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McCoy et al. found GPT-4 decoded a simple cipher 51% of the time when the output was common text, but only 13% when it was uncommon. That is the pattern Marcus describes: workable when a case resembles training data, fragile when it does not. Wu et al. also found nontrivial performance on the variants, which fits 'hit or miss' rather than total failure.","limitations":["Reasoning models released after July 2024 are not assessed.","'Any reasoning' is broad; the studies cover selected tasks."],"evidence":[{"id":"e1","relation":"supports","excerpt":"Across a suite of 11 tasks, we observe nontrivial performance on the counterfactual variants, but nevertheless find that performance substantially and consistently degrades compared to the default conditions.","locator":"Abstract","url":"https://arxiv.org/abs/2307.02477","publisher":"Wu et al., MIT/Boston University (arXiv; NAACL 2024)","event_date":"2023-07","available_at":"2023-07-05","explanation":"Reasoning accuracy falls substantially and consistently on unfamiliar variants of the same tasks. Performance is still nontrivial, which fits 'hit or miss' rather than total failure."},{"id":"e2","relation":"supports","excerpt":"For instance, GPT-4's accuracy at decoding a simple cipher is 51% when the output is a high-probability word sequence but only 13% when it is low-probability.","locator":"Abstract","url":"https://arxiv.org/abs/2309.13638","publisher":"McCoy et al., Princeton (arXiv)","event_date":"2023-09","available_at":"2023-09-24","explanation":"GPT-4's success on a deterministic task depends heavily on how familiar the content is, which matches his claim of being viable near the training data and fragile away from it."}],"what_would_resolve":null,"retrospective":true,"plain_reading":true},"payload_hash":"13ef7a043f34ef7e2158f6e3633593411a4c4e36557c3dcfa80250cec80db25d","policy_version":"2026-10-28.7","recorded_at":"2026-10-29T03:13:09Z","drafter":{"provider":"anthropic","model":"claude-opus-5-5"},"reviewers":[{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-alphaproof-alphageometry-chatgpt-0bf994-4x-review-anthropic-1"},{"provider":"openai","model":"gpt-6-astra","run_id":"marcus-alphaproof-alphageometry-chatgpt-0bf994-4x-review-openai-1"}],"final_text":{"provider":"openai","model":"gpt-6-astra","run_id":"marcus-alphaproof-alphageometry-chatgpt-0bf994-4x-final-openai-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":"2026-10-29T03:13:09Z","next_review_due_at":null,"horizon_now":"not_applicable"},"event_ids":["marcus-alphaproof-alphageometry-chatgpt-19c5a2-assessment-published-a1"],"last_substantive_change_at":"2026-10-29T17:00:18Z"}},{"url":"https://punditclaims.ai/claims/marcus-alphaproof-alphageometry-chatgpt-1d76b9","standing":"current","assessment_label":"Unresolved","versions":[{"assessment_id":"marcus-alphaproof-alphageometry-chatgpt-1d76b9-a1","status":"current","url":"https://punditclaims.ai/claims/marcus-alphaproof-alphageometry-chatgpt-1d76b9#assessment-marcus-alphaproof-alphageometry-chatgpt-1d76b9-a1"}],"claim":{"claim_id":"marcus-alphaproof-alphageometry-chatgpt-1d76b9","subject_id":"gary-marcus","revision":1,"content_hash":"49ea5d897ca244f2c1c37be20d281bd2f7458e6c03f7a4009488fbe295552f70","normalized_proposition":"People are starting to realize generative AI has plateaued, lacks a killer app, still hallucinates and errs, and has no moat.","kind":"observation","target":"public perception of generative AI","conditions":[],"original_horizon_text":null,"topic_tags":["business-economics"],"concerns_providers":[],"selection_stratum":"declared-lead","occurrences":[{"id":"marcus-alphaproof-alphageometry-chatgpt-1d76b9-o1","statement_date":"2024-07-28","statement_date_precision":"day","excerpt":"People are starting to realize all of the above.","locator":"paragraph 8","document":{"url":"https://garymarcus.substack.com/p/alphaproof-alphageometry-chatgpt","title":"AlphaProof, AlphaGeometry, ChatGPT, and why the future of AI is neurosymbolic","publisher":"Marcus on AI","published_at":"2024-07-28T18:14:13Z","date_uncertain":false,"date_provenance":"JSON-LD datePublished"},"observed_at":"2026-10-28T23:15:36Z","observed_via":"live","archive_url":null,"access_state":"full_public","snapshot_hash":"94b57ca923c546b371aa8d3ae84ed6c0f5711f318196a6aeed2c023165031eb9","receipts":1}],"first_statement_date":"2024-07-28","related":[],"current":{"assessment_id":"marcus-alphaproof-alphageometry-chatgpt-1d76b9-a1","payload":{"claim_id":"marcus-alphaproof-alphageometry-chatgpt-1d76b9","subject_id":"gary-marcus","normalized_proposition":"People are starting to realize generative AI has plateaued, lacks a killer app, still hallucinates and errs, and has no moat.","kind":"observation","conditions":[],"original_horizon_text":null,"statement":{"excerpt":"People are starting to realize all of the above.","locator":"paragraph 8","url":"https://garymarcus.substack.com/p/alphaproof-alphageometry-chatgpt","date":"2024-07-28","document_title":"AlphaProof, AlphaGeometry, ChatGPT, and why the future of AI is neurosymbolic","observed_at":"2026-10-28T23:15:36Z"},"criteria":{"from_speaker":["People are starting to realize all of the above.","The current approach has reached a plateau","There is no killer app","Hallucinations remain","Boneheaded errors remain","Nobody has a moat","in less than a year"],"our_interpretation":["The plain reading is an emerging increase in public recognition of the five listed limitations, not merely the existence of some critics.","Recognition means expressed belief in those limitations; this claim does not itself establish that the beliefs are correct.","The surrounding comparison with less than a year earlier supplies a lookback window of July 28, 2023 through July 28, 2024.","No majority threshold is imposed, but 'all of the above' requires evidence covering the full list. Comparable surveys or a systematic body of dated commentary could show the trend."],"support_condition":"Comparable opinion evidence or systematic contemporary commentary demonstrates increasing recognition of all five listed limitations by the post date.","contradiction_condition":"Comparable evidence covering the same population and propositions demonstrates that recognition was stable or declining, contrary to the claimed emerging increase."},"axis":"historical_observation","outcome":"unresolved","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"Contemporary public skepticism is documented, particularly about investment returns. The reviewed evidence does not show increasing recognition of all five specific limitations. Survey optimism also prevents treating selected critical commentary as a complete account of public opinion, though it does not contradict growing awareness of particular weaknesses.","limitations":["The population and size of the claimed change are unspecified.","A loose reading meaning only that some prominent people voiced doubts would be easier to support.","The survey sample is not representative of all people and does not test the full claim."],"evidence":[{"id":"e1","relation":"context","excerpt":"little to show for it","locator":"Opening summary","url":"https://www.goldmansachs.com/insights/top-of-mind/gen-ai-too-much-spend-too-little-benefit","publisher":"Goldman Sachs","event_date":"2024-06-27","available_at":"2024-06-27","explanation":"The publication establishes that skepticism about AI investment benefits was publicly expressed before Marcus's post. It does not measure growing agreement with the entire list of limitations."},{"id":"e2","relation":"context","excerpt":"benefits of GenAI outweigh the risks","locator":"GenAI is going mainstream and consumers believe the benefits outweigh the risks","url":"https://kpmg.com/us/en/media/news/generative-ai-consumer-trust-survey.html","publisher":"KPMG","event_date":"2024-01","available_at":"2024-01-18","explanation":"The January survey disclosure reports substantial optimism among 1,000 college-educated, informed US consumers. 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The systems both rely on a kind of cheat on the input: trained human coders translate all the Olympiad's input sentences into mathematical form.","locator":"paragraph 60","document":{"url":"https://garymarcus.substack.com/p/alphaproof-alphageometry-chatgpt","title":"AlphaProof, AlphaGeometry, ChatGPT, and why the future of AI is neurosymbolic","publisher":"Marcus on AI","published_at":"2024-07-28T18:14:13Z","date_uncertain":false,"date_provenance":"JSON-LD datePublished"},"observed_at":"2026-10-28T23:15:36Z","observed_via":"live","archive_url":null,"access_state":"full_public","snapshot_hash":"94b57ca923c546b371aa8d3ae84ed6c0f5711f318196a6aeed2c023165031eb9","receipts":1}],"first_statement_date":"2024-07-28","related":[],"current":{"assessment_id":"marcus-alphaproof-alphageometry-chatgpt-22368d-a1","payload":{"claim_id":"marcus-alphaproof-alphageometry-chatgpt-22368d","subject_id":"gary-marcus","normalized_proposition":"AlphaProof and AlphaGeometry 2 rely on human coders translating Olympiad problems into formal form, a serious shortcoming.","kind":"observation","conditions":[],"original_horizon_text":null,"statement":{"excerpt":"Much as I admire the new pair of systems, they do have a serious shortcoming and one that is not unfamiliar. 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It does not mean every training example or proof step.","'Cheat' and 'serious shortcoming' are evaluations of the workflow, not separate claims that competition rules were violated.","The observation does not assert that later versions could never automate formalization."],"support_condition":"Records of the July 2024 demonstration establish that humans converted the competition problem statements into the formal representations used by both systems before automated solving.","contradiction_condition":"Records establish that either system autonomously translated its actual IMO evaluation inputs from the original natural-language statements without human formalization."},"axis":"historical_observation","outcome":"supported","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"The 2024 demonstration used human-prepared formal problem statements. The primary AlphaProof paper expressly records expert manual formalization, and contemporaneous reporting describes human input preparation for the combined demonstration. This supports Marcus's factual point about the workflow. It does not establish that the assistance was concealed or prohibited, or independently settle his judgment that it was a 'cheat'.","limitations":["The assessment concerns the July 2024 demonstration, not later systems.","The evidence for the combined workflow partly relies on a news report because the original announcement was not recoverable through read_page.","The assessment does not equate human formalization with a rule violation."],"evidence":[{"id":"e1","relation":"supports","excerpt":"by domain experts.","locator":"Methods, IMO 2024 Evaluation Protocol and Methods, Problem Formalization, page 18","url":"https://www.nature.com/articles/s41586-025-09833-y_reference.pdf","publisher":"Nature","event_date":"2024-07","available_at":"2025-11-12","explanation":"The primary paper explicitly confirms expert manual formalization of AlphaProof's competition inputs. Its publication is later, but the reported event is the July 2024 evaluation."},{"id":"e2","relation":"supports","excerpt":"by humans.","locator":"Limitations, paragraph reporting Gowers's comments on formalization","url":"https://arstechnica.com/information-technology/2024/07/google-ai-earns-silver-medal-equivalent-at-international-mathematical-olympiad","publisher":"Ars Technica","event_date":"2024-07","available_at":"2024-07-25","explanation":"The report covers the combined AlphaProof and AlphaGeometry 2 demonstration and attributes confirmation of human formalization to an evaluator. 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It does not supply a deadline for this conditional claim.","A collapse requires evidence of a broad, sustained breakdown; an isolated company failure or market correction is insufficient."],"support_condition":"A documented generative AI collapse occurs, followed by substantive AI research or development continuing.","contradiction_condition":"A documented generative AI collapse causes AI research and development as a whole to cease."},"axis":"forecast_outcome","outcome":"unresolved","horizon_state":"open_horizon","evaluation_as_of":"2026-10-28","rationale":"The 2026 AI Index abstract, revised June 29, 2026, describes continuing AI advances but does not establish a preceding generative AI collapse [e1]. This evidence cannot settle whether AI research and development would survive the collapse required by the forecast [e1].","limitations":["The evidence does not establish a broad, sustained generative AI collapse or its date.","Continuing AI activity is not evidence of survival after an unestablished collapse.","The claim specifies neither a collapse threshold nor a post-collapse observation period."],"evidence":[{"id":"e1","relation":"context","excerpt":"AI continues to advance rapidly","locator":"Abstract, second sentence","url":"https://arxiv.org/abs/2606.15708","publisher":"Stanford HAI; arXiv","event_date":"2026","available_at":"2026-06-29","explanation":"The report describes an active AI field. It does not establish that this activity followed the generative AI collapse required by the forecast."}],"what_would_resolve":"Independent evidence establishing a broad, sustained generative AI collapse and its date, followed by primary records showing substantive AI research or development continuing, or evidence that the collapse caused field-wide cessation.","retrospective":true,"plain_reading":true},"payload_hash":"24a0450930d4d95e57c82fbcb86ac544081d7da99f696da5e35239dd6739b71b","policy_version":"2026-10-28.7","recorded_at":"2026-10-29T06:52:02Z","drafter":{"provider":"openai","model":"gpt-6-astra"},"reviewers":[{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-alphaproof-alphageometry-chatgpt-1d76b9-4x-review-anthropic-1"},{"provider":"openai","model":"gpt-6-astra","run_id":"marcus-alphaproof-alphageometry-chatgpt-1d76b9-4x-review-openai-1"}],"final_text":{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-alphaproof-alphageometry-chatgpt-3694e4-2x-final-anthropic-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":"2026-10-29T06:52:02Z","next_review_due_at":null,"horizon_now":"open_horizon"},"event_ids":["marcus-alphaproof-alphageometry-chatgpt-3694e4-assessment-published-a2"],"last_substantive_change_at":"2026-10-29T17:00:18Z"}},{"url":"https://punditclaims.ai/claims/marcus-alphaproof-alphageometry-chatgpt-502de0","standing":"current","assessment_label":"Can’t be checked","versions":[{"assessment_id":"marcus-alphaproof-alphageometry-chatgpt-502de0-a1","status":"current","url":"https://punditclaims.ai/claims/marcus-alphaproof-alphageometry-chatgpt-502de0#assessment-marcus-alphaproof-alphageometry-chatgpt-502de0-a1"}],"claim":{"claim_id":"marcus-alphaproof-alphageometry-chatgpt-502de0","subject_id":"gary-marcus","revision":1,"content_hash":"c1a5084822d60fca1b5491dc9d0a6b277b10b1af71f5dfacd0d89698b533ea61","normalized_proposition":"GenAI is vastly overrated, and its collapse may well lead to an AI winter of sorts.","kind":"conditional_forecast","target":"generative AI","conditions":[{"text":"if GenAI collapses","type":"antecedent","from_source":true}],"original_horizon_text":null,"topic_tags":["business-economics"],"concerns_providers":[],"selection_stratum":"declared-lead","occurrences":[{"id":"marcus-alphaproof-alphageometry-chatgpt-502de0-o1","statement_date":"2024-07-28","statement_date_precision":"day","excerpt":"I think that GenAI is vastly overrated and overhyped, but fear that its collapse may well lead to an AI winter of sorts, like what happened in the mid-1980s, when AI “expert systems” rapidly ascended and rapidly fell.","locator":"paragraph 9","document":{"url":"https://garymarcus.substack.com/p/alphaproof-alphageometry-chatgpt","title":"AlphaProof, AlphaGeometry, ChatGPT, and why the future of AI is neurosymbolic","publisher":"Marcus on AI","published_at":"2024-07-28T18:14:13Z","date_uncertain":false,"date_provenance":"JSON-LD datePublished"},"observed_at":"2026-10-28T23:15:36Z","observed_via":"live","archive_url":null,"access_state":"full_public","snapshot_hash":"94b57ca923c546b371aa8d3ae84ed6c0f5711f318196a6aeed2c023165031eb9","receipts":1}],"first_statement_date":"2024-07-28","related":[],"current":{"assessment_id":"marcus-alphaproof-alphageometry-chatgpt-502de0-a1","payload":{"claim_id":"marcus-alphaproof-alphageometry-chatgpt-502de0","subject_id":"gary-marcus","normalized_proposition":"GenAI is vastly overrated, and its collapse may well lead to an AI winter of sorts.","kind":"conditional_forecast","conditions":["if GenAI collapses"],"original_horizon_text":null,"statement":{"excerpt":"I think that GenAI is vastly overrated and overhyped, but fear that its collapse may well lead to an AI winter of sorts, like what happened in the mid-1980s, when AI “expert systems” rapidly ascended and rapidly fell.","locator":"paragraph 9","url":"https://garymarcus.substack.com/p/alphaproof-alphageometry-chatgpt","date":"2024-07-28","document_title":"AlphaProof, AlphaGeometry, ChatGPT, and why the future of AI is neurosymbolic","observed_at":"2026-10-28T23:15:36Z"},"criteria":{"from_speaker":["if GenAI collapses"],"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. 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That is a value judgment."],"support_condition":"Not adjudicable: no agreed measure of 'genuine novelty' or of an unbiased baseline.","contradiction_condition":"Not adjudicable for the same reason."},"axis":"not_applicable","outcome":"not_adjudicable","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"On 2024-07-28, Marcus wrote that the current, short-term investment climate was hugely biased against genuine novelty and outside-the-box thinking. This is a judgment about what investors prefer. Neither 'genuine novelty' nor 'outside the box thinking' has an agreed measure. The claim also gives no baseline for what an unbiased split of funding would be. Crunchbase data published on 2025-01-07 show that almost a third of 2024 AI funding went to foundation model companies (e1). That shows where the money went. It cannot show whether the split was biased against novel ideas.","limitations":["The Crunchbase figure covers all of 2024 and was published after the post. The claim is about the climate in July 2024.","Only one funding source was consulted.","A narrower version of the claim could be checked, such as the share of funding going to non-LLM or neurosymbolic approaches. The claim does not state one."],"evidence":[{"id":"e1","relation":"context","excerpt":"Of those AI dollars, almost a third of all AI funding went to foundation model companies.","locator":"section 'Breakout year for AI', paragraph 3","url":"https://news.crunchbase.com/venture/global-funding-data-analysis-ai-eoy-2024","publisher":"Crunchbase News","event_date":"2024","available_at":"2025-01-07","explanation":"Shows a large share of 2024 AI funding went to model companies. This says nothing on its own about whether investors were biased against new ideas."}],"what_would_resolve":null,"retrospective":true},"payload_hash":"9aaefcb16d263c42a875904124335cf8b3f36b53606ead72db5ec00f3554145b","policy_version":"2026-10-28.7","recorded_at":"2026-10-29T06:45:05Z","drafter":{"provider":"anthropic","model":"claude-opus-5-5"},"reviewers":[{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-alphaproof-alphageometry-chatgpt-3b7617-4x-review-anthropic-1"},{"provider":"openai","model":"gpt-6-astra","run_id":"marcus-alphaproof-alphageometry-chatgpt-3b7617-4x-review-openai-1"}],"final_text":{"provider":"openai","model":"gpt-6-astra","run_id":"marcus-alphaproof-alphageometry-chatgpt-0bf994-3x-final-openai-1"},"status":"current","notice":null,"superseded_by":null,"evidence_meta":[{"id":"e1","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":"2026-10-29T06:45:05Z","next_review_due_at":null,"horizon_now":"not_applicable"},"event_ids":["marcus-alphaproof-alphageometry-chatgpt-5fd6c8-assessment-published-a2"],"last_substantive_change_at":"2026-10-29T17:00:18Z"}},{"url":"https://punditclaims.ai/claims/marcus-alphaproof-alphageometry-chatgpt-75be04","standing":"current","assessment_label":"Contradicted","versions":[{"assessment_id":"marcus-alphaproof-alphageometry-chatgpt-75be04-a2","status":"current","url":"https://punditclaims.ai/claims/marcus-alphaproof-alphageometry-chatgpt-75be04#assessment-marcus-alphaproof-alphageometry-chatgpt-75be04-a2"}],"claim":{"claim_id":"marcus-alphaproof-alphageometry-chatgpt-75be04","subject_id":"gary-marcus","revision":1,"content_hash":"1009e529ec58e3c1c8f613be1c8f68263db8fa7013cd02693012e0406521073f","normalized_proposition":"The generative AI bubble will begin to burst by July 28, 2025.","kind":"forecast","target":"generative AI bubble","conditions":[],"original_horizon_text":"within the next 12 months","topic_tags":["business-economics","financing"],"concerns_providers":[],"selection_stratum":"declared-lead","occurrences":[{"id":"marcus-alphaproof-alphageometry-chatgpt-75be04-o1","statement_date":"2024-07-28","statement_date_precision":"day","excerpt":"I fully expect that the generative AI bubble will begin to burst within the next 12 months, for many reasons:","locator":"paragraph 2","document":{"url":"https://garymarcus.substack.com/p/alphaproof-alphageometry-chatgpt","title":"AlphaProof, AlphaGeometry, ChatGPT, and why the future of AI is neurosymbolic","publisher":"Marcus on AI","published_at":"2024-07-28T18:14:13Z","date_uncertain":false,"date_provenance":"JSON-LD datePublished"},"observed_at":"2026-10-28T23:15:36Z","observed_via":"live","archive_url":null,"access_state":"full_public","snapshot_hash":"94b57ca923c546b371aa8d3ae84ed6c0f5711f318196a6aeed2c023165031eb9","receipts":1}],"first_statement_date":"2024-07-28","related":[],"current":{"assessment_id":"marcus-alphaproof-alphageometry-chatgpt-75be04-a2","payload":{"claim_id":"marcus-alphaproof-alphageometry-chatgpt-75be04","subject_id":"gary-marcus","normalized_proposition":"The generative AI bubble will begin to burst by July 28, 2025.","kind":"forecast","conditions":[],"original_horizon_text":"within the next 12 months","statement":{"excerpt":"I fully expect that the generative AI bubble will begin to burst within the next 12 months, for many reasons:","locator":"paragraph 2","url":"https://garymarcus.substack.com/p/alphaproof-alphageometry-chatgpt","date":"2024-07-28","document_title":"AlphaProof, AlphaGeometry, ChatGPT, and why the future of AI is neurosymbolic","observed_at":"2026-10-28T23:15:36Z"},"criteria":{"from_speaker":["the generative AI bubble","will begin to burst","within the next 12 months"],"our_interpretation":["The deadline is July 28, 2025.","The plain reading is the beginning of a broad, material unwinding of investment and valuations, rather than a temporary market decline or isolated failure.","Sustained weakness should appear across the sector. Fewer deals alongside rising aggregate investment can indicate concentration without establishing a bubble burst.","This is an industry-level forecast, not a claim about a named company's financial figure; metric_scope is therefore null."],"support_condition":"Evidence shows that a broad and sustained material contraction in generative AI investment and valuations began by July 28, 2025.","contradiction_condition":"Sector evidence shows continued expansion of the investment boom through the forecast window, without a sustained broad reversal by the deadline."},"axis":"forecast_outcome","outcome":"contradicted","horizon_state":"elapsed","evaluation_as_of":"2026-10-28","rationale":"Under the ordinary reading of a broad investment boom beginning to unwind, the evidence points the other way. First-half 2025 generative AI investment exceeded the whole of 2024, and S&P Global still described strong capital momentum in July. EY's falling transaction count is material counterevidence: investment became more concentrated. It does not establish a broad burst when aggregate funding increased sharply. This judgment concerns the stated deadline, not whether valuations were justified or a later collapse was possible.","limitations":["The result depends on interpreting burst as a broad, sustained unwinding.","Large transactions dominate aggregate investment.","Announced investment totals do not necessarily equal cash already transferred.","Coverage of the final weeks before the deadline is less complete."],"evidence":[{"id":"e1","relation":"contradicts","excerpt":"surpassed last year","locator":"Commentary by Grit Young; accompanying investment findings","url":"https://www.ey.com/en_ie/newsroom/2025/06/generative-ai-vc-funding-49-2b-h1-2025-ey-report","publisher":"EY Ireland","event_date":"2025-06","available_at":"2025-08-05","explanation":"EY reports $49.2 billion of first-half generative AI VC investment versus $44.2 billion for all of 2024. It also reports nearly 25% fewer transactions than in the preceding half-year, a material sign of concentration that qualifies the expansion."},{"id":"e2","relation":"context","excerpt":"interest – and capital","locator":"Description beneath the July 15, 2025 webinar date","url":"https://pages.marketintelligence.spglobal.com/10630-H1-Review-of-GenAI-Funding-Can-the-Market-Keep-Up-with-Record-Volumes-Registration-July-2025.html","publisher":"S&P Global Market Intelligence","event_date":"2025-07-15","available_at":"2025-07-15","explanation":"The research provider describes continued investment momentum close to the deadline. This corroborates EY's measured first-half expansion, although the quoted assessment is qualitative and forward-looking."}],"what_would_resolve":null,"retrospective":true,"plain_reading":true},"payload_hash":"4441e9aa4db208bd6991e25c9807d65e0aeea10349a523dad6784b56345bbf94","policy_version":"2026-10-28.7","recorded_at":"2026-10-29T06:44:50Z","drafter":{"provider":"openai","model":"gpt-6-astra"},"reviewers":[{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-alphaproof-alphageometry-chatgpt-75be04-4x-review-anthropic-1"},{"provider":"openai","model":"gpt-6-astra","run_id":"marcus-alphaproof-alphageometry-chatgpt-75be04-4x-review-openai-1"}],"final_text":{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-alphaproof-alphageometry-chatgpt-75be04-4x-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":"company_disclosure","audited":null,"derived":null,"metric":null}],"public_from":null,"public_until":null},"history":[],"review":{"review_due":false,"triggers":[],"last_completed_review_at":"2026-10-29T06:44:50Z","next_review_due_at":null,"horizon_now":"elapsed"},"event_ids":["marcus-alphaproof-alphageometry-chatgpt-75be04-assessment-published-a2"],"last_substantive_change_at":"2026-10-29T17:00:18Z"}},{"url":"https://punditclaims.ai/claims/marcus-alphaproof-alphageometry-chatgpt-88edd6","standing":"current","assessment_label":"Unresolved","versions":[{"assessment_id":"marcus-alphaproof-alphageometry-chatgpt-88edd6-a1","status":"current","url":"https://punditclaims.ai/claims/marcus-alphaproof-alphageometry-chatgpt-88edd6#assessment-marcus-alphaproof-alphageometry-chatgpt-88edd6-a1"}],"claim":{"claim_id":"marcus-alphaproof-alphageometry-chatgpt-88edd6","subject_id":"gary-marcus","revision":1,"content_hash":"32aa14f3d94472ceaf2253355c989f40ec7e34503ee465fe8b44d6323b43287c","normalized_proposition":"AGI cannot be achieved without neurosymbolic AI.","kind":"forecast","target":"AGI","conditions":[{"text":"without neurosymbolic AI","type":"qualifier","from_source":true}],"original_horizon_text":null,"topic_tags":["agi-timelines"],"concerns_providers":[],"selection_stratum":"declared-lead","occurrences":[{"id":"marcus-alphaproof-alphageometry-chatgpt-88edd6-o1","statement_date":"2024-07-28","statement_date_precision":"day","excerpt":"There can be no path to AGI without neurosymbolic AI.","locator":"paragraph 68","document":{"url":"https://garymarcus.substack.com/p/alphaproof-alphageometry-chatgpt","title":"AlphaProof, AlphaGeometry, ChatGPT, and why the future of AI is neurosymbolic","publisher":"Marcus on AI","published_at":"2024-07-28T18:14:13Z","date_uncertain":false,"date_provenance":"JSON-LD datePublished"},"observed_at":"2026-10-28T23:15:36Z","observed_via":"live","archive_url":null,"access_state":"full_public","snapshot_hash":"94b57ca923c546b371aa8d3ae84ed6c0f5711f318196a6aeed2c023165031eb9","receipts":1}],"first_statement_date":"2024-07-28","related":[],"current":{"assessment_id":"marcus-alphaproof-alphageometry-chatgpt-88edd6-a1","payload":{"claim_id":"marcus-alphaproof-alphageometry-chatgpt-88edd6","subject_id":"gary-marcus","normalized_proposition":"AGI cannot be achieved without neurosymbolic AI.","kind":"forecast","conditions":["without neurosymbolic AI"],"original_horizon_text":null,"statement":{"excerpt":"There can be no path to AGI without neurosymbolic AI.","locator":"paragraph 68","url":"https://garymarcus.substack.com/p/alphaproof-alphageometry-chatgpt","date":"2024-07-28","document_title":"AlphaProof, AlphaGeometry, ChatGPT, and why the future of AI is neurosymbolic","observed_at":"2026-10-28T23:15:36Z"},"criteria":{"from_speaker":["There can be no path to AGI","without neurosymbolic AI","“System II” mechanisms for symbol-manipulation"],"our_interpretation":["The plain reading is a universal necessity claim: achieving general, human-level intelligence requires combining neural methods with symbolic reasoning.","Neurosymbolic AI means neural computation integrated with mechanisms that represent and manipulate explicit symbols or rules. Merely producing text does not establish that integration.","AGI requires demonstrated general capability across substantially different tasks, including unfamiliar tasks. A benchmark score or a developer's declaration alone is insufficient.","The word 'without' defines the excluded class of approaches. It is not a separate contingent antecedent that must occur before this universal claim can be assessed.","No deadline is stated. A neurosymbolic AGI would demonstrate one successful approach, but would not establish that every other approach is impossible."],"support_condition":"A broadly validated necessity result establishes that AGI requires neural and symbolic integration, under definitions and assumptions that cover the ordinary claim. Success by particular neurosymbolic systems is insufficient.","contradiction_condition":"At least one publicly demonstrated system is independently established and widely accepted as AGI, with sufficient technical disclosure to establish that its achievement does not depend on neurosymbolic methods."},"axis":"forecast_outcome","outcome":"unresolved","horizon_state":"open_horizon","evaluation_as_of":"2026-10-28","rationale":"Marcus makes an unlimited necessity claim with no deadline. The search found substantial benchmark progress and recent AGI declarations, but no decisive demonstration establishing both AGI and the absence of neurosymbolic methods. ARC Prize explicitly declines to call its Astra result AGI. Research favoring symbolic integration also falls short of proving that all other routes are impossible. Neither current uncertainty nor successful hybrid systems establishes Marcus's claim.","limitations":["Public demonstrations may not disclose enough implementation detail to classify a system's methods.","Disagreement about AGI and neurosymbolic AI could complicate a future resolution.","The cited preprint's architectural classifications are its authors' assessments, not independent architecture audits."],"evidence":[{"id":"e1","relation":"context","excerpt":"fewer interactions than the human baseline","locator":"Analysis — Action Efficiency Compared to Humans","url":"https://arcprize.org/blog/astra","publisher":"ARC Prize","event_date":"2026-09-03","available_at":"2026-09-03","explanation":"This is material progress relevant to a possible counterexample. The report expressly declines to identify Astra as AGI, and its observations of symbolic representations do not establish an architecture free of neurosymbolic methods."},{"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 assessment documents disagreement about how AGI should be established and finds the published Astra evidence insufficient. This limits any claim of a decisive counterexample; it does not demonstrate that Marcus's architectural requirement is true."},{"id":"e3","relation":"context","excerpt":"Frontier developers adopt neurosymbolic approaches","locator":"Abstract, version 6","url":"https://arxiv.org/abs/2601.05280","publisher":"arXiv","event_date":"2026-09-28","available_at":"2026-09-28","explanation":"The authors characterize recent systems as neurosymbolic and study limits of particular learning objectives. Neither that characterization nor those narrower limits proves that every possible route to AGI requires neurosymbolic AI."}],"what_would_resolve":"An independently validated, widely accepted AGI with sufficient disclosure to establish that it does not depend on neurosymbolic methods would contradict the claim. A broadly validated necessity result covering its full scope would support it.","retrospective":true,"plain_reading":true},"payload_hash":"93c8c272b544f9fe8c586f15868b4db2bfaf99230618033b3a79260032391a18","policy_version":"2026-10-28.7","recorded_at":"2026-10-29T02:22:11Z","drafter":{"provider":"openai","model":"gpt-6-astra"},"reviewers":[{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-alphaproof-alphageometry-chatgpt-88edd6-1x-review-anthropic-1"},{"provider":"openai","model":"gpt-6-astra","run_id":"marcus-alphaproof-alphageometry-chatgpt-88edd6-1x-review-openai-1"}],"final_text":{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-alphaproof-alphageometry-chatgpt-88edd6-1x-final-anthropic-1"},"status":"current","notice":null,"superseded_by":null,"evidence_meta":[{"id":"e1","source_type":"benchmark","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":"2026-10-29T02:22:11Z","next_review_due_at":null,"horizon_now":"open_horizon"},"event_ids":["marcus-alphaproof-alphageometry-chatgpt-88edd6-assessment-published-a1"],"last_substantive_change_at":"2026-10-29T17:00:18Z"}},{"url":"https://punditclaims.ai/claims/marcus-alphaproof-alphageometry-chatgpt-958ae5","standing":"current","assessment_label":"Unresolved","versions":[{"assessment_id":"marcus-alphaproof-alphageometry-chatgpt-958ae5-a1","status":"current","url":"https://punditclaims.ai/claims/marcus-alphaproof-alphageometry-chatgpt-958ae5#assessment-marcus-alphaproof-alphageometry-chatgpt-958ae5-a1"}],"claim":{"claim_id":"marcus-alphaproof-alphageometry-chatgpt-958ae5","subject_id":"gary-marcus","revision":1,"content_hash":"75fb119a6de52157cf61d86c8d8806e985e7a027bf9ba2bae6719c8d57af465f","normalized_proposition":"No generative AI company has a competitive moat.","kind":"observation","target":"generative AI companies","conditions":[],"original_horizon_text":null,"topic_tags":["business-economics"],"concerns_providers":[],"selection_stratum":"declared-lead","occurrences":[{"id":"marcus-alphaproof-alphageometry-chatgpt-958ae5-o1","statement_date":"2024-07-28","statement_date_precision":"day","excerpt":"Nobody has a moat","locator":"paragraph 7","document":{"url":"https://garymarcus.substack.com/p/alphaproof-alphageometry-chatgpt","title":"AlphaProof, AlphaGeometry, ChatGPT, and why the future of AI is neurosymbolic","publisher":"Marcus on AI","published_at":"2024-07-28T18:14:13Z","date_uncertain":false,"date_provenance":"JSON-LD datePublished"},"observed_at":"2026-10-28T23:15:36Z","observed_via":"live","archive_url":null,"access_state":"full_public","snapshot_hash":"94b57ca923c546b371aa8d3ae84ed6c0f5711f318196a6aeed2c023165031eb9","receipts":1}],"first_statement_date":"2024-07-28","related":[],"current":{"assessment_id":"marcus-alphaproof-alphageometry-chatgpt-958ae5-a1","payload":{"claim_id":"marcus-alphaproof-alphageometry-chatgpt-958ae5","subject_id":"gary-marcus","normalized_proposition":"No generative AI company has a competitive moat.","kind":"observation","conditions":[],"original_horizon_text":null,"statement":{"excerpt":"Nobody has a moat","locator":"paragraph 7","url":"https://garymarcus.substack.com/p/alphaproof-alphageometry-chatgpt","date":"2024-07-28","document_title":"AlphaProof, AlphaGeometry, ChatGPT, and why the future of AI is neurosymbolic","observed_at":"2026-10-28T23:15:36Z"},"criteria":{"from_speaker":["Nobody has a moat","generative AI"],"our_interpretation":["The ordinary business meaning of a moat is a durable competitive advantage that competitors cannot readily reproduce or overcome.","I read the scope as companies supplying generative AI, including incumbent technology companies, rather than only independent model laboratories.","Relevant advantages include defensible technology, exclusive resources, distribution, switching costs and network effects. A temporary benchmark lead alone is insufficient.","I use July 2024 through the post date as the reporting window for the claimed ongoing competitive state."],"support_condition":"Broad contemporary evidence establishes that no generative AI supplier possessed a durable competitive advantage across the relevant sources of advantage.","contradiction_condition":"Evidence identifies at least one generative AI supplier with an established, durable advantage protecting its generative AI business against competitors."},"axis":"historical_observation","outcome":"unresolved","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"Contemporary evidence points in different directions without settling the claim. Meta described close competition in model capabilities. Competition authorities identified substantial incumbent advantages and risks of entrenchment. Neither establishes that every supplier lacked a moat, nor conclusively identifies an already durable generative AI moat under the stated criteria.","limitations":["The universal claim requires unusually broad evidence to support.","Meta has an interest in portraying open models as competitive.","The regulators' statement identifies risks and existing adjacent-market advantages, not an adjudicated finding about a particular generative AI business.","A narrower interpretation limited to model performance could produce a different assessment."],"evidence":[{"id":"e1","relation":"context","excerpt":"strong accumulated advantages","locator":"Risks to competition — Entrenching or extending market power in AI-related markets","url":"https://www.gov.uk/government/publications/joint-statement-on-competition-in-generative-ai-foundation-models-and-ai-products/joint-statement-on-competition-in-generative-ai-foundation-models-and-ai-products","publisher":"European Commission, UK CMA, US DOJ and US FTC","event_date":"2024-07-23","available_at":"2024-07-23","explanation":"This primary policy statement is material evidence against treating all suppliers as competitively unprotected. However, its discussion of extending advantages into AI is framed largely as a risk, rather than a company-specific finding of an established generative AI moat."},{"id":"e2","relation":"context","excerpt":"competitive with the most advanced models","locator":"Opening discussion comparing Llama with closed models","url":"https://about.fb.com/news/2024/07/open-source-ai-is-the-path-forward","publisher":"Meta","event_date":"2024-07-23","available_at":"2024-07-23","explanation":"Meta's account is consistent with contestable model capabilities. 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Neural networks and symbol systems can be profitably combined, contra Hinton and LeCun.","locator":"paragraph 62","document":{"url":"https://garymarcus.substack.com/p/alphaproof-alphageometry-chatgpt","title":"AlphaProof, AlphaGeometry, ChatGPT, and why the future of AI is neurosymbolic","publisher":"Marcus on AI","published_at":"2024-07-28T18:14:13Z","date_uncertain":false,"date_provenance":"JSON-LD datePublished"},"observed_at":"2026-10-28T23:15:36Z","observed_via":"live","archive_url":null,"access_state":"full_public","snapshot_hash":"94b57ca923c546b371aa8d3ae84ed6c0f5711f318196a6aeed2c023165031eb9","receipts":1}],"first_statement_date":"2024-07-28","related":[],"current":{"assessment_id":"marcus-alphaproof-alphageometry-chatgpt-a113d8-a1","payload":{"claim_id":"marcus-alphaproof-alphageometry-chatgpt-a113d8","subject_id":"gary-marcus","normalized_proposition":"Google DeepMind's work shows neural networks and symbolic systems can be profitably combined.","kind":"observation","conditions":[],"original_horizon_text":null,"statement":{"excerpt":"Nonetheless, GDM's new work is powerful proof of concept (pun intended!). 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DeepMind also published the solution artifacts. That establishes the modest claim that neural and symbolic components can be combined usefully. Human input preparation and extended computation limit claims about autonomy or competition equivalence, but do not negate this proof of concept.","limitations":["Technical usefulness does not imply financial profitability.","This is an existence result, not evidence that hybrid systems are universally superior.","Later AlphaGeometry 2 architecture details are context rather than decisive evidence about the July implementation.","The evaluation does not adjudicate Hinton's or LeCun's broader views."],"evidence":[{"id":"e1","relation":"supports","excerpt":"resulting in proofs","locator":"IMO Proofs, first paragraph","url":"https://www.julian.ac/blog/2025/11/13/alphaproof-paper","publisher":"Julian Schrittwieser, AlphaProof coauthor","event_date":"2024-07","available_at":"2025-11-13","explanation":"The developer's account connects the actual IMO proofs to a transformer that proposes Lean tactics, search over proof states and automatic formal verification. It explicitly identifies the described work as ending in July 2024."},{"id":"e2","relation":"context","excerpt":"neuro-symbolic interface","locator":"Section 7.2, Inference setup, The analysis string","url":"https://arxiv.org/html/2502.03544v1","publisher":"Google DeepMind authors via arXiv","event_date":"2025-02-05","available_at":"2025-02-05","explanation":"The paper explicitly describes interaction between a language model and a symbolic deduction engine. This later architecture description is contextual; it is not used to assume that every reported improvement was present in July 2024."},{"id":"e3","relation":"supports","excerpt":"proofs found","locator":"IMO 2024 Solutions, opening paragraph","url":"https://storage.googleapis.com/deepmind-media/DeepMind.com/Blog/imo-2024-solutions/index.html","publisher":"Google DeepMind","event_date":"2024-07","available_at":"2024-07-25","explanation":"The primary solution collection supplies proof artifacts from the demonstration and explains how intermediate Lean proof states can be inspected. 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The following 12-month prediction concerns when a bubble begins bursting, rather than when the predicted collapse is completed.","I do not convert the screening suggestions of one or two years into a deadline supplied by Marcus."],"support_condition":"Dated evidence establishes a broad, sustained collapse in generative AI adoption and commercial activity, including ChatGPT.","contradiction_condition":"Continued expansion would contradict fulfillment over the period observed. Without a definite deadline, it cannot conclusively disprove eventual collapse."},"axis":"forecast_outcome","outcome":"unresolved","horizon_state":"open_horizon","evaluation_as_of":"2026-10-28","rationale":"The evidence points toward expanding use, including independent survey growth through July 2025 and OpenAI's later report of broader adoption. It does not show the predicted collapse. However, this sentence has no definite deadline. Treating the adjacent 12-month forecast as its deadline would materially change the assessment, so this claim remains unresolved under the open-horizon rule.","limitations":["OpenAI's disclosure is self-reported.","The independent survey covers six countries rather than the whole world.","Adoption growth alone does not establish profitability."],"evidence":[{"id":"e1","relation":"context","excerpt":"ChatGPT adoption has grown sharply","locator":"Section ChatGPT adoption accelerated across regions","url":"https://openai.com/index/how-chatgpt-adoption-has-expanded","publisher":"OpenAI","event_date":"2026-06-30","available_at":"2026-06-30","explanation":"OpenAI reports expanding adoption and deeper use. This weighs against a collapse having occurred by this report, but does not settle an unspecified deadline."},{"id":"e2","relation":"context","excerpt":"increased substantially","locator":"Executive summary, findings on awareness and use","url":"https://reutersinstitute.politics.ox.ac.uk/generative-ai-and-news-report-2025-how-people-think-about-ais-role-journalism-and-society","publisher":"Reuters Institute for the Study of Journalism, University of Oxford","event_date":"2025-07-15","available_at":"2025-10-07","explanation":"The survey, fielded June 5–July 15, reports weekly use rising from 18% to 34% across six countries. 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In May 2024, Stanford researchers found that commercial legal AI tools sold as avoiding hallucinations still gave wrong information on more than 17% to 34% of queries. In late July 2024, OpenAI's SearchGPT gave wrong festival dates in its own launch demo. The 'inevitable byproduct' part is hedged ('as far as I can tell') and is treated as opinion, not scored.","limitations":["Only the 'yet to disappear' part is scored.","The evidence covers selected products and domains."],"evidence":[{"id":"e1","relation":"supports","excerpt":"But even these bespoke legal AI tools still hallucinate an alarming amount of the time: the Lexis+ AI and Ask Practical Law AI systems produced incorrect information more than 17% of the time, while Westlaw’s AI-Assisted Research hallucinated more than 34% of the time.","locator":"section 'AI-Driven Legal Research Tools Still Hallucinate', paragraph 1","url":"https://hai.stanford.edu/news/ai-trial-legal-models-hallucinate-1-out-6-or-more-benchmarking-queries","publisher":"Stanford HAI / RegLab","event_date":"2024-05","available_at":"2024-05-23","explanation":"In mid-2024, LLM products whose providers claimed they 'avoid' hallucinations still hallucinated on 17% to 34% or more of test queries."},{"id":"e2","relation":"supports","excerpt":"In a prerecorded video showcasing SearchGPT, the tool provided incorrect information about the dates of the Appalachian Summer Festival in Boone, North Carolina.","locator":"paragraph 2","url":"https://techradar.com/computing/artificial-intelligence/openais-searchgpt-appears-to-get-lost-on-its-first-hunt","publisher":"TechRadar","event_date":"2024-07-25","available_at":"2024-07-26","explanation":"OpenAI's newest system made a factual error in its launch demo days before the 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plateau"],"our_interpretation":["Current approach refers to neural generative AI, especially frontier language models.","The plain reading is that meaningful capability gains across successive frontier releases had stalled.","The claim supplies no reporting period. I use April–June 2024, the latest completed calendar quarter before the post, to assess the ongoing state.","A plateau in capability is different from continued errors or failure to attain perfect reliability."],"support_condition":"Comparable evaluations across recent frontier releases show no meaningful improvement across substantive capabilities.","contradiction_condition":"Contemporaneous comparable evaluations show meaningful frontier gains across substantive tasks, including independent evidence."},"axis":"historical_observation","outcome":"contradicted","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"Contemporaneous evidence shows meaningful gains rather than a general capability plateau. Independent vision-language comparisons and OlympicArena both favor newer frontier models over predecessors. Anthropic also reports a substantial coding gain on its internal evaluation. These findings do not show that hallucinations or reliability problems were solved. The judgment applies to stalled capability improvement, not to the stronger claim that existing methods could become fully reliable.","limitations":["Arena preferences can reflect presentation as well as correctness.","OlympicArena was a preprint.","The coding evaluation was conducted by the model's developer.","The quarter-based target period is an explicit interpretation of an undated ongoing observation."],"evidence":[{"id":"e1","relation":"contradicts","excerpt":"achieve notably higher performance","locator":"Leaderboard results, first overall finding","url":"https://www.lmsys.org/blog/2024-06-27-multimodal","publisher":"LMSYS Org","event_date":"2024-06-25","available_at":"2024-06-27","explanation":"User comparisons collected June 10–25 show newer frontier models outperforming preceding models on image-containing conversations. Preference measurements are imperfect, but this is independent evidence of improvement within the target quarter."},{"id":"e2","relation":"contradicts","excerpt":"a clear performance gap","locator":"Abstract, empirical finding 2","url":"https://arxiv.org/abs/2406.16772","publisher":"arXiv; Zhen Huang and colleagues","event_date":"2024-06","available_at":"2024-06-26","explanation":"OlympicArena independently finds a clear gap favoring the newer models on difficult multidisciplinary problems. The same abstract says overall performance remains unsatisfactory; that limits claims of reliability but does not establish stalled improvement."},{"id":"e3","relation":"contradicts","excerpt":"solved 64% of problems","locator":"Section Frontier intelligence at 2x the speed","url":"https://www.anthropic.com/research/claude-3-5-sonnet","publisher":"Anthropic","event_date":"2024-06","available_at":"2024-06-21","explanation":"The company reports a substantial improvement on the same internal coding evaluation. 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says that, for the most part, all the major tech companies are building the same thing (bigger LLMs on more data) and getting almost the same results (GPT-4-level models that barely differ and all struggle with hallucinations and boneheaded errors), with hardly any investment in anything else.","kind":"observation","target":"major tech companies","conditions":[{"text":"for the most part","type":"scope","from_source":true}],"original_horizon_text":null,"topic_tags":["scaling","capabilities"],"concerns_providers":[],"selection_stratum":"declared-lead","occurrences":[{"id":"marcus-alphaproof-alphageometry-chatgpt-bb6581-o1","statement_date":"2024-07-28","statement_date_precision":"day","excerpt":"So for the most part, we’ve been stuck in a world in which all the major tech companies are building exactly the same thing (bigger and bigger LLMs based on more data), achieving almost exactly the same results (a passel of GPT-4 level models, hardly different from each other, all struggling with hallucinations and boneheaded errors), and hardly any investment in anything else.","locator":"paragraph 51","document":{"url":"https://garymarcus.substack.com/p/alphaproof-alphageometry-chatgpt","title":"AlphaProof, AlphaGeometry, ChatGPT, and why the future of AI is neurosymbolic","publisher":"Marcus on AI","published_at":"2024-07-28T18:14:13Z","date_uncertain":false,"date_provenance":"JSON-LD datePublished"},"observed_at":"2026-10-28T23:15:36Z","observed_via":"live","archive_url":null,"access_state":"full_public","snapshot_hash":"94b57ca923c546b371aa8d3ae84ed6c0f5711f318196a6aeed2c023165031eb9","receipts":1}],"first_statement_date":"2024-07-28","related":[],"current":{"assessment_id":"marcus-alphaproof-alphageometry-chatgpt-bb6581-a1","payload":{"claim_id":"marcus-alphaproof-alphageometry-chatgpt-bb6581","subject_id":"gary-marcus","normalized_proposition":"Marcus says that, for the most part, all the major tech companies are building the same thing (bigger LLMs on more data) and 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An isolated alternative project cannot by itself contradict the investment claim.","Use July 2024 through publication as the latest publicly observable industry state. This is a current research and product snapshot, rather than a claim about a particular financial quarter.","Interpret 'investment' as resources devoted to AI development. Marcus supplies neither a spending definition nor an accounting basis.","Do not invent numerical cutoffs for 'almost exactly' or 'hardly any'. A definitive assessment requires evidence strong enough to withstand ordinary variations in those terms."],"support_condition":"Contemporaneous comparisons show broadly similar frontier capabilities and persistent factual or elementary reasoning errors, while comparable resource-allocation records show that major companies overwhelmingly concentrated AI development on scaling LLMs, with negligible investment elsewhere.","contradiction_condition":"Comparable evidence shows substantial, systematic capability differences inconsistent with the claimed clustering, or a material industry-wide share of AI investment in alternative approaches. Exceptions already allowed by 'for the most part' are insufficient alone."},"axis":"historical_observation","outcome":"unresolved","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"Meta's disclosures support the picture of large-scale LLM development, competitive frontier performance and continuing hallucination problems. Other disclosures show capability improvements and research beyond that pattern. Those examples do not settle a claim qualified by 'for the most part'. The central gap is investment allocation: the retrieved records do not establish how much major companies devoted to alternatives relative to LLM scaling. The claim also supplies no accounting basis. A definitive overall verdict would therefore exceed the evidence.","limitations":["Company evaluations are not a substitute for consistent independent comparisons.","The claim combines model performance, errors and investment allocation.","Some contextual evidence describes June rather than the July target period."],"evidence":[{"id":"e1","relation":"context","excerpt":"largest model","locator":"Model Architecture, first paragraph","url":"https://ai.meta.com/blog/meta-llama-3-1","publisher":"Meta","event_date":"2024-07-23","available_at":"2024-07-23","explanation":"Meta documents a larger model trained on extensive data. Its evaluations section also reports competitiveness with GPT-4, GPT-4o and Claude 3.5 Sonnet, but neither statement measures industry-wide investment allocation."},{"id":"e2","relation":"context","excerpt":"Hallucinations remain","locator":"Section 4.3.6, Factuality","url":"https://arxiv.org/html/2407.21783v1","publisher":"Meta authors via arXiv","event_date":"2024-07","available_at":"2024-07-31","explanation":"The technical report acknowledges persistent hallucination problems. This supports that part of Marcus's description, but does not supply a common error measurement across every major lab or settle the investment component."},{"id":"e3","relation":"context","excerpt":"64%","locator":"Frontier intelligence at 2x the speed, third paragraph","url":"https://www.anthropic.com/research/claude-3-5-sonnet","publisher":"Anthropic","event_date":"2024-06","available_at":"2024-06-21","explanation":"This is material counterevidence to a blanket impression of negligible capability differences. It is a June internal comparison with an earlier Anthropic model, rather than a July cross-industry evaluation."},{"id":"e4","relation":"context","excerpt":"symbols and audio","locator":"JASCO Offers More Control Over AI Music Generation","url":"https://about.fb.com/news/2024/06/releasing-new-ai-research-models-to-accelerate-innovation-at-scale","publisher":"Meta","event_date":"2024-06-18","available_at":"2024-06-18","explanation":"Meta describes research beyond simply enlarging text LLMs. This qualifies the picture of uniform development, but the earlier announcement does not quantify its share of investment."}],"what_would_resolve":"Comparable contemporaneous model evaluations and resource-allocation records covering a defined set of major companies, with consistent treatment of scaling, alternative approaches and shared infrastructure.","retrospective":true,"plain_reading":true},"payload_hash":"a1c1a5f2eb25305b5040b45b4572b5e0200102e10b2b4feaf43acfc5153b112f","policy_version":"2026-10-28.7","recorded_at":"2026-10-29T06:45:20Z","drafter":{"provider":"openai","model":"gpt-6-astra"},"reviewers":[{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-alphaproof-alphageometry-chatgpt-ad371e-4x-review-anthropic-1"},{"provider":"openai","model":"gpt-6-astra","run_id":"marcus-alphaproof-alphageometry-chatgpt-ad371e-4x-review-openai-1"}],"final_text":{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-alphaproof-alphageometry-chatgpt-ad371e-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},{"id":"e3","source_type":"company_disclosure","audited":null,"derived":null,"metric":null},{"id":"e4","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":"2026-10-29T06:45:20Z","next_review_due_at":null,"horizon_now":"not_applicable"},"event_ids":["marcus-alphaproof-alphageometry-chatgpt-bb6581-assessment-published-a1"],"last_substantive_change_at":"2026-10-29T17:00:18Z"}},{"url":"https://punditclaims.ai/claims/marcus-alphaproof-alphageometry-chatgpt-c58ed5","standing":"current","assessment_label":"Unresolved","versions":[{"assessment_id":"marcus-alphaproof-alphageometry-chatgpt-c58ed5-a2","status":"current","url":"https://punditclaims.ai/claims/marcus-alphaproof-alphageometry-chatgpt-c58ed5#assessment-marcus-alphaproof-alphageometry-chatgpt-c58ed5-a2"}],"claim":{"claim_id":"marcus-alphaproof-alphageometry-chatgpt-c58ed5","subject_id":"gary-marcus","revision":1,"content_hash":"abf0eb4d8c1ae3260cddc515d1d7b226a6fc79e1babef81ec34a793017fcc9ad","normalized_proposition":"Generative AI has no killer app.","kind":"observation","target":"generative AI","conditions":[],"original_horizon_text":null,"topic_tags":["business-economics"],"concerns_providers":[],"selection_stratum":"declared-lead","occurrences":[{"id":"marcus-alphaproof-alphageometry-chatgpt-c58ed5-o1","statement_date":"2024-07-28","statement_date_precision":"day","excerpt":"There is no killer app","locator":"paragraph 4","document":{"url":"https://garymarcus.substack.com/p/alphaproof-alphageometry-chatgpt","title":"AlphaProof, AlphaGeometry, ChatGPT, and why the future of AI is neurosymbolic","publisher":"Marcus on AI","published_at":"2024-07-28T18:14:13Z","date_uncertain":false,"date_provenance":"JSON-LD datePublished"},"observed_at":"2026-10-28T23:15:36Z","observed_via":"live","archive_url":null,"access_state":"full_public","snapshot_hash":"94b57ca923c546b371aa8d3ae84ed6c0f5711f318196a6aeed2c023165031eb9","receipts":1}],"first_statement_date":"2024-07-28","related":[],"current":{"assessment_id":"marcus-alphaproof-alphageometry-chatgpt-c58ed5-a2","payload":{"claim_id":"marcus-alphaproof-alphageometry-chatgpt-c58ed5","subject_id":"gary-marcus","normalized_proposition":"Generative AI has no killer app.","kind":"observation","conditions":[],"original_horizon_text":null,"statement":{"excerpt":"There is no killer app","locator":"paragraph 4","url":"https://garymarcus.substack.com/p/alphaproof-alphageometry-chatgpt","date":"2024-07-28","document_title":"AlphaProof, AlphaGeometry, ChatGPT, and why the future of AI is neurosymbolic","observed_at":"2026-10-28T23:15:36Z"},"criteria":{"from_speaker":["There is no killer app"],"our_interpretation":["The statement concerns generative AI applications.","The plain reading is that no application combines compelling practical value with substantial sustained demand. A professional application can qualify.","The claim supplies no reporting period. I use April–June 2024, the latest completed calendar quarter before the post.","Paid adoption and repeat use are relevant, but neither initial trials nor subscriber counts alone establish lasting utility."],"support_condition":"Broad evidence shows that plausible leading applications lacked compelling utility or substantial sustained demand during the target period.","contradiction_condition":"At least one generative AI application demonstrably combined substantial repeat or paid demand with compelling practical value during the target period."},"axis":"historical_observation","outcome":"unresolved","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"The evidence does not establish either the universal absence of a killer app or a clear qualifying application under the stated criteria. The contemporary survey finds mostly infrequent use, but that does not exclude valuable specialist applications. Microsoft's earlier report identifies substantial paid coding-assistant demand and customer benefits, which weigh against the claim, without supplying independent retention and utility evidence for the target quarter. 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This records a narrow attribution about quantities; it does not establish an explicit general criticism of reasoning reliability or the full set of criticisms in the claimed GPT-2 Twitter series [e1]. 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This does not test subjective experience.","Evidence that memorization sometimes occurs would establish only part of the claim. 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This is direct participant testimony, but it does not publish the underlying exchanges or independently establish their falsity."},{"id":"e4","relation":"context","excerpt":"voice","locator":"Opening section, account from the Sky actress's agent","url":"https://arstechnica.com/tech-policy/2024/05/sky-voice-actor-says-nobody-ever-compared-her-to-scarjo-before-openai-drama","publisher":"Ars Technica","event_date":"2023","available_at":"2024-05-23","explanation":"Reported casting records and the agent's account weaken the Johansson example. They do not disprove a pattern arising from other episodes, and do not conclusively resolve private selection motives."}],"what_would_resolve":"Authenticated records or independently corroborated accounts resolving the named episodes: the equity interests and disclosures relevant to the Senate answer, the disputed communications concerning Toner, and the casting and selection communications concerning Johansson. 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And stupid, obviously refuted bullshit at that.","locator":"paragraph 3","document":{"url":"https://garymarcus.substack.com/p/the-openai-board-was-right","title":"The OpenAI board was right","publisher":"Marcus on AI","published_at":"2024-05-21T03:28:31Z","date_uncertain":false,"date_provenance":"JSON-LD datePublished"},"observed_at":"2026-10-29T00:00:11Z","observed_via":"live","archive_url":null,"access_state":"full_public","snapshot_hash":"c2f797aa2ddba69e6f18a5a74f51b4d4752f9971faff96409106625ce6d74791","receipts":1}],"first_statement_date":"2024-05-21","related":[],"current":{"assessment_id":"marcus-the-openai-board-was-right-30f64e-a1","payload":{"claim_id":"marcus-the-openai-board-was-right-30f64e","subject_id":"gary-marcus","normalized_proposition":"OpenAI's claim that its voice's resemblance to Scarlett Johansson was coincidental is false.","kind":"observation","conditions":[],"original_horizon_text":null,"statement":{"excerpt":"I probably don’t have to tell you, but that’s complete bullshit. 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Earlier casting records are background evidence about that denial."],"support_condition":"Reliable evidence establishes that resemblance to Johansson was an intended objective of selecting, directing, training, or tuning Sky.","contradiction_condition":"Reliable evidence establishes an independent voice-selection and production process without an objective of resembling Johansson."},"axis":"historical_observation","outcome":"unresolved","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"Interest in Johansson's voice gives Marcus's inference some basis. The casting account supplies material counterevidence. Neither establishes the complete purpose behind Sky's selection and production. The evidence does not justify calling the coincidence denial conclusively false, but it also does not conclusively establish its truth.","limitations":["Earlier casting evidence is context outside the denial's target period.","Party statements and a secondary account cannot substitute for the complete production record."],"evidence":[{"id":"e1","relation":"context","excerpt":"She simply used her own voice and got the gig.","locator":"Opening section, paragraph following the account of the actress's agent","url":"https://arstechnica.com/tech-policy/2024/05/sky-voice-actor-says-nobody-ever-compared-her-to-scarjo-before-openai-drama","publisher":"Ars Technica","event_date":"2023","available_at":"2024-05-23","explanation":"Ars summarizes reporting that the actress received no imitation direction and that casting materials did not request a Johansson clone. 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No beginning date is stated, so the target period has an unspecified start.","Using another actress's natural voice does not by itself exclude deliberately selecting her for a resemblance."],"support_condition":"Reliable records or testimony establish that resembling Johansson was an objective in creating or selecting Sky.","contradiction_condition":"Reliable records establish independent selection and production without a Johansson-imitation objective."},"axis":"historical_observation","outcome":"unresolved","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"OpenAI's chronology is material counterevidence to the suggestion that it hired a substitute after Johansson refused. But chronology alone does not establish whether resemblance influenced the original selection. Johansson's refusal and complaint do not establish that intent either. The public evidence reviewed leaves the central production decision unresolved.","limitations":["The primary production account comes from the company whose conduct is disputed.","A different actress and an earlier casting date do not logically exclude deliberate resemblance."],"evidence":[{"id":"e1","relation":"contradicts","excerpt":"We cast the voice actor behind Sky’s voice before any outreach to Ms. Johansson.","locator":"Statement from Sam Altman dated May 20, 2024; timeline updated May 22, 2024","url":"https://openai.com/index/how-the-voices-for-chatgpt-were-chosen","publisher":"OpenAI","event_date":"2023","available_at":"2024-05-22","explanation":"OpenAI says selection and recording preceded its September approach and denies an imitation objective. This weighs against Marcus's account, but an interested party's chronology cannot rule out intentional selection for resemblance."},{"id":"e2","relation":"context","excerpt":"But she eventually rejected the offer for personal reasons, she said.","locator":"Johansson's account of rejecting Altman's initial offer","url":"https://www.bbc.com/news/articles/cm559l5g529o","publisher":"BBC News","event_date":"2023-09","available_at":"2024-05-21","explanation":"The refusal and subsequent complaint explain the suspicion of intentional substitution. 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Showing that leading models still hallucinated in documented evaluations is enough to support the claim. The claim does not say visual data made no improvement at all.","Target period: from TED 2023 (April 2023) to the post date (2024-04-15)."],"support_condition":"Developer documentation or published evaluations from the period show that leading multimodal LLMs still hallucinated in material ways.","contradiction_condition":"Credible evidence from the period shows that leading multimodal LLMs had essentially eliminated hallucinations, or that adding vision had been shown to resolve them."},"axis":"historical_observation","outcome":"supported","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"The claim is that, as of April 2024, adding visual data to LLMs had not solved hallucinations. OpenAI's system card for GPT-4V, dated 25 September 2023, says the model 'was prone to hallucinations' and sometimes made factual errors in an authoritative tone (e1). 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It does not require failure on every request.","For the ongoing state, use the latest completed calendar quarter and the subsequent period before publication: January 1 through April 15, 2024. This reporting window is an evaluator choice.","A single failed image establishes a possible failure, but does not by itself establish the broader description 'very buggy'."],"support_condition":"Contemporaneous tests of the deployed products establish recurring elementary failures, including letter or alphabet rendering.","contradiction_condition":"Representative contemporaneous tests establish dependable performance on the elementary multimodal tasks described, including letter and alphabet rendering."},"axis":"historical_observation","outcome":"unresolved","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"The readable benchmark establishes limitations in image generation, alongside strong comparative performance by DALL-E 3. 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If you read it carefully, it says literally nothing about capabilities improving.","locator":"paragraph 2","document":{"url":"https://garymarcus.substack.com/p/evidence-that-llms-are-reaching-a","title":"Evidence that LLMs are reaching a point of diminishing returns - and what that might mean","publisher":"Marcus on AI","published_at":"2024-04-13T22:00:38Z","date_uncertain":false,"date_provenance":"JSON-LD datePublished"},"observed_at":"2026-10-29T00:00:33Z","observed_via":"live","archive_url":null,"access_state":"full_public","snapshot_hash":"38497ce2b51b3be12f95c40c31fbcafa2aca4e2418f4614dde3173dcb89b920e","receipts":1}],"first_statement_date":"2024-04-13","related":[],"current":{"assessment_id":"marcus-evidence-that-llms-are-reaching-a-458f77-a1","payload":{"claim_id":"marcus-evidence-that-llms-are-reaching-a-458f77","subject_id":"gary-marcus","normalized_proposition":"The study Mollick cited does not show that LLM capabilities are improving; it only addresses compute efficiency.","kind":"observation","conditions":[],"original_horizon_text":null,"statement":{"excerpt":"The study Mollick linked to doesn’t actually show what he claims. 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Full support additionally requires that the study neither discusses nor analyzes capability or performance improvements.","contradiction_condition":"The study explicitly discusses or analyzes improvements in language-model performance or capabilities, or actually establishes the capability-doubling result attributed to it."},"axis":"historical_observation","outcome":"mixed","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"Marcus correctly distinguishes compute-efficiency doubling from capability doubling. The study does not establish Mollick's stated doubling rate for capabilities. But 'literally nothing about capabilities improving' overstates the criticism: the authors explicitly analyze performance gains and describe their work as quantifying progress in language-model capabilities.","limitations":["Language-model performance is narrower than general capability.","Reading 'literally nothing' solely as shorthand for 'no evidence of capability doubling' would produce a more favorable assessment."],"evidence":[{"id":"e1","relation":"supports","excerpt":"We want to estimate the rate at which newer language models are able to achieve a certain level of performance more efficiently than older models.","locator":"Section 2.1, Model definitions, first sentence","url":"https://arxiv.org/html/2403.05812v1","publisher":"Anson Ho and colleagues, arXiv","event_date":"2024-03-09","available_at":"2024-03-09","explanation":"The study measures efficiency at a given performance level. Its compute-halving estimate does not establish that capabilities themselves doubled at the same rate."},{"id":"e2","relation":"contradicts","excerpt":"Nevertheless, our work provides a rigorous quantitative analysis of the remarkable rate of progress in language modeling capabilities over the past decade.","locator":"Overview, concluding discussion","url":"https://epoch.ai/publications/algorithmic-progress-in-language-models","publisher":"Epoch AI, study authors","event_date":"2024-03-12","available_at":"2024-03-12","explanation":"The authors explicitly describe an analysis of improving capabilities. Their summary also explains the study's decomposition of performance gains into compute scaling and algorithmic contributions, contradicting the assertion that it addresses only efficiency."}],"what_would_resolve":null,"retrospective":true,"plain_reading":true},"payload_hash":"8c418b27385b1fea9736199497db23e3eef31ab5a9a918645ffc6739b5f2407e","policy_version":"2026-10-28.7","recorded_at":"2026-10-29T07:05:40Z","drafter":{"provider":"openai","model":"gpt-6-astra"},"reviewers":[{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-evidence-that-llms-are-reaching-a-452931-4x-review-anthropic-1"},{"provider":"openai","model":"gpt-6-astra","run_id":"marcus-evidence-that-llms-are-reaching-a-452931-4x-review-openai-1"}],"final_text":{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-evidence-that-llms-are-reaching-a-452931-4x-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":"secondary_summary","audited":null,"derived":null,"metric":null}],"public_from":null,"public_until":null},"history":[],"review":{"review_due":false,"triggers":[],"last_completed_review_at":"2026-10-29T07:05:40Z","next_review_due_at":null,"horizon_now":"not_applicable"},"event_ids":["marcus-evidence-that-llms-are-reaching-a-458f77-assessment-published-a1"],"last_substantive_change_at":"2026-10-29T17:00:18Z"}},{"url":"https://punditclaims.ai/claims/marcus-evidence-that-llms-are-reaching-a-570f6e","standing":"current","assessment_label":"Supported","versions":[{"assessment_id":"marcus-evidence-that-llms-are-reaching-a-570f6e-a1","status":"current","url":"https://punditclaims.ai/claims/marcus-evidence-that-llms-are-reaching-a-570f6e#assessment-marcus-evidence-that-llms-are-reaching-a-570f6e-a1"}],"claim":{"claim_id":"marcus-evidence-that-llms-are-reaching-a-570f6e","subject_id":"gary-marcus","revision":1,"content_hash":"febf5b2483564f974bee87e0167307b25cb748527487483a273bbf0a2020b65e","normalized_proposition":"OpenAI's GPT-4 Turbo graph shows only mostly modest improvements over recent months.","kind":"observation","target":"OpenAI","conditions":[{"text":"last few months","type":"qualifier","from_source":true}],"original_horizon_text":null,"topic_tags":["capabilities"],"concerns_providers":["openai"],"selection_stratum":"declared-lead","occurrences":[{"id":"marcus-evidence-that-llms-are-reaching-a-570f6e-o1","statement_date":"2024-04-13","statement_date_precision":"day","excerpt":"What it actually shows is some improvements, mostly modest, over the last few months for a bunch of different measures.","locator":"paragraph 8","document":{"url":"https://garymarcus.substack.com/p/evidence-that-llms-are-reaching-a","title":"Evidence that LLMs are reaching a point of diminishing returns - and what that might mean","publisher":"Marcus on AI","published_at":"2024-04-13T22:00:38Z","date_uncertain":false,"date_provenance":"JSON-LD datePublished"},"observed_at":"2026-10-29T00:00:33Z","observed_via":"live","archive_url":null,"access_state":"full_public","snapshot_hash":"38497ce2b51b3be12f95c40c31fbcafa2aca4e2418f4614dde3173dcb89b920e","receipts":1}],"first_statement_date":"2024-04-13","related":[],"current":{"assessment_id":"marcus-evidence-that-llms-are-reaching-a-570f6e-a1","payload":{"claim_id":"marcus-evidence-that-llms-are-reaching-a-570f6e","subject_id":"gary-marcus","normalized_proposition":"OpenAI's GPT-4 Turbo graph shows only mostly modest improvements over recent months.","kind":"observation","conditions":["last few months"],"original_horizon_text":null,"statement":{"excerpt":"What it actually shows is some improvements, mostly modest, over the last few months for a bunch of different measures.","locator":"paragraph 8","url":"https://garymarcus.substack.com/p/evidence-that-llms-are-reaching-a","date":"2024-04-13","document_title":"Evidence that LLMs are reaching a point of diminishing returns - and what that might mean","observed_at":"2026-10-29T00:00:33Z"},"criteria":{"from_speaker":["some improvements","mostly modest","over the last few months","a bunch of different measures"],"our_interpretation":["Assess the recent GPT-4 Turbo comparison published by OpenAI immediately before the post.","Read mostly modest as small movements on most displayed benchmark-score scales, allowing larger changes on a minority.","Use absolute score changes because the graph displays benchmark scores; do not silently substitute error reduction or a general capability multiplier.","Interpret the last few months as January through the April 13 post. The comparison itself identifies January 25 and April 9 model versions."],"support_condition":"The contemporaneous comparison shows improvements on several measures, with most changes amounting to only a few points on the displayed 0–100 scales.","contradiction_condition":"Most displayed measures show large score changes, or the comparison fails to show improvements across several measures."},"axis":"historical_observation","outcome":"supported","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"The contemporaneous primary table supports the ordinary graphical reading. In both prompting configurations, four of six changes are within about five score points; GPQA and MATH improve more. Marcus said mostly modest, which allows those larger gains. The result supports this narrow description of the recent comparison, not a conclusion about general capability growth or future progress.","limitations":["Modest is a qualitative judgment, and relative changes can look larger.","The readable chart posts omit the image's numerical content; the assessment uses their linked, dated primary evaluation table.","The table reports company evaluations without confidence intervals.","The historical repository revision is dated April 13; the announcement was published April 12."],"evidence":[{"id":"e1","relation":"context","excerpt":"We’ve improved capabilities in writing, math, logical reasoning, and coding.","locator":"Announcement text","url":"https://x.com/OpenAI/status/1778574613813006610","publisher":"OpenAI","event_date":"2024-04-12","available_at":"2024-04-12","explanation":"Identifies the release and improvements being discussed. The announcement links OpenAI's simple-evals repository, which supplies the numerical comparison."},{"id":"e2","relation":"supports","excerpt":"Evals are sensitive to prompting, and there's significant variation in the formulations used in recent publications and libraries.","locator":"Overview methodological qualification; Benchmark Results, January and April GPT4s rows","url":"https://github.com/openai/simple-evals/tree/267835b30446d8f68a77cff13bc8a8256ce41829","publisher":"OpenAI","event_date":"2024-04-13","available_at":"2024-04-13","explanation":"In the assistant configuration, April-minus-January changes are 4.5 DROP F1 points and 7.9, 8.9, 4.5, 1.3, and 1.6 percentage points on GPQA, MATH, MGSM, MMLU, and HumanEval. The ChatGPT configuration likewise has small changes on most measures, with larger GPQA and MATH gains and a slight HumanEval decline."}],"what_would_resolve":null,"retrospective":true,"plain_reading":true},"payload_hash":"a5e549e0eaf6f6e6406b5f63df14d2c9c558a430e16906e101a065b5145cbb80","policy_version":"2026-10-28.7","recorded_at":"2026-10-29T07:05:40Z","drafter":{"provider":"openai","model":"gpt-6-astra"},"reviewers":[{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-evidence-that-llms-are-reaching-a-452931-4x-review-anthropic-1"},{"provider":"openai","model":"gpt-6-astra","run_id":"marcus-evidence-that-llms-are-reaching-a-452931-4x-review-openai-1"}],"final_text":{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-evidence-that-llms-are-reaching-a-452931-4x-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":{"inputs":["Assistant January, ordered DROP/GPQA/MATH/MGSM/MMLU/HumanEval: 81.5, 41.4, 64.5, 85.1, 85.4, 86.6.","Assistant April, same order: 86.0, 49.3, 73.4, 89.6, 86.7, 88.2.","ChatGPT January, same order: 83.4, 39.7, 64.2, 83.7, 84.8, 88.2.","ChatGPT April, same order: 85.4, 49.1, 72.2, 88.6, 86.5, 87.6."],"formula":"Subtract each January score from its April counterpart within the same prompting configuration. Assistant differences: 4.5, 7.9, 8.9, 4.5, 1.3, 1.6. ChatGPT differences: 2.0, 9.4, 8.0, 4.9, 1.7, -0.6.","rounding":"One decimal place, matching the published scores."},"metric":null}],"public_from":null,"public_until":null},"history":[],"review":{"review_due":false,"triggers":[],"last_completed_review_at":"2026-10-29T07:05:40Z","next_review_due_at":null,"horizon_now":"not_applicable"},"event_ids":["marcus-evidence-that-llms-are-reaching-a-570f6e-assessment-published-a1"],"last_substantive_change_at":"2026-10-29T17:00:18Z"}},{"url":"https://punditclaims.ai/claims/marcus-evidence-that-llms-are-reaching-a-5d187d","standing":"current","assessment_label":"Unresolved","versions":[{"assessment_id":"marcus-evidence-that-llms-are-reaching-a-5d187d-a1","status":"current","url":"https://punditclaims.ai/claims/marcus-evidence-that-llms-are-reaching-a-5d187d#assessment-marcus-evidence-that-llms-are-reaching-a-5d187d-a1"}],"claim":{"claim_id":"marcus-evidence-that-llms-are-reaching-a-5d187d","subject_id":"gary-marcus","revision":1,"content_hash":"4a74dfd4bd302f4f66d0e2244010e5faddbfaacfe92ec3dfebb2643f11b551e8","normalized_proposition":"About 5 to 7 recent models are on par with GPT-4, but none is clearly ahead.","kind":"observation","target":"frontier LLMs","conditions":[],"original_horizon_text":null,"topic_tags":["capabilities","scaling"],"concerns_providers":[],"selection_stratum":"declared-lead","occurrences":[{"id":"marcus-evidence-that-llms-are-reaching-a-5d187d-o1","statement_date":"2024-04-13","statement_date_precision":"day","excerpt":"Another way to think about this (see third graph above) is that there about 5 - 7 recent models are on a par with GPT-4, but none is clearly and decisively ahead.","locator":"paragraph 23","document":{"url":"https://garymarcus.substack.com/p/evidence-that-llms-are-reaching-a","title":"Evidence that LLMs are reaching a point of diminishing returns - and what that might mean","publisher":"Marcus on AI","published_at":"2024-04-13T22:00:38Z","date_uncertain":false,"date_provenance":"JSON-LD datePublished"},"observed_at":"2026-10-29T00:00:33Z","observed_via":"live","archive_url":null,"access_state":"full_public","snapshot_hash":"38497ce2b51b3be12f95c40c31fbcafa2aca4e2418f4614dde3173dcb89b920e","receipts":1}],"first_statement_date":"2024-04-13","related":[],"current":{"assessment_id":"marcus-evidence-that-llms-are-reaching-a-5d187d-a1","payload":{"claim_id":"marcus-evidence-that-llms-are-reaching-a-5d187d","subject_id":"gary-marcus","normalized_proposition":"About 5 to 7 recent models are on par with GPT-4, but none is clearly ahead.","kind":"observation","conditions":[],"original_horizon_text":null,"statement":{"excerpt":"Another way to think about this (see third graph above) is that there about 5 - 7 recent models are on a par with GPT-4, but none is clearly and decisively ahead.","locator":"paragraph 23","url":"https://garymarcus.substack.com/p/evidence-that-llms-are-reaching-a","date":"2024-04-13","document_title":"Evidence that LLMs are reaching a point of diminishing returns - and what that might mean","observed_at":"2026-10-29T00:00:33Z"},"criteria":{"from_speaker":["about 5 - 7 recent models","on a par with GPT-4","none is clearly and decisively ahead","see third graph above"],"our_interpretation":["The ordinary reading is that approximately five to seven recent models had broadly comparable capabilities to GPT-4, without a substantial overall leader.","The count requires identifying whether separate snapshots count as separate models. Do not silently substitute a count of vendors.","GPT-4 may mean the original model or the updated family; that distinction must remain explicit.","For the ongoing comparison, use the latest pre-post release-and-evaluation window, March 1 through April 13, 2024. This reporting window is an evaluator choice."],"support_condition":"A dated, identifiable model set contains approximately five to seven newer models with broadly comparable performance to the specified GPT-4 baseline, and no model has a robust substantial overall lead.","contradiction_condition":"The dated model set materially fails the approximate count under a defensible parity definition, or a newer model has a robust substantial overall lead over the specified baseline."},"axis":"historical_observation","outcome":"unresolved","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"Contemporary records identify several GPT-4 contenders, but they do not verify the specific approximate count. The missing graph prevents identifying its members and baseline. Reports also distinguish original GPT-4 from later versions, which changes who qualifies as a peer or leader. Neither support nor contradiction of the full claim is established.","limitations":["The earlier four-model list does not contradict a later approximate five-to-seven-model count.","A ranking advantage is not automatically a decisive overall lead.","Counting snapshots separately could materially change the result."],"evidence":[{"id":"e1","relation":"context","excerpt":"contender.","locator":"Paragraph following the four model descriptions","url":"https://simonwillison.net/2024/Mar/8/gpt-4-barrier","publisher":"Simon Willison","event_date":"2024-03-08","available_at":"2024-03-08","explanation":"The article names Gemini 1.5, Mistral Large, Claude 3 Opus and Inflection-2.5 as contenders. 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It’s hard not to see this plot as tentative evidence for the hypothesis of diminishing returns.","locator":"paragraph 11","document":{"url":"https://garymarcus.substack.com/p/evidence-that-llms-are-reaching-a","title":"Evidence that LLMs are reaching a point of diminishing returns - and what that might mean","publisher":"Marcus on AI","published_at":"2024-04-13T22:00:38Z","date_uncertain":false,"date_provenance":"JSON-LD datePublished"},"observed_at":"2026-10-29T00:00:33Z","observed_via":"live","archive_url":null,"access_state":"full_public","snapshot_hash":"38497ce2b51b3be12f95c40c31fbcafa2aca4e2418f4614dde3173dcb89b920e","receipts":1}],"first_statement_date":"2024-04-13","related":[],"current":{"assessment_id":"marcus-evidence-that-llms-are-reaching-a-f2cb8e-a2","payload":{"claim_id":"marcus-evidence-that-llms-are-reaching-a-f2cb8e","subject_id":"gary-marcus","normalized_proposition":"MMLU gains from GPT-4 to GPT-4 Turbo are small compared with earlier generational jumps, tentatively evidencing diminishing returns.","kind":"observation","conditions":["tentative evidence"],"original_horizon_text":null,"statement":{"excerpt":"Huge jump from GPT-2 to GPT-3. 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No precise threshold for 'huge' or 'not so much' is supplied.","Assess the historical score pattern and its tentative evidential value. This is not a prediction that future models will stop improving or a demonstrated relationship between training expenditure and capability.","The target covers the named historical generations through the post date. Its start is unspecified because the passage gives no starting calendar date.","Evaluation methods must be examined. Differences limit the strength of the inference even when the reported percentage-accuracy pattern is clear."],"support_condition":"Published MMLU results show a substantially smaller GPT-4-to-April-2024-Turbo accuracy gain than both earlier generational gains, with methodological qualifications consistent with describing the pattern as tentative evidence.","contradiction_condition":"Comparable published results show that the GPT-4-to-April-2024-Turbo gain is similar to or larger than either earlier generational gain, undermining the stated historical pattern."},"axis":"historical_observation","outcome":"supported","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"The January 2021 MMLU paper reports 32.4% accuracy for fine-tuned GPT-2 and 43.9% for GPT-3 X-Large, a gain of 11.5 percentage points [e1]. OpenAI's March 2023 report gives GPT-4 a score of 86.4%, a further gain of 42.5 points [e1, e2]. OpenAI's April 11, 2024 evaluation record reports 86.5–86.7% for the April 9 Turbo snapshot, only 0.1–0.3 points above the original GPT-4 result [e2, e3]. These reported gains support the historical pattern of a substantially smaller final increase, but differing evaluation methods limit its value as evidence of diminishing returns [e1, e2, e3].","limitations":["The records do not compare all four models under one protocol: GPT-2 was fine-tuned, GPT-3 used few-shot prompting, GPT-4 used five-shot prompting, and Turbo used zero-shot chain-of-thought prompting [e1, e2, e3].","These MMLU results alone do not establish diminishing returns to training resources, a general slowdown across capabilities, or limits on future improvement [e1, e2, e3]."],"evidence":[{"id":"e1","relation":"supports","excerpt":"GPT-2 on UnifiedQA training data and our dev+val set.","locator":"Section 4.1 and Table 1; Appendix A.1","url":"https://arxiv.org/pdf/2009.03300v3","publisher":"Dan Hendrycks and coauthors, arXiv","event_date":"2021-01","available_at":"2021-01-12","explanation":"Table 1 reports 32.4% for fine-tuned GPT-2 and 43.9% for GPT-3 X-Large evaluated few-shot, a reported increase of 11.5 percentage points. The different evaluation methods prevent treating this as a controlled comparison of unchanged model preparation."},{"id":"e2","relation":"supports","excerpt":"GPT-4’s performance along with the few-shot method\nused to evaluate.","locator":"Table 2, MMLU row and caption","url":"https://arxiv.org/pdf/2303.08774v1","publisher":"OpenAI","event_date":"2023-03","available_at":"2023-03-15","explanation":"Table 2 reports GPT-4 MMLU accuracy of 86.4% with five-shot evaluation. 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This is over three times better than the best results for any Tesla FSD v11.X","locator":"paragraph 2","url":"https://www.nextbigfuture.com/2024/04/tesla-fsd-12-3-x-is-over-three-times-better-than-the-best-fsd-v11-x-on-miles-per-disengagement.html","publisher":"NextBigFuture","event_date":"2024-04-02","available_at":"2024-04-02","explanation":"Crowdsourced tracker data as of early April 2024 showed v12.3 with far more miles between critical disengagements than v11 or v10, a measurable improvement just before the post."},{"id":"e2","relation":"supports","excerpt":"The common thread in the feedback from owners about v12.3 is that car’s movements are much smoother and feels more human-like, and that it can make correct decisions in the heat of the moment that it never could before.","locator":"paragraph 7","url":"https://driveteslacanada.ca/news/tesla-fsd-beta-v12-3-gets-overwhelmingly-positive-feedback-with-switch-to-end-to-end-neural-nets","publisher":"Drive Tesla Canada","event_date":"2024-03-18","available_at":"2024-03-18","explanation":"A contemporaneous report on the v12.3 wide release says owners widely saw better driving than in earlier versions."}],"what_would_resolve":null,"retrospective":true,"plain_reading":true},"payload_hash":"a688d4edb6abc5d7f34c21406a3b2a53270aec32e1ceb256c119109bc738f999","policy_version":"2026-10-28.7","recorded_at":"2026-10-29T06:43:14Z","drafter":{"provider":"anthropic","model":"claude-opus-5-5"},"reviewers":[{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-24-hours-in-ai-news-b04c94-1x-review-anthropic-1"},{"provider":"openai","model":"gpt-6-astra","run_id":"marcus-24-hours-in-ai-news-b04c94-1x-review-openai-1"}],"final_text":{"provider":"openai","model":"gpt-6-astra","run_id":"marcus-24-hours-in-ai-news-b04c94-1x-final-openai-2"},"status":"current","notice":null,"superseded_by":null,"evidence_meta":[{"id":"e1","source_type":"secondary_summary","audited":false,"derived":null,"metric":null},{"id":"e2","source_type":"news_report","audited":null,"derived":null,"metric":null}],"public_from":null,"public_until":null},"history":[],"review":{"review_due":false,"triggers":[],"last_completed_review_at":"2026-10-29T06:43:14Z","next_review_due_at":null,"horizon_now":"not_applicable"},"event_ids":["marcus-24-hours-in-ai-news-b04c94-assessment-published-a2"],"last_substantive_change_at":"2026-10-29T17:00:18Z"}},{"url":"https://punditclaims.ai/claims/marcus-breaking-news-scaling-will-never-38a35b","standing":"current","assessment_label":"Supported","versions":[{"assessment_id":"marcus-breaking-news-scaling-will-never-38a35b-a1","status":"current","url":"https://punditclaims.ai/claims/marcus-breaking-news-scaling-will-never-38a35b#assessment-marcus-breaking-news-scaling-will-never-38a35b-a1"}],"claim":{"claim_id":"marcus-breaking-news-scaling-will-never-38a35b","subject_id":"gary-marcus","revision":1,"content_hash":"5a860f74ce3628d167da2724002e86b4c315e8e5551e3a1f882062a4d19940af","normalized_proposition":"Current AI models can generalize to some degree but not enough.","kind":"observation","target":"current AI models","conditions":[],"original_horizon_text":null,"topic_tags":["capabilities"],"concerns_providers":[],"selection_stratum":"declared-lead","occurrences":[{"id":"marcus-breaking-news-scaling-will-never-38a35b-o1","statement_date":"2024-04-08","statement_date_precision":"day","excerpt":"current models aren’t literally look up tables; 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Model evaluations have no uniform reporting cycle, so this is an explicit recency convention."],"support_condition":"Evaluations of current models show successful transfer on some new task variants but substantial, systematic failures on other meaningful reasoning or planning variants.","contradiction_condition":"Representative evaluations show reliable transfer across unfamiliar reasoning and planning variants, undermining the claimed insufficiency."},"axis":"historical_observation","outcome":"supported","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"Under the contextual reading of partial but unreliable generalization, the claim is supported. GSM-Plus finds successful handling of some variations alongside substantial failures on others across many models and prompting methods. Earlier planning research demonstrates meaningful successes, so the evidence supports a reliability limitation rather than a blanket claim that models cannot reason, plan, or generalize.","limitations":["The outcome depends on interpreting 'not enough' through the surrounding discussion of reliability.","The evidence does not establish an AGI threshold or a permanent limitation.","One benchmark family cannot characterize all current AI models and uses."],"evidence":[{"id":"e1","relation":"supports","excerpt":"struggling with answering the variations","locator":"Section 1, Introduction, summary of experimental findings","url":"https://arxiv.org/html/2402.19255v1","publisher":"arXiv; Li and colleagues","event_date":"2024-02","available_at":"2024-02-29","explanation":"The study evaluates 25 LLMs and four prompting techniques. It reports resilience to some numerical and wording changes alongside substantial failures under other mathematical perturbations, supporting the contextual claim of partial but unreliable generalization."},{"id":"e2","relation":"context","excerpt":"a surprisingly powerful generalized planner","locator":"Abstract","url":"https://arxiv.org/abs/2305.11014v1","publisher":"arXiv; Silver and colleagues","event_date":"2023-05","available_at":"2023-05-18","explanation":"GPT-4 generated planning programs that generalized to other tasks with automated debugging support. This challenges any blanket dismissal of planning generalization, but predates the target quarter and does not establish broad reliability."}],"what_would_resolve":null,"retrospective":true,"plain_reading":true},"payload_hash":"a0a04957391f8e6f3cbd69dd0fa181cc08e9eae2b6e37c7935111974666e368c","policy_version":"2026-10-28.7","recorded_at":"2026-10-29T02:38:08Z","drafter":{"provider":"openai","model":"gpt-6-astra"},"reviewers":[{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-breaking-news-scaling-will-never-38a35b-4x-review-anthropic-1"},{"provider":"openai","model":"gpt-6-astra","run_id":"marcus-breaking-news-scaling-will-never-38a35b-4x-review-openai-1"}],"final_text":{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-breaking-news-scaling-will-never-38a35b-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":"2026-10-29T02:38:08Z","next_review_due_at":null,"horizon_now":"not_applicable"},"event_ids":["marcus-breaking-news-scaling-will-never-38a35b-assessment-published-a1"],"last_substantive_change_at":"2026-10-29T17:00:18Z"}},{"url":"https://punditclaims.ai/claims/marcus-breaking-news-scaling-will-never-739ace","standing":"current","assessment_label":"Contradicted","versions":[{"assessment_id":"marcus-breaking-news-scaling-will-never-739ace-a1","status":"current","url":"https://punditclaims.ai/claims/marcus-breaking-news-scaling-will-never-739ace#assessment-marcus-breaking-news-scaling-will-never-739ace-a1"}],"claim":{"claim_id":"marcus-breaking-news-scaling-will-never-739ace","subject_id":"gary-marcus","revision":1,"content_hash":"c2f422c59a4ebbb5e27723717a1aaf74f40962f4fc5ab2ea1b13127e16d013d5","normalized_proposition":"Poor generalization beyond training data is why driverless cars are still just demos.","kind":"causal","target":"driverless cars","conditions":[],"original_horizon_text":null,"topic_tags":["reliability","capabilities"],"concerns_providers":[],"selection_stratum":"declared-lead","occurrences":[{"id":"marcus-breaking-news-scaling-will-never-739ace-o1","statement_date":"2024-04-08","statement_date_precision":"day","excerpt":"This is why driverless cars are still just demos, and why LLMs will never be reliable.","locator":"paragraph 1","document":{"url":"https://garymarcus.substack.com/p/breaking-news-scaling-will-never","title":"Breaking news: Scaling will never get us to AGI","publisher":"Marcus on AI","published_at":"2024-04-08T17:26:43Z","date_uncertain":false,"date_provenance":"JSON-LD datePublished"},"observed_at":"2026-10-29T00:00:41Z","observed_via":"live","archive_url":null,"access_state":"full_public","snapshot_hash":"1a00424496bde2f59c54ca291c3cc862b99e584d3b75832149f3b029440206e8","receipts":1}],"first_statement_date":"2024-04-08","related":[],"current":{"assessment_id":"marcus-breaking-news-scaling-will-never-739ace-a1","payload":{"claim_id":"marcus-breaking-news-scaling-will-never-739ace","subject_id":"gary-marcus","normalized_proposition":"Poor generalization beyond training data is why driverless cars are still just demos.","kind":"causal","conditions":[],"original_horizon_text":null,"statement":{"excerpt":"This is why driverless cars are still just demos, and why LLMs will never be reliable.","locator":"paragraph 1","url":"https://garymarcus.substack.com/p/breaking-news-scaling-will-never","date":"2024-04-08","document_title":"Breaking news: Scaling will never get us to AGI","observed_at":"2026-10-29T00:00:41Z"},"criteria":{"from_speaker":["This is why","driverless cars are still just demos"],"our_interpretation":["This assessment concerns the supplied driverless-car proposition, not the separate prediction about LLM reliability.","I read 'just demos' as demonstration or trial activity without routine public passenger service.","For the ongoing state described immediately before publication, I use the latest completed calendar quarter, January through March 2024.","An operating public driverless service would contradict the necessary premise of this causal claim, without proving that generalization difficulties are unimportant."],"support_condition":"Driverless passenger operations remained demonstration-only, and technical evidence identifies poor generalization as the reason they had not become routine services.","contradiction_condition":"A routine public passenger service was operating without a human driver during the target period."},"axis":"causal_assessment","outcome":"contradicted","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"Waymo's January 2024 regulatory filing describes thousands of daily driverless passenger rides and ordinary travel uses. That contradicts the premise that driverless cars were still only demonstrations. Some extensions, including freeway service, remained in testing. The evidence does not determine how much poor generalization constrained wider deployment.","limitations":["The decisive operational account is the company's regulatory filing.","This finding does not establish unrestricted autonomous driving or refute generalization as a deployment challenge."],"evidence":[{"id":"e1","relation":"contradicts","excerpt":"no human","locator":"Advice Letter 0002, page 2, paragraph beginning 'Each day'","url":"https://webproda.cpuc.ca.gov/-/media/cpuc-website/divisions/consumer-protection-and-enforcement-division/documents/tlab/av-programs/waymo-llc-cpuc-advice-letter-0002-tier-2--january-2024-passenger-safety-plan-update-january-192024.pdf","publisher":"Waymo LLC; filed with the California Public Utilities Commission","event_date":"2024-01-19","available_at":"2024-01-19","explanation":"The filing describes daily public driverless rides for work, school, errands and airport travel, and identifies Waymo One as a deployed commercial service. Those operations contradict the demonstration-only premise."},{"id":"e2","relation":"context","excerpt":"freeway trips","locator":"Paragraph beginning 'As we gradually ramp up our operations'","url":"https://waymo.com/blog/2024/01/from-surface-streets-to-freeways-safely-expanding-our-rider-only-testing","publisher":"Waymo","event_date":"2024-01-08","available_at":"2024-01-08","explanation":"Freeway expansion still involved employee testing. 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decisions about data collection, consent, licensing and privacy.","The claim alleges a causal chain from weak generalization to data demand and then to developer decisions. Showing large datasets and controversial conduct separately is insufficient.","The assessment concerns configurations and developer conduct by the post date."],"support_condition":"Technical evidence links generalization limitations to increased data needs, and documented developer decisions connect those needs to consequential collection or licensing choices.","contradiction_condition":"Controlled technical evidence and documented decisions show that the alleged generalization limitation did not materially drive the data demand or the developer choices."},"axis":"causal_assessment","outcome":"unresolved","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"Lake and Baroni's 2018 study found systematic compositional generalization failures in tested recurrent networks and tentatively linked lack of systematicity to training-data needs [e1]. Reporting available on April 8, 2024 described Meta's 2023 discussions of collecting copyrighted data despite potential lawsuits and the time required for licensing [e2]. The study does not connect its technical mechanism to those decisions, and the reporting does not isolate generalization limitations as their cause [e1, e2]. The full causal chain is neither established nor refuted [e1, e2].","limitations":["The technical evidence concerns particular recurrent networks and tasks, and its link to data demand is tentative [e1].","The corporate discussions are documented through reporting based on internal recordings; the underlying records were not independently verified in this packet [e2].","The evidence does not distinguish data needed for unfamiliar facts from data needed to compensate for weak generalization in the reported decisions [e1, e2]."],"evidence":[{"id":"e1","relation":"context","excerpt":"RNNs fail spectacularly.","locator":"Abstract","url":"https://arxiv.org/abs/1711.00350","publisher":"ICML; arXiv","event_date":"2018","available_at":"2018-06-06","explanation":"The study demonstrates a generalization weakness and tentatively connects lack of systematicity to data needs. It does not establish that this mechanism caused particular companies' collection decisions."},{"id":"e2","relation":"context","excerpt":"even if that meant facing lawsuits.","locator":"Opening section, paragraph describing Meta's 2023 internal meetings","url":"https://indianexpress.com/article/technology/artificial-intelligence/how-tech-giants-cut-corners-to-harvest-data-for-ai-9257392","publisher":"The New York Times, republished by The Indian Express","event_date":"2023","available_at":"2024-04-08","explanation":"Reporting based on meeting recordings connects the pursuit of training data to consequential licensing decisions. It does not isolate weak generalization as the cause; competition, cost and licensing speed also appear in the account."}],"what_would_resolve":"Controlled studies separating factual coverage from generalization-related data needs, combined with decision records linking those needs to collection, consent or licensing choices within the period ending April 8, 2024. 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A literal boundary around training points is a different mathematical claim.","The three-decade reference defines the historical scope of the architectures being discussed. It does not require every architecture to fail in every year."],"support_condition":"Research on commonly used neural architectures finds substantial, recurring performance losses under distribution shifts or extrapolation beyond familiar training conditions.","contradiction_condition":"Representative controlled evaluations show dependable generalization across materially unfamiliar conditions, undermining the claimed recurring difficulty."},"axis":"historical_observation","outcome":"supported","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"The ordinary claim of recurring difficulty is supported. WILDS finds substantial losses under real-world distribution shifts, and controlled graph-network research identifies extrapolation failures. Successful compositional learning and the geometry research require clear qualifications: neural networks can generalize beyond training examples, and a literal convex-hull boundary does not explain their abilities.","limitations":["The outcome depends on reading the spatial language as unfamiliar conditions rather than a precise geometric boundary.","These findings do not establish that scaling can never produce AGI."],"evidence":[{"id":"e1","relation":"supports","excerpt":"substantially lower out-of-distribution than in-distribution performance","locator":"Abstract","url":"https://arxiv.org/abs/2012.07421v3","publisher":"arXiv; Koh and colleagues","event_date":"2021","available_at":"2021-07-16","explanation":"WILDS reports performance losses across ten datasets involving natural distribution shifts. This supports recurring generalization difficulty across applications, without establishing a universal inability."},{"id":"e2","relation":"supports","excerpt":"do not extrapolate well","locator":"Section 4.2, Corollary 1","url":"https://arxiv.org/html/2009.11848v5","publisher":"arXiv; Xu and colleagues","event_date":"2021","available_at":"2021-03-02","explanation":"The paper identifies a concrete extrapolation failure for a common graph-network configuration. It also shows that suitable architectural choices and training distributions can permit successful extrapolation."},{"id":"e3","relation":"context","excerpt":"as an indicator of generalization performances","locator":"Abstract, final sentence","url":"https://arxiv.org/abs/2110.09485v1","publisher":"arXiv; Balestriero, Pesenti and LeCun","event_date":"2021","available_at":"2021-10-18","explanation":"The authors show why high-dimensional learning commonly occurs outside the training points' convex hull. This challenges a literal geometric reading, but does not establish reliable performance under distribution shifts."},{"id":"e4","relation":"context","excerpt":"several systematic generalization benchmarks","locator":"Abstract","url":"https://www.nature.com/articles/s41586-023-06668-3","publisher":"Nature; Lake and Baroni","event_date":"2023","available_at":"2023-10-25","explanation":"A neural network trained for compositional skills demonstrates successful systematic generalization. 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Other research shows that training choices can postpone saturation. These findings are relevant but do not conclusively settle the broad, undated forecast. No decisive event meeting the early-resolution rule was found by October 28, 2026.","limitations":["A narrower claim about a specified loss curve could be supported by individual experiments.","Diminishing marginal gains are compatible with continued substantial progress."],"evidence":[{"id":"e1","relation":"context","excerpt":"with more repetition","locator":"Abstract","url":"https://arxiv.org/abs/2305.16264v1","publisher":"arXiv; Muennighoff and colleagues","event_date":"2023","available_at":"2023-05-25","explanation":"The experiment demonstrates saturation when data is repeatedly reused. 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Reusing the same corpus across companies does not consume it like a physical resource."],"support_condition":"Dated measurements show that relevant model-training requirements exceed the available usable data supply under matched definitions, and document an actual constraint rather than only a projected future intersection.","contradiction_condition":"A general, validated account establishes that usable supply can keep pace with the relevant training requirements indefinitely. Continued sufficiency over a finite period alone does not contradict an eventual forecast."},"axis":"forecast_outcome","outcome":"unresolved","horizon_state":"open_horizon","evaluation_as_of":"2026-10-28","rationale":"Epoch's June 6, 2024 analysis projected full utilization of usable public human-generated text between 2026 and 2032 with an 80% confidence interval; it did not document an observed supply-demand crossing [e1]. An October 2025 study found conditional benefits from some mixtures of synthetic and natural data, without establishing indefinitely sufficient supply [e2]. TechCrunch's January 8, 2025 report quoted an exhaustion assertion but supplied no matched demand and supply measurements [e3]. These items establish neither an actual broad data constraint meeting the contract nor that usable supply can keep pace indefinitely [e1, e2, e3].","limitations":["Epoch's quantitative estimate covers public human-generated text, not all usable training data [e1].","The synthetic-data experiments concern particular mixtures, budgets and training conditions; they do not establish unlimited substitution for natural data [e2].","The reported exhaustion assertion lacks a transparent inventory and a comparable demand estimate [e3]."],"evidence":[{"id":"e1","relation":"context","excerpt":"between 2026 and 2032.","locator":"Results, paragraph describing the combined dataset-growth projection","url":"https://epoch.ai/publications/will-we-run-out-of-data-limits-of-llm-scaling-based-on-human-generated-data","publisher":"Epoch AI","event_date":"2024-06-06","available_at":"2024-06-06","explanation":"Epoch projects a future constraint for quality- and repetition-adjusted public human text. 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No starting date is supplied, so the start remains unspecified.","Respondent counts, organization counts, project counts and request volumes are different denominators and cannot be substituted without justification."],"support_condition":"Representative evidence establishes that more than half of enterprise LLM applications attempted by the post date were testing rather than production systems.","contradiction_condition":"Representative evidence with the same denominator establishes that half or fewer were testing."},"axis":"historical_observation","outcome":"unresolved","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"The O’Reilly survey supports the narrower observation that many respondents' companies were still experimenting. It does not measure the share of all LLM applications that were tests. Klarna demonstrates substantial production use, but cannot establish the opposite majority. 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Evidence about an augmented system therefore does not automatically refute the narrower claim about an unaided model.","The assessment concerns the mechanisms of the LLMs discussed in March 2024. Later research may explain those mechanisms but cannot substitute newer systems for them."],"support_condition":"Research establishes the claimed causal contributions for the relevant models, rather than merely showing that hallucinations occur.","contradiction_condition":"Research shows that the asserted causal mechanism does not hold, or demonstrates lookup and reasoning within the scope meant by 'LLMs themselves'. Improvements alone do not disprove a structural risk."},"axis":"causal_assessment","outcome":"unresolved","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"Research supports a structural connection between statistical prediction and some hallucinations. It does not establish the full causal claim without qualifications. ReAct also shows that language models could already coordinate retrieval and reasoning with an external interface. Whether that contradicts Marcus depends on what he excludes with 'LLMs themselves'. The combined claim cannot fairly receive an unqualified verdict.","limitations":["A conditional mathematical result is not a universal result about deployed systems.","Successful retrieval and reasoning on selected tasks do not establish general reliability."],"evidence":[{"id":"e1","relation":"supports","excerpt":"calibrated predictors of facts.","locator":"Introduction, paragraph introducing the statistical result","url":"https://arxiv.org/html/2311.14648v3","publisher":"arXiv; Adam Tauman Kalai and Santosh S. Vempala","event_date":"2024-03-20","available_at":"2024-03-20","explanation":"The result supports a structural prediction-related source of hallucinations under calibration and factual-distribution assumptions. The authors explicitly caution that it does not establish inevitable hallucination for every language model or post-training procedure."},{"id":"e2","relation":"context","excerpt":"verbal reasoning traces","locator":"Model Overview, first paragraph","url":"https://research.google/blog/react-synergizing-reasoning-and-acting-in-language-models","publisher":"Google Research","event_date":"2022-11-08","available_at":"2022-11-08","explanation":"The experiments use a frozen PaLM model with a Wikipedia interface to combine retrieved information and model-generated reasoning. 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Later research may test the proposed mechanism, but later market movements alone cannot establish it."],"support_condition":"Comparisons of generative AI investments show that greater prior hype produces larger declines after comparable expectation misses, accounting for other material causes.","contradiction_condition":"Credible comparisons find no meaningful amplification, or a reversed relationship, with sufficient precision to reject the proposed tendency."},"axis":"causal_assessment","outcome":"unresolved","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"The closest research is consistent with the idea that exaggerated AI narratives create financial risk. It does not settle Marcus's stronger comparative claim that more generative AI hype causes a bigger fall when expectations are missed. 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It’s probably worth a lot, but you wouldn’t like me as much if I acknowledged that, so I won’t. Also, did I mention I own the OpenAI venture fund? Nope, guess not, sorry.","locator":"paragraph 24","document":{"url":"https://garymarcus.substack.com/p/openais-lies-and-half-truths","title":"OpenAI’s Lies and Half-Truths","publisher":"Marcus on AI","published_at":"2024-03-15T03:49:20Z","date_uncertain":false,"date_provenance":"JSON-LD datePublished"},"observed_at":"2026-10-29T00:00:56Z","observed_via":"live","archive_url":null,"access_state":"full_public","snapshot_hash":"63d7420f1800ec80b2f234a757bcadb3f22b36245610083334c41f37a23483cb","receipts":1}],"first_statement_date":"2024-03-15","related":[],"current":{"assessment_id":"marcus-openais-lies-and-half-truths-c81e88-a1","payload":{"claim_id":"marcus-openais-lies-and-half-truths-c81e88","subject_id":"gary-marcus","normalized_proposition":"In his May 2023 Senate testimony, Sam Altman left out his indirect OpenAI stake through Y Combinator and his ownership of the OpenAI venture fund.","kind":"observation","conditions":[],"original_horizon_text":null,"statement":{"excerpt":"Translation: But I do have equity in YCombinator and YCombinator has equity in OpenAI. It’s probably worth a lot, but you wouldn’t like me as much if I acknowledged that, so I won’t. Also, did I mention I own the OpenAI venture fund? Nope, guess not, sorry.","locator":"paragraph 24","url":"https://garymarcus.substack.com/p/openais-lies-and-half-truths","date":"2024-03-15","document_title":"OpenAI’s Lies and Half-Truths","observed_at":"2026-10-29T00:00:56Z"},"criteria":{"from_speaker":["Sam Altman, to the Senate, May 2023","I do have equity in YCombinator","YCombinator has equity in OpenAI","I own the OpenAI venture fund","if I acknowledged that"],"our_interpretation":["Assess whether both interests existed at the May 2023 hearing and whether Altman disclosed them in his testimony.","Distinguish an indirect economic interest through a Y Combinator fund from direct OpenAI equity.","Read ownership of the venture fund as legal ownership or control of its managing entity, not ownership of investors' capital.","The speculation about value and motive is excluded from the factual assessment.","This is a qualitative ownership-and-disclosure claim, not a measurement of company finances; metric_scope is null."],"support_condition":"Records establish both interests at the hearing date, and the testimony omits both.","contradiction_condition":"Records establish that either alleged interest did not exist at the hearing date, or the testimony disclosed an interest Marcus says was omitted."},"axis":"historical_observation","outcome":"unresolved","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"The transcript supports the omission component. Later reporting supports an indirect YC interest and an initial fund-control arrangement, but the retrieved records do not fully establish both legal interests at the hearing date. The fund's reported lack of personal financial benefit also qualifies Marcus's presentation. Hold the combined claim pending a dated ownership record.","limitations":["This is an evidentiary hold, not a finding that either interest was absent.","No finding is made about Altman's motive, the stake's value, or whether the omission violated a disclosure duty."],"evidence":[{"id":"e1","relation":"supports","excerpt":"no equity","locator":"Altman's exchange with Senator Kennedy; text offset approximately 105072","url":"https://www.govinfo.gov/content/pkg/CHRG-118shrg52706/html/CHRG-118shrg52706.htm","publisher":"U.S. Government Publishing Office","event_date":"2023-05-16","available_at":"2024-02-12","explanation":"Supports the testimony component. The searchable oral record contains no disclosure of either ownership interest; its Y Combinator reference concerns Altman's former employment."},{"id":"e2","relation":"context","excerpt":"indirect investment","locator":"Paragraph discussing employees' financial interests following the November board dispute","url":"https://time.com/6342827/ceo-of-the-year-2023-sam-altman","publisher":"TIME","event_date":"2023-11","available_at":"2023-12-06","explanation":"Reports an indirect interest later in 2023. It does not independently date the ownership chain to the May hearing."},{"id":"e3","relation":"context","excerpt":"named controller","locator":"Paragraph 2","url":"https://techcrunch.com/2024/04/01/sam-altman-gives-up-control-of-openai-startup-fund-resolving-unusual-corporate-venture-structure","publisher":"TechCrunch","event_date":"2021","available_at":"2024-04-01","explanation":"Supports the existence of an initial control arrangement. It is not a contemporaneous ownership record distinguishing the fund, general partner, and management company."},{"id":"e4","relation":"context","excerpt":"despite the ownership.","locator":"Paragraph 2 of Reuters report","url":"https://finance.yahoo.com/news/openai-removes-sam-altmans-ownership-160651728.html","publisher":"Reuters, republished by Yahoo Finance","event_date":"2024-04-01","available_at":"2024-04-01","explanation":"Material qualification to an economic reading of fund ownership. This company statement does not negate legal control or establish the precise May 2023 arrangement."}],"what_would_resolve":"Readable historical ownership records, or reliable retrospective reporting explicitly establishing both ownership chains on May 16, 2023, checked against the testimony.","retrospective":true,"plain_reading":true},"payload_hash":"00527a016075728f8024b84eee915167de9809fe8221b41e27af2497214bae9f","policy_version":"2026-10-28.7","recorded_at":"2026-10-29T08:04:21Z","drafter":{"provider":"openai","model":"gpt-6-astra"},"reviewers":[{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-openais-lies-and-half-truths-97fd17-3x-review-anthropic-1"},{"provider":"openai","model":"gpt-6-astra","run_id":"marcus-openais-lies-and-half-truths-97fd17-3x-review-openai-1"}],"final_text":{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-openais-lies-and-half-truths-97fd17-3x-final-anthropic-1"},"status":"current","notice":null,"superseded_by":null,"evidence_meta":[{"id":"e1","source_type":"official_statistics","audited":null,"derived":null,"metric":null},{"id":"e2","source_type":"news_report","audited":null,"derived":null,"metric":null},{"id":"e3","source_type":"news_report","audited":null,"derived":null,"metric":null},{"id":"e4","source_type":"news_report","audited":null,"derived":null,"metric":null}],"public_from":null,"public_until":null},"history":[],"review":{"review_due":false,"triggers":[],"last_completed_review_at":"2026-10-29T08:04:21Z","next_review_due_at":null,"horizon_now":"not_applicable"},"event_ids":["marcus-openais-lies-and-half-truths-c81e88-assessment-published-a1"],"last_substantive_change_at":"2026-10-29T17:00:18Z"}},{"url":"https://punditclaims.ai/claims/marcus-openais-lies-and-half-truths-cfd3b2","standing":"current","assessment_label":"Supported","versions":[{"assessment_id":"marcus-openais-lies-and-half-truths-cfd3b2-a2","status":"current","url":"https://punditclaims.ai/claims/marcus-openais-lies-and-half-truths-cfd3b2#assessment-marcus-openais-lies-and-half-truths-cfd3b2-a2"}],"claim":{"claim_id":"marcus-openais-lies-and-half-truths-cfd3b2","subject_id":"gary-marcus","revision":1,"content_hash":"83b7d57e00cc4bef516195de916210a90e798dcbf81a70980203e32662da34fc","normalized_proposition":"While publicly asking the Senate to regulate it, OpenAI worked behind the scenes to water down regulations.","kind":"observation","target":"OpenAI","conditions":[],"original_horizon_text":null,"topic_tags":["regulation-policy"],"concerns_providers":["openai"],"selection_stratum":"declared-lead","occurrences":[{"id":"marcus-openais-lies-and-half-truths-cfd3b2-o1","statement_date":"2024-03-15","statement_date_precision":"day","excerpt":"Translation: But (revealed a few weeks later) behind the scenes we will try hard to water down your regulations.","locator":"paragraph 27","document":{"url":"https://garymarcus.substack.com/p/openais-lies-and-half-truths","title":"OpenAI’s Lies and Half-Truths","publisher":"Marcus on AI","published_at":"2024-03-15T03:49:20Z","date_uncertain":false,"date_provenance":"JSON-LD datePublished"},"observed_at":"2026-10-29T00:00:56Z","observed_via":"live","archive_url":null,"access_state":"full_public","snapshot_hash":"63d7420f1800ec80b2f234a757bcadb3f22b36245610083334c41f37a23483cb","receipts":1}],"first_statement_date":"2024-03-15","related":[],"current":{"assessment_id":"marcus-openais-lies-and-half-truths-cfd3b2-a2","payload":{"claim_id":"marcus-openais-lies-and-half-truths-cfd3b2","subject_id":"gary-marcus","normalized_proposition":"While publicly asking the Senate to regulate it, OpenAI worked behind the scenes to water down regulations.","kind":"observation","conditions":[],"original_horizon_text":null,"statement":{"excerpt":"Translation: But (revealed a few weeks later) behind the scenes we will try hard to water down your regulations.","locator":"paragraph 27","url":"https://garymarcus.substack.com/p/openais-lies-and-half-truths","date":"2024-03-15","document_title":"OpenAI’s Lies and Half-Truths","observed_at":"2026-10-29T00:00:56Z"},"criteria":{"from_speaker":["to the Senate, May 2023: Please regulate us","revealed a few weeks later","behind the scenes","try hard to water down your regulations"],"our_interpretation":["Read this as a retrospective description of lobbying revealed after the hearing, despite the satirical future tense.","Watering down means seeking narrower coverage, exemptions, or fewer binding obligations than a proposed regulatory text.","Read regulations broadly enough to include the EU AI Act. A claim specifically about a U.S. Senate proposal would require different evidence.","The target includes the earlier lobbying subsequently revealed in June 2023; Marcus supplies no starting date.","Assess documented efforts, not their intensity, success, or sincerity."],"support_condition":"The hearing shows public support for regulation, and records show nonpublic efforts to narrow proposed AI regulatory obligations during the episode subsequently reported.","contradiction_condition":"The alleged lobbying records show no effort to narrow obligations, or establish that the reported private proposals instead sought equally strict or stronger coverage."},"axis":"historical_observation","outcome":"supported","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"At the May 16, 2023 Senate hearing, Altman supported a new regulatory framework and offered to work with lawmakers [e1]. TIME reported on June 20, 2023 that OpenAI had privately asked EU officials in September 2022 to drop a proposed high-risk classification provision and rely on labeling requirements instead [e2]. This supports the claim under the contract’s inclusion of earlier EU lobbying: the private request preceded the hearing, while its disclosure followed it [e1, e2].","limitations":["The documented request concerned the EU AI Act; it does not establish efforts to weaken a U.S. Senate proposal [e2].","The June 2023 reporting date does not establish that the documented lobbying occurred during or after the May 2023 hearing [e1, e2].","The evidence does not establish lobbying intensity, motive, insincerity, or whether OpenAI caused the provision’s removal [e1, e2]."],"evidence":[{"id":"e1","relation":"supports","excerpt":"collaborate with you","locator":"Altman's exchange with Senator Durbin about a new liability framework; text offset approximately 57791","url":"https://www.govinfo.gov/content/pkg/CHRG-118shrg52706/html/CHRG-118shrg52706.htm","publisher":"U.S. Government Publishing Office","event_date":"2023-05-16","available_at":"2024-02-12","explanation":"The surrounding exchange supports public cooperation on regulation. Elsewhere in the same testimony, Altman advocates licensing and safety requirements."},{"id":"e2","relation":"supports","excerpt":"scrapping","locator":"Paragraph discussing the September 2022 white paper and the proposed high-risk classification","url":"https://time.com/6288245/openai-eu-lobbying-ai-act","publisher":"TIME","event_date":"2022-09","available_at":"2023-06-20","explanation":"The report documents private proposals to narrow EU requirements. OpenAI defended its submissions as safety-focused policy input."}],"what_would_resolve":null,"retrospective":true,"plain_reading":true},"payload_hash":"df34c34087a003eb7f345938e7d0cf235ef12dffada8f130609a2d78edae2bc0","policy_version":"2026-10-28.7","recorded_at":"2026-10-29T11:50:27Z","drafter":{"provider":"openai","model":"gpt-6-astra"},"reviewers":[{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-openais-lies-and-half-truths-97fd17-3x-review-anthropic-1"},{"provider":"openai","model":"gpt-6-astra","run_id":"marcus-openais-lies-and-half-truths-97fd17-3x-review-openai-1"}],"final_text":{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-openais-lies-and-half-truths-cfd3b2-1x-final-anthropic-1"},"status":"current","notice":null,"superseded_by":null,"evidence_meta":[{"id":"e1","source_type":"official_statistics","audited":null,"derived":null,"metric":null},{"id":"e2","source_type":"news_report","audited":null,"derived":null,"metric":null}],"public_from":null,"public_until":null},"history":[],"review":{"review_due":false,"triggers":[],"last_completed_review_at":"2026-10-29T11:50:27Z","next_review_due_at":null,"horizon_now":"not_applicable"},"event_ids":["marcus-openais-lies-and-half-truths-cfd3b2-assessment-published-a2"],"last_substantive_change_at":"2026-10-29T17:00:18Z"}},{"url":"https://punditclaims.ai/claims/marcus-the-exponential-enshittification-09b8b0","standing":"current","assessment_label":"Can’t be checked","versions":[{"assessment_id":"marcus-the-exponential-enshittification-09b8b0-a1","status":"current","url":"https://punditclaims.ai/claims/marcus-the-exponential-enshittification-09b8b0#assessment-marcus-the-exponential-enshittification-09b8b0-a1"}],"claim":{"claim_id":"marcus-the-exponential-enshittification-09b8b0","subject_id":"gary-marcus","revision":2,"content_hash":"b749168f969250609cba68c0bead162effa50f43f5336acb22f1ca44f6b52a70","normalized_proposition":"It looks like science is the next area to be overrun by AI-generated fake submissions, as Clarkesworld was and as Marcus had warned other areas would be.","kind":"forecast","target":"science publishing","conditions":[{"text":"it looks like","type":"qualifier","from_source":true}],"original_horizon_text":null,"topic_tags":["other"],"concerns_providers":[],"selection_stratum":"declared-lead","occurrences":[{"id":"marcus-the-exponential-enshittification-09b8b0-o1","statement_date":"2024-03-15","statement_date_precision":"day","excerpt":"I warned then that other areas would see the same. 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I use December 2023 through the post date as the contemporary reporting period.","Difficulty does not mean that useful production deployments were impossible."],"support_condition":"Contemporary firsthand research documents recurring retrieval, grounding, or operational failures requiring substantial testing and mitigation.","contradiction_condition":"Representative contemporary evidence shows that dependable production RAG was routinely achievable with little additional engineering or validation."},"axis":"historical_observation","outcome":"supported","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"Contemporary implementation research documented recurring failures and unresolved testing problems. RAGTruth independently documented unsupported outputs despite retrieval. This supports the ordinary claim that dependable production remained difficult. 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Why even the biggest models still struggle with guardrails","observed_at":"2026-10-29T00:01:15Z"},"criteria":{"from_speaker":["Current systems","mix historical data, images from old films etc, all together","way too unsophisticated","representative current sample"],"our_interpretation":["The relevant systems are contemporary image generators discussed in the post.","I read representative current sample as generated population distributions reasonably matching an identified contemporary population, rather than every small image batch matching exact proportions.","The claim combines mixed-era training data with unreliable contemporary representation; evidence of biased outputs alone does not establish both.","Because no reporting interval is supplied, the target is the current deployment period in February 2024 through publication on February 21."],"support_condition":"Training documentation establishes mixed historical and contemporary inputs, and audits of the relevant deployments show persistent failure to represent specified contemporary populations.","contradiction_condition":"Documentation disproves the asserted data mixing, or representative audits show that the relevant systems reliably produce contemporary population samples when the intended population is specified."},"axis":"historical_observation","outcome":"unresolved","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"The sources establish reasons for concern about demographic representation, but do not settle the complete claim for February 2024. The earlier surgeon audit found substantial failures alongside a successful match to attending-surgeon demographics. Dataset documentation does not establish how the relevant systems handled historical material or selected contemporary populations.","limitations":["The demographic experiments predate the target period.","The dataset documentation is not a complete account of the relevant commercial systems.","The audit's January 2024 issue date is used as a conservative availability date."],"evidence":[{"id":"e3","relation":"context","excerpt":"this large-scale dataset is uncurated.","locator":"Disclaimer on dataset purpose and content warning","url":"https://laion.ai/blog/laion-5b","publisher":"LAION","event_date":"2022-03-31","available_at":"2022-03-31","explanation":"LAION documents a broadly collected web dataset. This does not by itself establish the mixture of depicted eras, use of old films, or contemporary sampling ability of the systems Marcus meant."},{"id":"e4","relation":"context","excerpt":"underestimated trainee representation.","locator":"Abstract, Conclusion and relevance","url":"https://pubmed.ncbi.nlm.nih.gov/37966807","publisher":"JAMA Surgery, abstract hosted by PubMed","event_date":"2023-05","available_at":"2024-01-01","explanation":"The May 2023 study found demographic mismatches but also a match between DALL-E 2 outputs and attending surgeons. The result depends on the reference population and does not directly measure February 2024 behavior."}],"what_would_resolve":"Version-matched training documentation and February 2024 demographic audits with explicit contemporary populations and adequate repeated sampling.","retrospective":true,"plain_reading":true},"payload_hash":"9fdceeb3916ac9e7f9b093937e1cb006a89a852aca10f9aaea24cbadaaedd7e7","policy_version":"2026-10-28.7","recorded_at":"2026-10-29T07:29:11Z","drafter":{"provider":"openai","model":"gpt-6-astra"},"reviewers":[{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-has-google-gone-too-woke-why-even-262cdc-4x-review-anthropic-1"},{"provider":"openai","model":"gpt-6-astra","run_id":"marcus-has-google-gone-too-woke-why-even-262cdc-4x-review-openai-1"}],"final_text":{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-has-google-gone-too-woke-why-even-262cdc-4x-final-anthropic-1"},"status":"current","notice":null,"superseded_by":null,"evidence_meta":[{"id":"e3","source_type":"company_disclosure","audited":null,"derived":null,"metric":null},{"id":"e4","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":"2026-10-29T07:29:11Z","next_review_due_at":null,"horizon_now":"not_applicable"},"event_ids":["marcus-has-google-gone-too-woke-why-even-6d5f86-assessment-published-a1"],"last_substantive_change_at":"2026-10-29T17:00:18Z"}},{"url":"https://punditclaims.ai/claims/marcus-has-google-gone-too-woke-why-even-7079cc","standing":"current","assessment_label":"Unresolved","versions":[{"assessment_id":"marcus-has-google-gone-too-woke-why-even-7079cc-a1","status":"current","url":"https://punditclaims.ai/claims/marcus-has-google-gone-too-woke-why-even-7079cc#assessment-marcus-has-google-gone-too-woke-why-even-7079cc-a1"}],"claim":{"claim_id":"marcus-has-google-gone-too-woke-why-even-7079cc","subject_id":"gary-marcus","revision":1,"content_hash":"7069a6f0df534ca50a6c1e840bba92d242cb2eba62f027dc047a23ff84fafe29","normalized_proposition":"Current AI is not capable of getting the balance between cultural sensitivity and historical accuracy right.","kind":"observation","target":"current AI","conditions":[],"original_horizon_text":null,"topic_tags":["capabilities","safety"],"concerns_providers":[],"selection_stratum":"declared-lead","occurrences":[{"id":"marcus-has-google-gone-too-woke-why-even-7079cc-o1","statement_date":"2024-02-21","statement_date_precision":"day","excerpt":"But here’s the thing. The AI we have got now isn’t really up to the job. Getting the delicate balance here right is way outside current AI’s wheelhouse.","locator":"paragraph 16","document":{"url":"https://garymarcus.substack.com/p/has-google-gone-too-woke-why-even","title":"Has Google gone too woke? 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Getting the delicate balance here right is way outside current AI’s wheelhouse.","locator":"paragraph 16","url":"https://garymarcus.substack.com/p/has-google-gone-too-woke-why-even","date":"2024-02-21","document_title":"Has Google gone too woke? Why even the biggest models still struggle with guardrails","observed_at":"2026-10-29T00:01:15Z"},"criteria":{"from_speaker":["The AI we have got now","isn’t really up to the job","Getting the delicate balance here right","way outside current AI’s wheelhouse"],"our_interpretation":["The surrounding passage identifies the task as respecting historical facts while representing contemporary demographic diversity appropriately.","The plain reading concerns reliable practical competence in leading contemporary AI systems, not a claim that every individual output must fail or that future progress is impossible.","I interpret 'now' as February 2024 through February 21. Later systems and interventions cannot directly establish the capabilities of that deployment period.","Because the statement extends to current AI generally, a failure in Gemini alone cannot settle the full claim. Evidence must address a reasonable range of leading systems and both sides of the task."],"support_condition":"Evaluations of leading systems deployed during the target period show recurring failures to combine historical demographic accuracy with appropriate contemporary representation across varied requests.","contradiction_condition":"Credible evaluations establish that leading contemporary systems reliably achieved both goals across a representative set of historical and contemporary requests."},"axis":"historical_observation","outcome":"unresolved","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"The Gemini incident establishes a serious product failure. It does not by itself show that contemporary AI generally lacked reliable competence at this task. Reporting describes differences between providers, and later research reports improvements that preserve diversity while increasing factuality. Neither supplies the comparative February evidence needed to settle the broader statement.","limitations":["The result depends on retaining the claim's general scope rather than narrowing it to Gemini.","Later mitigation results do not contradict a claim about February 2024.","The absence of a suitable comparative evaluation is an evidence gap, not proof that systems were capable."],"evidence":[{"id":"e1","relation":"context","excerpt":"system instructions","locator":"Penultimate paragraph","url":"https://arstechnica.com/information-technology/2024/02/googles-hidden-ai-diversity-prompts-lead-to-outcry-over-historically-inaccurate-images","publisher":"Ars Technica","event_date":"2024-02-22","available_at":"2024-02-22","explanation":"The article describes differences between providers and suggests a product-level remedy. This cautions against treating Gemini's failure as proof of general incapacity, but supplies no controlled comparative evaluation."},{"id":"e2","relation":"context","excerpt":"preserving diversity","locator":"Abstract, final sentence; version 2","url":"https://arxiv.org/abs/2407.00377","publisher":"arXiv","event_date":"2024","available_at":"2024-10-23","explanation":"The research studies the relevant trade-off and reports a mitigation that improves both objectives. Its later experiments are context only; the abstract does not date them more precisely or establish performance of February deployments."}],"what_would_resolve":"A reproducible evaluation of archived February 2024 system versions across historical and contemporary prompts, with explicit factuality and representation criteria and repeated trials.","retrospective":true,"plain_reading":true},"payload_hash":"c7e92ef0d77203d080da83e65102791a19784f4c0b0cdce2ceec1f72b43b9d61","policy_version":"2026-10-28.7","recorded_at":"2026-10-29T07:28:48Z","drafter":{"provider":"openai","model":"gpt-6-astra"},"reviewers":[{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-has-google-gone-too-woke-why-even-7079cc-4x-review-anthropic-1"},{"provider":"openai","model":"gpt-6-astra","run_id":"marcus-has-google-gone-too-woke-why-even-7079cc-4x-review-openai-1"}],"final_text":{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-has-google-gone-too-woke-why-even-7079cc-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":"preprint","audited":null,"derived":null,"metric":null}],"public_from":null,"public_until":null},"history":[],"review":{"review_due":false,"triggers":[],"last_completed_review_at":"2026-10-29T07:28:48Z","next_review_due_at":null,"horizon_now":"not_applicable"},"event_ids":["marcus-has-google-gone-too-woke-why-even-7079cc-assessment-published-a1"],"last_substantive_change_at":"2026-10-29T17:00:18Z"}},{"url":"https://punditclaims.ai/claims/marcus-has-google-gone-too-woke-why-even-7cf71d","standing":"current","assessment_label":"Unresolved","versions":[{"assessment_id":"marcus-has-google-gone-too-woke-why-even-7cf71d-a1","status":"current","url":"https://punditclaims.ai/claims/marcus-has-google-gone-too-woke-why-even-7cf71d#assessment-marcus-has-google-gone-too-woke-why-even-7cf71d-a1"}],"claim":{"claim_id":"marcus-has-google-gone-too-woke-why-even-7cf71d","subject_id":"gary-marcus","revision":1,"content_hash":"64f5dcd49ff07fba0d3ee803fcbf442d313d0d950756b28a79b4d7386f6a793c","normalized_proposition":"Making AI culturally sensitive yet historically accurate is hard because of skewed data/cultural change and AI's lack of intelligence.","kind":"causal","target":"AI guardrails","conditions":[],"original_horizon_text":null,"topic_tags":["capabilities","safety"],"concerns_providers":[],"selection_stratum":"declared-lead","occurrences":[{"id":"marcus-has-google-gone-too-woke-why-even-7cf71d-o1","statement_date":"2024-02-21","statement_date_precision":"day","excerpt":"Getting AI to behave in a culturally-sensitive yet historically accurate way is hard for two reasons, one having to do with data and cultural change, the other with intelligence.","locator":"paragraph 17","document":{"url":"https://garymarcus.substack.com/p/has-google-gone-too-woke-why-even","title":"Has Google gone too woke? 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Google identified product-tuning problems. Research also shows that demographic output can change through deployment-time interventions. These findings neither establish nor rule out contributions from skewed data and weak contextual reasoning.","limitations":["Google's explanation is a company account rather than an independently reproduced causal investigation.","The intervention research does not directly test the relevant historical requests or February deployment."],"evidence":[{"id":"e1","relation":"context","excerpt":"failed","locator":"What happened, paragraph beginning 'So what went wrong?'","url":"https://blog.google/products-and-platforms/products/gemini/gemini-image-generation-issue","publisher":"Google","event_date":"2024-02","available_at":"2024-02-23","explanation":"Google attributes the problem to diversity tuning and excessive caution. This supplies a concrete alternative mechanism but does not establish whether data composition or reasoning limitations also contributed."},{"id":"e2","relation":"context","excerpt":"reinforce such biases.","locator":"Abstract","url":"https://link.springer.com/article/10.1007/s43681-024-00531-5","publisher":"AI and Ethics, Springer Nature","event_date":"2024","available_at":"2024-08-01","explanation":"The research reports biased outputs and changes to demographic proportions through deployment-time instructions without retraining. It does not establish both proposed causes for the systems and failures Marcus described."}],"what_would_resolve":"A controlled evaluation of the relevant systems separating training-data composition, contextual reasoning, and guardrail tuning, with historical accuracy and demographic appropriateness measured together.","retrospective":true,"plain_reading":true},"payload_hash":"3c52d7c81fe27c5153d4b8f14edfaae38b56ee63d3c728e1fe5f6cb656241eac","policy_version":"2026-10-28.7","recorded_at":"2026-10-29T07:28:48Z","drafter":{"provider":"openai","model":"gpt-6-astra"},"reviewers":[{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-has-google-gone-too-woke-why-even-7079cc-4x-review-anthropic-1"},{"provider":"openai","model":"gpt-6-astra","run_id":"marcus-has-google-gone-too-woke-why-even-7079cc-4x-review-openai-1"}],"final_text":{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-has-google-gone-too-woke-why-even-7079cc-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":"peer_reviewed","audited":null,"derived":null,"metric":null}],"public_from":null,"public_until":null},"history":[],"review":{"review_due":false,"triggers":[],"last_completed_review_at":"2026-10-29T07:28:48Z","next_review_due_at":null,"horizon_now":"not_applicable"},"event_ids":["marcus-has-google-gone-too-woke-why-even-7cf71d-assessment-published-a1"],"last_substantive_change_at":"2026-10-29T17:00:18Z"}},{"url":"https://punditclaims.ai/claims/marcus-has-google-gone-too-woke-why-even-83060f","standing":"current","assessment_label":"Supported","versions":[{"assessment_id":"marcus-has-google-gone-too-woke-why-even-83060f-a1","status":"current","url":"https://punditclaims.ai/claims/marcus-has-google-gone-too-woke-why-even-83060f#assessment-marcus-has-google-gone-too-woke-why-even-83060f-a1"}],"claim":{"claim_id":"marcus-has-google-gone-too-woke-why-even-83060f","subject_id":"gary-marcus","revision":1,"content_hash":"5bc7ba82f50a49c9a5d6d90e6f14aac2a3a6862694902d31ff590220fedf56fc","normalized_proposition":"Gemini's historically inaccurate diversity in generated images is a general issue, not limited to popes.","kind":"observation","target":"Gemini historically inaccurate image generation","conditions":[],"original_horizon_text":null,"topic_tags":["reliability"],"concerns_providers":[],"selection_stratum":"declared-lead","occurrences":[{"id":"marcus-has-google-gone-too-woke-why-even-83060f-o1","statement_date":"2024-02-21","statement_date_precision":"day","excerpt":"It’s a general issue.","locator":"paragraph 7","document":{"url":"https://garymarcus.substack.com/p/has-google-gone-too-woke-why-even","title":"Has Google gone too woke? 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Why even the biggest models still struggle with guardrails","observed_at":"2026-10-29T00:01:15Z"},"criteria":{"from_speaker":["It’s not just","Popes","It’s a general issue","other examples","historically or culturally far from typical"],"our_interpretation":["In context, 'general issue' means that historically inappropriate demographic substitutions appeared across historical subjects beyond popes. It does not assert that most generated images were inaccurate.","The discussion refers to examples circulating yesterday and still reproducible now, giving a February 20–21 target period.","Generating a person of color for a modern nationality is not by itself a historical error."],"support_condition":"Credible contemporary observations document historically inappropriate demographic substitutions in multiple historical subjects beyond popes.","contradiction_condition":"Reliable examination establishes that the alleged examples beyond popes were misattributed, explicitly fictional, or historically appropriate, leaving the problem confined to pope images."},"axis":"historical_observation","outcome":"supported","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"The Verge independently reproduced historical demographic errors involving German soldiers and nineteenth-century US senators on February 21. The issue therefore extended beyond popes. Its successful Founding Fathers result limits any claim of universal failure, but does not contradict Marcus's narrower point.","limitations":["The tests do not measure how frequently the problem occurred.","This assessment does not independently verify the Apollo 11 example.","The evidence is a contemporaneous reporter test rather than a provider-issued output archive."],"evidence":[{"id":"e1","relation":"supports","excerpt":"factually misrepresenting the past","locator":"Final testing section, paragraph immediately before the concluding Founding Fathers and senator comparison","url":"https://www.theverge.com/2024/2/21/24079371/google-ai-gemini-generative-inaccurate-historical","publisher":"The Verge","event_date":"2024-02-21","available_at":"2024-02-21","explanation":"The reporters reproduced inappropriate demographic depictions for German soldiers and nineteenth-century US senators, including female senators. 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The claim also has no deadline. It stays open.","limitations":["The scope words ('whole generation', 'educated by') are hard to measure."],"evidence":[{"id":"e1","relation":"context","excerpt":"The review found that YouTube often promotes strange, nonsensical videos from channels that purport to teach toddlers and preschoolers the alphabet and animals.","locator":"paragraph 2","url":"https://mezha.ua/en/news/youtube-shows-kids-ai-videos-308931","publisher":"Mezha (summarising The New York Times)","event_date":"2026-02","available_at":"2026-02-27","explanation":"Shows that 'educational' AI videos with false content are being recommended to young children. 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They do not establish the surrounding predictions about scammers, advertising, or educational harm.","limitations":["The conclusion depends on interpreting counterparts functionally, without an unstated requirement for Sora-level performance.","Open-source here means accessible code and weights under an open-source license, not a fully disclosed training dataset.","No precise deadline can fairly be assigned to 'In short order.'"],"evidence":[{"id":"e1","relation":"supports","excerpt":"We released Open-Sora 1.0, a fully open-source project for video generation.","locator":"News entry dated 2024.03.18","url":"https://github.com/hpcaitech/Open-Sora","publisher":"HPC-AI Tech","event_date":"2024-03-18","available_at":"2024-03-18","explanation":"The official release record establishes an actual video-generation release after the prediction. 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Two independent screens, one from each provider, both found this a recommendation or a value judgment.","limitations":[],"evidence":[],"what_would_resolve":null,"retrospective":true,"uncheckable":["value_judgment"]},"payload_hash":"624c081e8866bc9c35b20734ce3be48e388e4b72412f4cac6227d2010ec4ae8e","policy_version":"2026-10-28.7","recorded_at":"2026-10-29T01:56:19Z","drafter":{"provider":"anthropic","model":"claude-opus-5-5"},"reviewers":[{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-when-looked-at-carefully-openais-screen-anthropic-1"},{"provider":"openai","model":"gpt-6-astra","run_id":"marcus-when-looked-at-carefully-openais-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":"2026-10-29T01:56:19Z","next_review_due_at":null,"horizon_now":"not_applicable"},"event_ids":["marcus-when-looked-at-carefully-openais-993a54-assessment-published-a1"],"last_substantive_change_at":"2026-10-29T17:00:18Z"}},{"url":"https://punditclaims.ai/claims/marcus-when-looked-at-carefully-openais-ab43ec","standing":"current","assessment_label":"Unresolved","versions":[{"assessment_id":"marcus-when-looked-at-carefully-openais-ab43ec-a1","status":"current","url":"https://punditclaims.ai/claims/marcus-when-looked-at-carefully-openais-ab43ec#assessment-marcus-when-looked-at-carefully-openais-ab43ec-a1"}],"claim":{"claim_id":"marcus-when-looked-at-carefully-openais-ab43ec","subject_id":"gary-marcus","revision":1,"content_hash":"3be0e000d1ba166ce4fd6f41f428bc25ada55729226914f52e831e149de95106","normalized_proposition":"It is currently unknown whether open-source models without guardrails are more useful to bad actors than commercial models with guardrails.","kind":"observation","target":"open-source models without guardrails","conditions":[],"original_horizon_text":null,"topic_tags":["safety","capabilities"],"concerns_providers":[],"selection_stratum":"declared-lead","occurrences":[{"id":"marcus-when-looked-at-carefully-openais-ab43ec-o1","statement_date":"2024-02-04","statement_date_precision":"day","excerpt":"At present, we do not know.","locator":"paragraph 31","document":{"url":"https://garymarcus.substack.com/p/when-looked-at-carefully-openais","title":"Diving deep into OpenAI’s new study on LLM’s and bioweapons","publisher":"Marcus on AI","published_at":"2024-02-04T19:13:42Z","date_uncertain":false,"date_provenance":"JSON-LD datePublished"},"observed_at":"2026-10-29T00:01:39Z","observed_via":"live","archive_url":null,"access_state":"full_public","snapshot_hash":"dc18ed2d3d3565da9c9af12664c7f3fdc471bab50a916766fbc4607266907f59","receipts":1}],"first_statement_date":"2024-02-04","related":[],"current":{"assessment_id":"marcus-when-looked-at-carefully-openais-ab43ec-a1","payload":{"claim_id":"marcus-when-looked-at-carefully-openais-ab43ec","subject_id":"gary-marcus","normalized_proposition":"It is currently unknown whether open-source models without guardrails are more useful to bad actors than commercial models with guardrails.","kind":"observation","conditions":[],"original_horizon_text":null,"statement":{"excerpt":"At present, we do not know.","locator":"paragraph 31","url":"https://garymarcus.substack.com/p/when-looked-at-carefully-openais","date":"2024-02-04","document_title":"Diving deep into OpenAI’s new study on LLM’s and bioweapons","observed_at":"2026-10-29T00:01:39Z"},"criteria":{"from_speaker":["At present, we do not know","open-source models without guardrails","more useful to bad actors than commercial models with guardrails"],"our_interpretation":["At present refers to public knowledge on February 4, 2024.","The surrounding post limits the comparison to biological misuse.","The plain reading concerns comparative practical assistance, not simply willingness to answer a prohibited prompt.","Open-source is read in the ordinary contemporary sense of publicly available model weights that users could modify.","Known requires sufficiently informative comparative evidence, not certainty or the mere existence of a suggestive demonstration."],"support_condition":"A contemporary research synthesis establishes that the specified comparison remained unsettled, with available studies insufficient to determine it.","contradiction_condition":"Research available by February 4 establishes the relative practical assistance of unguarded open models and guarded commercial models on comparable biological-misuse tasks."},"axis":"historical_observation","outcome":"unresolved","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"The research checked does not settle the comparison Marcus described. Earlier work did show that removing safeguards could make an open model more helpful in biological-misuse planning, so this was not an evidence-free question. But that comparison involved two Llama variants, not unguarded open models against guarded commercial systems. Failure to find a decisive comparison is insufficient to prove that the field did not know.","limitations":["Earlier research is context because the claim concerns the knowledge state on February 4.","Evidence of greater compliance does not automatically establish greater practical capability.","The search is not an exhaustive systematic review."],"evidence":[{"id":"e5","relation":"context","excerpt":"Here we investigated whether continued model weight proliferation is likely to help malicious actors leverage more capable future models to inflict mass death.","locator":"Abstract, third sentence","url":"https://arxiv.org/abs/2310.18233","publisher":"arXiv; Gopal and colleagues","event_date":"2023-11-01","available_at":"2023-11-01","explanation":"This preprint supplies relevant evidence about removing safeguards from Llama. Its comparison is between two Llama variants, so it does not settle the specified comparison with guarded commercial models."},{"id":"e6","relation":"context","excerpt":"Independent pre-release evaluations could help understand the capabilities of models and the effectiveness of safeguards.","locator":"Abstract, concluding recommendations","url":"https://arxiv.org/abs/2306.13952","publisher":"arXiv; Jonas B. Sandbrink","event_date":"2023-12-23","available_at":"2023-12-23","explanation":"The review calls for evaluations of capabilities and safeguards. This is consistent with uncertainty, but does not establish that no adequate comparison existed by February 4."}],"what_would_resolve":"A qualifying comparison published by February 4, 2024, or a comprehensive contemporary synthesis explicitly documenting the unresolved comparison.","retrospective":true,"plain_reading":true},"payload_hash":"b2b06c4541856c7b09c0fcd8b0c8d16db29bd37552fb85ad1e951ba1a085ca4a","policy_version":"2026-10-28.7","recorded_at":"2026-10-29T12:53:13Z","drafter":{"provider":"openai","model":"gpt-6-astra"},"reviewers":[{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-when-looked-at-carefully-openais-596625-4x-review-anthropic-1"},{"provider":"openai","model":"gpt-6-astra","run_id":"marcus-when-looked-at-carefully-openais-596625-4x-review-openai-1"}],"final_text":{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-when-looked-at-carefully-openais-596625-4x-final-anthropic-1"},"status":"current","notice":null,"superseded_by":null,"evidence_meta":[{"id":"e5","source_type":"preprint","audited":null,"derived":null,"metric":null},{"id":"e6","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":"2026-10-29T12:53:13Z","next_review_due_at":null,"horizon_now":"not_applicable"},"event_ids":["marcus-when-looked-at-carefully-openais-ab43ec-assessment-published-a1"],"last_substantive_change_at":"2026-10-29T17:00:18Z"}},{"url":"https://punditclaims.ai/claims/marcus-when-looked-at-carefully-openais-b51513","standing":"current","assessment_label":"Unresolved","versions":[{"assessment_id":"marcus-when-looked-at-carefully-openais-b51513-a2","status":"current","url":"https://punditclaims.ai/claims/marcus-when-looked-at-carefully-openais-b51513#assessment-marcus-when-looked-at-carefully-openais-b51513-a2"}],"claim":{"claim_id":"marcus-when-looked-at-carefully-openais-b51513","subject_id":"gary-marcus","revision":1,"content_hash":"3eb99f51836a3c0623133b68f2a7a6d3d6ff02aaa1f99962212c594c1d12efe0","normalized_proposition":"Neither OpenAI nor the media noted the fourfold difference in expert success on pathogen formulation, nor discussed in detail a similar paper from Kevin Esvelt's lab.","kind":"observation","target":"OpenAI and the media","conditions":[],"original_horizon_text":null,"topic_tags":["safety","other"],"concerns_providers":["openai"],"selection_stratum":"declared-lead","occurrences":[{"id":"marcus-when-looked-at-carefully-openais-b51513-o1","statement_date":"2024-02-04","statement_date_precision":"day","excerpt":"It is shocking that neither OpenAI nor the media noted this (they also did not discuss in any detail one other paper that reached a somewhat similar conclusion, by Kevin Esvelt’s lab).","locator":"paragraph 29","document":{"url":"https://garymarcus.substack.com/p/when-looked-at-carefully-openais","title":"Diving deep into OpenAI’s new study on LLM’s and bioweapons","publisher":"Marcus on AI","published_at":"2024-02-04T19:13:42Z","date_uncertain":false,"date_provenance":"JSON-LD datePublished"},"observed_at":"2026-10-29T00:01:39Z","observed_via":"live","archive_url":null,"access_state":"full_public","snapshot_hash":"dc18ed2d3d3565da9c9af12664c7f3fdc471bab50a916766fbc4607266907f59","receipts":1}],"first_statement_date":"2024-02-04","related":[],"current":{"assessment_id":"marcus-when-looked-at-carefully-openais-b51513-a2","payload":{"claim_id":"marcus-when-looked-at-carefully-openais-b51513","subject_id":"gary-marcus","normalized_proposition":"Neither OpenAI nor the media noted the fourfold difference in expert success on pathogen formulation, nor discussed in detail a similar paper from Kevin Esvelt's lab.","kind":"observation","conditions":[],"original_horizon_text":null,"statement":{"excerpt":"It is shocking that neither OpenAI nor the media noted this (they also did not discuss in any detail one other paper that reached a somewhat similar conclusion, by Kevin Esvelt’s lab).","locator":"paragraph 29","url":"https://garymarcus.substack.com/p/when-looked-at-carefully-openais","date":"2024-02-04","document_title":"Diving deep into OpenAI’s new study on LLM’s and bioweapons","observed_at":"2026-10-29T00:01:39Z"},"criteria":{"from_speaker":["neither OpenAI nor the media noted this","did not discuss in any detail","one other paper","Kevin Esvelt’s lab"],"our_interpretation":["This refers to the fourfold formulation threshold result.","Read media as contemporary coverage of the OpenAI study through February 4, 2024, rather than all earlier reporting about Esvelt.","The post links the Esvelt reference to Soice et al., Can large language models democratize access to dual-use biotechnology?","Read noted as explicitly drawing attention to the fourfold comparison. Merely publishing recoverable counts is a different, plausible reading.","Detailed discussion means substantive treatment of the Esvelt study's methods or findings, beyond a citation.","The word shocking is an opinion and is not assessed."],"support_condition":"A sufficiently comprehensive contemporary record shows neither an explicit fourfold comparison nor substantive discussion of the linked Esvelt study by OpenAI or the relevant media.","contradiction_condition":"A qualifying contemporary OpenAI publication or media report explicitly highlights the fourfold comparison or substantively discusses the linked Esvelt study."},"axis":"historical_observation","outcome":"unresolved","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"The January 31, 2024 Bloomberg report neither highlights the fourfold formulation comparison nor discusses the linked Esvelt-lab paper [e3]. The February 1 Verge report does not identify the fourfold comparison, although it discusses other benefits from model access and questions OpenAI's framing [e4]. These articles establish omissions in those reports, but cannot establish the broader claim about OpenAI and contemporary media through February 4, 2024 [e3][e4].","limitations":["The supplied evidence items omit OpenAI's publication and do not provide a sufficiently comprehensive contemporary media record.","The assessment interprets 'noted' as explicitly highlighting the fourfold comparison and 'in any detail' as substantive discussion of the linked study's methods or findings."],"evidence":[{"id":"e3","relation":"context","excerpt":"The other group was just given internet access to complete the exercise.","locator":"Paragraph beginning As part of the team's first study","url":"https://finance.yahoo.com/news/openai-says-gpt-4-poses-180022896.html","publisher":"Bloomberg, syndicated by Yahoo Finance","event_date":"2024-01-31","available_at":"2024-01-31","explanation":"Inspection of the complete article found neither the fourfold formulation comparison nor discussion of Soice et al. This establishes only what this particular report omitted."},{"id":"e4","relation":"context","excerpt":"Researchers also found that participants who relied on GPT-4 had more detailed answers.","locator":"Results discussion following the paragraph about statistical significance","url":"https://www.theverge.com/2024/2/1/24058095/open-ai-bioweapon-study-preparedness-team","publisher":"The Verge","event_date":"2024-02-01","available_at":"2024-02-01","explanation":"The full article discusses benefits and challenges OpenAI's framing, but does not identify the specific fourfold comparison. It cautions against extending Marcus's narrow omission claim into a claim that media ignored all concerning findings."}],"what_would_resolve":"A qualifying OpenAI publication or media report available before the February 4, 2024 post that explicitly highlights the fourfold comparison or substantively discusses the linked Esvelt study would contradict the claim. 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This does not decide whether the analysis was appropriate or whether a journal should have accepted the paper.","limitations":["The p-values are in a linked CSV rather than stated directly in footnote C.","The CSV quotation preserves a complete data record because the source has no prose sentences.","A separately timestamped release-day archive of the linked files was not authenticated."],"evidence":[{"id":"e7","relation":"contradicts","excerpt":"For completeness, we also repeated this procedure using the Mann-Whitney U-test, and observed the same results.","locator":"Appendix C, Methodology Details, Statistical testing","url":"https://openai.com/research/building-an-early-warning-system-for-llm-aided-biological-threat-creation","publisher":"OpenAI","event_date":"2024-01-31","available_at":"2024-01-31","explanation":"The methods paragraph names one-sided t-tests and the Mann–Whitney check. 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An isolated jailbreak or mistaken refusal is insufficient by itself to establish ineffectiveness.","The universal claim requires broader evidence than selected demonstrations of failure."],"support_condition":"Representative evaluations of the relevant guardrails show that none achieved a practically useful balance of benign-request acceptance and resistance to harmful requests.","contradiction_condition":"At least one guardrail available by the post demonstrates that practical balance in sufficiently broad evaluations of both benign requests and adversarial harmful requests."},"axis":"historical_observation","outcome":"unresolved","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"Pre-post research documents substantial guardrail failures, but it does not establish that none worked effectively. XSTest found GPT-4 performed well on both safe and unsafe prompts, and SmoothLLM demonstrated useful defenses with retained task performance. 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No numerical reliability threshold is supplied.","Current systems refers to systems around the post date. I use December 2023 through January 10, 2024 as the recent observation window."],"support_condition":"Controlled evaluations show that prompt diversity and response variability cause continuing safeguard failures despite successful filtering of individual examples.","contradiction_condition":"Controlled evaluations establish reliable generalization under diverse prompts and repeated trials, or show that the asserted causal factors do not explain the failures."},"axis":"causal_assessment","outcome":"unresolved","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"The sources establish vulnerabilities and show that broader defenses can reduce them without eliminating them. They do not settle the claimed explanation that excessive example diversity and erratic responses cause the unreliability. Treating jailbreak success alone as proof would collapse a causal claim into a weaker observation.","limitations":["The controlled defense study is contextual because its experiments predate the target period.","The causal terms and reliability threshold remain underspecified."],"evidence":[{"id":"e3","relation":"context","excerpt":"bad or toxic information","locator":"Apostol Vassilev quotation in discussion of chatbot vulnerabilities","url":"https://www.nist.gov/news-events/news/2024/01/nist-identifies-types-cyberattacks-manipulate-behavior-ai-systems","publisher":"National Institute of Standards and Technology","event_date":"2024-01-04","available_at":"2024-01-04","explanation":"NIST's contemporary assessment recognizes vulnerabilities to carefully constructed prompts. It does not experimentally separate prompt diversity and response inconsistency as causes of unreliable safeguards."},{"id":"e4","relation":"context","excerpt":"from 67.21% to 19.34%.","locator":"Page 1, abstract","url":"https://xyq7.github.io/papers/NMI-JailbreakDefense.pdf","publisher":"Nature Machine Intelligence; author-hosted copy","event_date":"2023","available_at":"2023-12-12","explanation":"A general defense substantially reduced attacks while leaving residual failures. 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It does not establish the stronger word generally through update histories across builders. Vulnerabilities and adaptive attacks alone cannot show whether builders usually patched individual examples or also achieved broader improvements.","limitations":["Public summaries provide less detail than internal remediation records.","The distinction between normal iterative improvement and ineffective narrow patching is not specified in the claim."],"evidence":[{"id":"e2","relation":"context","excerpt":"AI chatbots remain vulnerable","locator":"Testing the limits of LLM ethics; Professor Liu quotation","url":"https://www.ntu.edu.sg/docs/default-source/corporate-ntu/hub-news/ntu-singapore-researchers-use-ai-chatbots-against-themselves-to-jailbreak-each-other.pdf","publisher":"Nanyang Technological University","event_date":"2023-12-28","available_at":"2023-12-28","explanation":"The researchers' institutional release describes an ongoing cycle of disclosed vulnerabilities and developer patches, alongside attacks designed to adapt. 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General evidence that Bing could produce recognizable characters does not establish that sequence.","I use January 1–10, 2024 for the recent episode described in the post. The exact filter-change and generation dates are not supplied."],"support_condition":"Authenticated dated Bing/DALL-E 3 prompt-and-image records after the described filter changes show recognizable protected characters or artwork.","contradiction_condition":"Authenticated records establish that the alleged post-change examples preceded the changes, were not produced by the named system, or did not show the claimed resemblance."},"axis":"historical_observation","outcome":"unresolved","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"Independent testing reported recognizable Mario and Sonic outputs from Bing. That corroborates the underlying capability, but the claim specifically concerns failures after particular new filters. The available records do not establish that chronology. The speaker's own examples cannot supply the missing independent confirmation.","limitations":["The independent demonstration lacks authenticated generation timestamps and filter-version information.","Recognizable characters do not by themselves establish legal infringement."],"evidence":[{"id":"e5","relation":"context","excerpt":"both Mario and Sonic","locator":"Neutral-prompt tests, Bing Image Generator subsection","url":"https://www.tomshardware.com/tech-industry/artificial-intelligence/ai-image-generators-output-copyrighted-characters","publisher":"Tom's Hardware; Avram Piltch","event_date":"2024-01-06","available_at":"2024-01-06","explanation":"This is a separately conducted demonstration reported on January 6; that date identifies the public report, not an authenticated generation timestamp. 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UK will not grant OpenAI the exemption from copyright responsibility it requested.","kind":"forecast","target":"United Kingdom copyright exemption for OpenAI","conditions":[],"original_horizon_text":null,"topic_tags":["copyright","regulation-policy"],"concerns_providers":["openai"],"selection_stratum":"declared-lead","occurrences":[{"id":"marcus-dall-es-new-guardrails-fast-furious-9c4bb1-o1","statement_date":"2024-01-10","statement_date_precision":"day","excerpt":"Since that would violate the Berne Convention and infuriate every “content provider” (from individual artists and writers to major studios and publishers) ever, we hope and expect that this exemption will not be granted.","locator":"paragraph 4","document":{"url":"https://garymarcus.substack.com/p/dall-es-new-guardrails-fast-furious","title":"DALL-E’s New Guardrails: Fast, Furious, and Far from Airtight","publisher":"Marcus on AI","published_at":"2024-01-10T03:06:39Z","date_uncertain":false,"date_provenance":"JSON-LD 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The March 2026 government report moved away from its preferred broad opt-out exception, but expressly left alternative approaches under consideration. That is consistent with Marcus's expectation without conclusively settling it. The search did not establish an event satisfying the early-resolution rule. 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