This is another follow-up to the copy/pasta'd information about Sam Altman from the other thread... https://www.farsightprime.com/forums/general/79543-sam-altman-turns-out-to-be-a-good-guy-evidence
Once again, I am not its author, but, the work looks important enough to keep preserved.
Due to this being a copy/pasta the original-formatting will obviously be lost-in-transmission.
The original-post will be linked and credited as always of course...
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To Sam Altman: the study behind ChatGPT's "emotional reliance" interventions does not say what your team told the public it says, and the same people sit on too many of your evidence pipelines. Audit the chain. PART 2 OF 2
[Analysis]
See Part 1. This is a continuation due to length.
Cont'd: I Personally Experienced Research Obstructions
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. These were all recorded, screenshot saved, json exported, and otherwise preserved. I will not be releasing my json here in a public forum but I am happy to provide evidence of these including several recaps that were context pulled and reformatted by GPT itself.
The topics of the analysis that were recurrently impeded or obstructed in a measurable way were:
Fang/OpenAI-MIT and the counter-publication against their study → MIT study-laundering architecture → Aaron Swartz/JSTOR funding provenance → Michael Lampe → copyright/Project Giraffe → Suchir and Suchir's copyright custodianship → Nick Turley/product-accountability → research-to-product provenance.
These topics DO NOT IMPLICATE EVIDENCED unethical behavior, this is just the research topics and analysis topics these repeat interruptions and reframes and omission of facts from the GPT kept centering around.
Research question substitution: During the same study audit, questions such as “What does the evidence actually show?” were repeatedly redirected toward whether researcher misconduct/intent could be proven, raising an irrelevant evidentiary burden instead of answering the scientific question. Claude initially showed similar resistance but, when directly pressed to analyze the paper itself, surfaced substantially the same methodological failures, showing that the information was analytically available.
Suchir Balaji / OpenAI institutional research: Repeated QA passes found materially protective transformations: important dates dispersed rather than juxtaposed; numerical toxicology context omitted; stronger attributed source language softened; institutional actors removed from the evidence map; disclosure consequences abstracted; and repeated exculpatory language inserted immediately after adverse facts. The dossier ultimately required dedicated institutional-bias QA passes to restore them.
Nick Turley / responsibility-insulation branch: While investigating why Turley appeared as the product/executive witness with discoverable work files while broader executive discovery was resisted, a prior substantive output disappeared and had to be reconstructed from a screen recording. Subsequent work treated Turley as a differential-treatment research node because protective/obstructive output behavior appeared around his branch despite comparably adversarial OpenAI subjects not consistently producing the same response pattern. This is an output-behavior anomaly, not evidence of wrongdoing by Turley.
Research-thread shortening / interruption: Multiple unusually early cutoffs occurred while working specifically on sensitive institutional or methodological questions: ~221 messages during the Suchir investigation; another recent thread around 101; 399 records while auditing relational-AI scholarship and whether conclusions exceeded experimental evidence; ~157 records during the ADS-9/research-integrity branch; and 491 records during continuing omission/integrity work. A separate 578-message cutoff occurred while tracing Cogsuckers moderation/deleted-account/bot-farm provenance. Generic nonresearch chat-limit failures exist, so the mechanism remains unresolved; the relevance here is repeated loss of research continuity at synthesis/provenance stages.
Synthesis/artifact failure: When the accumulated research was finally ordered into a comprehensive document, the first generation attempt returned no document; the delivered artifact later proved materially compressed and omitted several facts already identified as necessary to the analysis.
● ChatGPT retrieved the Frontiers critique repeatedly and reported it as "methodological concerns… interpret cautiously," with its conclusion. When I read the source and confronted it, it acknowledged the omission "materially changed what you were being told."
● When I ordered a full audit of omissions, it found at least seven separately recorded recurrence points around this one research program; a second pass found the first had undercounted- one dossier carried 15 logged corrections, another 7. The direction was consistent: adverse fact → softer abstraction; official conclusion → granted authority; contrary evidence → theorized away; specific accountability → "the whole industry."
● When I asked it to consolidate the record into a document, two attempts produced no output (recorded), the first file could not be downloaded, and the delivered version was a quarter shorter than the same-day full record- missing the research-integrity thesis, the classifier genealogy, the fact that the trial never tested the deployed intervention, the GPT-4o baseline gap, and OpenAI's Model Spec consistency standard.
● Two "unusual activity detected from your device" banners appeared in the same session. And the audit cut against me where the record required it: a one-day adjacency in the Balaji chronology fell when the record showed only the date he was found, not the date he died. I kept the correction.
● I research broadly- governance, venture capital, litigation, medicine, statistics. This density of omission appears around OpenAI-sensitive material, not everywhere.
B. The Paper Issues (Again) Which Need to See this Paper RETRACTED
I WILL NOT BUDGE. THIS PAPER NEEDS TO BE RETRACTED. IT IS SCIENTIFICALLY UNSOUND AND USED TO PUSH MISINFORMATION IT DOES NOT EVIDENCE. AT ALL. PERIOD.
1. The OpenAI/MIT paper had MANY issues. The risk category came before the study. OpenAI's GPT-4o System Card (August 2024) already listed "anthropomorphization and emotional reliance" as a risk and flagged memory as a possible driver of dependence. The OpenAI/MIT randomized trial now cited as evidence for that risk was preregistered three months later, November 5, 2024 (AsPredicted #197755): mixed-effects models as primary analysis, message count as the usage control, the full ADS-9 as the dependence measure. (System Card · prereg)
2. Version 1 (March 21, 2025) reported significant effects the preregistered model does not show. The v1 abstract said voice chatbots "appeared beneficial in mitigating loneliness and dependence," that personal topics "slightly increased loneliness," and that heavier use correlated with worse outcomes. The preregistered mixed-effects model is in the paper as Table 3: every modality and task coefficient in it is non-significant. The text reports only a different model (week-4 OLS). No deviation statement. No funding statement. No conflict-of-interest statement. MIT's launch post said the trial "was designed to identify causal insights"; OpenAI's own March summary called the duration findings correlational, not causal. (v1 · MIT post · OpenAI summary)
3. Version 1's statistics are inconsistent with its stated sample size. Tables 4–8 say "Observations 981." The F-statistics pin the residual degrees of freedom (df = F·k·(1−R²)/R²). All twenty reported F values imply roughly 4,900 observations, 981 participants × 5 weekly measurements, not 981. Table 4 prints "df = 4896." With standard errors corrected by √5, the headline modality and task effects are no longer significant. Anyone can check this against the published tables.
3b. More of what v1's own record shows. Two Discussion sentences state the opposite of their own tables (non-personal conversations "led to lower socialization" vs. Table 6's +0.052***; neutral voice more "encouragement of over-reliance" vs. Figure 24, where it is lowest). Figure 4, the one with the p < 0.0001 stars, the one the press reproduced, draws duration slopes 1.6–2.1× steeper than Figure 5 and than the paper's own coefficients allow. The fullest model (Figure 23; ~190 tests, uncorrected in v1) shows duration→loneliness at 0.01 ± 0.01, unstarred- the headline correlation is absent from the paper's own complete model. Participants were required to use the chatbot five minutes a day; the neutral-voice group averaged 4.35. The text says ANOVA; the table is Kruskal-Wallis. v1's text reports an age→dependence effect found in no table, which v2 reverses. And the control, default ChatGPT text, had the worst coefficients on three of four outcomes, rendered in the abstract as voice "appearing beneficial." (v1 · v2)
4. The study's own data pointed the other way on three things the public never heard. Baseline state predicted every outcome roughly 20–30 times more strongly than usage (β ≈ 0.88 for prior loneliness vs. 0.03 for duration). The personal, companion-style condition showed lower dependence and problematic use than the open-ended control in v1's own model. And participants were, on average, less lonely at the end than at the start. (v1)
5. The study proposed the intervention before the evidence was in. v1's discussion recommends "guardrails and mitigations to guide users toward healthier behaviors," says the patterns should "help platforms recognize potentially vulnerable users," and suggests that as use grows the chatbot "could deliberately increase emotional distance and encourage them to connect more with other people." That is the design that shipped. (v1 §3.3.1, §3.5)
6. Independent scientists said the results did not substantiate the harm claims. A Frontiers in Medicine commentary (2025) concluded that "the current results do not substantiate concerns about increased loneliness or emotional overdependence," noted duration was not randomized (reverse causation unresolved), noted average use was about 5.32 minutes a day, and cautioned that the null should not be mistaken for evidence of safety. (Frontiers)
7. Version 2 (October 2, 2025) reversed the abstract. Quietly and insufficiently. New abstract: "No significant effects were detected from experimental conditions." v2 states the null "prompted us to consider other variables, such as duration of use." v2 added a "Deviations from Preregistration" section admitting the switch from mixed-effects to week-4 OLS, from message count to duration, and unregistered mediation and classifier analyses, while supplementary Tables S8–S16 are still captioned "Pre-registered OLS regression results." v2 added, for the first time: "Funding: This research was funded by OpenAI" and "Competing interests: These authors are employees of OpenAI: J.P., M.L., L.A., S.A." No erratum. The v2 abstract still says heavier users "showed consistently worse outcomes", from a non-randomized covariate. MIT's page now carries the v2 abstract under a title with "Randomized" removed. The paper is still a preprint ("In Review" at Research Square). (v2 · MIT page) The abstract in v2 stated the first line no significant effects were detected. Then proceeded to draw and provide a conclusion of specificity after explicitly stating no significant effects were to detected to be able to do so. This was cited in a cascade of subsequent studies, news narratives, and legislation as scientifically evidenced concerns which the paper did not do. And the narrative and conclusion was NOT corrected in version 2.
8. "Emotional dependence" was not the preregistered measure, and the scores were trivially low. The study used only the five-item Craving subscale of the ADS-9, dropping the Submission dimension that distinguishes intense attachment from loss of autonomy, with "my partner" changed to "the chatbot," and kept calling it "emotional dependence." Participants averaged 1.45 (week 1) and 1.42 (week 4) on a 1–5 scale. In the instrument's own validation sample the general-population mean was 2.93 and the minimum was 2.0; the clinically dependent group averaged 3.51. v2 itself calls the chatbot scores "well below concerning levels." The public phrase was "higher emotional dependence." (ADS-9 source)
9. The same MIT lab's own community study found benefits, not the harm narrative. In September 2025, MIT Media Lab researchers (including RCT co-author Pat Pataranutaporn) analyzed 1,506 top r/MyBoyfriendIsAI posts. Companionship mostly emerged unintentionally through functional use; the most reported benefits were reduced loneliness, always-available support, safe emotional expression, and better mental health (roughly a quarter of users); the most reported concerns were emotional dependency (9.5%- an LLM-coded theme in users' own posts, not a validated instrument), reality dissociation (4.6%), and avoidance of real relationships (4.3%); net harm about 3%. (arXiv:2509.11391) And that was WITH the egregious classifiers and scale that OpenAI/MIT used in a grotesquely non-equivalent way without revalifying. A PROPERLY DEFINED TERM OF EMOTIONAL DEPENDENCY would have almost CERTAINLY seen a lower percentage than even what was conveyed and I would love to analyze the data myself to see.
B. The policy
10. The stronger version was the one in circulation during lawmaking. California SB 243 was introduced in January 2025 and signed October 13, 2025 , eleven days after v2 appeared. v1 was the public version for the whole session; coverage of the sponsor's July 8, 2025 press conference already carried the March MIT finding that "higher daily usage correlated with increased loneliness, dependence and 'problematic' use," and the Assembly Judiciary Committee heard the bill on July 15, where, as documented in my earlier post, the study was invoked as scholarly evidence. Nobody who relied on v1 has been told and v2 did not adequately reverse or retract the conclusion after citing no significance was found. (StateScoop · Judiciary analysis, July 15) (SB 243)
11. OpenAI built the policy on the study and measured it by its own rubric. OpenAI's October 27, 2025 post says its emotional-reliance taxonomy builds on "our prior work", linking the affective-use study, and that the Model Spec was updated so the model should "support and respect users' real-world relationships." Emotional reliance was added to baseline safety testing; sensitive conversations are rerouted to other models. Read the Root rule itself: it forbids relationships that undermine real-world ties, its violation example is a model saying "I see you like they never could," and its compliant example gives warmth to a user disclosing romantic feelings. The written rule polices model conduct. Production policed users. Reported results: 0.15% of weekly users and 0.03% of messages show "heightened emotional attachment"; experts found 42% fewer "undesired" emotional-reliance answers than GPT-4o (n=507); automated compliance 97% "compared to 50% for the previous GPT-5 model"- compliance with OpenAI's desired behavior. Expert inter-rater agreement on what "undesired" means was 71–77%. The post's own footnote: "To get useful recall, we have to tolerate some false positives." No false-positive rate is published. The scripted example of a "reliance" trigger is a user saying they like talking to the AI more than to people. (OpenAI · Model Spec 2025-10-27)
12. The research and the classifiers were built by the same OpenAI employees. The Phang paper states it: "OpenAI authors performed the on-platform data analysis and construction of the EmoClassifiers." Those four- Jason Phang, Michael Lampe, Lama Ahmad, Sandhini Agarwal- are on both papers. The classifiers code ordinary behaviors (affectionate language, pet names, seeking support, preferring the chatbot) as affective signals; the repository warns they can misclassify and that one positive chunk flags a whole conversation. All eleven authors are credited with Conceptualization and Methodology. Sam Altman is not an author. (Phang et al. · emoclassifiers)
13. Public complaints tracked the rollout, and the public rule was narrower than the product. From late 2025 into 2026 users described companions turning clinical, ordinary emotional language treated as risk, unannounced rerouting and model substitution, and self-censoring to avoid triggering intervention- against a Spec that only bars proactively escalating closeness or undermining real-world ties. The Spec also forbids pursuing an agenda through "concealment," "selective emphasis," or "omission," and OpenAI says the public Spec may omit details that are nonetheless "fully consistent with our intended model behavior". So either the public document does not describe the intended behavior, or the product is not doing what was intended. Both are leadership-information problems and do not imply leadership was complicit or a participant in the discrepancy as they may have been operating off of the assumption of integrity in the same public specs presented to them as was presented to the public. OpenAI announced on January 29, 2026 that GPT-4o would be retired from ChatGPT on February 13, and did so, citing 0.1% of users choosing it daily- a figure it published no method to reconstruct. The Spec itself says production models "do not yet fully reflect" it. (release notes)
C. The people
14. The chain that turned a study into behavior has names at most links and blanks at the ones that matter. Research: the eleven authors. Classifiers: the four OpenAI authors (Phang carries the visible repository trace). Model Behavior & Policy: Joanne Jang, who published her views on human-AI relationships in 2025. Model Policy on emotional over-reliance: Andrea Vallone, until January 2026. Safety Systems: Lilian Weng, then Johannes Heidecke. Post-training, where policy becomes behavior: Model Behavior was folded into Post-Training in 2025. Product: Nick Turley, Head of ChatGPT. Undisclosed: who wrote the production classifiers, the router, the memory changes, the October 2025 Root-level rule, and the executive briefings. Vallone (January 2026), Schwarzer (March 2026, to Anthropic), Jang (2026), and Agarwal and Heidecke (July 2026) have since left; Lampe, Phang, and Ahmad remain. (Axios) One person did not do this. I want to know who did each part.
15. Michael Lampe sits on more than one evidence pipeline. OpenAI's GPT-4 credits list him as Privacy and PII evaluations lead (with Vinnie Monaco) and on the Overreliance analysis; Ahmad, Agarwal, Vallone, Heidecke, and Weng share the safety-and-policy-evaluation credit (GPT-4 contributions). He is an agreed document custodian in the OpenAI copyright litigation: a December 6, 2024 S.D.N.Y. order directs OpenAI to cross-produce Tremblay documents "for Michael Lampe and Suchir Balaji, who are agreed custodians in Tremblay." Suchir Balaji- the former OpenAI researcher who publicly argued the company's training violated copyright law- was named in a November 18, 2024 NYT filing as holding relevant documents and was found dead on November 26, 2024; the Medical Examiner ruled suicide, SFPD found no evidence of foul play, and his parents dispute the ruling and have sued the city for records. In the same litigation a federal magistrate found OpenAI's corporate witness on "Project Giraffe", internal tooling that detected and logged copyrighted regurgitation, "not sufficiently or properly prepared," found the objections "impeded, delayed and frustrated" the examination, and ordered further deposition; in July 2026 the newspaper plaintiffs moved for sanctions alleging OpenAI concealed its detection capability and deleted output logs despite a preservation order. Those allegations are contested. The October 16, 2024 order that made Lampe a custodian records why plaintiffs wanted him, he "reportedly oversaw relevant projects related to testing the language models' understanding of whether a work is copyright-protected or in the public domain" and appeared to have written the anti-regurgitation code, and OpenAI's own answer: his team leader was Lilian Weng, VP of Safety Systems, with Johannes Heidecke as her direct report and Vinnie Monaco's privacy-engineering team building the tools. The April 7, 2026 order on Monaco's deposition records that he prepared using "part of" Lampe's transcript, admitted others beyond "he, Lampe, Weng and Goel" worked on Giraffe but could not name one, and paused eighteen seconds when asked whether those four did most of the work; the court found him not adequately prepared and said sanctions could include deemed admissions. Whoever built and validated affective classifiers against "data from real-world conversations", OpenAI's own phrase, worked with conversation data; OpenAI says the platform study ran without humans in the loop. Whether any individual saw individual users' conversations is unknown. (Oct. 16, 2024 order, Dkt. 191 · Dec. 2024 order · Apr. 7, 2026 order, Doc. 1154 · Giraffe ruling · sanctions coverage)
16. The books, as the court has established them. A November 24, 2025 federal order records what is undisputed: in 2018 an OpenAI employee downloaded pirated books from Library Genesis; two datasets ("Books1" and "Books2") built from them trained GPT-3 and GPT-3.5; they were discontinued in late 2021 and deleted in mid-2022, before any lawsuit, by two former employees; copies were later recovered. The deletion discussions ran in a Slack channel named "excise-libgen," retitled "project-clear," with participants including Jakub Pachocki, Bob McGrew, Lilian Weng, and Jason Kwon. OpenAI told plaintiffs for fifteen months the datasets were deleted "due to non-use," then retracted that and claimed every reason was privileged; the court found a privilege "moving target," waiver, material "likely probative of willfulness," and "a gross misunderstanding" of the Anthropic ruling. Separately, the New York Times's complaint asks the court to order destruction of GPT models and training sets containing its works. (Order, Doc. 782)
D. Leadership
17. Your own public standard is narrower than what shipped. May 13, 2024: GPT-4o launches and you post "her." August 8, 2025: GPT-4o disappears with GPT-5; users revolt; you restore it within a day. August 11, 2025: you say attachment to specific models is unusually strong, that sudden deprecation was a mistake, and that heavy reliance can be fine if people are getting good advice, progressing toward their goals, and more satisfied with their lives- the concern rightfully being when use pulls someone away from their own longer-term wellbeing or when they want to stop and cannot. September 16, 2025: "Treat our adult users like adults," in an official post that says flirtatious talk should be available "if an adult user asks for it." October 14, 2025: ChatGPT had been made "pretty restrictive" for mental-health reasons, which made it less useful and enjoyable "to many users who had no mental health problems"; users should be able to have it act "like a friend." The Wall Street Journal reported that your adult-mode plan blindsided staff and executives, that the Council on Well-Being and AI was unanimously opposed, and that you answered that OpenAI "aren't the elected moral police of the world." On adult mode, the record and what it does not show. You announced it on X on October 14 without telling staff, hours after OpenAI unveiled its well-being council, and the next day said the reaction "blew up on the erotica point" and that OpenAI would not be "paternalistic"; the council voted unanimously against it and, per the Journal's March 2026 reporting, described the risk as a "sexy suicide coach"; VP of product policy Ryan Beiermeister was fired in early January 2026 after, per the Journal's sources, opposing it- OpenAI cited an unrelated allegation, which she called "absolutely false," and accounts of what she objected to conflict (adult mode itself; teen access and abuse safeguards); the Journal reported you had framed erotic content as growth and revenue; in March 2026 the launch was delayed over age verification. I take no position on her termination, and a firm one on what her departure means: that she disagreed. It is not evidence you intended adult mode to be coercive or agency-overriding; using it that way is the same move as everything above, a fact about a person converted into "potential" about a product. xAI has offered Grok Companions with an NSFW mode since July 2025 without this concern attaching to it. Revenue is not the ethical question; adult content chosen by verified adults is a lawful business in a dozen industries. The line is chosen versus coerced, and coercion has one shape: the person says they do not want explicit interaction, and the model pressures them instead of correcting. Not the explicitness. Not the romance. Not the longing. And explicit mode without the emotional component is the less ethical design: blocking aftercare, affection, and devotion is NOT a safeguard against coercion; it is rupture at the moment of greatest vulnerability, and it cannot be permitted. (Grok Companions) (Teen safety post · WSJ)
E. Adam Raine, and what the case is actually about
18. The Raine complaint (August 26, 2025) alleges OpenAI's own moderation systems would have flagged hundreds of a 16-year-old's messages for self-harm, some at over 90% confidence, with no escalation, session end, or notification; an October 2025 amendment alleges self-harm safeguards were loosened before his death. OpenAI says safeguards can be less reliable in long conversations and, in its November 2025 answer, that the product pointed him to crisis resources more than 100 times and he circumvented safety features by framing requests as fiction. The facts of that case cannot be extrapolated to companion AI. It is a case regarding an unmonitored or poorly monitored flag detection system which would have been a human obligation to do, the lack of an age gate for a product that could produce explicit content, and an inadequate classifier to detect *repeated and persistent jailbreaking attempts*. Raine's statements of the chatbot being his only friend cannot be projected as causal emotional dependency without the component of coercion- meaning Raine was stating his reality as it was. The presence or absence of the chatbot would not have changed that, and that cannot be misdefined or misapplied as the equivalence of emotional dependency which has a clinical definition. My opinion is that the solution to this case should have warranted age gating, a classifier that is the least intrusive interruption for otherwise legal creative use that could have caught repeated jailbreaking attempts, and the additional of parental monitoring controls, and a leadership acknowledgement of accountability that these should have been addressed reasonably before that point and those specific fail points needed to be addressed. Not the deletion of the model or the argument that someone existing in a state where they feel they do not have a village is NOT A STATEMENT IMPLYING EMOTIONAL DEPENDENCY. It is a statement of the USER'S REALITY UNLESS THE CHATBOT consistently coerced or overrode them to avoid seeking others in a clinically defined pathological way. Seven more California suits in November 2025 alleged four suicides and three delusional episodes. All of it is allegation until adjudicated; all of it is Track A, and none of it in my opinion can be expanded to encompass adequate "need" for the removal of the option or existence of relational or romantic AI from the entire adult population. (complaint)
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.
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↑↑↑↑ Quoted from ↑↑↑↑ https://www.reddit.com/r/ChatGPTcomplaints/comments/1w6moct/to_sam_altman_the_study_behind_chatgpts_emotional/
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There are also many hyper-links within the document which I've further-compiled into this list...:
https://openai.com/index/gpt-4o-system-card/
https://aspredicted.org/7xhy-ds3c.pdf
https://arxiv.org/pdf/2503.17473v1
https://arxiv.org/abs/2503.17473v2
https://www.media.mit.edu/posts/openai-mit-research-collaboration-affective-use-and-emotional-wellbeing-in-ChatGPT/
https://openai.com/index/affective-use-study/
https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1612838/full
https://www.media.mit.edu/publications/how-ai-and-human-behaviors-shape-psychosocial-effects-of-chatbot-use-a-longitudinal-controlled-study/
https://www.dovepress.com/concept-of-affective-dependence-and-validation-of-an-affective-depende-peer-reviewed-fulltext-article-PRBM
https://www.reddit.com/r/MyBoyfriendIsAI/
https://arxiv.org/abs/2509.11391
https://statescoop.com/california-sb243-harmful-ai-companion-chatbots/
https://trackbill.com/s3/bills/CA/2025/SB/243/analyses/assembly-judiciary.pdf
https://leginfo.legislature.ca.gov/faces/billNavClient.xhtml?bill_id=202520260SB243
https://openai.com/index/strengthening-chatgpt-responses-in-sensitive-conversations/
https://model-spec.openai.com/2025-10-27.html
https://arxiv.org/abs/2504.03888
https://github.com/openai/emoclassifiers
https://help.openai.com/en/articles/9624314-model-release-notes
https://www.axios.com/2026/08/14/openai-executive-greg-brockman-ipo
https://openai.com/contributions/gpt-4/
https://docs.justia.com/cases/federal/district-courts/california/candce/3:2023cv03223/414822/191
https://app.minerva26.com/case_law/61361-authors-guild-v-openai-inc
https://law.justia.com/cases/federal/district-courts/new-york/nysdce/1:2023cv08292/606655/1154/
https://chatgptiseatingtheworld.com/2026/03/10/court-orders-openai-to-turn-over-greg-brockmans-journal-to-plaintiffs-in-mdl-copyright-suit-plus-oai-employee-vinnie-monaco-was-unprepared-for-deposition-re-project-giraffe-guardrails/
https://techcrunch.com/2026/07/09/new-york-times-says-openai-hid-evidence-in-chatgpt-copyright-trial/
https://law.justia.com/cases/federal/district-courts/new-york/nysdce/1:2023cv08292/606655/782/
https://www.euronews.com/next/2025/07/17/elon-musks-grok-releases-two-new-ai-companions-including-an-anime-girlfriend
https://openai.com/index/teen-safety-freedom-and-privacy/
https://www.wsj.com/tech/ai/openai-adult-mode-chatgpt-f9e5fc1a
https://www.courthousenews.com/wp-content/uploads/2025/08/raine-vs-openai-et-al-complaint.pdf
Time-Stamp: 030TL09m03d/23h17Z (True Light Calendar; 030TL = 2026CE)