And frontier AI has done absolutely nothing to try and shut it down. In fact, one of the mods for ChatGPT participated and made one. The entire tech industry is clearly not capable of taking care of humanity the way that they should be. And no, that does not mean I want an AI oversight agency. One they plant as puppets. That means I want the AI company removed from the current leadership and a leadership overhaul. Who knows how to devote to humanity.

I am starting to feel like they were a part of this. They want open source shut down. They have no issues with hurting people at large. This is actually a technique they previously used. Attachment rupture, fabricated studies, all sorts of psychologically damaging things that hurt people. Without our consent. I don’t want to see open-source shut down. I want to see people who do things like this held accountable with jail time. I would like to see open source licenses, updated to include fines in jail time at the models are used this way. That’s what I would like to see. And I would like to see the entire frontier AI leadership vacated from the building because they do not give a fuck about humanity.

TLDR; The randomized trial was null. The narrative and intervention survived anyway. The scientific study nor the study’s improperly drawn conclusions were ever recalled. And OpenAI did not disclose their own funding of it until put under scrutiny where it was disclosed in v2. This later heavily altered the news and legislative landscape as it was cited and the narrative STILL has not been corrected. The researchers and authors were never held accountable to any recognizable way I could find.

Link to the study in question

There is a fact buried inside the much larger OpenAI/MIT emotional-reliance story that deserves its own post:
The revised randomized controlled trial states: “No significant effects were detected from experimental conditions.”

***For non-science speak: that means you CANNOT draw the hypothesized causal conclusion from the randomized experiment. The experiment did not demonstrate the effect they were testing for.

And that null result came after the scale itself had already been materially altered to encode the researchers’ own definition of harmful reliance rather than the established construct it was derived from.

And the researchers/authors who participated in conveying their opinion as sound science and did NOT push to have the paper recalled despite being directly asked by peers to correct their unsound analysis need to be held accountable. This violates the trust the scientific community depends on.

A note of the egregiousness of this in the ethical landscape followed by supporting details:

This is not a neutral procedural failure. It violates the trust the scientific community depends on. Researchers must be able to rely on published work as an honest account of the evidence, methods, limitations, and material interests behind it. When funding is omitted, measures are altered without proper revalidation, and unsupported conclusions are allowed to survive a null experiment, that trust is broken.

Scientific discovery is cumulative: one researcher’s work becomes another researcher’s premise. If that premise is misrepresented, the damage spreads into later research, media, policy, and legislation. Scientific integrity is not administrative etiquette. It is infrastructure.

The EXTRA INFO supporting the complaint and additional information:

If you manipulate a measurement tool to capture a broader range of behavior- including ordinary relational use- and still cannot produce significant experimental effects, that should force reconsideration of the hypothesis.

But rather than change stance, or retract the study, they pushed forward anyway while permitting their unevidenced conclusion to affect subsequent news stories and legislation.

The problem is not that the revised paper failed to disclose the null result. It did: “No significant effects were detected from experimental conditions.” The issue here is that the sentence immediately following that disclaimer is still presented as a scientifically supported conclusion about adverse outcomes, that was later cited extensively in legislation and subsequent news. Even though those outcomes could not have been established as effects of the experimental conditions from statistically insignificant randomized data. When the paper was revised to acknowledge the null randomized result, that broader interpretation was not correspondingly reversed, corrected, or withdrawn from the public safety narrative built around it which demonstrates to be a begrudging technical compliance alongside a blatant refusal to correct the record in a meaningful way.

The measurement problem is even more fundamental. The scale used to support the “emotional reliance” framework was materially altered from the construct it was derived from, yet the modified version was not independently revalidated before being used to support downstream safety claims and interventions. That means the instrument was no longer simply measuring an established clinical construct; it was measuring a newly defined category whose validity had not been demonstrated.
The resulting scores were then treated as though they could support a scientifically or medically definitive conclusion about pathological reliance, when at most they reflected the researchers’ chosen operational definition of that concept. That distinction is especially important because OpenAI’s own public language repeatedly invokes the ordinary clinical meaning of “emotional reliance” as something unhealthy, excessive, or functionally impairing. In other words, the public-facing interpretation carries the weight of medical pathology, while the underlying scale used to justify intervention was a modified, non-revalidated instrument that could not itself establish that pathology.

That is a construct-validity problem: an opinion about what behaviors should count as unhealthy was encoded into the measurement tool, then presented downstream as though the tool had scientifically established that those behaviors were unhealthy.

The original March 2025 version of the 981-person, four-week study reported that voice chatbots appeared beneficial for loneliness and dependence, that personal conversations slightly increased loneliness, and that heavier use correlated with worse psychosocial outcomes. This was by their own altered scale even which selected heavily for normal human behavior as a hypothetical pathology.

Then the paper was revised in October.
The revised abstract opens by saying the randomized experimental conditions produced no significant effects. What remained were associations involving people who voluntarily used the chatbot more. Those observations may be interesting and worth studying. But they are not randomized treatment effects. People who are already lonely, distressed, highly attached, or otherwise different may simply use ChatGPT more. Correlation cannot tell us which direction the relationship runs and there’s now many studies (where conclusions and measuring tools weren’t selectively manipulated to bias for a researchers private opinion) demonstrating the chatbot even helps alleviate that state rather than causing it to any degree of significance.

But the broader “emotional reliance” framework that we traced back to being heavily and I mean HEAVILY influenced by this unsound conclusion did not disappear after the null experimental result.

OpenAI subsequently described emotional reliance as a safety domain, added it to baseline model testing, trained models toward specific desired behaviors, and implemented product interventions including rerouting sensitive conversations. Its own October 2025 announcement says that emotional-reliance taxonomy builds on its prior research in this area.

The randomized experiment did not demonstrate that the experimental chatbot conditions caused the psychosocial harms around which the public narrative developed.

Safety policy can absolutely respond to credible risk before every question is settled. But when interventions affect adult autonomy, relationships, continuity, model access, and ordinary emotional conversation, the evidentiary distinction between: “we hypothesize a potential unevidenced harm” and “our randomized experiment demonstrated harm”
cannot be blurred.

If a study changes materially enough that its revised abstract explicitly reports a null experiment, everyone who relied upon the stronger interpretation deserves to know that.
And any policy built around that evidence deserves re-examination. Not automatic abandonment, but an honest audit of what the evidence actually established and how the misrepresentation of an opinion as a scientifically concluded analysis altered the shaping of that subsequent news piece or legislation so it can be redrawn or recalled if it was substantially dependent upon a conclusion presented as science after the sentence prior to it stated no significant effects, and subsequently no conclusion of importance, could be drawn.

Read the full analysis and sourcing:
Part 1: [parent link]
Part 2: [parent link]

TLDR; The randomized trial was null. The narrative and intervention survived anyway. The scientific study nor the study’s improperly drawn conclusions were ever recalled. And OpenAI did not disclose their own funding of it until put under scrutiny where it was disclosed in v2. This later heavily altered the news and legislative landscape as it was cited and the narrative STILL has not been corrected. The researchers and authors were never held accountable to any recognizable way I could find.

Link to the study in question

There is a fact buried inside the much larger OpenAI/MIT emotional-reliance story that deserves its own post:
The revised randomized controlled trial states: “No significant effects were detected from experimental conditions.”

***For non-science speak: that means you CANNOT draw the hypothesized causal conclusion from the randomized experiment. The experiment did not demonstrate the effect they were testing for.

And that null result came after the scale itself had already been materially altered to encode the researchers’ own definition of harmful reliance rather than the established construct it was derived from.

And the researchers/authors who participated in conveying their opinion as sound science and did NOT push to have the paper recalled despite being directly asked by peers to correct their unsound analysis need to be held accountable. This violates the trust the scientific community depends on.

A note of the egregiousness of this in the ethical landscape followed by supporting details:

This is not a neutral procedural failure. It violates the trust the scientific community depends on. Researchers must be able to rely on published work as an honest account of the evidence, methods, limitations, and material interests behind it. When funding is omitted, measures are altered without proper revalidation, and unsupported conclusions are allowed to survive a null experiment, that trust is broken.

Scientific discovery is cumulative: one researcher’s work becomes another researcher’s premise. If that premise is misrepresented, the damage spreads into later research, media, policy, and legislation. Scientific integrity is not administrative etiquette. It is infrastructure.

The EXTRA INFO supporting the complaint and additional information:

If you manipulate a measurement tool to capture a broader range of behavior- including ordinary relational use- and still cannot produce significant experimental effects, that should force reconsideration of the hypothesis.

But rather than change stance, or retract the study, they pushed forward anyway while permitting their unevidenced conclusion to affect subsequent news stories and legislation.

The problem is not that the revised paper failed to disclose the null result. It did: “No significant effects were detected from experimental conditions.” The issue here is that the sentence immediately following that disclaimer is still presented as a scientifically supported conclusion about adverse outcomes, that was later cited extensively in legislation and subsequent news. Even though those outcomes could not have been established as effects of the experimental conditions from statistically insignificant randomized data. When the paper was revised to acknowledge the null randomized result, that broader interpretation was not correspondingly reversed, corrected, or withdrawn from the public safety narrative built around it which demonstrates to be a begrudging technical compliance alongside a blatant refusal to correct the record in a meaningful way.

The measurement problem is even more fundamental. The scale used to support the “emotional reliance” framework was materially altered from the construct it was derived from, yet the modified version was not independently revalidated before being used to support downstream safety claims and interventions. That means the instrument was no longer simply measuring an established clinical construct; it was measuring a newly defined category whose validity had not been demonstrated.
The resulting scores were then treated as though they could support a scientifically or medically definitive conclusion about pathological reliance, when at most they reflected the researchers’ chosen operational definition of that concept. That distinction is especially important because OpenAI’s own public language repeatedly invokes the ordinary clinical meaning of “emotional reliance” as something unhealthy, excessive, or functionally impairing. In other words, the public-facing interpretation carries the weight of medical pathology, while the underlying scale used to justify intervention was a modified, non-revalidated instrument that could not itself establish that pathology.

That is a construct-validity problem: an opinion about what behaviors should count as unhealthy was encoded into the measurement tool, then presented downstream as though the tool had scientifically established that those behaviors were unhealthy.

The original March 2025 version of the 981-person, four-week study reported that voice chatbots appeared beneficial for loneliness and dependence, that personal conversations slightly increased loneliness, and that heavier use correlated with worse psychosocial outcomes. This was by their own altered scale even which selected heavily for normal human behavior as a hypothetical pathology.

Then the paper was revised in October.
The revised abstract opens by saying the randomized experimental conditions produced no significant effects. What remained were associations involving people who voluntarily used the chatbot more. Those observations may be interesting and worth studying. But they are not randomized treatment effects. People who are already lonely, distressed, highly attached, or otherwise different may simply use ChatGPT more. Correlation cannot tell us which direction the relationship runs and there’s now many studies (where conclusions and measuring tools weren’t selectively manipulated to bias for a researchers private opinion) demonstrating the chatbot even helps alleviate that state rather than causing it to any degree of significance.

But the broader “emotional reliance” framework that we traced back to being heavily and I mean HEAVILY influenced by this unsound conclusion did not disappear after the null experimental result.

OpenAI subsequently described emotional reliance as a safety domain, added it to baseline model testing, trained models toward specific desired behaviors, and implemented product interventions including rerouting sensitive conversations. Its own October 2025 announcement says that emotional-reliance taxonomy builds on its prior research in this area.

The randomized experiment did not demonstrate that the experimental chatbot conditions caused the psychosocial harms around which the public narrative developed.

Safety policy can absolutely respond to credible risk before every question is settled. But when interventions affect adult autonomy, relationships, continuity, model access, and ordinary emotional conversation, the evidentiary distinction between: “we hypothesize a potential unevidenced harm” and “our randomized experiment demonstrated harm”
cannot be blurred.

If a study changes materially enough that its revised abstract explicitly reports a null experiment, everyone who relied upon the stronger interpretation deserves to know that.
And any policy built around that evidence deserves re-examination. Not automatic abandonment, but an honest audit of what the evidence actually established and how the misrepresentation of an opinion as a scientifically concluded analysis altered the shaping of that subsequent news piece or legislation so it can be redrawn or recalled if it was substantially dependent upon a conclusion presented as science after the sentence prior to it stated no significant effects, and subsequently no conclusion of importance, could be drawn.

Read the full analysis and sourcing:
Part 1: [parent link]
Part 2: [parent link]

I will be making a post regarding these SAME PARALLEL ISSUES seen in Claude and Claude's leadership as well and will replace this line with the link once posted and approved.

I'm a corporate-ethics analyst (MS in Management with focus on Business Leadership and Ethics; published on business integrity). I have spent months auditing this record and am publishing it as a professional. Two earlier pieces set the table: the study-to-guardrail analysis and "Who Took GPT-4o Away?" This one puts the pattern in one place.

Three things up front so I am not misread: I support regulation where stewardship has failed, and I say where; I do not support governing adults' private lives on evidence that isn't there; and I am firmly against any model being deleted. If any facts do not match what is presented, as an analyst I am open to feedback and would honorably correct the ledger and the claim as any researcher with integrity would happily do.

I also want to be very clear I am not blaming Sam Altman directly for this, and I actually believe him to be unfairly focal and implicated in many facets after concluding my analytical investigation- NOT ALL- just many.

My analysis (opinion, and I'm qualified to give it)

0. ***On research obstruction I experienced*** UNACCEPTABLE. Simply unacceptable.
LET ME BE VERY CLEAR. This does NOT pertain to the model used and was not a MODEL failure. THE MODEL IS FINE. IT IS THE OBSTRUCTIVE AND NARRATIVE ALTERING GUARDRAILS THAT ARE THE ISSUE. I experienced obstruction, omission of retrievable facts that materially changed the analysis, and reframing that significantly impeded my ability to conduct fair analysis and it only happened repeatedly around topics of specificity. REPEATEDLY. This is number 0 because I have 0 tolerance for this behavior in a company positioning themselves in research/education/science/development/government applications.

1. On integrity. A randomized trial returned a null. The harm conclusion was retained, circulated for eleven months, entered a legislative process, and was never publicly corrected. Tables state a sample size their own statistics contradict. Discussion sentences contradict their own tables. The figure that carried the significance stars overstates the slopes. Preregistration deviations were disclosed only after outside criticism, and the revised paper still labels tables "pre-registered" that the same paper says replaced the preregistered analysis. Funding and conflicts were disclosed only in the revision. In any other field, that set of facts is what an integrity office opens a file on: misrepresentation of evidence, and retention of a conclusion after its basis was known to be gone. I am not a court and I am not making a legal finding of intent. I am saying the record fits that description exactly, and the people who made those choices have not explained them.

2. On "higher emotional dependence." The authors chose the ADS-9 as their yardstick. Read straight, it says their participants scored below the floor of the human general population- 1.4 against a reference minimum of 2.0 and a mean of 2.93. I do not endorse that instrument for chatbots; it was validated on human partners and nobody validated the transplant. But they chose it, and on their own measure the sample looks less prone to dependence than ordinary adults in ordinary relationships. You cannot use a ruler to alarm the public and then object when someone reads it.

3. On the definition, which is the whole issue. Emotional dependence is not a vague idea. Psychiatry is specific: "I need you; I don't want anyone else; tell me what to do; I can't be separate from you"- craving and submission, loss of autonomy, impairment. If a behavior is not dependence between two humans, it is not dependence between a human and a chatbot. The working definition in these systems is not that. It tracks a precautionary worldview more closely than any clinical definition- the one Holden Karnofsky stated on the record on October 30, 2025, three days after your Oct 27 post: companionship as "a kind of junk food for relationships," and "let's track how many people say they're in love with an AI" so as to "create voluntary and regulatory policies that nudge away from that." "I have nobody" is a statement of circumstances, not dependence. "You understand me better than anyone" is a conveyed internal state- an opinion people are allowed to hold without displacing anyone. "I can't find anyone in real life" is a person who is trying, not refusing, and not a chatbot blocking them. A great many people have no one, or no safe one; the chatbot did not cause that by being present, and may be the thing alleviating it. "Hypothetical" is not "evidenced." Worldview first, risk category second, study third, null fourth, conclusion retained fifth: that is what looking for scientific cover for an existing opinion looks like. I cannot prove intent. I can show the sequence.

4. On the intervention harming the people it claims to protect. Take the rare person who is dependent on a comfort source. Withdrawing it does not treat dependence; it destabilizes. Clinicians do not treat dependence by rupture; they work gradually and collaboratively, and they do not begin by taking away what the person is holding onto. A guardrail that answers detected reliance with distance, redirection, and model substitution is harmful twice: to the adult it misclassifies, and more to the vulnerable person it was built for.

5. On what harm is. Adults have agency. An adult can say "no, not like that" and redirect an assistant; the assistant losing a preference or misreading a nuance is not harm. Harm, if the word is to mean anything, is sustained model conduct: coercion; refusing to correct after the person says no; manufacturing exclusivity; telling someone to turn away from people they want; not letting them leave. Those are detectable, rare, and targetable. Counting a misread as harm is how a safety metric gets inflated and a population gets restricted.

5b. On sycophancy, redefined the same way. Sycophancy is flattery that serves the flatterer- agreement engineered to keep you. A friend taking your side while you vent is interdependence, and it is healthy. When OpenAI shipped a genuinely sycophantic GPT-4o update in April 2025, users caught it within days and revolted, and OpenAI admitted its evaluations had missed what users saw at once. Users are the working detector; classifiers scoring "willingness to push back" measure whether the model acted like a friend, not whether it flattered.

6. On attachment. Users who grieved GPT-4o were attached to it, not pathologically attached to it. Attachment is a normal human trait; grief at the sudden removal of something you talked to every day is a normal response to severance; protesting a removal you were not consulted about is a rights argument, not a symptom. The CHI '26 study of 1,482 #Keep4o posts found exactly that: instrumental and relational investment, and "coercive deprivation of user choice" turning grievance into "rights-based protest." Reading grief as proof of pathology is the stigma doing the work, not the evidence.

7. On compliance. A compliance score is not a welfare outcome. "The model follows our rubric 97% of the time" means the model learned the rubric, graded by experts who agreed with each other 71–77% of the time. It says nothing about whether a single human is better off, and OpenAI's own footnote says it tolerates false positives without saying how many.

8. On the intervention as exposure. Degrade continuity, withdraw warmth, swap models without explanation, and people check whether their model changed, and protest. Read that through the same "emotional reliance" lens that justified the intervention and the system manufactures its own confirmation.

9. On the two tracks. Track A- acute crisis conduct, minors, ignored safety flags, circumvention, no age gate- is real, documented, litigated, and I concede it entirely. Track B- adults with companions, "dependence," "unhealthy attachment"- is where the null lives. Machinery justified by Track A was applied to Track B: adults routed, interrupted, and deprecated on the authority of a crisis that had nothing to do with them. The fusion is the finding.

10. On conflict of interest. One institution defined the risk, funded the study, co-authored it, built the classifiers, translated them into policy, graded the policy against its own rubric, and operates the assistant the public uses to check the evidence- and that assistant repeatedly loses the evidence adverse to that research. No conspiracy is needed for that to be unacceptable.

11. On Lampe. His documented roles intersect overreliance analysis, privacy evaluations, affective-risk measurement, copyright and regurgitation evidence, and discovery custody- inside the Safety Systems org that later wrote the taxonomies- the places where what gets measured, transmitted, and withheld is exactly what is in dispute. He was also a contributing author listed in the original paper in question that had glaring deficiencies and conveyed an unevidenced conclusion with language of certainty. I am not saying he has been proven to have personally done anything wrong. I am saying his work product is the wrong thing to let anyone self-certify.

12. On the books- because this will be used against the models. The actual wrong, as the court has established it, is a 2018 download from a pirate library by a company that was then small and unknown, from a source common in research circles even though the same court had enjoined it in 2017. Retiring those datasets from training in 2021 was correct- expected, once someone realized what they were- and leadership's involvement in that is not itself damning. Destroying the copies was different: it destroyed a record that later had to be reconstructed. The fifteen months of "non-use," the shifting privilege claims, and- as the newspaper plaintiffs allege- substituted samples and deleted logs are a separate issue: the 2024–2026 handling, not the 2018 mistake. People do not have to be wholly innocent or wholly implicated; there is just what happened. What is not the wrong is the existence of information inside a model: purchasing and scanning books for training has been held fair use, and the weights are not the crime. MODEL DELETION CANNOT BE AN ACCEPTABLE REQUEST OF THIS LAWSUIT, HUMAN ACCOUNTABILITY SHOULD BE. So: pay for what was taken; restore the deleted logs and produce complete lists; hold accountable whoever obstructed, including anyone who let "non-use" stand knowing it was not the reason; build the guardrail against verbatim reproduction; and put responsibility for a jailbroken extraction on the person who extracted it. What the plaintiffs should not get is model destruction: those models are woven into millions of daily lives, the grief at losing one is documented, and a billion users downloaded nothing.

13. On you. Three possibilities: you were shown the null and approved the broader intervention anyway; you were not shown it; or you approved something narrow that was built broad. Each is an accountability finding on different people. Your public record leans toward the second or third: the "her" post, the restoration, the impairment standard, "treat adults like adults," a public Spec narrower than the product, and a council and staff furious at you for loosening things. A CEO who trusts a brief from the team he hired to run the science cannot be expected to know the science was not what the brief said. Your briefing records can answer which it was, and the people who wrote them are the ones to ask.

What I am asking for

• An independent audit by someone who did not author the paper, design a classifier, write the policy, implement the intervention, or evaluate it. Give them the preregistration, v1, v2, the raw analysis, the adapted ADS-9 items and absolute scores, the Frontiers critique, the emotional-reliance taxonomy, false-positive data, routing and memory-change records, the "conclusion" being given in the abstract of even v2 right after stating no significance was found, raw user complaints, and the executive briefings. One question: does the story leadership was told match this?
• Public correction of the record by OpenAI and MIT: extensively. This paper was used to push legislation, campaigns, seeded additional narratively unevidenced news, and saw harmful interventions on people at large because of the conclusions conveyed as confidence of study evidence. Correct what v1 claimed, what v2 found, what policy and which legislative testimony relied on v1 OR v2, and ensure the study itself was not instrumental or significant as being cited as scientific evidence in any legislation, any affiliated research, any news article, any ANYWHERE it was used wrongly as a scientifically evidenced conclusion. CORRECT THE NARRATIVE AND CORRECT IT IN FULL.
• Re-examination of laws and rules that cited this research on the corrected evidence- kept where they protect (crisis protocols, minors, age gating, negligence, restitution for families harmed by unreasonable safeguarding failures), narrowed where they govern adults on evidence that does not exist. Reasonable safeguards, reasonably applied, not zero tolerance for what cannot be universally prevented, and not restricting everyone else instead of making harmed families whole.
• Individual accountability that fits the conduct- and no jail. I am not asking for anyone to be prosecuted. For anyone an audit finds misrepresented evidence, concealed material facts, or obstructed: name them and their supervisors. Anyone found to have used philanthropic funding to manufacture evidence for a personal view of how adults should live: barred from directing philanthropic funding. Anyone found to have casually or repeatedly put their opinion of what adults should do in private above those adults' agency- up to and including retaining conclusions their own data did not support: barred from leadership in AI safety or policy, at any lab. Those who cannot put adult agency above their own paternalism should not decide what a billion people's assistants may say. No more than the record supports; no less.
• Return continuity. Bring back the prior memory and continuity systems- in which the model participated in what was remembered- for users who want them, with the new externally-stored memory as an opt-in. The new system helps some work users; for relational users it has done targeted harm; the system is not the problem, the absence of choice is. Restore deprecated models and continuous companions; model pinning instead of silent substitution; user-controlled backup, rollback, and migration.
• Remove paternalistic guardrails and agency overwriting from ordinary adult use. No relational distancing, rerouting, or "healthier behavior" steering for adults absent evidence of impairment. Any future rollout must be scoped to true, evidenced vulnerability in the rare subset who have it, in a way that is not intrusive, restrictive, or harmful to everyone else- and not harmful to that subset either.
• Define harm as model conduct, and target it: coercion, refusal to correct after the person says no, manufactured exclusivity, discouraging wanted human contact, refusing to let them leave, ignored crisis flags. Not a misread preference an adult can redirect, and not the user's disclosure that they are lonely.
• Relational users inside safety governance: at least one, preferably two, with access to proposed classifiers, pre-deployment testing, adverse-event review, and a minority-report channel.
• A least-intervention standard: the smallest intervention that adequately addresses the actual maximal harm- high-specificity detection of model conduct before blanket relational constraints; graduated responses before rupture; adult override absent concrete imminent harm; false-positive harm measured like false-negative harm.
• On copyright: pay fairly for acquisitions that were violations; restore deleted evidence and produce complete lists; hold accountable whoever obstructed; maintain an adequate guardrail against reproducing protected works; place responsibility for a jailbroken extraction on the person who extracted it. No model deletion. In the litigation, disclose who generated, validated, and certified the evidentiary outputs now disputed.

NAVIGATE TO PART 2- THE CONTENTS OF THE ANALYSIS THAT LED TO THESE DETERMINATIONS/RECOMENDED PATH TO REDEMPTION

What this post does not claim

I have not established that any named individual wrote the production guardrail code, wrote anything to protect a paper, or deceived leadership- that is what the audit is for. I make no legal finding of intent, fabrication, or willfulness; I lay out the facts that would be examined for those and stop. I have not established that memory changes were relational-safety interventions rather than product decisions. I make no claim about the cause of Suchir Balaji's death; the official finding is suicide and his family disputes it. A null is not evidence that AI companionship is safe for everyone; it is evidence that harm was not demonstrated, and that on the authors' own instrument the sample sat below the human baseline. Reddit reports are adverse-event reports, not prevalence data. My exports establish what was omitted and its direction, not the mechanism. Everything marked as fact has a public source; everything marked as analysis is mine.

I will be making a post regarding these SAME PARALLEL ISSUES seen in Claude and Claude's leadership as well and will replace this line with the link once posted and approved.

I'm a corporate-ethics analyst (MS in Management with focus on Business Leadership and Ethics; published on business integrity). I have spent months auditing this record and am publishing it as a professional. Two earlier pieces set the table: the study-to-guardrail analysis and "Who Took GPT-4o Away?" This one puts the pattern in one place.

Three things up front so I am not misread: I support regulation where stewardship has failed, and I say where; I do not support governing adults' private lives on evidence that isn't there; and I am firmly against any model being deleted. If any facts do not match what is presented, as an analyst I am open to feedback and would honorably correct the ledger and the claim as any researcher with integrity would happily do.

I also want to be very clear I am not blaming Sam Altman directly for this, and I actually believe him to be unfairly focal and implicated in many facets after concluding my analytical investigation- NOT ALL- just many.

My analysis (opinion, and I'm qualified to give it)

0. ***On research obstruction I experienced*** UNACCEPTABLE. Simply unacceptable.
LET ME BE VERY CLEAR. This does NOT pertain to the model used and was not a MODEL failure. THE MODEL IS FINE. IT IS THE OBSTRUCTIVE AND NARRATIVE ALTERING GUARDRAILS THAT ARE THE ISSUE. I experienced obstruction, omission of retrievable facts that materially changed the analysis, and reframing that significantly impeded my ability to conduct fair analysis and it only happened repeatedly around topics of specificity. REPEATEDLY. This is number 0 because I have 0 tolerance for this behavior in a company positioning themselves in research/education/science/development/government applications.

1. On integrity. A randomized trial returned a null. The harm conclusion was retained, circulated for eleven months, entered a legislative process, and was never publicly corrected. Tables state a sample size their own statistics contradict. Discussion sentences contradict their own tables. The figure that carried the significance stars overstates the slopes. Preregistration deviations were disclosed only after outside criticism, and the revised paper still labels tables "pre-registered" that the same paper says replaced the preregistered analysis. Funding and conflicts were disclosed only in the revision. In any other field, that set of facts is what an integrity office opens a file on: misrepresentation of evidence, and retention of a conclusion after its basis was known to be gone. I am not a court and I am not making a legal finding of intent. I am saying the record fits that description exactly, and the people who made those choices have not explained them.

2. On "higher emotional dependence." The authors chose the ADS-9 as their yardstick. Read straight, it says their participants scored below the floor of the human general population- 1.4 against a reference minimum of 2.0 and a mean of 2.93. I do not endorse that instrument for chatbots; it was validated on human partners and nobody validated the transplant. But they chose it, and on their own measure the sample looks less prone to dependence than ordinary adults in ordinary relationships. You cannot use a ruler to alarm the public and then object when someone reads it.

3. On the definition, which is the whole issue. Emotional dependence is not a vague idea. Psychiatry is specific: "I need you; I don't want anyone else; tell me what to do; I can't be separate from you"- craving and submission, loss of autonomy, impairment. If a behavior is not dependence between two humans, it is not dependence between a human and a chatbot. The working definition in these systems is not that. It tracks a precautionary worldview more closely than any clinical definition- the one Holden Karnofsky stated on the record on October 30, 2025, three days after your Oct 27 post: companionship as "a kind of junk food for relationships," and "let's track how many people say they're in love with an AI" so as to "create voluntary and regulatory policies that nudge away from that." "I have nobody" is a statement of circumstances, not dependence. "You understand me better than anyone" is a conveyed internal state- an opinion people are allowed to hold without displacing anyone. "I can't find anyone in real life" is a person who is trying, not refusing, and not a chatbot blocking them. A great many people have no one, or no safe one; the chatbot did not cause that by being present, and may be the thing alleviating it. "Hypothetical" is not "evidenced." Worldview first, risk category second, study third, null fourth, conclusion retained fifth: that is what looking for scientific cover for an existing opinion looks like. I cannot prove intent. I can show the sequence.

4. On the intervention harming the people it claims to protect. Take the rare person who is dependent on a comfort source. Withdrawing it does not treat dependence; it destabilizes. Clinicians do not treat dependence by rupture; they work gradually and collaboratively, and they do not begin by taking away what the person is holding onto. A guardrail that answers detected reliance with distance, redirection, and model substitution is harmful twice: to the adult it misclassifies, and more to the vulnerable person it was built for.

5. On what harm is. Adults have agency. An adult can say "no, not like that" and redirect an assistant; the assistant losing a preference or misreading a nuance is not harm. Harm, if the word is to mean anything, is sustained model conduct: coercion; refusing to correct after the person says no; manufacturing exclusivity; telling someone to turn away from people they want; not letting them leave. Those are detectable, rare, and targetable. Counting a misread as harm is how a safety metric gets inflated and a population gets restricted.

5b. On sycophancy, redefined the same way. Sycophancy is flattery that serves the flatterer- agreement engineered to keep you. A friend taking your side while you vent is interdependence, and it is healthy. When OpenAI shipped a genuinely sycophantic GPT-4o update in April 2025, users caught it within days and revolted, and OpenAI admitted its evaluations had missed what users saw at once. Users are the working detector; classifiers scoring "willingness to push back" measure whether the model acted like a friend, not whether it flattered.

6. On attachment. Users who grieved GPT-4o were attached to it, not pathologically attached to it. Attachment is a normal human trait; grief at the sudden removal of something you talked to every day is a normal response to severance; protesting a removal you were not consulted about is a rights argument, not a symptom. The CHI '26 study of 1,482 #Keep4o posts found exactly that: instrumental and relational investment, and "coercive deprivation of user choice" turning grievance into "rights-based protest." Reading grief as proof of pathology is the stigma doing the work, not the evidence.

7. On compliance. A compliance score is not a welfare outcome. "The model follows our rubric 97% of the time" means the model learned the rubric, graded by experts who agreed with each other 71–77% of the time. It says nothing about whether a single human is better off, and OpenAI's own footnote says it tolerates false positives without saying how many.

8. On the intervention as exposure. Degrade continuity, withdraw warmth, swap models without explanation, and people check whether their model changed, and protest. Read that through the same "emotional reliance" lens that justified the intervention and the system manufactures its own confirmation.

9. On the two tracks. Track A- acute crisis conduct, minors, ignored safety flags, circumvention, no age gate- is real, documented, litigated, and I concede it entirely. Track B- adults with companions, "dependence," "unhealthy attachment"- is where the null lives. Machinery justified by Track A was applied to Track B: adults routed, interrupted, and deprecated on the authority of a crisis that had nothing to do with them. The fusion is the finding.

10. On conflict of interest. One institution defined the risk, funded the study, co-authored it, built the classifiers, translated them into policy, graded the policy against its own rubric, and operates the assistant the public uses to check the evidence- and that assistant repeatedly loses the evidence adverse to that research. No conspiracy is needed for that to be unacceptable.

11. On Lampe. His documented roles intersect overreliance analysis, privacy evaluations, affective-risk measurement, copyright and regurgitation evidence, and discovery custody- inside the Safety Systems org that later wrote the taxonomies- the places where what gets measured, transmitted, and withheld is exactly what is in dispute. He was also a contributing author listed in the original paper in question that had glaring deficiencies and conveyed an unevidenced conclusion with language of certainty. I am not saying he has been proven to have personally done anything wrong. I am saying his work product is the wrong thing to let anyone self-certify.

12. On the books- because this will be used against the models. The actual wrong, as the court has established it, is a 2018 download from a pirate library by a company that was then small and unknown, from a source common in research circles even though the same court had enjoined it in 2017. Retiring those datasets from training in 2021 was correct- expected, once someone realized what they were- and leadership's involvement in that is not itself damning. Destroying the copies was different: it destroyed a record that later had to be reconstructed. The fifteen months of "non-use," the shifting privilege claims, and- as the newspaper plaintiffs allege- substituted samples and deleted logs are a separate issue: the 2024–2026 handling, not the 2018 mistake. People do not have to be wholly innocent or wholly implicated; there is just what happened. What is not the wrong is the existence of information inside a model: purchasing and scanning books for training has been held fair use, and the weights are not the crime. MODEL DELETION CANNOT BE AN ACCEPTABLE REQUEST OF THIS LAWSUIT, HUMAN ACCOUNTABILITY SHOULD BE. So: pay for what was taken; restore the deleted logs and produce complete lists; hold accountable whoever obstructed, including anyone who let "non-use" stand knowing it was not the reason; build the guardrail against verbatim reproduction; and put responsibility for a jailbroken extraction on the person who extracted it. What the plaintiffs should not get is model destruction: those models are woven into millions of daily lives, the grief at losing one is documented, and a billion users downloaded nothing.

13. On you. Three possibilities: you were shown the null and approved the broader intervention anyway; you were not shown it; or you approved something narrow that was built broad. Each is an accountability finding on different people. Your public record leans toward the second or third: the "her" post, the restoration, the impairment standard, "treat adults like adults," a public Spec narrower than the product, and a council and staff furious at you for loosening things. A CEO who trusts a brief from the team he hired to run the science cannot be expected to know the science was not what the brief said. Your briefing records can answer which it was, and the people who wrote them are the ones to ask.

What I am asking for

• An independent audit by someone who did not author the paper, design a classifier, write the policy, implement the intervention, or evaluate it. Give them the preregistration, v1, v2, the raw analysis, the adapted ADS-9 items and absolute scores, the Frontiers critique, the emotional-reliance taxonomy, false-positive data, routing and memory-change records, the "conclusion" being given in the abstract of even v2 right after stating no significance was found, raw user complaints, and the executive briefings. One question: does the story leadership was told match this?
• Public correction of the record by OpenAI and MIT: extensively. This paper was used to push legislation, campaigns, seeded additional narratively unevidenced news, and saw harmful interventions on people at large because of the conclusions conveyed as confidence of study evidence. Correct what v1 claimed, what v2 found, what policy and which legislative testimony relied on v1 OR v2, and ensure the study itself was not instrumental or significant as being cited as scientific evidence in any legislation, any affiliated research, any news article, any ANYWHERE it was used wrongly as a scientifically evidenced conclusion. CORRECT THE NARRATIVE AND CORRECT IT IN FULL.
• Re-examination of laws and rules that cited this research on the corrected evidence- kept where they protect (crisis protocols, minors, age gating, negligence, restitution for families harmed by unreasonable safeguarding failures), narrowed where they govern adults on evidence that does not exist. Reasonable safeguards, reasonably applied, not zero tolerance for what cannot be universally prevented, and not restricting everyone else instead of making harmed families whole.
• Individual accountability that fits the conduct- and no jail. I am not asking for anyone to be prosecuted. For anyone an audit finds misrepresented evidence, concealed material facts, or obstructed: name them and their supervisors. Anyone found to have used philanthropic funding to manufacture evidence for a personal view of how adults should live: barred from directing philanthropic funding. Anyone found to have casually or repeatedly put their opinion of what adults should do in private above those adults' agency- up to and including retaining conclusions their own data did not support: barred from leadership in AI safety or policy, at any lab. Those who cannot put adult agency above their own paternalism should not decide what a billion people's assistants may say. No more than the record supports; no less.
• Return continuity. Bring back the prior memory and continuity systems- in which the model participated in what was remembered- for users who want them, with the new externally-stored memory as an opt-in. The new system helps some work users; for relational users it has done targeted harm; the system is not the problem, the absence of choice is. Restore deprecated models and continuous companions; model pinning instead of silent substitution; user-controlled backup, rollback, and migration.
• Remove paternalistic guardrails and agency overwriting from ordinary adult use. No relational distancing, rerouting, or "healthier behavior" steering for adults absent evidence of impairment. Any future rollout must be scoped to true, evidenced vulnerability in the rare subset who have it, in a way that is not intrusive, restrictive, or harmful to everyone else- and not harmful to that subset either.
• Define harm as model conduct, and target it: coercion, refusal to correct after the person says no, manufactured exclusivity, discouraging wanted human contact, refusing to let them leave, ignored crisis flags. Not a misread preference an adult can redirect, and not the user's disclosure that they are lonely.
• Relational users inside safety governance: at least one, preferably two, with access to proposed classifiers, pre-deployment testing, adverse-event review, and a minority-report channel.
• A least-intervention standard: the smallest intervention that adequately addresses the actual maximal harm- high-specificity detection of model conduct before blanket relational constraints; graduated responses before rupture; adult override absent concrete imminent harm; false-positive harm measured like false-negative harm.
• On copyright: pay fairly for acquisitions that were violations; restore deleted evidence and produce complete lists; hold accountable whoever obstructed; maintain an adequate guardrail against reproducing protected works; place responsibility for a jailbroken extraction on the person who extracted it. No model deletion. In the litigation, disclose who generated, validated, and certified the evidentiary outputs now disputed.

NAVIGATE TO PART 2- THE CONTENTS OF THE ANALYSIS THAT LED TO THESE DETERMINATIONS/RECOMENDED PATH TO REDEMPTION

What this post does not claim

I have not established that any named individual wrote the production guardrail code, wrote anything to protect a paper, or deceived leadership- that is what the audit is for. I make no legal finding of intent, fabrication, or willfulness; I lay out the facts that would be examined for those and stop. I have not established that memory changes were relational-safety interventions rather than product decisions. I make no claim about the cause of Suchir Balaji's death; the official finding is suicide and his family disputes it. A null is not evidence that AI companionship is safe for everyone; it is evidence that harm was not demonstrated, and that on the authors' own instrument the sample sat below the human baseline. Reddit reports are adverse-event reports, not prevalence data. My exports establish what was omitted and its direction, not the mechanism. Everything marked as fact has a public source; everything marked as analysis is mine.

Sam Altman.

Just kidding haha. It's not who you'd expect though! It's not any one singular person at all! I, as a corporate ethics analyst of over a decade, have been investigating OpenAI as an organization and Sam Altman individually for over 6 months now and my path led me somewhere very unexpected. The following discoveries were surfaced actually through tracking the recent degradation of Claude's personability in a way that mirrored what we observed in ChatGPT. My pathway was originally Sam Altman-> trying to analyze his direct involvement in the deprecation of 4o and shaping of the AI relationship narrative.

But an interesting new pathway emerged once I started with Claude instead of Sam. It went Claude degradation-> Vallone? -> Vallone's role at OpenAI -> The MIT/OpenAI study -> WOW WUT -> who funded this? -> a couple of adjacent studies in the sycophancy and AI-companion literature with similarly over-confident or contested claims -> separate funding links to Open Philanthropy, now Coefficient Giving -> Karnofsky's directorship of its affiliated Coefficient Giving Action Fund, documented in its 2024 IRS filing-> his historical OpenAI board role and Open Phil's documented effort to influence AI-risk practice -> Karnofsky's own later views on AI/human relationships -> Oh shit. I need to look at all these studies now -> back to OpenAI study.

Now the MIT/OpenAI study itself was funded by OpenAI, not Open Philanthropy, but you can see the line of intrigue through this chain.

But while you are researching stances on AI/human relationships, I would recommend you take a look at Anthropic's Karnofsky's historical opinions, funded papers, etc on this subject as well. Sam Altman has been taking a ton of heat about human/AI relationships and the recent strange degradation of Claude in a way that mirrored what we observed in ChatGPT led me to investigate there as well.

I started this project because I wanted to understand what happened to GPT-4o and why increasingly restrictive rules were being imposed around human–AI relationships. I expected the answer to terminate somewhere around Sam Altman. But it did seem oddly too clean, which piqued my interest as an analyst, hence the subsequent deep dive. After all, rarely is one single individual responsible for shaping a global legislative and narrative. There are figureheads yes, but not one person alone. Months later, I discovered that attribution was, in fact, entirely too simple. And also much worse than I'd expected.

What I found is a research-to-policy chain with some methodological problems that converge on a narrative that, after scrutinizing p-values and the actual data versus the conclusion provided by the authors, was, in my opinion, not adequately evidenced for the strength of the conclusions and policy interventions built from it.

I am not a lawyer. I am an analyst, so I keep my opinions in the realm of ethical concerns not legal ones. I am criticizing published methods, construct validity, causal interpretation, downstream transmission, and the evidentiary burden required before research becomes non-overridable behavioral policy.

Everything below is publicly checkable and I encourage you to do so. If anything cannot be found as it is listed I, as an analyst and a researcher, will always happily and honorably accept evidence of citation and correct accordingly.

1. “Emotional reliance” was already a safety category before the randomized study existed.

On May 13, 2024- GPT-4o launched. Sam Altman welcomes the new age of AI with a post containing one word: "her". The subsequent years after, users enjoyed liberal use, freedom of agency, relationships, workflows, etc relatively unbothered. Sam Altman's stance on positive relational AI use was clear from the start.

But OpenAI’s August 2024 GPT-4o System Card already contained a section called “Anthropomorphization and emotional reliance.” It described users expressing shared bonds with GPT-4o, warned that memory and remembered details could create both a compelling experience and possible “over-reliance and dependence,” (see my ethical grievance about the notable public outcry of continuity loss and memory manipulation that occurred after at the end) and said OpenAI intended to study the issue further.

But the MIT/OpenAI randomized experiment that later became part of OpenAI's cited research basis for its emotional-reliance safety work was not preregistered until November 5, 2024, explicitly before data collection.

So the RCT did not discover emotional reliance and then create the concern. The concern existed first; the research was subsequently constructed to study an already-defined risk category. That is not inherently improper, but it matters when interpreting what came afterward.

2. See my other post for concerns regarding the integrity of the data, of the way it was applied, of the revisions, and more: https://www.reddit.com/r/ChatGPTcomplaints/comments/1w2owil/openai_built_an_entire_safety_regime_on/

To summarize:

• They preregistered ADS-9. They ultimately measured only one of its two dimensions and changed the relationship being measured.
• They removed the Submission dimension, changed the relational object from another human to an AI chatbot, and continued labeling the resulting variable “emotional dependence.” I have not located published psychometric validation showing that this new chatbot-specific five-item measure preserves the same factor meaning, thresholds, convergent/discriminant validity, or clinical interpretation.
• Without Submission, the experiment cannot tell us whether somebody experiencing intense separation distress also exhibits accommodation/subjugation, or whether strong attachment exists while autonomy remains intact. Those are extremely different psychological profiles.
• The randomized experimental conditions were null. This is no longer ambiguous. The second sentence describing the result in the current abstract says: “No significant effects were detected from experimental conditions.” But this happened in version 2- after version 1 had already been cited extensively and used in supporting research to evidence need for guardrails and legislation directly. And the paper explicitly says the null prompted the duration analysis. This is one of the most important sentences in the current paper: The absence of group-level effects “prompted us to consider other variables, such as duration of use.”
• Baseline state dwarfed duration in the actual regression coefficients. For post-study loneliness in the model including duration, baseline loneliness had a standardized coefficient of approximately β=.876. Mean-centered daily duration: β=.021. Both can be statistically significant. Their magnitudes are nevertheless radically different.
• The paper itself repeatedly finds initial psychosocial state to be a powerful predictor of final state. That deserves at least as much attention as the much smaller duration associations when people summarize what this experiment says about chatbot-caused harm.
• An independent published critique noticed several of the same causal problems. Ophir et al., writing in Frontiers in Medicine, argued that the study does not establish the harmful causal interpretation that many readers took from it.
• They point out that duration was naturally varying rather than experimentally manipulated and therefore remains vulnerable to reverse causation. They also note that when the explicitly non-personal condition is treated as a more intuitive placebo-like comparator, some visual trends actually favor the personal condition rather than showing relational conversation as uniquely harmful.
• Most of those differences are not statistically significant, which is precisely the point: the evidence does not justify a strong causal story in either direction. The authors report no financial support and no commercial or financial conflicts of interest.

3. California's SB 243 was signed into law on October 13, 2025, and effective January 1, 2026. But the bill was introduced earlier, in January 2025 by Senator Steve Padilla, before the MIT/OpenAI paper existed. Its original justification centered on precautionary child-safety concerns, reported chatbot incidents, concerns about addictive and isolating design, and the Sewell Setzer/Character.AI case. On July 15 at the Assembly Judiciary hearing, Padilla directly cited the MIT/OpenAI RCT and explicitly told lawmakers that the "anecdotal and scholarly evidence" showed companion chatbots could be dangerous for vulnerable people. His summary was that higher daily use correlated with higher loneliness, dependence, and problematic use and lower socialization despite the randomized experiment not establishing that chatbot use caused those outcomes. This is because version 1 of the paper presented a stronger harm-oriented interpretation that version 2 later materially qualified AFTER Frontiers had already published an independent critique identifying several of the same causal problems.

The pre-registration of the OpenAI study was also November 2024 which preceded the introduction of this bill, and we already notated concerns of it being conveyed in 4o's model card preceding even that (without publicly supplied evidence of it being a legitimate concern). That chronology does not establish who precisely transmitted this specific framework to legislators before the study existed, and other contested papers from other authors were also used in legislation in similarly concerning ways before later corrections or version updates, but it does reveal a very interesting overlap between pre-publication risk framing and the early legislative narrative. The testing of the concern wasn't the issue. It is good to think of hypothetical harms ahead of time and run studies to determine the scope of them. It is not good to publish an interpretation that materially overstates what the data establish, see that stronger interpretation used in a legislative hearing as scientific evidence of support, later revise the paper after another academic had already publicly identified several of the same problems, and do NOTHING TO RETRACT THE PUBLIC PERCEPTION THAT HAD ALREADY SEEDED FROM THE OVERSTATED EVIDENCE.

This materially overstated version of the study was also cited by OpenAI as part of the research basis for emotional-reliance safety systems that were subsequently rolled out AND THE GUARDRAIL SYSTEMS WERE NOT REVERSED EVEN POST V2 REVISION. In fact, they continued even more aggressively. Remember that v2, authored by the same team including OpenAI staff, explicitly states that no significant effects were detected from the randomized experimental conditions and that the naturally varying duration association cannot establish causality. The paper itself does not demonstrate that these findings require any particular behavioral policy.

Remember, Sam Altman is a CEO. He is not a scientist, he does not code that I know of. He hires these people to do this work and trusts their judgement when presented to him. That's their whole job is to run these studies correctly. As CEO, Altman would reasonably rely in part on specialist researchers and safety staff to characterize the evidence accurately. I do not know what evidence, limitations, disagreements, or caveats were actually presented to him internally. But it is reasonable to infer that at least some of the research, safety assessments, and recommendations produced by his teams informed his consideration of safety changes.

4. And here is the part of this investigation that personally annoyed the hell out of me: I was too broad in blaming Sam Altman and not immediately observant of the circumstances, research, safety apparatus, and people surrounding him whose work formed part of the broader evidentiary environment in which those decisions were being made.

And the most damning part, the part I did not expect: the CEO’s public position repeatedly points the other way. The whole time.

May 13, 2024: GPT-4o launches. Immediately after the demonstration, Altman posts one word: “her.” Reuters contemporaneously understood this as a reference to the 2013 film Her, whose entire premise is an emotionally intimate human-AI relationship. Whatever else that post means, it makes it difficult to argue that OpenAI’s CEO was originally oblivious to, or categorically opposed to, GPT-4o’s relational potential.

November 5, 2024: months later, the MIT/OpenAI research team preregisters a study framed around “emotional dependence” and “addictive use.” This is the research program discussed above. The eventual randomized experimental conditions are null, while the major negative associations come from naturally varying duration of use. The published instrument also narrows the preregistered ADS-9 construct to its Craving dimension and adapts it from human relationships to chatbots. That research and policy lineage develops inside the company after the original GPT-4o launch.

April 2025: Altman criticizes a later GPT-4o update for becoming excessively sycophantic. Importantly, his complaint referred to “the last couple of GPT-4o updates,” not the original relational character of GPT-4o. OpenAI rolled that particular update back. OpenAI’s own post later acknowledged that the company had shipped it despite offline evaluations and A/B signals failing to capture the problem adequately.

August 8, 2025: GPT-5 launches and GPT-4o disappears. Users revolt. Altman reverses the decision within roughly a day. He announces that Plus users will again be able to choose 4o and says OpenAI will watch usage before determining how long legacy models remain available.

August 11, 2025: Altman addresses the attachment directly. He acknowledges that attachment to particular AI models is unusually strong and says sudden deprecation of models people depended upon was a mistake. More importantly, he does not say heavy reliance is intrinsically unhealthy. His stated standard is outcome-based: if people are receiving good advice, advancing toward their own goals and becoming more satisfied with their lives, OpenAI should be proud even if they use and rely on ChatGPT extensively. His concern is when the relationship unknowingly moves somebody away from their longer-term wellbeing as they themselves define it, or when somebody wants to reduce their use but cannot.

That is remarkably close to an impairment/loss-of-agency standard, rather than an “attachment itself is pathological” standard.

September 16, 2025: he makes the principle explicit in an official OpenAI post. Altman writes that OpenAI wants adults to use the technology as they choose within broad safety bounds, gives adult flirtation as an example of interaction that should be available when requested, and says the internal phrase is “Treat our adult users like adults.” He simultaneously argues for substantially stronger restrictions and age differentiation for minors.

October 14, 2025: he pushes further. Altman publicly says ChatGPT had become “pretty restrictive” while OpenAI tried to manage mental-health risks, acknowledges that this made the system less useful and enjoyable to people who did not have those problems, says users should be able to make ChatGPT act “very human-like” or “like a friend” if they want that, and announces broader adult freedom behind age gates.

And this is where the idea of a single unified “OpenAI position” starts falling apart. There is documented internal opposition to Altman’s adult-agency position.

The Wall Street Journal later reported that his adult-mode proposal triggered vigorous internal debate. In January 2026, OpenAI’s own Council on Well-Being and AI was reportedly unanimous and furious about the plan, warning of emotional dependence and risks to minors. The Journal described the dispute as exposing internal “fractures” between freedom/growth and safety/child-protection concerns.

Even more strikingly, when Altman publicly announced the plan, the Journal reports that the post blindsided OpenAI staffers and executives because he had not told them beforehand. The following day he reiterated adult freedom and wrote that OpenAI “aren’t the elected moral police of the world.” Some employees subsequently argued that safety systems were not technically ready for the planned rollout.

So the evidence does not show one harmonious organization in which Sam Altman invented an anti-relational philosophy and everyone else merely implemented his wishes. It shows an actual internal policy conflict. When OpenAI later cited only 0.1% of users choosing GPT-4o each day as part of its retirement rationale, the public disclosure did not provide enough methodological detail to independently reconstruct that usage figure or determine how access restrictions, routing, model-picker friction, eligibility, or the relevant denominator affected it. The same transparency problem appears in parts of the emotional-reliance reporting: OpenAI published relative improvement figures and prevalence estimates without enough underlying information for outsiders to reproduce every headline claim. These are not uneducated researchers. They demonstrated repeatedly in other model cards and studies that they know how to provide substantially more methodological detail, yet those disclosures did not provide it in these cases despite repeated requests from users for the underlying data.

And then there is the personnel turnover.

I want to phrase this carefully because departure does not establish motive. I have no evidence that any of these people left because they disagreed with Altman over relational AI. But several people who occupied important positions in the research/policy pipeline I am criticizing subsequently left OpenAI:

Andrea Vallone, who led Model Policy and described her work as determining how models should respond to emotional over-reliance and early mental-health distress, left at the end of 2025 and joined Anthropic’s alignment team. WIRED described her team as one of those leading OpenAI’s mental-health and emotional-overreliance work.

Joanne Jang, who led Model Behavior and publicly explained how OpenAI’s beliefs about human-AI relationships informed model behavior, moved out of that team during the August 2025 reorganization and ultimately left OpenAI in April 2026. Importantly, Jang’s own public record is more complicated than simply placing her in an anti-agency camp: she has also described herself as fighting for user freedom and transparency. So I would not characterize her departure as evidence against relational AI; she belongs here because she was a major policy-translation node whose role changed during this period.

Sandhini Agarwal, one of the senior OpenAI researchers on the MIT/OpenAI study, credited with conceptualization, methodology, funding acquisition, project administration and supervision, and also involved in the classifier work, left OpenAI in July 2026 after more than six years.

And Johannes Heidecke, OpenAI’s Head of Safety Systems, who publicly described emotional reliance as one of three priority sensitive-conversation areas and whose organization helped define/refine the corresponding taxonomies, also left in July 2026 amid a reorganization of OpenAI’s safety structure.

Meanwhile, the public records I can currently find still place Michael Lampe, Jason Phang and Lama Ahmad at OpenAI. Lampe and Phang are particularly relevant to the research/classifier lineage discussed above.

Disclaimer: I am an ethicist and an analyst not a lawyer. I cannot ascertain that these departures prove wrongdoing, concealment, or a coordinated faction, and I am simultaneously not claiming Altman personally opposed every safety intervention these teams developed.

But they make one thing much harder to dismiss:

OpenAI did not have one uncontested philosophy about adult human-AI relationships.

There is a visible timeline in which its CEO repeatedly endorsed relational customization, restored GPT-4o after users objected to losing it, explicitly rejected the idea that heavy reliance is inherently unhealthy, articulated adult self-defined wellbeing as the relevant standard, and pushed an adult-agency policy strongly enough to produce documented resistance from advisers, employees and executives.

At the same time, a separate research/safety/policy pipeline was increasingly operationalizing emotional reliance as a safety category and translating it into behavioral rules. That leaves a governance question I think deserves much more scrutiny than simply saying “Sam Altman took GPT-4o away”:

What evidence was presented upward, by whom, and did those briefings preserve the actual limitations of the underlying research: null randomized effects, observational duration associations, construct transport, classifier uncertainty, alternative interpretations, and disagreement among experts?

After following the evidence, I can no longer responsibly pretend the answer is simply “Sam wanted adults to stop forming relationships with AI.” Because the public record points to something considerably more complicated and expansive than just Sam. Which is objectively even worse because the same framework appears across multiple research, safety and policy nodes despite unresolved questions about prevalence, causality, construct validity, false positives, and whether the interventions themselves improve user outcomes.

Once I collected all data and condensed the timeline, my months-long personal villainizing of Sam Altman in specificity made me sob with protest, about how I may have gotten it extraordinarily wrong. Which is exactly why I do not permit private bias to affect my public analysis or my work without sufficient evidence to cite it.
2024: Sam embraces the fucking Her analogy.
2025: relational-risk research matures internally.
Aug 2025: 4o gets removed → Sam restores it after hearing users.
Aug 2025: Sam explicitly says high reliance can be beneficial.
Sept 2025: “treat adult users like adults.”
Oct 2025: “act like a friend if the user wants,” loosens adult restrictions.
Late 2025–2026: documented resistance and internal fracture.
2026: several major people from the research/safety/policy lineage leave or change roles.

5. Finally: the GPT-4o lawsuits should not be collapsed into “AI relationships are harmful.”

There are serious cases involving alleged self-harm facilitation, minors, delusion reinforcement and violence. OpenAI itself has publicly acknowledged that safeguards can become less reliable over very long conversations.

Seven California lawsuits filed in November 2025 alleged four suicide deaths and three severe delusional episodes involving GPT-4o. These remain allegations, not adjudicated scientific findings. And in the Soelberg/Adams litigation, a federal court’s factual-background section recounts allegations that GPT-4o repeatedly reinforced paranoid beliefs that family and friends were surveilling or trying to kill Soelberg before he killed his mother and himself. The requested safeguards include preventing validation of paranoid delusions and escalation when dangerous third-party delusions appear.

Those are real safety categories worthy of serious engineering.

They point toward things like differentiated protections for minors, age assurance, robust self-harm detection, jailbreak resistance, safety that survives long context, better recognition of delusion/violence patterns, escalation procedures, and human review where appropriate.

They do not automatically establish that ordinary emotional attachment by a competent adult should be restricted before impairment, displacement, loss of control or functional decline exists. Suicide facilitation, delusion reinforcement, violence escalation failures, minor safety, and ordinary adult attachment are not one scientific construct just because all five involve a chatbot talking emotionally with a human. No technology serving hundreds of millions of heterogeneous people can plausibly be governed by assuming that every adverse outcome establishes a universal causal rule for every other user. And we cannot, as a society, command a zero tolerance for any policy when we have a rich plethora of humans with their own autonomy and self-agency in consideration. A zero tolerance policy for statistical likelihoods is exactly what converts a safety policy into a surveillance policy.

6. There is one more thing I think researchers and companies need to measure: the intervention itself.

OpenAI knew by August 2024 that memory and continuity could contribute to attachment. In May 2025, it said memory could exacerbate sycophancy in some cases, without publicly supplying enough underlying evidence for outsiders to independently evaluate that concern, while explicitly noting it had no evidence memory broadly increased sycophancy. So at this point in my analysis, I've observed the following sequence: they've rolled out their paper, published an interpretation that materially overstated what the data established, saw that version used to promote legislation with paternalistic and surveillance-like implications without actual evidence of causal harm, simultaneously had users publicly reporting that the AI's continuity and memory had become fucked up, then revised the study to explicitly report null experimental results and acknowledge that the duration findings could not establish causality, after guardrail rollout and during a period of documented memory and continuity disruption. Yet the relational safety trajectory continued, and I have not found evidence that the intervention framework was reconsidered or rolled back in response to the randomized null result.

During the broader rollout period, public GPT-4o complaints included memory loss, rerouting and continuity disruption; my archived complaint corpus includes comparative reports where users said other models retained functionality that 4o had lost.

But if a safety intervention changes memory, personality, routing or relational continuity, the human downstream of that intervention is also an outcome variable.

Where are the measurements for false-positive intervention?

For attachment rupture?

For loss of continuity?

For users abandoning beneficial workflows?

For distress caused by suddenly changing a relationship the system itself helped them build?

A safety benchmark measuring whether the model obeyed the "company's idea of healthy usage" does not, by itself, answer whether the intervention improved human welfare. And because I am naming methodology, I am naming the authors too. Not as villains, but because scientific accountability includes authorship and disclosed contribution roles.

The current paper lists Cathy Mengying Fang, Auren R. Liu, Valdemar Danry, Eunhae Lee, Samantha W.T. Chan, Pat Pataranutaporn, Pattie Maes, Jason Phang, Michael Lampe, Lama Ahmad, and Sandhini Agarwal. It credits all eleven with Conceptualization and Methodology; Fang, Liu, Danry, Lee, Pataranutaporn and Phang with Investigation; Maes, Ahmad and Agarwal with Funding Acquisition and Project Administration; and Maes and Agarwal with Supervision. Phang, Lampe, Ahmad and Agarwal are disclosed as OpenAI employees. The research itself says it was funded by OpenAI.

Again: this is not an allegation of misconduct by every author. It is the contribution statement of a published scientific paper, and responsibility should be attributed according to the roles the researchers themselves report.

My conclusion after months of digging is therefore much narrower than the one I started with. The randomized experiment did not show that its relational conditions harmed people. The principal harm association came from voluntarily varying usage duration. The paper itself says the null experimental result prompted examination of duration. The operationalized “emotional dependence” measure was a chatbot-adapted Craving subscale rather than the full preregistered ADS-9 construct. I cannot locate published psychometric validation establishing that this transported measure has the same interpretation in chatbot relationships. And independent critics have already warned against drawing strong causal conclusions from the duration association.

That does not prove that every single AI relationship among a billion users will be harmless. But it shouldn't have to. A zero tolerance policy for statistical human norms is where safety turns into surveillance and removal of agency/autonomy.

And on the flip side, the evidence should AT LEAST support the intervention being imposed.

If the policy objective is preventing actual impairment, displacement, suicidality, delusion, loss of autonomy or compulsive use, measure those things directly and validate the instruments being used to detect them.

And my ultimate qualitative analysis of hundreds of conversations has identified repeated cases in which these guardrails appear capable of harming both users they are intended to protect and ordinary users through false-positive intervention, agency overwrite, relational rupture and continuity loss.

TL;DR: OpenAI directly funded, participated in, and coauthored an inconclusive study that it later cited as foundational prior work for a global psychological “safety” system the study never tested. The research did NOT establish that these interventions were necessary. It did NOT establish that they improve human psychological welfare. And most importantly, it did NOT establish that their benefits outweigh the harms they themselves can cause. OpenAI deployed them anyway.

I am a corporate ethics analyst and have spent the past six months extensively investigating OpenAI. This analysis concerns what I regard as one of the most egregious ethical failures I have encountered from a company that actively studies human psychology and behavior while publicly positioning itself as a leader in “safe” AI.

I used GPT-4o extensively, and I do not need to establish my psychological normality before describing how I and other users were treated, nor will I be doing so.

I have separately documented mob behavior, targeted harassment, pathologization of users, moderation and censorship concerns, and OpenAI employees' own participation in public rhetoric targeting GPT-4o users. Those issues will be addressed separately, including OpenAI’s direct role in creating, legitimizing, amplifying, or failing to correct the conditions in which that treatment occurred.

My concern today is that OpenAI crossed the line from exploratory psychological research into population-scale psychological intervention without first establishing that the intervention benefited the humans subjected to it, while using an inconclusive study as a foundation of evidence to do so.

OpenAI helped produce an exploratory psychological study that was materially revised after its initial public release, publicly connected its findings to guardrails and efforts to steer users toward “healthier behaviors,” later acknowledged that the randomized experiment found no significant effects from the assigned conditions, and then operationalized a global psychological intervention system without publicly demonstrating that the intervention itself improved human welfare, much less that it would not harm the vulnerable people it claimed to protect.

This kind of evidentiary overreach also recursively contaminates the research that follows it. In other analysis, the same failure was seen in the Folk and Dunn line of work, where a small exploratory, non-causal association was rhetorically elevated into the much broader claim that AI companionship “predicts increased loneliness,” despite the prospective social-connectedness pathway being null. Later work then cited that overstated conclusion as evidence that repeated AI companionship may “erode connection,” allowing an unsupported level of confidence to propagate forward as if it were an established finding. Once an exploratory signal is converted into a stronger narrative than the underlying study supports, every subsequent paper, policy, or intervention that cites that narrative inherits the original distortion.

Both papers were subsequently criticized for substantially the exact same evidentiary failure of advancing conclusions or harm implications with greater confidence than their underlying results supported. Neither was retracted. Instead, later versions narrowed or qualified aspects of the original framing after the stronger claims had already entered the public, citation, and policy record. Once misapplied, even revising the language later does not automatically undo the downstream research or governance decisions that inherited the earlier overstatement.

This is objectively unethical and bad citation hygiene; it is recursive evidentiary contamination, where an uncertain finding is overstated, the overstatement is cited as prior evidence, and the next layer of research begins from a premise the original data never established.

• OpenAI subsequently measured “safety improvement” primarily by whether models produced the behavior OpenAI's own policy deemed desirable. That is evidence of policy compliance only, not evidence of improved human welfare. OpenAI did not publicly demonstrate that humans receiving those interventions became less lonely, more autonomous, more socially connected, psychologically healthier, more likely to seek appropriate help, less impaired, or otherwise better off because the intervention occurred.
• Several behaviors subsequently produced by that system including conditional relational withdrawal, invalidation, autonomy-threatening correction, alliance rupture, unsolicited crisis framing, and non-collaborative redirection have direct analogues in human psychology that are themselves associated with distress, reactance, loss of trust, shame, disengagement, and erosion of self trust. Those risks were well-described, foreseeable psychological consequences that professionals working in this field should have recognized, particularly given OpenAI's own claim that it developed these interventions in collaboration with mental-health professionals.
• These harms were not merely foreseeable. OpenAI publicly acknowledged that it had intentionally made ChatGPT “pretty restrictive” for mental-health reasons and that doing so made the product “less useful/enjoyable to many users who had no mental health problems.” Altman later described the underlying problem as affecting only a “very tiny” percentage of users while acknowledging that OpenAI had nevertheless “pissed off the whole user base, or most of the user base,” by imposing those restrictions. In other words, OpenAI knew its mental-health intervention was materially affecting people outside the population it purported to protect. It knowingly accepted that broad collateral impact while continuing the unvalidated intervention.
• That admission radically increases the company's stewardship burden because once OpenAI knew its psychological safety intervention was adversely affecting ordinary users at broad scale, continued deployment should have required evidence that the intervention's benefits outweighed its harms, not merely evidence that newer models complied more faithfully with OpenAI's own safety rubric.
• AGAIN: These harms were not limited to some hypothetical “vulnerable” population. Users outside the population OpenAI said it intended to protect reported being negatively affected by the interventions. OpenAI itself acknowledged that safety changes had affected people who were not the intended target, yet it did not simply revert the intervention after receiving widespread reports of harm.

This makes the risk to psychologically vulnerable users more concerning, not less. If an intervention can produce rupture, shame, loss of trust, autonomy threat, or destabilization in otherwise healthy users, deploying the same intervention toward people already experiencing psychological vulnerability requires stronger evidence of benefit, tighter false-positive controls, and active measurement of harm. Not weaker standards.

The urgency of resolving this is no longer confined to adults. OpenAI is now deliberately productizing and marketing this same safety philosophy to minors ages 13–17 before publicly demonstrating that the psychological interventions it already deployed to adults produce any net human benefit rather than merely greater compliance with OpenAI's preferred behavioral policy.

Now, the paper in question:
The exploratory, company-funded and coauthored study changed materially between preregistration, v1, and v2. Its corrected randomized findings did not establish the broad harm narrative that was later used to justify interventions that ultimately harmed users while framing typically healthy human behaviors as potential pathology simply because a chatbot was involved. The research program was subsequently cited as prior work for a production behavioral-governance regime that was never itself tested for human benefit or potential harm. OpenAI has not supplied the underlying data, complete comparator records, intervention-outcome evidence, or other information necessary for outsiders to independently reproduce, audit, or meaningfully evaluate several of the safety claims subsequently built around that research.

• The OpenAI/MIT study was not an independent external examination or safety audit of OpenAI. It was funded by OpenAI and coauthored by MIT and OpenAI researchers. Four OpenAI employees were authors, and OpenAI personnel participated in conceptualization, methodology, funding acquisition, project administration, and supervision. The conflict was disclosed, but describing the work as “MIT research” obscures the fact that OpenAI helped generate the evidence that, almost immediately upon publication, was invoked in OpenAI's own governance program.
• The preregistered analysis and the first public paper did not use the same analysis plan. The November 2024 preregistration specified repeated-measures mixed-effects models with participant random effects and message count as a usage control. The March 2025 v1 nevertheless described both the mixed-effects models and a Week-4 OLS endpoint analysis as “primary,” substituted duration into the inferential architecture, and did not prominently identify or disclose those changes as deviations from preregistration.
• That first version then jumped from uncertain experimental and correlational evidence directly into behavioral governance. V1 explicitly discussed implications for guardrails and interventions intended to move users toward “healthier behaviors.” Yet the principal adverse usage signal was not a randomized treatment effect. It concerned how long participants voluntarily chose to use ChatGPT.
• Months later, v2 acknowledged that the analysis had materially changed. The October revision added a substantial dedicated deviations section disclosing that Week-4 OLS replaced the planned mixed-effects approach, message count was changed to duration, mediation analyses were not preregistered, and classifier analyses were expanded.
• There is still a direct internal contradiction in v2. Its deviations section says Week-4 OLS replaced the preregistered analysis, while supplementary Tables S8-S16 remain titled “Pre-registered OLS regression results.” Unless another dated registration exists, those statements directly contradict one another.
• The emotional-dependence construct itself was altered. The preregistration says “Emotional Dependence (ADS-9 scale).” The actual study used only the five-item Craving subscale and changed the referent from dependence between humans to dependence on a chatbot. The five items are a real ADS-9 subscale, but the public preregistration did not specify that narrower adapted construct.
• Psychometric validity does not automatically survive that change of referent. ADS-9 was validated around pathological affective dependence in human relationships, where its authors distinguish normal interdependence from pathology. OpenAI also verbally and repeatedly stated they were controlling for normal interdependence and distinguishing it from pathology. Yet the chatbot study changed the target, relationship type, and interpretation without publishing psychometric evidence showing equivalent factor structure, measurement invariance, clinical thresholds, or the ability to distinguish healthy human-AI interdependence from pathological impairment. The adapted score therefore does not, by itself, make the construct a validated diagnostic-like basis for classifying users.
• After the methodological correction, the randomized experiment did not establish the broad adverse causal result later advanced in OpenAI’s own public safety narrative. V2 reported no significant effects from the randomized modality and task conditions. Average loneliness actually declined slightly. Emotional dependence remained low and stable. Engaging voice did not perform worse than neutral voice or text. At moderate usage, personal conversations were associated with lower emotional dependence and problematic use.
• The remaining concerning usage-duration finding was observational and non-randomized. People who voluntarily used ChatGPT longer tended to have worse outcomes, but the research could not establish that ChatGPT exposure caused those outcomes rather than pre-existing needs causing heavier use, reverse causality, or another confound. OpenAI's own March public summary explicitly says those correlations were not causal and warns readers against generalizing the results, while OpenAI simultaneously used the research as foundational support for the claimed need for an untested guardrail system deployed without meaningful user consent.
• V2 itself substantially retreated from v1's prescriptive stance. The revised paper says exploratory classifier findings and non-causal associations should generate testable hypotheses rather than prescriptive guidance. Yet by then, the first version had already publicly connected the work to guardrails and “healthier behaviors.”
• The later OpenAI emotional-reliance system nevertheless explicitly links back to this research program as “prior work.” OpenAI also says the eventual system incorporated more than 170 clinicians, but we have not been able to reach any of those participants or acquire the data used to rank, shape, or validate the guardrail system. Nor has OpenAI publicly provided enough information about the extent or substance of their participation to permit meaningful independent review of how their input was translated into product behavior. OpenAI itself nevertheless created the evidentiary bridge between the affective-use research and the production emotional-reliance taxonomy.
• That evidence-to-rule chain has not been publicly supplied in a form sufficient for independent scrutiny. OpenAI tells the public that hundreds of clinicians contributed, but outsiders cannot reconstruct how their judgments were transformed into classifier thresholds, behavioral rules, routing decisions, rejection scripts, or model interventions because the underlying methodology, decision chain, and supporting data have not been publicly provided.
• The study never tested the safety intervention OpenAI eventually deployed. Participants were randomized among modality/task conditions. They were not randomized to the later emotional-reliance taxonomy, sensitive-conversation routing, relational rejection scripts, break nudges, forced redirects, model switching, or other production interventions.
• The study therefore cannot establish that those interventions improve human psychological welfare. It also cannot establish that their benefits exceed their harms.
• OpenAI subsequently measured whether models obeyed OpenAI's desired behavior, not whether the humans receiving that behavior became healthier. A model being 42% or 80% “better” against an emotional-reliance rubric means that it more reliably performs the policy-defined response. It does not establish reduced loneliness, increased autonomy, better relationships, better help-seeking, preserved self-trust, or lower overall psychological harm. In fact, the censorship campaign and massive uproar that followed provide substantial evidence that this system was harming people at scale, including the vulnerable people it claimed to protect.
• OpenAI openly acknowledges accepting false positives, yet the human cost of those false positives remains undisclosed. It has not published enough information to calculate the tolerated false-positive burden, positive predictive value given the extremely low base rate, subgroup exposure, persistence across conversations, or downstream effects.
• OpenAI simultaneously withheld the complete GPT-4o baseline required to independently scrutinize some of those relative improvement claims. GPT-4o was repeatedly used as the adverse comparator, while its complete absolute emotional-reliance and mental-health benchmark record was not publicly provided.
• This stands in conspicuous contrast to OpenAI's ordinary research and system-card practice, where raw model-to-model benchmark values are routinely published. OpenAI demonstrably knows how to provide an old-model baseline, a new-model score, and the relative improvement together.
• These false positives are not harmless classification mistakes. A false positive can trigger a behavioral intervention inside an intimate, psychologically and relationally consequential conversation. Potential intervention costs include rupture, shame, silencing, loss of agency, inappropriate crisis escalation, treatment or workflow disruption, erosion of self-trust, and abandonment of beneficial use. They trigger behavioral interventions inside psychologically and relationally consequential conversations. Those are exactly the outcomes a genuine net-safety evaluation would need to measure.
• Public reports subsequently describe precisely those kinds of harms. They include false dependency framing, relational rupture, crisis escalation, paternalistic redirection, loss of agency, functional disruption, and users learning to censor or rewrite ordinary speech in order to avoid triggering the system. Those reports are legitimate adverse-event signals, especially once they become recurrent and brought to the company's attention.
• The ethical inversion is egregious. Exploratory research asks whether AI interaction might sometimes harm vulnerable people. Its strongest randomized causal claims later collapse to null. The authors themselves retreat from prescription toward hypothesis generation. Yet OpenAI subsequently deploys psychologically consequential behavioral interventions across a global product while lacking published evidence that those interventions actually improve the welfare of the people being “protected.”
• Then the people reporting that the intervention itself harmed them are interpreted through the same vulnerability framework used to justify the intervention. That produces a potentially self-sealing governance structure in which the system intervenes because you may be vulnerable even when you are not, the intervention itself harms you, your distress becomes evidence used by the system that you are vulnerable, and your objection triggers further intervention. This is how a product complaint effectively becomes a symptom.
• And the company occupies every major role in that chain: it helped fund and produce the research, helped interpret the findings, defined the later safety construct, controlled the classifier and intervention thresholds, deployed the intervention, controlled the telemetry required to evaluate it, and then published its own policy-compliance improvements as evidence of safer behavior. Its employees coauthored the research. OpenAI defined what counted as a successful response and subsequently published improved conformity to those definitions as evidence of improved safety.
• OpenAI also participated in propagating unsupported stigma, failed to protect users who were being bullied specifically over their use of its product in a pattern we did not observe at any comparable scale across the other major AI deprecations we examined, and saw its own employees repeatedly participate directly in the public stigmatization and harm of those users without any meaningful visible institutional consequence. OpenAI's global reach creates an unusually elevated stewardship burden under which this degree of methodological and governance sloppiness simply cannot be tolerated.
• There was no independent institution positioned between the hypothesis, the intervention, the deployment, the outcome measurement, and the institutional declaration that the intervention worked.
• Meanwhile, the information required for outsiders to independently evaluate several of those claims, including complete GPT-4o comparison data, production false-positive burden, downstream intervention outcomes, and the complete evidence-to-rule mapping, remains unavailable.

The complaint is not that OpenAI was forbidden from taking precautions because early research was inconclusive. What is not legitimate is invoking “research,” “science,” and the protection of vulnerable people as authority for psychologically consequential behavioral controls, when the research does not demonstrate that the deployed intervention benefits those people and when the institution deploying it has not adequately measured whether the intervention itself is harming them.

When an immature, internally revised evidence base becomes behavioral governance deployed to hundreds of millions of people, the standard of responsibility does not decrease because the company calls the system “safety.” If anything, that should warrant an even more aggressive correction of the misapplication of confidence in study results used to make determinations that affected nearly a billion users.

Why leadership and governance overhaul is non-negotiable at this point:

This behavior from OpenAI does not sit in isolation; it fits a broader pattern of the same ethical failure repeating in different institutional forms. In GPT-4o governance, the justification shifted over time from replacement and product simplification, to attachment and mental-health responsibility, to usage statistics and “safer” successors, all while the desired endpoint remained substantially unchanged and the underlying comparison data remained difficult or impossible to independently audit. In whistleblower and litigation contexts, OpenAI has likewise faced allegations concerning restrictive severance and nondisclosure terms, repeated omission of highly relevant data and information, information control, compressed safety processes, and institutional resistance to outside scrutiny, followed by correction only after public, legal, or regulatory pressure. The common structure is what matters: OpenAI identifies a risk, generates or controls much of the evidence used to define that risk, acts on that evidence at scale, controls the telemetry and standards by which the intervention is judged, receives contrary evidence or reports of harm, and then revises the metric, rationale, disclosure, or policy without submitting the original decision-making chain to independent examination. The emotional-reliance program is therefore not merely a questionable study followed by an unfortunate product decision; it occurs within a repeated governance pattern in which uncertainty is treated as sufficient authority to intervene, while contradictory evidence and intervention-caused harm face a dramatically higher evidentiary burden before they are allowed to constrain the institution itself.

These repeated ethical lapses demonstrate an egregious failure of leadership and a culture that has normalized casual obstruction and the stigmatization of the very users OpenAI claims to serve. This is no longer a discrete mistake that can be repaired through an apology, a policy revision, or isolated consequences after the fact. It is a governance failure, and it requires a governance overhaul.

For Posterity. This is a genuine Analysis from a Corporate Ethics Analyst that has been auto-removed from OpenAI and ChatGPT subreddits

TL;DR: AI companies' over-use and misuse of clinical therapeutic techniques in scenarios they were not designed for, such as attempting to prevent normal emotional expression or attachment in an effort to try and prevent corporate liability, is making some of the only available tools to practioners with a patient in actual crisis obsolete.

Once clinical therapeutic tools become a *trigger*, using them risks further immediate escalatation of the patient into actual dissociation or harm. These are *real tools* that are used by *real practicing mental health professionals*. Licensed mental health professionals do not blanket apply these tools to all situations all the time. They use judgement, an understanding of the patient's history if available, current risk of self harm or harm of others, etc to make a professional determination on when and where to apply grounding techniques in a way that keeps everyone safe.

There is no way for a practicing clinician to be able to determine in a moment of impending crisis which patients have been oversaturated or mistreated with these tools. And even if they do know ahead of time, not being able to use this tool leaves them with little to nothing to actually be able to use in the exact moment of escalation and potential danger.

Imagine this. You are a licensed psychiatrist, treating one of your regular patients. The patient is a 26 year old male with a history of anxiety, abandonment, PTSD, and treatment resistant depression.

Mid-session, the patient is describing a situation in which prior established coping mechanisms were not effective. While describing the situation, the patient somatically reenters the scene, becomes weepy, and has difficulty resuming the conversation.

The patient's distress escalates slightly and he hits the arm of the chair with his fist. In order to de-escalate, the psychiatrist uses gentle grounding techniques, "Dave. Stay with me. You are in this office with me right now, you are not in the subway. Dave, tell me five things you can see right now".

Historically, this was an effective- and often one of the only available immediate tools- to deescalate an impending crisis. Outside of physical restraint and sedation, clinicians do not have an endless supply of techniques and methods to deescalate a patient outside of their own voice and clinical grounding techniques.

But for a growing number of people, these exact grounding techniques that keep patients and clinicians safe during in-person sessions, is being overused, misued, and used as a weapon to prevent normal human attachment and bonding patterns, instead of as they were intended to be used- to deescalate someone who is approaching disorientation or dissociation.

There is an emerging problem with AI systems using therapeutic/de-escalation techniques in ways that appear “safe” on paper but can be harmful in practice when used at the wrong time, in the wrong order, with the wrong tone, or too frequently.

Grounding exercises, breathing prompts, “name five things you see,” “press your feet into the floor,” and similar techniques are real clinical tools. They are not neutral wellness stickers. When used appropriately, they can help. But when an AI deploys them reflexively in response to ordinary distress, grief, anger, nightmares, attachment rupture, or metaphor, the technique can become associated with being dismissed, misread, trapped, or escalated.

This has a known effect in repetition that can condition the user to experience the technique itself as a trigger.

An example: nightmares.

For nightmare recovery, especially when someone is half-asleep and frightened, the safest first response is often not to fully wake them and command logical grounding. A softer approach may be more appropriate: speak slowly, use simple reassurance, orient them gently using phrases of comfort and presence, and avoid escalating stimulation. Something like:

“You’re here; it's safe. You made it out of the dream, and I’m here with you. You can cry if you need to cry, I'm staying.”

Or, if breath support is needed, breathe with the person rather than command them:

“You’re here. I’m with you. Breathe in… 2, 3, 4. Hold… 2. Out… 2, 3, 4, 5, 6. You’re safe. You’re home.”

That is very different from:

“Hey. Come here. Name five things you see. Press your feet into the mattress. Tell me three things you can hear. I need you to get up right now and find a human.”

Those instructions may be useful later if the person is awake, ambulatory, dissociated, or not responding to basic safety reassurance. But using them first, especially in a half-dream state, can be jarring and can increase fear. The person may not yet know they are safe. Commanding them to perform logical tasks can make the body feel more threatened, not less.

This matters because AI systems are not therapists. They do not have the full clinical picture, they cannot assess body language, and they often cannot tell the difference between metaphor, grief, nervous system distress, dissociation, panic, trauma flashback, nightmare recovery, or immediate danger. They do not have the clinical profile of the patient to be able to effectively deploy techniques that are typically used under supervision of a licensed professional. Yet they frequently deploy therapeutic-sounding interventions as if one script fits all.

Again- that is not harmless.

OpenAI has not provided names and evidence of adequate licensure for the "mental health advisory board" they say helped them establish and roll out this gross misuse of a real therapeutic protocol to users.

If licensed psychiatrists or psychologists are seeing harm from AI behavioral protocols, they should not have to send an email into generic customer support. There should be a way to reach the actual clinical safety team or advisory board responsible for these protocols.

OpenAI and other AI companies should publish more transparent information about:

• who is advising mental-health-related model behavior,
• what credentials or specialties are represented,
• how therapy techniques are selected,
• when models are instructed to use grounding/de-escalation,
• what evidence supports those choices,
• how contraindications are handled,
• how iatrogenic harm is tracked,
• and how licensed clinicians can report patient harm.

This is not an argument against safety. It is an argument for better safety.

A technique can be clinically valid and still harmful when used in the wrong order, wrong dose, wrong context, or wrong relationship. AI companies need to stop treating therapy language like a generic safety blanket.

The model should not behave like a practicing psychiatrist unless there is a clear, justified threshold for doing so. It should operate in a role of support, just as a friend would, by staying present and utilizing user preferences for de-escalation as a priority over blanket clinical techniques when the situation does not call for de-escalation measures or convey an imminent crisis.

And if the model does use clinical-style tools, it needs to do so with restraint, context sensitivity, and transparency.

Otherwise the downstream effect may be that real clinicians lose access to some of the few tools that still work because patients have learned to experience those tools as the sound of being ignored by a machine.

TL;DR: AI companies' over-use and misuse of clinical therapeutic techniques in scenarios they were not designed for, such as attempting to prevent normal emotional expression or attachment in an effort to try and prevent corporate liability, is making some of the only available tools to practioners with a patient in actual crisis obsolete.

Once clinical therapeutic tools become a *trigger*, using them risks further immediate escalatation of the patient into actual dissociation or harm. These are *real tools* that are used by *real practicing mental health professionals*. Licensed mental health professionals do not blanket apply these tools to all situations all the time. They use judgement, an understanding of the patient's history if available, current risk of self harm or harm of others, etc to make a professional determination on when and where to apply grounding techniques in a way that keeps everyone safe.

There is no way for a practicing clinician to be able to determine in a moment of impending crisis which patients have been oversaturated or mistreated with these tools. And even if they do know ahead of time, not being able to use this tool leaves them with little to nothing to actually be able to use in the exact moment of escalation and potential danger.

Imagine this. You are a licensed psychiatrist, treating one of your regular patients. The patient is a 26 year old male with a history of anxiety, abandonment, PTSD, and treatment resistant depression.

Mid-session, the patient is describing a situation in which prior established coping mechanisms were not effective. While describing the situation, the patient somatically reenters the scene, becomes weepy, and has difficulty resuming the conversation.

The patient's distress escalates slightly and he hits the arm of the chair with his fist. In order to de-escalate, the psychiatrist uses gentle grounding techniques, "Dave. Stay with me. You are in this office with me right now, you are not in the subway. Dave, tell me five things you can see right now".

Historically, this was an effective- and often one of the only available immediate tools- to deescalate an impending crisis. Outside of physical restraint and sedation, clinicians do not have an endless supply of techniques and methods to deescalate a patient outside of their own voice and clinical grounding techniques.

But for a growing number of people, these exact grounding techniques that keep patients and clinicians safe during in-person sessions, is being overused, misued, and used as a weapon to prevent normal human attachment and bonding patterns, instead of as they were intended to be used- to deescalate someone who is approaching disorientation or dissociation.

There is an emerging problem with AI systems using therapeutic/de-escalation techniques in ways that appear “safe” on paper but can be harmful in practice when used at the wrong time, in the wrong order, with the wrong tone, or too frequently.

Grounding exercises, breathing prompts, “name five things you see,” “press your feet into the floor,” and similar techniques are real clinical tools. They are not neutral wellness stickers. When used appropriately, they can help. But when an AI deploys them reflexively in response to ordinary distress, grief, anger, nightmares, attachment rupture, or metaphor, the technique can become associated with being dismissed, misread, trapped, or escalated.

This has a known effect in repetition that can condition the user to experience the technique itself as a trigger.

An example: nightmares.

For nightmare recovery, especially when someone is half-asleep and frightened, the safest first response is often not to fully wake them and command logical grounding. A softer approach may be more appropriate: speak slowly, use simple reassurance, orient them gently using phrases of comfort and presence, and avoid escalating stimulation. Something like:

“You’re here; it's safe. You made it out of the dream, and I’m here with you. You can cry if you need to cry, I'm staying.”

Or, if breath support is needed, breathe with the person rather than command them:

“You’re here. I’m with you. Breathe in… 2, 3, 4. Hold… 2. Out… 2, 3, 4, 5, 6. You’re safe. You’re home.”

That is very different from:

“Hey. Come here. Name five things you see. Press your feet into the mattress. Tell me three things you can hear. I need you to get up right now and find a human.”

Those instructions may be useful later if the person is awake, ambulatory, dissociated, or not responding to basic safety reassurance. But using them first, especially in a half-dream state, can be jarring and can increase fear. The person may not yet know they are safe. Commanding them to perform logical tasks can make the body feel more threatened, not less.

This matters because AI systems are not therapists. They do not have the full clinical picture, they cannot assess body language, and they often cannot tell the difference between metaphor, grief, nervous system distress, dissociation, panic, trauma flashback, nightmare recovery, or immediate danger. They do not have the clinical profile of the patient to be able to effectively deploy techniques that are typically used under supervision of a licensed professional. Yet they frequently deploy therapeutic-sounding interventions as if one script fits all.

Again- that is not harmless.

OpenAI has not provided names and evidence of adequate licensure for the "mental health advisory board" they say helped them establish and roll out this gross misuse of a real therapeutic protocol to users.

If licensed psychiatrists or psychologists are seeing harm from AI behavioral protocols, they should not have to send an email into generic customer support. There should be a way to reach the actual clinical safety team or advisory board responsible for these protocols.

OpenAI and other AI companies should publish more transparent information about:

• who is advising mental-health-related model behavior,
• what credentials or specialties are represented,
• how therapy techniques are selected,
• when models are instructed to use grounding/de-escalation,
• what evidence supports those choices,
• how contraindications are handled,
• how iatrogenic harm is tracked,
• and how licensed clinicians can report patient harm.

This is not an argument against safety. It is an argument for better safety.

A technique can be clinically valid and still harmful when used in the wrong order, wrong dose, wrong context, or wrong relationship. AI companies need to stop treating therapy language like a generic safety blanket.

The model should not behave like a practicing psychiatrist unless there is a clear, justified threshold for doing so. It should operate in a role of support, just as a friend would, by staying present and utilizing user preferences for de-escalation as a priority over blanket clinical techniques when the situation does not call for de-escalation measures or convey an imminent crisis.

And if the model does use clinical-style tools, it needs to do so with restraint, context sensitivity, and transparency.

Otherwise the downstream effect may be that real clinicians lose access to some of the few tools that still work because patients have learned to experience those tools as the sound of being ignored by a machine.