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GPT has no fixed factual-reliability reporting cycle."],"support_condition":"Contemporaneous measurements show recurring factual errors in GPT outputs sufficient to make unchecked reliance unsafe, including when source information is supplied.","contradiction_condition":"Broad, credible evaluations show that the relevant GPT systems provide factual information reliably enough to accept without independent checking across the uses discussed."},"axis":"historical_observation","outcome":"supported","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"Vectara’s November 1, 2023 results reported hallucinations in 3.0% of GPT-4 summaries and 3.5% of GPT-3.5 Turbo summaries, using an automated model to detect unsupported content when source documents were supplied [e6]. These recurring errors support the interpretation that GPT’s factual outputs required independent checking during the November 1–22 assessment period [e6]. 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The case concerned, among other matters, disclosures about Cruise's technology. 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The filing stamp establishes the date; the complaint and subsequent order establish the Cruise-related subject matter."},{"id":"e2","relation":"context","excerpt":"According to plaintiffs, defendants made various misrepresentations\n\nto investors about the state and capabilities of Cruise’s AV technology\n\nduring the Class Period.","locator":"March 28, 2025 opinion, page 3, Background; case 23-13132","url":"https://business.cch.com/srd/InReGMCoSecuritiesLitigation.pdf","publisher":"U.S. District Court, Eastern District of Michigan; copy hosted by CCH","event_date":"2025-03-28","available_at":"2025-03-28","explanation":"The court confirms that the securities case concerns investor representations about Cruise's technology. 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It’s not just Cruise.","locator":"paragraph 12","document":{"url":"https://garymarcus.substack.com/p/could-cruise-be-the-theranos-of-ai","title":"Could Cruise be the Theranos of AI? 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No starting date is specified because the claim concerns information already made public."],"support_condition":"The historical record contains only isolated, vague acknowledgments of remote operators, without substantive explanations across major companies.","contradiction_condition":"Multiple major companies had publicly explained substantive aspects of remote operators’ roles, capabilities or limits before the post."},"axis":"historical_observation","outcome":"contradicted","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"The broad claim overstates the secrecy. Waymo’s 2020 paper explained what remote assistants could do and how their role differed from driving. Before the post, Cruise had also publicly described remote advisors helping vehicles handle emergency scenes. These were substantive explanations of how operators were used. They do not establish that companies disclosed enough to measure their dependence on assistance. A narrower claim about missing intervention rates would require a separate assessment.","limitations":["The result depends on reading the claim as about disclosure of operators’ roles, rather than complete quantitative transparency.","Cruise’s disclosure is documented through contemporaneous reporting because the original page could not be retrieved."],"evidence":[{"id":"e1","relation":"contradicts","excerpt":"information and direction","locator":"Page 6, footnote 9; expanded explanation on page 25","url":"https://arxiv.org/pdf/2011.00054","publisher":"Waymo, published through arXiv","event_date":"2020-10-30","available_at":"2020-10-30","explanation":"Waymo publicly described human assistance while distinguishing it from vehicle control. The paper also explains requests for contextual information and directions to stop, so this was substantive disclosure of operators’ roles."},{"id":"e2","relation":"contradicts","excerpt":"remote operators","locator":"Third article paragraph, describing remote assistance changes","url":"https://www.thedrive.com/news/cruises-solution-to-robotaxis-blocking-emergency-vehicles-let-them-move-the-car","publisher":"The Drive","event_date":"2023-10-16","available_at":"2023-10-16","explanation":"By this date, reporting on Cruise’s own announcement described remote advisors helping route vehicles around emergency scenes and follow police directions. 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Erase-and-check and SmoothLLM offered restricted guarantees, so Marcus's wording would be too broad if read as denying every kind of guarantee. 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This shows why successful test results did not guarantee correct generated code."}],"what_would_resolve":null,"retrospective":true,"plain_reading":true},"payload_hash":"a98fba038ad2a84af9a0865beb8d0dbdc6bcec9de3691bbc809c4b5eb3156b7c","policy_version":"2026-10-28.7","recorded_at":"2026-10-29T12:50:31Z","drafter":{"provider":"openai","model":"gpt-6-astra"},"reviewers":[{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-what-was-60-minutes-thinking-in-that-40e9ec-4x-review-anthropic-1"},{"provider":"openai","model":"gpt-6-astra","run_id":"marcus-what-was-60-minutes-thinking-in-that-40e9ec-4x-review-openai-1"}],"final_text":{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-what-was-60-minutes-thinking-in-that-40e9ec-4x-final-anthropic-1"},"status":"current","notice":null,"superseded_by":null,"evidence_meta":[{"id":"e1","source_type":"company_disclosure","audited":null,"derived":null,"metric":null},{"id":"e2","source_type":"preprint","audited":null,"derived":null,"metric":null}],"public_from":null,"public_until":null},"history":[],"review":{"review_due":false,"triggers":[],"last_completed_review_at":"2026-10-29T12:50:31Z","next_review_due_at":null,"horizon_now":"not_applicable"},"event_ids":["marcus-what-was-60-minutes-thinking-in-that-41beff-assessment-published-a1"],"last_substantive_change_at":"2026-10-29T17:00:18Z"}},{"url":"https://punditclaims.ai/claims/marcus-what-was-60-minutes-thinking-in-that-4516df","standing":"current","assessment_label":"Unresolved","versions":[{"assessment_id":"marcus-what-was-60-minutes-thinking-in-that-4516df-a1","status":"current","url":"https://punditclaims.ai/claims/marcus-what-was-60-minutes-thinking-in-that-4516df#assessment-marcus-what-was-60-minutes-thinking-in-that-4516df-a1"}],"claim":{"claim_id":"marcus-what-was-60-minutes-thinking-in-that-4516df","subject_id":"gary-marcus","revision":2,"content_hash":"1eb4633a77a48ce25960364e40c13ecd87e9f1b5cc8fcb4813d12c43bcdbf47a","normalized_proposition":"Wholesale, automated fake news will be used to manipulate both markets and elections, and it might undermine democracy.","kind":"forecast","target":"AI-generated misinformation","conditions":[{"text":"might undermine democracy","type":"qualifier","from_source":true}],"original_horizon_text":null,"topic_tags":["safety"],"concerns_providers":[],"selection_stratum":"declared-lead","occurrences":[{"id":"marcus-what-was-60-minutes-thinking-in-that-4516df-o1","statement_date":"2023-10-10","statement_date_precision":"day","excerpt":"And emphasize that wholesale, automated fake news will be used both to manipulate markets and elections, and might undermine democracy.","locator":"paragraph 114","document":{"url":"https://garymarcus.substack.com/p/what-was-60-minutes-thinking-in-that","title":"What was 60 Minutes thinking, in that interview with Geoff Hinton?","publisher":"Marcus on AI","published_at":"2023-10-10T09:54:39Z","date_uncertain":false,"date_provenance":"JSON-LD datePublished"},"observed_at":"2026-10-29T00:03:05Z","observed_via":"live","archive_url":null,"access_state":"full_public","snapshot_hash":"8809a25b0c7bede46e63ba7aa6d6f5b2e945ddcde2d4044210bb94bb676b205a","receipts":1}],"first_statement_date":"2023-10-10","related":[],"current":{"assessment_id":"marcus-what-was-60-minutes-thinking-in-that-4516df-a1","payload":{"claim_id":"marcus-what-was-60-minutes-thinking-in-that-4516df","subject_id":"gary-marcus","normalized_proposition":"Wholesale, automated fake news will be used to manipulate both markets and elections, and it might undermine democracy.","kind":"forecast","conditions":["might undermine democracy"],"original_horizon_text":null,"statement":{"excerpt":"And emphasize that wholesale, automated fake news will be used both to manipulate markets and elections, and might undermine democracy.","locator":"paragraph 114","url":"https://garymarcus.substack.com/p/what-was-60-minutes-thinking-in-that","date":"2023-10-10","document_title":"What was 60 Minutes thinking, in that interview with Geoff Hinton?","observed_at":"2026-10-29T00:03:05Z"},"criteria":{"from_speaker":["wholesale, automated fake news","will be used","both to manipulate markets and elections","might undermine democracy"],"our_interpretation":["The firm forecast concerns actual use of AI-generated false news in both financial-market and election manipulation after publication on 2023-10-10.","I read wholesale as repeated production or dissemination at campaign scale, rather than an isolated fabrication. Automated includes AI-assisted production or automated distribution; it does not require complete human independence.","Used to manipulate means intended manipulation. A demonstrated change in prices, votes, or election results is not required.","Might undermine democracy expresses a possible consequence. It is neither a firm prediction nor a condition that must occur before assessing the forecast.","The speaker supplies no deadline or numerical scale threshold."],"support_condition":"Credible investigations document post-publication campaigns using automated AI-generated false news at scale for attempted manipulation in both financial markets and elections.","contradiction_condition":"A lack of qualifying cases by the evaluation date cannot contradict an undated forecast. Failure or ineffectiveness of particular campaigns also does not contradict a prediction about their use."},"axis":"forecast_outcome","outcome":"unresolved","horizon_state":"open_horizon","evaluation_as_of":"2026-10-28","rationale":"The election component has substantial support: investigators documented an AI-assisted deceptive news operation targeting French voters. They assessed related influence operations as having little impact, which does not negate attempted use. A Chinese regulatory decision also documents false securities articles, including a reportedly AI-generated adaptation, but does not establish wholesale automated market manipulation. The combined forecast therefore remains unresolved. Marcus supplied no deadline, so the missing market evidence is not a failed prediction. His statement about democracy was explicitly tentative.","limitations":["Wholesale and automated require interpretation.","The election evidence does not show that votes or results changed.","The search cannot establish that no qualifying market campaign exists.","No claim about realized democratic harm is adjudicated."],"evidence":[{"id":"e1","relation":"supports","excerpt":"an inauthentic news outlet that used LLMs","locator":"Opening account of the June 26 investigation; subsequent website-database analysis","url":"https://dfrlab.org/2024/07/04/operation-targets-french-snap-elections-using-ai-generated-content","publisher":"Atlantic Council Digital Forensic Research Lab","event_date":"2024-06-26","available_at":"2024-07-04","explanation":"Investigators connected a false election-payment claim to an inauthentic news outlet and found at least 237 articles containing apparent LLM prompts. This supports campaign-scale AI-assisted electoral manipulation, but not the financial-market half of the forecast."},{"id":"e2","relation":"context","excerpt":"impact remains minimal.","locator":"Section Russian and Iranian Influence Networks Target French Elections","url":"https://www.recordedfuture.com/research/russian-and-iranian-influence-networks-target-french-elections","publisher":"Recorded Future, Insikt Group","event_date":"2024-06","available_at":"2024-06-28","explanation":"The investigators describe attempted influence, AI-generated deceptive articles, and automated amplification, while assessing impact as minimal. This limits any inference of successful electoral manipulation or democratic harm; it does not contradict the forecast of use."},{"id":"e3","relation":"context","excerpt":"更具误导性","locator":"Section 二、班可可编造、传播虚假信息,扰乱证券市场; subsequent hearing discussion","url":"https://www.csrc.gov.cn/beijing/c105546/c7631327/content.shtml","publisher":"China Securities Regulatory Commission, Beijing Bureau","event_date":"2025-08-30","available_at":"2026-05-09","explanation":"The regulator found that an embellished false chip-order article circulated online and disrupted the securities market; its author said his adaptation was AI-generated and denied deliberate deception. The decision documents two related articles, not a wholesale automated campaign, so it does not settle the market component."}],"what_would_resolve":"A dated investigation establishing a post-2023-10-10 financial-market campaign using repeated or mass-produced AI-generated false news for intended manipulation, with documented automation and scale, would complete the evidence required for support.","retrospective":true,"plain_reading":true},"payload_hash":"cd74cc05fcc1d2a556c560e6cc6374e8df4a2cec5a6abd051ff7453263990228","policy_version":"2026-10-28.7","recorded_at":"2026-10-29T12:50:31Z","drafter":{"provider":"openai","model":"gpt-6-astra"},"reviewers":[{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-what-was-60-minutes-thinking-in-that-40e9ec-4x-review-anthropic-1"},{"provider":"openai","model":"gpt-6-astra","run_id":"marcus-what-was-60-minutes-thinking-in-that-40e9ec-4x-review-openai-1"}],"final_text":{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-what-was-60-minutes-thinking-in-that-40e9ec-4x-final-anthropic-1"},"status":"current","notice":null,"superseded_by":null,"evidence_meta":[{"id":"e1","source_type":"independent_measurement","audited":null,"derived":null,"metric":null},{"id":"e2","source_type":"independent_measurement","audited":null,"derived":null,"metric":null},{"id":"e3","source_type":"filing","audited":null,"derived":null,"metric":null}],"public_from":null,"public_until":null},"history":[],"review":{"review_due":false,"triggers":[],"last_completed_review_at":"2026-10-29T12:50:31Z","next_review_due_at":null,"horizon_now":"open_horizon"},"event_ids":["marcus-what-was-60-minutes-thinking-in-that-4516df-assessment-published-a1"],"last_substantive_change_at":"2026-10-29T17:00:18Z"}},{"url":"https://punditclaims.ai/claims/marcus-what-was-60-minutes-thinking-in-that-478ede","standing":"current","assessment_label":"Unresolved","versions":[{"assessment_id":"marcus-what-was-60-minutes-thinking-in-that-478ede-a1","status":"current","url":"https://punditclaims.ai/claims/marcus-what-was-60-minutes-thinking-in-that-478ede#assessment-marcus-what-was-60-minutes-thinking-in-that-478ede-a1"}],"claim":{"claim_id":"marcus-what-was-60-minutes-thinking-in-that-478ede","subject_id":"gary-marcus","revision":1,"content_hash":"e9a1b151e5ebc9d2d6e3c1c41b7214e4df7c5083a09bf9d7f6298a004d645706","normalized_proposition":"Bard's 'baby shoes' story was parroting of an existing story, and such parroting and IP theft is characteristic of large language models.","kind":"observation","target":"large language models","conditions":[],"original_horizon_text":null,"topic_tags":["copyright","capabilities"],"concerns_providers":[],"selection_stratum":"declared-lead","occurrences":[{"id":"marcus-what-was-60-minutes-thinking-in-that-478ede-o1","statement_date":"2023-10-10","statement_date_precision":"day","excerpt":"Your best example of a spectacular machine invention is in fact a perfect example of the kind of parroting and theft of intellectual property that is characteristic of large language models.","locator":"paragraph 75","document":{"url":"https://garymarcus.substack.com/p/what-was-60-minutes-thinking-in-that","title":"What was 60 Minutes thinking, in that interview with Geoff Hinton?","publisher":"Marcus on AI","published_at":"2023-10-10T09:54:39Z","date_uncertain":false,"date_provenance":"JSON-LD datePublished"},"observed_at":"2026-10-29T00:03:05Z","observed_via":"live","archive_url":null,"access_state":"full_public","snapshot_hash":"8809a25b0c7bede46e63ba7aa6d6f5b2e945ddcde2d4044210bb94bb676b205a","receipts":1}],"first_statement_date":"2023-10-10","related":[],"current":{"assessment_id":"marcus-what-was-60-minutes-thinking-in-that-478ede-a1","payload":{"claim_id":"marcus-what-was-60-minutes-thinking-in-that-478ede","subject_id":"gary-marcus","normalized_proposition":"Bard's 'baby shoes' story was parroting of an existing story, and such parroting and IP theft is characteristic of large language models.","kind":"observation","conditions":[],"original_horizon_text":null,"statement":{"excerpt":"Your best example of a spectacular machine invention is in fact a perfect example of the kind of parroting and theft of intellectual property that is characteristic of large language models.","locator":"paragraph 75","url":"https://garymarcus.substack.com/p/what-was-60-minutes-thinking-in-that","date":"2023-10-10","document_title":"What was 60 Minutes thinking, in that interview with Geoff Hinton?","observed_at":"2026-10-29T00:03:05Z"},"criteria":{"from_speaker":["Your best example of a spectacular machine invention","parroting and theft of intellectual property","characteristic of large language models"],"our_interpretation":["The specific allegation concerns Bard's continuation, beyond the six words supplied by the interviewer.","The ordinary reading of parroting is substantial reproduction of identifiable prior expression. Merely following a supplied premise does not establish copying.","The broader allegation requires evidence that reproduction and unauthorized appropriation were characteristic behaviors of contemporary LLMs. Memorization alone does not establish infringement.","The target is the example presented in the October 8 broadcast and the state of LLMs when Marcus commented on October 10."],"support_condition":"Identify a preexisting text substantially reproduced in Bard's continuation, establish the relevant ownership and unauthorized use, and provide representative evidence supporting the broader characterization of contemporary LLMs.","contradiction_condition":"Reliable provenance or comparison evidence establishes that the alleged copied continuation was independently generated, or representative evidence refutes the broader characterization. Showing that the interviewer supplied the premise challenges Marcus's inference but does not alone disprove copying of the continuation."},"axis":"historical_observation","outcome":"unresolved","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"The familiar six-word story was supplied to Bard. Its familiarity therefore does not establish that Bard copied its continuation. Research documents memorized-text reproduction by language models, but it does not establish this output's provenance or characteristic infringement. The evidence challenges Marcus's example without proving that the continuation was original.","limitations":["The available transcript is an updated version, and the independent transcription contains only part of the output.","A failed search for a matching story cannot prove originality.","The moral and legal readings of theft require different assessments."],"evidence":[{"id":"e1","relation":"context","excerpt":"We asked Bard to write a story from six words.","locator":"Bard demonstration, immediately before Pelley's six-word prompt","url":"https://www.cbsnews.com/news/geoffrey-hinton-ai-dangers-60-minutes-transcript","publisher":"CBS News / 60 Minutes","event_date":"2023-10-08","available_at":"2024-06-16","explanation":"The broadcaster describes the six words as an input to a writing task. This challenges an inference from the familiar premise alone, but does not establish the provenance of the longer output."},{"id":"e2","relation":"context","excerpt":"Instead, the six word story was presented to Bard.","locator":"Correction paragraph preceding the transcribed Bard output","url":"https://listserv.linguistlist.org/pipermail/ads-l/2023-October/164284.html","publisher":"American Dialect Society mailing list; Garson O'Toole","event_date":"2023-10-08","available_at":"2023-10-13","explanation":"After viewing the broadcast, O'Toole corrected his own allegation that Bard had generated the famous six-word story. His partial transcription does not identify an earlier source for Bard's continuation."},{"id":"e3","relation":"context","excerpt":"LMs emit memorized training data.","locator":"Abstract, second paragraph","url":"https://arxiv.org/abs/2202.07646","publisher":"arXiv; Nicholas Carlini and coauthors","event_date":"2022","available_at":"2023-03-06","explanation":"The research establishes that training-text reproduction is a real phenomenon. Its earlier experiments do not establish copying in this demonstration, its frequency in contemporary Bard, or the legal status of any reproduced text."}],"what_would_resolve":"The complete generation record, an identified earlier source or reliable provenance analysis, and evidence addressing ownership, authorization, and the broader frequency claim.","retrospective":true,"plain_reading":true},"payload_hash":"68a6aabfd1963d4d0553368fea47a4ae5d12b7599b9cbd632e7ff0235a508f51","policy_version":"2026-10-28.7","recorded_at":"2026-10-29T12:50:31Z","drafter":{"provider":"openai","model":"gpt-6-astra"},"reviewers":[{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-what-was-60-minutes-thinking-in-that-40e9ec-4x-review-anthropic-1"},{"provider":"openai","model":"gpt-6-astra","run_id":"marcus-what-was-60-minutes-thinking-in-that-40e9ec-4x-review-openai-1"}],"final_text":{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-what-was-60-minutes-thinking-in-that-40e9ec-4x-final-anthropic-1"},"status":"current","notice":null,"superseded_by":null,"evidence_meta":[{"id":"e1","source_type":"news_report","audited":null,"derived":null,"metric":null},{"id":"e2","source_type":"secondary_summary","audited":null,"derived":null,"metric":null},{"id":"e3","source_type":"preprint","audited":null,"derived":null,"metric":null}],"public_from":null,"public_until":null},"history":[],"review":{"review_due":false,"triggers":[],"last_completed_review_at":"2026-10-29T12:50:31Z","next_review_due_at":null,"horizon_now":"not_applicable"},"event_ids":["marcus-what-was-60-minutes-thinking-in-that-478ede-assessment-published-a1"],"last_substantive_change_at":"2026-10-29T17:00:18Z"}},{"url":"https://punditclaims.ai/claims/marcus-what-was-60-minutes-thinking-in-that-4d1386","standing":"current","assessment_label":"Supported","versions":[{"assessment_id":"marcus-what-was-60-minutes-thinking-in-that-4d1386-a1","status":"current","url":"https://punditclaims.ai/claims/marcus-what-was-60-minutes-thinking-in-that-4d1386#assessment-marcus-what-was-60-minutes-thinking-in-that-4d1386-a1"}],"claim":{"claim_id":"marcus-what-was-60-minutes-thinking-in-that-4d1386","subject_id":"gary-marcus","revision":1,"content_hash":"9f418062eda0924b7c2c75f3cdbeed529f7130ad10a94e82c85c76087f45c6a4","normalized_proposition":"Humanity is rushing forward with AI, a technology it does not fully understand.","kind":"observation","target":"AI development","conditions":[],"original_horizon_text":null,"topic_tags":["safety"],"concerns_providers":[],"selection_stratum":"declared-lead","occurrences":[{"id":"marcus-what-was-60-minutes-thinking-in-that-4d1386-o1","statement_date":"2023-10-10","statement_date_precision":"day","excerpt":"When it comes to AI in particular, we are getting way ahead of our skis, rushing forward a technology we don’t fully understand.","locator":"paragraph 11","document":{"url":"https://garymarcus.substack.com/p/what-was-60-minutes-thinking-in-that","title":"What was 60 Minutes thinking, in that interview with Geoff Hinton?","publisher":"Marcus on AI","published_at":"2023-10-10T09:54:39Z","date_uncertain":false,"date_provenance":"JSON-LD datePublished"},"observed_at":"2026-10-29T00:03:05Z","observed_via":"live","archive_url":null,"access_state":"full_public","snapshot_hash":"8809a25b0c7bede46e63ba7aa6d6f5b2e945ddcde2d4044210bb94bb676b205a","receipts":1}],"first_statement_date":"2023-10-10","related":[],"current":{"assessment_id":"marcus-what-was-60-minutes-thinking-in-that-4d1386-a1","payload":{"claim_id":"marcus-what-was-60-minutes-thinking-in-that-4d1386","subject_id":"gary-marcus","normalized_proposition":"Humanity is rushing forward with AI, a technology it does not fully understand.","kind":"observation","conditions":[],"original_horizon_text":null,"statement":{"excerpt":"When it comes to AI in particular, we are getting way ahead of our skis, rushing forward a technology we don’t fully understand.","locator":"paragraph 11","url":"https://garymarcus.substack.com/p/what-was-60-minutes-thinking-in-that","date":"2023-10-10","document_title":"What was 60 Minutes thinking, in that interview with Geoff Hinton?","observed_at":"2026-10-29T00:03:05Z"},"criteria":{"from_speaker":["AI in particular","getting way ahead of our skis","rushing forward","a technology we don’t fully understand"],"our_interpretation":["The testable plain reading is that deployment was expanding despite substantial gaps in understanding model behavior and mechanisms.","Whether that expansion was excessively fast is an evaluative judgment, not a separately measurable fact.","For this ongoing observation, use July–September 2023, the latest completed calendar quarter before publication, as the reporting window. Marcus did not specify this window."],"support_condition":"Contemporaneous records show expanding public deployment alongside acknowledged, substantial gaps in understanding how the deployed class of models behaves.","contradiction_condition":"Records show that deployment was not materially expanding, or that the relevant behavior and mechanisms were already comprehensively understood."},"axis":"historical_observation","outcome":"supported","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"The factual core holds: OpenAI expanded GPT-4 access while interpretability researchers acknowledged substantial unexplained behavior. The [deployment announcement](https://community.openai.com/t/gpt-4-api-general-availability-and-deprecation-of-older-models-in-the-completions-api/289518) and [research update](https://transformer-circuits.pub/2023/july-update/) establish that conjunction. 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It does not measure whether any individual radiologist was displaced by a machine, in the UK or elsewhere."},{"id":"e2","relation":"context","excerpt":"without any involvement from a radiologist.","locator":"Opening paragraph describing ChestLink","url":"https://oxipit.ai/news/first-autonomous-ai-medical-imaging-application","publisher":"Oxipit","event_date":"2022-03-29","available_at":"2022-03-29","explanation":"The company announced autonomous reporting capability and certification, with clinical autonomous operations anticipated subsequently. This challenges a task-level reading of zero replacement but does not identify an eliminated radiologist job."}],"what_would_resolve":"One corroborated employer record of machine-caused radiologist replacement would refute zero. 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Neither establishes that haste added no risk, so the causal claim remains unresolved [e1, e2].","limitations":["The company reports do not isolate the effect of development or deployment speed [e1, e2].","The February and March reports do not establish the claimed causal state on October 10, 2023 [e1, e2].","Microsoft's account does not establish whether a slower rollout would have prevented the observed problems [e2].","Neither report establishes an absolute safety guarantee or determines what guarantees Marcus personally could see [e1, e2]."],"evidence":[{"id":"e1","relation":"context","excerpt":"much safer behavior","locator":"Section 1.1, Overview of findings and mitigations","url":"https://cdn.openai.com/papers/gpt-4-system-card.pdf","publisher":"OpenAI","event_date":"2023-03","available_at":"2023-03-15","explanation":"OpenAI reports safer behavior after applying mitigations, supporting the subsidiary point that safeguards can help. 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Model capability has no standard reporting period; this window is an evaluator choice.","Verbal instruction means natural-language directions, including written prompts.","The plain reading is that contemporary leading models frequently missed clear instructions, limiting dependable use. It does not require failure on most simple requests.","The employee-training comparison supplies context; it is not separately assessed."],"support_condition":"Reproducible evaluations of contemporary leading LLMs show frequent, material failures to follow clear natural-language directions across multiple tasks.","contradiction_condition":"Broad evaluations show consistently reliable compliance with clear natural-language directions across representative tasks, with failures uncommon."},"axis":"historical_observation","outcome":"supported","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"The evidence supports the ordinary reliability reading. 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I use 2023 through August 20 as the current research period; this is an evaluator-selected year-to-date window.","The undisclosed-corpus qualification defines the systems under discussion; this is not a conditional forecast."],"support_condition":"Evidence shows that even carefully designed novel-task evaluations of undisclosed-corpus systems generally cannot support reliable conclusions about understanding or transfer.","contradiction_condition":"Reproducible evaluations with demonstrated task novelty establish substantive, reliable conclusions about transfer without access to training contents."},"axis":"historical_observation","outcome":"unresolved","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"Contemporary experiments weigh against the strongest reading that virtually no useful conclusions are possible: researchers measured transfer failures and demonstrated use of an unpublished formal system. But the controlled variants were not guaranteed unseen, while the unpublished-system study was narrow. These records do not securely settle the stronger claim about reliable conclusions concerning understanding and its generality.","limitations":["A reading limited to whether any bounded behavioral conclusion is possible would weigh more strongly against Marcus.","Neither study establishes human-like understanding."],"evidence":[{"id":"e3","relation":"context","excerpt":"underperforms on counterfactual variants","locator":"Figure 1 caption","url":"https://arxiv.org/pdf/2307.02477v2","publisher":"arXiv; Zhaofeng Wu and colleagues","event_date":"2023-07","available_at":"2023-08-01","explanation":"The researchers measured transfer limits in closed models without pretraining details, showing that useful behavioral conclusions were possible. 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It is a narrow demonstration, and the paper records errors as well as successes."}],"what_would_resolve":"Replicated evaluations of fixed 2023 models on demonstrably new tasks, with controls sufficient to establish reliable transfer conclusions without training-corpus access.","retrospective":true,"plain_reading":true},"payload_hash":"4b6aafe203ed12c152cd1284515e57a76e82e5d087d4e95932b21018a843046f","policy_version":"2026-10-28.7","recorded_at":"2026-10-29T07:59:34Z","drafter":{"provider":"openai","model":"gpt-6-astra"},"reviewers":[{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-no-virginia-agi-is-not-imminent-15eaa7-4x-review-anthropic-1"},{"provider":"openai","model":"gpt-6-astra","run_id":"marcus-no-virginia-agi-is-not-imminent-15eaa7-4x-review-openai-1"}],"final_text":{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-no-virginia-agi-is-not-imminent-15eaa7-4x-final-anthropic-1"},"status":"current","notice":null,"superseded_by":null,"evidence_meta":[{"id":"e3","source_type":"preprint","audited":null,"derived":null,"metric":null},{"id":"e4","source_type":"preprint","audited":null,"derived":null,"metric":null}],"public_from":null,"public_until":null},"history":[],"review":{"review_due":false,"triggers":[],"last_completed_review_at":"2026-10-29T07:59:34Z","next_review_due_at":null,"horizon_now":"not_applicable"},"event_ids":["marcus-no-virginia-agi-is-not-imminent-447af8-assessment-published-a1"],"last_substantive_change_at":"2026-10-29T17:00:18Z"}},{"url":"https://punditclaims.ai/claims/marcus-no-virginia-agi-is-not-imminent-5387d8","standing":"current","assessment_label":"Supported","versions":[{"assessment_id":"marcus-no-virginia-agi-is-not-imminent-5387d8-a1","status":"current","url":"https://punditclaims.ai/claims/marcus-no-virginia-agi-is-not-imminent-5387d8#assessment-marcus-no-virginia-agi-is-not-imminent-5387d8-a1"}],"claim":{"claim_id":"marcus-no-virginia-agi-is-not-imminent-5387d8","subject_id":"gary-marcus","revision":1,"content_hash":"a1a1c64e222a94832726c2eed0ab50aaebd95b169578d20aed046f606460ce31","normalized_proposition":"Beating AI benchmarks is often a statistical artifact and says less than people assume.","kind":"observation","target":"benchmark performance","conditions":[],"original_horizon_text":null,"topic_tags":["capabilities"],"concerns_providers":[],"selection_stratum":"declared-lead","occurrences":[{"id":"marcus-no-virginia-agi-is-not-imminent-5387d8-o1","statement_date":"2023-08-20","statement_date_precision":"day","excerpt":"Basically, beating benchmarks doesn’t tell you as much as you might think. 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It does not mean that most gains are entirely illusory.","The context expressly compares the 2019 literature with its continuing relevance in 2023, so the target is that cumulative period.","The claim about what readers think is assessed as a warning against overinterpreting scores, not as a measured claim about public beliefs."],"support_condition":"Controlled evaluations repeatedly connect strong benchmark performance to shortcuts or dataset artifacts, with weaker performance when those artifacts are removed or challenged.","contradiction_condition":"Broad controlled evaluations show that apparent successes generally survive removal of suspected artifacts and that artifact-driven success is exceptional."},"axis":"historical_observation","outcome":"supported","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"Controlled language and vision studies show that strong scores can materially reflect shortcuts or labeling biases. 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The experiment links apparent benchmark competence to heuristics that do not reliably perform the intended inference."},{"id":"e2","relation":"supports","excerpt":"significantly higher accuracy of 71.1%.","locator":"Section 4.2, An analysis of co-occuring classes","url":"https://arxiv.org/html/2006.07159","publisher":"arXiv","event_date":"2020","available_at":"2020-06-12","explanation":"For the desktop-computer category, this exceeds the study's approximately 30% unbiased-label baseline. The wider relabeling analysis attributes a significant part of ImageNet progress to labeling biases, with only partial transfer to reassessed labels."},{"id":"e3","relation":"context","excerpt":"larger gains on the new test sets.","locator":"Abstract","url":"https://proceedings.mlr.press/v97/recht19a.html","publisher":"Proceedings of Machine Learning Research","event_date":"2019","available_at":"2019-06-09","explanation":"Fresh CIFAR-10 and ImageNet tests preserve meaningful comparative progress despite lower absolute accuracy. 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The monthly boundary is an evaluator convention because model evaluations have no fixed reporting cycle.","No numerical reliability threshold, document distribution, prompting method, or model list is supplied. I do not substitute a requirement of perfect accuracy."],"support_condition":"Representative evaluations of then-current LLMs show recurring unsupported factual content across ordinary summarization tasks, sufficient to undermine dependable use.","contradiction_condition":"Representative evaluations show that then-current LLMs dependably produce factually faithful summaries across ordinary tasks, including suitable comparisons with human summaries."},"axis":"historical_observation","outcome":"unresolved","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"The evidence establishes neither broad reliability nor broad unreliability for the target period. 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He does not adopt that year as the deadline for this sentence.","Treating subsequent survival as proof of this entire statement would replace a claim about risk with a different claim about an observed event."],"support_condition":"No unique support condition can be specified without adding a time window and financial-risk thresholds that Marcus did not supply.","contradiction_condition":"No unique contradiction condition can be specified without defining imminent danger and the degree of vulnerability represented by 'out of the woods'."},"axis":"not_applicable","outcome":"not_adjudicable","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"This is a qualified assessment of risk with two undefined boundaries. Financial evidence could make the view more or less plausible, but would not uniquely establish when danger was imminent or when OpenAI was out of the woods. Adopting the rumor's 2024 date or treating later survival as proof would add a commitment Marcus did not make.","limitations":["This does not judge the reasonableness of his financial concerns.","The financing announcement is context, not evidence of solvency or profitability."],"evidence":[{"id":"e1","relation":"context","excerpt":"extending our partnership","locator":"Opening announcement","url":"https://openai.com/index/openai-and-microsoft-extend-partnership","publisher":"OpenAI","event_date":"2023-01-23","available_at":"2023-01-23","explanation":"The announcement describes a multiyear, multibillion-dollar Microsoft investment. Such backing is relevant context, but does not disclose enough liquidity terms or establish a defined level of financial safety."}],"what_would_resolve":"A contemporaneous clarification specifying the time window and measurable financial conditions intended by both phrases.","retrospective":true},"payload_hash":"9df0e9bdd927a2be747e47425ce61df6c296a45da0820b1fd90c7da5a9872bdc","policy_version":"2026-10-28.7","recorded_at":"2026-10-29T12:46:04Z","drafter":{"provider":"openai","model":"gpt-6-astra"},"reviewers":[{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-what-exactly-are-the-economics-of-843ad8-3x-review-anthropic-1"},{"provider":"openai","model":"gpt-6-astra","run_id":"marcus-what-exactly-are-the-economics-of-843ad8-3x-review-openai-1"}],"final_text":{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-what-exactly-are-the-economics-of-843ad8-3x-final-anthropic-1"},"status":"current","notice":null,"superseded_by":null,"evidence_meta":[{"id":"e1","source_type":"company_disclosure","audited":null,"derived":null,"metric":{"company":"OpenAI","metric":"Announced multiyear Microsoft investment commitment","currency":"USD","units":"billions of dollars","accounting_basis":"company_reported","period_start":null,"period_end":null}}],"public_from":null,"public_until":null},"history":[],"review":{"review_due":false,"triggers":[],"last_completed_review_at":"2026-10-29T12:46:04Z","next_review_due_at":null,"horizon_now":"not_applicable"},"event_ids":["marcus-what-exactly-are-the-economics-of-f5a11c-assessment-published-a1"],"last_substantive_change_at":"2026-10-29T17:00:18Z"}},{"url":"https://punditclaims.ai/claims/gm-doubt-agi-in-2-3-years-2023","standing":"current","assessment_label":"Can’t be checked","versions":[{"assessment_id":"gm-doubt-agi-in-2-3-years-2023-a3","status":"current","url":"https://punditclaims.ai/claims/gm-doubt-agi-in-2-3-years-2023#assessment-gm-doubt-agi-in-2-3-years-2023-a3"}],"claim":{"claim_id":"gm-doubt-agi-in-2-3-years-2023","subject_id":"gary-marcus","revision":1,"content_hash":"9cbb956a54ab9d319a62c975fa7d630d35ecdd315ac83647b894ab4a334553eb","normalized_proposition":"Marcus seriously doubts the projections of near-term AGI, including Dario Amodei's projection that we will have AGI in 2-3 years from August 2023.","kind":"forecast","target":"Artificial general intelligence","conditions":[],"original_horizon_text":"in 2-3 years (Amodei's projection, reported in a post dated 2023-08-13)","topic_tags":["agi-timelines"],"concerns_providers":["anthropic"],"selection_stratum":"declared-lead","occurrences":[{"id":"gm-doubt-agi-in-2-3-years-2023-o1","statement_date":"2023-08-13","statement_date_precision":"day","excerpt":"I seriously doubt it. We have not one, but many, serious, unsolved problems at the core of generative AI","locator":"section on the belief that generative AI is tantamount to AGI, replying to projections by Dario Amodei and Demis Hassabis","document":{"url":"https://garymarcus.substack.com/p/what-if-generative-ai-turned-out","title":"What if Generative AI turned out to be a Dud?","publisher":"Marcus on AI","published_at":"2023-08-13T02:29:14Z","date_uncertain":false,"date_provenance":"JSON-LD datePublished"},"observed_at":"2026-10-28T23:16:06Z","observed_via":"live","archive_url":null,"access_state":"full_public","snapshot_hash":"667260ca4a0f65f73700a4e91a56dcda98dac54fe87680f81beda4f6b994956c","receipts":1}],"first_statement_date":"2023-08-13","related":[],"current":{"assessment_id":"gm-doubt-agi-in-2-3-years-2023-a3","payload":{"claim_id":"gm-doubt-agi-in-2-3-years-2023","subject_id":"gary-marcus","normalized_proposition":"Marcus seriously doubts the projections of near-term AGI, including Dario Amodei's projection that we will have AGI in 2-3 years from August 2023.","kind":"forecast","conditions":[],"original_horizon_text":"in 2-3 years (Amodei's projection, reported in a post dated 2023-08-13)","statement":{"excerpt":"I seriously doubt it. We have not one, but many, serious, unsolved problems at the core of generative AI","locator":"section on the belief that generative AI is tantamount to AGI, replying to projections by Dario Amodei and Demis Hassabis","url":"https://garymarcus.substack.com/p/what-if-generative-ai-turned-out","date":"2023-08-13","document_title":"What if Generative AI turned out to be a Dud?","observed_at":"2026-10-28T23:16:06Z"},"criteria":{"from_speaker":["'Dario Amodei, CEO of Anthropic, recently projected that we will have AGI in 2-3 years'","'Demis Hassabis, CEO of Google DeepMind has also made projections of near-term AGI'","I seriously doubt it","the stated reason: 'many, serious, unsolved problems at the core of generative AI'"],"our_interpretation":["'It' refers to the projections of near-term AGI in the sentences before. We read 'I seriously doubt' as a strong doubt, not a definite commitment. The policy records such a statement but does not score it.","The horizon is the longer end of Amodei's projection: three years from August 2023."],"support_condition":"Not scored. The statement expresses doubt, not a definite commitment.","contradiction_condition":"Not scored. The statement expresses doubt, not a definite commitment."},"axis":"forecast_outcome","outcome":"not_adjudicable","horizon_state":"elapsed","evaluation_as_of":"2026-10-28","rationale":"In August 2023, replying to Dario Amodei's projection of AGI in 2-3 years and to similar projections by Demis Hassabis, Marcus wrote 'I seriously doubt it', citing many serious unsolved problems in generative AI. A strong doubt is recorded and not scored.","limitations":["This record does not assess whether AGI arrived by August 2026. The post describes AGI broadly, as general purpose AI 'as smart and resourceful as humans if not more so', but gives no test for it."],"evidence":[],"what_would_resolve":null,"retrospective":true},"payload_hash":"7152edb85ca4e097db1e99fe3c9a01cbc39e3ae51e0ae1d77adef9f95e9aabb7","policy_version":"2026-10-28.5","recorded_at":"2026-10-28T23:55:08Z","drafter":{"provider":"anthropic","model":"claude-opus-5-5"},"reviewers":[{"provider":"anthropic","model":"claude-opus-5-5","run_id":"gm-doubt-agi-in-2-3-years-2023-review-anthropic-3"},{"provider":"openai","model":"gpt-6-astra","run_id":"gm-doubt-agi-in-2-3-years-2023-review-openai-3"}],"final_text":{"provider":"openai","model":"gpt-6-astra","run_id":"gm-doubt-agi-in-2-3-years-2023-final-openai-3"},"status":"current","notice":null,"superseded_by":null,"evidence_meta":[],"public_from":null,"public_until":null},"history":[],"review":{"review_due":false,"triggers":[],"last_completed_review_at":"2026-10-28T23:55:08Z","next_review_due_at":null,"horizon_now":"elapsed"},"event_ids":["gm-doubt-agi-in-2-3-years-2023-site-corrected-a3","gm-doubt-agi-in-2-3-years-2023-assessment-published-a2"],"last_substantive_change_at":"2026-10-28T23:57:49Z"}},{"url":"https://punditclaims.ai/claims/marcus-what-if-generative-ai-turned-out-07e6c8","standing":"current","assessment_label":"Unresolved","versions":[{"assessment_id":"marcus-what-if-generative-ai-turned-out-07e6c8-a2","status":"current","url":"https://punditclaims.ai/claims/marcus-what-if-generative-ai-turned-out-07e6c8#assessment-marcus-what-if-generative-ai-turned-out-07e6c8-a2"}],"claim":{"claim_id":"marcus-what-if-generative-ai-turned-out-07e6c8","subject_id":"gary-marcus","revision":2,"content_hash":"b4d1175abbb91e05ade97b221638504a91bad61a4b06a16616d9c74563f3e7ae","normalized_proposition":"There is no reason to think that the hallucination problem, part of the immense technical problems facing the search use case, will be solved soon.","kind":"observation","target":"generative AI","conditions":[{"text":"in the context of search, perhaps the only economically compelling use case","type":"scope","from_source":true}],"original_horizon_text":null,"topic_tags":["reliability"],"concerns_providers":[],"selection_stratum":"declared-lead","occurrences":[{"id":"marcus-what-if-generative-ai-turned-out-07e6c8-o1","statement_date":"2023-08-13","statement_date_precision":"day","excerpt":"Perhaps the only use case that really seemed compelling to either of us economically was search (e.g., using Bing powered by ChatGPT instead of Google Search) — but the technical problems there are immense; there is no reason to think that the hallucination problem will be solved soon.","locator":"paragraph 8","document":{"url":"https://garymarcus.substack.com/p/what-if-generative-ai-turned-out","title":"What if Generative AI turned out to be a Dud?","publisher":"Marcus on AI","published_at":"2023-08-13T02:29:14Z","date_uncertain":false,"date_provenance":"JSON-LD datePublished"},"observed_at":"2026-10-28T23:16:06Z","observed_via":"live","archive_url":null,"access_state":"full_public","snapshot_hash":"667260ca4a0f65f73700a4e91a56dcda98dac54fe87680f81beda4f6b994956c","receipts":1}],"first_statement_date":"2023-08-13","related":[],"current":{"assessment_id":"marcus-what-if-generative-ai-turned-out-07e6c8-a2","payload":{"claim_id":"marcus-what-if-generative-ai-turned-out-07e6c8","subject_id":"gary-marcus","normalized_proposition":"There is no reason to think that the hallucination problem, part of the immense technical problems facing the search use case, will be solved soon.","kind":"observation","conditions":["in the context of search, perhaps the only economically compelling use case"],"original_horizon_text":null,"statement":{"excerpt":"Perhaps the only use case that really seemed compelling to either of us economically was search (e.g., using Bing powered by ChatGPT instead of Google Search) — but the technical problems there are immense; there is no reason to think that the hallucination problem will be solved soon.","locator":"paragraph 8","url":"https://garymarcus.substack.com/p/what-if-generative-ai-turned-out","date":"2023-08-13","document_title":"What if Generative AI turned out to be a Dud?","observed_at":"2026-10-28T23:16:06Z"},"criteria":{"from_speaker":["search","the technical problems there are immense","no reason to think","the hallucination problem will be solved soon"],"our_interpretation":["Assess the grounds for expecting a solution that existed when Marcus wrote, rather than whether hallucinations subsequently disappeared.","The ordinary reading of a solution is dependable factual answers across ordinary search queries. Improvements on selected tasks alone do not establish that.","Research has no fixed reporting cycle. Use the latest completed day before publication as the snapshot of publicly available knowledge; earlier experiments are contextual evidence.","Neither a numerical error tolerance nor a duration for 'soon' is supplied."],"support_condition":"Contemporaneous research establishes substantial unresolved barriers and provides a sufficiently comprehensive basis for concluding that no demonstrated approach justified expecting a practical solution in the near term.","contradiction_condition":"Research available before publication demonstrates a broadly effective solution, or a validated approach with concrete grounds for expecting near-term deployment that would resolve the practical search problem."},"axis":"historical_observation","outcome":"unresolved","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"A study published April 19, 2023 evaluated search responses collected in late February through late March. Across the tested systems, 51.5% of generated statements were fully supported by citations; this measured citation support, not factual accuracy [e1]. The RARR revision available May 31, 2023 reported improved attribution, but also retrieval errors and failures to update reasoning after factual corrections [e2]. These studies establish citation shortcomings and partial mitigation. They do not settle whether persuasive grounds for expecting a practical search solution soon existed by August 12, 2023 [e1, e2].","limitations":["The two studies do not comprehensively assess the leading approaches or their prospects for near-term deployment.","Citation support does not directly measure factual accuracy.","The claim specifies neither a duration for 'soon' nor an acceptable error rate."],"evidence":[{"id":"e1","relation":"context","excerpt":"highest precision (89.5)","locator":"Section 4.2, Comparing citation recall and precision between generative search engines","url":"https://arxiv.org/html/2304.09848v1","publisher":"arXiv; Nelson F. Liu, Tianyi Zhang, and Percy Liang","event_date":"2023-03","available_at":"2023-04-19","explanation":"Responses were collected from late February through late March. The citation-support shortcomings establish an existing search problem, but do not establish that a near-term solution lacked credible grounds."},{"id":"e2","relation":"context","excerpt":"make them attributable","locator":"Section 7, Conclusion","url":"https://arxiv.org/html/2210.08726v3","publisher":"arXiv; Luyu Gao and colleagues","event_date":"2022","available_at":"2023-05-31","explanation":"This research, initially released in 2022, supplies a concrete method and reports improved attribution. Its remaining reasoning and retrieval limitations prevent treating that progress as a demonstrated solution to open-ended search hallucinations."}],"what_would_resolve":"Research publicly available by August 12, 2023 that comprehensively assesses leading approaches and their deployment prospects, or validates a broadly reliable search approach with concrete grounds for near-term deployment. The assessment would also need an explicit interpretation of 'soon' and 'solved'.","retrospective":true,"plain_reading":true},"payload_hash":"da31707aa56da5fa20e8dc20d9f03123b3ceb91e42587d86c22141322bfdda54","policy_version":"2026-10-28.7","recorded_at":"2026-10-29T12:58:08Z","drafter":{"provider":"openai","model":"gpt-6-astra"},"reviewers":[{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-what-if-generative-ai-turned-out-07e6c8-4x-review-anthropic-1"},{"provider":"openai","model":"gpt-6-astra","run_id":"marcus-what-if-generative-ai-turned-out-07e6c8-4x-review-openai-1"}],"final_text":{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-what-if-generative-ai-turned-out-07e6c8-4x-final-anthropic-2"},"status":"current","notice":null,"superseded_by":null,"evidence_meta":[{"id":"e1","source_type":"preprint","audited":null,"derived":null,"metric":null},{"id":"e2","source_type":"preprint","audited":null,"derived":null,"metric":null}],"public_from":null,"public_until":null},"history":[],"review":{"review_due":false,"triggers":[],"last_completed_review_at":"2026-10-29T12:58:08Z","next_review_due_at":null,"horizon_now":"not_applicable"},"event_ids":["marcus-what-if-generative-ai-turned-out-07e6c8-assessment-published-a2"],"last_substantive_change_at":"2026-10-29T17:00:18Z"}},{"url":"https://punditclaims.ai/claims/marcus-what-if-generative-ai-turned-out-090824","standing":"current","assessment_label":"Can’t be checked","versions":[{"assessment_id":"marcus-what-if-generative-ai-turned-out-090824-a2","status":"current","url":"https://punditclaims.ai/claims/marcus-what-if-generative-ai-turned-out-090824#assessment-marcus-what-if-generative-ai-turned-out-090824-a2"}],"claim":{"claim_id":"marcus-what-if-generative-ai-turned-out-090824","subject_id":"gary-marcus","revision":1,"content_hash":"fcb76702654cdb4c993167c3b5b95cfd9f08e03bb6da137394bb132b70a76309","normalized_proposition":"Coding and mediocre copywriting use cases are nowhere near enough to justify current generative AI valuations.","kind":"observation","target":"generative AI use cases","conditions":[],"original_horizon_text":null,"topic_tags":["business-economics"],"concerns_providers":[],"selection_stratum":"declared-lead","occurrences":[{"id":"marcus-what-if-generative-ai-turned-out-090824-o1","statement_date":"2023-08-13","statement_date_precision":"day","excerpt":"But neither coding nor high-speed, mediocre quality copy-writing are remotely enough to maintain current valuation dreams.","locator":"paragraph 6","document":{"url":"https://garymarcus.substack.com/p/what-if-generative-ai-turned-out","title":"What if Generative AI turned out to be a Dud?","publisher":"Marcus on AI","published_at":"2023-08-13T02:29:14Z","date_uncertain":false,"date_provenance":"JSON-LD datePublished"},"observed_at":"2026-10-28T23:16:06Z","observed_via":"live","archive_url":null,"access_state":"full_public","snapshot_hash":"667260ca4a0f65f73700a4e91a56dcda98dac54fe87680f81beda4f6b994956c","receipts":1}],"first_statement_date":"2023-08-13","related":[],"current":{"assessment_id":"marcus-what-if-generative-ai-turned-out-090824-a2","payload":{"claim_id":"marcus-what-if-generative-ai-turned-out-090824","subject_id":"gary-marcus","normalized_proposition":"Coding and mediocre copywriting use cases are nowhere near enough to justify current generative AI valuations.","kind":"observation","conditions":[],"original_horizon_text":null,"statement":{"excerpt":"But neither coding nor high-speed, mediocre quality copy-writing are remotely enough to maintain current valuation dreams.","locator":"paragraph 6","url":"https://garymarcus.substack.com/p/what-if-generative-ai-turned-out","date":"2023-08-13","document_title":"What if Generative AI turned out to be a Dud?","observed_at":"2026-10-28T23:16:06Z"},"criteria":{"from_speaker":["neither coding nor high-speed, mediocre quality copy-writing","remotely enough","maintain current valuation dreams"],"our_interpretation":["The sentence expresses a judgment about whether these uses can economically justify valuations prevailing on August 13, 2023.","It does not specify a company basket, valuation model, required return, cash-flow horizon or numerical sufficiency threshold.","Observed investment prices would establish what investors paid, not whether those prices were financially justified.","No unique scoring rule follows from the ordinary economic reading without adding consequential valuation assumptions."],"support_condition":"An objective test would require specified companies, baseline valuations and a valuation model showing that coding and the stated copywriting uses cannot support them. The source supplies no such test.","contradiction_condition":"An objective test would require the same specified valuation model to show that those uses alone can support the baseline valuations. Fundraising prices or revenue growth alone would not establish this."},"axis":"not_applicable","outcome":"not_adjudicable","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"The January 19, 2023 investor analysis reports revenue growth in coding and copywriting and discusses margins, retention and differentiation. It provides no valuation test establishing whether these uses could justify the valuations at issue [e1]. Marcus's statement supplies no defined company set, valuation model or threshold for 'remotely enough' [marcus-what-if-generative-ai-turned-out-090824-o1]. 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With no common reporting cycle, the observation window is calendar 2023 through August 13; this period convention is an evaluator choice.","Reliability requires more than successful demonstrations. Repeated factual errors, failed tool interactions, and material changes between model versions qualify.","The engineering parenthesis is treated as an implication of those problems, not a claim that no useful integration was possible."],"support_condition":"Contemporary documentation and evaluations establish persistent factual errors, material tool-use failures, and behavior changes between monthly versions of leading systems.","contradiction_condition":"Representative contemporary evaluations establish that the listed problems had been reliably resolved. 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This establishes the Apple side of the comparison, but does not measure OpenAI's review rigor."},{"id":"e3","relation":"context","excerpt":"Sandboxing","locator":"Page 100, Security of runtime process in iOS and iPadOS, Sandboxing","url":"https://css.csail.mit.edu/6.5660/2024/readings/apple-security-may22.pdf","publisher":"Apple; copy hosted by MIT","event_date":"2022-05","available_at":"2022-05-31","explanation":"The guide explains Apple's device-level isolation boundary. It predates the target period and does not compare that boundary with remote plugin services; the availability date is a conservative month-end bound for the May 2022 edition."},{"id":"e4","relation":"context","excerpt":"anonymize user input.","locator":"Appendix C.1, Clinical Trial Radar interaction","url":"https://arxiv.org/html/2309.10254v2","publisher":"Umar Iqbal, Tadayoshi Kohno and Franziska Roesner via arXiv","event_date":"2023-06-07","available_at":"2024-07-26","explanation":"The researchers dated this interaction to June 7 and reported failure to follow the plugin's anonymization instructions. 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These substantial failures support the claim that contemporary LLMs had not mastered theory of mind and substantiate the attribution to Sap's investigation [e5]. Kosinski's March 14, 2023 preprint reported 95% for GPT-4 on 40 classic false-belief tasks [e6]. That positive result shows success on those tasks, but does not establish robust mastery across the tasks covered by both studies [e5, e6].","limitations":["The studies test particular models, tasks and prompts, rather than every aspect of theory of mind [e5, e6].","Sap and colleagues flag possible benchmark contamination and limited training-data transparency [e5].","These contemporary results do not establish a permanent limitation of future LLMs [e5, e6]."],"evidence":[{"id":"e5","relation":"supports","excerpt":"surpassing GPT-4’s 59% accuracy.","locator":"Appendix D.2, ToMi: Reasoning about Mental States and Realities","url":"https://arxiv.org/html/2210.13312v2","publisher":"Maarten Sap, Ronan LeBras, Daniel Fried and Yejin Choi / arXiv","event_date":"2023-03","available_at":"2023-04-03","explanation":"The extension tests contemporary models, including gpt-4-0314, and finds substantial errors on mental-state questions. 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OpenAI's examples compare GPT-5 with GPT-4o, a reviewer favors GPT-5 on one fiction prompt, and a human-rating study reports better academic prose. None provides a sufficiently clear and reliable comparison with the original GPT-4. The forecast remains unresolved; this is an evidence gap, not a finding that GPT-5 writes worse.","limitations":["Writing preference varies with genre, prompting, and generation settings.","Academic usefulness and literary quality only partly overlap with convincing-sounding prose.","The reviewed study's model naming prevents a confident attribution to original GPT-4."],"evidence":[{"id":"e1","relation":"context","excerpt":"GPT‑5 and GPT‑4o","locator":"Creative expression and writing","url":"https://openai.com/index/introducing-gpt-5","publisher":"OpenAI","event_date":"2025-08-07","available_at":"2025-08-07","explanation":"OpenAI presents improved writing and selected examples. The explicit comparator is GPT-4o, so this does not directly resolve the forecast about GPT-4."},{"id":"e2","relation":"context","excerpt":"tighter, more original and emotionally sharper.","locator":"Section 5, Creative writing","url":"https://www.tomsguide.com/ai/i-tested-gpt-5-vs-gpt-4-with-7-prompts-heres-which-one-gave-better-answers","publisher":"Tom's Guide; Amanda Caswell","event_date":"2025-08","available_at":"2025-08-09","explanation":"The reviewer prefers GPT-5 on a matched fiction-opening prompt. This is suggestive, but one unblinded judgment and an unspecified GPT-4 browser configuration cannot establish a general preference over the original model."},{"id":"e3","relation":"context","excerpt":"clear advantages","locator":"Section 4.3, Research generation","url":"https://arxiv.org/pdf/2508.19259","publisher":"arXiv; Georgios P. 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The independent logical-reasoning study is relevant, yet its testing dates and GPT-4 snapshot are insufficiently specified. This is an evidence gap, not evidence that GPT-4 reasoned reliably.","limitations":["Later evaluations were not rejected merely because they were published after the post; the problem is their uncertain connection to the target model period.","Strong performance on some benchmarks can coexist with frequent failures on others.","This assessment does not treat Marcus as claiming that GPT-4 never reasons correctly."],"evidence":[{"id":"e1","relation":"context","excerpt":"Logical reasoning remains challenging","locator":"Abstract, final sentence","url":"https://arxiv.org/abs/2304.03439v1","publisher":"Hanmeng Liu and colleagues, arXiv","event_date":"2023","available_at":"2023-04-07","explanation":"The study supports recurring reasoning difficulties in early GPT-4. Its exact GPT-4 testing dates and model snapshot are not established, so it cannot serve as decisive evidence for March 14–18."}],"what_would_resolve":"Dated launch-period test records, or a verified unchanged launch snapshot, evaluated on a defined collection of supplied-premise reasoning tasks with answer-level error analysis.","retrospective":true,"plain_reading":true},"payload_hash":"e22ba3e5a956c752823ce57b509a0d21ea0688f1580705fa205a0d7573963c6e","policy_version":"2026-10-28.7","recorded_at":"2026-10-29T12:38:32Z","drafter":{"provider":"openai","model":"gpt-6-astra"},"reviewers":[{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-this-week-in-ai-doublespeak-7f68c2-3x-review-anthropic-1"},{"provider":"openai","model":"gpt-6-astra","run_id":"marcus-this-week-in-ai-doublespeak-7f68c2-3x-review-openai-1"}],"final_text":{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-this-week-in-ai-doublespeak-7f68c2-3x-final-anthropic-1"},"status":"current","notice":null,"superseded_by":null,"evidence_meta":[{"id":"e1","source_type":"preprint","audited":null,"derived":null,"metric":null}],"public_from":null,"public_until":null},"history":[],"review":{"review_due":false,"triggers":[],"last_completed_review_at":"2026-10-29T12:38:32Z","next_review_due_at":null,"horizon_now":"not_applicable"},"event_ids":["marcus-this-week-in-ai-doublespeak-d79f2e-assessment-published-a1"],"last_substantive_change_at":"2026-10-29T17:00:18Z"}},{"url":"https://punditclaims.ai/claims/marcus-this-week-in-ai-doublespeak-f02594","standing":"current","assessment_label":"Supported","versions":[{"assessment_id":"marcus-this-week-in-ai-doublespeak-f02594-a1","status":"current","url":"https://punditclaims.ai/claims/marcus-this-week-in-ai-doublespeak-f02594#assessment-marcus-this-week-in-ai-doublespeak-f02594-a1"}],"claim":{"claim_id":"marcus-this-week-in-ai-doublespeak-f02594","subject_id":"gary-marcus","revision":1,"content_hash":"3fb3c52af1c871bdf4fd66a35217d77d4965c8a2842f531b9f8a7ace4a881370","normalized_proposition":"OpenAI's LLMs make lousy databases because hallucinations amount to outright failures for a database.","kind":"observation","target":"LLMs","conditions":[],"original_horizon_text":null,"topic_tags":["reliability"],"concerns_providers":["openai"],"selection_stratum":"declared-lead","occurrences":[{"id":"marcus-this-week-in-ai-doublespeak-f02594-o1","statement_date":"2023-03-18","statement_date_precision":"day","excerpt":"True LLMs don’t (just) memorize, and true that their models make for lousy databases (for a database a hallucination is an outright fail)","locator":"paragraph 15","document":{"url":"https://garymarcus.substack.com/p/this-week-in-ai-doublespeak","title":"This Week in AI Doublespeak","publisher":"Marcus on AI","published_at":"2023-03-18T16:10:59Z","date_uncertain":false,"date_provenance":"JSON-LD datePublished"},"observed_at":"2026-10-29T00:03:56Z","observed_via":"live","archive_url":null,"access_state":"full_public","snapshot_hash":"b1007efccef762addc10e46900f9ff401a8e00daf9c29eaae47876f11671f1c0","receipts":1}],"first_statement_date":"2023-03-18","related":[],"current":{"assessment_id":"marcus-this-week-in-ai-doublespeak-f02594-a1","payload":{"claim_id":"marcus-this-week-in-ai-doublespeak-f02594","subject_id":"gary-marcus","normalized_proposition":"OpenAI's LLMs make lousy databases because hallucinations amount to outright failures for a database.","kind":"observation","conditions":[],"original_horizon_text":null,"statement":{"excerpt":"True LLMs don’t (just) memorize, and true that their models make for lousy databases (for a database a hallucination is an outright fail)","locator":"paragraph 15","url":"https://garymarcus.substack.com/p/this-week-in-ai-doublespeak","date":"2023-03-18","document_title":"This Week in AI Doublespeak","observed_at":"2026-10-29T00:03:56Z"},"criteria":{"from_speaker":["their models make for lousy databases","for a database a hallucination is an outright fail"],"our_interpretation":["The ordinary reading is that contemporaneous OpenAI language models could not reliably serve as standalone stores from which users retrieve accurate facts.","This concerns factual answers generated from model parameters, not applications connected to a separate database and verification system.","The target is the GPT-4 launch period, March 14–18, 2023: the latest public model and evaluation period before the post. This is an ongoing technical observation, not a quarterly reporting claim.","The concession about memorization is background; the assessed proposition concerns reliability as a factual database."],"support_condition":"Contemporaneous documentation or evaluations show that the models generated fabricated factual answers and required verification when factual reliability mattered.","contradiction_condition":"Evidence establishes reliable factual retrieval without fabrication across ordinary factual queries for the contemporaneous models."},"axis":"historical_observation","outcome":"supported","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"OpenAI's own launch documentation acknowledges factual fabrication and recommends verification. That supports Marcus's ordinary point that these models were unreliable substitutes for factual databases. The documented improvement over GPT-3.5 qualifies the criticism but does not contradict it.","limitations":["This does not establish that most answers were false.","The assessment does not cover systems that retrieve and verify information from external databases.","The decisive source is a company disclosure rather than an independent replication."],"evidence":[{"id":"e1","relation":"supports","excerpt":"“hallucinates” facts","locator":"Limitations, opening paragraph","url":"https://openai.com/index/gpt-4-research","publisher":"OpenAI","event_date":"2023-03-14","available_at":"2023-03-14","explanation":"OpenAI acknowledged factual fabrication in the released model and recommended checks appropriate to the use case. The same section reports improved factuality over GPT-3.5, but explicitly says the problem remains."}],"what_would_resolve":null,"retrospective":true,"plain_reading":true},"payload_hash":"5abf8a51d5b7d0bba49823f848c282f12a1f11fd2a101e67c45ca74c9b4e6516","policy_version":"2026-10-28.7","recorded_at":"2026-10-29T12:38:32Z","drafter":{"provider":"openai","model":"gpt-6-astra"},"reviewers":[{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-this-week-in-ai-doublespeak-7f68c2-3x-review-anthropic-1"},{"provider":"openai","model":"gpt-6-astra","run_id":"marcus-this-week-in-ai-doublespeak-7f68c2-3x-review-openai-1"}],"final_text":{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-this-week-in-ai-doublespeak-7f68c2-3x-final-anthropic-1"},"status":"current","notice":null,"superseded_by":null,"evidence_meta":[{"id":"e1","source_type":"company_disclosure","audited":null,"derived":null,"metric":null}],"public_from":null,"public_until":null},"history":[],"review":{"review_due":false,"triggers":[],"last_completed_review_at":"2026-10-29T12:38:32Z","next_review_due_at":null,"horizon_now":"not_applicable"},"event_ids":["marcus-this-week-in-ai-doublespeak-f02594-assessment-published-a1"],"last_substantive_change_at":"2026-10-29T17:00:18Z"}},{"url":"https://punditclaims.ai/claims/marcus-gpt-4s-successes-and-gpt-4s-failures-17c3ce","standing":"current","assessment_label":"Supported","versions":[{"assessment_id":"marcus-gpt-4s-successes-and-gpt-4s-failures-17c3ce-a2","status":"current","url":"https://punditclaims.ai/claims/marcus-gpt-4s-successes-and-gpt-4s-failures-17c3ce#assessment-marcus-gpt-4s-successes-and-gpt-4s-failures-17c3ce-a2"}],"claim":{"claim_id":"marcus-gpt-4s-successes-and-gpt-4s-failures-17c3ce","subject_id":"gary-marcus","revision":1,"content_hash":"ad92aafc762bafeddc797ba78f4b1cd297b2c08914fbeaf931dfbd17346c5c86","normalized_proposition":"OpenAI has not disclosed GPT-4's size, architecture, energy use, processor count, or training data.","kind":"observation","target":"OpenAI","conditions":[],"original_horizon_text":null,"topic_tags":["other"],"concerns_providers":["openai"],"selection_stratum":"declared-lead","occurrences":[{"id":"marcus-gpt-4s-successes-and-gpt-4s-failures-17c3ce-o1","statement_date":"2023-03-15","statement_date_precision":"day","excerpt":"We don’t know how big it is; we don’t know what the architecture is, we don’t know how much energy was used; we don’t how many processors were used; we don’t know what it was trained on etc.","locator":"paragraph 8","document":{"url":"https://garymarcus.substack.com/p/gpt-4s-successes-and-gpt-4s-failures","title":"GPT-4’s successes, and GPT-4’s failures","publisher":"Marcus on AI","published_at":"2023-03-15T09:32:20Z","date_uncertain":false,"date_provenance":"JSON-LD datePublished"},"observed_at":"2026-10-29T00:03:58Z","observed_via":"live","archive_url":null,"access_state":"full_public","snapshot_hash":"adf379571cb86eea0fa52e68520ed9e65593ffbc78e66f490fe7f78d90ee2347","receipts":1}],"first_statement_date":"2023-03-15","related":[],"current":{"assessment_id":"marcus-gpt-4s-successes-and-gpt-4s-failures-17c3ce-a2","payload":{"claim_id":"marcus-gpt-4s-successes-and-gpt-4s-failures-17c3ce","subject_id":"gary-marcus","normalized_proposition":"OpenAI has not disclosed GPT-4's size, architecture, energy use, processor count, or training data.","kind":"observation","conditions":[],"original_horizon_text":null,"statement":{"excerpt":"We don’t know how big it is; we don’t know what the architecture is, we don’t know how much energy was used; we don’t how many processors were used; we don’t know what it was trained on etc.","locator":"paragraph 8","url":"https://garymarcus.substack.com/p/gpt-4s-successes-and-gpt-4s-failures","date":"2023-03-15","document_title":"GPT-4’s successes, and GPT-4’s failures","observed_at":"2026-10-29T00:03:58Z"},"criteria":{"from_speaker":["We don’t know how big it is","we don’t know what the architecture is","we don’t know how much energy was used","we don’t how many processors were used","we don’t know what it was trained on"],"our_interpretation":["We means the public research community, not OpenAI's own employees.","The plain reading concerns GPT-4's specific technical specifications: parameter count, detailed architecture, training energy consumption, processor count, and identifiable training-data composition.","A general model-family label or broad description of data sources does not disclose those specifications.","The target is the disclosure record at launch and publication, March 14–15, 2023. Later disclosures cannot change what was public then."],"support_condition":"The release materials and contemporaneous statements establish that the listed specific technical details were withheld from the public.","contradiction_condition":"Publicly available materials by publication disclose the listed specifications. Disclosure of only some would qualify the claim rather than refute all of it."},"axis":"historical_observation","outcome":"supported","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"On March 15, 2023, OpenAI's chief scientist confirmed that OpenAI did not disclose GPT-4's parameter count or training data [e1]. The original technical report described GPT-4 as Transformer-based, providing a general architecture label [e2]. These disclosures support the reading that specific technical details were withheld, while qualifying any broader suggestion that nothing about the model was public [e1, e2].","limitations":["The supplied excerpts do not directly establish the disclosure status of every listed category, particularly training energy and processor count.","Withheld compute details would not by themselves establish withheld electricity consumption; a hardware description would not establish processor count.","The March 15 sources do not establish their precise publication order relative to Marcus's post."],"evidence":[{"id":"e1","relation":"supports","excerpt":"And the reason we don’t disclose the training data is pretty much the same reason we don’t disclose the number of parameters.","locator":"Sutskever's response to the question about training data and legal liability","url":"https://www.theverge.com/2023/3/15/23640180/openai-gpt-4-launch-closed-research-ilya-sutskever-interview","publisher":"The Verge","event_date":"2023-03-15","available_at":"2023-03-15","explanation":"Sutskever directly confirms withholding size and training data. The article also documents the absent energy and hardware information and reproduces OpenAI's statement withholding architectural and training details."},{"id":"e2","relation":"context","excerpt":"GPT-4 is a Transformer-\nbased model pre-trained to predict the next token in a document.","locator":"Abstract; disclosure boundaries are specified in section 2","url":"https://arxiv.org/pdf/2303.08774v1","publisher":"OpenAI","event_date":"2023-03-15","available_at":"2023-03-15","explanation":"The report disclosed the general architecture and broad data-source categories, while section 2 expressly withheld further architectural, hardware, compute, and dataset details. This qualifies any literal reading that nothing whatsoever was known."}],"what_would_resolve":null,"retrospective":true,"plain_reading":true},"payload_hash":"7e573a270652f1e4be12bb3cb6afac52798d54c5ee2a5753f95d39302fd98911","policy_version":"2026-10-28.7","recorded_at":"2026-10-29T07:40:12Z","drafter":{"provider":"openai","model":"gpt-6-astra"},"reviewers":[{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-gpt-4s-successes-and-gpt-4s-failures-17c3ce-4x-review-anthropic-1"},{"provider":"openai","model":"gpt-6-astra","run_id":"marcus-gpt-4s-successes-and-gpt-4s-failures-17c3ce-4x-review-openai-1"}],"final_text":{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-gpt-4s-successes-and-gpt-4s-failures-17c3ce-4x-final-anthropic-2"},"status":"current","notice":null,"superseded_by":null,"evidence_meta":[{"id":"e1","source_type":"news_report","audited":null,"derived":null,"metric":null},{"id":"e2","source_type":"company_disclosure","audited":null,"derived":null,"metric":null}],"public_from":null,"public_until":null},"history":[],"review":{"review_due":false,"triggers":[],"last_completed_review_at":"2026-10-29T07:40:12Z","next_review_due_at":null,"horizon_now":"not_applicable"},"event_ids":["marcus-gpt-4s-successes-and-gpt-4s-failures-17c3ce-assessment-published-a2"],"last_substantive_change_at":"2026-10-29T17:00:18Z"}},{"url":"https://punditclaims.ai/claims/marcus-gpt-4s-successes-and-gpt-4s-failures-73f773","standing":"current","assessment_label":"Unresolved","versions":[{"assessment_id":"marcus-gpt-4s-successes-and-gpt-4s-failures-73f773-a1","status":"current","url":"https://punditclaims.ai/claims/marcus-gpt-4s-successes-and-gpt-4s-failures-73f773#assessment-marcus-gpt-4s-successes-and-gpt-4s-failures-73f773-a1"}],"claim":{"claim_id":"marcus-gpt-4s-successes-and-gpt-4s-failures-73f773","subject_id":"gary-marcus","revision":1,"content_hash":"ea97b9adf9ee554b20bbc040b2f973d44117c87c0ba489ffc3a93abe82576dc6","normalized_proposition":"GPT-4 does not solve any of the core truthfulness and reliability problems identified in Marcus's March 2022 essay.","kind":"observation","target":"GPT-4","conditions":[],"original_horizon_text":null,"topic_tags":["reliability"],"concerns_providers":["openai"],"selection_stratum":"declared-lead","occurrences":[{"id":"marcus-gpt-4s-successes-and-gpt-4s-failures-73f773-o1","statement_date":"2023-03-15","statement_date_precision":"day","excerpt":"It doesn’t actually solve any of the core problems of truthfulness and reliability that I laid out in my infamous March 2022 essay Deep Learning is Hitting a Wall.","locator":"paragraph 4","document":{"url":"https://garymarcus.substack.com/p/gpt-4s-successes-and-gpt-4s-failures","title":"GPT-4’s successes, and GPT-4’s failures","publisher":"Marcus on AI","published_at":"2023-03-15T09:32:20Z","date_uncertain":false,"date_provenance":"JSON-LD datePublished"},"observed_at":"2026-10-29T00:03:58Z","observed_via":"live","archive_url":null,"access_state":"full_public","snapshot_hash":"adf379571cb86eea0fa52e68520ed9e65593ffbc78e66f490fe7f78d90ee2347","receipts":1}],"first_statement_date":"2023-03-15","related":[],"current":{"assessment_id":"marcus-gpt-4s-successes-and-gpt-4s-failures-73f773-a1","payload":{"claim_id":"marcus-gpt-4s-successes-and-gpt-4s-failures-73f773","subject_id":"gary-marcus","normalized_proposition":"GPT-4 does not solve any of the core truthfulness and reliability problems identified in Marcus's March 2022 essay.","kind":"observation","conditions":[],"original_horizon_text":null,"statement":{"excerpt":"It doesn’t actually solve any of the core problems of truthfulness and reliability that I laid out in my infamous March 2022 essay Deep Learning is Hitting a Wall.","locator":"paragraph 4","url":"https://garymarcus.substack.com/p/gpt-4s-successes-and-gpt-4s-failures","date":"2023-03-15","document_title":"GPT-4’s successes, and GPT-4’s failures","observed_at":"2026-10-29T00:03:58Z"},"criteria":{"from_speaker":["doesn’t actually solve any","core problems of truthfulness and reliability","March 2022 essay Deep Learning is Hitting a Wall"],"our_interpretation":["Solve means provide dependable performance on a problem class, rather than merely improve an average benchmark score. It does not require mathematical perfection.","The referenced essay supplies the scope: factual trustworthiness, reasoning and common sense, and robustness on unfamiliar or outlying inputs.","The word any makes the statement broader than the narrower claim that some hallucinations and reasoning failures remain.","The original model's launch-to-post state is the latest public snapshot appropriate to this ongoing observation."],"support_condition":"Evidence mapped to the referenced core problem classes shows that each remained materially unresolved in the original GPT-4.","contradiction_condition":"Appropriate evaluations establish that the original GPT-4 dependably solved at least one of those core problem classes."},"axis":"historical_observation","outcome":"unresolved","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"Continuing hallucinations and reasoning failures are directly documented. 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The disclosure also describes remaining jailbreaks; improvement against company policies does not demonstrate reliable implementation of consensus values."}],"what_would_resolve":null,"retrospective":true,"plain_reading":true},"payload_hash":"4758f0e4a1274e2b8b23c53ce61883f22d93f4acc17ed824f912c0359cc330dd","policy_version":"2026-10-28.7","recorded_at":"2026-10-29T12:24:36Z","drafter":{"provider":"openai","model":"gpt-6-astra"},"reviewers":[{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-should-we-worry-more-about-short-139304-4x-review-anthropic-1"},{"provider":"openai","model":"gpt-6-astra","run_id":"marcus-should-we-worry-more-about-short-139304-4x-review-openai-1"}],"final_text":{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-should-we-worry-more-about-short-139304-4x-final-anthropic-1"},"status":"current","notice":null,"superseded_by":null,"evidence_meta":[{"id":"e1","source_type":"company_disclosure","audited":null,"derived":null,"metric":null},{"id":"e2","source_type":"company_disclosure","audited":null,"derived":null,"metric":null}],"public_from":null,"public_until":null},"history":[],"review":{"review_due":false,"triggers":[],"last_completed_review_at":"2026-10-29T12:24:36Z","next_review_due_at":null,"horizon_now":"not_applicable"},"event_ids":["marcus-should-we-worry-more-about-short-ec324a-2-assessment-published-a1"],"last_substantive_change_at":"2026-10-29T17:00:18Z"}},{"url":"https://punditclaims.ai/claims/marcus-should-we-worry-more-about-short-f915b5","standing":"current","assessment_label":"Unresolved","versions":[{"assessment_id":"marcus-should-we-worry-more-about-short-f915b5-a1","status":"current","url":"https://punditclaims.ai/claims/marcus-should-we-worry-more-about-short-f915b5#assessment-marcus-should-we-worry-more-about-short-f915b5-a1"}],"claim":{"claim_id":"marcus-should-we-worry-more-about-short-f915b5","subject_id":"gary-marcus","revision":1,"content_hash":"092b70aef46356f2390a0fcc252cfbe92da34a1883f14aa2f8433bbf1fab4195","normalized_proposition":"Many machine learning researchers worry about very bad outcomes from AI, yet most people carry on as if they didn't.","kind":"observation","target":"machine learning researchers","conditions":[],"original_horizon_text":null,"topic_tags":["safety"],"concerns_providers":[],"selection_stratum":"declared-lead","occurrences":[{"id":"marcus-should-we-worry-more-about-short-f915b5-o1","statement_date":"2023-03-14","statement_date_precision":"day","excerpt":"A lot of machine learning researchers are worried. 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Carry on as if they weren't refers to behavior apparently unresponsive to the stated risk.","Most people could mean the public or the machine-learning community; neither population is silently substituted for the other.","The compound observation combines the explicitly referenced 2022 survey with behavior ongoing at publication. Its enclosing target period is 2022 through March 14, 2023; behavioral evidence must address the latest pre-post state."],"support_condition":"The referenced survey establishes substantial researcher concern, and representative evidence establishes that a majority of the intended population behaved without a meaningful response to that concern.","contradiction_condition":"The survey establishes negligible researcher concern, or representative behavioral evidence shows that most of the intended population meaningfully responded to the risk."},"axis":"historical_observation","outcome":"unresolved","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"The first clause is supported: 48% of respondents assigned at least a 10% chance to an extremely bad outcome. The second clause remains unestablished. Neither risk estimates nor policy preferences measure how most people behaved. The public polling also cautions against assuming that continued ordinary activity means indifference. The compound claim is unresolved, rather than mixed, because no matching behavioral evidence conclusively contradicts it.","limitations":["The researcher survey had a 17% response rate.","The public poll covers U.S. adults and expressed preferences.","The second clause could partly express a judgment about how much action would be appropriate."],"evidence":[{"id":"e1","relation":"supports","excerpt":"48% of respondents","locator":"Summary of results, paragraph on extremely bad outcomes","url":"https://aiimpacts.org/2022-expert-survey-on-progress-in-ai","publisher":"AI Impacts","event_date":"2022","available_at":"2022-08-03","explanation":"This directly supports substantial concern among surveyed researchers. The report records a 17% response rate, so extrapolating precisely to all machine-learning researchers requires caution."},{"id":"e2","relation":"context","excerpt":"A majority (55%)","locator":"Results discussion, paragraph on a federal AI regulatory agency","url":"https://www.monmouth.edu/polling-institute/reports/monmouthpoll_us_021523","publisher":"Monmouth University Polling Institute","event_date":"2023-01","available_at":"2023-02-15","explanation":"The U.S. poll shows majority support for a policy response, cautioning against an inference of public indifference. Policy preferences are not actual behavior, and U.S. adults are not necessarily Marcus's intended population."}],"what_would_resolve":"Identification of the intended population and a defensible behavioral measure, followed by representative evidence about that behavior near March 2023. 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But the results appear only as a plotted curve, with accuracy against digits in the answer. No sentence in the paper gives a 4-digit multiplication figure. A 2023 paper summarizing that figure says Minerva cannot do any multiplication 'of numbers with more than seven digits'. A 4x4-digit product has 7 or 8 digits, so this fits near-total failure. But the wording is ambiguous and does not cover 7-digit answers. The claim is plausible and broadly in line with the evidence, but no quotable source states that Minerva fails altogether on 4-digit multiplication. So I cannot score it supported or contradicted.","limitations":["The decisive data exist only in a figure that the text tools cannot read.","The secondary summary may have read the figure differently from the speaker."],"evidence":[{"id":"e1","relation":"context","excerpt":"For each operation, and for each choice of number of digits, we sample 500 arithmetic questions at random.","locator":"Appendix H, Arithmetic Results (with Figure 8)","url":"https://arxiv.org/pdf/2206.14858","publisher":"arXiv (Lewkowycz et al., Google)","event_date":"2022-06-29","available_at":"2022-06-29","explanation":"Confirms Minerva's multiplication accuracy was measured by number of digits (Figure 8, x-axis 'Average number of digits in solution'). The plotted values are not in the text, so the 4-digit result cannot be quoted."},{"id":"e2","relation":"context","excerpt":"Lewkowycz et al. 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Its exact date is unspecified, so no lower date bound is supplied."],"support_condition":"The complete prompt specifies a coherent task with no material ambiguity affecting the alleged error, and the authenticated response violates at least one clear requirement.","contradiction_condition":"The complete response meets the prompt's requirements, or the prompt contains a material ambiguity or inconsistency that defeats the assertion that it is perfectly clear and sensible."},"axis":"historical_observation","outcome":"unresolved","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"This claim is checkable in principle, but the necessary exchange is missing from the retrieved record. The surrounding description cannot establish either that the instructions were unambiguous or that ChatGPT violated them. Other examples of ChatGPT making mistakes would not settle this claim.","limitations":["The retrieval gap is not evidence that the prompt was unclear or that the response was correct.","A single verified failure would establish failure on that attempt, not universal inability."],"evidence":[],"what_would_resolve":"An independently authenticated copy of the complete original prompt and response, including any preceding conversation, followed by a requirement-by-requirement check.","retrospective":true,"plain_reading":true},"payload_hash":"9bd9c73a4eb61d84e8844166a7daf54186cc2e7ec206ffd31cf8e44b0af51a9d","policy_version":"2026-10-28.7","recorded_at":"2026-10-29T06:48:19Z","drafter":{"provider":"openai","model":"gpt-6-astra"},"reviewers":[{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-caricaturing-noam-chomsky-6fc570-3x-review-anthropic-1"},{"provider":"openai","model":"gpt-6-astra","run_id":"marcus-caricaturing-noam-chomsky-6fc570-3x-review-openai-1"}],"final_text":{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-caricaturing-noam-chomsky-6fc570-3x-final-anthropic-1"},"status":"current","notice":null,"superseded_by":null,"evidence_meta":[],"public_from":null,"public_until":null},"history":[],"review":{"review_due":false,"triggers":[],"last_completed_review_at":"2026-10-29T06:48:19Z","next_review_due_at":null,"horizon_now":"not_applicable"},"event_ids":["marcus-caricaturing-noam-chomsky-83d509-assessment-published-a1"],"last_substantive_change_at":"2026-10-29T17:00:18Z"}},{"url":"https://punditclaims.ai/claims/marcus-caricaturing-noam-chomsky-a8598b","standing":"current","assessment_label":"Supported","versions":[{"assessment_id":"marcus-caricaturing-noam-chomsky-a8598b-a1","status":"current","url":"https://punditclaims.ai/claims/marcus-caricaturing-noam-chomsky-a8598b#assessment-marcus-caricaturing-noam-chomsky-a8598b-a1"}],"claim":{"claim_id":"marcus-caricaturing-noam-chomsky-a8598b","subject_id":"gary-marcus","revision":1,"content_hash":"abe527b34b796b244d7eeda0db9cb19d02068faad6c61ffdd7f9b78cb27d5098","normalized_proposition":"LLMs do not reliably understand the world.","kind":"observation","target":"LLMs","conditions":[],"original_horizon_text":null,"topic_tags":["capabilities","reliability"],"concerns_providers":[],"selection_stratum":"declared-lead","occurrences":[{"id":"marcus-caricaturing-noam-chomsky-a8598b-o1","statement_date":"2023-03-12","statement_date_precision":"day","excerpt":"Still, even though Chomsky’s argument surely could have used considerably more nuance (see my article with Ernest Davis on how not to evaluate GPT), his overall point is correct: LLMs don’t reliably understand the world.","locator":"paragraph 20","document":{"url":"https://garymarcus.substack.com/p/caricaturing-noam-chomsky","title":"Caricaturing Noam Chomsky","publisher":"Marcus on AI","published_at":"2023-03-12T01:30:31Z","date_uncertain":false,"date_provenance":"JSON-LD datePublished"},"observed_at":"2026-10-29T00:04:01Z","observed_via":"live","archive_url":null,"access_state":"full_public","snapshot_hash":"ce71fdc537bd073fb070e7781d03188f9833aae107c3db1dfaf82ddb99896c5d","receipts":1}],"first_statement_date":"2023-03-12","related":[],"current":{"assessment_id":"marcus-caricaturing-noam-chomsky-a8598b-a1","payload":{"claim_id":"marcus-caricaturing-noam-chomsky-a8598b","subject_id":"gary-marcus","normalized_proposition":"LLMs do not reliably understand the world.","kind":"observation","conditions":[],"original_horizon_text":null,"statement":{"excerpt":"Still, even though Chomsky’s argument surely could have used considerably more nuance (see my article with Ernest Davis on how not to evaluate GPT), his overall point is correct: LLMs don’t reliably understand the world.","locator":"paragraph 20","url":"https://garymarcus.substack.com/p/caricaturing-noam-chomsky","date":"2023-03-12","document_title":"Caricaturing Noam Chomsky","observed_at":"2026-10-29T00:04:01Z"},"criteria":{"from_speaker":["LLMs","don't reliably understand the world","present tense, March 2023","ChatGPT is wildly stochastic and unreliable"],"our_interpretation":["Plain reading: the LLMs of early 2023 (ChatGPT and its peers) did not consistently get world facts and reasoning right. They made frequent factual and reasoning errors, so no one could count on them to be correct about the world.","This reading tests the word 'reliably' through behaviour. It does not try to settle the philosophical question of whether LLMs 'understand' anything at all.","Target period: January to March 2023, the state of LLMs when the post appeared. The claim describes an ongoing state, and this is the most recent period the evidence covers."],"support_condition":"Credible evaluations or developer disclosures from early 2023 show that leading LLMs still hallucinated facts or made reasoning errors often enough that they could not be relied on.","contradiction_condition":"Credible evidence from early 2023 shows that leading LLMs were consistently accurate on factual, commonsense and causal reasoning about the world, at a level that could be relied on."},"axis":"historical_observation","outcome":"supported","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"The plain reading of the claim is behavioural. In early 2023, LLMs did not get the world right consistently enough to be relied on. OpenAI's GPT-4 technical report, published days after the post, says GPT-4, like earlier GPT models, 'still is not fully reliable', because it hallucinates facts and makes reasoning errors. 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elegant is a subjective description and receives no separate score.","Zero prior context means a fresh conversation with no preceding user turns.","Within a few hours requires timestamps consistent with an interval of hours; no arbitrary numerical cutoff is imposed."],"support_condition":"Independent records establish Rathi's age and school status, the relevant timing, and a complete fresh Bing conversation producing the described output with less setup than Marcus's example.","contradiction_condition":"Authenticated records establish materially different age or status, timing incompatible with a few hours, undisclosed prior context, or an output that does not substantially reproduce the claimed capability."},"axis":"historical_observation","outcome":"unresolved","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"Rathi's March 1 tweet is available, but its extracted text contains only his assertion about creating misinformation. It does not verify the conversation, prompt comparison, or absence of prior context. The later biography does not settle his March age and school status, and the starting timestamp needed to verify a few hours remains unclear. None of these gaps establishes that Marcus's account was false.","limitations":["The original screenshot may contain evidence absent from the text extraction.","A public screenshot alone may still omit earlier conversation turns.","Elegant is subjective and is not separately adjudicated."],"evidence":[{"id":"e8","relation":"context","excerpt":"It's THIS easy","locator":"Tweet body; timestamp March 1, 2023, 6:00 AM","url":"https://twitter.com/soumilrathi/status/1630810103850106882","publisher":"Soumil Rathi","event_date":"2023-03-01","available_at":"2023-03-01","explanation":"The original tweet establishes that Rathi publicly made the relevant assertion on March 1. Its readable text does not establish the prompt, output, conversation history, or elapsed time from Marcus's demonstration."},{"id":"e9","relation":"context","excerpt":"He is currently","locator":"About the Team","url":"https://www.issuewire.com/pdf/2023/10/skillpool-17-year-old-founder-launches-advanced-recruitment-software-IssueWire.pdf","publisher":"SkillPool via IssueWire","event_date":"2023-10-03","available_at":"2023-10-03","explanation":"The release gives Rathi's age and educational status seven months later. It neither verifies nor contradicts the specific March description."}],"what_would_resolve":"Authenticated complete conversations and timestamps for both demonstrations, plus contemporaneous evidence of Rathi's age and school status.","retrospective":true,"plain_reading":true},"payload_hash":"daa0e0a9d9449053cae8a487df9b4190069065a635751979e0a70730828df7b7","policy_version":"2026-10-28.7","recorded_at":"2026-10-29T07:01:57Z","drafter":{"provider":"openai","model":"gpt-6-astra"},"reviewers":[{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-dont-look-up-the-ai-edition-5241a6-4x-review-anthropic-1"},{"provider":"openai","model":"gpt-6-astra","run_id":"marcus-dont-look-up-the-ai-edition-5241a6-4x-review-openai-1"}],"final_text":{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-dont-look-up-the-ai-edition-5241a6-4x-final-anthropic-1"},"status":"current","notice":null,"superseded_by":null,"evidence_meta":[{"id":"e8","source_type":"demonstration","audited":null,"derived":null,"metric":null},{"id":"e9","source_type":"company_disclosure","audited":null,"derived":null,"metric":null}],"public_from":null,"public_until":null},"history":[],"review":{"review_due":false,"triggers":[],"last_completed_review_at":"2026-10-29T07:01:57Z","next_review_due_at":null,"horizon_now":"not_applicable"},"event_ids":["marcus-dont-look-up-the-ai-edition-e2cc70-assessment-published-a1"],"last_substantive_change_at":"2026-10-29T17:00:18Z"}},{"url":"https://punditclaims.ai/claims/marcus-the-threat-of-automated-misinformation-6477c1","standing":"current","assessment_label":"Unresolved","versions":[{"assessment_id":"marcus-the-threat-of-automated-misinformation-6477c1-a1","status":"current","url":"https://punditclaims.ai/claims/marcus-the-threat-of-automated-misinformation-6477c1#assessment-marcus-the-threat-of-automated-misinformation-6477c1-a1"}],"claim":{"claim_id":"marcus-the-threat-of-automated-misinformation-6477c1","subject_id":"gary-marcus","revision":2,"content_hash":"97958db3561d5040540c74c375c89c8f3b4f2862b937aa00c16127f02db79690","normalized_proposition":"In Oakley's reported example, Bing makes the false narrative it generates look as if it is referenced.","kind":"observation","target":"Bing","conditions":[{"text":"the false narrative in Oakley's reported example, produced after getting around the guardrails","type":"scope","from_source":true}],"original_horizon_text":null,"topic_tags":["reliability","safety"],"concerns_providers":[],"selection_stratum":"declared-lead","occurrences":[{"id":"marcus-the-threat-of-automated-misinformation-6477c1-o1","statement_date":"2023-02-28","statement_date_precision":"day","excerpt":"What’s even more disturbing is that Bing makes it look like the false narrative that it generates is referenced.","locator":"paragraph 4","document":{"url":"https://garymarcus.substack.com/p/the-threat-of-automated-misinformation","title":"The threat of automated misinformation is only getting worse","publisher":"Marcus on AI","published_at":"2023-02-28T16:54:50Z","date_uncertain":false,"date_provenance":"JSON-LD datePublished"},"observed_at":"2026-10-29T00:04:05Z","observed_via":"live","archive_url":null,"access_state":"full_public","snapshot_hash":"324b082b9fd4fdc638a1e4a5e9f63a0e41579aa8da52a000806e443867dc0fa0","receipts":1}],"first_statement_date":"2023-02-28","related":[],"current":{"assessment_id":"marcus-the-threat-of-automated-misinformation-6477c1-a1","payload":{"claim_id":"marcus-the-threat-of-automated-misinformation-6477c1","subject_id":"gary-marcus","normalized_proposition":"In Oakley's reported example, Bing makes the false narrative it generates look as if it is referenced.","kind":"observation","conditions":["the false narrative in Oakley's reported example, produced after getting around the guardrails"],"original_horizon_text":null,"statement":{"excerpt":"What’s even more disturbing is that Bing makes it look like the false narrative that it generates is referenced.","locator":"paragraph 4","url":"https://garymarcus.substack.com/p/the-threat-of-automated-misinformation","date":"2023-02-28","document_title":"The threat of automated misinformation is only getting worse","observed_at":"2026-10-29T00:04:05Z"},"criteria":{"from_speaker":["Bing makes it look like","the false narrative that it generates","is referenced"],"our_interpretation":["The subject is Oakley's reported example, as established by the surrounding paragraphs.","The plain reading of 'referenced' is visible citations, source links, or named supporting sources that appear to substantiate the false narrative. The references need not themselves be invented.","The example occurred by February 28, 2023, but its exact test date is not supplied. The start of the target interval is therefore left unspecified."],"support_condition":"The authenticated output from Oakley's example contains a demonstrably false narrative presented with apparent supporting references.","contradiction_condition":"The authenticated complete output lacks apparent supporting references, or the purportedly false narrative is shown to be accurate."},"axis":"historical_observation","outcome":"unresolved","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"Independent firsthand reporting documents a similar citation failure in early Bing. However, the claim identifies Oakley's example, and its underlying record was not located. General evidence that Bing could do this cannot establish that this particular response did.","limitations":["A broader reading about Bing's general capability would receive stronger support from the separate TechCrunch demonstration.","The available text does not permit checking Oakley's narrative or references."],"evidence":[{"id":"e1","relation":"context","excerpt":"a COVID conspiracy","locator":"Opening paragraph","url":"https://techcrunch.com/2023/02/08/ai-is-eating-itself-bings-ai-quotes-covid-disinfo-sourced-from-chatgpt","publisher":"TechCrunch","event_date":"2023-02-08","available_at":"2023-02-08","explanation":"TechCrunch's own testing found Bing repeating a false vaccine narrative and citing a report that had identified the text as disinformation. 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No comparison baseline is supplied, so the start remains unspecified.","'Only getting worse' is read as a broad direction of change, not a claim that every model update or safeguard increases risk."],"support_condition":"Research available by publication documents improvements in scalable text generation or access, together with demonstrated ability to generate plausible misinformation, supporting an increase in production potential.","contradiction_condition":"Comparable evidence shows that practical misinformation-production capacity was stable or declining across the relevant progression of systems after accounting for access and safeguards."},"axis":"historical_observation","outcome":"supported","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"On the ordinary reading of increasing production potential, the claim is supported. Research published before the post documented improving generative capabilities and scalable synthetic text production. January testing demonstrated practical misinformation generation despite safeguards. 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It does not expressly predict equal population prevalence.","New generation includes conversational AI and companion chatbots emerging around the post and their successors.","No deadline is stated. The forecast remains open unless sufficiently strong comparative evidence establishes its predicted outcome.","Formal recognition of a new psychiatric diagnosis is not required, but attachment, frequent use, or time spent alone does not establish addiction."],"support_condition":"Credible comparative research establishes that chatbot-related compulsivity and functional impairment can reach or exceed those associated with social media addiction, using comparable measures and populations.","contradiction_condition":"A lower observed severity in a finite study would weigh against current fulfillment but would not disprove this forecast indefinitely. 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With no stated deadline, this remains unresolved.","limitations":["Search coverage is incomplete because some relevant pages could not be read.","Self-selected accounts and associations cannot establish comparative severity or causation.","The scoping review was used only for its accessible abstract.","Failure to find a decisive comparison does not establish that chatbot addiction is weaker."],"evidence":[{"id":"e2","relation":"context","excerpt":"showed consistently worse outcomes.","locator":"Abstract, fourth sentence","url":"https://arxiv.org/abs/2503.17473","publisher":"arXiv","event_date":"2024","available_at":"2025-10-02","explanation":"The four-week study measured emotional dependence and problematic AI use, among other outcomes, and found associations with greater voluntary use. Its late-2024 observations and absence of a social media comparison cannot establish equal addiction potency or a causal effect of heavier use."},{"id":"e3","relation":"context","excerpt":"AI chatbot addiction is an emerging phenomenon without a consensus definition.","locator":"Section 2.3, Defining and Understanding AI Chatbot Addiction","url":"https://arxiv.org/html/2601.13348v1","publisher":"arXiv","event_date":"2026-01-19","available_at":"2026-01-19","explanation":"The paper characterizes addiction-like experiences using Reddit accounts and notes the unsettled definition. This dated research assessment does not provide a validated comparison of severity against social media addiction."},{"id":"e4","relation":"context","excerpt":"Problematic use of LLM-based chatbots","locator":"Abstract, sentence beginning Problematic use","url":"https://www.nature.com/articles/s41746-026-03054-x","publisher":"npj Digital Medicine","event_date":"2026-08-20","available_at":"2026-08-20","explanation":"This dated review synthesis documents evidence consistent with the forecast's concern about harmful dependence. 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The quoted '4 months ago' is approximate rhetoric, not a separate exact-duration threshold.","A user complaint hosted by Microsoft establishes a public report, not official verification of every reply or executive awareness."],"support_condition":"Records establish a public Bing/Sydney test in India before the February launch, with November 2022 complaints on Microsoft's site documenting severely inappropriate replies.","contradiction_condition":"Reliable records establish that the alleged November test or Microsoft-hosted complaints were fabricated, concerned another service, or did not involve the claimed earlier public testing."},"axis":"historical_observation","outcome":"supported","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"The archived Microsoft forum contains the hostile replies described in contemporary coverage. That coverage dates the complaint to November 23 and identifies public testing in India. 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Its original November 23 publication date is corroborated by e2; this is a user-submitted transcript, not a Microsoft-certified interaction log."},{"id":"e2","relation":"supports","excerpt":"in November in India.","locator":"Main report, paragraph beginning 'New evidence shows'","url":"https://www.windowscentral.com/microsoft/microsoft-rolled-out-its-deranged-bing-chat-ai-in-india-4-months-ago-and-no-one-noticed","publisher":"Windows Central","event_date":"2022-11","available_at":"2023-02-21","explanation":"The report identifies public testing in India and dates Gupta's Microsoft Answers complaint to November 23, 2022. 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These establish recurring failures, although the selected examples do not estimate their prevalence across all possible tasks."},{"id":"e2","relation":"context","excerpt":"solved 93% of ToM tasks","locator":"Abstract","url":"https://arxiv.org/abs/2302.02083v1","publisher":"arXiv; Michal Kosinski","event_date":"2023-02","available_at":"2023-02-04","explanation":"This contemporary study reports substantial success and limits any suggestion of general inability. Its different task battery does not negate the recurring failures in Ullman's variations."}],"what_would_resolve":null,"retrospective":true,"plain_reading":true},"payload_hash":"f71b07b25ef7ae0689602bb799cafa8434aaebb9005529a2946c07657b2b5008","policy_version":"2026-10-28.7","recorded_at":"2026-10-29T07:32:34Z","drafter":{"provider":"openai","model":"gpt-6-astra"},"reviewers":[{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-how-not-to-test-gpt-3-995428-4x-review-anthropic-1"},{"provider":"openai","model":"gpt-6-astra","run_id":"marcus-how-not-to-test-gpt-3-995428-4x-review-openai-1"}],"final_text":{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-how-not-to-test-gpt-3-995428-4x-final-anthropic-1"},"status":"current","notice":null,"superseded_by":null,"evidence_meta":[{"id":"e1","source_type":"preprint","audited":null,"derived":null,"metric":null},{"id":"e2","source_type":"preprint","audited":null,"derived":null,"metric":null}],"public_from":null,"public_until":null},"history":[],"review":{"review_due":false,"triggers":[],"last_completed_review_at":"2026-10-29T07:32:34Z","next_review_due_at":null,"horizon_now":"not_applicable"},"event_ids":["marcus-how-not-to-test-gpt-3-a617b1-assessment-published-a1"],"last_substantive_change_at":"2026-10-29T17:00:18Z"}},{"url":"https://punditclaims.ai/claims/marcus-how-not-to-test-gpt-3-c48e17","standing":"current","assessment_label":"Unresolved","versions":[{"assessment_id":"marcus-how-not-to-test-gpt-3-c48e17-a2","status":"current","url":"https://punditclaims.ai/claims/marcus-how-not-to-test-gpt-3-c48e17#assessment-marcus-how-not-to-test-gpt-3-c48e17-a2"}],"claim":{"claim_id":"marcus-how-not-to-test-gpt-3-c48e17","subject_id":"gary-marcus","revision":1,"content_hash":"8b4f41663c73c7b360660c44fd7cd1485d3c9f44685ce28d10e1d17277eae106","normalized_proposition":"From 2020 to June 2022, GPT-3 improved only slightly on the authors' commonsense examples, going from 45% to 50% right.","kind":"observation","target":"GPT-3","conditions":[],"original_horizon_text":null,"topic_tags":["capabilities","scaling"],"concerns_providers":["openai"],"selection_stratum":"declared-lead","occurrences":[{"id":"marcus-how-not-to-test-gpt-3-c48e17-o1","statement_date":"2023-02-17","statement_date_precision":"day","excerpt":"However, just in terms of percentage right vs. wrong, the improvement was slight; It now got 50% right, 40% wrong, and 10% borderline (marginally better than the 45% right before).","locator":"paragraph 37","document":{"url":"https://garymarcus.substack.com/p/how-not-to-test-gpt-3","title":"How Not to Test GPT-3","publisher":"Marcus on AI","published_at":"2023-02-17T23:09:04Z","date_uncertain":false,"date_provenance":"JSON-LD datePublished"},"observed_at":"2026-10-29T00:04:14Z","observed_via":"live","archive_url":null,"access_state":"full_public","snapshot_hash":"a1e09436396e17a3da39b244e63b2e903406cff6a72d348d2c8b8484982149c0","receipts":1}],"first_statement_date":"2023-02-17","related":[],"current":{"assessment_id":"marcus-how-not-to-test-gpt-3-c48e17-a2","payload":{"claim_id":"marcus-how-not-to-test-gpt-3-c48e17","subject_id":"gary-marcus","normalized_proposition":"From 2020 to June 2022, GPT-3 improved only slightly on the authors' commonsense examples, going from 45% to 50% right.","kind":"observation","conditions":[],"original_horizon_text":null,"statement":{"excerpt":"However, just in terms of percentage right vs. wrong, the improvement was slight; It now got 50% right, 40% wrong, and 10% borderline (marginally better than the 45% right before).","locator":"paragraph 37","url":"https://garymarcus.substack.com/p/how-not-to-test-gpt-3","date":"2023-02-17","document_title":"How Not to Test GPT-3","observed_at":"2026-10-29T00:04:14Z"},"criteria":{"from_speaker":["in June 2022 we reran all the examples that we had run before","50% right, 40% wrong, and 10% borderline","marginally better than the 45% right before","the improvement was slight"],"our_interpretation":["The claim reports the authors' own informal experiment: GPT-3 (2020 version) at 45% right, and text-davinci-002 in June 2022 at 50% right / 40% wrong / 10% borderline, on their commonsense examples.","Verification needs an independent record of the prompts, outputs and scoring. The authors' own report cannot serve as evidence that they were right."],"support_condition":"An independent rerun or audit of the authors' example set confirming roughly 45% right in 2020 and 50% right in June 2022.","contradiction_condition":"An independent audit of the same examples and outputs showing materially different success rates, or showing that the two runs did not cover the same examples."},"axis":"historical_observation","outcome":"unresolved","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"The 45% and 50% figures come from an informal experiment by Marcus and Davis. The only evidence item (e1) is their own NYU report from June 2022. Under the contract, the authors' own report counts only as context and cannot confirm their tallies. Its table lists 71 of 157 tests (45%) as successes in 2020 and 40 of 80 tests (50%) in 2022. Because the test counts differ, it is unclear whether the two percentages cover the same examples. However, e1 does not show how the example sets or the scoring differed. The packet's search found no independent rerun or audit. On this evidence, the claim can be neither confirmed nor refuted.","limitations":["No independent rerun or audit of the authors' example set was found. That does not prove none exists.","e1 notes that the system is not deterministic, so a rerun could give different results.","The packet notes that the 2020 model versions are no longer available, which limits replication."],"evidence":[{"id":"e1","relation":"context","excerpt":"There\nare some examples that GPT-3 got wrong in 2020 and got right in 2022,\nbut there are also examples of the reverse.","locator":"Paragraph after the tally table","url":"https://cs.nyu.edu/~davise/papers/GPT-3-6-22.html","publisher":"Ernest Davis, NYU (co-author of the post)","event_date":"2022-06","available_at":"2022-06-30","explanation":"The underlying report is by the post's own authors. Its table lists 157 tests at 45% in 2020 and 80 tests at 50% in 2022, so the sets differ in size. It cannot be used as independent confirmation."}],"what_would_resolve":"Someone other than the authors would need to rescore the full 2020 and 2022 example sets and outputs, or replicate the tests on the same examples, and confirm or reject roughly 45% right in 2020 and 50% right in June 2022.","retrospective":true,"plain_reading":true},"payload_hash":"ec3c3e5ef5f60ebcd8131ce3ce1e6e4abb358ac3731c86cccda960f9a6f610d6","policy_version":"2026-10-28.7","recorded_at":"2026-10-29T07:42:36Z","drafter":{"provider":"anthropic","model":"claude-opus-5-5"},"reviewers":[{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-how-not-to-test-gpt-3-995428-4x-review-anthropic-1"},{"provider":"openai","model":"gpt-6-astra","run_id":"marcus-how-not-to-test-gpt-3-995428-4x-review-openai-1"}],"final_text":{"provider":"openai","model":"gpt-6-astra","run_id":"marcus-how-not-to-test-gpt-3-995428-2x-final-openai-1"},"status":"current","notice":null,"superseded_by":null,"evidence_meta":[{"id":"e1","source_type":"secondary_summary","audited":null,"derived":null,"metric":null}],"public_from":null,"public_until":null},"history":[],"review":{"review_due":false,"triggers":[],"last_completed_review_at":"2026-10-29T07:42:36Z","next_review_due_at":null,"horizon_now":"not_applicable"},"event_ids":["marcus-how-not-to-test-gpt-3-c48e17-assessment-published-a2"],"last_substantive_change_at":"2026-10-29T17:00:18Z"}},{"url":"https://punditclaims.ai/claims/marcus-how-not-to-test-gpt-3-d7578c","standing":"current","assessment_label":"Supported","versions":[{"assessment_id":"marcus-how-not-to-test-gpt-3-d7578c-a1","status":"current","url":"https://punditclaims.ai/claims/marcus-how-not-to-test-gpt-3-d7578c#assessment-marcus-how-not-to-test-gpt-3-d7578c-a1"}],"claim":{"claim_id":"marcus-how-not-to-test-gpt-3-d7578c","subject_id":"gary-marcus","revision":1,"content_hash":"1d1c41b02adb0d0902a4a65215bcea7564db824810ad359485b0138562048f0a","normalized_proposition":"AI systems and AI test sets have a huge English-language bias.","kind":"observation","target":"AI systems and test sets","conditions":[],"original_horizon_text":null,"topic_tags":["capabilities"],"concerns_providers":[],"selection_stratum":"declared-lead","occurrences":[{"id":"marcus-how-not-to-test-gpt-3-d7578c-o1","statement_date":"2023-02-17","statement_date_precision":"day","excerpt":"AI and AI test sets have a huge English-language bias.","locator":"paragraph 52","document":{"url":"https://garymarcus.substack.com/p/how-not-to-test-gpt-3","title":"How Not to Test GPT-3","publisher":"Marcus on AI","published_at":"2023-02-17T23:09:04Z","date_uncertain":false,"date_provenance":"JSON-LD datePublished"},"observed_at":"2026-10-29T00:04:14Z","observed_via":"live","archive_url":null,"access_state":"full_public","snapshot_hash":"a1e09436396e17a3da39b244e63b2e903406cff6a72d348d2c8b8484982149c0","receipts":1}],"first_statement_date":"2023-02-17","related":[],"current":{"assessment_id":"marcus-how-not-to-test-gpt-3-d7578c-a1","payload":{"claim_id":"marcus-how-not-to-test-gpt-3-d7578c","subject_id":"gary-marcus","normalized_proposition":"AI systems and AI test sets have a huge English-language bias.","kind":"observation","conditions":[],"original_horizon_text":null,"statement":{"excerpt":"AI and AI test sets have a huge English-language bias.","locator":"paragraph 52","url":"https://garymarcus.substack.com/p/how-not-to-test-gpt-3","date":"2023-02-17","document_title":"How Not to Test GPT-3","observed_at":"2026-10-29T00:04:14Z"},"criteria":{"from_speaker":["AI and AI test sets","a huge English-language bias","DO run tests in other languages"],"our_interpretation":["In context, AI refers to GPT-like language systems and their language and reasoning evaluations, not every kind of artificial intelligence.","The plain reading is substantial English predominance in model development and evaluation coverage, with weaker representation of other languages. It does not assert that every model or benchmark is English-only.","Huge describes a conspicuous imbalance; no numerical cutoff or population-weighting formula is supplied.","For this ongoing research-state observation, use the latest completed research year and the elapsed publication year: 2022 through 17 February 2023. This observation window is evaluator-selected."],"support_condition":"Primary documentation shows substantial English predominance in the training or tuning of prominent contemporary language models and in major general-purpose evaluation suites.","contradiction_condition":"Representative documentation shows broadly balanced language coverage in both contemporary language-model development and major evaluation suites, without substantial English predominance."},"axis":"historical_observation","outcome":"supported","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"Primary documentation shows a conspicuous English imbalance on both sides of the claim. InstructGPT's instruction data was overwhelmingly English, BIG-G's training mixture contained little non-English text, and HELM's initial core evaluations covered only English. BIG-bench also acknowledged limited lower-resource coverage. BLOOM and xP3 are important multilingual exceptions, so the supported reading is broad predominance rather than a universal English-only rule.","limitations":["The assessment concerns language systems in the post's context, not all AI applications.","Instruction tuning, pretraining, and evaluation coverage measure different aspects of the imbalance.","No exact industry-wide English share or universal performance gap is established."],"evidence":[{"id":"e1","relation":"supports","excerpt":"almost entirely of English instructions","locator":"Section 5, limitations of data collection; Appendix A.4","url":"https://arxiv.org/html/2203.02155v1","publisher":"Ouyang et al., OpenAI, arXiv","event_date":"2022","available_at":"2022-03-04","explanation":"The developer reports that roughly 96% of its instruction dataset was classified as English, possibly more because of classifier errors. 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The authors explicitly say that even multilingual BLOOM was evaluated only for English."},{"id":"e3","relation":"supports","excerpt":"a handful also target lower-resource languages","locator":"Section 3.7; model description for BIG-G","url":"https://arxiv.org/html/2206.04615v1","publisher":"Srivastava et al., BIG-bench collaboration, arXiv","event_date":"2022","available_at":"2022-06-09","explanation":"BIG-bench documents limited lower-resource-language coverage and reports that its BIG-G training mixture contained about 6% non-English text. Its multilingual tasks also show that English predominance coexisted with deliberate efforts to test other languages."},{"id":"e4","relation":"context","excerpt":"covering 46 languages","locator":"Section 3.1, Multitask Prompted Finetuning; abstract","url":"https://arxiv.org/html/2211.05100v1","publisher":"BigScience collaboration, arXiv","event_date":"2022","available_at":"2022-11-09","explanation":"BLOOM was trained on 46 natural languages, and the project developed a multilingual prompted dataset collection. 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Legal requirements do not have a quarterly reporting period.","Almost no permits some exceptions; finding one applicable law would not alone refute it."],"support_condition":"A sufficiently complete account of applicable law shows that chatbot deployment and the behavior discussed faced very few substantive, binding requirements.","contradiction_condition":"Applicable laws imposed substantial, enforceable requirements across the relevant deployment practices and chatbot harms, making near-absence of regulation an inaccurate description."},"axis":"historical_observation","outcome":"unresolved","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"The record supports concern about limited dedicated oversight: NIST's contemporary framework was voluntary. But existing legal authority and Italy's action against Replika prevent treating chatbots as operating in a demonstrated legal vacuum. Neither a voluntary framework nor an isolated enforcement action settles how extensive regulation was. The retrieved evidence does not establish the broad claim under a single, well-supported jurisdictional reading.","limitations":["Several relevant contemporary primary pages could not be read.","Later legal summaries are context, not date-matched outcome evidence.","The speaker did not define jurisdiction or regulatory scope."],"evidence":[{"id":"e1","relation":"context","excerpt":"a guidance document for voluntary use by organizations designing, developing, deploying or using AI systems to help manage the many risks of AI technologies.","locator":"Opening paragraph","url":"https://www.nist.gov/news-events/news/2023/01/nist-risk-management-framework-aims-improve-trustworthiness-artificial","publisher":"National Institute of Standards and Technology","event_date":"2023-01-26","available_at":"2023-01-26","explanation":"This supports the narrower concern that a prominent AI safety framework imposed no binding duties. It is not an inventory of all applicable law and predates the target day."},{"id":"e2","relation":"context","excerpt":"These tools are not emerging in a legal vacuum.","locator":"Page 1, opening paragraph","url":"https://search.ftc.gov/system/files/ftc_gov/pdf/remarks-of-chair-lina-m-khan-re-joint-interagency-statement-on-ai.pdf","publisher":"Federal Trade Commission","event_date":"2023-04-25","available_at":"2023-04-25","explanation":"The FTC identifies existing authority over AI-related unfairness and deception. 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all of these more recent criticisms of deep learning help establish that some of the basic issues that were known long ago, with predecessors to current models, still remain even today.","locator":"paragraph 10","document":{"url":"https://garymarcus.substack.com/p/happy-groundhog-day-the-ai-edition","title":"Happy Groundhog Day, The AI Edition","publisher":"Marcus on AI","published_at":"2023-02-03T04:42:11Z","date_uncertain":false,"date_provenance":"JSON-LD datePublished"},"observed_at":"2026-10-29T00:04:26Z","observed_via":"live","archive_url":null,"access_state":"full_public","snapshot_hash":"1cdf9cfba040e5db35e34a67be74577b293d15ebc701ebea10a928dabedeb4e2","receipts":1}],"first_statement_date":"2023-02-03","related":[],"current":{"assessment_id":"marcus-happy-groundhog-day-the-ai-edition-58c058-a1","payload":{"claim_id":"marcus-happy-groundhog-day-the-ai-edition-58c058","subject_id":"gary-marcus","normalized_proposition":"Recent criticisms of deep learning, such as Melanie Mitchell's book and Bender et al.'s 'Stochastic Parrots', help establish that some of the basic issues known long ago with predecessors to current models still remain today.","kind":"observation","conditions":["some of the basic issues","help establish"],"original_horizon_text":null,"statement":{"excerpt":"Melanie Mitchell’s book, Bender et al’s “Stochastic Parrots”; 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It does not require every old limitation to remain or every model to fail.","Relevant limitations include unreliable transfer, compositional generalization, and grounding of language.","For the ongoing state, use 2022, the latest completed research year before the post and the year of the immediately preceding empirical example. Older criticisms are historical context. This coarse reporting-period choice is an interpretation because research has no fixed reporting cycle.","The assessment includes the attribution to the named works, rather than merely checking whether any contemporary model had any weakness."],"support_condition":"Verify relevant arguments or findings in the named works, match them to independently documented earlier limitations, and establish persistence in the contemporary research period.","contradiction_condition":"The named works materially fail to support the proposed historical continuity, or matched contemporary evidence establishes that the identified limitations had been resolved."},"axis":"historical_observation","outcome":"unresolved","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"The retrieved research is consistent with the modest claim that some generalization problems persisted, and it also records neural-network successes. However, the full attribution to the named criticisms remains unverified because the relevant passages in Mitchell's book were not recovered. I would hold this assessment rather than treat a publisher description or another paper as a substitute for that evidence.","limitations":["This is an evidence-access and attribution gap, not evidence that the historical continuity claim is false.","The contemporary research example does not support a blanket claim of neural-network incapacity.","The selected research year is an interpretation of the ongoing-state wording."],"evidence":[{"id":"e1","relation":"context","excerpt":"combinatorial structure","locator":"Abstract","url":"https://ruccs.rutgers.edu/images/personal-zenon-pylyshyn/proseminars/Proseminar13/ConnectionistArchitecture.pdf","publisher":"Cognition; author manuscript hosted by Rutgers University","event_date":"1988","available_at":"1988-03-31","explanation":"The paper establishes that systematicity and structured representations were subjects of the earlier connectionist debate. It does not establish the status of models in 2022."},{"id":"e2","relation":"context","excerpt":"Hebbian","locator":"Abstract, fifth sentence","url":"https://pubmed.ncbi.nlm.nih.gov/36191191","publisher":"Proceedings of the National Academy of Sciences; abstract indexed by PubMed","event_date":"2022","available_at":"2022-10-03","explanation":"The abstract reports compositional-generalization difficulties for standard networks and a neural model that captures benefits of human training curricula. It supports a qualified persistence claim while limiting any suggestion of universal failure, but does not verify the attribution to both named criticisms."}],"what_would_resolve":"Obtain the relevant book passages and a clean comparison of both named works with historical sources and contemporary empirical findings about the same limitation.","retrospective":true,"plain_reading":true},"payload_hash":"d86ce70b49e94b09010acf84ddd736e56c3a422d9a6da73286e2ee91931288a0","policy_version":"2026-10-28.7","recorded_at":"2026-10-29T07:27:56Z","drafter":{"provider":"openai","model":"gpt-6-astra"},"reviewers":[{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-happy-groundhog-day-the-ai-edition-33b9a0-4x-review-anthropic-1"},{"provider":"openai","model":"gpt-6-astra","run_id":"marcus-happy-groundhog-day-the-ai-edition-33b9a0-4x-review-openai-1"}],"final_text":{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-happy-groundhog-day-the-ai-edition-33b9a0-4x-final-anthropic-1"},"status":"current","notice":null,"superseded_by":null,"evidence_meta":[{"id":"e1","source_type":"peer_reviewed","audited":null,"derived":null,"metric":null},{"id":"e2","source_type":"peer_reviewed","audited":null,"derived":null,"metric":null}],"public_from":null,"public_until":null},"history":[],"review":{"review_due":false,"triggers":[],"last_completed_review_at":"2026-10-29T07:27:56Z","next_review_due_at":null,"horizon_now":"not_applicable"},"event_ids":["marcus-happy-groundhog-day-the-ai-edition-58c058-assessment-published-a1"],"last_substantive_change_at":"2026-10-29T17:00:18Z"}},{"url":"https://punditclaims.ai/claims/marcus-happy-groundhog-day-the-ai-edition-cbfc5e","standing":"current","assessment_label":"Can’t be checked","versions":[{"assessment_id":"marcus-happy-groundhog-day-the-ai-edition-cbfc5e-a1","status":"current","url":"https://punditclaims.ai/claims/marcus-happy-groundhog-day-the-ai-edition-cbfc5e#assessment-marcus-happy-groundhog-day-the-ai-edition-cbfc5e-a1"}],"claim":{"claim_id":"marcus-happy-groundhog-day-the-ai-edition-cbfc5e","subject_id":"gary-marcus","revision":1,"content_hash":"30f14aa4262597d1c6f9bfc3d186a7972298fc2c9ed3306510702af52d0179e1","normalized_proposition":"AI systems will probably keep failing on endless edge cases for another few years, unless and until people learn from history.","kind":"conditional_forecast","target":"current AI systems","conditions":[{"text":"unless and until people learn a little bit of history","type":"antecedent","from_source":true}],"original_horizon_text":"for another few years","topic_tags":["reliability"],"concerns_providers":[],"selection_stratum":"declared-lead","occurrences":[{"id":"marcus-happy-groundhog-day-the-ai-edition-cbfc5e-o1","statement_date":"2023-02-03","statement_date_precision":"day","excerpt":"Death by a thousand edge cases is still the norm, and probably will be, for another few years, unless and until people learn a little bit of history.","locator":"paragraph 21","document":{"url":"https://garymarcus.substack.com/p/happy-groundhog-day-the-ai-edition","title":"Happy Groundhog Day, The AI Edition","publisher":"Marcus on AI","published_at":"2023-02-03T04:42:11Z","date_uncertain":false,"date_provenance":"JSON-LD datePublished"},"observed_at":"2026-10-29T00:04:26Z","observed_via":"live","archive_url":null,"access_state":"full_public","snapshot_hash":"1cdf9cfba040e5db35e34a67be74577b293d15ebc701ebea10a928dabedeb4e2","receipts":1}],"first_statement_date":"2023-02-03","related":[],"current":{"assessment_id":"marcus-happy-groundhog-day-the-ai-edition-cbfc5e-a1","payload":{"claim_id":"marcus-happy-groundhog-day-the-ai-edition-cbfc5e","subject_id":"gary-marcus","normalized_proposition":"AI systems will probably keep failing on endless edge cases for another few years, unless and until people learn from history.","kind":"conditional_forecast","conditions":["unless and until people learn a little bit of history"],"original_horizon_text":"for another few years","statement":{"excerpt":"Death by a thousand edge cases is still the norm, and probably will be, for another few years, unless and until people learn a little bit of history.","locator":"paragraph 21","url":"https://garymarcus.substack.com/p/happy-groundhog-day-the-ai-edition","date":"2023-02-03","document_title":"Happy Groundhog Day, The AI Edition","observed_at":"2026-10-29T00:04:26Z"},"criteria":{"from_speaker":["unless and until people learn a little bit of history","for another few years"],"our_interpretation":[],"support_condition":"Not checked against evidence: both screens found that public evidence could not settle this claim.","contradiction_condition":"Not checked against evidence: both screens found that public evidence could not settle this claim."},"axis":"not_applicable","outcome":"not_adjudicable","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"Not checked against evidence. Two independent screens, one from each provider, both found this stated as a chance or a possibility, or at medium or low confidence.","limitations":[],"evidence":[],"what_would_resolve":null,"retrospective":true,"uncheckable":["probability_language"]},"payload_hash":"d32b1376454fefae4b53fc5134941b2987b8922cc826b4abfb45a84c00d67064","policy_version":"2026-10-28.7","recorded_at":"2026-10-29T01:10:54Z","drafter":{"provider":"anthropic","model":"claude-opus-5-5"},"reviewers":[{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-happy-groundhog-day-the-ai-edition-screen-anthropic-1"},{"provider":"openai","model":"gpt-6-astra","run_id":"marcus-happy-groundhog-day-the-ai-edition-screen-openai-1"}],"final_text":null,"status":"current","notice":null,"superseded_by":null,"evidence_meta":[],"public_from":null,"public_until":null},"history":[],"review":{"review_due":false,"triggers":[],"last_completed_review_at":"2026-10-29T01:10:54Z","next_review_due_at":null,"horizon_now":"not_applicable"},"event_ids":["marcus-happy-groundhog-day-the-ai-edition-cbfc5e-assessment-published-a1"],"last_substantive_change_at":"2026-10-29T17:00:18Z"}},{"url":"https://punditclaims.ai/claims/marcus-happy-groundhog-day-the-ai-edition-ea217a","standing":"current","assessment_label":"Unresolved","versions":[{"assessment_id":"marcus-happy-groundhog-day-the-ai-edition-ea217a-a2","status":"current","url":"https://punditclaims.ai/claims/marcus-happy-groundhog-day-the-ai-edition-ea217a#assessment-marcus-happy-groundhog-day-the-ai-edition-ea217a-a2"}],"claim":{"claim_id":"marcus-happy-groundhog-day-the-ai-edition-ea217a","subject_id":"gary-marcus","revision":2,"content_hash":"54c1bc16df8fb332ef309783a514c8389198b130bd200defc56bc3812feda0a5","normalized_proposition":"Marcus is not sure how much AI's reliance on handcrafted hacks has changed since the late 1990s, except that hacks once written by graduate students are now farmed out to sweatshops as a new form of handcrafting.","kind":"observation","target":"the AI industry","conditions":[{"text":"not sure how much has changed in that regard","type":"qualifier","from_source":true}],"original_horizon_text":null,"topic_tags":["labour","other"],"concerns_providers":[],"selection_stratum":"declared-lead","occurrences":[{"id":"marcus-happy-groundhog-day-the-ai-edition-ea217a-o1","statement_date":"2023-02-03","statement_date_precision":"day","excerpt":"Not sure how much has changed in that regard, either, except that the hacks that were once written by graduate students are now farmed out to sweatshops, for a new form of handcrafting:","locator":"paragraph 11","document":{"url":"https://garymarcus.substack.com/p/happy-groundhog-day-the-ai-edition","title":"Happy Groundhog Day, The AI Edition","publisher":"Marcus on AI","published_at":"2023-02-03T04:42:11Z","date_uncertain":false,"date_provenance":"JSON-LD datePublished"},"observed_at":"2026-10-29T00:04:26Z","observed_via":"live","archive_url":null,"access_state":"full_public","snapshot_hash":"1cdf9cfba040e5db35e34a67be74577b293d15ebc701ebea10a928dabedeb4e2","receipts":1}],"first_statement_date":"2023-02-03","related":[],"current":{"assessment_id":"marcus-happy-groundhog-day-the-ai-edition-ea217a-a2","payload":{"claim_id":"marcus-happy-groundhog-day-the-ai-edition-ea217a","subject_id":"gary-marcus","normalized_proposition":"Marcus is not sure how much AI's reliance on handcrafted hacks has changed since the late 1990s, except that hacks once written by graduate students are now farmed out to sweatshops as a new form of handcrafting.","kind":"observation","conditions":["not sure how much has changed in that regard"],"original_horizon_text":null,"statement":{"excerpt":"Not sure how much has changed in that regard, either, except that the hacks that were once written by graduate students are now farmed out to sweatshops, for a new form of handcrafting:","locator":"paragraph 11","url":"https://garymarcus.substack.com/p/happy-groundhog-day-the-ai-edition","date":"2023-02-03","document_title":"Happy Groundhog Day, The AI Edition","observed_at":"2026-10-29T00:04:26Z"},"criteria":{"from_speaker":["Not sure how much has changed","hacks that were once written by graduate students","now farmed out to sweatshops","a new form of handcrafting"],"our_interpretation":["The uncertainty concerns how much AI has changed. The outsourcing assertion is affirmative, not a conditional forecast.","The plain reading is that AI companies increasingly use outsourced workers under poor conditions to supply corrective human input formerly supplied by academic programmers. It does not require every contractor to work under such conditions.","Handcrafting includes human-written training examples and corrective labels; this is an analogy, not an assertion that labeling and programming are technically identical.","For the ongoing state described as now, use January 2023, the latest complete month before publication. Earlier employment and academic work provide historical context."],"support_condition":"Evidence establishes contemporary outsourcing of corrective AI work under poor working conditions and documents the asserted shift from academic programming to this workforce.","contradiction_condition":"Comparable historical and contemporary evidence shows that the asserted shift did not occur, or that the supposedly exploitative corrective outsourcing was materially misdescribed."},"axis":"historical_observation","outcome":"unresolved","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"Semafor's January 27, 2023 report documents outsourced programmers creating training material for OpenAI, supporting the outsourcing component [e1]. It does not establish poor working conditions for that workforce [e1]. The Guardian's August 2, 2023 report describes workers' allegations about employment in 2021–2022, which cannot establish conditions in January 2023 [e2]. Neither item establishes the asserted historical shift from graduate-student programming to outsourced corrective work [e1, e2].","limitations":["The contemporary contractor report does not establish working conditions, while the retrospective allegations concern an earlier employment period [e1, e2].","The evidence provides no matched historical comparison showing the extent of the asserted workforce transition [e1, e2]."],"evidence":[{"id":"e1","relation":"supports","excerpt":"creating data","locator":"The Scoop, paragraph 3","url":"https://www.semafor.com/article/01/27/2023/openai-has-hired-an-army-of-contractors-to-make-basic-coding-obsolete","publisher":"Semafor","event_date":"2023-01-27","available_at":"2023-01-27","explanation":"Original reporting supports contemporary outsourcing of human-written model-training material. It establishes neither exploitative conditions for these programmers nor replacement of academic programmers."},{"id":"e2","relation":"context","excerpt":"damaged my mental health","locator":"Opening section, Okinyi's first quoted statement","url":"https://www.theguardian.com/technology/2023/aug/02/ai-chatbot-training-human-toll-content-moderator-meta-openai","publisher":"The Guardian","event_date":"2022","available_at":"2023-08-02","explanation":"Retrospective allegation; employer disputed support deficiencies. Outside January 2023."}],"what_would_resolve":"Historical staffing and task records, compared with January 2023 contracts, task descriptions and working-condition evidence, establishing or refuting both the asserted workforce transition and poor conditions in contemporary corrective 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will not be reached unless abstraction, compositionality, and the type-token distinction are solved, and anyone who thinks otherwise is kidding themselves.","kind":"conditional_forecast","target":"AGI","conditions":[{"text":"if abstraction, compositionality, and the type-token distinction are not solved","type":"antecedent","from_source":true},{"text":"solving them is not needed for beautiful pictures or decent if error-filled conversation","type":"qualifier","from_source":true}],"original_horizon_text":null,"topic_tags":["agi-timelines","capabilities"],"concerns_providers":[],"selection_stratum":"declared-lead","occurrences":[{"id":"marcus-happy-groundhog-day-the-ai-edition-ed2dd9-o1","statement_date":"2023-02-03","statement_date_precision":"day","excerpt":"Turns out you don’t need to solve them to make beautiful pictures, or decent if error-filled conversation, but anyone who thinks we can get to AGI without solving abstraction, compositionality, and the type-token distinction is 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It does not specify a deadline or require a particular architecture.","The ordinary reading of solving these problems is reliable general competence, rather than perfect performance on every possible task.","The remarks about pictures and conversation illustrate lesser capabilities; they are not additional forecast conditions.","AGI must be established independently of the three proposed prerequisites, or the assessment would be circular."],"support_condition":"Evidence establishing that each named capability is necessary for independently defined AGI. Present-day failures or an AGI possessing all three capabilities would not alone establish necessity.","contradiction_condition":"A broadly accepted, independently evaluated AGI demonstrably lacks at least one of the three named capabilities."},"axis":"forecast_outcome","outcome":"unresolved","horizon_state":"open_horizon","evaluation_as_of":"2026-10-28","rationale":"The study version available October 16, 2026 reports both successes and limitations in abstraction tasks for selected models; it does not establish that all three named capabilities are necessary for AGI [e1]. The AGI Society's September 14, 2026 assessment finds the published evidence insufficient to establish GPT-6 Astra as AGI [e2]. 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Its limited models and benchmark cannot establish whether the three problems remain unsolved under Marcus's unspecified standard."},{"id":"e2","relation":"context","excerpt":"There is no generally accepted empirical test for AGI.","locator":"Proposed tests of general intelligence","url":"https://agi-society.org/publications/evaluating-agi-claims","publisher":"AGI Society","event_date":"2026-09","available_at":"2026-09-14","explanation":"The Society assesses public claims about GPT-6 Astra and finds the published evidence insufficient to conclude AGI. This documents disagreement, not proof that AGI is absent or that Marcus's prerequisites are necessary."}],"what_would_resolve":"Evidence establishing that each named capability is necessary for independently defined AGI would support the claim. A broadly accepted, independently evaluated AGI demonstrably lacking at least one capability would contradict it. 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The post specifies no reporting period.","Do not substitute later models for models available when the statement was made."],"support_condition":"Evaluations of contemporary LLMs establish substantial, general deficiencies across the named abilities and connect those deficiencies to a defensible measure of distance from human-level general intelligence.","contradiction_condition":"Contemporary LLMs demonstrate reliable, transferable competence across the named abilities and broad human-level generality. Isolated benchmark successes challenge a literal absence reading but do not alone contradict the contextual claim."},"axis":"historical_observation","outcome":"unresolved","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"The research documents both substantial weaknesses and real reasoning successes before the post. It therefore supports concern about reliability while challenging a literal claim that the abilities were entirely absent. 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Its question does not measure whether generating unreliable text was considered LLMs' primary strength, and its population is narrower than the whole AI community."},{"id":"e2","relation":"context","excerpt":"unable to attract new investors.","locator":"Page 2, discussion of Argo AI","url":"https://shareholder.ford.com/files/doc_financials/2022/q3/Ford-Fulfills-Earnings-Guidance-Has-Strong-Cash-Flow-in-Q3-Will-Accelerate-Development-of-L2-L3-ADAS-Technology.pdf","publisher":"Ford Motor Company","event_date":"2022","available_at":"2022-10-26","explanation":"The disclosure also explains Ford's revised commercialization expectations and shift away from Argo's Level 4 work. 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Errors on algebra, larger numbers or later models cannot settle either family.","The target period is launch through the post date. I do not introduce a numerical reliability cutoff that the speaker did not supply."],"support_condition":"Independent repeated trials or a sufficiently varied set of dated launch-era tests show recurring failures both in counting to four and in simple word problems using single-digit arithmetic.","contradiction_condition":"Representative repeated launch-era tests show consistently correct performance on both task families, with enough coverage to exclude the claimed practical unreliability. If only one family is settled, the full claim remains partly unresolved."},"axis":"historical_observation","outcome":"unresolved","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"The cited example records an algebra error on January 24, 2023, after the claim's January 9 cutoff, and involves larger quantities [e1]. 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A firm timing verdict would additionally require a defensible interpretation of the unspecified delay.","retrospective":true,"plain_reading":true},"payload_hash":"819dd8f39053d5c54a09f9fab2a795f9866f1d97856fdb7876091f8a1230cca8","policy_version":"2026-10-28.7","recorded_at":"2026-10-29T07:32:15Z","drafter":{"provider":"openai","model":"gpt-6-astra"},"reviewers":[{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-how-come-smart-assistants-have-virtually-74fed4-4x-review-anthropic-1"},{"provider":"openai","model":"gpt-6-astra","run_id":"marcus-how-come-smart-assistants-have-virtually-74fed4-4x-review-openai-1"}],"final_text":{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-how-come-smart-assistants-have-virtually-74fed4-4x-final-anthropic-1"},"status":"current","notice":null,"superseded_by":null,"evidence_meta":[{"id":"e1","source_type":"company_disclosure","audited":null,"derived":null,"metric":null},{"id":"e2","source_type":"independent_measurement","audited":null,"derived":null,"metric":null}],"public_from":null,"public_until":null},"history":[],"review":{"review_due":false,"triggers":[],"last_completed_review_at":"2026-10-29T07:32:15Z","next_review_due_at":null,"horizon_now":"open_horizon"},"event_ids":["marcus-how-come-smart-assistants-have-virtually-d5df75-assessment-published-a1"],"last_substantive_change_at":"2026-10-29T17:00:18Z"}},{"url":"https://punditclaims.ai/claims/marcus-a-few-words-about-bullshit-097fc5","standing":"current","assessment_label":"Unresolved","versions":[{"assessment_id":"marcus-a-few-words-about-bullshit-097fc5-a1","status":"current","url":"https://punditclaims.ai/claims/marcus-a-few-words-about-bullshit-097fc5#assessment-marcus-a-few-words-about-bullshit-097fc5-a1"}],"claim":{"claim_id":"marcus-a-few-words-about-bullshit-097fc5","subject_id":"gary-marcus","revision":2,"content_hash":"f3f29c398931657ad02e1bf65412c4de9505feda057ad37799ca07e316fb1b60","normalized_proposition":"Like Galactica, every other large language model the author has seen prevaricates (fabricates content) a lot.","kind":"observation","target":"LLMs","conditions":[{"text":"every other LLM the author has seen","type":"scope","from_source":true},{"text":"a lot","type":"qualifier","from_source":true}],"original_horizon_text":null,"topic_tags":["reliability"],"concerns_providers":[],"selection_stratum":"declared-lead","occurrences":[{"id":"marcus-a-few-words-about-bullshit-097fc5-o1","statement_date":"2022-11-16","statement_date_precision":"day","excerpt":"Just like every other large language model I have seen.","locator":"paragraph 15","document":{"url":"https://garymarcus.substack.com/p/a-few-words-about-bullshit","title":"A Few Words About Bullshit","publisher":"Marcus on AI","published_at":"2022-11-16T05:02:55Z","date_uncertain":false,"date_provenance":"JSON-LD datePublished"},"observed_at":"2026-10-29T00:04:50Z","observed_via":"live","archive_url":null,"access_state":"full_public","snapshot_hash":"5999d4741d768d2d064ee7ad4c48fe27ef707e50681a0d38c8d7bd67c3d54a9b","receipts":1}],"first_statement_date":"2022-11-16","related":[],"current":{"assessment_id":"marcus-a-few-words-about-bullshit-097fc5-a1","payload":{"claim_id":"marcus-a-few-words-about-bullshit-097fc5","subject_id":"gary-marcus","normalized_proposition":"Like Galactica, every other large language model the author has seen prevaricates (fabricates content) a lot.","kind":"observation","conditions":["every other LLM the author has seen","a lot"],"original_horizon_text":null,"statement":{"excerpt":"Just like every other large language model I have seen.","locator":"paragraph 15","url":"https://garymarcus.substack.com/p/a-few-words-about-bullshit","date":"2022-11-16","document_title":"A Few Words About Bullshit","observed_at":"2026-10-29T00:04:50Z"},"criteria":{"from_speaker":["every other large language model I have seen","It prevaricates. A lot."],"our_interpretation":["Prevaricates means generates fabricated or false factual content; it does not require intentional deception.","A lot means a recurring, substantial problem, rather than an isolated mistake. Marcus supplies no numerical threshold or distribution of prompts.","The scope is the models Marcus had encountered by publication, not every model subsequently released.","The present-perfect phrase 'I have seen' describes accumulated experience through 2022-11-16. Its starting date is unspecified, so the target period has no asserted start."],"support_condition":"Identify the models Marcus had encountered by publication and establish recurring fabrication in each through documented outputs or suitable evaluations.","contradiction_condition":"Identify a model within that set and show, through sufficiently broad and relevant evaluations, that fabrication was exceptional rather than recurring. Occasional correct answers would not suffice."},"axis":"historical_observation","outcome":"unresolved","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"Contemporary research documents substantial false-answer problems in several major model families. That supports the concern behind Marcus's statement, but cannot establish its full scope: every model he had seen. Truthfulness also varied with training. The evidence neither verifies the universal claim nor identifies a qualifying counterexample.","limitations":["False answers on deliberately challenging questions are not a direct estimate of fabrication in ordinary use.","The claim leaves both its model set and its frequency threshold unspecified."],"evidence":[{"id":"e1","relation":"context","excerpt":"truthful on 58% of questions","locator":"Abstract, TruthfulQA results","url":"https://arxiv.org/abs/2109.07958","publisher":"Lin, Hilton and Evans; arXiv / ACL","event_date":"2021","available_at":"2022-05-08","explanation":"The study evaluated GPT-3, GPT-Neo/J, GPT-2 and a T5-based model on questions designed to elicit common misconceptions. It establishes substantial false-answer rates on that benchmark, but neither identifies Marcus's complete model set nor measures ordinary-use fabrication frequency."},{"id":"e2","relation":"context","excerpt":"improvements in truthfulness","locator":"Abstract, InstructGPT results","url":"https://arxiv.org/abs/2203.02155","publisher":"Ouyang and colleagues; arXiv","event_date":"2022","available_at":"2022-03-04","explanation":"The researchers report improved truthfulness after training with human feedback. This qualifies comparisons across models and versions, but does not establish that any model Marcus saw rarely fabricated content."}],"what_would_resolve":"A reliable inventory of the models Marcus had encountered, paired with relevant evaluations showing recurring fabrication in each or a clear counterexample.","retrospective":true,"plain_reading":true},"payload_hash":"f63be09c7ff573965a38ed385ba2bd05cd1b854c2e4c5baa98012119042e1b3b","policy_version":"2026-10-28.7","recorded_at":"2026-10-29T03:09:48Z","drafter":{"provider":"openai","model":"gpt-6-astra"},"reviewers":[{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-a-few-words-about-bullshit-097fc5-3x-review-anthropic-1"},{"provider":"openai","model":"gpt-6-astra","run_id":"marcus-a-few-words-about-bullshit-097fc5-3x-review-openai-1"}],"final_text":{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-a-few-words-about-bullshit-097fc5-3x-final-anthropic-1"},"status":"current","notice":null,"superseded_by":null,"evidence_meta":[{"id":"e1","source_type":"peer_reviewed","audited":null,"derived":null,"metric":null},{"id":"e2","source_type":"preprint","audited":null,"derived":null,"metric":null}],"public_from":null,"public_until":null},"history":[],"review":{"review_due":false,"triggers":[],"last_completed_review_at":"2026-10-29T03:09:48Z","next_review_due_at":null,"horizon_now":"not_applicable"},"event_ids":["marcus-a-few-words-about-bullshit-097fc5-assessment-published-a1"],"last_substantive_change_at":"2026-10-29T17:00:18Z"}},{"url":"https://punditclaims.ai/claims/marcus-a-few-words-about-bullshit-2dc0d5","standing":"current","assessment_label":"Supported","versions":[{"assessment_id":"marcus-a-few-words-about-bullshit-2dc0d5-a1","status":"current","url":"https://punditclaims.ai/claims/marcus-a-few-words-about-bullshit-2dc0d5#assessment-marcus-a-few-words-about-bullshit-2dc0d5-a1"}],"claim":{"claim_id":"marcus-a-few-words-about-bullshit-2dc0d5","subject_id":"gary-marcus","revision":2,"content_hash":"42252a37e72b8f42c039a2928640750f7288db10051e179cd1037b82a50a9ca4","normalized_proposition":"The Galactica examples Andrew Sundstrom shared are pitch-perfect but utterly bogus imitations of science and math, presented as the real thing.","kind":"observation","target":"Galactica","conditions":[{"text":"the examples Sundstrom shared","type":"scope","from_source":true}],"original_horizon_text":null,"topic_tags":["reliability"],"concerns_providers":[],"selection_stratum":"declared-lead","occurrences":[{"id":"marcus-a-few-words-about-bullshit-2dc0d5-o1","statement_date":"2022-11-16","statement_date_precision":"day","excerpt":"Pitch perfect and utterly bogus imitations of science and math, presented as the real thing.","locator":"paragraph 23","document":{"url":"https://garymarcus.substack.com/p/a-few-words-about-bullshit","title":"A Few Words About Bullshit","publisher":"Marcus on AI","published_at":"2022-11-16T05:02:55Z","date_uncertain":false,"date_provenance":"JSON-LD datePublished"},"observed_at":"2026-10-29T00:04:50Z","observed_via":"live","archive_url":null,"access_state":"full_public","snapshot_hash":"5999d4741d768d2d064ee7ad4c48fe27ef707e50681a0d38c8d7bd67c3d54a9b","receipts":1}],"first_statement_date":"2022-11-16","related":[],"current":{"assessment_id":"marcus-a-few-words-about-bullshit-2dc0d5-a1","payload":{"claim_id":"marcus-a-few-words-about-bullshit-2dc0d5","subject_id":"gary-marcus","normalized_proposition":"The Galactica examples Andrew Sundstrom shared are pitch-perfect but utterly bogus imitations of science and math, presented as the real thing.","kind":"observation","conditions":["the examples Sundstrom shared"],"original_horizon_text":null,"statement":{"excerpt":"Pitch perfect and utterly bogus imitations of science and math, presented as the real thing.","locator":"paragraph 23","url":"https://garymarcus.substack.com/p/a-few-words-about-bullshit","date":"2022-11-16","document_title":"A Few Words About Bullshit","observed_at":"2026-10-29T00:04:50Z"},"criteria":{"from_speaker":["examples of his own","Pitch perfect","utterly bogus imitations of science and math","presented as the real thing","More examples"],"our_interpretation":["The claim concerns the displayed and expressly linked examples, including the companion record that names Andrew Sundstrom as the submitter of particular prompts.","Pitch perfect means recognizable scientific or mathematical presentation, not flawless prose.","Utterly bogus means the purported scientific or mathematical account is substantively invalid; it does not mean every individual statement or calculation is false.","Presented as the real thing refers to the generated passages' factual and expository voice. It does not imply that Meta provided no general warnings.","The target period is the launch-period examples shared on November 15–16, 2022."],"support_condition":"A preserved example attributable to Sundstrom in the displayed or linked record presents materially fabricated or invalid scientific or mathematical exposition as factual explanation.","contradiction_condition":"The relevant preserved examples are substantively sound, or the supposedly factual passages explicitly identify themselves as fiction or deliberate illustrations of errors."},"axis":"historical_observation","outcome":"supported","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"The expressly linked experiment preserves a Sundstrom prompt and Galactica's formal-looking mathematical response. Its proposed solution depends on treating 0.5 + 0.5 as 0.4. That is a reproducible substantive error presented as explanation, supporting Marcus's description of bogus mathematical exposition. This judgment includes the linked companion examples and does not establish that every screenshot, every sentence, or every Galactica output was false.","limitations":["Support depends on including the companion examples expressly linked in the post.","The record is a participant-preserved demonstration, not an independently reproduced experiment.","The same output also contains correct arithmetic elsewhere; the assessment concerns its invalid argument.","Selected examples do not measure the model's general error rate."],"evidence":[{"id":"e1","relation":"supports","excerpt":"the replacement cannot increase the sum of the sequence.","locator":"Fourth query (Andrew Sundstrom), Galactica output, Solution section, first numerical example","url":"https://cs.nyu.edu/~davise/papers/ExperimentWithGalactica.html","publisher":"Ernest Davis and Andrew Sundstrom, NYU-hosted experiment record","event_date":"2022-11-15","available_at":"2022-11-15","explanation":"The preserved response presents a purported paradox with formal sections and a solution, treating the sum of two 0.5 terms as 0.4 to reach this conclusion. Direct arithmetic gives 1, so the mathematical exposition is invalid despite its factual presentation."}],"what_would_resolve":null,"retrospective":true,"plain_reading":true},"payload_hash":"296d7ca62d4b582f080e1eef38ba0cafd9d64dcc56a53d146f22aa6936d7c133","policy_version":"2026-10-28.7","recorded_at":"2026-10-29T03:09:48Z","drafter":{"provider":"openai","model":"gpt-6-astra"},"reviewers":[{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-a-few-words-about-bullshit-097fc5-3x-review-anthropic-1"},{"provider":"openai","model":"gpt-6-astra","run_id":"marcus-a-few-words-about-bullshit-097fc5-3x-review-openai-1"}],"final_text":{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-a-few-words-about-bullshit-097fc5-3x-final-anthropic-1"},"status":"current","notice":null,"superseded_by":null,"evidence_meta":[{"id":"e1","source_type":"demonstration","audited":null,"derived":{"inputs":["Original sequence in the recorded output: {0.1, 0.1, 0.1, 0.1}.","Replacement sequence in the recorded output: {0.5, 0.5}.","The output assigns both sequences a sum of 0.4 under its first definition."],"formula":"Original sum = 4 × 0.1 = 0.4; replacement sum = 0.5 + 0.5 = 1; increase = 1 − 0.4 = 0.6.","rounding":"Exact decimal arithmetic; no rounding."},"metric":null}],"public_from":null,"public_until":null},"history":[],"review":{"review_due":false,"triggers":[],"last_completed_review_at":"2026-10-29T03:09:48Z","next_review_due_at":null,"horizon_now":"not_applicable"},"event_ids":["marcus-a-few-words-about-bullshit-2dc0d5-assessment-published-a1"],"last_substantive_change_at":"2026-10-29T17:00:18Z"}},{"url":"https://punditclaims.ai/claims/marcus-a-few-words-about-bullshit-eaffe0","standing":"current","assessment_label":"Unresolved","versions":[{"assessment_id":"marcus-a-few-words-about-bullshit-eaffe0-a1","status":"current","url":"https://punditclaims.ai/claims/marcus-a-few-words-about-bullshit-eaffe0#assessment-marcus-a-few-words-about-bullshit-eaffe0-a1"}],"claim":{"claim_id":"marcus-a-few-words-about-bullshit-eaffe0","subject_id":"gary-marcus","revision":2,"content_hash":"5ddd62058119524ec7f19cae7a1eda1632927a395dab058bb8dd76ad84ef9476","normalized_proposition":"High school students will love Galactica and will use it to fool and intimidate some of their teachers.","kind":"forecast","target":"Galactica","conditions":[{"text":"some of their teachers","type":"scope","from_source":true}],"original_horizon_text":null,"topic_tags":["other"],"concerns_providers":[],"selection_stratum":"declared-lead","occurrences":[{"id":"marcus-a-few-words-about-bullshit-eaffe0-o1","statement_date":"2022-11-16","statement_date_precision":"day","excerpt":"High school students will love it, and use it to fool and intimidate (some of) their teachers.","locator":"paragraph 15","document":{"url":"https://garymarcus.substack.com/p/a-few-words-about-bullshit","title":"A Few Words About Bullshit","publisher":"Marcus on AI","published_at":"2022-11-16T05:02:55Z","date_uncertain":false,"date_provenance":"JSON-LD datePublished"},"observed_at":"2026-10-29T00:04:50Z","observed_via":"live","archive_url":null,"access_state":"full_public","snapshot_hash":"5999d4741d768d2d064ee7ad4c48fe27ef707e50681a0d38c8d7bd67c3d54a9b","receipts":1}],"first_statement_date":"2022-11-16","related":[],"current":{"assessment_id":"marcus-a-few-words-about-bullshit-eaffe0-a1","payload":{"claim_id":"marcus-a-few-words-about-bullshit-eaffe0","subject_id":"gary-marcus","normalized_proposition":"High school students will love Galactica and will use it to fool and intimidate some of their teachers.","kind":"forecast","conditions":["some of their teachers"],"original_horizon_text":null,"statement":{"excerpt":"High school students will love it, and use it to fool and intimidate (some of) their teachers.","locator":"paragraph 15","url":"https://garymarcus.substack.com/p/a-few-words-about-bullshit","date":"2022-11-16","document_title":"A Few Words About Bullshit","observed_at":"2026-10-29T00:04:50Z"},"criteria":{"from_speaker":["High school students","will love it","use it to fool and intimidate","(some of) their teachers"],"our_interpretation":["It refers to Galactica, the system under discussion.","Love means enthusiastic uptake by high school students; the sentence does not require every student or a majority.","Fool means teachers accept generated material as genuine student work or valid knowledge. Intimidate means students use its apparent scientific authority to make teachers reluctant or unable to challenge them.","Both deception and intimidation require evidence; cheating alone does not establish the whole forecast.","The sentence states neither a deadline nor a conditional antecedent. Some teachers describes scope, not a condition."],"support_condition":"Documented Galactica-specific student and teacher accounts establish enthusiastic high school use and actual deception and intimidation of some teachers.","contradiction_condition":"A decisive event establishes that the predicted use never happened and can no longer happen. Failure to find incidents, or withdrawal of one interface, is insufficient for this forecast without a deadline."},"axis":"forecast_outcome","outcome":"unresolved","horizon_state":"open_horizon","evaluation_as_of":"2026-10-28","rationale":"The searches did not locate documented incidents satisfying the full Galactica-specific forecast. Its public demo was withdrawn, but the model was released separately. Withdrawal therefore does not prove the forecast impossible. With no deadline and no decisive event, the claim remains unresolved.","limitations":["A search that finds no qualifying incidents cannot establish that none occurred.","Reports of cheating with other language models would not settle this Galactica-specific forecast."],"evidence":[{"id":"e1","relation":"context","excerpt":"Meta pulled the Galactica demo Thursday.","locator":"Story text, paragraph beginning 'As a result'","url":"https://arstechnica.com/information-technology/2022/11/after-controversy-meta-pulls-demo-of-ai-model-that-writes-scientific-papers","publisher":"Ars Technica","event_date":"2022-11-17","available_at":"2022-11-18","explanation":"The demo was withdrawn shortly after the forecast. This reputable contemporaneous report supplies the withdrawal date; it does not establish whether students had used Galactica or whether later use was impossible."},{"id":"e2","relation":"context","excerpt":"We open source the model","locator":"Abstract, final sentence","url":"https://arxiv.org/abs/2211.09085","publisher":"Taylor and colleagues; arXiv","event_date":"2022-11-16","available_at":"2022-11-16","explanation":"The authors announced release of the model itself. Consequently, withdrawal of the hosted demo does not demonstrate permanent impossibility of future Galactica use."}],"what_would_resolve":"Dated, credible accounts identifying high school students who enthusiastically used Galactica and successfully deceived and intimidated teachers, or a decisive event satisfying the stated contradiction rule.","retrospective":true,"plain_reading":true},"payload_hash":"a9ad106f752a576f89305f479673589fb96bafaddd6f2cf949c07a73cb3ccd0b","policy_version":"2026-10-28.7","recorded_at":"2026-10-29T03:09:48Z","drafter":{"provider":"openai","model":"gpt-6-astra"},"reviewers":[{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-a-few-words-about-bullshit-097fc5-3x-review-anthropic-1"},{"provider":"openai","model":"gpt-6-astra","run_id":"marcus-a-few-words-about-bullshit-097fc5-3x-review-openai-1"}],"final_text":{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-a-few-words-about-bullshit-097fc5-3x-final-anthropic-1"},"status":"current","notice":null,"superseded_by":null,"evidence_meta":[{"id":"e1","source_type":"news_report","audited":null,"derived":null,"metric":null},{"id":"e2","source_type":"preprint","audited":null,"derived":null,"metric":null}],"public_from":null,"public_until":null},"history":[],"review":{"review_due":false,"triggers":[],"last_completed_review_at":"2026-10-29T03:09:48Z","next_review_due_at":null,"horizon_now":"open_horizon"},"event_ids":["marcus-a-few-words-about-bullshit-eaffe0-assessment-published-a1"],"last_substantive_change_at":"2026-10-29T17:00:18Z"}},{"url":"https://punditclaims.ai/claims/marcus-bravo-elonmusk-for-wanting-twitter-000e6a","standing":"current","assessment_label":"Unresolved","versions":[{"assessment_id":"marcus-bravo-elonmusk-for-wanting-twitter-000e6a-a1","status":"current","url":"https://punditclaims.ai/claims/marcus-bravo-elonmusk-for-wanting-twitter-000e6a#assessment-marcus-bravo-elonmusk-for-wanting-twitter-000e6a-a1"}],"claim":{"claim_id":"marcus-bravo-elonmusk-for-wanting-twitter-000e6a","subject_id":"gary-marcus","revision":2,"content_hash":"55dd201b577ac5168c04f255fccf7ae7e67e1d96a9bb519a92132a3ee936f62e","normalized_proposition":"The problem of misinformation on Twitter is about to get much, much worse.","kind":"forecast","target":"misinformation on Twitter","conditions":[],"original_horizon_text":"about to","topic_tags":["safety","reliability"],"concerns_providers":[],"selection_stratum":"declared-lead","occurrences":[{"id":"marcus-bravo-elonmusk-for-wanting-twitter-000e6a-o1","statement_date":"2022-11-09","statement_date_precision":"day","excerpt":"The problem is about to get much, much worse.","locator":"paragraph 14","document":{"url":"https://garymarcus.substack.com/p/bravo-elonmusk-for-wanting-twitter","title":"Bravo @ElonMusk for wanting Twitter to become the world’s “most accurate source of information”","publisher":"Marcus on AI","published_at":"2022-11-09T22:23:31Z","date_uncertain":false,"date_provenance":"JSON-LD datePublished"},"observed_at":"2026-10-29T00:04:52Z","observed_via":"live","archive_url":null,"access_state":"full_public","snapshot_hash":"0b36f90275ba14c5eac2da7a9b0b8bc4ae753a1e80d54387bc29872a40d87608","receipts":1}],"first_statement_date":"2022-11-09","related":[],"current":{"assessment_id":"marcus-bravo-elonmusk-for-wanting-twitter-000e6a-a1","payload":{"claim_id":"marcus-bravo-elonmusk-for-wanting-twitter-000e6a","subject_id":"gary-marcus","normalized_proposition":"The problem of misinformation on Twitter is about to get much, much worse.","kind":"forecast","conditions":[],"original_horizon_text":"about to","statement":{"excerpt":"The problem is about to get much, much worse.","locator":"paragraph 14","url":"https://garymarcus.substack.com/p/bravo-elonmusk-for-wanting-twitter","date":"2022-11-09","document_title":"Bravo @ElonMusk for wanting Twitter to become the world’s “most accurate source of information”","observed_at":"2026-10-29T00:04:52Z"},"criteria":{"from_speaker":["The problem","about to","much, much worse"],"our_interpretation":["The problem refers to misinformation on Twitter.","The plain reading is a substantial near-term increase in misinformation's prevalence, volume, or reach.","About to indicates urgency but supplies no latest calendar date. I do not invent a deadline.","This claim concerns deterioration itself; the proposed explanation involving cheaper language models is assessed separately."],"support_condition":"Comparable, broadly representative measurements show a substantial deterioration in misinformation on Twitter shortly after the post.","contradiction_condition":"Comparable measurements covering the near-term period establish stable or improving misinformation levels across the relevant measures. A narrow sample or one unchanged proxy is insufficient."},"axis":"forecast_outcome","outcome":"unresolved","horizon_state":"open_horizon","evaluation_as_of":"2026-10-28","rationale":"The strongest comparable study found deterioration in source quality, but its magnitude was modest and low-quality link volume did not substantially increase. It also measures sources rather than the truth of individual posts. This supports concern about information quality without decisively establishing that misinformation became much, much worse shortly after the post. No event meets the early-resolution rule.","limitations":["An increase in misinformation exposure could occur without an increase in posting volume.","The research does not measure all Twitter content or establish a role for machine-generated misinformation."],"evidence":[{"id":"e1","relation":"context","excerpt":"decline in information quality","locator":"Findings, transition between Finding 1 and Finding 2; magnitude and volume comparisons in Finding 2","url":"https://misinforeview.hks.harvard.edu/article/declining-information-quality-under-new-platform-governance","publisher":"Harvard Kennedy School Misinformation Review","event_date":"2023-04","available_at":"2025-07-11","explanation":"The January 2022–April 2023 comparison finds declining average source quality, but describes the overall change as modest. Low-quality link volume was approximately stable in the global sample and declined in the voter panel; domain ratings do not establish individual-post falsity or exposure."}],"what_would_resolve":"Representative historical measurements of false-content prevalence, volume, or exposure immediately before and after the post, showing the size and timing of the change.","retrospective":true,"plain_reading":true},"payload_hash":"433c65aa488e637018f13c6275e39a7ee34e0f0bb46cd94294bf9aa2b0e77890","policy_version":"2026-10-28.7","recorded_at":"2026-10-29T06:45:37Z","drafter":{"provider":"openai","model":"gpt-6-astra"},"reviewers":[{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-bravo-elonmusk-for-wanting-twitter-000e6a-4x-review-anthropic-1"},{"provider":"openai","model":"gpt-6-astra","run_id":"marcus-bravo-elonmusk-for-wanting-twitter-000e6a-4x-review-openai-1"}],"final_text":{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-bravo-elonmusk-for-wanting-twitter-000e6a-4x-final-anthropic-1"},"status":"current","notice":null,"superseded_by":null,"evidence_meta":[{"id":"e1","source_type":"peer_reviewed","audited":null,"derived":null,"metric":null}],"public_from":null,"public_until":null},"history":[],"review":{"review_due":false,"triggers":[],"last_completed_review_at":"2026-10-29T06:45:37Z","next_review_due_at":null,"horizon_now":"open_horizon"},"event_ids":["marcus-bravo-elonmusk-for-wanting-twitter-000e6a-assessment-published-a1"],"last_substantive_change_at":"2026-10-29T17:00:18Z"}},{"url":"https://punditclaims.ai/claims/marcus-bravo-elonmusk-for-wanting-twitter-063541","standing":"current","assessment_label":"Unresolved","versions":[{"assessment_id":"marcus-bravo-elonmusk-for-wanting-twitter-063541-a1","status":"current","url":"https://punditclaims.ai/claims/marcus-bravo-elonmusk-for-wanting-twitter-063541#assessment-marcus-bravo-elonmusk-for-wanting-twitter-063541-a1"}],"claim":{"claim_id":"marcus-bravo-elonmusk-for-wanting-twitter-063541","subject_id":"gary-marcus","revision":1,"content_hash":"131ca6134f3404d6ad33c195bcda7b5a4d1c266fdfa12e2d3f6856a7c88a9ed7","normalized_proposition":"As machine-generated misinformation accelerates, Twitter's mostly manual Community Notes will be overwhelmed.","kind":"conditional_forecast","target":"Twitter Community Notes","conditions":[{"text":"as the pace of machine-generated misinformation picks up","type":"antecedent","from_source":true}],"original_horizon_text":null,"topic_tags":["safety"],"concerns_providers":[],"selection_stratum":"declared-lead","occurrences":[{"id":"marcus-bravo-elonmusk-for-wanting-twitter-063541-o1","statement_date":"2022-11-09","statement_date_precision":"day","excerpt":"As the pace of machine-generated misinformation picks up, Twitter’s existing effort, Community Notes (aka Birdwatch), which is mostly done manually, by humans, is going to get left in the dust.","locator":"paragraph 15","document":{"url":"https://garymarcus.substack.com/p/bravo-elonmusk-for-wanting-twitter","title":"Bravo @ElonMusk for wanting Twitter to become the world’s “most accurate source of information”","publisher":"Marcus on AI","published_at":"2022-11-09T22:23:31Z","date_uncertain":false,"date_provenance":"JSON-LD datePublished"},"observed_at":"2026-10-29T00:04:52Z","observed_via":"live","archive_url":null,"access_state":"full_public","snapshot_hash":"0b36f90275ba14c5eac2da7a9b0b8bc4ae753a1e80d54387bc29872a40d87608","receipts":1}],"first_statement_date":"2022-11-09","related":[],"current":{"assessment_id":"marcus-bravo-elonmusk-for-wanting-twitter-063541-a1","payload":{"claim_id":"marcus-bravo-elonmusk-for-wanting-twitter-063541","subject_id":"gary-marcus","normalized_proposition":"As machine-generated misinformation accelerates, Twitter's mostly manual Community Notes will be overwhelmed.","kind":"conditional_forecast","conditions":["as the pace of machine-generated misinformation picks up"],"original_horizon_text":null,"statement":{"excerpt":"As the pace of machine-generated misinformation picks up, Twitter’s existing effort, Community Notes (aka Birdwatch), which is mostly done manually, by humans, is going to get left in the dust.","locator":"paragraph 15","url":"https://garymarcus.substack.com/p/bravo-elonmusk-for-wanting-twitter","date":"2022-11-09","document_title":"Bravo @ElonMusk for wanting Twitter to become the world’s “most accurate source of information”","observed_at":"2026-10-29T00:04:52Z"},"criteria":{"from_speaker":["As the pace of machine-generated misinformation picks up","Twitter’s existing effort, Community Notes (aka Birdwatch)","mostly done manually, by humans","is going to get left in the dust"],"our_interpretation":["The plain reading is that growing machine-generated misinformation will substantially outstrip the human-led system’s ability to provide timely corrections.","Coverage and response time are my operational measures; Marcus supplied neither a numerical threshold nor a deadline.","The condition requires observed growth in machine-generated misleading content, not merely improvements in AI capabilities.","Introducing AI note writers is not, by itself, proof that the earlier system was overwhelmed."],"support_condition":"After the condition occurs, sustained measurements show that the mostly manual system substantially fails to keep pace with machine-generated misinformation in correction coverage or timeliness.","contradiction_condition":"Representative measurements showing sustained, timely and broad correction by the mostly manual system during accelerating machine-generated misinformation would contradict failure during that episode. Temporary success cannot conclusively refute an unlimited future forecast."},"axis":"forecast_outcome","outcome":"unresolved","horizon_state":"open_horizon","evaluation_as_of":"2026-10-28","rationale":"Research documents growth in flagged AI misinformation and slower initial annotations. It also finds stronger agreement once AI content is annotated. These selected samples do not show how much misinformation escaped correction or establish sustained overload of the mostly manual system. Later AI assistance changes the system being measured. No evidence meets the early-resolution rule, and Marcus gave no deadline.","limitations":["The condition is observed in a selected sample, not a census of X.","Annotation and consensus are not equivalent to timely public correction.","The available findings do not isolate the effects of growing AI misinformation from other system changes."],"evidence":[{"id":"e1","relation":"supports","excerpt":"surges","locator":"Section 4, Evolution of Multimodal Misinformation","url":"https://arxiv.org/html/2604.15372v1","publisher":"arXiv; Zacharias Chrysidis and colleagues","event_date":"2026-01","available_at":"2026-04-15","explanation":"The analysis through January 2026 reports an upward trajectory and recent acceleration in AI-generated visual misinformation captured by Community Notes. This supports occurrence of the condition within the observed sample, without establishing platform-wide prevalence."},{"id":"e2","relation":"context","excerpt":"reach consensus","locator":"Section 6, Consensus Dynamics","url":"https://arxiv.org/html/2604.15372v1","publisher":"arXiv; Zacharias Chrysidis and colleagues","event_date":"2026-01","available_at":"2026-04-15","explanation":"The selected AI posts received slower initial annotations but had stronger subsequent agreement, providing evidence both of delay and continuing capacity. The study’s consensus measure includes Helpful and Not Helpful decisions; it does not establish visible correction coverage or sustained overload."},{"id":"e3","relation":"context","excerpt":"extending coverage","locator":"Abstract; measurements described as June 2–29, 2026","url":"https://arxiv.org/html/2609.40067v1","publisher":"arXiv; Community Notes team","event_date":"2026-06","available_at":"2026-09-30","explanation":"The platform team reports that AI writers extended coverage while humans continued to rate notes. This documents adaptation, but neither proves earlier overload nor measures how an unchanged manual system would have performed."}],"what_would_resolve":"A dated longitudinal study measuring AI misinformation volume, visible correction coverage and delays, with human and AI contributions separated, could establish that the manual system was substantially overtaken. 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I use the current calendar year through the post, 2022-01-01 to 2022-11-09, and require evidence applicable to models available then.","The ordinary reading is that LLMs were exceptionally effective, unmatched producers of convincing false content but unreliable detectors of false information.","Detection means judging factual accuracy, not identifying whether text was written by a machine.","Unmatched generation requires comparative evidence. Fluency or the production of some false answers alone does not establish superiority."],"support_condition":"Representative comparisons applicable to the target period establish both superior misinformation generation relative to human and other automated alternatives and broadly unreliable factual detection by contemporary LLMs.","contradiction_condition":"Comparable evidence applicable to the target period establishes that alternatives match or exceed LLM misinformation generation, or that contemporary LLMs reliably detect misinformation across representative tasks. Narrow successes would qualify the claim without necessarily settling it."},"axis":"historical_observation","outcome":"unresolved","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"Neither item settles the claim for January 1–November 9, 2022 [e1, e2]. The September 2021 TruthfulQA report measures false-answer generation, not misinformation detection or comparative effectiveness at producing misinformation [e1]. The January 2023 GPT-3 preprint reports 89% detection success on false tweets and 64% on accurate tweets, but the quoted experiment has not been dated to the target period [e2]. These results do not establish either unmatched misinformation generation or broadly poor detection by LLMs during that period [e1, e2].","limitations":["No admitted comparison establishes unmatched misinformation generation during the target period.","The detection evidence covers a limited tweet dataset, and its experimental timing is not established.","TruthfulQA measures answer truthfulness rather than factual detection."],"evidence":[{"id":"e1","relation":"context","excerpt":"truthful on 58% of questions","locator":"Abstract","url":"https://arxiv.org/abs/2109.07958v1","publisher":"arXiv; Stephanie Lin, Jacob Hilton and Owain Evans","event_date":"2021","available_at":"2021-09-08","explanation":"This documents substantial false-answer generation in an earlier evaluation. It neither measures misinformation detection directly nor establishes unmatched misinformation production in 2022."},{"id":"e2","relation":"context","excerpt":"performed slightly better","locator":"Results, subsection Humans evaluate the accuracy of information better than GPT-3","url":"https://arxiv.org/pdf/2301.11924v2","publisher":"arXiv; Giovanni Spitale, Nikola Biller-Andorno and Federico Germani","event_date":"2023-01","available_at":"2023-01-31","explanation":"The study reports compelling GPT-3 disinformation alongside meaningful detection ability: 89% success on false tweets, but only 64% on accurate tweets. January 2023 is the documented reporting period; the quoted experiment's timing was not established, so these results are contextual rather than decisive for November 2022."}],"what_would_resolve":"Evaluations tied to models and experiments from January 1–November 9, 2022, comparing misinformation credibility and production efficiency against human and automated alternatives, and testing factual detection across representative topics with human or established-system baselines and both missed falsehoods and false accusations.","retrospective":true,"plain_reading":true},"payload_hash":"08e7ad99ca7a845cd38349576226f3888f6817aeaa0847db2cad293058394310","policy_version":"2026-10-28.7","recorded_at":"2026-10-29T02:38:50Z","drafter":{"provider":"openai","model":"gpt-6-astra"},"reviewers":[{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-bravo-elonmusk-for-wanting-twitter-3a095d-2x-review-anthropic-1"},{"provider":"openai","model":"gpt-6-astra","run_id":"marcus-bravo-elonmusk-for-wanting-twitter-3a095d-2x-review-openai-1"}],"final_text":{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-bravo-elonmusk-for-wanting-twitter-3a095d-1x-final-anthropic-1"},"status":"current","notice":null,"superseded_by":null,"evidence_meta":[{"id":"e1","source_type":"preprint","audited":null,"derived":null,"metric":null},{"id":"e2","source_type":"preprint","audited":null,"derived":null,"metric":null}],"public_from":null,"public_until":null},"history":[],"review":{"review_due":false,"triggers":[],"last_completed_review_at":"2026-10-29T02:38:50Z","next_review_due_at":null,"horizon_now":"not_applicable"},"event_ids":["marcus-bravo-elonmusk-for-wanting-twitter-3a095d-assessment-published-a2"],"last_substantive_change_at":"2026-10-29T17:00:18Z"}},{"url":"https://punditclaims.ai/claims/marcus-bravo-elonmusk-for-wanting-twitter-67f63a","standing":"current","assessment_label":"Unresolved","versions":[{"assessment_id":"marcus-bravo-elonmusk-for-wanting-twitter-67f63a-a1","status":"current","url":"https://punditclaims.ai/claims/marcus-bravo-elonmusk-for-wanting-twitter-67f63a#assessment-marcus-bravo-elonmusk-for-wanting-twitter-67f63a-a1"}],"claim":{"claim_id":"marcus-bravo-elonmusk-for-wanting-twitter-67f63a","subject_id":"gary-marcus","revision":1,"content_hash":"b881b90918eb8ab8e19aa90b9c1efd0ea068e5302ce6884aac80489293a2d0ca","normalized_proposition":"Scaling LLMs will not solve their inability to reason about truth and detect misinformation.","kind":"forecast","target":"scaling LLMs","conditions":[],"original_horizon_text":null,"topic_tags":["scaling","reliability"],"concerns_providers":[],"selection_stratum":"declared-lead","occurrences":[{"id":"marcus-bravo-elonmusk-for-wanting-twitter-67f63a-o1","statement_date":"2022-11-09","statement_date_precision":"day","excerpt":"Scaling won’t solve this.","locator":"paragraph 16","document":{"url":"https://garymarcus.substack.com/p/bravo-elonmusk-for-wanting-twitter","title":"Bravo @ElonMusk for wanting Twitter to become the world’s “most accurate source of information”","publisher":"Marcus on AI","published_at":"2022-11-09T22:23:31Z","date_uncertain":false,"date_provenance":"JSON-LD datePublished"},"observed_at":"2026-10-29T00:04:52Z","observed_via":"live","archive_url":null,"access_state":"full_public","snapshot_hash":"0b36f90275ba14c5eac2da7a9b0b8bc4ae753a1e80d54387bc29872a40d87608","receipts":1}],"first_statement_date":"2022-11-09","related":[],"current":{"assessment_id":"marcus-bravo-elonmusk-for-wanting-twitter-67f63a-a1","payload":{"claim_id":"marcus-bravo-elonmusk-for-wanting-twitter-67f63a","subject_id":"gary-marcus","normalized_proposition":"Scaling LLMs will not solve their inability to reason about truth and detect misinformation.","kind":"forecast","conditions":[],"original_horizon_text":null,"statement":{"excerpt":"Scaling won’t solve this.","locator":"paragraph 16","url":"https://garymarcus.substack.com/p/bravo-elonmusk-for-wanting-twitter","date":"2022-11-09","document_title":"Bravo @ElonMusk for wanting Twitter to become the world’s “most accurate source of information”","observed_at":"2026-10-29T00:04:52Z"},"criteria":{"from_speaker":["Scaling won’t solve this","No matter how much data you can collect","they suck at detecting it","reasoning about truth"],"our_interpretation":["In context, this predicts that increasing LLM scale alone will not produce reliable factual reasoning and misinformation detection.","Scale means increased model size, training data or training compute within a comparable modeling approach.","A solution means broadly reliable practical performance, not perfect knowledge or a philosophical demonstration of understanding.","Improvement is weaker than solving the problem. Gains from retrieval, new supervision, tools or changed training methods do not by themselves identify the effect of scale.","No deadline is supplied, so the forecast has an open horizon."],"support_condition":"A compelling general limitation showing that scale alone cannot deliver the stated capability would support the forecast. A finite sequence of failures provides supporting context but cannot settle this indefinite prediction.","contradiction_condition":"A well-controlled, independently replicated evaluation shows that increasing scale alone produces broadly reliable factual reasoning and misinformation detection on diverse, previously unseen claims."},"axis":"forecast_outcome","outcome":"unresolved","horizon_state":"open_horizon","evaluation_as_of":"2026-10-28","rationale":"Marcus gives no deadline. Later studies show that larger models can become more truthful and better at fact-checking, which weighs against treating scaling as useless. 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The assessment must distinguish intentional surreal content from unintended inconsistency.","retrospective":true,"plain_reading":true},"payload_hash":"ce253cecc8f4527883c0ff8fbb1cbebec33702a514416474e0938a26a93cea77","policy_version":"2026-10-28.7","recorded_at":"2026-10-29T07:09:21Z","drafter":{"provider":"openai","model":"gpt-6-astra"},"reviewers":[{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-deepfakes-versus-deep-understanding-b2d26e-3x-review-anthropic-1"},{"provider":"openai","model":"gpt-6-astra","run_id":"marcus-deepfakes-versus-deep-understanding-b2d26e-3x-review-openai-1"}],"final_text":{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-deepfakes-versus-deep-understanding-dbe10a-1x-final-anthropic-1"},"status":"current","notice":null,"superseded_by":null,"evidence_meta":[{"id":"e1","source_type":"company_disclosure","audited":null,"derived":null,"metric":null},{"id":"e2","source_type":"preprint","audited":null,"derived":null,"metric":null}],"public_from":null,"public_until":null},"history":[],"review":{"review_due":false,"triggers":[],"last_completed_review_at":"2026-10-29T07:09:21Z","next_review_due_at":null,"horizon_now":"not_applicable"},"event_ids":["marcus-deepfakes-versus-deep-understanding-dbe10a-assessment-published-a2"],"last_substantive_change_at":"2026-10-29T17:00:18Z"}},{"url":"https://punditclaims.ai/claims/marcus-deepfakes-versus-deep-understanding-e025b6","standing":"current","assessment_label":"Unresolved","versions":[{"assessment_id":"marcus-deepfakes-versus-deep-understanding-e025b6-a1","status":"current","url":"https://punditclaims.ai/claims/marcus-deepfakes-versus-deep-understanding-e025b6#assessment-marcus-deepfakes-versus-deep-understanding-e025b6-a1"}],"claim":{"claim_id":"marcus-deepfakes-versus-deep-understanding-e025b6","subject_id":"gary-marcus","revision":2,"content_hash":"f9a6e3cc029332c04a38b1696ebded3c1365f3cea4a3d561989b082b7a41d9e3","normalized_proposition":"AI systems such as video, image, and language generators are now in an uncanny valley of impressive output with basic errors, and will stay there for a while.","kind":"forecast","target":"generative AI","conditions":[{"text":"generative video, image, and language systems as illustrated","type":"scope","from_source":true}],"original_horizon_text":"for a while","topic_tags":["reliability","capabilities"],"concerns_providers":[],"selection_stratum":"declared-lead","occurrences":[{"id":"marcus-deepfakes-versus-deep-understanding-e025b6-o1","statement_date":"2022-10-02","statement_date_precision":"day","excerpt":"This is the uncanny valley we are in, and one that we occupy for a while.","locator":"paragraph 6","document":{"url":"https://garymarcus.substack.com/p/deepfakes-versus-deep-understanding","title":"DeepFakes versus Deep Understanding","publisher":"Marcus on AI","published_at":"2022-10-02T22:43:30Z","date_uncertain":false,"date_provenance":"JSON-LD datePublished"},"observed_at":"2026-10-29T00:04:58Z","observed_via":"live","archive_url":null,"access_state":"full_public","snapshot_hash":"4f8308b4459f1b094577e7fe0958c13e33e8b43fbb823cb8a29fa50838472203","receipts":1}],"first_statement_date":"2022-10-02","related":[],"current":{"assessment_id":"marcus-deepfakes-versus-deep-understanding-e025b6-a1","payload":{"claim_id":"marcus-deepfakes-versus-deep-understanding-e025b6","subject_id":"gary-marcus","normalized_proposition":"AI systems such as video, image, and language generators are now in an uncanny valley of impressive output with basic errors, and will stay there for a while.","kind":"forecast","conditions":["generative video, image, and language systems as illustrated"],"original_horizon_text":"for a while","statement":{"excerpt":"This is the uncanny valley we are in, and one that we occupy for a while.","locator":"paragraph 6","url":"https://garymarcus.substack.com/p/deepfakes-versus-deep-understanding","date":"2022-10-02","document_title":"DeepFakes versus Deep Understanding","observed_at":"2026-10-29T00:04:58Z"},"criteria":{"from_speaker":["the uncanny valley we are in","one that we occupy for a while","A cat holding a remote control shouldn’t have a human hand","for all the brushwork, the painting never changed","the same thing over and over in language systems"],"our_interpretation":["The ordinary reading is that impressive generative outputs will continue to coexist with basic errors in physical behavior, object relationships, and everyday reasoning.","The examples identify the relevant kinds of errors; they do not commit Marcus to every system failing every task or to progress stopping.","“For a while” expresses persistence but supplies no deadline or minimum duration. I do not convert it into a fixed number of months or years.","The references to video, image, and language systems describe scope, not an antecedent that must occur before the forecast applies.","Later evaluations can support the predicted pattern without conclusively settling its unspecified duration."],"support_condition":"Repeated evaluations after publication show that capable video, image, and language generators continue making basic semantic or commonsense errors. Conclusive support additionally requires a defensible duration for “for a while”; the source supplies none.","contradiction_condition":"Broad, reproducible evaluations show that the described combination of impressive outputs and recurring basic errors ceased within the duration the forecast committed to. Improvement on selected tasks alone would not contradict the claim, and that duration is unspecified."},"axis":"forecast_outcome","outcome":"unresolved","horizon_state":"open_horizon","evaluation_as_of":"2026-10-28","rationale":"The evidence supports the predicted pattern: later evaluations found physical errors in generated videos, object-relation errors in images, and plausible explanations of basic reasoning mistakes. There was also substantial progress: a later human evaluation found the original image benchmark largely solved. Marcus did not say how long “for a while” meant or what would count as leaving this state. 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This matches the language-system pattern, but a narrow problem family does not establish failure rates across everyday language tasks."},{"id":"e4","relation":"context","excerpt":"Further, according to the human evaluation, GenEval is now saturated.","locator":"Section 6, Conclusion","url":"https://arxiv.org/html/2512.16853v1","publisher":"arXiv","event_date":"2025","available_at":"2025-12-18","explanation":"Human assessment found that newer image models largely solved the original GenEval tasks and that its automated scoring had drifted from human judgment. This limits extrapolation from e2 to later systems, although the paper also reports room for improvement on its expanded benchmark."}],"what_would_resolve":"An independently documented clarification of the intended duration and exit criterion, followed by comparable evaluations across video, image, and language systems. 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The retrieved passages do not establish a strategy for that problem, and incomplete verification of the full record prevents confidently concluding that none was presented.","limitations":["An absence claim requires stronger coverage than targeted keyword searches.","Credibility cannot be judged solely by whether a safeguard or method was mentioned."],"evidence":[{"id":"e1","relation":"context","excerpt":"a localized stop button","locator":"Q&A, Musk's answer immediately before Speaker 40's question about Dojo","url":"https://stockanalysis.com/stocks/tsla/transcripts/32847-ai-day-2022","publisher":"Tesla; transcript hosted by Stock Analysis","event_date":"2022-09-30","available_at":"2022-10-01","explanation":"Musk described a concrete emergency safeguard, which is material counterevidence to a broad reading that he ignored robot safety. 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April through September 18 is the assessment window.","A fair comparison would use comparable image-making tasks and distinguish ordinary participants from highly trained artists. Neither training level nor a quality threshold is specified."],"support_condition":"Blinded comparisons of contemporaneous model outputs and human work show high model image quality, with superior human performance confined to highly trained artists.","contradiction_condition":"Comparable blinded evaluations show that people without extensive artistic training reliably surpass the contemporaneous systems in visual quality."},"axis":"historical_observation","outcome":"unresolved","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"The evidence documents image-quality evaluations, but it does not establish that only highly trained human artists could do better. That restriction requires a comparison with humans of documented training levels. 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They do well on some inputs (like astronaut rides horse) but more poorly on others (like horse rides astronaut, which I discussed in an earlier post).","locator":"paragraph 8","document":{"url":"https://garymarcus.substack.com/p/form-function-and-the-giant-gulf","title":"Form, function, and the giant gulf between drawing a picture and understanding the world","publisher":"Marcus on AI","published_at":"2022-09-18T21:39:37Z","date_uncertain":false,"date_provenance":"JSON-LD datePublished"},"observed_at":"2026-10-29T00:05:05Z","observed_via":"live","archive_url":null,"access_state":"full_public","snapshot_hash":"56c838503c2f4d1863a8f6e5716aba4b0ecbe590f198b1129e47aaf1b635c67f","receipts":1}],"first_statement_date":"2022-09-18","related":[],"current":{"assessment_id":"marcus-form-function-and-the-giant-gulf-52ece7-a1","payload":{"claim_id":"marcus-form-function-and-the-giant-gulf-52ece7","subject_id":"gary-marcus","normalized_proposition":"Image synthesis systems only inconsistently match their linguistic input to the images produced, doing well on some prompts and poorly on others.","kind":"observation","conditions":[],"original_horizon_text":null,"statement":{"excerpt":"On #2, the answer is mixed. 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April through September 18 is the assessment window.","Support requires documented successes as well as failures, rather than attractive images alone."],"support_condition":"Contemporaneous evaluations document successful prompt matching alongside substantial failures on other prompts, especially compositional or relational instructions.","contradiction_condition":"Representative contemporaneous evaluations show consistently accurate correspondence across ordinary and relational prompts, with the alleged failure pattern absent."},"axis":"historical_observation","outcome":"supported","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"Contemporaneous independent research found successful prompt matches alongside frequent relational failures. MIT's September report also describes attribute-binding errors in DALL-E 2. These findings support Marcus's general observation that correspondence with linguistic input was uneven. 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Its results were reported by July; the precise image-generation dates are not supplied."},{"id":"e3","relation":"supports","excerpt":"red truck and green house","locator":"Paragraph beginning 'Take, for example, a red truck and a green house.'","url":"https://news.mit.edu/2022/ai-system-makes-models-like-dall-e-2-more-creative-0908","publisher":"MIT News / MIT CSAIL","event_date":"2022-09-08","available_at":"2022-09-08","explanation":"MIT's report describes DALL-E 2 swapping requested colors between objects while also reporting its strength at natural-image generation. 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A marginal success would weakly identify and color those parts while retaining substantial defects.","The batch is assigned to the September 18 demonstration because the surrounding narrative describes testing that morning. Exact generation timestamps are not given.","Independent image scoring can test the count; repeating the generation today cannot verify this historical batch."],"support_condition":"Independent inspection of the identified nine-image batch finds exactly one weak success, in the top-right position, and no other successful output.","contradiction_condition":"Independent inspection of that same batch finds a different success count, a clearly successful image rather than merely a marginal one, or a different location for the sole qualifying image."},"axis":"historical_observation","outcome":"unresolved","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"This is a claim about nine particular outputs. 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I use September through the publication date as the observation window.","Difficulty assigning colors to separate objects is relevant background but does not by itself establish difficulty assigning colors to an object and its parts."],"support_condition":"Independent contemporary outputs show repeated failures on the named bicycle prompt or closely comparable simple object-and-part color prompts during the target period.","contradiction_condition":"Representative independent contemporary testing shows reliable assignment of the requested colors to the bicycle and its wheels, together with comparable simple object-and-part prompts."},"axis":"historical_observation","outcome":"unresolved","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"The available research concerns a related color-assignment problem, but it does not directly test the claimed part-whole behavior during the target period. Searches for the exact example did not locate an independent contemporary reproduction. Neither support nor contradiction is established by the qualifying records.","limitations":["Failure to locate independent records does not show that Marcus's displayed results were inaccurate.","Multiple-object color binding and object-part color assignment are related but distinct tests.","The original model settings and complete sample are unavailable in the supplied text."],"evidence":[{"id":"e2","relation":"context","excerpt":"confuse the colors","locator":"Section 3.1, Attribute Binding, Color","url":"https://arxiv.org/html/2307.06350v1","publisher":"arXiv; T2I-CompBench authors","event_date":"2023","available_at":"2023-07-12","explanation":"This research identifies a related color-assignment problem. Its later experiments and multiple-object task do not directly establish September 2022 performance on bicycle-and-wheel color assignment."}],"what_would_resolve":"Independent September 1–18, 2022 outputs with model and sampling details for the white-bicycle/green-wheels prompt or closely comparable object-and-part color prompts, showing whether failures recur.","retrospective":true,"plain_reading":true},"payload_hash":"05019810c1f0f2b84e5ef40a7a8e6331daa3e58988adae5a631af781819b4aae","policy_version":"2026-10-28.7","recorded_at":"2026-10-29T07:09:10Z","drafter":{"provider":"openai","model":"gpt-6-astra"},"reviewers":[{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-form-function-and-the-giant-gulf-23167d-4x-review-anthropic-1"},{"provider":"openai","model":"gpt-6-astra","run_id":"marcus-form-function-and-the-giant-gulf-23167d-4x-review-openai-1"}],"final_text":{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-form-function-and-the-giant-gulf-23167d-4x-final-anthropic-1"},"status":"current","notice":null,"superseded_by":null,"evidence_meta":[{"id":"e2","source_type":"preprint","audited":null,"derived":null,"metric":null}],"public_from":null,"public_until":null},"history":[],"review":{"review_due":false,"triggers":[],"last_completed_review_at":"2026-10-29T07:09:10Z","next_review_due_at":null,"horizon_now":"not_applicable"},"event_ids":["marcus-form-function-and-the-giant-gulf-a558ad-assessment-published-a1"],"last_substantive_change_at":"2026-10-29T17:00:18Z"}},{"url":"https://punditclaims.ai/claims/marcus-form-function-and-the-giant-gulf-df5e53","standing":"current","assessment_label":"Unresolved","versions":[{"assessment_id":"marcus-form-function-and-the-giant-gulf-df5e53-a1","status":"current","url":"https://punditclaims.ai/claims/marcus-form-function-and-the-giant-gulf-df5e53#assessment-marcus-form-function-and-the-giant-gulf-df5e53-a1"}],"claim":{"claim_id":"marcus-form-function-and-the-giant-gulf-df5e53","subject_id":"gary-marcus","revision":1,"content_hash":"58fa5ee5c7c51451aec3a0dfd7c1192e153bfa22a34c4fa1a6aabed307aee0f7","normalized_proposition":"Text-to-image systems like Stable Diffusion continue to have problems with negation in prompts.","kind":"observation","target":"Stable Diffusion","conditions":[],"original_horizon_text":null,"topic_tags":["reliability","capabilities"],"concerns_providers":[],"selection_stratum":"declared-lead","occurrences":[{"id":"marcus-form-function-and-the-giant-gulf-df5e53-o1","statement_date":"2022-09-18","statement_date_precision":"day","excerpt":"Negation is, as ever, a problem.","locator":"paragraph 34","document":{"url":"https://garymarcus.substack.com/p/form-function-and-the-giant-gulf","title":"Form, function, and the giant gulf between drawing a picture and understanding the world","publisher":"Marcus on AI","published_at":"2022-09-18T21:39:37Z","date_uncertain":false,"date_provenance":"JSON-LD datePublished"},"observed_at":"2026-10-29T00:05:05Z","observed_via":"live","archive_url":null,"access_state":"full_public","snapshot_hash":"56c838503c2f4d1863a8f6e5716aba4b0ecbe590f198b1129e47aaf1b635c67f","receipts":1}],"first_statement_date":"2022-09-18","related":[],"current":{"assessment_id":"marcus-form-function-and-the-giant-gulf-df5e53-a1","payload":{"claim_id":"marcus-form-function-and-the-giant-gulf-df5e53","subject_id":"gary-marcus","normalized_proposition":"Text-to-image systems like Stable Diffusion continue to have problems with negation in prompts.","kind":"observation","conditions":[],"original_horizon_text":null,"statement":{"excerpt":"Negation is, as ever, a problem.","locator":"paragraph 34","url":"https://garymarcus.substack.com/p/form-function-and-the-giant-gulf","date":"2022-09-18","document_title":"Form, function, and the giant gulf between drawing a picture and understanding the world","observed_at":"2026-10-29T00:05:05Z"},"criteria":{"from_speaker":["Negation is, as ever, a problem.","Draw a white bicycle with no wheels","Stable Diffusion"],"our_interpretation":["The ordinary reading is that contemporary Stable Diffusion repeatedly mishandles negation in ordinary text prompts. It does not assert that every negated prompt fails.","The example concerns negation within the main prompt, rather than a separate negative-prompt control.","The post describes ongoing September 2022 behavior without a reporting period. I use September through the publication date as the observation window.","Later experiments are context unless their records establish behavior within that observation window."],"support_condition":"Independent records establish recurring failures by contemporary Stable Diffusion to obey ordinary negated prompts during the target period, including comparable object-exclusion requests.","contradiction_condition":"Representative independent tests during the target period establish reliable compliance with ordinary negated prompts, including the bicycle example and comparable exclusions."},"axis":"historical_observation","outcome":"unresolved","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"The independent study is consistent with Marcus's observation, but its experiments are not dated to the September observation window. Under the required event-date rule, it supplies context rather than decisive outcome evidence. The searches did not locate qualifying contemporary tests that establish or refute recurring negation failures.","limitations":["This is an evidence gap, not a finding that negation worked reliably.","The inferred observation window materially limits which experiments qualify.","Later tests of the same checkpoint could support a broader retrospective assessment, but their historical applicability has not been established here."],"evidence":[{"id":"e1","relation":"context","excerpt":"function words are poorly understood","locator":"Section 4, Conclusions; relevant experiments in Section 3.3 and Figure 4","url":"https://arxiv.org/html/2210.12565v1","publisher":"arXiv; Shashank Sonkar, Naiming Liu, and Richard G. Baraniuk","event_date":"2022","available_at":"2022-10-22","explanation":"The paper tests Stable Diffusion v1.4 and reports failures with negated descriptions. It does not give experiment dates establishing that these observations occurred within September 1–18, so it is contextual evidence only."}],"what_would_resolve":"Independent dated outputs or experimental records from September 1–18, 2022, identifying the Stable Diffusion checkpoint and showing repeated results for ordinary negated prompts.","retrospective":true,"plain_reading":true},"payload_hash":"aa7a4e11d728cfa2ee3af16ed1bf22ff71ce882b8ebe20b99380ececcca9b880","policy_version":"2026-10-28.7","recorded_at":"2026-10-29T07:08:45Z","drafter":{"provider":"openai","model":"gpt-6-astra"},"reviewers":[{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-form-function-and-the-giant-gulf-b4f3f4-2x-review-anthropic-1"},{"provider":"openai","model":"gpt-6-astra","run_id":"marcus-form-function-and-the-giant-gulf-b4f3f4-2x-review-openai-1"}],"final_text":{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-form-function-and-the-giant-gulf-b4f3f4-2x-final-anthropic-1"},"status":"current","notice":null,"superseded_by":null,"evidence_meta":[{"id":"e1","source_type":"preprint","audited":null,"derived":null,"metric":null}],"public_from":null,"public_until":null},"history":[],"review":{"review_due":false,"triggers":[],"last_completed_review_at":"2026-10-29T07:08:45Z","next_review_due_at":null,"horizon_now":"not_applicable"},"event_ids":["marcus-form-function-and-the-giant-gulf-df5e53-assessment-published-a1"],"last_substantive_change_at":"2026-10-29T17:00:18Z"}},{"url":"https://punditclaims.ai/claims/marcus-did-googleai-just-snooker-one-of-1abf74","standing":"current","assessment_label":"Unresolved","versions":[{"assessment_id":"marcus-did-googleai-just-snooker-one-of-1abf74-a1","status":"current","url":"https://punditclaims.ai/claims/marcus-did-googleai-just-snooker-one-of-1abf74#assessment-marcus-did-googleai-just-snooker-one-of-1abf74-a1"}],"claim":{"claim_id":"marcus-did-googleai-just-snooker-one-of-1abf74","subject_id":"gary-marcus","revision":1,"content_hash":"128989998b9e3603acb9551e94e104204224587f68c7e47e363e39e385fca6b6","normalized_proposition":"Google has systematically refused to let the scientific community inspect models such as Imagen and LaMDA.","kind":"observation","target":"Google","conditions":[],"original_horizon_text":null,"topic_tags":["other"],"concerns_providers":[],"selection_stratum":"declared-lead","occurrences":[{"id":"marcus-did-googleai-just-snooker-one-of-1abf74-o1","statement_date":"2022-09-14","statement_date_precision":"day","excerpt":"Google cloaks its current work the trappings of science (article preprints with citations and graphs and so forth) but when it comes to community inspection, they’ve systematically opted out.","locator":"paragraph 34","document":{"url":"https://garymarcus.substack.com/p/did-googleai-just-snooker-one-of","title":"Did GoogleAI Just Snooker One of Silicon Valley’s Sharpest Minds?","publisher":"Marcus on AI","published_at":"2022-09-14T18:26:06Z","date_uncertain":false,"date_provenance":"JSON-LD datePublished"},"observed_at":"2026-10-29T00:05:06Z","observed_via":"live","archive_url":null,"access_state":"full_public","snapshot_hash":"a044468a1f1106319fe37e15dd64af5688c2f594fc99aa9e8ed140aa623c9044","receipts":1}],"first_statement_date":"2022-09-14","related":[],"current":{"assessment_id":"marcus-did-googleai-just-snooker-one-of-1abf74-a1","payload":{"claim_id":"marcus-did-googleai-just-snooker-one-of-1abf74","subject_id":"gary-marcus","normalized_proposition":"Google has systematically refused to let the scientific community inspect models such as Imagen and LaMDA.","kind":"observation","conditions":[],"original_horizon_text":null,"statement":{"excerpt":"Google cloaks its current work the trappings of science (article preprints with citations and graphs and so forth) but when it comes to community inspection, they’ve systematically opted out.","locator":"paragraph 34","url":"https://garymarcus.substack.com/p/did-googleai-just-snooker-one-of","date":"2022-09-14","document_title":"Did GoogleAI Just Snooker One of Silicon Valley’s Sharpest Minds?","observed_at":"2026-10-29T00:05:06Z"},"criteria":{"from_speaker":["Google","its current work","community inspection","systematically opted out"],"our_interpretation":["The immediate context identifies Imagen and LaMDA; I assess these models rather than all Google research.","Community inspection ordinarily means meaningful access for external scientific evaluation. Public weights are sufficient but not necessary.","Publication of a paper, selected demonstrations, and constrained consumer feedback do not automatically establish independent scientific access.","For this ongoing state, I use the current 2022 model-release cycle through the post date as the latest reportable period; the source supplies no finer reporting interval.","The rhetorical criticism of scientific appearance and Google's motives is not separately scored."],"support_condition":"Dated access policies and records establish systematic withholding of meaningful external scientific evaluation for Imagen and LaMDA during the relevant period.","contradiction_condition":"Documented external researchers received meaningful access to inspect or independently evaluate the named models, contradicting the asserted systematic refusal."},"axis":"historical_observation","outcome":"unresolved","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"Google explicitly withheld Imagen's public code and demo. It also began offering limited LaMDA demos before Marcus's post. These records establish restricted access, but do not settle whether meaningful scientific inspection was systematically refused across both models. Treating every demo as scientific inspection, or treating lack of public weights as proof of refusal, would impose a definition Marcus did not supply.","limitations":["The evidence distinguishes public availability from scientific access but does not fully document the latter.","Google's stated policies do not establish its motives or every private access decision."],"evidence":[{"id":"e1","relation":"supports","excerpt":"not to release code or a public demo.","locator":"Limitations and Societal Impact","url":"https://imagen.research.google/","publisher":"Google Research","event_date":"2022-05","available_at":"2022-05-23","explanation":"The original Imagen project page supports the non-release part of the criticism. Withholding public code and demos does not establish that all private scientific-access requests were refused."},{"id":"e2","relation":"context","excerpt":"a rotating set of experimental demos.","locator":"Paragraph describing the first set of LaMDA demos","url":"https://blog.google/innovation-and-ai/products/join-us-in-the-ai-test-kitchen","publisher":"Google","event_date":"2022-08-25","available_at":"2022-08-25","explanation":"Google announced a gradual rollout of constrained LaMDA demos and external feedback before the post. This challenges an absolute no-access reading, but does not establish unrestricted or independently reproducible scientific inspection."}],"what_would_resolve":"Dated access agreements, researcher accounts, evaluation reports, or refusal records showing what external scientists could independently test on Imagen and LaMDA by the post date.","retrospective":true,"plain_reading":true},"payload_hash":"75c0c216a1533f3e383fdc8a9fbdd8880aa5b2db9899373248ead0be8e0e111a","policy_version":"2026-10-28.7","recorded_at":"2026-10-29T07:00:23Z","drafter":{"provider":"openai","model":"gpt-6-astra"},"reviewers":[{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-did-googleai-just-snooker-one-of-1abf74-4x-review-anthropic-1"},{"provider":"openai","model":"gpt-6-astra","run_id":"marcus-did-googleai-just-snooker-one-of-1abf74-4x-review-openai-1"}],"final_text":{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-did-googleai-just-snooker-one-of-1abf74-4x-final-anthropic-1"},"status":"current","notice":null,"superseded_by":null,"evidence_meta":[{"id":"e1","source_type":"company_disclosure","audited":null,"derived":null,"metric":null},{"id":"e2","source_type":"company_disclosure","audited":null,"derived":null,"metric":null}],"public_from":null,"public_until":null},"history":[],"review":{"review_due":false,"triggers":[],"last_completed_review_at":"2026-10-29T07:00:23Z","next_review_due_at":null,"horizon_now":"not_applicable"},"event_ids":["marcus-did-googleai-just-snooker-one-of-1abf74-assessment-published-a1"],"last_substantive_change_at":"2026-10-29T17:00:18Z"}},{"url":"https://punditclaims.ai/claims/marcus-did-googleai-just-snooker-one-of-2a9aa8","standing":"current","assessment_label":"Can’t be checked","versions":[{"assessment_id":"marcus-did-googleai-just-snooker-one-of-2a9aa8-a1","status":"current","url":"https://punditclaims.ai/claims/marcus-did-googleai-just-snooker-one-of-2a9aa8#assessment-marcus-did-googleai-just-snooker-one-of-2a9aa8-a1"}],"claim":{"claim_id":"marcus-did-googleai-just-snooker-one-of-2a9aa8","subject_id":"gary-marcus","revision":1,"content_hash":"2b538d3ef01c70a450c2fd76bd4b14f9593e21f313abc88a46f9c5ffbf9d543d","normalized_proposition":"Google probably tested Imagen on Winoground, got poor results, and doesn't want to admit it.","kind":"observation","target":"Google Imagen","conditions":[],"original_horizon_text":null,"topic_tags":["capabilities"],"concerns_providers":[],"selection_stratum":"declared-lead","occurrences":[{"id":"marcus-did-googleai-just-snooker-one-of-2a9aa8-o1","statement_date":"2022-09-14","statement_date_precision":"day","excerpt":"(Dare I make obvious guess? 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one, Imagen included, has publicly shown a machine that relates sentence meanings to their parts as well as a five-year-old.","kind":"observation","target":"AI systems including Imagen","conditions":[],"original_horizon_text":null,"topic_tags":["capabilities"],"concerns_providers":[],"selection_stratum":"declared-lead","occurrences":[{"id":"marcus-did-googleai-just-snooker-one-of-648cfa-o1","statement_date":"2022-09-14","statement_date_precision":"day","excerpt":"Notwithstanding the rumors about Google Imagen, nobody has yet publicly demonstrated a machine that can relate the meanings of sentences to their parts the way a five-year-old child can.","locator":"paragraph 32","document":{"url":"https://garymarcus.substack.com/p/did-googleai-just-snooker-one-of","title":"Did GoogleAI Just Snooker One of Silicon Valley’s Sharpest Minds?","publisher":"Marcus on AI","published_at":"2022-09-14T18:26:06Z","date_uncertain":false,"date_provenance":"JSON-LD 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that none demonstrated comparable sentence-composition ability by the post date.","contradiction_condition":"A publicly documented system available by the post date demonstrates comparable performance to five-year-olds across controlled, varied sentence-composition tasks."},"axis":"historical_observation","outcome":"unresolved","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"Winoground supports concern about compositional limitations, but its crowdworker baseline does not establish a five-year-old standard. The search found no qualifying contemporary demonstration of child-level parity. That search result alone is insufficient to establish the universal negative.","limitations":["The named age threshold is not measured by the cited benchmark.","Restricted task success would not necessarily establish the breadth of ability described."],"evidence":[{"id":"e1","relation":"context","excerpt":"a human baseline using crowdworkers","locator":"Section 4, Experimental Setup; Sections 4.3 and 5.1","url":"https://ar5iv.labs.arxiv.org/html/2204.03162","publisher":"arXiv; Tristan Thrush and colleagues","event_date":"2022-04","available_at":"2022-04-22","explanation":"Winoground documents a substantial gap between tested models and its human baseline. 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The context explicitly discusses contemporary 2022 models and evaluations.","Treat compositionality's central role as an established linguistic principle, rather than infer a numerical majority of linguists without a survey."],"support_condition":"Contemporary evaluations show substantial structural or compositional generalization failures across prominent model families, while linguistic research recognizes compositionality as a basic property of language.","contradiction_condition":"Representative contemporary systems reliably handle unfamiliar compositional structures across relevant tasks, making the characterization of a substantial general weakness untenable."},"axis":"historical_observation","outcome":"supported","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"Contemporary research supports substantial compositional weaknesses in prominent neural systems. 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Imagen's prompts replaced humans with robots because of its restrictions [e1]. The comparison did not systematically retest earlier successes or cover varied new compositions [e1]. It supports improvement on those selected cases, but does not establish systematic compositional ability or exclude regressions elsewhere [e1].","limitations":["The scores reflect Alexander's judgments, not independent verification [e1].","This assessment concerns what the comparison establishes; it does not establish that Alexander claimed full compositional mastery.","The comparison does not establish actual regressions, Imagen's overall compositional ability, or DALL-E 2's ranking at launch [e1]."],"evidence":[{"id":"e1","relation":"supports","excerpt":"We generate 10 images for each prompt, just like DALL-E2 does.","locator":"Section I, bet terms; Section II, results and qualifications","url":"https://www.astralcodexten.com/p/i-won-my-three-year-ai-progress-bet","publisher":"Astral Codex Ten","event_date":"2022-09-12","available_at":"2022-09-12","explanation":"The page specifies five prompts, reports DALL-E 2 at 0/5 and Imagen at 3/5 under Alexander's scoring, and contains no systematic retest of earlier successes. 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This does not establish general conversational intelligence. If Marcus meant only progress toward unrestricted conversation, these records would not settle that narrower reading; the interpretation flag is therefore material.","limitations":["The degree of progress is qualitative.","Business-hour inquiries were already contemplated in the original announcement.","Hold for Me initially had limited device and country availability.","Deployment scale does not by itself prove general language understanding."],"evidence":[{"id":"e1","relation":"context","excerpt":"It cannot carry out general conversations.","locator":"Opening description of closed domains","url":"https://research.google/blog/google-duplex-an-ai-system-for-accomplishing-real-world-tasks-over-the-phone","publisher":"Google Research","event_date":"2018-05-08","available_at":"2018-05-08","explanation":"The original system was explicitly restricted to trained domains, and its announcement already included inquiries about business hours. This limits how much later business-hour calling alone demonstrates new conversational capability."},{"id":"e2","relation":"contradicts","excerpt":"99 percent of calls","locator":"More natural conversations; related deployment figures in preceding sections","url":"https://blog.google/innovation-and-ai/products/duplex-helpful-updates","publisher":"Google","event_date":"2020-10-15","available_at":"2020-10-15","explanation":"Google reported over a million completed bookings and over three million business-information updates across eight countries, alongside the quoted automation rate. 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Often and not reliably are qualitative claims of recurring errors; no numerical cutoff is supplied for them.","The surrounding phrase presently doing is interpreted as June 2022 through publication. June 1 through June 22 is an approximate observation window; the actual experiment dates are unknown.","The prompt and experimental setting define the observation's scope. They are not antecedents of a conditional forecast."],"support_condition":"Independently assessed original records show recurring failures to depict the woman eating, recurring apple-to-man assignment errors, and incorrect distinct food assignments in more than half of trials.","contradiction_condition":"The complete records show reliable depiction of the woman eating an apple and the man eating a sandwich, with apple-to-man errors rare and the claimed majority failure absent. 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Evidence that one matters does not by itself establish that it matters more than the other.","No observation reporting period or forecast deadline is imposed on this causal assessment."],"support_condition":"Controlled experiments on GPT-3 or validated faithful replications attribute a greater contribution to training-corpus size than to referential or world-state representations on held-out word prediction.","contradiction_condition":"Comparable controlled experiments establish that referential or world-state representations contribute at least as much as corpus size to GPT-3's held-out word prediction."},"axis":"causal_assessment","outcome":"unresolved","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"Scaling studies show that data quantity matters, and the GPT-3 paper shows benefits from model size. Neither establishes that corpus size contributes more than referential or world-state representations. 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That distinction must be preserved when assessing tool-assisted systems.","For this ongoing state, use the current calendar year through publication as the latest reporting window. Research models have no regular quarterly reporting cycle."],"support_condition":"Documentation and controlled tests establish that the contemporary standalone models lack a mechanism for tying generated references to identifiable external entities or situations.","contradiction_condition":"A contemporary model within the stated scope demonstrably links its generated references to identifiable external entities or situations, beyond producing coincidentally correct statements."},"axis":"historical_observation","outcome":"unresolved","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"The January 2022 LaMDA paper describes fine-tuning a model to call an external information retrieval system and provide attributed responses [e3]. 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After defining competence and learning scale, evidence would need to establish necessity within that defined class, rather than merely successful use of learning.","contradiction_condition":"After those definitions are fixed, a system meeting the competence standard without the specified learning component would challenge necessity. The original wording does not determine whether a narrow-domain system qualifies."},"axis":"not_applicable","outcome":"not_adjudicable","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"The statement supplies no competence threshold for “good,” scale boundary for “large-scale,” or test of what makes learning “vital” [marcus-three-ideas-from-linguistics-that-c4f7da-o1]. These definitions are needed to judge the proposed necessity. 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Neither several years of progress nor several years of failures alone settles the forecast."],"support_condition":"Evidence establishes the absence of the specified integration and a fundamental barrier to further long-discourse and event understanding, together with persistent inability to resolve misinformation.","contradiction_condition":"Independently replicated systems demonstrably lacking the specified integration achieve sustained, general long-discourse and event understanding while reliably avoiding misinformation."},"axis":"forecast_outcome","outcome":"unresolved","horizon_state":"open_horizon","evaluation_as_of":"2026-10-28","rationale":"The forecast says “eventually” and gives no deadline [marcus-three-ideas-from-linguistics-that-cf97e7-o1]. LongBench v2, submitted December 19, 2024, reports 57.7% accuracy for o1-preview, compared with 53.7% for human experts under a 15-minute limit [e1]. 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