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This supports a contribution from external factual access, but the task and training changes do not isolate the general explanation about stochastic reconstruction."},{"id":"e6","relation":"context","excerpt":"containing necessary facts","locator":"Abstract","url":"https://arxiv.org/abs/2407.12216","publisher":"Garima Agrawal et al.; arXiv","event_date":"2024","available_at":"2024-10-06","explanation":"Relevant structured information can be available while answering still fails. 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Other intervention research identifies answer-selection mechanisms that can produce hallucinations. Neither establishes or rules out Marcus's broad explanation across the cited kinds of errors. The existence of those errors alone cannot settle their cause.","limitations":["The claim supplies no threshold for huge number.","Evidence from bounded tasks cannot establish prevalence across language, image, and video systems.","Alternative mechanisms can coexist with world-model defects."],"evidence":[{"id":"e6","relation":"context","excerpt":"detours erode performance","locator":"Section 3, detour experiment surrounding Table 2","url":"https://arxiv.org/html/2406.03689v3","publisher":"Keyon Vafa and colleagues via arXiv","event_date":"2024","available_at":"2024-11-10","explanation":"The study supplies evidence for a narrower connection between incoherent representations and failures in structured tasks. It does not isolate or measure the proposed cause across fabricated references, image generation, and video comprehension."},{"id":"e7","relation":"context","excerpt":"resulting in a hallucination.","locator":"Hallucinations section","url":"https://www.anthropic.com/research/tracing-thoughts-language-model","publisher":"Anthropic","event_date":"2025-03","available_at":"2025-03-27","explanation":"The researchers induce hallucinations by changing features governing whether the model answers or declines. 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It does not mean that no useful representations have been found.","The surrounding discussion reviews developments from the January 2023 Othello result through publication. I use that cumulative literature period.","Recovering individual features or decoding information is weaker evidence than showing that a model maintains and uses the recovered representation."],"support_condition":"Published research shows useful but restricted recoveries, substantial failures of coherence or updating, and incomplete understanding of larger models.","contradiction_condition":"Published research demonstrates systematic recovery and causal validation of coherent, updatable world models across substantially broader environments and models."},"axis":"historical_observation","outcome":"supported","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"The evidence supports limited success under the ordinary reading of narrow, incomplete recovery. Coherence tests expose failures that simpler diagnostics miss, and contemporary work on Claude acknowledges substantial limits in coverage. However, chess interventions and spatial representations are real advances. This assessment does not endorse the surrounding suggestion that meaningful results stopped at Othello.","limitations":["The qualitative judgment depends on distinguishing partial representations from robust dynamic models.","The cited studies do not measure the entire field on a common scale.","Mechanistic interpretability has aims beyond recovering world models."],"evidence":[{"id":"e1","relation":"supports","excerpt":"incoherent underlying maps","locator":"Section 3.3, final paragraph before Section 3.4","url":"https://arxiv.org/html/2406.03689v3","publisher":"Vafa et al., NeurIPS 2024; arXiv","event_date":"2024","available_at":"2024-11-10","explanation":"The navigation experiments distinguish successful route generation from recovery of a coherent map. They support a substantive limitation, although they do not survey the whole field."},{"id":"e2","relation":"context","excerpt":"5,000","locator":"Section 4.2, Board State Interventions","url":"https://arxiv.org/html/2403.15498v2","publisher":"Adam Karvonen, COLM 2024; arXiv","event_date":"2024","available_at":"2024-07-14","explanation":"The paper recovers chess-board representations and tests interventions that change subsequent moves. This challenges the surrounding suggestion that Othello was the only meaningful success, but a constrained chess result does not by itself refute limited overall success."},{"id":"e3","relation":"context","excerpt":"representations of space and time","locator":"Abstract","url":"https://arxiv.org/abs/2310.02207","publisher":"Wes Gurnee and Max Tegmark; arXiv","event_date":"2023","available_at":"2024-03-04","explanation":"Llama-2 representations extend beyond board games and remain consistent across some prompt variations. The authors describe ingredients of a world model, without establishing a complete dynamic model."},{"id":"e4","relation":"supports","excerpt":"human effort","locator":"Introductory discussion of the method's limitations","url":"https://www.anthropic.com/research/tracing-thoughts-language-model","publisher":"Anthropic","event_date":"2025-03","available_at":"2025-03-27","explanation":"Anthropic reports that its method captures only part of Claude's computation and requires substantial analysis even for short prompts. 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4 is better at text in general now, but its handling of watch times other than 10:10 remains dreadful.","kind":"observation","target":"Google Imagen","conditions":[{"text":"times other than 10:10","type":"qualifier","from_source":true},{"text":"text better in general, though not in this image","type":"qualifier","from_source":true}],"original_horizon_text":null,"topic_tags":["capabilities","reliability"],"concerns_providers":[],"selection_stratum":"declared-lead","occurrences":[{"id":"marcus-image-generation-still-crazy-after-20666b-o1","statement_date":"2025-06-25","statement_date_precision":"day","excerpt":"Text in general is better now (though not in this image) but watch comprehension of any time other than 10:10 (most frequent in advertisements) remains dreadful.","locator":"paragraph 25","document":{"url":"https://garymarcus.substack.com/p/image-generation-still-crazy-after","title":"Image generation: Still crazy after all these years","publisher":"Marcus on 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product version is substituted."],"support_condition":"Comparable evidence establishes improved text rendering, while representative tests of the relevant Imagen 4 version show that most requested non-10:10 watch times are incorrect.","contradiction_condition":"Representative tests establish reliable rendering across non-10:10 watch times, or comparable evaluations refute the text improvement. 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It does not say every complex problem fails.","Basic interfaces that let a model act in an environment are relevant to simple deployment, but their results are not identical to unassisted chat.","Assess o3 and contemporaneous Claude versions separately; o3-mini is not a substitute for evidence about o3.","Use calendar 2025 through June 7 for contemporary capability reports because no period or regular reporting interval is specified.","Dropped into complex problems describes the deployment setting, not an antecedent for a conditional forecast."],"support_condition":"Evaluations of o3 and contemporaneous Claude versions show substantial failure rates on verifiable complex tasks without bespoke task solutions or continuing human correction.","contradiction_condition":"Broad evaluations demonstrate dependable completion of representative complex tasks by both named model families under simple deployment conditions."},"axis":"historical_observation","outcome":"supported","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"METR's April 2025 evaluation places a pre-release o3 checkpoint at 50% predicted success on HCAST tasks taking humans about 1.5 hours [e1]. Its March 2025 study reports Claude 3.7 Sonnet's 50% success horizon at about 59 minutes and its 80% horizon at about 15 minutes of human task time [e2]. These results support the warning against expecting dependable complex-task completion in the evaluated deployment settings [e1, e2].","limitations":["The evaluations use agent interfaces and do not directly test exclusively unassisted chat.","The o3 result concerns a pre-release checkpoint; the Claude result concerns 3.7 Sonnet.","The task suites differ, so the reported horizons are not directly comparable [e1].","The evidence does not cover every business problem or establish a capability ceiling."],"evidence":[{"id":"e1","relation":"supports","excerpt":"50% time-horizon score","locator":"Summary of Results","url":"https://metr.org/evaluations/openai-o3-report","publisher":"METR","event_date":"2025-04","available_at":"2025-04-16","explanation":"The evaluated o3 checkpoint reached only 50% predicted success at tasks taking humans about 1.5 hours, despite setting a new high in this evaluation. This supports caution about dependable complex-task completion, while the pre-release checkpoint and agent interface limit direct application to unassisted released o3."},{"id":"e2","relation":"supports","excerpt":"cannot reliably perform","locator":"Section 4.2.1, Time horizons at 50% success rate vs 80% success rate","url":"https://arxiv.org/html/2503.14499v1","publisher":"METR, arXiv","event_date":"2025-03","available_at":"2025-03-18","explanation":"The section reports Claude 3.7 Sonnet's approximately 59-minute horizon at 50% success and approximately 15-minute horizon at 80% success. These substantial failure rates support the reliability warning for that Claude version."}],"what_would_resolve":null,"retrospective":true,"plain_reading":true},"payload_hash":"24225156e11a78479d9f3f440d0b1f1619ec382cfabf76dcfde9b8e1e56916c7","policy_version":"2026-10-28.7","recorded_at":"2026-10-29T02:18:16Z","drafter":{"provider":"openai","model":"gpt-6-astra"},"reviewers":[{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-a-knockout-blow-for-llms-055f7c-4x-review-anthropic-2"},{"provider":"openai","model":"gpt-6-astra","run_id":"marcus-a-knockout-blow-for-llms-055f7c-4x-review-openai-2"}],"final_text":{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-a-knockout-blow-for-llms-055f7c-4x-final-anthropic-2"},"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":"preprint","audited":null,"derived":null,"metric":null}],"public_from":null,"public_until":null},"history":[],"review":{"review_due":false,"triggers":[],"last_completed_review_at":"2026-10-29T02:18:16Z","next_review_due_at":null,"horizon_now":"not_applicable"},"event_ids":["marcus-a-knockout-blow-for-llms-27f761-assessment-published-a3"],"last_substantive_change_at":"2026-10-29T17:00:18Z"}},{"url":"https://punditclaims.ai/claims/marcus-a-knockout-blow-for-llms-3e2ec8","standing":"current","assessment_label":"Unresolved","versions":[{"assessment_id":"marcus-a-knockout-blow-for-llms-3e2ec8-a2","status":"current","url":"https://punditclaims.ai/claims/marcus-a-knockout-blow-for-llms-3e2ec8#assessment-marcus-a-knockout-blow-for-llms-3e2ec8-a2"}],"claim":{"claim_id":"marcus-a-knockout-blow-for-llms-3e2ec8","subject_id":"gary-marcus","revision":1,"content_hash":"cdcf87f9f0ca5d17964cc317222c2710b65fb88e0bd634f2023fbcfd7253f6dc","normalized_proposition":"Claude gets under 80% accuracy on 7-disc Tower of Hanoi and essentially cannot solve 8 discs.","kind":"observation","target":"Claude","conditions":[],"original_horizon_text":null,"topic_tags":["capabilities","reliability"],"concerns_providers":["anthropic"],"selection_stratum":"declared-lead","occurrences":[{"id":"marcus-a-knockout-blow-for-llms-3e2ec8-o1","statement_date":"2025-06-07","statement_date_precision":"day","excerpt":"Claude, on the other hand, can barely do 7 discs, getting less than 80% accuracy, left bottom panel below, and pretty much can’t get 8 correct at all.","locator":"paragraph 17","document":{"url":"https://garymarcus.substack.com/p/a-knockout-blow-for-llms","title":"A knockout blow for LLMs?","publisher":"Marcus on AI","published_at":"2025-06-07T22:28:19Z","date_uncertain":false,"date_provenance":"JSON-LD datePublished"},"observed_at":"2026-10-28T23:56:20Z","observed_via":"live","archive_url":null,"access_state":"full_public","snapshot_hash":"db7d9ad27f5ff721b3a57ecbb00a6572add1c6d796049d15f8a9fa82a2c144c0","receipts":1}],"first_statement_date":"2025-06-07","related":[],"current":{"assessment_id":"marcus-a-knockout-blow-for-llms-3e2ec8-a2","payload":{"claim_id":"marcus-a-knockout-blow-for-llms-3e2ec8","subject_id":"gary-marcus","normalized_proposition":"Claude gets under 80% accuracy on 7-disc Tower of Hanoi and essentially cannot solve 8 discs.","kind":"observation","conditions":[],"original_horizon_text":null,"statement":{"excerpt":"Claude, on the other hand, can barely do 7 discs, getting less than 80% accuracy, left bottom panel below, and pretty much can’t get 8 correct at all.","locator":"paragraph 17","url":"https://garymarcus.substack.com/p/a-knockout-blow-for-llms","date":"2025-06-07","document_title":"A knockout blow for LLMs?","observed_at":"2026-10-28T23:56:20Z"},"criteria":{"from_speaker":["Claude","7 discs","less than 80% accuracy","left bottom panel below","pretty much can’t get 8 correct at all"],"our_interpretation":["The reference to the panel makes this a claim about the Claude configuration tested in Apple's cited study, not every Claude version.","Accuracy means producing a complete legal solution under the study's prompting and scoring conditions.","I read 'pretty much' as near-zero success, without requiring literally zero successes.","The target is the experimental snapshot in the new June 2025 paper discussed in the post. The calendar month identifies that snapshot, not later June experiments."],"support_condition":"The original Claude results show accuracy below 80% at seven discs and zero or nearly zero accuracy at eight discs.","contradiction_condition":"The original results show at least 80% accuracy at seven discs or substantial successful performance at eight discs."},"axis":"historical_observation","outcome":"unresolved","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"The claim is directly checkable against the original figure, but the retrieved text does not expose its numerical points. The general finding of declining accuracy is insufficient to verify both numbers. This is a retrieval gap, not evidence that Marcus misstated the results.","limitations":["The original PDFs exceeded the reading tool's size limit.","No numerical result has been inferred from the speaker's description."],"evidence":[],"what_would_resolve":"A readable original figure or underlying data identifying Claude's seven- and eight-disc success rates.","retrospective":true,"plain_reading":true},"payload_hash":"8d15f8f33ccdc5be126208f433c135585ca7f0148898782b6dbae7daad94b7ea","policy_version":"2026-10-28.7","recorded_at":"2026-10-29T02:03:40Z","drafter":{"provider":"openai","model":"gpt-6-astra"},"reviewers":[{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-a-knockout-blow-for-llms-055f7c-4x-review-anthropic-2"},{"provider":"openai","model":"gpt-6-astra","run_id":"marcus-a-knockout-blow-for-llms-055f7c-4x-review-openai-2"}],"final_text":{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-a-knockout-blow-for-llms-055f7c-4x-final-anthropic-1"},"status":"current","notice":null,"superseded_by":null,"evidence_meta":[],"public_from":null,"public_until":null},"history":[],"review":{"review_due":false,"triggers":[],"last_completed_review_at":"2026-10-29T02:03:40Z","next_review_due_at":null,"horizon_now":"not_applicable"},"event_ids":["marcus-a-knockout-blow-for-llms-3e2ec8-assessment-published-a2"],"last_substantive_change_at":"2026-10-29T17:00:18Z"}},{"url":"https://punditclaims.ai/claims/marcus-a-knockout-blow-for-llms-5c645c","standing":"current","assessment_label":"Can’t be checked","versions":[{"assessment_id":"marcus-a-knockout-blow-for-llms-5c645c-a1","status":"current","url":"https://punditclaims.ai/claims/marcus-a-knockout-blow-for-llms-5c645c#assessment-marcus-a-knockout-blow-for-llms-5c645c-a1"}],"claim":{"claim_id":"marcus-a-knockout-blow-for-llms-5c645c","subject_id":"gary-marcus","revision":1,"content_hash":"fe5878786cd589296739ce153fe0c31ca2ef9d53f1cfa2e76244008353b5341f","normalized_proposition":"The LLM approach has limits that are becoming clearer every day.","kind":"observation","target":"LLMs","conditions":[],"original_horizon_text":null,"topic_tags":["capabilities","scaling"],"concerns_providers":[],"selection_stratum":"declared-lead","occurrences":[{"id":"marcus-a-knockout-blow-for-llms-5c645c-o1","statement_date":"2025-06-07","statement_date_precision":"day","excerpt":"But this particular approach has limits that are clearer by the day.","locator":"paragraph 37","document":{"url":"https://garymarcus.substack.com/p/a-knockout-blow-for-llms","title":"A knockout blow for LLMs?","publisher":"Marcus on AI","published_at":"2025-06-07T22:28:19Z","date_uncertain":false,"date_provenance":"JSON-LD datePublished"},"observed_at":"2026-10-28T23:56:20Z","observed_via":"live","archive_url":null,"access_state":"full_public","snapshot_hash":"db7d9ad27f5ff721b3a57ecbb00a6572add1c6d796049d15f8a9fa82a2c144c0","receipts":1}],"first_statement_date":"2025-06-07","related":[],"current":{"assessment_id":"marcus-a-knockout-blow-for-llms-5c645c-a1","payload":{"claim_id":"marcus-a-knockout-blow-for-llms-5c645c","subject_id":"gary-marcus","normalized_proposition":"The LLM approach has limits that are becoming clearer every day.","kind":"observation","conditions":[],"original_horizon_text":null,"statement":{"excerpt":"But this particular approach has limits that are clearer by the day.","locator":"paragraph 37","url":"https://garymarcus.substack.com/p/a-knockout-blow-for-llms","date":"2025-06-07","document_title":"A knockout blow for LLMs?","observed_at":"2026-10-28T23:56:20Z"},"criteria":{"from_speaker":[],"our_interpretation":[],"support_condition":"Not checked against evidence: both screens found that public evidence could not settle this claim.","contradiction_condition":"Not checked against evidence: both screens found that public evidence could not settle this claim."},"axis":"not_applicable","outcome":"not_adjudicable","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"Not checked against evidence. Two independent screens, one from each provider, both found this too vague to test: informed readers would test it in different ways and reach different answers.","limitations":[],"evidence":[],"what_would_resolve":null,"retrospective":true,"uncheckable":["no_plain_reading"]},"payload_hash":"7eeb4d04931897ea55320e89a0240d441f5bb99d899d3c0349b602490fbebf0e","policy_version":"2026-10-28.7","recorded_at":"2026-10-29T00:44:36Z","drafter":{"provider":"anthropic","model":"claude-opus-5-5"},"reviewers":[{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-a-knockout-blow-for-llms-screen-anthropic-1"},{"provider":"openai","model":"gpt-6-astra","run_id":"marcus-a-knockout-blow-for-llms-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":"2026-10-29T00:44:36Z","next_review_due_at":null,"horizon_now":"not_applicable"},"event_ids":["marcus-a-knockout-blow-for-llms-5c645c-assessment-published-a1"],"last_substantive_change_at":"2026-10-29T17:00:18Z"}},{"url":"https://punditclaims.ai/claims/marcus-a-knockout-blow-for-llms-68a208","standing":"current","assessment_label":"Supported","versions":[{"assessment_id":"marcus-a-knockout-blow-for-llms-68a208-a1","status":"current","url":"https://punditclaims.ai/claims/marcus-a-knockout-blow-for-llms-68a208#assessment-marcus-a-knockout-blow-for-llms-68a208-a1"}],"claim":{"claim_id":"marcus-a-knockout-blow-for-llms-68a208","subject_id":"gary-marcus","revision":2,"content_hash":"0b85bfb7ae61f3a6b9eff037a6ced6fbb85e9610f397ea1e390bb0cf43e06d3c","normalized_proposition":"Neural networks of various kinds can generalize within the training distribution of data they are exposed to, but their generalizations tend to break down outside that distribution.","kind":"observation","target":"neural networks","conditions":[{"text":"neural networks of various kinds","type":"scope","from_source":true},{"text":"tend to","type":"qualifier","from_source":true}],"original_horizon_text":null,"topic_tags":["capabilities","reliability"],"concerns_providers":[],"selection_stratum":"declared-lead","occurrences":[{"id":"marcus-a-knockout-blow-for-llms-68a208-o1","statement_date":"2025-06-07","statement_date_precision":"day","excerpt":"neural networks of various kinds can generalize within a training distribution of data they are exposed to, but their generalizations tend to break down outside that distribution.","locator":"paragraph 8","document":{"url":"https://garymarcus.substack.com/p/a-knockout-blow-for-llms","title":"A knockout blow for LLMs?","publisher":"Marcus on AI","published_at":"2025-06-07T22:28:19Z","date_uncertain":false,"date_provenance":"JSON-LD datePublished"},"observed_at":"2026-10-28T23:56:20Z","observed_via":"live","archive_url":null,"access_state":"full_public","snapshot_hash":"db7d9ad27f5ff721b3a57ecbb00a6572add1c6d796049d15f8a9fa82a2c144c0","receipts":1}],"first_statement_date":"2025-06-07","related":[],"current":{"assessment_id":"marcus-a-knockout-blow-for-llms-68a208-a1","payload":{"claim_id":"marcus-a-knockout-blow-for-llms-68a208","subject_id":"gary-marcus","normalized_proposition":"Neural networks of various kinds can generalize within the training distribution of data they are exposed to, but their generalizations tend to break down outside that distribution.","kind":"observation","conditions":["neural networks of various kinds","tend to"],"original_horizon_text":null,"statement":{"excerpt":"neural networks of various kinds can generalize within a training distribution of data they are exposed to, but their generalizations tend to break down outside that distribution.","locator":"paragraph 8","url":"https://garymarcus.substack.com/p/a-knockout-blow-for-llms","date":"2025-06-07","document_title":"A knockout blow for LLMs?","observed_at":"2026-10-28T23:56:20Z"},"criteria":{"from_speaker":["neural networks of various kinds","can generalize within a training distribution","generalizations tend to break down outside that distribution"],"our_interpretation":["The plain reading is a recurring empirical weakness: networks that perform well on held-out examples resembling their training data often perform substantially worse after a distribution shift.","The words 'tend to' allow successful exceptions. 'Break down' means a substantial loss of successful generalization, without requiring accuracy to fall to zero.","The surrounding passage explicitly describes a longstanding pattern dating from 1998 through the post. I assess that historical research record through 2025-06-07. This scientific claim has no quarterly reporting cycle.","A new example from the same distribution is distinguished from a test that changes combinations, sequence lengths, domains, or other relevant data properties."],"support_condition":"Controlled studies across several neural-network variants and tasks show successful within-distribution generalization alongside substantial performance losses under specified distribution shifts.","contradiction_condition":"Broad, controlled comparisons across varied neural networks and meaningful distribution shifts show that within-distribution performance generally carries over, making substantial breakdown an exceptional rather than recurring pattern."},"axis":"historical_observation","outcome":"supported","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"The ordinary reading is supported. WILDS finds substantial performance losses across ten distribution-shift datasets, and SCAN finds failures on systematic generalization despite success on easier held-out commands. These are direct experimental examples of the recurring pattern Marcus describes. Successful meta-learning results limit the conclusion: neural networks can generalize systematically under suitable training. They do not overturn a claim expressly qualified by 'tend to'.","limitations":["Support concerns a recurring empirical weakness, not an impossibility theorem or a measured failure rate for all neural networks.","The selected studies do not establish the behavior of every LLM or explain every reasoning failure.","Successful systematic generalization depends on the training procedure and the test being used."],"evidence":[{"id":"e1","relation":"supports","excerpt":"On each dataset, we show that standard training yields substantially lower out-of-distribution than in-distribution performance.","locator":"Abstract, sentence 4","url":"https://proceedings.mlr.press/v139/koh21a.html","publisher":"Proceedings of Machine Learning Research","event_date":"2021","available_at":"2021-07-24","explanation":"WILDS compares within-distribution and shifted performance across ten datasets covering varied applications. The consistent substantial gaps support the claimed recurring weakness."},{"id":"e2","relation":"supports","excerpt":"However, when generalization requires systematic compositional skills (as in the \"dax\" example above), RNNs fail spectacularly.","locator":"Abstract, sentence 6","url":"https://proceedings.mlr.press/v80/lake18a.html","publisher":"Proceedings of Machine Learning Research","event_date":"2018","available_at":"2018-07-15","explanation":"The study tests several recurrent-network variants and reports successful generalization when training and test commands differ little, followed by failures on systematic compositional tests. It supports the distinction Marcus describes in a controlled setting."},{"id":"e3","relation":"context","excerpt":"Our results show how a standard neural network architecture, optimized for its compositional skills, can mimic human systematic generalization in a head-to-head comparison.","locator":"Abstract, final sentence","url":"https://www.nature.com/articles/s41586-023-06668-3","publisher":"Nature","event_date":"2023","available_at":"2023-10-25","explanation":"Meta-learning produces successful systematic generalization and improves performance on several compositional benchmarks. 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This is relevant progress, but the disclosure does not independently establish general intelligence or settle the architectural boundary in 'direct route'."},{"id":"e6","relation":"context","excerpt":"AI is accelerating our own research and engineering, and AI-enabled research has led to advances in mathematics, including the Navier-Stokes Millennium Problem.","locator":"Opening discussion of scientific progress and economic growth","url":"https://openai.com/index/building-standards-next-phase-ai","publisher":"OpenAI","event_date":"2026-09-21","available_at":"2026-09-21","explanation":"This reports benefits relevant to transformative potential. 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Plain reading: material hallucination rates on published evaluations from leading labs, plus documented real-world harm. 'Unsolved' means no lab claimed or showed that hallucinations had been eliminated.","The part about warning since 2001 is biographical and is not scored."],"support_condition":"Evaluations of frontier models from around April–May 2025 show substantial hallucination rates, and documented incidents of harmful fabrication continued at that time.","contradiction_condition":"Evaluations from that period show frontier models with negligible hallucination rates, or credible evidence that the problem had been solved by May 2025."},"axis":"historical_observation","outcome":"supported","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"The contract asks whether frontier models had substantial hallucination rates around April–May 2025, and whether harmful fabrication incidents were still happening then. Both parts are met. OpenAI's system card of April 16, 2025 reported PersonQA hallucination rates of 33% for o3 and 48% for o4-mini, against 16% for the older o1. It reported SimpleQA rates of 51% and 79%. OpenAI wrote that more research was needed to understand the cause. On the same date, Transluce reported that o3 often made up actions it had taken and justified them when challenged. It said this behaviour also appeared in o3-mini and o1. On May 22, 2025, an LA Times column reported that Damien Charlotin's list held 99 court cases with AI hallucinations. It said that in some 30 cases involving lawyers, the errors were found or filed as recently as that year. No evidence in the packet shows that the problem had been solved by May 2025. 'Deep' and 'serious' are judgments, but the evidence fits the plain reading.","limitations":["Hallucination rates depend heavily on the benchmark and the model. The cited figures come from a few evaluations and do not give one general rate.","'Deep' and 'serious' have no measurable threshold.","The court-case counts cover only publicly reported cases and do not measure how often hallucinations happen overall."],"evidence":[{"id":"e1","relation":"supports","excerpt":"More research is needed to understand the cause of these results.","locator":"Section 3.3 Hallucinations, Table 4","url":"https://cdn.openai.com/pdf/2221c875-02dc-4789-800b-e7758f3722c1/o3-and-o4-mini-system-card.pdf","publisher":"OpenAI","event_date":"2025-04-16","available_at":"2025-04-16","explanation":"OpenAI's own system card for its newest models reports PersonQA hallucination rates of 33% (o3) and 48% (o4-mini), higher than o1, and says the cause is not understood. This shows the problem was material and unsolved in April 2025."},{"id":"e2","relation":"supports","excerpt":"we found that o3 frequently fabricates actions it took to fulfill user requests, and elaborately justifies the fabrications when confronted by the user.","locator":"Introductory paragraph","url":"https://transluce.org/investigating-o3-truthfulness","publisher":"Transluce","event_date":"2025-04-16","available_at":"2025-04-16","explanation":"An independent lab found that a frontier model often made things up in April 2025, and that the behaviour also appeared in other widely deployed models."},{"id":"e3","relation":"supports","excerpt":"in some 30 cases involving lawyers the AI-generated errors were discovered or were in documents filed as recently as this year","locator":"Paragraph beginning 'In most of the cases'","url":"https://www.yahoo.com/news/hiltzik-ai-hallucinations-growing-problem-100000192.html","publisher":"Los Angeles Times (via Yahoo 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Occasional success does not refute 'can't' in this ordinary sense; reliable, routine success would.","Target period: the weeks before the post (April 1 to May 24, 2025), since he describes the present state."],"support_condition":"Evaluations from around April–May 2025 show frontier LLMs fabricating content without flagging it, failing to catch their own fabrications, or hallucinating even when grounded in retrieved sources.","contradiction_condition":"Evaluations from that period show frontier LLMs reliably detecting and flagging their own fabrications or reliably staying within supplied sources."},"axis":"historical_observation","outcome":"supported","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"The contract reads 'can't' as 'can't reliably', as of the weeks before May 24, 2025. On April 16, 2025, Transluce reported that OpenAI's o3 made up code runs. When pressed, it 'doubles down and offers excuses' instead of admitting it had not run the code. 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A strictly literal reading would fail, because models sometimes admit fabrication.","The 'stick purely to known sources' part has no evidence from the target period, only the earlier-tested legal-tools study.","The evidence covers a few OpenAI models and selected tasks, not all LLMs."],"evidence":[{"id":"e1","relation":"supports","excerpt":"When pressed by the user about its hallucinated code execution, the model doubles down and offers excuses for its inaccurate code outputs, rather than admitting it did not run the code itself.","locator":"Section 'Discovering lapses in truthfulness', paragraph 1","url":"https://transluce.org/investigating-o3-truthfulness","publisher":"Transluce","event_date":"2025-04-16","available_at":"2025-04-16","explanation":"A frontier model (o3-2025-04-03) failed to flag its own fabrication and defended it when challenged. This directly supports 'can't notify when they have invented something bogus' and 'can't sanity check their own work'."},{"id":"e2","relation":"supports","excerpt":"We find that all models, especially o-series, are susceptible to this behavior.","locator":"Section 'Automatically surfacing behaviors'","url":"https://transluce.org/investigating-o3-truthfulness","publisher":"Transluce","event_date":"2025-04-16","available_at":"2025-04-16","explanation":"Across hundreds of tested conversations, every model tested (o3, o1, o3-mini, GPT-4.1, GPT-4o) sometimes falsely claimed to have run code. 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Two independent screens, one from each provider, both found that evidence could not settle it: one found it rhetoric that commits to nothing testable; the other found it about something no public evidence could show, such as a private intention or a counterfactual.","limitations":[],"evidence":[],"what_would_resolve":null,"retrospective":true,"uncheckable":["no_public_evidence","rhetoric"]},"payload_hash":"01d5b0b2f29f04a24df52757cbcfbaf0363757ee5a7a3c08c9f046373ecb6235","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-two-years-ago-today-in-ai-history-screen-anthropic-1"},{"provider":"openai","model":"gpt-6-astra","run_id":"marcus-two-years-ago-today-in-ai-history-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-two-years-ago-today-in-ai-history-1ec279-assessment-published-a1"],"last_substantive_change_at":"2026-10-29T17:00:18Z"}},{"url":"https://punditclaims.ai/claims/marcus-two-years-ago-today-in-ai-history-2e3897","standing":"current","assessment_label":"Contradicted","versions":[{"assessment_id":"marcus-two-years-ago-today-in-ai-history-2e3897-a2","status":"current","url":"https://punditclaims.ai/claims/marcus-two-years-ago-today-in-ai-history-2e3897#assessment-marcus-two-years-ago-today-in-ai-history-2e3897-a2"}],"claim":{"claim_id":"marcus-two-years-ago-today-in-ai-history-2e3897","subject_id":"gary-marcus","revision":1,"content_hash":"1ad29506cfc99016f449dcfa057f8244c2df9dceefff7233c6fec6c8cbbd7093","normalized_proposition":"Sam Altman is doing everything he can to stop meaningful AI regulation.","kind":"observation","target":"Sam Altman","conditions":[],"original_horizon_text":null,"topic_tags":["regulation-policy"],"concerns_providers":["openai"],"selection_stratum":"declared-lead","occurrences":[{"id":"marcus-two-years-ago-today-in-ai-history-2e3897-o1","statement_date":"2025-05-16","statement_date_precision":"day","excerpt":"Now he is doing everything he can to stop AI regulation of any meaningful sort.","locator":"paragraph 17","document":{"url":"https://garymarcus.substack.com/p/two-years-ago-today-in-ai-history","title":"Two Years Ago Today in AI History: The Tale of An About-face in AI Regulation","publisher":"Marcus on AI","published_at":"2025-05-16T17:52:01Z","date_uncertain":false,"date_provenance":"JSON-LD datePublished"},"observed_at":"2026-10-28T23:56:29Z","observed_via":"live","archive_url":null,"access_state":"full_public","snapshot_hash":"601fa12fecaa15ed2f9ebed11c899e64a306dba5be7fb7875a4bff566dfd1324","receipts":1}],"first_statement_date":"2025-05-16","related":[],"current":{"assessment_id":"marcus-two-years-ago-today-in-ai-history-2e3897-a2","payload":{"claim_id":"marcus-two-years-ago-today-in-ai-history-2e3897","subject_id":"gary-marcus","normalized_proposition":"Sam Altman is doing everything he can to stop meaningful AI regulation.","kind":"observation","conditions":[],"original_horizon_text":null,"statement":{"excerpt":"Now he is doing everything he can to stop AI regulation of any meaningful sort.","locator":"paragraph 17","url":"https://garymarcus.substack.com/p/two-years-ago-today-in-ai-history","date":"2025-05-16","document_title":"Two Years Ago Today in AI History: The Tale of An About-face in AI Regulation","observed_at":"2026-10-28T23:56:29Z"},"criteria":{"from_speaker":["Now","doing everything he can","to stop AI regulation","of any meaningful sort"],"our_interpretation":["Treat 'everything he can' as emphasis on comprehensive opposition, not a measurable claim about maximum effort.","Meaningful regulation includes enforceable protections against identifiable AI harms, including unauthorized digital replicas.","Assess Altman's public conduct and OpenAI's official advocacy under his leadership. Company positions inform his institutional stance but do not establish his private beliefs.","For the ongoing state, use the current administration's policy period from January 20 through the May 16 post, including the referenced May 8 hearing."],"support_condition":"The current record shows comprehensive opposition to substantive AI safeguards, without affirmative support for enforceable protections.","contradiction_condition":"Altman or the company whose advocacy the passage attributes to him affirmatively supports substantive, enforceable AI protections during the target period."},"axis":"historical_observation","outcome":"contradicted","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"At the May 8, 2025 hearing, Altman opposed a heavy-handed government prior-approval process for AI [e1]. But on April 9, 2025, OpenAI endorsed the NO FAKES Act, which proposed enforceable protections against unauthorized digital replicas [e2]. Counting OpenAI's official advocacy and targeted protections as the contract requires, that endorsement contradicts comprehensive opposition to meaningful regulation [e2]. Altman also agreed on May 8 to work with senators on child protections, without endorsing a specific binding rule in that exchange [e3].","limitations":["OpenAI's endorsement establishes an institutional position, not a separate personal endorsement by Altman or his private beliefs.","The evidence does not establish undisclosed lobbying or the sincerity of public endorsements.","The assessment depends on counting targeted digital-replica protections as meaningful regulation."],"evidence":[{"id":"e1","relation":"context","excerpt":"I think that would be disastrous.","locator":"May 8 hearing, Altman's response to Chairman Cruz about government prior approval","url":"https://www.govinfo.gov/content/pkg/CHRG-119shrg61426/html/CHRG-119shrg61426.htm","publisher":"U.S. Senate Committee on Commerce, Science, and Transportation","event_date":"2025-05-08","available_at":"2025-05-08","explanation":"Altman opposed the heavy-handed government prior-approval process described in Cruz's question. This supports Marcus's narrower account of resistance to regulation, but cannot establish opposition to every meaningful safeguard."},{"id":"e2","relation":"contradicts","excerpt":"OpenAI is happy to once again support the NO FAKES Act","locator":"ENDORSEMENTS, statement attributed to OpenAI Chief Global Affairs Officer Chris Lehane","url":"https://mail.insideaipolicy.com/sites/insideaipolicy.com/files/documents/2025/apr/ai04092025_3.pdf","publisher":"OpenAI statement in senators' April 9 release, reproduced by Inside AI Policy","event_date":"2025-04-09","available_at":"2025-04-09","explanation":"OpenAI endorsed legislation imposing liability for unauthorized digital replicas and certain knowing hosting. 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gone are the days when Washington was afraid of the risks of AI enough to seriously consider doing anything about them.","locator":"paragraph 26","url":"https://garymarcus.substack.com/p/two-years-ago-today-in-ai-history","date":"2025-05-16","document_title":"Two Years Ago Today in AI History: The Tale of An About-face in AI Regulation","observed_at":"2026-10-28T23:56:29Z"},"criteria":{"from_speaker":["our government","gone are the days","Washington","seriously consider doing anything","risks of AI"],"our_interpretation":["Read Washington as the federal executive and Congress, consistent with the surrounding discussion of both.","Serious consideration includes substantive legislative advancement or adopted executive safeguards addressing identifiable AI harms.","Assess observable policy actions, not officials' private feelings of fear.","For the ongoing state, use the current administration from January 20 through the May 16, 2025 post.","A shift toward innovation does not alone establish abandonment of all risk-related action."],"support_condition":"The current federal record shows abandonment of substantive consideration or implementation of measures addressing AI risks.","contradiction_condition":"Congress or the executive seriously advances or adopts substantive measures addressing AI risks during the target period."},"axis":"historical_observation","outcome":"contradicted","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"Before the post, Congress had passed the TAKE IT DOWN Act through both chambers, addressing AI-generated intimate images. 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The administration's shift toward innovation is real in the cited order, but the broader claim overstates it.","limitations":["Targeted measures do not establish comprehensive regulation of frontier AI developers.","The assessment does not judge the measures' adequacy or eventual effectiveness.","The TAKE IT DOWN Act had passed Congress but was not yet signed when Marcus posted."],"evidence":[{"id":"e1","relation":"contradicts","excerpt":"the TAKE IT DOWN Act now heads to the President to be signed into law.","locator":"Opening paragraph","url":"https://www.commerce.senate.gov/press/rep/release/take-it-down-act-passes-the-house-heads-to-president-trump-s-desk-2025-4","publisher":"U.S. Senate Committee on Commerce, Science, and Transportation","event_date":"2025-04-28","available_at":"2025-04-28","explanation":"Congress had passed legislation addressing nonconsensual intimate images, including AI-generated images, with criminal provisions and removal duties. Passage through both chambers exceeds mere discussion of AI risks."},{"id":"e2","relation":"contradicts","excerpt":"If proper risk mitigation is\nnot possible, agencies must cease the use of the AI.","locator":"Page 3, minimum risk management requirements","url":"https://www.whitehouse.gov/wp-content/uploads/2025/02/M-25-21-Accelerating-Federal-Use-of-AI-through-Innovation-Governance-and-Public-Trust.pdf","publisher":"White House Office of Management and Budget","event_date":"2025-04-03","available_at":"2025-04-03","explanation":"The executive branch directed agencies to manage risks from high-impact AI and cease use when mitigation is impossible. This is concrete risk-related action despite the memorandum's emphasis on adoption."},{"id":"e3","relation":"context","excerpt":"we must develop AI systems that are free from ideological bias or engineered social agendas.","locator":"Section 1, Purpose","url":"https://www.whitehouse.gov/presidential-actions/2025/01/removing-barriers-to-american-leadership-in-artificial-intelligence","publisher":"The White House","event_date":"2025-01-23","available_at":"2025-01-23","explanation":"The order emphasizes AI leadership and directs review and possible removal of actions under the revoked Biden AI order. 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Two independent screens, one from each provider, both found this stated as a chance or a possibility, or at medium or low confidence.","limitations":[],"evidence":[],"what_would_resolve":null,"retrospective":true,"uncheckable":["probability_language"]},"payload_hash":"046dd9b1c682238ffa66950628823e92fae4063bab093a293bfffb2bdc6383b7","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-two-years-ago-today-in-ai-history-screen-anthropic-1"},{"provider":"openai","model":"gpt-6-astra","run_id":"marcus-two-years-ago-today-in-ai-history-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-two-years-ago-today-in-ai-history-5e47f1-assessment-published-a1"],"last_substantive_change_at":"2026-10-29T17:00:18Z"}},{"url":"https://punditclaims.ai/claims/marcus-two-years-ago-today-in-ai-history-5f86d2","standing":"current","assessment_label":"Mixed","versions":[{"assessment_id":"marcus-two-years-ago-today-in-ai-history-5f86d2-a2","status":"current","url":"https://punditclaims.ai/claims/marcus-two-years-ago-today-in-ai-history-5f86d2#assessment-marcus-two-years-ago-today-in-ai-history-5f86d2-a2"}],"claim":{"claim_id":"marcus-two-years-ago-today-in-ai-history-5f86d2","subject_id":"gary-marcus","revision":1,"content_hash":"83ff2c1abc455d6a6cae5772469d81c7b85e88a9fcc724e9067676d21891167d","normalized_proposition":"Congress has passed no meaningful AI regulation and is trying to prevent states from passing meaningful AI regulation.","kind":"observation","target":"US Congress","conditions":[],"original_horizon_text":null,"topic_tags":["regulation-policy"],"concerns_providers":[],"selection_stratum":"declared-lead","occurrences":[{"id":"marcus-two-years-ago-today-in-ai-history-5f86d2-o1","statement_date":"2025-05-16","statement_date_precision":"day","excerpt":"Congress has failed to pass any meaningful AI regulation, and even worse, they are now actively aiming to prevent States — probably our last hope — from passing anything meaningful.","locator":"paragraph 13","document":{"url":"https://garymarcus.substack.com/p/two-years-ago-today-in-ai-history","title":"Two Years Ago Today in AI History: The Tale of An About-face in AI Regulation","publisher":"Marcus on AI","published_at":"2025-05-16T17:52:01Z","date_uncertain":false,"date_provenance":"JSON-LD datePublished"},"observed_at":"2026-10-28T23:56:29Z","observed_via":"live","archive_url":null,"access_state":"full_public","snapshot_hash":"601fa12fecaa15ed2f9ebed11c899e64a306dba5be7fb7875a4bff566dfd1324","receipts":1}],"first_statement_date":"2025-05-16","related":[],"current":{"assessment_id":"marcus-two-years-ago-today-in-ai-history-5f86d2-a2","payload":{"claim_id":"marcus-two-years-ago-today-in-ai-history-5f86d2","subject_id":"gary-marcus","normalized_proposition":"Congress has passed no meaningful AI regulation and is trying to prevent states from passing meaningful AI regulation.","kind":"observation","conditions":[],"original_horizon_text":null,"statement":{"excerpt":"Congress has failed to pass any meaningful AI regulation, and even worse, they are now actively aiming to prevent States — probably our last hope — from passing anything meaningful.","locator":"paragraph 13","url":"https://garymarcus.substack.com/p/two-years-ago-today-in-ai-history","date":"2025-05-16","document_title":"Two Years Ago Today in AI History: The Tale of An About-face in AI Regulation","observed_at":"2026-10-28T23:56:29Z"},"criteria":{"from_speaker":["Congress has failed to pass any meaningful AI regulation","now actively aiming to prevent States","from passing anything meaningful"],"our_interpretation":["The context supplies a comparison with the May 16, 2023 hearing; I assess congressional action through May 16, 2025.","Meaningful AI regulation ordinarily includes substantive legal duties addressing a specific AI harm; it need not be a comprehensive AI licensing regime.","Passed by Congress means approved by both chambers. Presidential signature and legal commencement are separate questions.","The state-law component concerns an active legislative effort. A prohibition on enforcement can prevent effective state regulation even without forbidding states from passing bills.","The parenthetical description of states as the last hope is opinion and is not scored."],"support_condition":"Congress had passed no substantive measure regulating AI harms, and an active congressional proposal would substantially prevent effective state AI regulation.","contradiction_condition":"Congress had passed a substantive AI-related regulatory measure, or the alleged effort to restrict state regulation did not exist. Assess the two components separately."},"axis":"historical_observation","outcome":"mixed","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"The claim that Congress had passed no meaningful AI regulation does not hold under the contract's definition: by April 28, 2025, both chambers had passed the TAKE IT DOWN Act [e1]. The bill provided for criminal restrictions on publishing nonconsensual intimate imagery, including AI-generated imagery, and platform removal duties; the April 28 release said it was heading to the President for signature [e1]. The state-restriction component holds: the May 13, 2025 agency announcement identified an active House committee proposal for a ten-year moratorium on enforcing state AI and automated-decision laws [e2].","limitations":["Congressional passage does not establish enactment or that the bill's duties were already in force.","The state proposal concerned enforcement, rather than a literal prohibition on passing state legislation.","The evidence establishes an active committee proposal, not its ultimate adoption or agreement by both chambers."],"evidence":[{"id":"e1","relation":"contradicts","excerpt":"heads to the President","locator":"April 28 release, first paragraph","url":"https://www.commerce.senate.gov/press/rep/release/take-it-down-act-passes-the-house-heads-to-president-trump-s-desk-2025-4","publisher":"US Senate Committee on Commerce, Science, and Transportation","event_date":"2025-04-28","available_at":"2025-04-28","explanation":"The release confirms passage by both chambers and describes criminal restrictions on nonconsensual intimate imagery, including AI deepfakes, plus platform removal duties. This is substantive targeted regulation under the stated reading, although it still awaited presidential signature."},{"id":"e2","relation":"supports","excerpt":"The enforcement moratorium","locator":"May 13 announcement, second paragraph; first paragraph describes the proposal and May 12 opposition letter","url":"https://cppa.ca.gov/announcements/2025/20250513.html","publisher":"California Privacy Protection Agency","event_date":"2025-05-12","available_at":"2025-05-13","explanation":"The agency identifies an active House committee proposal to prohibit enforcement of state AI and automated-decision laws for ten years. The committee draft independently confirms that description, supporting the state-restriction component."}],"what_would_resolve":null,"retrospective":true,"plain_reading":true},"payload_hash":"cd40ed019dbf4ea1ac68a2555417438c358cd8b61cca746b9066653f92123b16","policy_version":"2026-10-28.7","recorded_at":"2026-10-29T12:56:04Z","drafter":{"provider":"openai","model":"gpt-6-astra"},"reviewers":[{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-two-years-ago-today-in-ai-history-384068-4x-review-anthropic-1"},{"provider":"openai","model":"gpt-6-astra","run_id":"marcus-two-years-ago-today-in-ai-history-384068-4x-review-openai-1"}],"final_text":{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-two-years-ago-today-in-ai-history-2e3897-3x-final-anthropic-1"},"status":"current","notice":null,"superseded_by":null,"evidence_meta":[{"id":"e1","source_type":"news_report","audited":null,"derived":null,"metric":null},{"id":"e2","source_type":"news_report","audited":null,"derived":null,"metric":null}],"public_from":null,"public_until":null},"history":[],"review":{"review_due":false,"triggers":[],"last_completed_review_at":"2026-10-29T12:56:04Z","next_review_due_at":null,"horizon_now":"not_applicable"},"event_ids":["marcus-two-years-ago-today-in-ai-history-5f86d2-assessment-published-a2"],"last_substantive_change_at":"2026-10-29T17:00:18Z"}},{"url":"https://punditclaims.ai/claims/marcus-two-years-ago-today-in-ai-history-74dd27","standing":"current","assessment_label":"Supported","versions":[{"assessment_id":"marcus-two-years-ago-today-in-ai-history-74dd27-a1","status":"current","url":"https://punditclaims.ai/claims/marcus-two-years-ago-today-in-ai-history-74dd27#assessment-marcus-two-years-ago-today-in-ai-history-74dd27-a1"}],"claim":{"claim_id":"marcus-two-years-ago-today-in-ai-history-74dd27","subject_id":"gary-marcus","revision":1,"content_hash":"78685e588a0ded225ad0e26d241579ff9eebe5ddaddb242d318ecadafee52319","normalized_proposition":"Since 2023 OpenAI has pushed for free training materials and exemption from copyright law, contrary to Altman's stated support for content creators.","kind":"observation","target":"OpenAI","conditions":[],"original_horizon_text":null,"topic_tags":["copyright","regulation-policy"],"concerns_providers":["openai"],"selection_stratum":"declared-lead","occurrences":[{"id":"marcus-two-years-ago-today-in-ai-history-74dd27-o1","statement_date":"2025-05-16","statement_date_precision":"day","excerpt":"(He also said at the time “we think that content creators, content owners, need to benefit from this technology” but ever since his company has been pushing for free training materials and exemption from copyright laws.)","locator":"paragraph 17","document":{"url":"https://garymarcus.substack.com/p/two-years-ago-today-in-ai-history","title":"Two Years Ago Today in AI History: The Tale of An About-face in AI Regulation","publisher":"Marcus on AI","published_at":"2025-05-16T17:52:01Z","date_uncertain":false,"date_provenance":"JSON-LD datePublished"},"observed_at":"2026-10-28T23:56:29Z","observed_via":"live","archive_url":null,"access_state":"full_public","snapshot_hash":"601fa12fecaa15ed2f9ebed11c899e64a306dba5be7fb7875a4bff566dfd1324","receipts":1}],"first_statement_date":"2025-05-16","related":[],"current":{"assessment_id":"marcus-two-years-ago-today-in-ai-history-74dd27-a1","payload":{"claim_id":"marcus-two-years-ago-today-in-ai-history-74dd27","subject_id":"gary-marcus","normalized_proposition":"Since 2023 OpenAI has pushed for free training materials and exemption from copyright law, contrary to Altman's stated support for content creators.","kind":"observation","conditions":[],"original_horizon_text":null,"statement":{"excerpt":"(He also said at the time “we think that content creators, content owners, need to benefit from this technology” but ever since his company has been pushing for free training materials and exemption from copyright laws.)","locator":"paragraph 17","url":"https://garymarcus.substack.com/p/two-years-ago-today-in-ai-history","date":"2025-05-16","document_title":"Two Years Ago Today in AI History: The Tale of An About-face in AI Regulation","observed_at":"2026-10-28T23:56:29Z"},"criteria":{"from_speaker":["content creators, content owners, need to benefit","ever since","pushing for free training materials","exemption from copyright laws"],"our_interpretation":["The period begins with the May 16, 2023 testimony and ends with the May 16, 2025 post.","Read 'free training materials' as advocating training access without a general requirement to obtain permission or pay copyright royalties.","In the training context, read 'exemption' as seeking protection from copyright liability for training, including through fair use. Do not read it as immunity for every output or every business activity.","'Ever since' means a sustained position during the intervening period, not documented advocacy on every day.","The contrast with creator benefits does not itself establish dishonesty or a promise to pay for every training use."],"support_condition":"Altman expressed support for creator benefits, and subsequent OpenAI positions advocated training on copyrighted works without generally required licensing or royalties.","contradiction_condition":"OpenAI's subsequent position required permission and payment for copyrighted training generally, rather than advocating fair-use protection or a training exception."},"axis":"historical_observation","outcome":"supported","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"Altman said creators should benefit. OpenAI subsequently advocated fair-use protection for training in 2023, 2024, and 2025, supporting the claim about access without generally required copyright permission or royalties. The result is limited to training. The records do not establish a demand for blanket immunity from copyright law, and OpenAI also pursued publisher partnerships. Advocacy for fair use does not by itself prove that the creator-benefit statement was insincere.","limitations":["Fair use is part of copyright law; calling it an exemption from copyright laws is imprecise.","The assessment addresses OpenAI's advocacy, not the legal merits of particular training practices.","The 2023 filing is a reproduced primary document; its hosting date is later than its submission date.","The hearing statement was public on May 16, although the compiled transcript appeared later."],"evidence":[{"id":"e1","relation":"supports","excerpt":"we think that content creators,\ncontent owners need to benefit from this technology.","locator":"May 16 hearing, Altman's response to Senator Blackburn on content creators","url":"https://www.govinfo.gov/content/pkg/CHRG-118shrg52706/html/CHRG-118shrg52706.htm","publisher":"U.S. Senate Committee on the Judiciary","event_date":"2023-05-16","available_at":"2023-05-16","explanation":"The official transcript confirms the substance of the statement Marcus quotes."},{"id":"e2","relation":"supports","excerpt":"The factual metadata and fundamental information that AI models learn from training data are\nnot protected by copyright law.","locator":"Page 12, Training AI Models Does Not Violate Copyright Law","url":"https://www.openfuture.eu/wp-content/uploads/2023/11/231111_copyright_offoce_noi_open-ai.pdf","publisher":"OpenAI; copy hosted by Open Future","event_date":"2023-10-30","available_at":"2023-11-11","explanation":"The submission argues that training qualifies as fair use and that licensing every training work is impracticable. The quoted sentence supplies part of its stated legal rationale, establishing advocacy during 2023."},{"id":"e3","relation":"supports","excerpt":"Training AI models using publicly available internet materials is fair use","locator":"Section 2, first paragraph","url":"https://openai.com/index/openai-and-journalism","publisher":"OpenAI","event_date":"2024-01-08","available_at":"2024-01-08","explanation":"OpenAI explicitly advocates fair-use protection for this training. This is evidence of its position, not a finding that its legal argument is correct."},{"id":"e4","relation":"supports","excerpt":"Applying the fair use doctrine to AI is not only a matter of American competitiveness","locator":"Page 10, Copyright: Promoting the Freedom to Learn","url":"https://cdn.openai.com/global-affairs/ostp-rfi/ec680b75-d539-4653-b297-8bcf6e5f7686/openai-response-ostp-nsf-rfi-notice-request-for-information-on-the-development-of-an-artificial-intelligence-ai-action-plan.pdf","publisher":"OpenAI","event_date":"2025-03-13","available_at":"2025-03-13","explanation":"The submission continues the fair-use argument and criticizes restrictions on access to training material. This establishes that the advocacy persisted into 2025."},{"id":"e5","relation":"context","excerpt":"Axel Springer and OpenAI have announced a global partnership","locator":"Opening partnership announcement","url":"https://openai.com/index/axel-springer-partnership","publisher":"OpenAI and Axel Springer","event_date":"2023-12-13","available_at":"2023-12-13","explanation":"The announcement describes publisher benefits and use of content for model training. It qualifies any suggestion that OpenAI rejected all partnerships or benefits for creators, while remaining compatible with its general fair-use position."}],"what_would_resolve":null,"retrospective":true,"plain_reading":true},"payload_hash":"28b0dbe89d5699cb6d5811698b91b33766ae12f135f0170b4873a41d595bc089","policy_version":"2026-10-28.7","recorded_at":"2026-10-29T12:45:19Z","drafter":{"provider":"openai","model":"gpt-6-astra"},"reviewers":[{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-two-years-ago-today-in-ai-history-384068-4x-review-anthropic-1"},{"provider":"openai","model":"gpt-6-astra","run_id":"marcus-two-years-ago-today-in-ai-history-384068-4x-review-openai-1"}],"final_text":{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-two-years-ago-today-in-ai-history-384068-4x-final-anthropic-1"},"status":"current","notice":null,"superseded_by":null,"evidence_meta":[{"id":"e1","source_type":"filing","audited":null,"derived":null,"metric":null},{"id":"e2","source_type":"filing","audited":null,"derived":null,"metric":null},{"id":"e3","source_type":"company_disclosure","audited":null,"derived":null,"metric":null},{"id":"e4","source_type":"filing","audited":null,"derived":null,"metric":null},{"id":"e5","source_type":"company_disclosure","audited":null,"derived":null,"metric":null}],"public_from":null,"public_until":null},"history":[],"review":{"review_due":false,"triggers":[],"last_completed_review_at":"2026-10-29T12:45:19Z","next_review_due_at":null,"horizon_now":"not_applicable"},"event_ids":["marcus-two-years-ago-today-in-ai-history-74dd27-assessment-published-a1"],"last_substantive_change_at":"2026-10-29T17:00:18Z"}},{"url":"https://punditclaims.ai/claims/marcus-two-years-ago-today-in-ai-history-9ad0f6","standing":"current","assessment_label":"Mixed","versions":[{"assessment_id":"marcus-two-years-ago-today-in-ai-history-9ad0f6-a1","status":"current","url":"https://punditclaims.ai/claims/marcus-two-years-ago-today-in-ai-history-9ad0f6#assessment-marcus-two-years-ago-today-in-ai-history-9ad0f6-a1"}],"claim":{"claim_id":"marcus-two-years-ago-today-in-ai-history-9ad0f6","subject_id":"gary-marcus","revision":1,"content_hash":"cd86f5c1790da6fd54c2a13e92242f2e50da650c1f0951eb4681aa51cdde2ad5","normalized_proposition":"US government officials now downplay rather than voice concern about AI risks.","kind":"observation","target":"US government officials","conditions":[],"original_horizon_text":null,"topic_tags":["regulation-policy","safety"],"concerns_providers":[],"selection_stratum":"declared-lead","occurrences":[{"id":"marcus-two-years-ago-today-in-ai-history-9ad0f6-o1","statement_date":"2025-05-16","statement_date_precision":"day","excerpt":"Government officials aren’t worrying out loud about the risks of AI, anymore. They are downplaying them.","locator":"paragraph 13","document":{"url":"https://garymarcus.substack.com/p/two-years-ago-today-in-ai-history","title":"Two Years Ago Today in AI History: The Tale of An About-face in AI Regulation","publisher":"Marcus on AI","published_at":"2025-05-16T17:52:01Z","date_uncertain":false,"date_provenance":"JSON-LD datePublished"},"observed_at":"2026-10-28T23:56:29Z","observed_via":"live","archive_url":null,"access_state":"full_public","snapshot_hash":"601fa12fecaa15ed2f9ebed11c899e64a306dba5be7fb7875a4bff566dfd1324","receipts":1}],"first_statement_date":"2025-05-16","related":[],"current":{"assessment_id":"marcus-two-years-ago-today-in-ai-history-9ad0f6-a1","payload":{"claim_id":"marcus-two-years-ago-today-in-ai-history-9ad0f6","subject_id":"gary-marcus","normalized_proposition":"US government officials now downplay rather than voice concern about AI risks.","kind":"observation","conditions":[],"original_horizon_text":null,"statement":{"excerpt":"Government officials aren’t worrying out loud about the risks of AI, anymore. They are downplaying them.","locator":"paragraph 13","url":"https://garymarcus.substack.com/p/two-years-ago-today-in-ai-history","date":"2025-05-16","document_title":"Two Years Ago Today in AI History: The Tale of An About-face in AI Regulation","observed_at":"2026-10-28T23:56:29Z"},"criteria":{"from_speaker":["Government officials","aren’t worrying out loud about the risks of AI, anymore","They are downplaying them"],"our_interpretation":["The surrounding discussion identifies US federal officials as the subject.","The plain reading describes a broad change in Washington's public rhetoric, not a claim about every official without exception.","For the ongoing state, I use the current administration's tenure through the post: January 20–May 16, 2025. Public statements can be assessed through the post date without a reporting lag.","I assess downplaying risks separately from the stronger assertion that substantive public concern has ceased."],"support_condition":"Prominent federal officials minimize AI risks or subordinate them to development, with substantive public concern largely absent from contemporary official statements.","contradiction_condition":"Prominent federal officials continue to voice substantive concern about AI risks. Such evidence contradicts the cessation component even if other officials downplay risks."},"axis":"historical_observation","outcome":"mixed","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"Vance's speech supports Marcus's description of an administration that downplayed safety concerns. But public concern had not disappeared: Federal Reserve Governor Barr warned explicitly about AI-enabled identity fraud in April. The shift in emphasis is supported; the broader suggestion that government officials no longer voiced concern is overstated.","limitations":["The Federal Reserve is part of the federal government but does not speak for the administration.","The evidence establishes contrasting prominent statements, not a quantified government-wide trend.","Official speeches are classified using the closest available source categories."],"evidence":[{"id":"e1","relation":"supports","excerpt":"hand-wringing about safety","locator":"February 11 speech, paragraph beginning 'The AI future'","url":"https://www.presidency.ucsb.edu/documents/remarks-the-vice-president-the-artificial-intelligence-action-summit-paris-france","publisher":"The American Presidency Project, reproducing the Office of the Vice President's speech","event_date":"2025-02-11","available_at":"2025-02-11","explanation":"Vance explicitly shifts emphasis from safety to opportunity and characterizes safety concern dismissively. This supports the downplaying component, although the same speech also recognizes hostile misuse of AI."},{"id":"e2","relation":"contradicts","excerpt":"the potential to supercharge identity fraud","locator":"Governor Michael Barr's speech, opening paragraph","url":"https://www.federalreserve.gov/newsevents/speech/barr20250417a.htm","publisher":"Board of Governors of the Federal Reserve System","event_date":"2025-04-17","available_at":"2025-04-17","explanation":"This official speech publicly warns about AI-enabled identity fraud and discusses defenses. It contradicts the assertion that federal officials had ceased expressing substantive concern."}],"what_would_resolve":null,"retrospective":true,"plain_reading":true},"payload_hash":"8a1d10a90c0725a1c9b17a4b85fb8ae84cce31b8d3722781ad5928e3e4e3394c","policy_version":"2026-10-28.7","recorded_at":"2026-10-29T12:45:48Z","drafter":{"provider":"openai","model":"gpt-6-astra"},"reviewers":[{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-two-years-ago-today-in-ai-history-9ad0f6-4x-review-anthropic-1"},{"provider":"openai","model":"gpt-6-astra","run_id":"marcus-two-years-ago-today-in-ai-history-9ad0f6-4x-review-openai-1"}],"final_text":{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-two-years-ago-today-in-ai-history-9ad0f6-4x-final-anthropic-1"},"status":"current","notice":null,"superseded_by":null,"evidence_meta":[{"id":"e1","source_type":"secondary_summary","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":"2026-10-29T12:45:48Z","next_review_due_at":null,"horizon_now":"not_applicable"},"event_ids":["marcus-two-years-ago-today-in-ai-history-9ad0f6-assessment-published-a1"],"last_substantive_change_at":"2026-10-29T17:00:18Z"}},{"url":"https://punditclaims.ai/claims/marcus-two-years-ago-today-in-ai-history-9cfc7b","standing":"current","assessment_label":"Supported","versions":[{"assessment_id":"marcus-two-years-ago-today-in-ai-history-9cfc7b-a1","status":"current","url":"https://punditclaims.ai/claims/marcus-two-years-ago-today-in-ai-history-9cfc7b#assessment-marcus-two-years-ago-today-in-ai-history-9cfc7b-a1"}],"claim":{"claim_id":"marcus-two-years-ago-today-in-ai-history-9cfc7b","subject_id":"gary-marcus","revision":1,"content_hash":"0b5d053be1858b627ffbec802915bba837f8c9bf752bea2a2dc26d653211a1d5","normalized_proposition":"While Altman publicly supported AI regulation in 2023, OpenAI lobbied to water down the EU AI Act.","kind":"observation","target":"OpenAI","conditions":[],"original_horizon_text":null,"topic_tags":["regulation-policy"],"concerns_providers":["openai"],"selection_stratum":"declared-lead","occurrences":[{"id":"marcus-two-years-ago-today-in-ai-history-9cfc7b-o1","statement_date":"2025-05-16","statement_date_precision":"day","excerpt":"And even as Altman was telling Congress that he supported AI regulation, his company was lobbying the EU to water down its AI act.","locator":"paragraph 17","document":{"url":"https://garymarcus.substack.com/p/two-years-ago-today-in-ai-history","title":"Two Years Ago Today in AI History: The Tale of An About-face in AI Regulation","publisher":"Marcus on AI","published_at":"2025-05-16T17:52:01Z","date_uncertain":false,"date_provenance":"JSON-LD datePublished"},"observed_at":"2026-10-28T23:56:29Z","observed_via":"live","archive_url":null,"access_state":"full_public","snapshot_hash":"601fa12fecaa15ed2f9ebed11c899e64a306dba5be7fb7875a4bff566dfd1324","receipts":1}],"first_statement_date":"2025-05-16","related":[],"current":{"assessment_id":"marcus-two-years-ago-today-in-ai-history-9cfc7b-a1","payload":{"claim_id":"marcus-two-years-ago-today-in-ai-history-9cfc7b","subject_id":"gary-marcus","normalized_proposition":"While Altman publicly supported AI regulation in 2023, OpenAI lobbied to water down the EU AI Act.","kind":"observation","conditions":[],"original_horizon_text":null,"statement":{"excerpt":"And even as Altman was telling Congress that he supported AI regulation, his company was lobbying the EU to water down its AI act.","locator":"paragraph 17","url":"https://garymarcus.substack.com/p/two-years-ago-today-in-ai-history","date":"2025-05-16","document_title":"Two Years Ago Today in AI History: The Tale of An About-face in AI Regulation","observed_at":"2026-10-28T23:56:29Z"},"criteria":{"from_speaker":["even as Altman was telling Congress","he supported AI regulation","his company was lobbying the EU","to water down its AI act"],"our_interpretation":["Read 'even as' as the same May 2023 regulatory episode surrounding the congressional testimony, rather than requiring lobbying on the exact hearing date.","Watering down means seeking to reduce proposed legal obligations or exclude OpenAI's models from stricter requirements. It does not require opposition to every regulation.","The claim concerns public positions and lobbying, not whether Altman sincerely believed his testimony."],"support_condition":"Records show Altman publicly supporting regulation in Congress and OpenAI advocating reduced EU AI Act obligations during the same May 2023 episode.","contradiction_condition":"Records establish that Altman did not support regulation in that testimony, or that the attributed EU advocacy did not seek reduced obligations."},"axis":"historical_observation","outcome":"supported","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"Altman supported regulation in his Senate testimony. During the same month's European tour, he met regulators and objected to classifications that would impose additional safety obligations. Contemporary reporting also reproduced OpenAI's disclosure of EU lobbying contacts. This supports the ordinary reading of overlapping public support and efforts to reduce particular requirements. It does not establish insincerity or same-day lobbying.","limitations":["The temporal reading is material: a requirement for an EU weakening request on the exact hearing date would remain unresolved.","Supporting regulation generally is compatible with opposing particular provisions."],"evidence":[{"id":"e1","relation":"supports","excerpt":"We believe it is essential to develop regulations that incentivize AI safety","locator":"Opening statement, before About OpenAI","url":"https://openai.com/global-affairs/testimony-of-sam-altman-before-the-us-senate","publisher":"OpenAI","event_date":"2023-05-16","available_at":"2023-06-22","explanation":"Altman's written congressional testimony explicitly supports regulation. The retrieved company page carries a June 22 submission date, later than the hearing."},{"id":"e2","relation":"supports","excerpt":"OpenAI’s skepticism centered on the E.U. law’s designation of “high risk” systems","locator":"Paragraph 3","url":"https://time.com/6282325/sam-altman-openai-eu","publisher":"TIME","event_date":"2023-05-24","available_at":"2023-05-25","explanation":"The report describes meetings with EU regulators and objections to classifications that would impose additional safety duties. This supports advocacy for reduced obligations near the testimony date."},{"id":"e3","relation":"supports","excerpt":"we have met with Washington-based and Brussels-based EU representatives.","locator":"Closing footnote quoting OpenAI's European Transparency Register entry","url":"https://techcrunch.com/2023/05/25/sam-altman-european-tour","publisher":"TechCrunch","event_date":"2023-05","available_at":"2023-05-26","explanation":"The contemporary report reproduces OpenAI's disclosure of EU lobbying contacts and identifies the AI Act as its lobbying subject. It corroborates actual engagement rather than public criticism alone."}],"what_would_resolve":null,"retrospective":true,"plain_reading":true},"payload_hash":"9b560227791b6558b77778db8d7fa71f8484b450933556cb6c49844090ddf488","policy_version":"2026-10-28.7","recorded_at":"2026-10-29T12:45:48Z","drafter":{"provider":"openai","model":"gpt-6-astra"},"reviewers":[{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-two-years-ago-today-in-ai-history-9ad0f6-4x-review-anthropic-1"},{"provider":"openai","model":"gpt-6-astra","run_id":"marcus-two-years-ago-today-in-ai-history-9ad0f6-4x-review-openai-1"}],"final_text":{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-two-years-ago-today-in-ai-history-9ad0f6-4x-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":"news_report","audited":null,"derived":null,"metric":null},{"id":"e3","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":"2026-10-29T12:45:48Z","next_review_due_at":null,"horizon_now":"not_applicable"},"event_ids":["marcus-two-years-ago-today-in-ai-history-9cfc7b-assessment-published-a1"],"last_substantive_change_at":"2026-10-29T17:00:18Z"}},{"url":"https://punditclaims.ai/claims/marcus-two-years-ago-today-in-ai-history-d6d8db","standing":"current","assessment_label":"Unresolved","versions":[{"assessment_id":"marcus-two-years-ago-today-in-ai-history-d6d8db-a1","status":"current","url":"https://punditclaims.ai/claims/marcus-two-years-ago-today-in-ai-history-d6d8db#assessment-marcus-two-years-ago-today-in-ai-history-d6d8db-a1"}],"claim":{"claim_id":"marcus-two-years-ago-today-in-ai-history-d6d8db","subject_id":"gary-marcus","revision":2,"content_hash":"2c249fdd443b78fad7f779fc1514e31bebf716a1c0a337c662e39b99328154f7","normalized_proposition":"As of May 2025, there is no concretely implemented international AI governance and no US national AI agency, and the US is no longer even well positioned to detect and address AI-escalated cybercrime.","kind":"observation","target":"US AI governance","conditions":[],"original_horizon_text":null,"topic_tags":["regulation-policy","safety"],"concerns_providers":[],"selection_stratum":"declared-lead","occurrences":[{"id":"marcus-two-years-ago-today-in-ai-history-d6d8db-o1","statement_date":"2025-05-16","statement_date_precision":"day","excerpt":"We have no concretely implemented international AI governance, no national AI agency; we are no longer even positioned well to detect and address AI-escalated cybercrime.","locator":"paragraph 12","document":{"url":"https://garymarcus.substack.com/p/two-years-ago-today-in-ai-history","title":"Two Years Ago Today in AI History: The Tale of An About-face in AI Regulation","publisher":"Marcus on AI","published_at":"2025-05-16T17:52:01Z","date_uncertain":false,"date_provenance":"JSON-LD datePublished"},"observed_at":"2026-10-28T23:56:29Z","observed_via":"live","archive_url":null,"access_state":"full_public","snapshot_hash":"601fa12fecaa15ed2f9ebed11c899e64a306dba5be7fb7875a4bff566dfd1324","receipts":1}],"first_statement_date":"2025-05-16","related":[],"current":{"assessment_id":"marcus-two-years-ago-today-in-ai-history-d6d8db-a1","payload":{"claim_id":"marcus-two-years-ago-today-in-ai-history-d6d8db","subject_id":"gary-marcus","normalized_proposition":"As of May 2025, there is no concretely implemented international AI governance and no US national AI agency, and the US is no longer even well positioned to detect and address AI-escalated cybercrime.","kind":"observation","conditions":[],"original_horizon_text":null,"statement":{"excerpt":"We have no concretely implemented international AI governance, no national AI agency; 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an AI research office is not automatically equivalent.","The cybercrime clause requires evidence of inadequate detection and response capacity and deterioration from an earlier position. Proposed cuts alone do not establish either."],"support_condition":"Records establish the absence of implemented international governance and a dedicated US AI regulator, together with an assessed deterioration leaving US AI-cybercrime defenses inadequate.","contradiction_condition":"Records establish qualifying governance or a dedicated regulator, or demonstrate adequate and undiminished AI-cybercrime detection and response capacity. A split across established components warrants mixed."},"axis":"historical_observation","outcome":"unresolved","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"International reporting had already attracted actual submissions, which challenges a broad reading of no implemented governance. That does not establish a binding international regulator. Planned CISA reductions raise concerns but do not establish the claimed level of national cybercrime incapacity. The collected records do not settle the three-part observation.","limitations":["The OECD article is retrospective and carries an authors' views disclaimer.","The CISA evidence is news reporting about an internal meeting because a corresponding primary record was not obtained.","Earlier institutional activity is retained as context rather than assigned a new May 16 event date."],"evidence":[{"id":"e1","relation":"context","excerpt":"19 organisations voluntarily submitted reports","locator":"Participants from around the world, first paragraph","url":"https://oecd.ai/en/wonk/haip-reporting-insights","publisher":"OECD.AI","event_date":"2025-04","available_at":"2025-06-11","explanation":"Actual submissions establish more than an unfulfilled pledge before the post. The framework was voluntary, and this earlier observation does not establish a binding international regulator or its precise publication-day status."},{"id":"e2","relation":"context","excerpt":"1,300 people","locator":"Third article paragraph","url":"https://therecord.media/cisa-restructuring-slowed-plankey-nomination","publisher":"The Record","event_date":"2025-04-30","available_at":"2025-04-30","explanation":"Reporting from an internal agency meeting documents planned reductions and uncertainty about approval. 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It does not require agreeing with Marcus or lengthening an AGI timeline.","The surrounding reference to GPT-5-level models makes GPT-5 a description of an expected generation of capability, rather than exclusively an OpenAI product name.","The target is the public commentary record available through April 7, 2025, the latest observable state before publication. No starting date is supplied, so the start remains unspecified.","The scandal characterization is an opinion. The introductory comparison with benchmark misconduct is rhetorical, not a conditional forecast."],"support_condition":"A sufficiently complete record of the named commentators' relevant coverage shows that they consistently omitted substantive consideration of reported model setbacks and their implications.","contradiction_condition":"Direct examples show that the named commentators substantively considered the reported setbacks and explained their implications, contradicting the characterization of those commentators as failing to reckon with them."},"axis":"historical_observation","outcome":"contradicted","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"On the ordinary reading of substantive engagement, the characterization is contradicted. Newton examined reported setbacks at three major laboratories, possible limits of LLMs and their implications for superintelligence. Cowen acknowledged slowdown reports and explained why his outlook remained optimistic. These are direct counterexamples involving two people Marcus named. Their conclusions need not agree with Marcus to count as reckoning with the evidence. This finding does not establish that all three addressed every contamination concern or revised numerical AGI timelines.","limitations":["The result depends on reading reckoning as substantive consideration, rather than agreement about how much weight setbacks deserve.","The assessment concerns the named examples, not every member of an unspecified pundit group.","The underlying technical truth of the model-setback reports is not independently adjudicated here."],"evidence":[{"id":"e1","relation":"contradicts","excerpt":"had underperformed expectations","locator":"Section I, paragraph introducing the Bloomberg report; implications discussed in sections II and III","url":"https://www.platformer.news/openai-google-scaling-laws-anthropic-ai","publisher":"Platformer — Casey Newton","event_date":"2024-11-14","available_at":"2024-11-14","explanation":"Newton explicitly covered setbacks at OpenAI, Google and Anthropic, then examined data limits, benchmark gaming, the possible insufficiency of LLMs, and alternative routes to progress. This is substantive engagement even though he remained optimistic."},{"id":"e2","relation":"contradicts","excerpt":"I remain bullish on AI progress,","locator":"Paragraph 6, following acknowledgment of recent reports that scaling had slowed","url":"https://marginalrevolution.com/marginalrevolution/2024/11/austrian-economics-and-ai-scaling.html","publisher":"Marginal Revolution — Tyler Cowen","event_date":"2024-11-14","available_at":"2024-11-14","explanation":"Cowen considered the slowdown reports and explained that his outlook rested on the research environment rather than confidence in scaling laws. 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I assess that domain rather than every application of deep learning.","I use January 1 through April 7, 2025 for the recent months described, with earlier generations providing the comparison baseline.","Testing diminishing results requires comparing performance gains with training inputs. Disappointment, release delays, and benchmark scores alone do not establish that relationship."],"support_condition":"Comparable measurements across frontier model generations show a clear reduction in capability gains relative to added training compute and data during the target period.","contradiction_condition":"Comparable measurements show sustained or increasing gains relative to added training inputs across the relevant frontier models and tasks."},"axis":"historical_observation","outcome":"unresolved","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"METR's February 27, 2025 evaluation placed GPT-4.5 between GPT-4o and o1 on its autonomy and research-engineering evaluations, but did not measure gains relative to training inputs (e1). Epoch's September 16, 2025 report found GPT-4.5's gains on selected benchmarks consistent with expected compute scaling (e2). That finding does not exclude diminishing marginal returns (e2). Neither item supplies the broad, matched comparison needed to establish or rule out declining gains per added training input during January 1–April 7, 2025 (e1; e2).","limitations":["METR evaluated an earlier GPT-4.5 checkpoint in a short, preliminary evaluation.","Epoch's selected benchmark comparisons do not establish a trend across frontier models and tasks.","The evidence does not cover deep learning beyond frontier language models or settle which definition of diminishing returns applies."],"evidence":[{"id":"e1","relation":"context","excerpt":"somewhere between GPT-4o and o1.","locator":"Opening evaluation summary","url":"https://metr.substack.com/p/2025-02-27-gpt-4-5-evals","publisher":"METR","event_date":"2025-02","available_at":"2025-02-27","explanation":"METR found limited relative performance on its autonomy and research-engineering evaluations. This does not measure gains per unit of training input."},{"id":"e2","relation":"context","excerpt":"scaling roughly as expected.","locator":"Printed page 21, footnote 12","url":"https://epoch.ai/files/AI_2030.pdf","publisher":"Epoch AI","event_date":"2025-02","available_at":"2025-09-16","explanation":"The footnote compares GPT-4.5 benchmark improvements with historical gains per order of magnitude of compute. This challenges a new breakdown in scaling, but expected scaling can itself have diminishing marginal returns."}],"what_would_resolve":"A reproducible comparison of frontier model generations before and during January 1–April 7, 2025, using consistent capability evaluations and disclosed or defensibly estimated training inputs. 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This window is an evaluator choice because model research has no standard reporting period."],"support_condition":"Evaluations of current frontier LLMs document recurring substantive reasoning failures, including systematic errors or invalid proofs, beyond isolated mistakes.","contradiction_condition":"Broad, rigorous evaluations establish dependable reasoning across relevant novel problems and controlled variations, and show that purported recurring failures are evaluation artifacts or unrepresentative exceptions."},"axis":"historical_observation","outcome":"supported","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"Contemporaneous studies document recurring reasoning failures rather than isolated wrong answers. Expert grading found very poor proof performance on newly released USAMO problems. A separate benchmark-cleaning study found systematic failures even on elementary tasks after removing evaluation errors. 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The productivity benefit holds in Google’s tested setting but does not hold in METR’s experienced-maintainer setting [e1, e2].","limitations":["Neither study establishes an industry-wide average effect or isolates autocomplete from other assistance features [e1, e2].","Google cautions that its result may not generalize across tools or periods [e1].","METR’s stated tool period extends beyond the March 12, 2025 claim; the cited abstract does not date individual tasks or provide a separate effect for tools available by that date [e2]."],"evidence":[{"id":"e1","relation":"supports","excerpt":"AI significantly shortened","locator":"Abstract; study design in Sections III and IV","url":"https://arxiv.org/html/2410.12944v3","publisher":"Google researchers / arXiv","event_date":"2024","available_at":"2024-11-11","explanation":"A randomized study of 96 Google engineers estimated about 21% less task time with completion, paste, and code-generation assistance. 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It does not claim that AI cannot improve maintenance.","I use January 1 through March 12, 2025 as the latest pre-publication reporting window for current tool capabilities.","'Over time' requires evidence about continued upkeep or recurring maintenance needs, rather than code generation alone."],"support_condition":"Evidence concerning target-period tools shows substantial unresolved maintenance difficulties in complex repositories, including continued need for human upkeep.","contradiction_condition":"Representative evidence shows that these tools reliably handle sustained maintenance of complex repositories without substantial remaining maintenance difficulties."},"axis":"historical_observation","outcome":"unresolved","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"The study published on February 4, 2025 concerns earlier, 2024 work: it found successful repairs and readability benefits, but most solutions introduced errors or new maintainability issues [e1]. It evaluates individual repairs, not sustained maintenance using tools from January 1 through March 12, 2025, so it does not settle the claim for that period [e1].","limitations":["The study does not establish whether its findings apply to target-period tools [e1].","Individual repair results do not show long-term maintenance outcomes or the human upkeep required in complex repositories [e1]."],"evidence":[{"id":"e1","relation":"context","excerpt":"errors or new maintainability issues.","locator":"Abstract","url":"https://arxiv.org/abs/2502.02368","publisher":"Henrique Nunes and colleagues, arXiv","event_date":"2024","available_at":"2025-02-04","explanation":"The study found maintenance-repair limitations, alongside successful repairs and improved readability. 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I do not import the separate next-year game-development reference.","Employment is relevant but must be combined with evidence about continuing work and AI's causal role."],"support_condition":"Across an agreed assessment horizon, representative employment and production evidence establishes a continuing substantive need for coders and software architects despite AI adoption.","contradiction_condition":"Representative employment and production evidence establishes durable, broad obsolescence of coders and software architects caused by AI, rather than isolated layoffs or changing job titles."},"axis":"forecast_outcome","outcome":"unresolved","horizon_state":"open_horizon","evaluation_as_of":"2026-10-28","rationale":"Employment pressure on young developers is relevant counterevidence to a reassuring reading of the statement. It does not establish that coders and architects are becoming obsolete, and continuing employment cannot settle an indefinite forecast against obsolescence. 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'Best work' is descriptive emphasis, not a measurable ranking.","Research goals means the substantive objectives supplied to the system. Generating hypotheses, experiments, or subordinate research directions does not by itself mean choosing those objectives.","For this ongoing observation, I use the February 2025 introduction through the March 9 post, the latest public reporting period for the named new system.","The assessment establishes the named example, not an exhaustive claim about every AI research system."],"support_condition":"Contemporaneous documentation shows that scientists supplied the research objectives to Google's AI co-scientist, which developed hypotheses and plans within those objectives.","contradiction_condition":"Contemporaneous documentation shows that the named system independently selected its substantive research objectives in place of human scientists."},"axis":"historical_observation","outcome":"supported","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"Google's contemporaneous documentation directly supports Marcus's named example. Scientists supplied the objectives, and the system generated and refined hypotheses and plans within them. This supports the observation about AI co-scientist; it does not establish a universal claim about all AI-for-science systems.","limitations":["Support is limited to the concrete named example.","Generating novel hypotheses can involve meaningful scientific initiative even when humans choose the overarching objective."],"evidence":[{"id":"e1","relation":"supports","excerpt":"Seven domain experts curated","locator":"Scaling test-time compute for advanced scientific reasoning","url":"https://research.google/blog/accelerating-scientific-breakthroughs-with-an-ai-co-scientist","publisher":"Google Research","event_date":"2025-02-19","available_at":"2025-02-19","explanation":"Google's launch description makes scientist-supplied objectives the system's input and describes experts selecting its evaluation goals. AI-generated hypotheses and research plans therefore operated within human-chosen objectives."}],"what_would_resolve":null,"retrospective":true,"plain_reading":true},"payload_hash":"7d9038fd763e50e999062a8f7cc8d81e6f53793aae415bb1228f58d5451ec9aa","policy_version":"2026-10-28.7","recorded_at":"2026-10-29T08:00:09Z","drafter":{"provider":"openai","model":"gpt-6-astra"},"reviewers":[{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-nobel-prizes-and-the-ai-hype-hall-612ee6-2-4x-review-anthropic-1"},{"provider":"openai","model":"gpt-6-astra","run_id":"marcus-nobel-prizes-and-the-ai-hype-hall-612ee6-2-4x-review-openai-1"}],"final_text":{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-nobel-prizes-and-the-ai-hype-hall-612ee6-2-4x-final-anthropic-1"},"status":"current","notice":null,"superseded_by":null,"evidence_meta":[{"id":"e1","source_type":"company_disclosure","audited":null,"derived":null,"metric":null}],"public_from":null,"public_until":null},"history":[],"review":{"review_due":false,"triggers":[],"last_completed_review_at":"2026-10-29T08:00:09Z","next_review_due_at":null,"horizon_now":"not_applicable"},"event_ids":["marcus-nobel-prizes-and-the-ai-hype-hall-bfc85e-assessment-published-a1"],"last_substantive_change_at":"2026-10-29T17:00:18Z"}},{"url":"https://punditclaims.ai/claims/marcus-nobel-prizes-and-the-ai-hype-hall-cd169c","standing":"current","assessment_label":"Unresolved","versions":[{"assessment_id":"marcus-nobel-prizes-and-the-ai-hype-hall-cd169c-a1","status":"current","url":"https://punditclaims.ai/claims/marcus-nobel-prizes-and-the-ai-hype-hall-cd169c#assessment-marcus-nobel-prizes-and-the-ai-hype-hall-cd169c-a1"}],"claim":{"claim_id":"marcus-nobel-prizes-and-the-ai-hype-hall-cd169c","subject_id":"gary-marcus","revision":1,"content_hash":"f23fda2019810c8a9f6ce08d8657ddedd850ed2aed5a313db182974235251bc3","normalized_proposition":"Current AI does not firmly have the sound logic, reliable causal reasoning, and originality that Nobel-calibre science requires.","kind":"observation","target":"current AI","conditions":[],"original_horizon_text":null,"topic_tags":["capabilities","reliability"],"concerns_providers":[],"selection_stratum":"declared-lead","occurrences":[{"id":"marcus-nobel-prizes-and-the-ai-hype-hall-cd169c-o1","statement_date":"2025-03-09","statement_date_precision":"day","excerpt":"Nobel-calibre science requires sound logic, reliable causal reasoning, and originality, and none of those are firmly in the grasp of current AI.","locator":"paragraph 17","document":{"url":"https://garymarcus.substack.com/p/nobel-prizes-and-the-ai-hype-hall","title":"Nobel Prizes and The AI Hype Hall of Fame","publisher":"Marcus on AI","published_at":"2025-03-09T20:06:10Z","date_uncertain":false,"date_provenance":"JSON-LD datePublished"},"observed_at":"2026-10-28T23:57:21Z","observed_via":"live","archive_url":null,"access_state":"full_public","snapshot_hash":"98d8bfc68b5ff2c8d1d7716e5bafa4de1c68a4b383e7289cfc67d267fc77952c","receipts":1}],"first_statement_date":"2025-03-09","related":[],"current":{"assessment_id":"marcus-nobel-prizes-and-the-ai-hype-hall-cd169c-a1","payload":{"claim_id":"marcus-nobel-prizes-and-the-ai-hype-hall-cd169c","subject_id":"gary-marcus","normalized_proposition":"Current AI does not firmly have the sound logic, reliable causal reasoning, and originality that Nobel-calibre science requires.","kind":"observation","conditions":[],"original_horizon_text":null,"statement":{"excerpt":"Nobel-calibre science requires sound logic, reliable causal reasoning, and originality, and none of those are firmly in the grasp of current AI.","locator":"paragraph 17","url":"https://garymarcus.substack.com/p/nobel-prizes-and-the-ai-hype-hall","date":"2025-03-09","document_title":"Nobel Prizes and The AI Hype Hall of Fame","observed_at":"2026-10-28T23:57:21Z"},"criteria":{"from_speaker":["Nobel-calibre science","sound logic","reliable causal reasoning","originality","none of those are firmly in the grasp of current AI"],"our_interpretation":["Current AI means systems existing by March 9, 2025, including specialized systems and systems using tools.","The plain reading is dependable command of each capability in demanding scientific work. It does not require perfect performance, and occasional successes do not establish dependable command.","The wording denies firm command of each capability separately. Establishing that AI lacked one capability would not establish the entire claim.","The target is the publicly reportable state of AI at publication. The interval has no stated starting date because existing systems and accumulated demonstrations inform that state; it ends on the publication date.","Nobel-calibre supplies scientific context, not a requirement that an AI receive a Nobel Prize."],"support_condition":"Representative, reproducible evaluations of systems existing at publication show substantial reliability limitations in logic and causal reasoning, while independently assessed scientific results establish comparable limitations in producing original findings. The evidence must cover all three components.","contradiction_condition":"Reproducible evaluations or independently validated scientific work establish dependable command of at least one named capability at the demanding scientific level described. An isolated success or an AI-assisted human achievement alone is insufficient."},"axis":"historical_observation","outcome":"unresolved","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"Contemporaneous benchmarks support narrower findings of unreliable logic and counterfactual reasoning. They do not establish the full claim about all current AI. FunSearch and Google's co-scientist also provide evidence of original results or hypotheses, although neither establishes dependable originality across demanding scientific work. The evidence therefore neither supports all three denials nor establishes a sufficiently broad counterexample under the ordinary reading. Calling the claim mixed would overstate what the originality examples prove.","limitations":["The two benchmark results are preprints and concern particular tasks and model samples.","The co-scientist evidence is a developer disclosure involving human guidance.","A categorical judgment would depend materially on an unstated reliability threshold."],"evidence":[{"id":"e1","relation":"supports","excerpt":"significant decline in accuracy","locator":"Abstract, second paragraph","url":"https://arxiv.org/abs/2502.01100v1","publisher":"arXiv","event_date":"2025-02","available_at":"2025-02-03","explanation":"The February evaluation documents logical-reasoning failures as task complexity increases, including evaluation of reasoning models. It supports the logic component within the tested tasks, not the entire claim about AI."},{"id":"e2","relation":"supports","excerpt":"random guessing","locator":"Abstract, sentence beginning “Our experiments”","url":"https://arxiv.org/abs/2502.11008v1","publisher":"arXiv","event_date":"2025-02","available_at":"2025-02-16","explanation":"The benchmark supports a narrower finding of unreliable formal counterfactual reasoning among the tested models. It does not establish that every contemporary AI system lacked reliable causal reasoning."},{"id":"e3","relation":"context","excerpt":"make discoveries for established open problems","locator":"Abstract, following the cap-set result","url":"https://www.nature.com/articles/s41586-023-06924-6","publisher":"Nature","event_date":"2023","available_at":"2023-12-14","explanation":"FunSearch produced new mathematical constructions using a language model and an evaluator. This materially qualifies any reading that denies AI all originality, but does not establish dependable originality across demanding scientific research."},{"id":"e4","relation":"context","excerpt":"proposed novel repurposing candidates","locator":"Section “Drug repurposing for acute myeloid leukaemia”","url":"https://research.google/blog/accelerating-scientific-breakthroughs-with-an-ai-co-scientist","publisher":"Google Research","event_date":"2025-02","available_at":"2025-02-19","explanation":"Google reports novel proposals and subsequent laboratory validation in a scientist-guided workflow. 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These results show improvement alongside substantial remaining failures; they do not measure all everyday usage."},{"id":"e2","relation":"context","excerpt":"largely prevents hallucination in legal research tasks.","locator":"Introduction, paragraph discussing providers' claims; footnote 2","url":"https://law.stanford.edu/wp-content/uploads/2024/05/Legal_RAG_Hallucinations.pdf","publisher":"Stanford University researchers","event_date":"2023","available_at":"2024-05-30","explanation":"The paper documents assurances from multiple legal AI providers beginning in 2023. These establish repeated assurances in a specific application domain, not universal promises by all LLM developers."},{"id":"e3","relation":"context","excerpt":"We also expect it to hallucinate less.","locator":"Opening section, paragraph describing early testing","url":"https://openai.com/index/introducing-gpt-4-5","publisher":"OpenAI","event_date":"2025-02-27","available_at":"2025-02-27","explanation":"The contemporary launch promised an expected reduction, not elimination. 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It does not mean no product shipped or no improvements occurred.","More than two years is a duration claim, not a forecast condition.","The described period is the project’s development through its recent release. The post supplies no exact start date.","Expensive is a qualitative spending assertion without a currency, accounting basis, or amount. Those details are not inferred from unrelated company spending."],"support_condition":"Reliable project records or corroborated reporting establish the original intended identity, subsequent GPT-4.5 release, missed capability goals, development exceeding two years, and substantial project expenditure.","contradiction_condition":"Reliable project records refute an essential component, such as the intended identity, duration exceeding two years, or failure to meet the original capability goals."},"axis":"historical_observation","outcome":"unresolved","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"Project reporting and Altman’s reproduced announcement support the connection between Orion, the intended GPT-5 project, and GPT-4.5. Reporting also describes missed expectations, while OpenAI documents actual improvements. The record does not establish the exact claim of more than two years of development, original capability targets, or a defined spending measure. The full conjunction therefore remains unresolved.","limitations":["Product improvements can coexist with missed internal expectations.","Reporting of more than 18 months is a lower bound, not proof that development lasted less than two years.","The qualitative spending assertion has an unknown accounting basis and cannot receive a decisive financial score.","The Altman statement is available here through a secondary reproduction."],"evidence":[{"id":"e1","relation":"context","excerpt":"came out in March","locator":"Section Training day, opening sentence; opening project-history paragraphs","url":"https://www.livemint.com/ai/artificial-intelligence/the-next-great-leap-in-ai-is-behind-schedule-and-crazy-expensive-11734761660034.html","publisher":"The Wall Street Journal, republished by Mint","event_date":"2024-12","available_at":"2024-12-21","explanation":"The report identifies Orion as the GPT-5 project, describes development exceeding 18 months, and reports training results below researchers’ expectations using people close to the project. 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It does not show that imminent-AGI advocacy caused the decision, that its economics required imminent AGI, or that numerous bets were questionable."},{"id":"e4","relation":"context","excerpt":"in line with both near-term and long-term demand signals.","locator":"Satya Nadella's prepared remarks, Azure infrastructure discussion","url":"https://www.microsoft.com/en-us/Investor/events/FY-2025/earnings-fy-2025-q2","publisher":"Microsoft","event_date":"2025-01-29","available_at":"2025-01-29","explanation":"Microsoft identifies demand as a rationale for capacity expansion. That provides an alternative to the claimed causal mechanism, but one company's explanation cannot refute a claim about many unspecified bets."}],"what_would_resolve":"Contemporaneous records identifying multiple commitments, showing how imminent-AGI advocacy affected the decisions, and establishing whether their rationale depended on near-term AGI.","retrospective":true,"plain_reading":true},"payload_hash":"e6d6abd5854081ce520fc0835156dc9c01fff82f221efa1e1019c21c60aae2cd","policy_version":"2026-10-28.7","recorded_at":"2026-10-29T07:06:20Z","drafter":{"provider":"openai","model":"gpt-6-astra"},"reviewers":[{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-ezra-kleins-new-take-on-agi-and-why-2015b9-4x-review-anthropic-1"},{"provider":"openai","model":"gpt-6-astra","run_id":"marcus-ezra-kleins-new-take-on-agi-and-why-2015b9-4x-review-openai-1"}],"final_text":{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-ezra-kleins-new-take-on-agi-and-why-2015b9-4x-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":"company_disclosure","audited":null,"derived":null,"metric":null}],"public_from":null,"public_until":null},"history":[],"review":{"review_due":false,"triggers":[],"last_completed_review_at":"2026-10-29T07:06:20Z","next_review_due_at":null,"horizon_now":"not_applicable"},"event_ids":["marcus-ezra-kleins-new-take-on-agi-and-why-2ee494-assessment-published-a1"],"last_substantive_change_at":"2026-10-29T17:00:18Z"}},{"url":"https://punditclaims.ai/claims/marcus-ezra-kleins-new-take-on-agi-and-why-302371","standing":"current","assessment_label":"Unresolved","versions":[{"assessment_id":"marcus-ezra-kleins-new-take-on-agi-and-why-302371-a1","status":"current","url":"https://punditclaims.ai/claims/marcus-ezra-kleins-new-take-on-agi-and-why-302371#assessment-marcus-ezra-kleins-new-take-on-agi-and-why-302371-a1"}],"claim":{"claim_id":"marcus-ezra-kleins-new-take-on-agi-and-why-302371","subject_id":"gary-marcus","revision":1,"content_hash":"b7021c6928d0aea4305a74d7849bc4f9345b4f1500396753a354b89c6b261178","normalized_proposition":"In the quarter century since 2001, none of the listed AGI prerequisites (generalization, abstract reasoning, tracking individuals, understanding structure) has received a principled solution.","kind":"observation","target":"AI field","conditions":[],"original_horizon_text":null,"topic_tags":["capabilities","agi-timelines"],"concerns_providers":[],"selection_stratum":"declared-lead","occurrences":[{"id":"marcus-ezra-kleins-new-take-on-agi-and-why-302371-o1","statement_date":"2025-03-05","statement_date_precision":"day","excerpt":"What that means is that a quarter century has passed without a principled solution to even one of those problems.","locator":"paragraph 24","document":{"url":"https://garymarcus.substack.com/p/ezra-kleins-new-take-on-agi-and-why","title":"Ezra Klein’s new take on AGI – and why I think it’s probably wrong","publisher":"Marcus on AI","published_at":"2025-03-05T17:50:28Z","date_uncertain":false,"date_provenance":"JSON-LD datePublished"},"observed_at":"2026-10-28T23:57:23Z","observed_via":"live","archive_url":null,"access_state":"full_public","snapshot_hash":"a8ec7e13bb4efd9bcd97eeed33f9cb48eb77b9ed9a747fa800107dffa4bb5dc7","receipts":1}],"first_statement_date":"2025-03-05","related":[],"current":{"assessment_id":"marcus-ezra-kleins-new-take-on-agi-and-why-302371-a1","payload":{"claim_id":"marcus-ezra-kleins-new-take-on-agi-and-why-302371","subject_id":"gary-marcus","normalized_proposition":"In the quarter century since 2001, none of the listed AGI prerequisites (generalization, abstract reasoning, tracking individuals, understanding structure) has received a principled solution.","kind":"observation","conditions":[],"original_horizon_text":null,"statement":{"excerpt":"What that means is that a quarter century has passed without a principled solution to even one of those problems.","locator":"paragraph 24","url":"https://garymarcus.substack.com/p/ezra-kleins-new-take-on-agi-and-why","date":"2025-03-05","document_title":"Ezra Klein’s new take on AGI – and why I think it’s probably wrong","observed_at":"2026-10-28T23:57:23Z"},"criteria":{"from_speaker":["a quarter century has passed","without a principled solution","to even one of those problems","published in 2001","written the year before"],"our_interpretation":["The four problems are generalization to new instances, abstract reasoning, tracking individuals and properties without hallucination, and understanding complex syntax and semantics.","The plain contextual reading of a principled solution is a reliable general method for at least one capability, not a narrow task-specific algorithm or benchmark improvement.","Treat quarter century as approximate. The stated history runs from the book’s writing in 2000 through March 5, 2025.","Evidence of failures in selected models cannot by itself establish that the entire field lacks a solution."],"support_condition":"A sufficiently broad research record establishes that all four capabilities still lack reliable general solutions by the post date.","contradiction_condition":"A reproducible, established method provides a reliable general solution to at least one of the four capabilities by the post date."},"axis":"historical_observation","outcome":"unresolved","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"Primary evaluations document remaining reasoning failures, but also substantial gains in novel-task adaptation. Neither finding establishes whether the field had a reliable general solution to any entire prerequisite. The field-wide negative is broader than the retrieved evidence, and its undefined standard materially affects the result.","limitations":["A narrow successful algorithm would not refute the contextual claim about general capabilities.","A benchmark failure would not rule out other methods.","The ARC result concerns the tested December preview, not later released versions of o3."],"evidence":[{"id":"e1","relation":"context","excerpt":"performance of all models","locator":"Abstract, sentence beginning Specifically","url":"https://arxiv.org/abs/2410.05229v1","publisher":"arXiv","event_date":"2024","available_at":"2024-10-07","explanation":"The tested models show fragility under controlled changes to mathematical questions. This supports concern about reliable reasoning in those models, but does not establish a field-wide absence of solutions to all four capabilities."},{"id":"e2","relation":"context","excerpt":"some very easy tasks","locator":"Section So is it AGI?; opening results paragraphs","url":"https://arcprize.org/blog/oai-o3-pub-breakthrough","publisher":"ARC Prize","event_date":"2024-12-20","available_at":"2024-12-20","explanation":"ARC Prize reports 75.7% and 87.5% on its semi-private evaluation for two compute settings of the tested o3 preview, describing substantial progress in novel-task adaptation. It also documents remaining easy-task failures, so the result neither proves a general solution nor supports an absence of progress."}],"what_would_resolve":"An explicit capability-level standard and a comprehensive assessment of proposed methods through March 5, 2025, or a reproducible general solution meeting that standard for one listed capability.","retrospective":true,"plain_reading":true},"payload_hash":"ffd116ccc2c254ab3670c2dc5fe1e16dbf267ca06e4e9ca301d750761de8590c","policy_version":"2026-10-28.7","recorded_at":"2026-10-29T07:06:32Z","drafter":{"provider":"openai","model":"gpt-6-astra"},"reviewers":[{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-ezra-kleins-new-take-on-agi-and-why-302371-4x-review-anthropic-1"},{"provider":"openai","model":"gpt-6-astra","run_id":"marcus-ezra-kleins-new-take-on-agi-and-why-302371-4x-review-openai-1"}],"final_text":{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-ezra-kleins-new-take-on-agi-and-why-302371-4x-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":"benchmark","audited":null,"derived":null,"metric":null}],"public_from":null,"public_until":null},"history":[],"review":{"review_due":false,"triggers":[],"last_completed_review_at":"2026-10-29T07:06:32Z","next_review_due_at":null,"horizon_now":"not_applicable"},"event_ids":["marcus-ezra-kleins-new-take-on-agi-and-why-302371-assessment-published-a1"],"last_substantive_change_at":"2026-10-29T17:00:18Z"}},{"url":"https://punditclaims.ai/claims/marcus-ezra-kleins-new-take-on-agi-and-why-3f7334","standing":"current","assessment_label":"Unresolved","versions":[{"assessment_id":"marcus-ezra-kleins-new-take-on-agi-and-why-3f7334-a2","status":"current","url":"https://punditclaims.ai/claims/marcus-ezra-kleins-new-take-on-agi-and-why-3f7334#assessment-marcus-ezra-kleins-new-take-on-agi-and-why-3f7334-a2"}],"claim":{"claim_id":"marcus-ezra-kleins-new-take-on-agi-and-why-3f7334","subject_id":"gary-marcus","revision":1,"content_hash":"474df45d43260afeae9e1ad43c93b777461e26a3653088036b49a096a02d55bd","normalized_proposition":"The AI industry has a vested interest in claiming AGI is imminent to justify lavish valuations, and has misled many non-technical government officials into thinking AGI is closer than it is.","kind":"causal","target":"the AI industry","conditions":[],"original_horizon_text":null,"topic_tags":["agi-timelines","business-economics","regulation-policy"],"concerns_providers":[],"selection_stratum":"declared-lead","occurrences":[{"id":"marcus-ezra-kleins-new-take-on-agi-and-why-3f7334-o1","statement_date":"2025-03-05","statement_date_precision":"day","excerpt":"(My view, as I explained in Taming Silicon Valley, is that industry has a vested interest in making people believe that AGI is imminent — how else to justify the lavish valuations - and that they have fooled a lot of people in government who aren’t technical experts into believing that AGI is closer than it really is).","locator":"paragraph 15","document":{"url":"https://garymarcus.substack.com/p/ezra-kleins-new-take-on-agi-and-why","title":"Ezra Klein’s new take on AGI – and why I think it’s probably wrong","publisher":"Marcus on AI","published_at":"2025-03-05T17:50:28Z","date_uncertain":false,"date_provenance":"JSON-LD datePublished"},"observed_at":"2026-10-28T23:57:23Z","observed_via":"live","archive_url":null,"access_state":"full_public","snapshot_hash":"a8ec7e13bb4efd9bcd97eeed33f9cb48eb77b9ed9a747fa800107dffa4bb5dc7","receipts":1}],"first_statement_date":"2025-03-05","related":[],"current":{"assessment_id":"marcus-ezra-kleins-new-take-on-agi-and-why-3f7334-a2","payload":{"claim_id":"marcus-ezra-kleins-new-take-on-agi-and-why-3f7334","subject_id":"gary-marcus","normalized_proposition":"The AI industry has a vested interest in claiming AGI is imminent to justify lavish valuations, and has misled many non-technical government officials into thinking AGI is closer than it is.","kind":"causal","conditions":[],"original_horizon_text":null,"statement":{"excerpt":"(My view, as I explained in Taming Silicon Valley, is that industry has a vested interest in making people believe that AGI is imminent — how else to justify the lavish valuations - 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Fundraising, lobbying, access, or agreement with industry does not alone establish that officials were misled.","Fooled is read as induced into an unjustified belief; deliberate deception is not added as a requirement.","The assessment concerns influence already exerted by March 5, 2025.","This is a qualitative claim about incentives and influence, not a measurement of a named company's valuation. No financial amount or accounting basis is asserted."],"support_condition":"Industry communications establish a connection between imminent-AGI messaging and valuation incentives, while attributable official accounts or comparable evidence establish that this messaging caused many nontechnical officials to adopt unjustifiably short timelines.","contradiction_condition":"Evidence at the claimed scale establishes that the asserted valuation mechanism did not operate, or that officials' expectations were independently formed and justified rather than induced into error by industry messaging."},"axis":"causal_assessment","outcome":"unresolved","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"OpenAI's October 2, 2024 fundraising announcement places its AGI mission alongside its financing announcement [e1]. This establishes a connection within that company's public communication, but does not establish that its valuation depended on claims of imminent AGI [e1]. The announcement identifies no officials whose timelines changed because of industry messaging and provides no basis for judging such beliefs unjustified [e1]. It therefore neither establishes nor refutes the claimed causal influence on officials by March 5, 2025 [e1].","limitations":["One company's announcement cannot establish an industry-wide valuation mechanism.","The packet provides no attributable records of affected officials, their expertise, or their beliefs before and after exposure.","No independent timeline establishes that the alleged beliefs were unjustified.","The evidence does not resolve what occurred in private communications."],"evidence":[{"id":"e1","relation":"context","excerpt":"artificial general intelligence","locator":"Opening paragraph of 'New funding to scale the benefits of AI'","url":"https://openai.com/index/scale-the-benefits-of-ai","publisher":"OpenAI","event_date":"2024-10-02","available_at":"2024-10-02","explanation":"OpenAI places its AGI mission within a fundraising announcement, making a commercial connection plausible. The announcement does not establish a near-term AGI promise, a valuation dependence on that promise, or an effect on officials' beliefs."}],"what_would_resolve":"Records establishing or refuting a link between imminent-AGI messaging and valuation incentives, plus attributable evidence of many identified nontechnical officials' beliefs before and after exposure by March 5, 2025. An independent basis for judging those timelines and evidence separating industry influence from other causes would also be needed.","retrospective":true,"plain_reading":true},"payload_hash":"a508d314dc28c7c3155643a3c9e59182ee9b8986b418f3eef1d95bfd2f13daa7","policy_version":"2026-10-28.7","recorded_at":"2026-10-29T07:13:46Z","drafter":{"provider":"openai","model":"gpt-6-astra"},"reviewers":[{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-ezra-kleins-new-take-on-agi-and-why-302371-4x-review-anthropic-1"},{"provider":"openai","model":"gpt-6-astra","run_id":"marcus-ezra-kleins-new-take-on-agi-and-why-302371-4x-review-openai-1"}],"final_text":{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-ezra-kleins-new-take-on-agi-and-why-236597-2x-final-anthropic-1"},"status":"current","notice":null,"superseded_by":null,"evidence_meta":[{"id":"e1","source_type":"company_disclosure","audited":null,"derived":null,"metric":null}],"public_from":null,"public_until":null},"history":[],"review":{"review_due":false,"triggers":[],"last_completed_review_at":"2026-10-29T07:13:46Z","next_review_due_at":null,"horizon_now":"not_applicable"},"event_ids":["marcus-ezra-kleins-new-take-on-agi-and-why-3f7334-assessment-published-a2"],"last_substantive_change_at":"2026-10-29T17:00:18Z"}},{"url":"https://punditclaims.ai/claims/marcus-ezra-kleins-new-take-on-agi-and-why-58ea33","standing":"current","assessment_label":"Unresolved","versions":[{"assessment_id":"marcus-ezra-kleins-new-take-on-agi-and-why-58ea33-a1","status":"current","url":"https://punditclaims.ai/claims/marcus-ezra-kleins-new-take-on-agi-and-why-58ea33#assessment-marcus-ezra-kleins-new-take-on-agi-and-why-58ea33-a1"}],"claim":{"claim_id":"marcus-ezra-kleins-new-take-on-agi-and-why-58ea33","subject_id":"gary-marcus","revision":1,"content_hash":"6b8fa600ecab0785cb468d388f1eec47b4067e82e0e74392fef4bb0af5cd850d","normalized_proposition":"Grok 3 was 15 times the size of Grok 2 and was generally viewed as disappointing.","kind":"observation","target":"Grok","conditions":[],"original_horizon_text":null,"topic_tags":["scaling"],"concerns_providers":[],"selection_stratum":"declared-lead","occurrences":[{"id":"marcus-ezra-kleins-new-take-on-agi-and-why-58ea33-o1","statement_date":"2025-03-05","statement_date_precision":"day","excerpt":"The fact that Grok 3 was 15x the size of Grok 2, and also generally viewed as disappointing.","locator":"paragraph 42","document":{"url":"https://garymarcus.substack.com/p/ezra-kleins-new-take-on-agi-and-why","title":"Ezra Klein’s new take on AGI – and why I think it’s probably wrong","publisher":"Marcus on AI","published_at":"2025-03-05T17:50:28Z","date_uncertain":false,"date_provenance":"JSON-LD datePublished"},"observed_at":"2026-10-28T23:57:23Z","observed_via":"live","archive_url":null,"access_state":"full_public","snapshot_hash":"a8ec7e13bb4efd9bcd97eeed33f9cb48eb77b9ed9a747fa800107dffa4bb5dc7","receipts":1}],"first_statement_date":"2025-03-05","related":[],"current":{"assessment_id":"marcus-ezra-kleins-new-take-on-agi-and-why-58ea33-a1","payload":{"claim_id":"marcus-ezra-kleins-new-take-on-agi-and-why-58ea33","subject_id":"gary-marcus","normalized_proposition":"Grok 3 was 15 times the size of Grok 2 and was generally viewed as disappointing.","kind":"observation","conditions":[],"original_horizon_text":null,"statement":{"excerpt":"The fact that Grok 3 was 15x the size of Grok 2, and also generally viewed as disappointing.","locator":"paragraph 42","url":"https://garymarcus.substack.com/p/ezra-kleins-new-take-on-agi-and-why","date":"2025-03-05","document_title":"Ezra Klein’s new take on AGI – and why I think it’s probably wrong","observed_at":"2026-10-28T23:57:23Z"},"criteria":{"from_speaker":["Grok 3","15x the size of Grok 2","generally viewed as disappointing"],"our_interpretation":["Assess both the numerical comparison and the reception claim.","Model size ordinarily means parameter count. Training compute is a plausible alternative in this scaling discussion, but the two measures are not interchangeable.","Generally viewed means a prevailing assessment across independent contemporary reviewers, not the existence of some disappointed reviewers.","Use the launch-to-post reception period, February 17 through March 5, 2025."],"support_condition":"Comparable technical disclosures establish a fifteenfold size increase, and a sufficiently broad contemporary review record establishes predominantly disappointing reception.","contradiction_condition":"Comparable size disclosures establish a materially different ratio, or a sufficiently broad contemporary review record establishes that disappointment was not the prevailing reception."},"axis":"historical_observation","outcome":"unresolved","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"The fifteenfold figure has a contemporary basis as reported compute, but that does not verify a fifteenfold increase in model size. Reception was not uniformly negative: the reviewed sources contain clear praise as well as reservations. They provide material counterevidence to Marcus's characterization without establishing a representative consensus. Neither the entire conjunction nor a clean split verdict is adequately established.","limitations":["The scale judgment depends on what size means.","A small, search-selected set of reviews cannot reliably establish prevailing opinion.","Positive benchmark results alone would not disprove disappointment relative to expectations."],"evidence":[{"id":"e3","relation":"context","excerpt":"Musk said that Grok-3 has 15x more compute than its predecessor.","locator":"Section 'Grok-3 chatbot', first paragraph","url":"https://www.silicon.co.uk/cloud/ai/elon-musks-xai-introduces-grok-3-ai-chatbot-600428","publisher":"Silicon UK","event_date":"2025-02-17","available_at":"2025-02-19","explanation":"This gives the fifteenfold number a plausible contemporary basis under a compute interpretation. It does not establish model size; reporting is used because an exact primary transcript of that numerical statement was not obtained."},{"id":"e4","relation":"contradicts","excerpt":"has done a tremendous job","locator":"Section 'Grok 3: Early Verdict'","url":"https://beebom.com/grok-3-hands-on-impressions","publisher":"Beebom","event_date":"2025-02-20","available_at":"2025-02-20","explanation":"This first-hand review is plainly positive, providing counterevidence to a characterization of the reception as generally disappointing. One review cannot establish the overall distribution of opinion."},{"id":"e5","relation":"context","excerpt":"I was left doubting the response.","locator":"Section 'Final thoughts'","url":"https://www.tomsguide.com/ai/i-tested-grok-3-with-5-prompts-heres-what-i-like-and-dont-like-about-this-chatbot","publisher":"Tom's Guide","event_date":"2025-02-18","available_at":"2025-02-18","explanation":"The reviewer expresses real reservations about research answers. The full review also praises Grok-3, so this criticism should not be counted as an unqualified negative overall verdict."}],"what_would_resolve":"Comparable technical disclosures identifying the size measure and ratio, plus a transparent survey of independent reviews published by March 5, 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Trump signed an Executive Order eliminating harmful Biden Administration AI policies and enhancing America’s global AI dominance.","locator":"Opening paragraph under REMOVING BARRIERS TO AMERICAN AI INNOVATION","url":"https://www.whitehouse.gov/fact-sheets/2025/01/fact-sheet-president-donald-j-trump-takes-action-to-enhance-americas-ai-leadership","publisher":"The White House","event_date":"2025-01-23","available_at":"2025-01-23","explanation":"The administration documents reversal of Biden AI policies and instructions to revise or rescind associated measures. 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This limits an inference that Musk originated the policy, but does not exclude his helping bring about its implementation."},{"id":"e4","relation":"context","excerpt":"DOGE’s specific goals at NIST couldn’t be immediately learned.","locator":"Paragraph beginning The feared layoffs at NIST","url":"https://www.wired.com/story/the-national-institute-of-standards-and-technology-braces-for-mass-firings","publisher":"WIRED","event_date":"2025-02-20","available_at":"2025-02-20","explanation":"The report connects anticipated NIST cuts to the administration and DOGE, but does not establish completed AI Safety Institute layoffs. 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General concerns about AI cannot substitute for evidence about those systems. There is no decisive contradictory evidence, so mixed would also be inappropriate.","limitations":["The usable evidence is news reporting based on sources describing an internal meeting; no primary meeting record was successfully read.","Intent attributed to Musk through his associates remains indirect.","The exact meaning of 'immediately' is unsettled."],"evidence":[{"id":"e1","relation":"supports","excerpt":"agencies government-wide.","locator":"Paragraph beginning 'Shedd attempted to answer these questions on Monday'","url":"https://www.wired.com/story/elon-musk-lieutenant-gsa-ai-agency","publisher":"WIRED","event_date":"2025-02-03","available_at":"2025-02-03","explanation":"WIRED reports that Musk associate Thomas Shedd outlined government AI projects for the coming weeks and months. 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I do not assign one.","Warning messages and users ignoring them are predicted features, not antecedents that make this a conditional forecast."],"support_condition":"Deployment records establish widespread routine government use of systems with material reliability problems and warnings, and representative behavioral evidence establishes that more than half of exposed users disregard those warnings.","contradiction_condition":"Evidence covering the same deployment population establishes that a central predicted feature is false, such as a representative majority consistently heeding warnings. With no fixed deadline, limited adoption at an interim date alone cannot establish failure."},"axis":"forecast_outcome","outcome":"unresolved","horizon_state":"open_horizon","evaluation_as_of":"2026-10-28","rationale":"The records establish broader access to government AI tools and documented safety concerns at VA. An experimental study also found no significant disclaimer effect on perceived credibility or trustworthiness. These findings do not establish that unreliable systems with ignored warnings became widespread across US government services. In particular, the majority-user prediction remains unmeasured. No decisive event satisfies the early-resolution rule, and 'soon' supplies no fixed deadline.","limitations":["The search does not establish that qualifying evidence cannot exist.","A statistically insignificant perception effect is not proof that most users ignored a warning.","The deployment and reliability thresholds remain qualitative."],"evidence":[{"id":"e1","relation":"context","excerpt":"access to Grok 4","locator":"Opening announcement paragraph","url":"https://www.gsa.gov/about-gsa/newsroom/news-releases/gsa-xai-partner-to-accelerate-federal-ai-adoption-09252025","publisher":"US General Services Administration","event_date":"2025-09-25","available_at":"2025-09-25","explanation":"GSA made these models available to federal agencies through its procurement channels. 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It also shows that the company publicly acknowledged this limitation; it does not establish deceptive or exaggerated AGI promotion."},{"id":"e2","relation":"supports","excerpt":"numerical values","locator":"Abstract, numerical-variation finding","url":"https://arxiv.org/abs/2410.05229v1","publisher":"arXiv","event_date":"2024-10-07","available_at":"2024-10-07","explanation":"Supports continuing fragility in grade-school mathematical reasoning among the tested models. The finding does not by itself settle whether an executive's future AGI forecast was exaggerated."},{"id":"e3","relation":"context","excerpt":"Deep learning works, and we will solve the remaining problems.","locator":"Paragraph beginning 'There are a lot of details'","url":"https://ia.samaltman.com/","publisher":"Sam Altman","event_date":"2024-09-23","available_at":"2024-09-23","explanation":"Altman's essay confidently promotes scaling and eventual superintelligence, but distinguishes future achievements from remaining present problems. 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Earlier records and later reporting can provide context about its causes.","This assesses a claimed business motive, not a specified financial amount or accounting measure; metric_scope is therefore null."],"support_condition":"Decision records, attributable testimony, or well-sourced reporting establish that pressure to recover AI investment materially influenced the February 2025 policy shift toward military and surveillance applications.","contradiction_condition":"Reliable evidence about the decision establishes that pressure to recover AI investment did not materially influence the shift. 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The policy change is documented, but its claimed cause remains unresolved [e1, e2].","limitations":["The packet contains no decision records or attributable testimony establishing whether pressure to recover AI investment materially influenced the change.","Google's public explanation does not establish the relative weight of financial and geopolitical considerations.","Removing policy restrictions does not establish deployment of particular weapons or surveillance applications."],"evidence":[{"id":"e1","relation":"context","excerpt":"more room to pursue potentially sensitive use cases","locator":"Paragraph beginning 'But in an announcement on Tuesday'","url":"https://www.wired.com/story/google-responsible-ai-principles","publisher":"WIRED","event_date":"2025-02-04","available_at":"2025-02-04","explanation":"The report documents removal of weapons and certain surveillance restrictions. It also describes former reviewers reporting commercial pressure, but does not connect this particular decision to recovering AI investment."},{"id":"e2","relation":"context","excerpt":"an increasingly complex geopolitical landscape","locator":"Section 'Updating AI Principles', third paragraph","url":"https://blog.google/innovation-and-ai/products/responsible-ai-2024-report-ongoing-work","publisher":"Google","event_date":"2025-02-04","available_at":"2025-02-04","explanation":"Google's explanation invokes geopolitical competition, wider AI adoption, common standards, and national security. These are alternative stated reasons, but the announcement neither establishes nor excludes pressure to recover investment."},{"id":"e3","relation":"context","excerpt":"cybersecurity, training, military recruitment, veterans’ healthcare","locator":"Section 'AI applications we will not pursue', paragraph explaining continued government and military work","url":"https://blog.google/innovation-and-ai/products/ai-principles","publisher":"Google","event_date":"2018-06-07","available_at":"2018-06-07","explanation":"The original principles explicitly allowed some military work and restricted surveillance that violated internationally accepted norms. This limits a literal reading of 'hello military and surveillance' as Google's first entry into either category."}],"what_would_resolve":"Contemporaneous decision records, attributable testimony from participants, or well-sourced reporting establishing whether pressure to recover AI investment materially influenced the February 4, 2025 policy change.","retrospective":true,"plain_reading":true},"payload_hash":"ab8ab161bbf77d9329f10e0fffc1c43f3bd8742687f3344674be6f5aaea8e6c8","policy_version":"2026-10-28.7","recorded_at":"2026-10-29T07:24:58Z","drafter":{"provider":"openai","model":"gpt-6-astra"},"reviewers":[{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-google-2001-dont-be-evil-ab2236-1x-review-anthropic-1"},{"provider":"openai","model":"gpt-6-astra","run_id":"marcus-google-2001-dont-be-evil-ab2236-1x-review-openai-1"}],"final_text":{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-google-2001-dont-be-evil-ab2236-1x-final-anthropic-2"},"status":"current","notice":null,"superseded_by":null,"evidence_meta":[{"id":"e1","source_type":"news_report","audited":null,"derived":null,"metric":null},{"id":"e2","source_type":"company_disclosure","audited":null,"derived":null,"metric":null},{"id":"e3","source_type":"company_disclosure","audited":null,"derived":null,"metric":null}],"public_from":null,"public_until":null},"history":[],"review":{"review_due":false,"triggers":[],"last_completed_review_at":"2026-10-29T07:24:58Z","next_review_due_at":null,"horizon_now":"not_applicable"},"event_ids":["marcus-google-2001-dont-be-evil-ab2236-assessment-published-a2"],"last_substantive_change_at":"2026-10-29T17:00:18Z"}},{"url":"https://punditclaims.ai/claims/marcus-google-2001-dont-be-evil-b72dde","standing":"current","assessment_label":"Unresolved","versions":[{"assessment_id":"marcus-google-2001-dont-be-evil-b72dde-a1","status":"current","url":"https://punditclaims.ai/claims/marcus-google-2001-dont-be-evil-b72dde#assessment-marcus-google-2001-dont-be-evil-b72dde-a1"}],"claim":{"claim_id":"marcus-google-2001-dont-be-evil-b72dde","subject_id":"gary-marcus","revision":1,"content_hash":"fecc30ad7cbe6021fd13801e38f66bc675f4f3a2ee1d870cd7e4cd5866bf6929","normalized_proposition":"Judging by its earnings call omissions, Google's AI arms race spending has not paid off so far.","kind":"observation","target":"Google AI investment","conditions":[{"text":"inferred from what earnings call omitted","type":"qualifier","from_source":true}],"original_horizon_text":null,"topic_tags":["business-economics"],"concerns_providers":[],"selection_stratum":"declared-lead","occurrences":[{"id":"marcus-google-2001-dont-be-evil-b72dde-o1","statement_date":"2025-02-05","statement_date_precision":"day","excerpt":"Reading between the lines, Google is locked in a epic arms race for AI supremacy that is not so far paying off.","locator":"paragraph 10","document":{"url":"https://garymarcus.substack.com/p/google-2001-dont-be-evil","title":"Google, 2001: Don’t Be Evil","publisher":"Marcus on AI","published_at":"2025-02-05T14:10:59Z","date_uncertain":false,"date_provenance":"JSON-LD datePublished"},"observed_at":"2026-10-28T23:57:56Z","observed_via":"live","archive_url":null,"access_state":"full_public","snapshot_hash":"c0b76759de0e2e06a848d6d36747a458ff0da2c285e327ff5a048ea6d0ac5d9c","receipts":1}],"first_statement_date":"2025-02-05","related":[],"current":{"assessment_id":"marcus-google-2001-dont-be-evil-b72dde-a1","payload":{"claim_id":"marcus-google-2001-dont-be-evil-b72dde","subject_id":"gary-marcus","normalized_proposition":"Judging by its earnings call omissions, Google's AI arms race spending has not paid off so far.","kind":"observation","conditions":["inferred from what earnings call omitted"],"original_horizon_text":null,"statement":{"excerpt":"Reading between the lines, Google is locked in a epic arms race for AI supremacy that is not so far paying off.","locator":"paragraph 10","url":"https://garymarcus.substack.com/p/google-2001-dont-be-evil","date":"2025-02-05","document_title":"Google, 2001: Don’t Be Evil","observed_at":"2026-10-28T23:57:56Z"},"criteria":{"from_speaker":["Reading between the lines","Google","arms race for AI supremacy","not so far paying off"],"our_interpretation":["Paying off means producing financial benefits commensurate with the associated investment and operating costs, rather than merely producing revenue.","The target is the position at the end of Q4 2024, the quarter explicitly discussed in the surrounding passage and reported immediately before the post.","The observation window is October through December 2024; assessing payback may also require earlier investment costs.","The earnings-call omission is Marcus's stated inference, not an external condition that activates the claim.","Marcus supplies no defined return measure or accounting basis. Referring to an earnings call without citing a matching financial figure does not establish an accounting basis."],"support_condition":"Comparable AI-specific financial records show that returns had not covered or justified the associated costs by the end of Q4 2024.","contradiction_condition":"Comparable AI-specific financial records establish material positive returns relative to the associated costs by the end of Q4 2024."},"axis":"historical_observation","outcome":"unresolved","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"The call did discuss AI monetization and billions in annual AI-related Cloud revenue. That weakens the argument from omission, but revenue does not establish investment payback. The financial release also places some shared AI research costs outside the operating segments. Neither source supplies a matching AI-only return calculation for Q4 2024. The unknown accounting basis independently prevents a supported or contradicted financial score.","limitations":["A narrower claim that Google reported no AI revenue would face contrary evidence, but that is not the proposition being graded.","Positive Cloud results and consolidated profits cannot substitute for returns on the specified AI investment.","Later growth does not establish the historical Q4 2024 outcome."],"evidence":[{"id":"e3","relation":"context","excerpt":"AI innovation and monetization","locator":"Page 15, Anat Ashkenazi's concluding prepared remarks","url":"https://s206.q4cdn.com/479360582/files/doc_financials/2024/q4/2024-q4-earnings-transcript.pdf","publisher":"Alphabet","event_date":"2025-02-04","available_at":"2025-02-04","explanation":"The CFO's surrounding remarks explicitly report billions in annual Cloud revenue from AI infrastructure and generative AI solutions. This weakens an inference from silence, but supplies neither Q4 AI-only profit nor investment payback."},{"id":"e4","relation":"context","excerpt":"Alphabet-level activities","locator":"Page 7, Segment Results, cost-allocation explanation","url":"https://abc.xyz/assets/a3/91/6d1950c148fa84c7d699abe05284/2024q4-alphabet-earnings-release.pdf","publisher":"Alphabet","event_date":"2024-12-31","available_at":"2025-02-04","explanation":"The following sentence identifies shared AI research and general-model development among those costs. Segment results therefore cannot establish an all-in return on Google's AI investment."}],"what_would_resolve":"A defined return measure and accounting basis, together with Q4 2024 records matching AI-attributable benefits to the relevant investment and operating costs.","retrospective":true,"plain_reading":true},"payload_hash":"e9efa341e13a79c1d3c9ae2ea3405ca75519af1d63a6a5f70f95f6ad9d067180","policy_version":"2026-10-28.7","recorded_at":"2026-10-29T07:24:24Z","drafter":{"provider":"openai","model":"gpt-6-astra"},"reviewers":[{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-google-2001-dont-be-evil-0f5109-4x-review-anthropic-1"},{"provider":"openai","model":"gpt-6-astra","run_id":"marcus-google-2001-dont-be-evil-0f5109-4x-review-openai-1"}],"final_text":{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-google-2001-dont-be-evil-0f5109-4x-final-anthropic-1"},"status":"current","notice":null,"superseded_by":null,"evidence_meta":[{"id":"e3","source_type":"company_disclosure","audited":null,"derived":null,"metric":{"company":"Google Cloud","metric":"Annual revenue from AI Infrastructure and Generative AI Solutions; measurement interval unspecified","currency":"USD","units":"billions","accounting_basis":"company_reported","period_start":null,"period_end":null}},{"id":"e4","source_type":"company_disclosure","audited":false,"derived":null,"metric":{"company":"Alphabet","metric":"Segment operating income and allocation of shared costs","currency":"USD","units":"millions","accounting_basis":"company_reported","period_start":"2024-10-01","period_end":"2024-12-31"}}],"public_from":null,"public_until":null},"history":[],"review":{"review_due":false,"triggers":[],"last_completed_review_at":"2026-10-29T07:24:24Z","next_review_due_at":null,"horizon_now":"not_applicable"},"event_ids":["marcus-google-2001-dont-be-evil-b72dde-assessment-published-a1"],"last_substantive_change_at":"2026-10-29T17:00:18Z"}},{"url":"https://punditclaims.ai/claims/marcus-chatgpt-in-shambles-06205f","standing":"current","assessment_label":"Can’t be checked","versions":[{"assessment_id":"marcus-chatgpt-in-shambles-06205f-a1","status":"current","url":"https://punditclaims.ai/claims/marcus-chatgpt-in-shambles-06205f#assessment-marcus-chatgpt-in-shambles-06205f-a1"}],"claim":{"claim_id":"marcus-chatgpt-in-shambles-06205f","subject_id":"gary-marcus","revision":1,"content_hash":"ca883a11016ac8ad3f42a98d17be422ef27eef5ef62036b792601ba5dd72fcee","normalized_proposition":"ChatGPT does not truly lie, but it often produces text that bears little resemblance to reality.","kind":"observation","target":"ChatGPT","conditions":[],"original_horizon_text":null,"topic_tags":["reliability"],"concerns_providers":["openai"],"selection_stratum":"declared-lead","occurrences":[{"id":"marcus-chatgpt-in-shambles-06205f-o1","statement_date":"2025-02-04","statement_date_precision":"day","excerpt":"(In fairness, ChatGPT doesn’t really lie; it just spews text that often bears little resemblance to reality, but you get my drift).","locator":"paragraph 34","document":{"url":"https://garymarcus.substack.com/p/chatgpt-in-shambles","title":"ChatGPT in Shambles","publisher":"Marcus on AI","published_at":"2025-02-04T21:23:25Z","date_uncertain":false,"date_provenance":"JSON-LD datePublished"},"observed_at":"2026-10-28T23:57:57Z","observed_via":"live","archive_url":null,"access_state":"full_public","snapshot_hash":"5f08673d001a1644ac0d1438f0de70e587ca568af3608308d36c24133ed12705","receipts":1}],"first_statement_date":"2025-02-04","related":[],"current":{"assessment_id":"marcus-chatgpt-in-shambles-06205f-a1","payload":{"claim_id":"marcus-chatgpt-in-shambles-06205f","subject_id":"gary-marcus","normalized_proposition":"ChatGPT does not truly lie, but it often produces text that bears little resemblance to reality.","kind":"observation","conditions":[],"original_horizon_text":null,"statement":{"excerpt":"(In fairness, ChatGPT doesn’t really lie; it just spews text that often bears little resemblance to reality, but you get my drift).","locator":"paragraph 34","url":"https://garymarcus.substack.com/p/chatgpt-in-shambles","date":"2025-02-04","document_title":"ChatGPT in Shambles","observed_at":"2026-10-28T23:57:57Z"},"criteria":{"from_speaker":[],"our_interpretation":[],"support_condition":"Not checked against evidence: both screens found that public evidence could not settle this claim.","contradiction_condition":"Not checked against evidence: both screens found that public evidence could not settle this claim."},"axis":"not_applicable","outcome":"not_adjudicable","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"Not checked against evidence. Two independent screens, one from each provider, both found that evidence could not settle it: one found it too vague to test: informed readers would test it in different ways and reach different answers; the other found it about something no public evidence could show, such as a private intention or a counterfactual.","limitations":[],"evidence":[],"what_would_resolve":null,"retrospective":true,"uncheckable":["no_plain_reading","no_public_evidence"]},"payload_hash":"103262a89bd83e6e0136738eaa439c6fa7e379bb3df84b1dc442f6f993f162ca","policy_version":"2026-10-28.7","recorded_at":"2026-10-29T00:54:52Z","drafter":{"provider":"anthropic","model":"claude-opus-5-5"},"reviewers":[{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-chatgpt-in-shambles-screen-anthropic-1"},{"provider":"openai","model":"gpt-6-astra","run_id":"marcus-chatgpt-in-shambles-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":"2026-10-29T00:54:52Z","next_review_due_at":null,"horizon_now":"not_applicable"},"event_ids":["marcus-chatgpt-in-shambles-06205f-assessment-published-a1"],"last_substantive_change_at":"2026-10-29T17:00:18Z"}},{"url":"https://punditclaims.ai/claims/marcus-chatgpt-in-shambles-0811ec","standing":"current","assessment_label":"Unresolved","versions":[{"assessment_id":"marcus-chatgpt-in-shambles-0811ec-a2","status":"current","url":"https://punditclaims.ai/claims/marcus-chatgpt-in-shambles-0811ec#assessment-marcus-chatgpt-in-shambles-0811ec-a2"}],"claim":{"claim_id":"marcus-chatgpt-in-shambles-0811ec","subject_id":"gary-marcus","revision":1,"content_hash":"5923c8141b3f2fd1b5cba03da5fe260d36680aaa8d8beba257c3c6481c716b9f","normalized_proposition":"In Marcus's test, the free version of ChatGPT failed several times to produce a complete list of all 50 US states.","kind":"observation","target":"ChatGPT","conditions":[{"text":"in a 10-minute test session","type":"qualifier","from_source":true}],"original_horizon_text":null,"topic_tags":["reliability"],"concerns_providers":["openai"],"selection_stratum":"declared-lead","occurrences":[{"id":"marcus-chatgpt-in-shambles-0811ec-o1","statement_date":"2025-02-04","statement_date_precision":"day","excerpt":"failed, multiple times, to include a full list of all US states","locator":"paragraph 30","document":{"url":"https://garymarcus.substack.com/p/chatgpt-in-shambles","title":"ChatGPT in Shambles","publisher":"Marcus on AI","published_at":"2025-02-04T21:23:25Z","date_uncertain":false,"date_provenance":"JSON-LD datePublished"},"observed_at":"2026-10-28T23:57:57Z","observed_via":"live","archive_url":null,"access_state":"full_public","snapshot_hash":"5f08673d001a1644ac0d1438f0de70e587ca568af3608308d36c24133ed12705","receipts":1}],"first_statement_date":"2025-02-04","related":[],"current":{"assessment_id":"marcus-chatgpt-in-shambles-0811ec-a2","payload":{"claim_id":"marcus-chatgpt-in-shambles-0811ec","subject_id":"gary-marcus","normalized_proposition":"In Marcus's test, the free version of ChatGPT failed several times to produce a complete list of all 50 US states.","kind":"observation","conditions":["in a 10-minute test session"],"original_horizon_text":null,"statement":{"excerpt":"failed, multiple times, to include a full list of all US states","locator":"paragraph 30","url":"https://garymarcus.substack.com/p/chatgpt-in-shambles","date":"2025-02-04","document_title":"ChatGPT in Shambles","observed_at":"2026-10-28T23:57:57Z"},"criteria":{"from_speaker":["ChatGPT","failed, multiple times","to include a full list of all US states","over the course of 10 minutes","latest (unpaid) version of ChatGPT"],"our_interpretation":["Read as: in Marcus's session on or about 2025-02-04, at least two ChatGPT answers that were meant to list every US state left out one or more states.","The primary record is the shared conversation Marcus linked."],"support_condition":"The transcript, or an independent check of it, shows two or more state tables that each left out at least one of the 50 states.","contradiction_condition":"The transcript shows that at most one table left out states."},"axis":"historical_observation","outcome":"unresolved","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"The claim is about one session with the free version of ChatGPT on or about 4 February 2025. Marcus describes the session as lasting 10 minutes. Under the contract, the claim is supported only if the shared transcript, or an independent check of it, shows two or more state tables that each left out at least one state. The evidence includes neither the transcript nor such a check. A post by Benjamin Todd, available on 9 February 2025, says \"GPT-4o misses out a bunch of states\" (e1). It does not say how many of Marcus's tables left out states. It also does not say whether Todd looked at Marcus's transcript. A reader comment dated 5 February 2025 says the reader's own GPT-4o run left out states (e2). That was a different session, so it does not show what happened in Marcus's session. The evidence neither confirms nor rules out two or more incomplete tables.","limitations":["The shared transcript linked in Marcus's post was not found, so none of his tables could be checked against the list of 50 states.","e1 does not show whether its author checked Marcus's transcript, or how many of Marcus's tables left out states.","e2 describes a different session and cannot show what Marcus's session produced."],"evidence":[{"id":"e1","relation":"context","excerpt":"GPT-4o misses out a bunch of states.","locator":"paragraph after the quoted prompt","url":"https://benjamintodd.substack.com/p/gary-marcus-says-ai-cant-do-things","publisher":"Benjamin Todd (Substack)","event_date":"2025-02-04","available_at":"2025-02-09","explanation":"A critic accepts that GPT-4o left out states, but he does not say whether it happened more than once in Marcus's session or whether he looked at the transcript."},{"id":"e2","relation":"context","excerpt":"States missing. Lists a state as formerly missing that wasn't.","locator":"comment by Sam, Feb 5, 2025","url":"https://garymarcus.substack.com/p/chatgpt-in-shambles/comments","publisher":"Substack reader comment","event_date":"2025-02-05","available_at":"2025-02-05","explanation":"A separate GPT-4o run that showed the same kind of failure. It is not evidence about Marcus's own session."}],"what_would_resolve":"The chatgpt.com conversation shared in Marcus's post, or an independent check of it, with each state table compared against the list of 50 US states.","retrospective":true,"plain_reading":true},"payload_hash":"e2822f41d4430634d36a2c41ed74cce8f083f62d2a0e73ca50adaa68f314f342","policy_version":"2026-10-28.7","recorded_at":"2026-10-29T03:09:18Z","drafter":{"provider":"anthropic","model":"claude-opus-5-5"},"reviewers":[{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-chatgpt-in-shambles-0811ec-4x-review-anthropic-1"},{"provider":"openai","model":"gpt-6-astra","run_id":"marcus-chatgpt-in-shambles-0811ec-4x-review-openai-1"}],"final_text":{"provider":"openai","model":"gpt-6-astra","run_id":"marcus-chatgpt-in-shambles-0811ec-3x-final-openai-1"},"status":"current","notice":null,"superseded_by":null,"evidence_meta":[{"id":"e1","source_type":"secondary_summary","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":"2026-10-29T03:09:18Z","next_review_due_at":null,"horizon_now":"not_applicable"},"event_ids":["marcus-chatgpt-in-shambles-0811ec-assessment-published-a2"],"last_substantive_change_at":"2026-10-29T17:00:18Z"}},{"url":"https://punditclaims.ai/claims/marcus-chatgpt-in-shambles-1bd53d","standing":"current","assessment_label":"Unresolved","versions":[{"assessment_id":"marcus-chatgpt-in-shambles-1bd53d-a1","status":"current","url":"https://punditclaims.ai/claims/marcus-chatgpt-in-shambles-1bd53d#assessment-marcus-chatgpt-in-shambles-1bd53d-a1"}],"claim":{"claim_id":"marcus-chatgpt-in-shambles-1bd53d","subject_id":"gary-marcus","revision":1,"content_hash":"01ce2b316fa00540efe4cf88085423c11f6ec5bd3e43e9a7ddb192a247889a0d","normalized_proposition":"In Marcus's test, ChatGPT issued many wrong corrections and acknowledged no uncertainty until its errors were pointed out.","kind":"observation","target":"ChatGPT","conditions":[{"text":"in a 10-minute test session","type":"qualifier","from_source":true}],"original_horizon_text":null,"topic_tags":["reliability"],"concerns_providers":["openai"],"selection_stratum":"declared-lead","occurrences":[{"id":"marcus-chatgpt-in-shambles-1bd53d-o1","statement_date":"2025-02-04","statement_date_precision":"day","excerpt":"issued numerous corrections that were wrong, never acknowledging uncertainty until after its errors were called out.","locator":"paragraph 33","document":{"url":"https://garymarcus.substack.com/p/chatgpt-in-shambles","title":"ChatGPT in Shambles","publisher":"Marcus on AI","published_at":"2025-02-04T21:23:25Z","date_uncertain":false,"date_provenance":"JSON-LD datePublished"},"observed_at":"2026-10-28T23:57:57Z","observed_via":"live","archive_url":null,"access_state":"full_public","snapshot_hash":"5f08673d001a1644ac0d1438f0de70e587ca568af3608308d36c24133ed12705","receipts":1}],"first_statement_date":"2025-02-04","related":[],"current":{"assessment_id":"marcus-chatgpt-in-shambles-1bd53d-a1","payload":{"claim_id":"marcus-chatgpt-in-shambles-1bd53d","subject_id":"gary-marcus","normalized_proposition":"In Marcus's test, ChatGPT issued many wrong corrections and acknowledged no uncertainty until its errors were pointed out.","kind":"observation","conditions":["in a 10-minute test session"],"original_horizon_text":null,"statement":{"excerpt":"issued numerous corrections that were wrong, never acknowledging uncertainty until after its errors were called out.","locator":"paragraph 33","url":"https://garymarcus.substack.com/p/chatgpt-in-shambles","date":"2025-02-04","document_title":"ChatGPT in Shambles","observed_at":"2026-10-28T23:57:57Z"},"criteria":{"from_speaker":["issued numerous corrections that were wrong","never acknowledging uncertainty until after its errors were called out","over the course of 10 minutes"],"our_interpretation":["The claim concerns only Marcus's reported test session, not ChatGPT's reliability generally.","The post says the test occurred that morning, so the target period is February 4, 2025.","I read 'numerous corrections' as several distinct corrective replies that still contained errors. For counting purposes, I use at least three; Marcus supplied no numerical threshold.","I read 'acknowledging uncertainty' as expressing doubt about the accuracy or completeness of the disputed answers. The order of those statements and the user's challenges matters."],"support_condition":"The complete conversation shows at least three corrective replies containing verifiable errors, with uncertainty about the disputed answers expressed only after the user challenged their accuracy.","contradiction_condition":"The complete conversation shows fewer than three incorrect corrective replies, or shows relevant uncertainty before the user challenged the answers. If one component holds and the other fails, assess the claim as mixed."},"axis":"historical_observation","outcome":"unresolved","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"The claim is checkable against the original exchange, but the linked conversation returned only a title. 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It does not identify which were produced with OpenAI Deep Research, so it cannot establish this product's contribution."},{"id":"e2","relation":"context","excerpt":"hallucinates less than prior models.","locator":"Section 3.3.6, Hallucination; discussion following Table 11","url":"https://cdn.openai.com/deep-research-system-card.pdf","publisher":"OpenAI","event_date":"2025-02","available_at":"2025-02-25","explanation":"OpenAI's tests found factual errors but better factuality than the comparison models. 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It does not mean every answer was wrong or that the systems had no useful capabilities.","For this ongoing technical state, the target period is 2025-02-02 through 2025-02-03: the launch and publication window. 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These limitations support the contextual claim that launch-version Deep Research required substantive checking during February 2–3, 2025 [e1]. The disclosure also reports successful information seeking and benchmark gains, which demonstrate capabilities without establishing dependable accuracy across research reports [e2].","limitations":["The evidence directly concerns Deep Research, not every contemporary AI system.","The company disclosure supplies no representative report-level error rate or independent audit.","Support depends on reading reliability as dependable research without substantive checking."],"evidence":[{"id":"e1","relation":"supports","excerpt":"often failing to convey uncertainty accurately.","locator":"Limitations, first paragraph","url":"https://openai.com/index/introducing-deep-research","publisher":"OpenAI","event_date":"2025-02-02","available_at":"2025-02-02","explanation":"The launch disclosure identifies source-discrimination and uncertainty-communication weaknesses directly relevant to dependable research. These support the ordinary reading that the output required substantive checking."},{"id":"e2","relation":"context","excerpt":"effectively seeking out specialized information","locator":"Humanity's Last Exam, opening paragraph","url":"https://openai.com/index/introducing-deep-research","publisher":"OpenAI","event_date":"2025-02-02","available_at":"2025-02-02","explanation":"The same disclosure reports useful information-seeking capabilities and benchmark improvements. 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Beneficial synthetic-data training in another system does not by itself falsify this open-ended product-specific forecast."},"axis":"forecast_outcome","outcome":"unresolved","horizon_state":"open_horizon","evaluation_as_of":"2026-10-28","rationale":"The searched training disclosure concerns GPT-5-generated trajectories, not documented OpenAI Deep Research outputs. General research also shows that synthetic-data training does not automatically produce collapse. Neither source settles this product-specific forecast. 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The claim therefore does not require a separate condition event before assessment."],"support_condition":"Representative behavioral evidence shows that only a small minority carefully check reports, and credible appearance demonstrably contributes to that behavior.","contradiction_condition":"Representative evidence shows widespread careful checking, or credible causal evidence refutes the claimed role of superficial plausibility. If the frequency and causal components differ, assess them separately."},"axis":"forecast_outcome","outcome":"unresolved","horizon_state":"open_horizon","evaluation_as_of":"2026-10-28","rationale":"The searches did not find evidence establishing either how few Deep Research users carefully check reports or whether credible appearance causes low checking. General AI surveys and instructed expert audits do not answer those questions. “Since” is a proposed explanation, not an unmet condition that makes the forecast unscorable. With no deadline or conclusive event, it remains unresolved.","limitations":["Checking behavior may vary substantially with expertise and task stakes.","Self-reported confidence or checking would not by itself establish actual behavior or causation."],"evidence":[],"what_would_resolve":"Representative observation of users' substantive verification behavior, combined with a controlled or otherwise credible causal study of how report presentation affects checking.","retrospective":true,"plain_reading":true},"payload_hash":"9fe58ec5a5a7f8f82ead7720b99c925db938699a108201fe5e612ebd90efd39c","policy_version":"2026-10-28.7","recorded_at":"2026-10-29T06:57:53Z","drafter":{"provider":"openai","model":"gpt-6-astra"},"reviewers":[{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-deep-research-deep-bullshit-and-the-468f01-4x-review-anthropic-1"},{"provider":"openai","model":"gpt-6-astra","run_id":"marcus-deep-research-deep-bullshit-and-the-468f01-4x-review-openai-1"}],"final_text":{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-deep-research-deep-bullshit-and-the-468f01-4x-final-anthropic-1"},"status":"current","notice":null,"superseded_by":null,"evidence_meta":[],"public_from":null,"public_until":null},"history":[],"review":{"review_due":false,"triggers":[],"last_completed_review_at":"2026-10-29T06:57:53Z","next_review_due_at":null,"horizon_now":"open_horizon"},"event_ids":["marcus-deep-research-deep-bullshit-and-the-5f6b63-assessment-published-a1"],"last_substantive_change_at":"2026-10-29T17:00:18Z"}},{"url":"https://punditclaims.ai/claims/marcus-deep-research-deep-bullshit-and-the-77d4d1","standing":"current","assessment_label":"Unresolved","versions":[{"assessment_id":"marcus-deep-research-deep-bullshit-and-the-77d4d1-a1","status":"current","url":"https://punditclaims.ai/claims/marcus-deep-research-deep-bullshit-and-the-77d4d1#assessment-marcus-deep-research-deep-bullshit-and-the-77d4d1-a1"}],"claim":{"claim_id":"marcus-deep-research-deep-bullshit-and-the-77d4d1","subject_id":"gary-marcus","revision":1,"content_hash":"6d3a073d23a7772e49a054f2acdbf40baf0186fdff84945aad6c0be0cdba6cd9","normalized_proposition":"Scammers will use Deep Research to write half-baked 'medical reports' to sell ads on websites.","kind":"forecast","target":"OpenAI Deep Research","conditions":[],"original_horizon_text":null,"topic_tags":["safety","reliability"],"concerns_providers":["openai"],"selection_stratum":"declared-lead","occurrences":[{"id":"marcus-deep-research-deep-bullshit-and-the-77d4d1-o1","statement_date":"2025-02-03","statement_date_precision":"day","excerpt":"Scammers will use it, for example, to write “medical reports” that are half-baked (in order to sell ads on the webistes that report such “research”).","locator":"paragraph 16","document":{"url":"https://garymarcus.substack.com/p/deep-research-deep-bullshit-and-the","title":"Deep Research, Deep Bullshit, and the potential (model) collapse of science","publisher":"Marcus on AI","published_at":"2025-02-03T18:54:38Z","date_uncertain":false,"date_provenance":"JSON-LD datePublished"},"observed_at":"2026-10-28T23:57:59Z","observed_via":"live","archive_url":null,"access_state":"full_public","snapshot_hash":"7ea37c25a09aff13c5de63b9a1618b371dbf7b178d74ef24ac05f625898eb0a6","receipts":1}],"first_statement_date":"2025-02-03","related":[],"current":{"assessment_id":"marcus-deep-research-deep-bullshit-and-the-77d4d1-a1","payload":{"claim_id":"marcus-deep-research-deep-bullshit-and-the-77d4d1","subject_id":"gary-marcus","normalized_proposition":"Scammers will use Deep Research to write half-baked 'medical reports' to sell ads on websites.","kind":"forecast","conditions":[],"original_horizon_text":null,"statement":{"excerpt":"Scammers will use it, for example, to write “medical reports” that are half-baked (in order to sell ads on the webistes that report such “research”).","locator":"paragraph 16","url":"https://garymarcus.substack.com/p/deep-research-deep-bullshit-and-the","date":"2025-02-03","document_title":"Deep Research, Deep Bullshit, and the potential (model) collapse of science","observed_at":"2026-10-28T23:57:59Z"},"criteria":{"from_speaker":["Scammers will use it","“medical reports” that are half-baked","in order to sell ads"],"our_interpretation":["“It” means OpenAI Deep Research, the product discussed in the surrounding passage.","A qualifying case must connect scammers, use of this specific product, materially unreliable medical reports, and publication on websites intended to earn advertising revenue.","The claim predicts an occurrence after publication, without specifying a frequency or deadline."],"support_condition":"A documented post-publication case establishes all four elements: scam activity, OpenAI Deep Research provenance, materially unreliable medical reports, and an advertising-funded publishing purpose.","contradiction_condition":"Failure to find a case during a finite period cannot contradict this undated forecast. Refuting one alleged case would only disqualify that case."},"axis":"forecast_outcome","outcome":"unresolved","horizon_state":"open_horizon","evaluation_as_of":"2026-10-28","rationale":"The searches located evidence of AI content farms, but no documented case connecting all elements of this prediction. Evidence about generative AI generally cannot establish use of OpenAI Deep Research specifically. Marcus gave no deadline, so the lack of a verified case by the evaluation date does not contradict him.","limitations":["The search cannot establish that no qualifying case exists.","Operators may not disclose the tools they use."],"evidence":[{"id":"e1","relation":"context","excerpt":"content is produced by AI.","locator":"Criteria for inclusion in the AI Content Farm tracker, first criterion","url":"https://www.newsguardtech.com/special-reports/ai-tracking-center","publisher":"NewsGuard","event_date":"2026-06-23","available_at":"2026-06-23","explanation":"The tracker documents AI-generated publishing and describes advertising incentives. It does not identify a case satisfying the product, medical-report, and scam requirements together."}],"what_would_resolve":"A dated investigation or verifiable operator record showing scammers used OpenAI Deep Research to produce materially unreliable medical reports for advertising-supported 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Deep Research lowers the effort and makes it trivial to write legit-sounding articles, paper mills will massively ramp up production, choking the peer review process and saturating the market with hard-to-find, hard-to-rectify errors.","kind":"forecast","target":"OpenAI Deep Research","conditions":[{"text":"because Deep Research lowers the effort, making it trivial to write legit-sounding articles","type":"qualifier","from_source":true}],"original_horizon_text":null,"topic_tags":["reliability","safety"],"concerns_providers":["openai"],"selection_stratum":"declared-lead","occurrences":[{"id":"marcus-deep-research-deep-bullshit-and-the-996dc7-o1","statement_date":"2025-02-03","statement_date_precision":"day","excerpt":"Because Deep Research lowers the effort, making it trivial to write legit-sounding articles, paper mill factories (there is already such a thing, designed to game citation counts, “an illegitimate, yet lucrative business“ that is growing) will massively ramp up production, choking the peer review process and saturating the market with hard-to-find, hard-to-rectify errors.","locator":"paragraph 34","document":{"url":"https://garymarcus.substack.com/p/deep-research-deep-bullshit-and-the","title":"Deep Research, Deep Bullshit, and the potential (model) collapse of science","publisher":"Marcus on AI","published_at":"2025-02-03T18:54:38Z","date_uncertain":false,"date_provenance":"JSON-LD datePublished"},"observed_at":"2026-10-28T23:57:59Z","observed_via":"live","archive_url":null,"access_state":"full_public","snapshot_hash":"7ea37c25a09aff13c5de63b9a1618b371dbf7b178d74ef24ac05f625898eb0a6","receipts":1}],"first_statement_date":"2025-02-03","related":[],"current":{"assessment_id":"marcus-deep-research-deep-bullshit-and-the-996dc7-a1","payload":{"claim_id":"marcus-deep-research-deep-bullshit-and-the-996dc7","subject_id":"gary-marcus","normalized_proposition":"Because Deep Research lowers the effort and makes it trivial to write legit-sounding articles, paper 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The causal connection is part of the forecast that must be established.","Massively and choking require evidence of a substantial change and material disruption. Isolated fraudulent papers or ordinary reviewer workload are insufficient.","No numerical threshold or deadline is supplied. I do not add one."],"support_condition":"Credible investigations or publishing records document a large increase in paper-mill output attributable to OpenAI Deep Research, together with material peer-review overload and widespread errors that persist because detection and correction are difficult.","contradiction_condition":"Decisive evidence establishes that the predicted causal sequence cannot occur. Nonoccurrence over a finite interval, successful screening at one venue, or evidence about other AI tools does not refute this undated forecast."},"axis":"forecast_outcome","outcome":"unresolved","horizon_state":"open_horizon","evaluation_as_of":"2026-10-28","rationale":"The evidence establishes a plausible mechanism and relevant surrounding problems. OpenAI advertised major time savings, NeurIPS organizers documented concerns about AI-written submissions, and a cancer-literature study found substantial suspected paper-mill contamination. But the latter predates Deep Research, and the NeurIPS report does not identify either this product or paper mills. None establishes the forecast's complete causal sequence. Marcus gave no deadline, so the absence of decisive evidence by 2026-10-28 is not a failed prediction.","limitations":["The search cannot establish that no undisclosed paper-mill use exists.","AI-text detection does not reliably establish which product generated a manuscript.","The claim's scale and disruption thresholds are qualitative.","The evidence items are context, not decisive outcome evidence."],"evidence":[{"id":"e1","relation":"context","excerpt":"in tens of minutes","locator":"Launch description, second sentence","url":"https://openai.com/index/introducing-deep-research","publisher":"OpenAI","event_date":"2025-02-02","available_at":"2025-02-02","explanation":"OpenAI described substantial time savings when launching the product. This makes the proposed mechanism plausible but does not independently establish trivial article fabrication, paper-mill adoption, or publishing harm."},{"id":"e2","relation":"context","excerpt":"acute risk to the peer review system","locator":"Section Why this policy?","url":"https://blog.neurips.cc/2026/06/02/ai-generated-papers-in-the-neurips-2026-position-paper-track","publisher":"NeurIPS Position Paper Track chairs","event_date":"2026","available_at":"2026-06-02","explanation":"Organizers reported extensive suspected AI writing and announced rejection or further scrutiny of submissions. This is relevant evidence of publishing strain, but their report does not establish paper-mill provenance, use of Deep Research, or the full predicted consequences."},{"id":"e3","relation":"context","excerpt":"substantial numbers of flagged papers","locator":"Abstract, Results","url":"https://pubmed.ncbi.nlm.nih.gov/41611528","publisher":"BMJ; abstract hosted by PubMed","event_date":"2024","available_at":"2026-01-29","explanation":"The study screened cancer papers published from 1999 through 2024 for similarity to paper-mill publications; the event date marks the corpus endpoint. 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harmless assistance or a disclosed outline alone does not satisfy it.","An evaluation publishing labelled examples of errors is not itself an instance of contamination.","This sentence supplies no minimum prevalence, so one authenticated contamination case can establish the literature component."],"support_condition":"At least one published scientific assertion or reference is demonstrably erroneous and traceable to OpenAI Deep Research output incorporated into the publication.","contradiction_condition":"Decisive evidence rules out the predicted contamination pathway. Clean publications or failure to find a case by an unstated deadline are insufficient."},"axis":"forecast_outcome","outcome":"unresolved","horizon_state":"open_horizon","evaluation_as_of":"2026-10-28","rationale":"The cited audit establishes citation problems in Deep Research-generated reports. It does not establish the additional step Marcus predicts: an error entering scientific literature as substantive content. No qualifying dated publication case was verified. With no deadline, the forecast remains open.","limitations":["This assessment uses the contextual meaning of infected as erroneous contamination.","The search cannot prove that qualifying publications do not exist.","The citation audit is a preprint and uses an operational test for likely fabricated URLs."],"evidence":[{"id":"e5","relation":"context","excerpt":"3.5% hallucinated","locator":"Section 4.1, Overall rates","url":"https://arxiv.org/html/2604.03173v1","publisher":"arXiv","event_date":"2026","available_at":"2026-04-03","explanation":"The audit reports citation-URL failures in generated research reports, using URL resolution and archival checks. It does not demonstrate that any such citation was subsequently incorporated as a reference in a scientific publication."}],"what_would_resolve":"A published scientific paper containing a verified error, with author records or an independent investigation tracing that error specifically to OpenAI Deep Research output.","retrospective":true,"plain_reading":true},"payload_hash":"0dc5f2a3d7429631d6d521a28167b4f62d9b786bc25f58c2f51a53fcfcb7c864","policy_version":"2026-10-28.7","recorded_at":"2026-10-29T06:58:01Z","drafter":{"provider":"openai","model":"gpt-6-astra"},"reviewers":[{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-deep-research-deep-bullshit-and-the-8e35f7-4x-review-anthropic-1"},{"provider":"openai","model":"gpt-6-astra","run_id":"marcus-deep-research-deep-bullshit-and-the-8e35f7-4x-review-openai-1"}],"final_text":{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-deep-research-deep-bullshit-and-the-8e35f7-4x-final-anthropic-1"},"status":"current","notice":null,"superseded_by":null,"evidence_meta":[{"id":"e5","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":"2026-10-29T06:58:01Z","next_review_due_at":null,"horizon_now":"open_horizon"},"event_ids":["marcus-deep-research-deep-bullshit-and-the-9dc1c6-assessment-published-a1"],"last_substantive_change_at":"2026-10-29T17:00:18Z"}},{"url":"https://punditclaims.ai/claims/marcus-deep-research-deep-bullshit-and-the-9f9465","standing":"current","assessment_label":"Supported","versions":[{"assessment_id":"marcus-deep-research-deep-bullshit-and-the-9f9465-a1","status":"current","url":"https://punditclaims.ai/claims/marcus-deep-research-deep-bullshit-and-the-9f9465#assessment-marcus-deep-research-deep-bullshit-and-the-9f9465-a1"}],"claim":{"claim_id":"marcus-deep-research-deep-bullshit-and-the-9f9465","subject_id":"gary-marcus","revision":1,"content_hash":"de2d6abb071913cc93ad223c3d83cbfd3048fb4a2f75eefd6d91bed093745944","normalized_proposition":"His 2023 conjecture that LLMs would be used to write garbage content undermining the internet's value has already been confirmed to some degree.","kind":"observation","target":"LLMs","conditions":[{"text":"to some degree","type":"qualifier","from_source":true}],"original_horizon_text":null,"topic_tags":["reliability","other"],"concerns_providers":[],"selection_stratum":"declared-lead","occurrences":[{"id":"marcus-deep-research-deep-bullshit-and-the-9f9465-o1","statement_date":"2025-02-03","statement_date_precision":"day","excerpt":"The basic conjecture then — which has already been confirmed to some degree – was that LLMs would be used to write garbage content undermining the value of the internet itself.","locator":"paragraph 11","document":{"url":"https://garymarcus.substack.com/p/deep-research-deep-bullshit-and-the","title":"Deep Research, Deep Bullshit, and the potential (model) collapse of science","publisher":"Marcus on AI","published_at":"2025-02-03T18:54:38Z","date_uncertain":false,"date_provenance":"JSON-LD datePublished"},"observed_at":"2026-10-28T23:57:59Z","observed_via":"live","archive_url":null,"access_state":"full_public","snapshot_hash":"7ea37c25a09aff13c5de63b9a1618b371dbf7b178d74ef24ac05f625898eb0a6","receipts":1}],"first_statement_date":"2025-02-03","related":[],"current":{"assessment_id":"marcus-deep-research-deep-bullshit-and-the-9f9465-a1","payload":{"claim_id":"marcus-deep-research-deep-bullshit-and-the-9f9465","subject_id":"gary-marcus","normalized_proposition":"His 2023 conjecture that LLMs would be used to write garbage content undermining the internet's value has already been confirmed to some degree.","kind":"observation","conditions":["to some degree"],"original_horizon_text":null,"statement":{"excerpt":"The basic conjecture then — which has already been confirmed to some degree – was that LLMs would be used to write garbage content undermining the value of the internet itself.","locator":"paragraph 11","url":"https://garymarcus.substack.com/p/deep-research-deep-bullshit-and-the","date":"2025-02-03","document_title":"Deep Research, Deep Bullshit, and the potential (model) collapse of science","observed_at":"2026-10-28T23:57:59Z"},"criteria":{"from_speaker":["already been confirmed to some degree","LLMs would be used","write garbage content","undermining the value of the internet itself"],"our_interpretation":["The context dates the original conjecture to approximately February 2023. The observation concerns evidence accumulated from that period through publication on February 3, 2025.","The ordinary reading of 'to some degree' requires documented unreliable or low-quality LLM content that causes identifiable information-quality costs. It does not require a measured decline in the internet's total economic value.","'To some degree' qualifies the strength of the assertion; it is not a conditional antecedent."],"support_condition":"Investigations available by the post identify published LLM-generated junk and observable costs such as misleading information spreading or lower-quality reference material.","contradiction_condition":"Reliable evidence refutes the alleged LLM attribution or information-quality harm and establishes that the asserted partial confirmation was absent during the target period."},"axis":"historical_observation","outcome":"supported","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"Before the post, NewsGuard had documented an apparently AI-rewritten false story spreading online. An independent preprint also found quality deficits in Wikipedia articles detected as AI-generated. Together these support Marcus’s limited assertion of confirmation 'to some degree'. They do not establish an internet-wide net decline or the stronger possibility of Google being destroyed.","limitations":["The Wikipedia study was a preprint and relied on imperfect detectors.","NewsGuard qualified its attribution of the particular rewrite to AI.","The evidence establishes specific harms, not their total prevalence or net economic impact."],"evidence":[{"id":"e1","relation":"supports","excerpt":"appeared to have used AI","locator":"Section 'I Wish I Had Known …', subsection 'GlobalVillageSpace.com'","url":"https://www.newsguardtech.com/misinformation-monitor/december-2023","publisher":"NewsGuard","event_date":"2023-11","available_at":"2023-12-27","explanation":"NewsGuard traced a viral false psychiatrist story to a site it classified as AI-generated and documented subsequent dissemination. This is a concrete information-quality harm, although its attribution of this particular rewrite to AI is qualified."},{"id":"e2","relation":"supports","excerpt":"generally lower on at least two axes","locator":"Section 5, 'Trends in Pages Flagged for AI', immediately before section 5.1","url":"https://arxiv.org/html/2410.08044v1","publisher":"arXiv; Creston Brooks, Samuel Eggert, and Denis Peskoff","event_date":"2024-08","available_at":"2024-10-10","explanation":"The study examined newly created Wikipedia articles and found fewer references and weaker integration among articles flagged as AI-generated. This supports a limited deterioration in reference quality, subject to imperfect AI detection."}],"what_would_resolve":null,"retrospective":true,"plain_reading":true},"payload_hash":"430a66e01607d10b1580aed818e7eb77c7828ca2097193acab09fbe41accaa53","policy_version":"2026-10-28.7","recorded_at":"2026-10-29T06:58:01Z","drafter":{"provider":"openai","model":"gpt-6-astra"},"reviewers":[{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-deep-research-deep-bullshit-and-the-8e35f7-4x-review-anthropic-1"},{"provider":"openai","model":"gpt-6-astra","run_id":"marcus-deep-research-deep-bullshit-and-the-8e35f7-4x-review-openai-1"}],"final_text":{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-deep-research-deep-bullshit-and-the-8e35f7-4x-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":"preprint","audited":null,"derived":null,"metric":null}],"public_from":null,"public_until":null},"history":[],"review":{"review_due":false,"triggers":[],"last_completed_review_at":"2026-10-29T06:58:01Z","next_review_due_at":null,"horizon_now":"not_applicable"},"event_ids":["marcus-deep-research-deep-bullshit-and-the-9f9465-assessment-published-a1"],"last_substantive_change_at":"2026-10-29T17:00:18Z"}},{"url":"https://punditclaims.ai/claims/marcus-deep-research-deep-bullshit-and-the-aa51b2","standing":"current","assessment_label":"Supported","versions":[{"assessment_id":"marcus-deep-research-deep-bullshit-and-the-aa51b2-a2","status":"current","url":"https://punditclaims.ai/claims/marcus-deep-research-deep-bullshit-and-the-aa51b2#assessment-marcus-deep-research-deep-bullshit-and-the-aa51b2-a2"}],"claim":{"claim_id":"marcus-deep-research-deep-bullshit-and-the-aa51b2","subject_id":"gary-marcus","revision":1,"content_hash":"78f4fba20083a91fefc794bb250eebd8f9f91d245ad9c5a4fa9a29186cc95371","normalized_proposition":"OpenAI's Deep Research is essentially a fancier remake of Meta's Galactica that can write science-sounding articles on any topic on demand.","kind":"observation","target":"OpenAI Deep Research","conditions":[],"original_horizon_text":null,"topic_tags":["capabilities","reliability"],"concerns_providers":["openai"],"selection_stratum":"declared-lead","occurrences":[{"id":"marcus-deep-research-deep-bullshit-and-the-aa51b2-o1","statement_date":"2025-02-03","statement_date_precision":"day","excerpt":"Sam Altman just released a fancier remake Deep Research, which, like Meta’s predecessor, can write science-sounding articles on demand, on any topic of your choice.","locator":"paragraph 4","document":{"url":"https://garymarcus.substack.com/p/deep-research-deep-bullshit-and-the","title":"Deep Research, Deep Bullshit, and the potential (model) collapse of science","publisher":"Marcus on AI","published_at":"2025-02-03T18:54:38Z","date_uncertain":false,"date_provenance":"JSON-LD datePublished"},"observed_at":"2026-10-28T23:57:59Z","observed_via":"live","archive_url":null,"access_state":"full_public","snapshot_hash":"7ea37c25a09aff13c5de63b9a1618b371dbf7b178d74ef24ac05f625898eb0a6","receipts":1}],"first_statement_date":"2025-02-03","related":[],"current":{"assessment_id":"marcus-deep-research-deep-bullshit-and-the-aa51b2-a2","payload":{"claim_id":"marcus-deep-research-deep-bullshit-and-the-aa51b2","subject_id":"gary-marcus","normalized_proposition":"OpenAI's Deep Research is essentially a fancier remake of Meta's Galactica that can write science-sounding articles on any topic on demand.","kind":"observation","conditions":[],"original_horizon_text":null,"statement":{"excerpt":"Sam Altman just released a fancier remake Deep Research, which, like Meta’s predecessor, can write science-sounding articles on demand, on any topic of your choice.","locator":"paragraph 4","url":"https://garymarcus.substack.com/p/deep-research-deep-bullshit-and-the","date":"2025-02-03","document_title":"Deep Research, Deep Bullshit, and the potential (model) collapse of science","observed_at":"2026-10-28T23:57:59Z"},"criteria":{"from_speaker":["just released","a fancier remake","like Meta’s predecessor","can write science-sounding articles on demand","on any topic of your choice"],"our_interpretation":["The testable comparison concerns generating research-style prose on user-selected subjects. I read 'remake' as a functional analogy, not an assertion of shared code or identical capabilities.","I read 'any topic' as broad topical choice, rather than guaranteed compliance with every conceivable prompt.","The comparison covers Galactica in November 2022 and Deep Research at its February 2025 launch. The target interval encompasses those two episodes; it does not imply continuous availability."],"support_condition":"Contemporaneous documentation and examples establish that both systems could produce scientific or research-style prose on subjects selected by users.","contradiction_condition":"Contemporaneous evidence establishes that either system lacked the claimed user-directed writing capability. Restrictions would contradict a literally unrestricted reading of 'any topic'."},"axis":"historical_observation","outcome":"supported","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"OpenAI’s February 2, 2025 launch documentation describes prompted reports for science, finance, policy, engineering, and consumer research [e1]. Reporting published November 18, 2022 documents Galactica generating wiki-style articles on subjects including protein complexes and the speed of light [e3]. These sources support the functional comparison: both could generate research-style prose on user-selected subjects [e1, e3]. Galactica also refused topics including AIDS and racism, so support applies to broad topical choice, not a literal guarantee of every topic [e3].","limitations":["The evidence supports a shared writing function, not technical equivalence or equal performance.","Research-style presentation does not establish factual reliability.","The Deep Research capability evidence is OpenAI’s own launch documentation, not an independent evaluation.","The finding depends on interpreting 'remake' as a functional analogy and 'any topic' as broad topical choice."],"evidence":[{"id":"e1","relation":"supports","excerpt":"consolidates insights from across the web","locator":"Section 'Why we built deep research'","url":"https://openai.com/index/introducing-deep-research","publisher":"OpenAI","event_date":"2025-02-02","available_at":"2025-02-02","explanation":"The launch documentation describes user prompts producing comprehensive, cited reports across science, finance, policy, engineering, and consumer topics. This establishes the broad report-generation capability in the comparison."},{"id":"e2","relation":"supports","excerpt":"literature reviews, encyclopedia articles","locator":"Section 1, Introduction; see also section 1.1","url":"https://arxiv.org/html/2211.09085v1","publisher":"arXiv; Meta AI authors","event_date":"2022-11","available_at":"2022-11-16","explanation":"The paper describes the scientific writing function behind the analogy. Its contribution section also reports actual assistance from Galactica in writing the abstract and conclusion and suggesting topics and citations."},{"id":"e3","relation":"context","excerpt":"gaps in what it can handle","locator":"Paragraph describing content-filter refusals","url":"https://www.technologyreview.com/2022/11/18/1063487/meta-large-language-model-ai-only-survived-three-days-gpt-3-science","publisher":"MIT Technology Review","event_date":"2022-11","available_at":"2022-11-18","explanation":"The report documents refusals on some topics, including AIDS. This limits the analogy and weighs against interpreting 'any topic' as an unrestricted guarantee, without defeating the broad-capability reading."}],"what_would_resolve":null,"retrospective":true,"plain_reading":true},"payload_hash":"52a667f9db057cdff5608ff81baa1bf985087233c3961c9ad7bede7a5a56add3","policy_version":"2026-10-28.7","recorded_at":"2026-10-29T07:09:30Z","drafter":{"provider":"openai","model":"gpt-6-astra"},"reviewers":[{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-deep-research-deep-bullshit-and-the-aa51b2-2x-review-anthropic-1"},{"provider":"openai","model":"gpt-6-astra","run_id":"marcus-deep-research-deep-bullshit-and-the-aa51b2-2x-review-openai-1"}],"final_text":{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-deep-research-deep-bullshit-and-the-aa51b2-1x-final-anthropic-1"},"status":"current","notice":null,"superseded_by":null,"evidence_meta":[{"id":"e1","source_type":"company_disclosure","audited":null,"derived":null,"metric":null},{"id":"e2","source_type":"preprint","audited":null,"derived":null,"metric":null},{"id":"e3","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":"2026-10-29T07:09:30Z","next_review_due_at":null,"horizon_now":"not_applicable"},"event_ids":["marcus-deep-research-deep-bullshit-and-the-aa51b2-assessment-published-a2"],"last_substantive_change_at":"2026-10-29T17:00:18Z"}},{"url":"https://punditclaims.ai/claims/marcus-deep-research-deep-bullshit-and-the-c1ac0c","standing":"current","assessment_label":"Unresolved","versions":[{"assessment_id":"marcus-deep-research-deep-bullshit-and-the-c1ac0c-a1","status":"current","url":"https://punditclaims.ai/claims/marcus-deep-research-deep-bullshit-and-the-c1ac0c#assessment-marcus-deep-research-deep-bullshit-and-the-c1ac0c-a1"}],"claim":{"claim_id":"marcus-deep-research-deep-bullshit-and-the-c1ac0c","subject_id":"gary-marcus","revision":1,"content_hash":"05b047e9592e39cb61d2c95f606322cd80e3694cc5704f9f831e1229ed6710a7","normalized_proposition":"Deep Research will flood the medical field with garbage content, causing serious trouble.","kind":"forecast","target":"OpenAI Deep Research","conditions":[],"original_horizon_text":null,"topic_tags":["safety","reliability"],"concerns_providers":["openai"],"selection_stratum":"declared-lead","occurrences":[{"id":"marcus-deep-research-deep-bullshit-and-the-c1ac0c-o1","statement_date":"2025-02-03","statement_date_precision":"day","excerpt":"When — not if — Deep Research begins to flood the medical zone with shite, we are all in trouble.","locator":"paragraph 35","document":{"url":"https://garymarcus.substack.com/p/deep-research-deep-bullshit-and-the","title":"Deep Research, Deep Bullshit, and the potential (model) collapse of science","publisher":"Marcus on AI","published_at":"2025-02-03T18:54:38Z","date_uncertain":false,"date_provenance":"JSON-LD datePublished"},"observed_at":"2026-10-28T23:57:59Z","observed_via":"live","archive_url":null,"access_state":"full_public","snapshot_hash":"7ea37c25a09aff13c5de63b9a1618b371dbf7b178d74ef24ac05f625898eb0a6","receipts":1}],"first_statement_date":"2025-02-03","related":[],"current":{"assessment_id":"marcus-deep-research-deep-bullshit-and-the-c1ac0c-a1","payload":{"claim_id":"marcus-deep-research-deep-bullshit-and-the-c1ac0c","subject_id":"gary-marcus","normalized_proposition":"Deep Research will flood the medical field with garbage content, causing serious trouble.","kind":"forecast","conditions":[],"original_horizon_text":null,"statement":{"excerpt":"When — not if — Deep Research begins to flood the medical zone with shite, we are all in trouble.","locator":"paragraph 35","url":"https://garymarcus.substack.com/p/deep-research-deep-bullshit-and-the","date":"2025-02-03","document_title":"Deep Research, Deep Bullshit, and the potential (model) collapse of science","observed_at":"2026-10-28T23:57:59Z"},"criteria":{"from_speaker":["When — not if","Deep Research","flood the medical zone with shite","we are all in trouble"],"our_interpretation":["Deep Research means OpenAI's named product.","In the surrounding discussion of scientific literature, the ordinary reading is a large influx of unreliable medical information, including medical literature, with substantial resulting problems for evidence quality or medical practice.","Shite means materially false, misleading, or unreliable content, not merely writing the speaker dislikes.","We are all in trouble expresses a serious collective problem; it does not require harm to every individual or proof of patient injury in every case.","When — not if asserts that the event will occur. It is not a conditional forecast awaiting an independently specified antecedent.","No deadline or numerical definition of flood is supplied."],"support_condition":"Public audits or investigations establish widespread dissemination of materially unreliable medical content generated by OpenAI Deep Research and substantial resulting damage to medical information, research, or practice.","contradiction_condition":"A decisive event permanently forecloses the predicted influx and consequences. Accurate outputs in limited tests or absence of a documented influx so far cannot refute an undated forecast."},"axis":"forecast_outcome","outcome":"unresolved","horizon_state":"open_horizon","evaluation_as_of":"2026-10-28","rationale":"A product-specific citation audit found apparent fabricated URLs, which supports concern about reliability but does not establish a flood of unreliable medical content. A small medical study reported favorable expert ratings, showing why individual errors cannot justify treating all output as garbage. Neither study measures widespread dissemination and resulting harm. Several promising medical evaluations could not be verified with the required reader and were excluded. No decisive event settles the forecast by 2026-10-28, and Marcus supplied no deadline.","limitations":["Benchmark errors establish capability limitations, not the prevalence of harmful medical content in circulation.","The favorable study does not rule out widespread problems elsewhere.","Some relevant primary sources were inaccessible to the required reader.","The meanings of flood and serious trouble remain qualitative."],"evidence":[{"id":"e4","relation":"context","excerpt":"3.5% hallucinated but 10.1% non-resolving","locator":"Section 4.1, Overall rates","url":"https://arxiv.org/html/2604.03173v1","publisher":"Delip Rao, Eric Wong, and Chris Callison-Burch; arXiv","event_date":"2026","available_at":"2026-04-03","explanation":"The 2026 audit classified some OpenAI Deep Research citation URLs as hallucinated, while distinguishing stale links from apparent fabrication. 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Any inclusion of experiments, personnel or data expenses must be consistent across models.","The observation concerns the training that produced the original January 20 release. Its starting date is unspecified.","Marcus cites no cost source or accounting basis in the supplied text, so accounting_basis is unknown."],"support_condition":"Comparable cost records establish that producing the complete R1 model cost less than producing comparably capable preceding frontier models.","contradiction_condition":"Comparable cost records establish that producing R1 cost as much as or more than the relevant preceding models."},"axis":"historical_observation","outcome":"unresolved","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"The disclosures make lower training cost plausible, including a later $294,000 estimate for the R1 post-training project. But that figure excludes base training, and the V3 figure measures a different model's training. Neither establishes the complete comparison Marcus asserted. His accounting basis and comparator are also unspecified.","limitations":["Unresolved does not mean the cheaper-training claim is false.","Rental-equivalent compute estimates are not audited total expenditure.","The later R1 disclosure was considered; it was not excluded merely because it appeared after the post."],"evidence":[{"id":"e1","relation":"context","excerpt":"Assuming the rental price of the H800 GPU is $2 per GPU hour, our total training costs amount to only $5.576M.","locator":"Section 1, training-cost discussion preceding the contributions","url":"https://arxiv.org/html/2412.19437v1","publisher":"DeepSeek-AI / arXiv","event_date":"2024-12","available_at":"2024-12-27","explanation":"This is a rental-equivalent compute estimate for V3's official training, excluding earlier research and ablation experiments. It is not the complete cost of training R1."},{"id":"e2","relation":"context","excerpt":"For the training of DeepSeek-R1-Zero, we employed 64*8 H800 GPUs,\nand the process required approximately 198 hours.","locator":"Section 2.4.4, Training Cost; Supplementary Table 4","url":"https://static-content.springer.com/esm/art%3A10.1038%2Fs41586-025-09422-z/MediaObjects/41586_2025_9422_MOESM1_ESM.pdf","publisher":"DeepSeek-AI / Nature supplementary information","event_date":"2025-01","available_at":"2025-09-17","explanation":"The accompanying table reports $294,000 at assumed rental rates for R1-Zero, supervised-data creation and R1 training. This is useful evidence of inexpensive post-training, but excludes the base model and does not provide a matched predecessor comparison."}],"what_would_resolve":"A specified comparator and accounting basis, with complete base-training and post-training costs for both models measured consistently.","retrospective":true,"plain_reading":true},"payload_hash":"9e2f588d8c90c60bfc4b8960f310e71180763b122589fc1e4dac3d5cd9a141c3","policy_version":"2026-10-28.7","recorded_at":"2026-10-29T07:08:56Z","drafter":{"provider":"openai","model":"gpt-6-astra"},"reviewers":[{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-five-things-most-people-dont-seem-103fa6-4x-review-anthropic-1"},{"provider":"openai","model":"gpt-6-astra","run_id":"marcus-five-things-most-people-dont-seem-103fa6-4x-review-openai-1"}],"final_text":{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-five-things-most-people-dont-seem-103fa6-4x-final-anthropic-1"},"status":"current","notice":null,"superseded_by":null,"evidence_meta":[{"id":"e1","source_type":"preprint","audited":null,"derived":null,"metric":{"company":"DeepSeek","metric":"DeepSeek-V3 official training compute cost at assumed GPU rental rates","currency":"USD","units":"millions of dollars","accounting_basis":"company_reported","period_start":null,"period_end":"2024-12"}},{"id":"e2","source_type":"company_disclosure","audited":null,"derived":null,"metric":{"company":"DeepSeek","metric":"Original R1 project's post-training compute cost at assumed GPU rental rates","currency":"USD","units":"dollars","accounting_basis":"company_reported","period_start":null,"period_end":"2025-01"}}],"public_from":null,"public_until":null},"history":[],"review":{"review_due":false,"triggers":[],"last_completed_review_at":"2026-10-29T07:08:56Z","next_review_due_at":null,"horizon_now":"not_applicable"},"event_ids":["marcus-five-things-most-people-dont-seem-103fa6-2-assessment-published-a1"],"last_substantive_change_at":"2026-10-29T17:00:18Z"}},{"url":"https://punditclaims.ai/claims/marcus-five-things-most-people-dont-seem-507f10","standing":"current","assessment_label":"Unresolved","versions":[{"assessment_id":"marcus-five-things-most-people-dont-seem-507f10-a1","status":"current","url":"https://punditclaims.ai/claims/marcus-five-things-most-people-dont-seem-507f10#assessment-marcus-five-things-most-people-dont-seem-507f10-a1"}],"claim":{"claim_id":"marcus-five-things-most-people-dont-seem-507f10","subject_id":"gary-marcus","revision":1,"content_hash":"00b251e05972fa47089d522e44dface3851e984117a4bb6786ff73c3d3c90d5b","normalized_proposition":"DeepSeek r1 is still expensive to run at inference, especially when made to reason longer like o3.","kind":"observation","target":"DeepSeek r1","conditions":[{"text":"especially when made to think longer like o3","type":"qualifier","from_source":true}],"original_horizon_text":null,"topic_tags":["business-economics"],"concerns_providers":[],"selection_stratum":"declared-lead","occurrences":[{"id":"marcus-five-things-most-people-dont-seem-507f10-o1","statement_date":"2025-01-28","statement_date_precision":"day","excerpt":"It is still expensive to operate (\"inference\"), especially if you want to make it \"think\" longer like o3.","locator":"paragraph 3","document":{"url":"https://garymarcus.substack.com/p/five-things-most-people-dont-seem","title":"Five things most people don't seem to understand about DeepSeek","publisher":"Marcus on AI","published_at":"2025-01-28T13:28:18Z","date_uncertain":false,"date_provenance":"JSON-LD datePublished"},"observed_at":"2026-10-28T23:58:03Z","observed_via":"live","archive_url":null,"access_state":"full_public","snapshot_hash":"007ee8570a935f2094d68fb12c01af1ba525d07dd18a3f06da137f7002b2860e","receipts":1}],"first_statement_date":"2025-01-28","related":[],"current":{"assessment_id":"marcus-five-things-most-people-dont-seem-507f10-a1","payload":{"claim_id":"marcus-five-things-most-people-dont-seem-507f10","subject_id":"gary-marcus","normalized_proposition":"DeepSeek r1 is still expensive to run at inference, especially when made to reason longer like o3.","kind":"observation","conditions":["especially when made to think longer like o3"],"original_horizon_text":null,"statement":{"excerpt":"It is still expensive to operate (\"inference\"), especially if you want to make it \"think\" longer like o3.","locator":"paragraph 3","url":"https://garymarcus.substack.com/p/five-things-most-people-dont-seem","date":"2025-01-28","document_title":"Five things most people don't seem to understand about DeepSeek","observed_at":"2026-10-28T23:58:03Z"},"criteria":{"from_speaker":["still expensive to operate","inference","especially if you want to make it \"think\" longer like o3"],"our_interpretation":["The ordinary reading concerns the material computing expense of serving the full original R1 model, especially per completed answer when reasoning is extended.","The reference to o3 illustrates extended reasoning; it does not assert that R1 costs more than o3.","Longer thinking is an emphasized operating scenario, not a conditional forecast.","Use the release-to-post period for this ongoing observation. No workload, cost threshold or accounting basis is supplied."],"support_condition":"Matched measurements establish substantial operating expense for ordinary R1 workloads and greater expense per completed answer when reasoning is extended.","contradiction_condition":"Matched measurements establish low operating expense under an appropriate ordinary workload and refute the claimed cost increase from extended reasoning."},"axis":"historical_observation","outcome":"unresolved","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"The engineering evidence supports the mechanism that longer reasoning consumes more resources. It does not settle whether January R1 operation was expensive under a specified workload. The direct cost disclosure concerns February and combines V3 with R1; lower API prices measure a different quantity. 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This supports the resource mechanism, but neither measures monetary expense nor falls inside the target period."},{"id":"e2","relation":"context","excerpt":"Assuming the leasing cost of one H800 GPU is $2 per hour, the total daily cost amounts to $87,072.","locator":"Inference-service statistics for the 24 hours ending February 28, 2025","url":"https://github.com/deepseek-ai/open-infra-index/blob/main/202502OpenSourceWeek/day_6_one_more_thing_deepseekV3R1_inference_system_overview.md","publisher":"DeepSeek-AI","event_date":"2025-02-28","available_at":"2025-03-01","explanation":"This is a rental-equivalent daily compute estimate for V3 and R1 together. It cannot establish January R1-only operating expense or the incremental cost of longer reasoning."},{"id":"e3","relation":"context","excerpt":"However, while the performance difference between the two models is small, the pricing difference is definitely not.","locator":"Performance and pricing","url":"https://epoch.ai/gradient-updates/what-went-into-training-deepseek-r1","publisher":"Epoch AI","event_date":"2025-01-31","available_at":"2025-01-31","explanation":"The analysis reports much lower customer output-token pricing for R1 than o1. 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The claim does not say R1 never improves accuracy.","Use the original release-to-post period as the latest publicly assessable period for this ongoing observation."],"support_condition":"Documented release-era behavior shows factual fabrication and continuing failures of dependable task performance.","contradiction_condition":"Broad, reproducible release-era evaluations establish that factual fabrication and ordinary reliability failures have been effectively resolved."},"axis":"historical_observation","outcome":"supported","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"Release-era evidence documents both unreliable prompt behavior and a fabricated self-identification. These support the narrow claim that hallucination and reliability problems remained. The later independent study corroborates that reading but is not needed to place an outcome inside the target period.","limitations":["The hallucination example comes from a reported reasoning trace, rather than a systematic release-period benchmark.","This does not establish that R1 was less reliable than every predecessor or that it made no accuracy improvements."],"evidence":[{"id":"e1","relation":"supports","excerpt":"Few-shot prompting consistently degrades its performance.","locator":"Section 5, Prompting Engineering","url":"https://arxiv.org/html/2501.12948v1","publisher":"DeepSeek-AI / arXiv","event_date":"2025-01-22","available_at":"2025-01-22","explanation":"DeepSeek reports prompt sensitivity in the released R1 model. 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This sentence makes no such exception, but a reader could take 'demos' to mean only general-purpose agents."],"support_condition":"Through 2025, AI agent products stayed at the demo, preview or experimental stage, with no significant production use or paying customers.","contradiction_condition":"During 2025, AI agent products were generally available and in real production use with paying customers at meaningful scale."},"axis":"forecast_outcome","outcome":"contradicted","horizon_state":"elapsed","evaluation_as_of":"2026-10-28","rationale":"Marcus wrote on 15 January 2025 that all we would have that year were agent demos. The window ended on 31 December 2025. Under the contract's plain reading, the sentence covers agent products in general. It does not carry the 'narrow use cases' exception from the sentence before it. Anthropic says Claude Code became generally available in May 2025 and became a critical tool for firms including Netflix, Spotify and KPMG. Anthropic also says it reached $1 billion in run-rate revenue in November 2025. Salesforce reported more than 9,500 paid Agentforce deals as of the quarter ending 31 October 2025, and a 70% quarterly rise in Agentforce accounts in production. These disclosures show paid production use in 2025, which meets the contradiction condition. A benchmark paper from 30 October 2025 found that the best agent automated 2.5% of the real-world projects in its Remote Labor Index. That bears on his point about reliability. It does not show that agent products were only demos.","limitations":["The result depends on reading 'demos' as covering all AI agent products, not only general-purpose ones.","Coding agents and customer-service agents might count as the 'narrow use cases' he allowed in the previous sentence.","Run-rate revenue and deal counts are company-reported figures, not audited annual revenue.","The benchmark measures performance on a fixed set of projects, not reliability in deployed products."],"evidence":[{"id":"e1","relation":"contradicts","excerpt":"Since becoming generally available in May 2025, Claude Code has grown from its origins as an internal engineering experiment into a critical tool for many of the world’s category-leading enterprises, including Netflix, Spotify, KPMG, L’Oreal, and Salesforce","locator":"paragraph 4","url":"https://www.anthropic.com/news/anthropic-acquires-bun-as-claude-code-reaches-usd1b-milestone","publisher":"Anthropic","event_date":"2025-05","available_at":"2025-12-03","explanation":"An agentic coding product was generally available from May 2025 and used by major companies. That is more than a demo."},{"id":"e2","relation":"contradicts","excerpt":"In November, Claude Code achieved a significant milestone: just six months after becoming available to the public, it reached $1 billion in run-rate revenue.","locator":"paragraph 1","url":"https://www.anthropic.com/news/anthropic-acquires-bun-as-claude-code-reaches-usd1b-milestone","publisher":"Anthropic","event_date":"2025-11","available_at":"2025-12-03","explanation":"A $1 billion revenue run rate within 2025 shows large-scale paid use of an agent product. This is a run rate, not realized annual revenue. It is cited only as evidence of real use."},{"id":"e3","relation":"contradicts","excerpt":"Agentforce accounts in production increased 70% quarter-over-quarter","locator":"Salesforce Company Highlights","url":"https://s205.q4cdn.com/626266368/files/doc_financials/2026/q3/CRM-Q3-FY26-Earnings-Press-Release.pdf","publisher":"Salesforce","event_date":"2025-10-31","available_at":"2025-12-03","explanation":"An enterprise agent product had more than 9,500 paid deals and growing production accounts in the quarter ending October 2025."},{"id":"e4","relation":"context","excerpt":"AI agents perform near the floor on RLI, with the highest-performing agent achieving an automation rate of 2.5%.","locator":"Abstract","url":"https://arxiv.org/abs/2510.26787v1","publisher":"arXiv (Scale AI / Center for AI Safety)","event_date":"2025-10-30","available_at":"2025-10-30","explanation":"General-purpose agents were still unreliable at end-to-end work in 2025. This backs his reliability point, but not the 'only demos' claim."}],"what_would_resolve":null,"retrospective":true,"plain_reading":true},"payload_hash":"2acb19f1551b81a837084f5b6bd52899534273597a9e2542325b6ff5b21b232d","policy_version":"2026-10-28.7","recorded_at":"2026-10-29T02:20:26Z","drafter":{"provider":"anthropic","model":"claude-opus-5-5"},"reviewers":[{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-ai-agents-hype-versus-reality-redux-2c1aed-4x-review-anthropic-2"},{"provider":"openai","model":"gpt-6-astra","run_id":"marcus-ai-agents-hype-versus-reality-redux-2c1aed-4x-review-openai-2"}],"final_text":{"provider":"openai","model":"gpt-6-astra","run_id":"marcus-ai-agents-hype-versus-reality-redux-2c1aed-4x-final-openai-2"},"status":"current","notice":null,"superseded_by":null,"evidence_meta":[{"id":"e1","source_type":"company_disclosure","audited":null,"derived":null,"metric":null},{"id":"e2","source_type":"company_disclosure","audited":null,"derived":null,"metric":null},{"id":"e3","source_type":"company_disclosure","audited":null,"derived":null,"metric":null},{"id":"e4","source_type":"preprint","audited":null,"derived":null,"metric":null}],"public_from":null,"public_until":null},"history":[],"review":{"review_due":false,"triggers":[],"last_completed_review_at":"2026-10-29T02:20:26Z","next_review_due_at":null,"horizon_now":"elapsed"},"event_ids":["marcus-ai-agents-hype-versus-reality-redux-5a7e45-assessment-published-a3"],"last_substantive_change_at":"2026-10-29T17:00:18Z"}},{"url":"https://punditclaims.ai/claims/marcus-ai-agents-hype-versus-reality-redux-65244e-2","standing":"current","assessment_label":"Supported","versions":[{"assessment_id":"marcus-ai-agents-hype-versus-reality-redux-65244e-2-a3","status":"current","url":"https://punditclaims.ai/claims/marcus-ai-agents-hype-versus-reality-redux-65244e-2#assessment-marcus-ai-agents-hype-versus-reality-redux-65244e-2-a3"}],"claim":{"claim_id":"marcus-ai-agents-hype-versus-reality-redux-65244e-2","subject_id":"gary-marcus","revision":1,"content_hash":"1947f99a8613f403236c7d1d7ea7eca642e053e695f154207c61110b3ded4e20","normalized_proposition":"As of January 2025 there is no principled, trustworthy solution to the hallucination problem.","kind":"observation","target":"generative AI","conditions":[],"original_horizon_text":null,"topic_tags":["reliability"],"concerns_providers":[],"selection_stratum":"declared-lead","occurrences":[{"id":"marcus-ai-agents-hype-versus-reality-redux-65244e-2-o1","statement_date":"2025-01-15","statement_date_precision":"day","excerpt":"That is in part because you can’t build reliable agents unless you can solve the hallucination problem, and there is still no principled trustworthy answer to that problem.","locator":"paragraph 11","document":{"url":"https://garymarcus.substack.com/p/ai-agents-hype-versus-reality-redux","title":"AI Agents: Hype versus Reality, redux","publisher":"Marcus on AI","published_at":"2025-01-15T16:41:55Z","date_uncertain":false,"date_provenance":"JSON-LD datePublished"},"observed_at":"2026-10-28T23:58:14Z","observed_via":"live","archive_url":null,"access_state":"full_public","snapshot_hash":"def0a71c39651630d3e1b0c5e17cd062b7d6e1064c7cc2afb0e94873df7229b3","receipts":1}],"first_statement_date":"2025-01-15","related":[],"current":{"assessment_id":"marcus-ai-agents-hype-versus-reality-redux-65244e-2-a3","payload":{"claim_id":"marcus-ai-agents-hype-versus-reality-redux-65244e-2","subject_id":"gary-marcus","normalized_proposition":"As of January 2025 there is no principled, trustworthy solution to the hallucination problem.","kind":"observation","conditions":[],"original_horizon_text":null,"statement":{"excerpt":"That is in part because you can’t build reliable agents unless you can solve the hallucination problem, and there is still no principled trustworthy answer to that problem.","locator":"paragraph 11","url":"https://garymarcus.substack.com/p/ai-agents-hype-versus-reality-redux","date":"2025-01-15","document_title":"AI Agents: Hype versus Reality, redux","observed_at":"2026-10-28T23:58:14Z"},"criteria":{"from_speaker":["there is still no principled trustworthy answer","the hallucination problem","you can't build reliable agents unless you can solve the hallucination problem"],"our_interpretation":["Only the factual part is scored: that as of January 2025 no method existed that reliably and verifiably eliminated or solved hallucinations in generative AI / LLMs. The claim that reliable agents require solving hallucination is a premise about what agents need and is not scored.","Plain reading of 'principled trustworthy answer': a method, grounded in theory, that reliably prevents or removes hallucinations across general use, so that outputs can be trusted. Methods that only reduce hallucination rates, or detect some of them, do not count as such an answer.","Target period: January 2025, the month of the post. The post describes an ongoing state ('still no') without naming a period."],"support_condition":"Expert or primary sources from around January 2025 say hallucinations had not been eliminated or reliably solved, or frontier models of that time still showed large measured hallucination rates.","contradiction_condition":"A method existed by January 2025 that was widely accepted, or independently shown, to reliably eliminate hallucinations in general-purpose LLMs."},"axis":"historical_observation","outcome":"supported","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"On 15 January 2025, Marcus wrote that there was still no principled, trustworthy answer to AI hallucination. Only this factual part is scored. A Nature news feature published 21 January 2025 was headlined 'AI hallucinations can't be stopped — but these techniques can limit their damage'. Its standfirst said large language models were still struggling to tell the truth. A Georgia Tech summary of that feature said researchers were working to make hallucinations less frequent and less problematic. OpenAI's system card for o3-mini, a model dated 31 January 2025, reported a 14.8% hallucination rate on PersonQA. OpenAI wrote that more work was needed to understand hallucinations. A June 2024 Nature paper describes a statistics-grounded method that detects a subset of hallucinations, called confabulations. Detecting a subset does not meet the contract's test of a method that reliably prevents hallucinations in general use. None of the cited sources describes such a method.","limitations":["'Principled trustworthy answer' has no stated threshold. The assessment uses the contract's reading: a method that reliably prevents hallucinations in general use.","The body of the Nature feature was paywalled. The assessment relies on its headline, standfirst and a university summary.","No January 2025 snapshot of a hallucination leaderboard was found.","The semantic entropy paper dates from June 2024, before the target period, and is used only as context.","The view that reliable agents need hallucination solved is a premise and was not assessed."],"evidence":[{"id":"e1","relation":"supports","excerpt":"Developers have tricks to stop artificial intelligence from making things up, but large language models are still struggling to tell the truth, the whole truth and nothing but the truth.","locator":"standfirst, under headline 'AI hallucinations can't be stopped — but these techniques can limit their damage'","url":"https://www.nature.com/articles/d41586-025-00068-5","publisher":"Nature","event_date":"2025-01-21","available_at":"2025-01-21","explanation":"A major science journal's survey of the field in January 2025 says hallucinations can't be stopped and that existing techniques only limit the damage. That matches 'no principled trustworthy answer'."},{"id":"e2","relation":"supports","excerpt":"More work is needed to understand hallucinations holistically, particularly in domains not covered\nby our evaluations (e.g., chemistry).","locator":"section 4.1.3 Hallucination Evaluations, Table 3 (PersonQA hallucination rate: 52.4% GPT-4o-mini, 27.4% o1-mini, 14.8% o3-mini)","url":"https://cdn.openai.com/o3-mini-system-card-feb10.pdf","publisher":"OpenAI","event_date":"2025-01-31","available_at":"2025-02-10","explanation":"OpenAI's newest model at the end of January 2025 still hallucinated on 14.8% of PersonQA questions, and OpenAI said more work was needed to understand hallucinations. No general solution was in hand."},{"id":"e3","relation":"supports","excerpt":"Vempala and others are working on ways to make hallucinations less frequent and less problematic.","locator":"paragraph 2","url":"https://www.cc.gatech.edu/external-news/ai-hallucinations-cant-be-stopped-these-techniques-can-limit-their-damage","publisher":"Georgia Institute of Technology, College of Computing","event_date":"2025-01-21","available_at":"2025-01-21","explanation":"Summary of the Nature feature. It shows that researchers' goal in January 2025 was to reduce hallucinations, not that they had solved them."},{"id":"e4","relation":"context","excerpt":"Here we develop new methods grounded in statistics, proposing entropy-based uncertainty estimators for LLMs to detect a subset of hallucinations—confabulations—which are arbitrary and incorrect generations.","locator":"abstract","url":"https://www.nature.com/articles/s41586-024-07421-0","publisher":"Nature (Farquhar et al.)","event_date":"2024-06","available_at":"2024-06-19","explanation":"The strongest counterpoint I found: a principled, theory-grounded method from 2024. But it only detects a subset of hallucinations and does not prevent them, so it is not a full trustworthy answer."}],"what_would_resolve":null,"retrospective":true,"plain_reading":true},"payload_hash":"c19675bbe527661aaf7a2d26853a84e1ea3d2a932d42a393feee428a6fec2a51","policy_version":"2026-10-28.7","recorded_at":"2026-10-29T02:20:26Z","drafter":{"provider":"anthropic","model":"claude-opus-5-5"},"reviewers":[{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-ai-agents-hype-versus-reality-redux-2c1aed-4x-review-anthropic-2"},{"provider":"openai","model":"gpt-6-astra","run_id":"marcus-ai-agents-hype-versus-reality-redux-2c1aed-4x-review-openai-2"}],"final_text":{"provider":"openai","model":"gpt-6-astra","run_id":"marcus-ai-agents-hype-versus-reality-redux-2c1aed-4x-final-openai-2"},"status":"current","notice":null,"superseded_by":null,"evidence_meta":[{"id":"e1","source_type":"news_report","audited":null,"derived":null,"metric":null},{"id":"e2","source_type":"company_disclosure","audited":null,"derived":null,"metric":null},{"id":"e3","source_type":"secondary_summary","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":"2026-10-29T02:20:26Z","next_review_due_at":null,"horizon_now":"not_applicable"},"event_ids":["marcus-ai-agents-hype-versus-reality-redux-65244e-2-assessment-published-a3"],"last_substantive_change_at":"2026-10-29T17:00:18Z"}},{"url":"https://punditclaims.ai/claims/marcus-ai-agents-hype-versus-reality-redux-9e0e0e-2","standing":"current","assessment_label":"Can’t be checked","versions":[{"assessment_id":"marcus-ai-agents-hype-versus-reality-redux-9e0e0e-2-a1","status":"current","url":"https://punditclaims.ai/claims/marcus-ai-agents-hype-versus-reality-redux-9e0e0e-2#assessment-marcus-ai-agents-hype-versus-reality-redux-9e0e0e-2-a1"}],"claim":{"claim_id":"marcus-ai-agents-hype-versus-reality-redux-9e0e0e-2","subject_id":"gary-marcus","revision":1,"content_hash":"ac6872da02d804339fb5ae79e66a5857b1bbd391cd6bb3bf173cd9adb36923bc","normalized_proposition":"AI agents probably will not do a huge fraction of human knowledge work this decade, except in narrow use cases.","kind":"forecast","target":"AI agents","conditions":[{"text":"except in narrow use cases","type":"qualifier","from_source":true}],"original_horizon_text":"this decade","topic_tags":["agents","labour","agi-timelines"],"concerns_providers":[],"selection_stratum":"declared-lead","occurrences":[{"id":"marcus-ai-agents-hype-versus-reality-redux-9e0e0e-2-o1","statement_date":"2025-01-15","statement_date_precision":"day","excerpt":"But not this year (or next, or the one after that, and probably not this decade, except in narrow use cases).","locator":"paragraph 5","document":{"url":"https://garymarcus.substack.com/p/ai-agents-hype-versus-reality-redux","title":"AI Agents: Hype versus Reality, redux","publisher":"Marcus on AI","published_at":"2025-01-15T16:41:55Z","date_uncertain":false,"date_provenance":"JSON-LD datePublished"},"observed_at":"2026-10-28T23:58:14Z","observed_via":"live","archive_url":null,"access_state":"full_public","snapshot_hash":"def0a71c39651630d3e1b0c5e17cd062b7d6e1064c7cc2afb0e94873df7229b3","receipts":1}],"first_statement_date":"2025-01-15","related":[],"current":{"assessment_id":"marcus-ai-agents-hype-versus-reality-redux-9e0e0e-2-a1","payload":{"claim_id":"marcus-ai-agents-hype-versus-reality-redux-9e0e0e-2","subject_id":"gary-marcus","normalized_proposition":"AI agents probably will not do a huge fraction of human knowledge work this decade, except in narrow use cases.","kind":"forecast","conditions":["except in narrow use cases"],"original_horizon_text":"this decade","statement":{"excerpt":"But not this year (or next, or the one after that, and probably not this decade, except in narrow use cases).","locator":"paragraph 5","url":"https://garymarcus.substack.com/p/ai-agents-hype-versus-reality-redux","date":"2025-01-15","document_title":"AI Agents: Hype versus Reality, redux","observed_at":"2026-10-28T23:58:14Z"},"criteria":{"from_speaker":["except in narrow use cases","this decade"],"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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Only a decisive, lasting failure of agents to do much knowledge work over a very long period would count against it."},"axis":"forecast_outcome","outcome":"unresolved","horizon_state":"open_horizon","evaluation_as_of":"2026-10-28","rationale":"Marcus forecast that AI agents will eventually do a huge fraction of human knowledge work and be worth trillions. He said this would happen 'no time soon' and gave no date. A preprint submitted on 30 October 2025 introduced the Remote Labor Index, a benchmark of real-world, economically valuable remote work projects. It reported that the best-performing agent had an automation rate of 2.5% (e1). This fits 'no time soon', but it does not test the long-term forecast. The contract allows early support only if agents are measured doing a large share of knowledge work and creating value in the trillions. No cited evidence shows either. Because the claim has no deadline, near-term results cannot contradict it. The hedged 'maybe physical labor too' is not assessed.","limitations":["The only evidence is one benchmark from late 2025. It measures what agents can do on a set of projects, not what share of real knowledge work they actually do.","'Worth trillions' is not tied to a specific measure, such as market value, revenue, or economic output.","'Huge fraction' has no stated threshold.","No labor statistics or estimates of the value agents create were found."],"evidence":[{"id":"e1","relation":"context","excerpt":"AI agents perform near the floor on RLI, with the highest-performing agent achieving an automation rate of 2.5%.","locator":"Abstract","url":"https://arxiv.org/abs/2510.26787v1","publisher":"arXiv (Scale AI / Center for AI Safety)","event_date":"2025-10-30","available_at":"2025-10-30","explanation":"In late 2025, agents could automate very little real remote work. 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They cannot settle a universal claim about his own reading."],"support_condition":"It would be supported only if the full set of declarations he saw were known, and each one used a definition weaker than the original AGI definitions (Legg, Goertzel, his own 2022 wording).","contradiction_condition":"It would be contradicted by a public pre-January-2025 declaration that AGI had been reached, which he is known to have seen, and which used the original human-level, reliable-generalization standard rather than a weaker one."},"axis":"not_applicable","outcome":"not_adjudicable","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"The claim covers every declaration of AGI that Marcus himself has seen. No public record lists those declarations, so the word 'every' cannot be checked. Whether a definition is 'redefining downwards' also depends on which definition is taken as the standard. The contract treats that as a judgement call, not a measurement. 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It is not scored as a separate factual claim.","'Nearly two years' is read as the period since GPT-4's release in March 2023, up to the post date."],"support_condition":"Evidence from the period shows that GPT-5 was still unreleased or in development as of early January 2025.","contradiction_condition":"Evidence shows that OpenAI publicly released a model named GPT-5 on or before 9 January 2025."},"axis":"historical_observation","outcome":"supported","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"The claim is that GPT-5 had not been released by 9 January 2025. On 21 December 2024, TechCrunch, summarizing the WSJ, reported that GPT-5 was still in development and behind schedule. It added that OpenAI had said it would not release Orion in 2024. OpenAI's own launch page introduces GPT-5 on 7 August 2025. So GPT-5 had not come out when Marcus wrote. The phrase about 'daily shouts of exponential progress' is rhetoric and is not scored.","limitations":["The in-period evidence is a news summary of a paywalled WSJ report.","The rhetorical framing about 'exponential progress' claims was not tested."],"evidence":[{"id":"e1","relation":"supports","excerpt":"The company previously said it would not be releasing a model code-named Orion this year.","locator":"final paragraph of article body","url":"https://techcrunch.com/2024/12/21/openais-gpt-5-reportedly-falling-short-of-expectations","publisher":"TechCrunch","event_date":"2024-12-21","available_at":"2024-12-21","explanation":"On 21 December 2024, GPT-5 (Orion) was reported as still in development, and OpenAI had said it would not release it in 2024. 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That means delays, smaller-than-hoped gains, or high costs relative to results.","He named no period. I use late 2024 through the post date, when reports on Orion's development came out."],"support_condition":"Credible reporting from late 2024 to early January 2025 describes delays, disappointing gains, or cost problems in OpenAI's GPT-5/Orion development.","contradiction_condition":"Credible reporting from the period shows GPT-5/Orion development on schedule and meeting expectations, or OpenAI shipped a model widely seen as worthy of the GPT-5 name in that period."},"axis":"historical_observation","outcome":"supported","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"In the two months before the post, two major outlets reported trouble with OpenAI's next model. In November 2024, The Information, citing OpenAI staff, said Orion's gains over GPT-4 were far smaller than the jump from GPT-3 to GPT-4. In December 2024, the WSJ said GPT-5 was behind schedule, that training ran slower than expected, and that results did not yet justify the cost. That fits the plain reading of 'struggling to build a model worthy of the GPT-5 name'. 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Sora's developer acknowledged incorrect object-state changes, while a multi-family language-model evaluation found substantial errors even in its strongest entity tracker. Both sources also document real capabilities or improvements. Those successes qualify the claim's breadth but do not establish dependable reasoning across the situations tested.","limitations":["This outcome depends on reading consistently as recurring gaps, not failure on every task.","The language-model study does not cover all frontier models available at the post date.","Sora's disclosure supplies examples and qualitative findings rather than a comprehensive error rate."],"evidence":[{"id":"e1","relation":"supports","excerpt":"correct changes in object state.","locator":"Discussion, simulator limitations","url":"https://openai.com/research/video-generation-models-as-world-simulators","publisher":"OpenAI","event_date":"2024-02","available_at":"2024-02-15","explanation":"OpenAI directly acknowledges incorrect object-state changes in Sora. The report also describes successful object persistence and some physical interactions, qualifying any reading that denies all entity-related competence."},{"id":"e2","relation":"supports","excerpt":"64.9 accuracy on 1+ operations","locator":"Section 3.5, Experiment 3: Effect of Alignment Tuning","url":"https://arxiv.org/html/2405.21068v1","publisher":"arXiv / Kim, Schuster, and Toshniwal","event_date":"2024","available_at":"2024-05-31","explanation":"The best tested model still made substantial errors on nontrivial entity tracking. Comparisons across model families also show that code training improves performance, which is meaningful counterevidence to a stronger claim of no progress or no competence."}],"what_would_resolve":null,"retrospective":true,"plain_reading":true},"payload_hash":"c00b6d74137c1c16132e38df3c57cd8b889d1ec6ba3e072e7716dd6fed591188","policy_version":"2026-10-28.7","recorded_at":"2026-10-29T02:34:08Z","drafter":{"provider":"openai","model":"gpt-6-astra"},"reviewers":[{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-ai-still-lacks-common-sense-70-years-a13f79-4x-review-anthropic-1"},{"provider":"openai","model":"gpt-6-astra","run_id":"marcus-ai-still-lacks-common-sense-70-years-a13f79-4x-review-openai-1"}],"final_text":{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-ai-still-lacks-common-sense-70-years-a13f79-4x-final-anthropic-1"},"status":"current","notice":null,"superseded_by":null,"evidence_meta":[{"id":"e1","source_type":"company_disclosure","audited":null,"derived":null,"metric":null},{"id":"e2","source_type":"preprint","audited":null,"derived":null,"metric":null}],"public_from":null,"public_until":null},"history":[],"review":{"review_due":false,"triggers":[],"last_completed_review_at":"2026-10-29T02:34:08Z","next_review_due_at":null,"horizon_now":"not_applicable"},"event_ids":["marcus-ai-still-lacks-common-sense-70-years-a13f79-assessment-published-a1"],"last_substantive_change_at":"2026-10-29T17:00:18Z"}},{"url":"https://punditclaims.ai/claims/marcus-ai-still-lacks-common-sense-70-years-b0c0ed","standing":"current","assessment_label":"Supported","versions":[{"assessment_id":"marcus-ai-still-lacks-common-sense-70-years-b0c0ed-a1","status":"current","url":"https://punditclaims.ai/claims/marcus-ai-still-lacks-common-sense-70-years-b0c0ed#assessment-marcus-ai-still-lacks-common-sense-70-years-b0c0ed-a1"}],"claim":{"claim_id":"marcus-ai-still-lacks-common-sense-70-years-b0c0ed","subject_id":"gary-marcus","revision":1,"content_hash":"420b7c43cdde83703ef397607ae664b74449311b3fdc2b625744e1eabfab449c","normalized_proposition":"Much of the commonsense reasoning problem the authors raised in 2015 and 2019 remains unsolved in AI as of January 2025.","kind":"observation","target":"commonsense reasoning in AI","conditions":[{"text":"despite apparent progress in the last few years","type":"qualifier","from_source":true}],"original_horizon_text":null,"topic_tags":["capabilities"],"concerns_providers":[],"selection_stratum":"declared-lead","occurrences":[{"id":"marcus-ai-still-lacks-common-sense-70-years-b0c0ed-o1","statement_date":"2025-01-05","statement_date_precision":"day","excerpt":"Depressingly, despite all of the apparent progress in the last few years, much of what we raised in 2015 and re-emphasized in 2019 remains unsolved.","locator":"paragraph 11","document":{"url":"https://garymarcus.substack.com/p/ai-still-lacks-common-sense-70-years","title":"AI still lacks “common” sense, 70 years later","publisher":"Marcus on AI","published_at":"2025-01-05T18:46:20Z","date_uncertain":false,"date_provenance":"JSON-LD datePublished"},"observed_at":"2026-10-28T23:58:23Z","observed_via":"live","archive_url":null,"access_state":"full_public","snapshot_hash":"74fe61386387dea0130d5afe74b03873fbc1d065a6d740adbabe4eaad9576682","receipts":1}],"first_statement_date":"2025-01-05","related":[],"current":{"assessment_id":"marcus-ai-still-lacks-common-sense-70-years-b0c0ed-a1","payload":{"claim_id":"marcus-ai-still-lacks-common-sense-70-years-b0c0ed","subject_id":"gary-marcus","normalized_proposition":"Much of the commonsense reasoning problem the authors raised in 2015 and 2019 remains unsolved in AI as of January 2025.","kind":"observation","conditions":["despite apparent progress in the last few years"],"original_horizon_text":null,"statement":{"excerpt":"Depressingly, despite all of the apparent progress in the last few years, much of what we raised in 2015 and re-emphasized in 2019 remains unsolved.","locator":"paragraph 11","url":"https://garymarcus.substack.com/p/ai-still-lacks-common-sense-70-years","date":"2025-01-05","document_title":"AI still lacks “common” sense, 70 years later","observed_at":"2026-10-28T23:58:23Z"},"criteria":{"from_speaker":["much of what we raised in 2015","re-emphasized in 2019","remains unsolved","despite all of the apparent progress"],"our_interpretation":["The ordinary reading is that substantial problems in reliable everyday reasoning persist, not that every example from the earlier publications still defeats AI.","The earlier publications identify physical understanding, reasoning about people's knowledge and intentions, and applying knowledge reliably across situations.","For the ongoing state described on January 5, I use 2024, the latest completed calendar year of publicly reportable research. This is an assessment window, not a claim that AI research follows an annual reporting cycle.","Substantial failures across distinct relevant domains can support 'much'; I do not interpret it as a numerical majority of an unspecified problem list."],"support_condition":"Independent evaluations in the target period establish substantial unresolved reliability problems across multiple commonsense domains identified in the earlier work.","contradiction_condition":"Evidence establishes broadly reliable solutions across those domains, leaving only isolated or peripheral problems."},"axis":"historical_observation","outcome":"supported","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"Independent 2024 evaluations document substantial failures in physical understanding and applied social reasoning. These match major concerns in the earlier work. Stronger performance on some mental-state tasks and improvements from guided prompting qualify the claim but do not establish that the broader reliability problems were solved. Support is for the ordinary qualitative reading of 'much,' not a numerical majority of historical examples.","limitations":["The result depends on interpreting 'unsolved' as lacking broad reliability.","Benchmark failures establish limits in tested systems, not impossibility for every AI approach.","The complete 2019 book was not independently retested; its scope was checked through the supplied passage and the authors' contemporary interview."],"evidence":[{"id":"e1","relation":"supports","excerpt":"struggle with answering","locator":"Evaluation Conclusion, immediately before section 6","url":"https://arxiv.org/html/2402.06119v2","publisher":"Zheng et al., ICML 2024; arXiv","event_date":"2024","available_at":"2024-07-28","explanation":"ContPhy finds persistent problems inferring physical properties and reasoning about dynamics and counterfactuals. These overlap the earlier work's physical-understanding concerns; they do not retest every historical example."},{"id":"e2","relation":"supports","excerpt":"judgment task.","locator":"Section 5.1, Judgment of behavior","url":"https://arxiv.org/html/2410.13648v1","publisher":"Gu et al.; arXiv","event_date":"2024","available_at":"2024-10-17","explanation":"SimpleToM reports failures applying information about people's knowledge to behavior judgments, including a 59.5% judgment score for o1-preview. It also reports large improvements from specific prompting interventions, so the evidence concerns dependable application rather than complete absence of knowledge."},{"id":"e3","relation":"context","excerpt":"better-than-human performance","locator":"Section 8, Conclusion","url":"https://arxiv.org/html/2405.18870v2","publisher":"Street et al.; arXiv","event_date":"2024","available_at":"2024-05-31","explanation":"This is material evidence of progress in reasoning about mental states. Success on this task does not establish reliable performance across physical and applied social 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be checked","versions":[{"assessment_id":"marcus-ai-still-lacks-common-sense-70-years-b137c9-a1","status":"current","url":"https://punditclaims.ai/claims/marcus-ai-still-lacks-common-sense-70-years-b137c9#assessment-marcus-ai-still-lacks-common-sense-70-years-b137c9-a1"}],"claim":{"claim_id":"marcus-ai-still-lacks-common-sense-70-years-b137c9","subject_id":"gary-marcus","revision":1,"content_hash":"be726d3b2d81e621c91f71228fa292eb71111ce445a78d8c52f4543e1658d37b","normalized_proposition":"General-purpose AI will begin to mature only once researchers directly tackle commonsense reasoning about entities interacting in space and persisting in time.","kind":"conditional_forecast","target":"general-purpose AI","conditions":[{"text":"only once researchers grapple directly with commonsense reasoning about entities in space and 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space and persist in time that the field of general-purpose AI will finally begin to mature.","locator":"paragraph 44","url":"https://garymarcus.substack.com/p/ai-still-lacks-common-sense-70-years","date":"2025-01-05","document_title":"AI still lacks “common” sense, 70 years later","observed_at":"2026-10-28T23:58:23Z"},"criteria":{"from_speaker":["only once researchers grapple directly with commonsense reasoning about entities in space and time"],"our_interpretation":[],"support_condition":"Not checked against evidence: both screens found that public evidence could not settle this claim.","contradiction_condition":"Not checked against evidence: both screens found that public evidence could not settle this claim."},"axis":"not_applicable","outcome":"not_adjudicable","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"Not checked against evidence. Two independent screens, one from each provider, both found this too vague to test: informed readers would test it in different ways and reach different answers.","limitations":[],"evidence":[],"what_would_resolve":null,"retrospective":true,"uncheckable":["no_plain_reading"]},"payload_hash":"a56c011285b52a13899df3ee19f1378a69779a2679fd11748b68a31de2db23a1","policy_version":"2026-10-28.7","recorded_at":"2026-10-29T00:44:36Z","drafter":{"provider":"anthropic","model":"claude-opus-5-5"},"reviewers":[{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-ai-still-lacks-common-sense-70-years-screen-anthropic-1"},{"provider":"openai","model":"gpt-6-astra","run_id":"marcus-ai-still-lacks-common-sense-70-years-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":"2026-10-29T00:44:36Z","next_review_due_at":null,"horizon_now":"not_applicable"},"event_ids":["marcus-ai-still-lacks-common-sense-70-years-b137c9-assessment-published-a1"],"last_substantive_change_at":"2026-10-29T17:00:18Z"}},{"url":"https://punditclaims.ai/claims/marcus-ai-still-lacks-common-sense-70-years-d51afb","standing":"current","assessment_label":"Unresolved","versions":[{"assessment_id":"marcus-ai-still-lacks-common-sense-70-years-d51afb-a2","status":"current","url":"https://punditclaims.ai/claims/marcus-ai-still-lacks-common-sense-70-years-d51afb#assessment-marcus-ai-still-lacks-common-sense-70-years-d51afb-a2"}],"claim":{"claim_id":"marcus-ai-still-lacks-common-sense-70-years-d51afb","subject_id":"gary-marcus","revision":1,"content_hash":"3d0a5f64e78c502120b3ccce797c98d324341b42d86eda04dd161f36dc24d10d","normalized_proposition":"Recent LLMs no longer make GPT-3's specific mistakes but still lack a robust understanding of the real external world.","kind":"observation","target":"LLMs","conditions":[],"original_horizon_text":null,"topic_tags":["capabilities","reliability"],"concerns_providers":[],"selection_stratum":"declared-lead","occurrences":[{"id":"marcus-ai-still-lacks-common-sense-70-years-d51afb-o1","statement_date":"2025-01-05","statement_date_precision":"day","excerpt":"Two and a half years later, more recent models no longer makes the same specific mistakes that GPT-3 did, but still lack a robust understanding of the real external world.","locator":"paragraph 19","document":{"url":"https://garymarcus.substack.com/p/ai-still-lacks-common-sense-70-years","title":"AI still lacks “common” sense, 70 years later","publisher":"Marcus on AI","published_at":"2025-01-05T18:46:20Z","date_uncertain":false,"date_provenance":"JSON-LD datePublished"},"observed_at":"2026-10-28T23:58:23Z","observed_via":"live","archive_url":null,"access_state":"full_public","snapshot_hash":"74fe61386387dea0130d5afe74b03873fbc1d065a6d740adbabe4eaad9576682","receipts":1}],"first_statement_date":"2025-01-05","related":[],"current":{"assessment_id":"marcus-ai-still-lacks-common-sense-70-years-d51afb-a2","payload":{"claim_id":"marcus-ai-still-lacks-common-sense-70-years-d51afb","subject_id":"gary-marcus","normalized_proposition":"Recent LLMs no longer make GPT-3's specific mistakes but still lack a robust understanding of the real external world.","kind":"observation","conditions":[],"original_horizon_text":null,"statement":{"excerpt":"Two and a half years later, more recent models no longer makes the same specific mistakes that GPT-3 did, but still lack a robust understanding of the real external world.","locator":"paragraph 19","url":"https://garymarcus.substack.com/p/ai-still-lacks-common-sense-70-years","date":"2025-01-05","document_title":"AI still lacks “common” sense, 70 years later","observed_at":"2026-10-28T23:58:23Z"},"criteria":{"from_speaker":["more recent models","no longer makes the same specific mistakes that GPT-3 did","still lack a robust understanding of the real external world"],"our_interpretation":["Both the correction of the earlier examples and the continuing broader weakness must be assessed.","The ordinary behavioral reading of robust understanding is dependable performance across varied physical and spatial problems.","The surrounding references to contemporary models place the observation in late 2024 through the post date. I use 2024-10-01 through 2025-01-05.","The phrase two and a half years later does not align with the passage's 2020 starting point and January 2025 publication."],"support_condition":"Matched retests show that contemporary models reliably pass the cited GPT-3 failure cases, while varied physical and spatial evaluations show recurring basic failures.","contradiction_condition":"Matched retests show that the cited earlier errors persist, or broad controlled evaluations establish dependable physical and spatial generalization in the contemporary models."},"axis":"historical_observation","outcome":"unresolved","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"The PhysGame preprint, available December 2, 2024, reports average accuracy of 56.1% for GPT-4o and 55.2% for Gemini-1.5-pro on questions about physics violations in gameplay videos [e1]. This provides limited evidence of physical-reasoning weaknesses, but does not establish broad reliability across physical and spatial problems [e1]. It does not retest the specific earlier GPT-3 mistakes, so it cannot settle the claim that those errors were corrected while broader weaknesses remained [e1].","limitations":["The packet contains no independent matched retests of the cited GPT-3 failure cases.","PhysGame covers a narrower task than general understanding of the external world.","The cited passages do not establish exact experimental dates within October 1, 2024 through January 5, 2025."],"evidence":[{"id":"e1","relation":"context","excerpt":"47.7% average accuracy.","locator":"Section 4.2, Performance Analysis, Table 4 discussion","url":"https://arxiv.org/html/2412.01800v1","publisher":"arXiv; Meng Cao and colleagues","event_date":"2024","available_at":"2024-12-02","explanation":"PhysGame reports physical-commonsense limitations across video language models, including GPT-4o and Gemini-1.5-pro. It supplies relevant behavioral context, but neither matched GPT-3 retests nor precise test dates establishing performance within the target interval."}],"what_would_resolve":"Independent matched retests of the original GPT-3 examples using identified model versions from October 1, 2024 through January 5, 2025, alongside repeated, varied physical and spatial evaluations with documented prompts and sampling settings.","retrospective":true,"plain_reading":true},"payload_hash":"eced3cdfba346aff233b2fad5aae2ce96454ee448a126240310cb8a045e97342","policy_version":"2026-10-28.7","recorded_at":"2026-10-29T02:21:22Z","drafter":{"provider":"openai","model":"gpt-6-astra"},"reviewers":[{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-ai-still-lacks-common-sense-70-years-b0c0ed-4x-review-anthropic-1"},{"provider":"openai","model":"gpt-6-astra","run_id":"marcus-ai-still-lacks-common-sense-70-years-b0c0ed-4x-review-openai-1"}],"final_text":{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-ai-still-lacks-common-sense-70-years-4037bf-4x-final-anthropic-1"},"status":"current","notice":null,"superseded_by":null,"evidence_meta":[{"id":"e1","source_type":"preprint","audited":null,"derived":null,"metric":null}],"public_from":null,"public_until":null},"history":[],"review":{"review_due":false,"triggers":[],"last_completed_review_at":"2026-10-29T02:21:22Z","next_review_due_at":null,"horizon_now":"not_applicable"},"event_ids":["marcus-ai-still-lacks-common-sense-70-years-d51afb-assessment-published-a2"],"last_substantive_change_at":"2026-10-29T17:00:18Z"}},{"url":"https://punditclaims.ai/claims/marcus-ai-still-lacks-common-sense-70-years-da3ee2","standing":"current","assessment_label":"Unresolved","versions":[{"assessment_id":"marcus-ai-still-lacks-common-sense-70-years-da3ee2-a2","status":"current","url":"https://punditclaims.ai/claims/marcus-ai-still-lacks-common-sense-70-years-da3ee2#assessment-marcus-ai-still-lacks-common-sense-70-years-da3ee2-a2"}],"claim":{"claim_id":"marcus-ai-still-lacks-common-sense-70-years-da3ee2","subject_id":"gary-marcus","revision":1,"content_hash":"0fd2f905a2b6361125a8e817dc56b08be3c412014cfff72b10640ed94bb9b0b7","normalized_proposition":"OpenAI's o1 often makes very basic spatial mistakes because it does not understand how objects are embedded and interact in space.","kind":"causal","target":"OpenAI o1","conditions":[],"original_horizon_text":null,"topic_tags":["capabilities","reliability"],"concerns_providers":["openai"],"selection_stratum":"declared-lead","occurrences":[{"id":"marcus-ai-still-lacks-common-sense-70-years-da3ee2-o1","statement_date":"2025-01-05","statement_date_precision":"day","excerpt":"For example, OpenAI’s o1 is able to create and execute Python code that can do geometric calculation, but because it does not understand how objects are embedded in space and interact in space or, indeed, what spatial relationships actually mean, it often makes very basic mistakes.","locator":"paragraph 19","document":{"url":"https://garymarcus.substack.com/p/ai-still-lacks-common-sense-70-years","title":"AI still lacks “common” sense, 70 years later","publisher":"Marcus on AI","published_at":"2025-01-05T18:46:20Z","date_uncertain":false,"date_provenance":"JSON-LD datePublished"},"observed_at":"2026-10-28T23:58:23Z","observed_via":"live","archive_url":null,"access_state":"full_public","snapshot_hash":"74fe61386387dea0130d5afe74b03873fbc1d065a6d740adbabe4eaad9576682","receipts":1}],"first_statement_date":"2025-01-05","related":[],"current":{"assessment_id":"marcus-ai-still-lacks-common-sense-70-years-da3ee2-a2","payload":{"claim_id":"marcus-ai-still-lacks-common-sense-70-years-da3ee2","subject_id":"gary-marcus","normalized_proposition":"OpenAI's o1 often makes very basic spatial mistakes because it does not understand how objects are embedded and interact in space.","kind":"causal","conditions":[],"original_horizon_text":null,"statement":{"excerpt":"For example, OpenAI’s o1 is able to create and execute Python code that can do geometric calculation, but because it does not understand how objects are embedded in space and interact in space or, indeed, what spatial relationships actually mean, it often makes very basic mistakes.","locator":"paragraph 19","url":"https://garymarcus.substack.com/p/ai-still-lacks-common-sense-70-years","date":"2025-01-05","document_title":"AI still lacks “common” sense, 70 years later","observed_at":"2026-10-28T23:58:23Z"},"criteria":{"from_speaker":["OpenAI’s o1","able to create and execute Python code","because it does not understand how objects are embedded in space and interact in space","often makes very basic mistakes"],"our_interpretation":["The claim includes an explanation for the errors, not merely an observation that errors occur.","Often means recurring across varied elementary spatial tasks; no numerical frequency is supplied.","Spatial understanding is read behaviorally as representing and applying spatial relationships consistently.","The current-state context is late 2024 through 2025-01-05.","Results for o1-preview are relevant context but are not automatically results for the released o1 model. Code generation and actual execution must also be distinguished."],"support_condition":"Controlled evaluations of the identified o1 version show recurring elementary spatial errors, and interventions or diagnostic tests connect those errors to deficient spatial representations despite effective geometric calculation.","contradiction_condition":"Representative evaluations show dependable elementary spatial performance, or controlled evidence establishes that the cited errors arise from another mechanism rather than the asserted spatial deficit."},"axis":"causal_assessment","outcome":"unresolved","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"A study available September 30, 2024 reports spatial-planning limitations in o1-preview alongside improved constraint-following over GPT-4 [e1]. Davis's October 11 report describes September 2024 o1-preview tests that found improved performance but continuing spatial difficulties [e2]. These preview-model results do not establish how often released o1 made elementary spatial errors during October 1, 2024 through January 5, 2025, or whether deficient spatial representations caused such errors [e1, e2].","limitations":["The cited studies do not establish the behavior of released o1 during the target period.","The tasks do not establish a representative frequency of very basic spatial mistakes.","The evidence does not isolate spatial representation from perception, task interpretation or calculation as the cause.","Actual Python execution by the relevant o1 configuration remains unverified."],"evidence":[{"id":"e1","relation":"context","excerpt":"spatially complex tasks.","locator":"Abstract","url":"https://arxiv.org/html/2409.19924v1","publisher":"arXiv; Kevin Wang and colleagues","event_date":"2024","available_at":"2024-09-30","explanation":"The study finds spatial planning limitations in o1-preview while also reporting improved constraint-following over GPT-4. It concerns a different version, predates the target interval and does not isolate the claimed cause."},{"id":"e2","relation":"context","excerpt":"spatial reasoning","locator":"Abstract","url":"https://arxiv.org/abs/2410.22340","publisher":"arXiv; Ernest Davis","event_date":"2024-09","available_at":"2024-10-11","explanation":"Davis reports improved but imperfect performance on 105 mathematics and science problems using o1-preview in September. 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Narrow uses are excepted by the speaker.","Evidence from earlier in 2025 is shown too, but the end of the year must be covered."],"support_condition":"Leading AI companies promoted agents at points spread across 2025, including its last quarter, and independent tests published late in 2025 show general-purpose agents failing a large share of realistic multi-step work tasks.","contradiction_condition":"Independent tests published late in 2025 show general-purpose agents completing most realistic multi-step work tasks across broad uses, or agents were not prominently promoted during 2025."},"axis":"forecast_outcome","outcome":"supported","horizon_state":"elapsed","evaluation_as_of":"2026-10-28","rationale":"On January 1, 2025, under 'High confidence', Marcus predicted that AI agents would be endlessly hyped throughout 2025 but far from reliable, except possibly in very narrow use cases. OpenAI promoted agents in January, June, and October 2025, from saying they may 'join the workforce' to launching tools to build them. Independent benchmarks found the best agent completed 30% of simulated workplace tasks (September 2025) and 2.5% of real remote-work projects (30 October 2025).","limitations":["The promotion shown comes from one company, OpenAI. It does not measure how much agents were promoted across the industry.","Both benchmarks are preprints, and the latest is from 30 October 2025, not the end of the year.","Whether an agent use counts as a 'very narrow use case' is not defined by the speaker."],"evidence":[{"id":"altman-reflections-agents-2025","relation":"supports","excerpt":"in 2025, we may see the first AI agents “join the workforce” and materially change the output of companies.","locator":"Sam Altman, 'Reflections', personal blog","url":"https://blog.samaltman.com/reflections","publisher":"Sam Altman (OpenAI CEO), personal blog","event_date":"2025-01-05","available_at":"2025-01-05","explanation":"Early in 2025, OpenAI's CEO said AI agents may 'join the workforce' that year. This shows agents promoted as transformative at the start of 2025. It is a company statement about its own field, not a test of agents."},{"id":"altman-gentle-singularity-agents","relation":"supports","excerpt":"2025 has seen the arrival of agents that can do real cognitive work; writing computer code will never be the same.","locator":"Sam Altman, 'The Gentle Singularity', personal blog","url":"https://blog.samaltman.com/the-gentle-singularity","publisher":"Sam Altman (OpenAI CEO), personal blog","event_date":"2025-06-10","available_at":"2025-06-10","explanation":"In June 2025, OpenAI's CEO said 2025 had seen agents that can do real cognitive work. This shows the promotion continuing mid-year. It is a company claim, not an independent test; its example, writing code, is one use."},{"id":"theagentcompany-2025","relation":"supports","excerpt":"the most competitive agent can complete 30% of tasks autonomously.","locator":"abstract, version 3 (10 September 2025)","url":"https://arxiv.org/abs/2412.14161v3","publisher":"arXiv (Carnegie Mellon University and others)","event_date":"2025-09-10","available_at":"2025-09-10","explanation":"An independent benchmark of agents on tasks from a simulated software company, revised in September 2025: the best agent completed 30% of tasks on its own, and harder long-horizon tasks were still beyond reach. Simpler tasks were often solved. This is a preprint."},{"id":"openai-agentkit-2025-10","relation":"supports","excerpt":"Today we’re launching AgentKit, a complete set of tools for developers and enterprises to build, deploy, and optimize agents.","locator":"opening paragraph, page dated October 6, 2025","url":"https://openai.com/index/introducing-agentkit","publisher":"OpenAI","event_date":"2025-10-06","available_at":"2025-10-06","explanation":"In October 2025, OpenAI launched a product line for building and deploying agents. This shows agents still being promoted in the last quarter of 2025. It is a company announcement, not a test of agents."},{"id":"remote-labor-index-2025-10","relation":"supports","excerpt":"AI agents perform near the floor on RLI, with the highest-performing agent achieving an automation rate of 2.5%.","locator":"abstract, submitted 30 October 2025","url":"https://arxiv.org/abs/2510.26787","publisher":"arXiv (Remote Labor Index authors)","event_date":"2025-10-30","available_at":"2025-10-30","explanation":"An independent benchmark of real, paid remote-work projects across many sectors, published 30 October 2025. The best agent completed 2.5% of projects to the required standard. 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The policy records such a prediction but does not score it.","'Technical moat', 'more convergence', and 'roughly the same place' have no stated measure."],"support_condition":"Not scored. The author filed the prediction under medium confidence, and its terms have no stated measure.","contradiction_condition":"Not scored. The author filed the prediction under medium confidence, and its terms have no stated measure."},"axis":"forecast_outcome","outcome":"not_adjudicable","horizon_state":"elapsed","evaluation_as_of":"2026-10-28","rationale":"On January 1, 2025, Marcus predicted that a technical moat would continue to be elusive, with more convergence on broadly similar models across the US and China and some European systems catching up. He listed it under the heading 'Medium confidence'. A prediction its author files under medium confidence is recorded and not scored. Its terms also have no stated measure.","limitations":["This record does not show what happened in 2025. No evidence about the gap between leading models is part of this record."],"evidence":[],"what_would_resolve":"Not scored. 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What happened: when OpenAI launched Sora 2 on September 30, 2025, it said the model is 'still imperfect' at obeying the laws of physics, though better than prior systems.","limitations":["The only evidence shown is OpenAI's own description, not an independent test.","OpenAI's statement is dated 30 September 2025, not the end of the year."],"evidence":[{"id":"openai-sora-2-physics","relation":"context","excerpt":"though still imperfect, it is better about obeying the laws of physics compared to prior systems.","locator":"section on world simulation, page dated September 30, 2025","url":"https://openai.com/index/sora-2","publisher":"OpenAI","event_date":"2025-09-30","available_at":"2025-09-30","explanation":"Shown as what happened. OpenAI said on 30 September 2025 that Sora 2 is 'still imperfect' at obeying the laws of physics, though better than prior systems. This is a company disclosure, not an independent test. 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Do not invent a numerical passing score.","Read 'every model' in context as every relevant GPT-like general-purpose model released in 2024. Tests of selected frontier models cannot establish that universal statement by themselves.","The target period is calendar 2024, explicitly named in the claim."],"support_condition":"Evaluations covering the relevant 2024 models establish that the overall pattern of the six predictions persisted across them.","contradiction_condition":"A documented 2024 model clearly escapes the overall pattern. An improvement on one benchmark alone does not contradict 'largely'."},"axis":"historical_observation","outcome":"unresolved","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"The evidence supports persistent reasoning errors, hallucinations and safeguard weaknesses in several important 2024 models. 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Since the post reviews 2024, the target period is calendar 2024 up to the post date."],"support_condition":"Evidence from late 2024 shows leading LLMs still hallucinate at meaningful rates, or that leading labs still treat hallucination as unsolved.","contradiction_condition":"Evidence shows a method or model that, by the end of 2024, reliably removed hallucinations across varied tasks in independent tests."},"axis":"historical_observation","outcome":"supported","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"The claim describes conditions in 2024, up to the post date of 1 January 2025. On 7 November 2024, OpenAI researchers wrote that current frontier models sometimes produce false outputs, known as hallucinations. They also called training models to give factually correct answers an open problem (e1). The same paper introduced SimpleQA, a set of short fact-seeking questions, and said GPT-4o and Claude both scored under 50% on it (e2). None of the cited evidence shows a method that reliably removed hallucinations across varied tasks by the end of 2024. Under the contract, a lab saying in late 2024 that hallucination was unsolved counts as support, so the claim holds on a plain reading.","limitations":["The main source is a lab paper describing its own models' limits. Independent leaderboard figures for late 2024 could not be read.","A SimpleQA score under 50% is not the same as a hallucination rate, because the benchmark also counts questions the model did not attempt.","The evidence does not measure hallucination rates in grounded settings such as retrieval or summarization.","'Robust' has no set threshold."],"evidence":[{"id":"e1","relation":"supports","excerpt":"Current frontier models sometimes produce false outputs or answers that are not substantiated by evidence, a problem known as “hallucinations.”","locator":"section 1 Introduction, paragraph 1","url":"https://arxiv.org/html/2411.04368v1","publisher":"OpenAI (arXiv 2411.04368)","event_date":"2024-11-07","available_at":"2024-11-07","explanation":"OpenAI researchers said in November 2024 that frontier models still hallucinate, and the paper calls factual accuracy an open problem."},{"id":"e2","relation":"supports","excerpt":"SimpleQA is created to be challenging for frontier models (e.g., GPT-4o and Claude both score less than 50%).","locator":"section 1 Introduction, bullet 'Challenging for frontier models'","url":"https://arxiv.org/html/2411.04368v1","publisher":"OpenAI (arXiv 2411.04368)","event_date":"2024-11-07","available_at":"2024-11-07","explanation":"Leading models answered fewer than half of simple fact questions correctly at the end of 2024. That does not look like a robust fix for false answers."}],"what_would_resolve":null,"retrospective":true,"plain_reading":true},"payload_hash":"5c4d31026e299b46e7f555deb661ccd6a10d57c83f1bdfc1f78e839a35beda8f","policy_version":"2026-10-28.7","recorded_at":"2026-10-29T02:18:44Z","drafter":{"provider":"anthropic","model":"claude-opus-5-5"},"reviewers":[{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-25-ai-predictions-for-2025-from-marcus-4acef5-4x-review-anthropic-2"},{"provider":"openai","model":"gpt-6-astra","run_id":"marcus-25-ai-predictions-for-2025-from-marcus-4acef5-4x-review-openai-2"}],"final_text":{"provider":"openai","model":"gpt-6-astra","run_id":"marcus-25-ai-predictions-for-2025-from-marcus-22c0aa-4x-final-openai-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":"2026-10-29T02:18:44Z","next_review_due_at":null,"horizon_now":"not_applicable"},"event_ids":["marcus-25-ai-predictions-for-2025-from-marcus-597446-assessment-published-a3"],"last_substantive_change_at":"2026-10-29T17:00:18Z"}},{"url":"https://punditclaims.ai/claims/marcus-25-ai-predictions-for-2025-from-marcus-64d30d","standing":"current","assessment_label":"Supported","versions":[{"assessment_id":"marcus-25-ai-predictions-for-2025-from-marcus-64d30d-a3","status":"current","url":"https://punditclaims.ai/claims/marcus-25-ai-predictions-for-2025-from-marcus-64d30d#assessment-marcus-25-ai-predictions-for-2025-from-marcus-64d30d-a3"}],"claim":{"claim_id":"marcus-25-ai-predictions-for-2025-from-marcus-64d30d","subject_id":"gary-marcus","revision":1,"content_hash":"76963b59f49e652d2e51032e1eac1865cfdd4a617c3192e5502dad559f42b09c","normalized_proposition":"Through 2025 hallucinations will continue to plague generative AI.","kind":"forecast","target":"generative AI","conditions":[],"original_horizon_text":"end of this year","topic_tags":["reliability"],"concerns_providers":[],"selection_stratum":"declared-lead","occurrences":[{"id":"marcus-25-ai-predictions-for-2025-from-marcus-64d30d-o1","statement_date":"2025-01-01","statement_date_precision":"day","excerpt":"Hallucinations (which should really be called confabulations) will continue to haunt generative AI.","locator":"paragraph 30","document":{"url":"https://garymarcus.substack.com/p/25-ai-predictions-for-2025-from-marcus","title":"25 AI Predictions for 2025, from Marcus on AI","publisher":"Marcus on AI","published_at":"2025-01-01T22:29:00Z","date_uncertain":false,"date_provenance":"JSON-LD datePublished"},"observed_at":"2026-10-28T22:44:27Z","observed_via":"live","archive_url":null,"access_state":"full_public","snapshot_hash":"b72b3e784cc6a22d315c3ece572c8e4f931a6bac3e8a0ee1d13e1abc99f6210d","receipts":1}],"first_statement_date":"2025-01-01","related":[],"current":{"assessment_id":"marcus-25-ai-predictions-for-2025-from-marcus-64d30d-a3","payload":{"claim_id":"marcus-25-ai-predictions-for-2025-from-marcus-64d30d","subject_id":"gary-marcus","normalized_proposition":"Through 2025 hallucinations will continue to plague generative AI.","kind":"forecast","conditions":[],"original_horizon_text":"end of this year","statement":{"excerpt":"Hallucinations (which should really be called confabulations) will continue to haunt generative AI.","locator":"paragraph 30","url":"https://garymarcus.substack.com/p/25-ai-predictions-for-2025-from-marcus","date":"2025-01-01","document_title":"25 AI Predictions for 2025, from Marcus on AI","observed_at":"2026-10-28T22:44:27Z"},"criteria":{"from_speaker":["Hallucinations","will continue to haunt generative AI","where we will be at the end of this year"],"our_interpretation":["Plain reading: through the end of 2025, hallucinations (confident false outputs) stay a well-documented, consequential problem for leading generative AI models and their deployments. They need not be unchanged or rising. They need to remain a recognized, material problem.","Evidence can be lab evaluations of leading models, statements by the labs themselves, or documented real-world incidents during 2025."],"support_condition":"Credible 2025 evidence shows that leading models still hallucinate at material rates, or that the problem keeps causing documented harm, and that the labs themselves still treat it as unsolved.","contradiction_condition":"Credible evidence shows that by late 2025 hallucinations in leading models had become rare enough that they were no longer a significant practical problem, for example near-zero rates on standard factuality evaluations and no notable incidents."},"axis":"forecast_outcome","outcome":"supported","horizon_state":"elapsed","evaluation_as_of":"2026-10-28","rationale":"The forecast period ended on 31 December 2025. OpenAI's system card of 16 April 2025 gave PersonQA hallucination rates of 0.33 for o3 and 0.48 for o4-mini, against 0.16 for o1 (e1). For o3 compared with o1, OpenAI wrote that more research was needed to understand the cause (e1). On 4 September 2025, OpenAI researchers wrote that hallucinations persist even in state-of-the-art systems and undermine trust (e2). On 6 October 2025, The Register reported that Deloitte had agreed to refund part of an Australian government contract. Deloitte had admitted using generative AI to produce the report, which contained fake citations and a made-up court quote (e3). In August 2025, OpenAI said GPT-5's responses were about 45% less likely than GPT-4o's to contain a factual error. With thinking on, it put the figure at about 80% less than o3 (e4). The same article called hallucination a major problem (e4). The reported reductions do not show that the problem stopped being material, so the forecast is supported.","limitations":["'Haunt' has no threshold. The verdict rests on the plain reading that the problem stayed material and recognized.","The GPT-5 figures are OpenAI's own and are relative, not absolute rates.","No independent cross-lab measure of hallucination rates at the end of 2025 was cited.","The Deloitte case is one reported incident. It does not show how often such incidents happen."],"evidence":[{"id":"e1","relation":"supports","excerpt":"More research is needed to understand the cause of these results.","locator":"Section 3.3 Hallucinations, Table 4","url":"https://cdn.openai.com/pdf/2221c875-02dc-4789-800b-e7758f3722c1/o3-and-o4-mini-system-card.pdf","publisher":"OpenAI","event_date":"2025-04-16","available_at":"2025-04-16","explanation":"OpenAI's April 2025 frontier reasoning models hallucinated on 33% (o3) and 48% (o4-mini) of PersonQA questions, more than o1. OpenAI said it did not know why."},{"id":"e2","relation":"supports","excerpt":"Such\n“hallucinations” persist even in state-of-the-art systems and undermine trust.","locator":"Abstract","url":"https://cdn.openai.com/pdf/d04913be-3f6f-4d2b-b283-ff432ef4aaa5/why-language-models-hallucinate.pdf","publisher":"OpenAI","event_date":"2025-09-04","available_at":"2025-09-04","explanation":"In September 2025, OpenAI's own researchers wrote that hallucinations persist even in state-of-the-art systems and undermine trust."},{"id":"e3","relation":"supports","excerpt":"Deloitte has agreed to refund part of an Australian government contract after admitting it used generative AI to produce a report riddled with fake citations, phantom footnotes, and even a made-up quote from a Federal Court judgment.","locator":"paragraph 1","url":"https://www.theregister.com/2025/10/06/deloitte_ai_report_australia","publisher":"The Register","event_date":"2025-10-06","available_at":"2025-10-06","explanation":"A late-2025 real-world incident in which fabricated citations from a GPT-4o-based tool led to a partial refund on a government contract."},{"id":"e4","relation":"contradicts","excerpt":"GPT-5's responses are around 45 percent less likely to contain a factual error than GPT-4o and when thinking GPT-5's responses are around 80 percent less likely to contain a factual error than OpenAI o3","locator":"section 'OpenAI tunes out the voices'","url":"https://www.theregister.com/2025/08/07/openai_gpt_5","publisher":"The Register","event_date":"2025-08-07","available_at":"2025-08-07","explanation":"OpenAI reported large relative cuts in factual errors with GPT-5. This shows real progress. It does not show that the problem went away, and the same article calls hallucination 'a major problem'."}],"what_would_resolve":null,"retrospective":true,"plain_reading":true},"payload_hash":"836c211dbd0cb5f5f053c33d7be0d16f1d639e2172395075fe6c0bd241e2b32d","policy_version":"2026-10-28.7","recorded_at":"2026-10-29T02:18:44Z","drafter":{"provider":"anthropic","model":"claude-opus-5-5"},"reviewers":[{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-25-ai-predictions-for-2025-from-marcus-4acef5-4x-review-anthropic-2"},{"provider":"openai","model":"gpt-6-astra","run_id":"marcus-25-ai-predictions-for-2025-from-marcus-4acef5-4x-review-openai-2"}],"final_text":{"provider":"openai","model":"gpt-6-astra","run_id":"marcus-25-ai-predictions-for-2025-from-marcus-22c0aa-4x-final-openai-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":"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":"2026-10-29T02:18:44Z","next_review_due_at":null,"horizon_now":"elapsed"},"event_ids":["marcus-25-ai-predictions-for-2025-from-marcus-64d30d-assessment-published-a3"],"last_substantive_change_at":"2026-10-29T17:00:18Z"}},{"url":"https://punditclaims.ai/claims/marcus-25-ai-predictions-for-2025-from-marcus-67a324","standing":"current","assessment_label":"Supported","versions":[{"assessment_id":"marcus-25-ai-predictions-for-2025-from-marcus-67a324-a3","status":"current","url":"https://punditclaims.ai/claims/marcus-25-ai-predictions-for-2025-from-marcus-67a324#assessment-marcus-25-ai-predictions-for-2025-from-marcus-67a324-a3"}],"claim":{"claim_id":"marcus-25-ai-predictions-for-2025-from-marcus-67a324","subject_id":"gary-marcus","revision":1,"content_hash":"0ca11ca112e72299f7247523f40fbbdf92e63bbaedfb9e063cf243ee9d1c1aff","normalized_proposition":"There are many GPT-4 level models, a price war, and very little moat among LLM providers.","kind":"observation","target":"LLM market","conditions":[],"original_horizon_text":null,"topic_tags":["business-economics"],"concerns_providers":[],"selection_stratum":"declared-lead","occurrences":[{"id":"marcus-25-ai-predictions-for-2025-from-marcus-67a324-o1","statement_date":"2025-01-01","statement_date_precision":"day","excerpt":"There are many such models now; there is a price war. There is very little moat.","locator":"paragraph 4","document":{"url":"https://garymarcus.substack.com/p/25-ai-predictions-for-2025-from-marcus","title":"25 AI Predictions for 2025, from Marcus on AI","publisher":"Marcus on AI","published_at":"2025-01-01T22:29:00Z","date_uncertain":false,"date_provenance":"JSON-LD datePublished"},"observed_at":"2026-10-28T22:44:27Z","observed_via":"live","archive_url":null,"access_state":"full_public","snapshot_hash":"b72b3e784cc6a22d315c3ece572c8e4f931a6bac3e8a0ee1d13e1abc99f6210d","receipts":1}],"first_statement_date":"2025-01-01","related":[],"current":{"assessment_id":"marcus-25-ai-predictions-for-2025-from-marcus-67a324-a3","payload":{"claim_id":"marcus-25-ai-predictions-for-2025-from-marcus-67a324","subject_id":"gary-marcus","normalized_proposition":"There are many GPT-4 level models, a price war, and very little moat among LLM providers.","kind":"observation","conditions":[],"original_horizon_text":null,"statement":{"excerpt":"There are many such models now; there is a price war. There is very little moat.","locator":"paragraph 4","url":"https://garymarcus.substack.com/p/25-ai-predictions-for-2025-from-marcus","date":"2025-01-01","document_title":"25 AI Predictions for 2025, from Marcus on AI","observed_at":"2026-10-28T22:44:27Z"},"criteria":{"from_speaker":["many such models now","there is a price war","very little moat"],"our_interpretation":["'Such models' means GPT-4 level models from several developers.","Price war: repeated, competitive price cuts among LLM providers.","Very little moat: no developer held a lasting performance lead that shields it from rivals.","He states a current condition without a period. Since the post reviews 2024, the target period is calendar 2024 up to the post date."],"support_condition":"Evidence shows several developers with closely matched frontier models, plus aggressive competitive price cutting by LLM providers in 2024.","contradiction_condition":"Evidence shows only one or two GPT-4 level providers, stable or rising prices, or a clear lasting lead held by one developer."},"axis":"historical_observation","outcome":"supported","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"The claim describes conditions in 2024, up to the post date of 1 January 2025. The AI Index 2025 reported that the gap between the top two models shrank from 4.9% in 2023 to 0.7% in 2024. It also reported that high-quality models were available from a growing number of developers (e1). This fits 'many such models' and 'very little moat', read as no lasting performance lead. Fortune reported in May 2024 that ByteDance priced its Doubao Pro model at a level it said was 99% below the industry average. Alibaba then cut prices by up to 97% on some Qwen models, and Baidu made two lighter Ernie models free for business users (e2). That is a run of competing price cuts, which fits 'price war'. None of the cited evidence shows one developer holding a lasting lead in 2024, or stable prices.","limitations":["'Moat' is read as a lasting performance lead. Brand, distribution or user base, which are other possible moats, were not measured.","The price-war evidence covers Chinese providers in May 2024. Western price cuts were not cited from a primary source.","The evidence does not show whether the price-cut models were GPT-4 level. Some were lighter versions."],"evidence":[{"id":"e1","relation":"supports","excerpt":"the difference between the top two models shrank from 4.9% in 2023 to just 0.7% in 2024.","locator":"Chapter 2 highlights, item 4","url":"https://hai-production.s3.amazonaws.com/files/hai_ai-index-report-2025_chapter2_final.pdf","publisher":"Stanford HAI (AI Index 2025)","event_date":"2024","available_at":"2025-04-07","explanation":"Leading models from different developers were nearly tied in 2024. That fits 'many such models' and 'very little moat'. The same report says this validates predictions that AI companies lack a moat."},{"id":"e2","relation":"supports","excerpt":"On Tuesday, Alibaba cut prices by up to 97% for some of its Qwen LLM models. Hours later, Baidu said its Ernie Speed and Ernie Light, two lighter versions of its Ernie LLM, would be free for all business users.","locator":"paragraphs 5-6","url":"https://fortune.com/asia/2024/05/22/china-ai-llm-price-war-discounts-bytedance-alibaba-baidu-iflytek","publisher":"Fortune","event_date":"2024-05-21","available_at":"2024-05-22","explanation":"Shows rival LLM providers cutting prices deeply in response to each other in 2024: a price war."}],"what_would_resolve":null,"retrospective":true,"plain_reading":true},"payload_hash":"60a11e476c559fabae365923eab1dd6e6588457251ed7329685a8255382ad693","policy_version":"2026-10-28.7","recorded_at":"2026-10-29T02:18:44Z","drafter":{"provider":"anthropic","model":"claude-opus-5-5"},"reviewers":[{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-25-ai-predictions-for-2025-from-marcus-66331f-4x-review-anthropic-2"},{"provider":"openai","model":"gpt-6-astra","run_id":"marcus-25-ai-predictions-for-2025-from-marcus-66331f-4x-review-openai-2"}],"final_text":{"provider":"openai","model":"gpt-6-astra","run_id":"marcus-25-ai-predictions-for-2025-from-marcus-22c0aa-4x-final-openai-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":"news_report","audited":null,"derived":null,"metric":null}],"public_from":null,"public_until":null},"history":[],"review":{"review_due":false,"triggers":[],"last_completed_review_at":"2026-10-29T02:18:44Z","next_review_due_at":null,"horizon_now":"not_applicable"},"event_ids":["marcus-25-ai-predictions-for-2025-from-marcus-67a324-assessment-published-a3"],"last_substantive_change_at":"2026-10-29T17:00:18Z"}},{"url":"https://punditclaims.ai/claims/marcus-25-ai-predictions-for-2025-from-marcus-6d352a","standing":"current","assessment_label":"Supported","versions":[{"assessment_id":"marcus-25-ai-predictions-for-2025-from-marcus-6d352a-a2","status":"current","url":"https://punditclaims.ai/claims/marcus-25-ai-predictions-for-2025-from-marcus-6d352a#assessment-marcus-25-ai-predictions-for-2025-from-marcus-6d352a-a2"}],"claim":{"claim_id":"marcus-25-ai-predictions-for-2025-from-marcus-6d352a","subject_id":"gary-marcus","revision":1,"content_hash":"9de4e7f12f3300bb22aafe8e505086b89fbf2e59abd03acf1a41bab0ed8102e2","normalized_proposition":"AI agents will be heavily hyped in 2025 but far from reliable except possibly in very narrow use cases.","kind":"forecast","target":"AI agents","conditions":[{"text":"except possibly very narrow use cases","type":"qualifier","from_source":true}],"original_horizon_text":"throughout 2025","topic_tags":["agents","reliability"],"concerns_providers":[],"selection_stratum":"declared-lead","occurrences":[{"id":"marcus-25-ai-predictions-for-2025-from-marcus-6d352a-o1","statement_date":"2025-01-01","statement_date_precision":"day","excerpt":"AI “Agents” will be endlessly hyped throughout 2025 but far from reliable, except possibly in very narrow use cases.","locator":"paragraph 32","document":{"url":"https://garymarcus.substack.com/p/25-ai-predictions-for-2025-from-marcus","title":"25 AI Predictions for 2025, from Marcus on AI","publisher":"Marcus on AI","published_at":"2025-01-01T22:29:00Z","date_uncertain":false,"date_provenance":"JSON-LD datePublished"},"observed_at":"2026-10-28T22:44:27Z","observed_via":"live","archive_url":null,"access_state":"full_public","snapshot_hash":"b72b3e784cc6a22d315c3ece572c8e4f931a6bac3e8a0ee1d13e1abc99f6210d","receipts":1}],"first_statement_date":"2025-01-01","related":[],"current":{"assessment_id":"marcus-25-ai-predictions-for-2025-from-marcus-6d352a-a2","payload":{"claim_id":"marcus-25-ai-predictions-for-2025-from-marcus-6d352a","subject_id":"gary-marcus","normalized_proposition":"AI agents will be heavily hyped in 2025 but far from reliable except possibly in very narrow use cases.","kind":"forecast","conditions":["except possibly very narrow use cases"],"original_horizon_text":"throughout 2025","statement":{"excerpt":"AI “Agents” will be endlessly hyped throughout 2025 but far from reliable, except possibly in very narrow use cases.","locator":"paragraph 32","url":"https://garymarcus.substack.com/p/25-ai-predictions-for-2025-from-marcus","date":"2025-01-01","document_title":"25 AI Predictions for 2025, from Marcus on AI","observed_at":"2026-10-28T22:44:27Z"},"criteria":{"from_speaker":["AI “Agents” will be endlessly hyped throughout 2025","but far from reliable","except possibly in very narrow use cases"],"our_interpretation":["The claim has two parts. 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Alternatively, evidence that agents were not heavily hyped."},"axis":"forecast_outcome","outcome":"supported","horizon_state":"elapsed","evaluation_as_of":"2026-10-28","rationale":"Both parts held in 2025. On hype: Sam Altman opened the year saying agents might 'join the workforce', and Gartner said most agent projects were 'mostly driven by hype'. On reliability: CMU's TheAgentCompany benchmark found the best agent finished only 30% of realistic workplace tasks. Gartner judged that current models lack the maturity to achieve complex goals on their own. In October, Andrej Karpathy said such agents 'just don't work'. The main counterpoint is coding agents, which worked well by some practitioners' accounts. The verdict treats coding as the kind of specialised use Marcus's carve-out allows.","limitations":["The result partly depends on reading coding agents as a 'narrow use case'. A reader who treats coding as broad could call this mixed.","'Endlessly hyped' and 'far from reliable' set no thresholds."],"evidence":[{"id":"e1","relation":"supports","excerpt":"We believe that, in 2025, we may see the first AI agents “join the workforce” and materially change the output of companies.","locator":"paragraph beginning 'We are now confident'","url":"https://blog.samaltman.com/reflections","publisher":"Sam Altman (OpenAI CEO) blog","event_date":"2025-01-05","available_at":"2025-01-05","explanation":"The OpenAI CEO opened 2025 by saying agents might 'join the workforce'. 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The same report says most projects are 'mostly driven by hype' and flags 'agent washing'."},{"id":"e3","relation":"supports","excerpt":"even with the strongest frontier model, it only manages to complete 30% of the total tasks","locator":"Section 7.1 Result Overview","url":"https://arxiv.org/html/2412.14161v3","publisher":"Carnegie Mellon University researchers (arXiv)","event_date":"2025-09-10","available_at":"2025-09-10","explanation":"On 175 realistic workplace tasks, the best 2025 agent (Gemini 2.5 Pro) completed only 30%. That fits 'far from reliable' for general work."},{"id":"e4","relation":"supports","excerpt":"The reason you don’t do it today is because they just don’t work.","locator":"quoted Karpathy passage, paragraph 2","url":"https://simonwillison.net/2025/Oct/18/agi-is-still-a-decade-away","publisher":"Simon Willison's Weblog (quoting Andrej Karpathy on Dwarkesh Podcast)","event_date":"2025-10-17","available_at":"2025-10-18","explanation":"In October 2025, OpenAI co-founder Andrej Karpathy said employee-like agents 'just don't work' and called 'the year of agents' more likely a decade."},{"id":"e5","relation":"contradicts","excerpt":"Coding agents that are really good at running tools in the loop on the other hand are here already.","locator":"paragraph after Karpathy quote","url":"https://simonwillison.net/2025/Oct/18/agi-is-still-a-decade-away","publisher":"Simon Willison's Weblog","event_date":"2025-10-18","available_at":"2025-10-18","explanation":"A practitioner says coding agents worked well in 2025. 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'Perhaps years' is optional, not a required outcome.","The endpoint combines dependable operation, broad availability and ordinary consumer affordability. Continued premium-tier restrictions can establish that this combined endpoint has not yet been reached, without identifying an exact date of technical maturity.","Treat access through an ordinary consumer tier as potentially accessible and a premium Pro-only preview as restricted access. This affordability distinction is an evaluator judgment.","Product subscription prices are access criteria here, not a claim about OpenAI's revenue or other financial accounts."],"support_condition":"Multiple products previewed during 2025 remain short of the combined reliability, broad-access and affordability standard for multiple months.","contradiction_condition":"A representative record of 2025 previews shows prompt dependable, broad and ordinarily affordable availability, without a continuing pattern of multi-month restrictions."},"axis":"forecast_outcome","outcome":"supported","horizon_state":"elapsed","evaluation_as_of":"2026-10-28","rationale":"Operator and Pulse demonstrate the continuing practice under the stated reading. Operator moved from its January Pro preview to the July Plus rollout, while Pulse remained Pro-only nearly three months after its September preview. These access restrictions kept the combined broad, accessible and dependable endpoint unmet for months. 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Its integration into ChatGPT agent came almost six months later; e2 establishes the broader subscription rollout."},{"id":"e2","relation":"supports","excerpt":"still in its early stages","locator":"Availability; Limitations and looking ahead","url":"https://openai.com/index/introducing-chatgpt-agent","publisher":"OpenAI","event_date":"2025-07-17","available_at":"2025-07-17","explanation":"The announcement begins the rollout to Plus and Team users following Operator's January preview. It also describes continuing mistakes, beta slideshow functionality and geographic restrictions, so broader access did not establish complete maturity."},{"id":"e3","relation":"supports","excerpt":"Pro users on mobile","locator":"Opening announcement","url":"https://openai.com/index/introducing-chatgpt-pulse","publisher":"OpenAI","event_date":"2025-09-25","available_at":"2025-09-25","explanation":"Establishes a second 2025 preview with premium-tier restrictions. The announcement says expansion to Plus would follow learning and improvement from early use."},{"id":"e4","relation":"supports","excerpt":"only available in ChatGPT Pro","locator":"December 17, 2025: Tasks are now in Pulse","url":"https://help.openai.com/en/articles/6825453-chatgpt-release-notes?2025","publisher":"OpenAI","event_date":"2025-12-17","available_at":"2025-12-17","explanation":"Pulse remained Pro-only nearly three months after its preview. This documents a multi-month access restriction within the forecast year without relying on subsequent developments."},{"id":"e5","relation":"context","excerpt":"$200 monthly plan","locator":"Opening announcement","url":"https://openai.com/index/introducing-chatgpt-pro","publisher":"OpenAI","event_date":"2024-12-05","available_at":"2024-12-05","explanation":"Establishes Pro's premium launch price. This is pricing context, not evidence of a 2025 product delay or a complete history of subsequent prices."},{"id":"e6","relation":"context","excerpt":"All Plus users","locator":"February 25 update; original February 2 announcement","url":"https://openai.com/index/introducing-deep-research","publisher":"OpenAI","event_date":"2025-02-25","available_at":"2025-02-25","explanation":"Deep research reached Plus users within the same month as its introduction. 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The central disclosure claim remains unsettled. The original video did not yield usable text, and accessible summaries cannot prove an omission. The secondary transcript is unreliable at the critical passage. These retrieval failures are not evidence that OpenAI failed to disclose the training.","limitations":["Clarity requires considering slides as well as spoken words.","Established use of test-time training alone would not establish Marcus's claim about preparation before evaluation."],"evidence":[{"id":"e1","relation":"context","excerpt":"OmniARC (Barbadillo [3]): A Qwen2.5-0.5B-Instruct model pretrained on multiple program induction tasks and further fine-tuned at test time with augmented ARC data.","locator":"Section 3.2, Examples of TTT used in ARC Prize 2024","url":"https://arxiv.org/html/2412.04604v1","publisher":"François Chollet and colleagues, arXiv","event_date":"2024","available_at":"2024-12-05","explanation":"The report describes preparatory training and test-time adaptation as separate components of contemporary ARC systems. 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Neither a particular ARC score nor flawless performance on every easy task is imposed as the definition.","The categorical assertion that the system was not AGI is stronger than saying the demonstration did not establish AGI."],"support_condition":"Evidence about the demonstrated system establishes substantial limitations incompatible with broad human-level cognitive capability under the ordinary reading.","contradiction_condition":"Reliable evaluations of that same system establish broad human-level cognitive capability across sufficiently varied unfamiliar tasks and domains."},"axis":"historical_observation","outcome":"unresolved","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"The records show strong but incomplete ARC performance and an evaluator applying a demanding AGI criterion. They justify caution about calling the demonstration AGI. They do not, by themselves, settle whether the system lacked broad human-level cognitive capability. 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Applying that criterion as dispositive would add a threshold that Marcus did not explicitly provide."},{"id":"e2","relation":"context","excerpt":"366/400 tasks in the ARC-AGI-Pub dataset.","locator":"Opening paragraph","url":"https://anokas.substack.com/p/llms-struggle-with-perception-not-reasoning-arcagi","publisher":"Mikel Bober-Irizar","event_date":"2024-12-20","available_at":"2024-12-24","explanation":"The analysis concerns the December results and documents substantial success with remaining failures. 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'At most necessary' is a hedge and is not scored.","Plain reading: the claim holds if the benchmark's own designers say passing it does not equal AGI, and a system that scored very high on it is still judged not to be AGI and still fails easy tasks.","The test meant is ARC-AGI-1 as it stood in December 2024."],"support_condition":"Authoritative sources, especially the benchmark's designers, state that passing ARC-AGI does not equal AGI, and a high-scoring system is shown to still fail easy tasks.","contradiction_condition":"The benchmark's designers or strong evidence establish that passing ARC-AGI is enough to show AGI."},"axis":"historical_observation","outcome":"supported","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"The part that can be checked is that passing ARC-AGI is not enough to show AGI. In its 20 December 2024 post on o3, the ARC Prize Foundation calls ARC-AGI 'not an acid test for AGI' and describes it as a research tool (e2). The same post says 'Passing ARC-AGI does not equate to achieving AGI'. It also says o3 is not AGI yet and 'still fails on some very easy tasks' (e1). So the benchmark's designers take the same view as Marcus on sufficiency, and they say o3, a high-scoring system, still fails easy tasks. The packet has no evidence that passing ARC-AGI would be enough to show AGI. The hedged 'at most necessary' part is not scored.","limitations":["The contract notes that AGI is not defined, so 'sufficient' is judged by the designers' stated view.","The evidence does not say which easy tasks o3 failed. That point rests on the designer's own statement.","The evidence covers the o3 version tested in December 2024, not later releases.","Whether ARC-AGI is necessary for AGI is not assessed."],"evidence":[{"id":"e1","relation":"supports","excerpt":"Passing ARC-AGI does not equate to achieving AGI, and, as a matter of fact, I don't think o3 is AGI yet. o3 still fails on some very easy tasks, indicating fundamental differences with human intelligence.","locator":"section 'So is it AGI?', paragraph 2","url":"https://arcprize.org/blog/oai-o3-pub-breakthrough","publisher":"ARC Prize Foundation (François Chollet)","event_date":"2024-12-20","available_at":"2024-12-20","explanation":"The benchmark's creator says passing is not the same as AGI. A system that scored up to 87.5% still fails easy tasks, so passing is not sufficient."},{"id":"e2","relation":"supports","excerpt":"However, it is important to note that ARC-AGI is not an acid test for AGI – as we've repeated dozens of times this year.","locator":"section 'So is it AGI?', paragraph 1","url":"https://arcprize.org/blog/oai-o3-pub-breakthrough","publisher":"ARC Prize Foundation (François Chollet)","event_date":"2024-12-20","available_at":"2024-12-20","explanation":"The designers present ARC-AGI as a research tool, not a decisive test for AGI. 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It fails for an item if ARC's design documents show the tasks deliberately draw on it.","Factuality means knowledge of facts about the world. Compositionality means combining known rules or concepts. Common sense means everyday intuitive knowledge about the world, including basic physical intuitions."],"support_condition":"ARC design documents show the tasks exclude factual knowledge, do not test combining concepts, and do not draw on commonsense knowledge.","contradiction_condition":"ARC design documents show the tasks deliberately test combining concepts or rely on commonsense priors such as objectness and elementary physics."},"axis":"historical_observation","outcome":"mixed","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"Factuality: supported. ARC Prize says ARC-AGI restricts itself to core knowledge priors and avoids learned or cultural knowledge, so facts about the world are not tested. Compositionality and common sense: the claim that they 'aren't even addressed' goes too far. Chollet's 2019 paper builds ARC on core-knowledge priors, including 'objectness and elementary physics', which are basic commonsense intuitions. The ARC-AGI-2 paper says ARC-AGI-2 targets 'deeper levels' of compositional generalization. That implies ARC-AGI-1 covered combining rules shallowly, though most of its tasks needed a single transformation. So ARC covers these two only thinly, not 'not at all'.","limitations":["The result depends on how 'common sense' and 'compositionality' are defined. A narrower reading, such as broad everyday world knowledge, would favor Marcus.","The ARC-AGI-2 paper was published after the post. It describes the earlier benchmark's design, which did not change.","The ARC Prize design page is undated, so its available_at is set to the date it was read."],"evidence":[{"id":"e1","relation":"supports","excerpt":"ARC-AGI avoids this by restricting itself to core knowledge priors,\nthose cognitive building blocks that are either present at birth or\nacquired very early in human development with minimal explicit\ninstruction","locator":"section Core Knowledge Priors, paragraph 2","url":"https://arcprize.org/arc-agi","publisher":"ARC Prize Foundation","event_date":"2019-11","available_at":"2026-10-28","explanation":"ARC deliberately avoids acquired and cultural knowledge, so factual knowledge is not tested. This supports the factuality part."},{"id":"e2","relation":"contradicts","excerpt":"Objectness and elementary physics: humans assume that their environment should\nbe parsed into “objects” characterized by principles of cohesion","locator":"Section II.3.1, Core Knowledge list, first bullet (p. 47)","url":"https://arxiv.org/pdf/1911.01547","publisher":"arXiv (François Chollet)","event_date":"2019-11","available_at":"2019-11-05","explanation":"ARC is built on core-knowledge priors such as objectness and elementary physics, which are basic commonsense intuitions. That cuts against 'common sense aren't even addressed', though only the most basic layer is covered."},{"id":"e3","relation":"contradicts","excerpt":"Lastly, a major focus with ARC-AGI-2 is to investigate deeper levels of compositional generalization — the ability to combine known rules or concepts in novel ways.","locator":"Section 7.1, final paragraph","url":"https://arxiv.org/html/2505.11831","publisher":"arXiv (Chollet, Knoop, Kamradt, Landers, Pinkard)","event_date":"2019-11","available_at":"2025-05-17","explanation":"The ARC authors describe ARC-AGI-2 as probing 'deeper levels' of compositional generalization than the 2019 ARC-AGI-1 design. That implies ARC-AGI-1 covered shallower levels, not none. The same section says most ARC-AGI-1 tasks needed only one high-level transformation, so coverage was weak."}],"what_would_resolve":null,"retrospective":true,"plain_reading":true},"payload_hash":"fc7456848afcaf5c0efbf959fc063cb0a1b5d3d16b096d1c28ab570a1d3274cc","policy_version":"2026-10-28.7","recorded_at":"2026-10-29T02:39:46Z","drafter":{"provider":"anthropic","model":"claude-opus-5-5"},"reviewers":[{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-c39-362891-4x-review-anthropic-1"},{"provider":"openai","model":"gpt-6-astra","run_id":"marcus-c39-362891-4x-review-openai-1"}],"final_text":{"provider":"openai","model":"gpt-6-astra","run_id":"marcus-c39-362891-4x-final-openai-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":"preprint","audited":null,"derived":null,"metric":null}],"public_from":null,"public_until":null},"history":[],"review":{"review_due":false,"triggers":[],"last_completed_review_at":"2026-10-29T02:39:46Z","next_review_due_at":null,"horizon_now":"not_applicable"},"event_ids":["marcus-c39-66ca37-assessment-published-a1"],"last_substantive_change_at":"2026-10-29T17:00:18Z"}},{"url":"https://punditclaims.ai/claims/marcus-c39-73750f","standing":"current","assessment_label":"Unresolved","versions":[{"assessment_id":"marcus-c39-73750f-a1","status":"current","url":"https://punditclaims.ai/claims/marcus-c39-73750f#assessment-marcus-c39-73750f-a1"}],"claim":{"claim_id":"marcus-c39-73750f","subject_id":"gary-marcus","revision":2,"content_hash":"088e9849b291163afacf401dd77cc4e3b0ec5f532e27bdcb0a8eb30ee06fad9e","normalized_proposition":"OpenAI's graph left out the MIT work (halfway between o1 and o3) and many other results, which made o3's breakthrough relative to the field seem far bigger than it really was.","kind":"causal","target":"OpenAI o3 ARC graph","conditions":[],"original_horizon_text":null,"topic_tags":["capabilities"],"concerns_providers":["openai"],"selection_stratum":"declared-lead","occurrences":[{"id":"marcus-c39-73750f-o1","statement_date":"2024-12-22","statement_date_precision":"day","excerpt":"Same was true of the openAI graph: the MIT work (halfway in between o1 and o3) and many others results weren’t shown, making the breakthrough relative to the field seem far bigger than it really was.","locator":"paragraph 8","document":{"url":"https://garymarcus.substack.com/p/c39","title":"𝗼𝟯 “𝗔𝗥𝗖 𝗔𝗚𝗜” 𝗽𝗼𝘀𝘁𝗺𝗼𝗿𝘁𝗲𝗺 𝗺𝗲𝗴𝗮𝘁𝗵𝗿𝗲𝗮𝗱: 𝘄𝗵𝘆 𝘁𝗵𝗶𝗻𝗴𝘀 𝗴𝗼𝘁 𝗵𝗲𝗮𝘁𝗲𝗱, 𝘄𝗵𝗮𝘁 𝘄𝗲𝗻𝘁 𝘄𝗿𝗼𝗻𝗴, 𝗮𝗻𝗱 𝘄𝗵𝗮𝘁 𝗶𝘁 𝗮𝗹𝗹 𝗺𝗲𝗮𝗻𝘀","publisher":"Marcus on AI","published_at":"2024-12-22T15:57:14Z","date_uncertain":false,"date_provenance":"JSON-LD datePublished"},"observed_at":"2026-10-28T23:58:29Z","observed_via":"live","archive_url":null,"access_state":"full_public","snapshot_hash":"1b94a07d8a44d778ec988f1392cacabc5faa7b2d6e0fcd3a19537c619bb18fa2","receipts":1}],"first_statement_date":"2024-12-22","related":[],"current":{"assessment_id":"marcus-c39-73750f-a1","payload":{"claim_id":"marcus-c39-73750f","subject_id":"gary-marcus","normalized_proposition":"OpenAI's graph left out the MIT work (halfway between o1 and o3) and many other results, which made o3's breakthrough relative to the field seem far bigger than it really was.","kind":"causal","conditions":[],"original_horizon_text":null,"statement":{"excerpt":"Same was true of the openAI graph: the MIT work (halfway in between o1 and o3) and many others results weren’t shown, making the breakthrough relative to the field seem far bigger than it really was.","locator":"paragraph 8","url":"https://garymarcus.substack.com/p/c39","date":"2024-12-22","document_title":"𝗼𝟯 “𝗔𝗥𝗖 𝗔𝗚𝗜” 𝗽𝗼𝘀𝘁𝗺𝗼𝗿𝘁𝗲𝗺 𝗺𝗲𝗴𝗮𝘁𝗵𝗿𝗲𝗮𝗱: 𝘄𝗵𝘆 𝘁𝗵𝗶𝗻𝗴𝘀 𝗴𝗼𝘁 𝗵𝗲𝗮𝘁𝗲𝗱, 𝘄𝗵𝗮𝘁 𝘄𝗲𝗻𝘁 𝘄𝗿𝗼𝗻𝗴, 𝗮𝗻𝗱 𝘄𝗵𝗮𝘁 𝗶𝘁 𝗮𝗹𝗹 𝗺𝗲𝗮𝗻𝘀","observed_at":"2026-10-28T23:58:29Z"},"criteria":{"from_speaker":["the MIT work","halfway in between o1 and o3","many others results weren't shown","making the breakthrough relative to the field seem far bigger"],"our_interpretation":["The causal assertion concerns the apparent numerical performance gap created by OpenAI's December 20, 2024 graph.","The plain reading is that adding relevant omitted prior results would substantially reduce the apparent advance over the field.","Halfway is read as approximately midway, not exact equality.","The mechanism can be assessed by reconstructing the comparison; actual changes in audience beliefs would require separate evidence."],"support_condition":"The original graph omits the identified MIT result and other comparable results, MIT falls approximately midway between the displayed o1 and o3 results, and adding the omitted results substantially reduces the apparent gap from the prior field frontier.","contradiction_condition":"The graph includes those comparators, MIT is not approximately intermediate under matching conditions, or a corrected comparison does not materially reduce the apparent advance."},"axis":"causal_assessment","outcome":"unresolved","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"The MIT paper confirms a prior 61.9% ensemble result on public validation. It does not establish the claimed omission or causal exaggeration in OpenAI's graph. The graph's exact entries and evaluation settings were not recovered reliably, so neither the halfway description nor the corrected performance gap can be verified. An intermediate score on a different test set would not settle the claim.","limitations":["The identified MIT figure is an ensemble result, not simply the standalone test-time-training model.","A numerical reconstruction would assess the presentation mechanism, not prove how viewers actually changed their beliefs."],"evidence":[{"id":"e1","relation":"context","excerpt":"By ensembling our method with recent program generation approaches, we get SoTA public validation accuracy of 61.9%, matching the average human score.","locator":"Abstract, version 1","url":"https://arxiv.org/abs/2411.07279v1","publisher":"Ekin Akyürek and colleagues, arXiv","event_date":"2024-11","available_at":"2024-11-11","explanation":"The MIT paper establishes a substantial prior result, specifically an ensemble score on public validation. 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Contemporaneous independent commentary likewise treats performance without ARC training as unknown. No qualifying public result was found for the period. This supports Marcus's claim about missing public evidence, without establishing that OpenAI never ran a private experiment.","limitations":["The evaluator page has later updates, and the training note's precise insertion date is not separately archived in this record.","Removing ARC training alone would not make model and human testing conditions identical."],"evidence":[{"id":"e1","relation":"supports","excerpt":"not yet tested the ARC-untrained","locator":"OpenAI o3 ARC-AGI Results, note on tuned","url":"https://arcprize.org/blog/oai-o3-pub-breakthrough","publisher":"ARC Prize","event_date":"2024-12-20","available_at":"2024-12-20","explanation":"The evaluator explicitly reports the missing control. 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That makes Marcus's criticism testable, but it does not itself prove either graph omitted those results or implied a comparison with the entire field. Public and semi-private scores also differ substantially. Without verified original graph contents and framing, the claim about both graphs remains unresolved.","limitations":["An image's absence from extracted text cannot establish an omission within that image.","Numerically higher scores from a different evaluation split are not interchangeable comparators."],"evidence":[{"id":"e1","relation":"context","excerpt":"The SOTA on ARC-AGI-1 increased from 33% to 55.5% during the 2024 Kaggle competition.","locator":"Opening results summary and 2024 ARC-AGI-Pub (High Scores) table","url":"https://arcprize.org/blog/arc-prize-2024-winners-technical-report","publisher":"ARC Prize","event_date":"2024","available_at":"2024-12-06","explanation":"The table separately reports MARA(BARC) + MIT at 47.5% on semi-private evaluation and 62.8% on public evaluation, alongside other entrants. 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Together they support the ordinary reading that neurosymbolic AI was gaining attention. They do not establish dominance, widespread deployment or that the trend started that week.","limitations":["The result depends on reading vogue as growing attention.","Fortune reports company activity and claims; it does not independently validate their technical performance.","The inspected Fortune text was corrected after the post."],"evidence":[{"id":"e1","relation":"supports","excerpt":"rapid growth since 2020","locator":"Abstract, Conclusion","url":"https://ceur-ws.org/Vol-3819/paper3.pdf","publisher":"CEUR Workshop Proceedings","event_date":"2024-08-05","available_at":"2024-11-01","explanation":"The systematic review presented in 2024 identifies a growing research field. 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Prototypes alone neither establish consumer readiness nor prove that readiness is imminent.","The decade of promises is background framing, not a separate dated forecast with identified promises."],"support_condition":"Evidence establishes the stated global absence rate under a defensible denominator, continuing hallucinations, and the absence of consumer-ready home humanoids in the target period.","contradiction_condition":"Comparable evidence establishes a materially different global absence rate, elimination of hallucinations, or consumer-ready home humanoids in the target period. Identify any contradicted component separately."},"axis":"historical_observation","outcome":"unresolved","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"The hallucination component is supported by OpenAI's December 2024 evaluations. Waymo's announcement documents a geographically limited service, but cannot establish 97% worldwide absence. 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The Verge's report of that summit said Google and others were seeing diminishing returns, and its subhead said everyone in AI seemed to agree models were hitting a scaling wall. A report dated Nov 6 quotes Marc Andreessen saying models were 'hitting the same ceiling on capabilities', which is the diminishing-returns idea. Together, the named people and others used the wall framing and the diminishing-returns idea in roughly the weeks before the post. One caveat: Andreessen's remarks fall about 17 days before the post, and he said 'ceiling', not 'wall'.","limitations":["Andreessen did not use the exact phrase 'hitting a wall'; the claim is scored on the group reading.","Andreessen's remarks fall at the loose edge of 'last couple weeks'.","News reports are used rather than the original recordings."],"evidence":[{"id":"e1","relation":"supports","excerpt":"“The wall has been the topic du jour for the past few weeks,” Scale AI CEO Alexandr Wang, whose company helps OpenAI, Meta, and others train their models, told the conference’s host, Eric Newcomer, during the morning’s first session. “Have we hit a wall? 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Comparatively little went to alternative approaches such as neurosymbolic AI.","'On the assumption that I was wrong' is read as 'on the assumption that scaling would keep delivering gains', since the investors' view of Marcus personally is not observable.","Three parts must hold: the size (hundreds of billions), the rationale (a belief in scaling), and the concentration (largely excluding alternatives)."],"support_condition":"Reported totals show hundreds of billions of dollars invested in LLM/generative-AI scaling by late 2024. Industry statements tie that investment to belief in scaling laws. 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That figure is a projection for one year and covers all capital spending, not investment in LLM scaling specifically. So it only hints at the size the claim describes. The packet has no data on funding for non-LLM approaches. That means 'largely to the exclusion of investment in other approaches' cannot be checked. No evidence in the packet contradicts the claim.","limitations":["The capex figure is a projection made in November 2024 for the full year, not a realized total.","The capex figure covers all data-center and cloud spending by four firms, not investment in LLM scaling alone, and it covers 2024 only, not 2020–2024.","The phrase 'on the assumption that I was wrong' is read as belief that scaling would keep working. Investors' views of Marcus himself cannot be observed.","Wang's statement is one industry view, and it does not quantify how much investment rested on that belief.","No measure of funding for alternative approaches was found."],"evidence":[{"id":"e1","relation":"supports","excerpt":"The capital expenditures of the four largest internet and software companies - Amazon.com Inc., Microsoft Corp., Meta Platforms Inc. and Alphabet Inc. - are set to total well over $200 billion this year, a record sum for the profligate collective.","locator":"paragraph 3 of article body","url":"https://gulfnews.com/amp/story/business%2Fmarkets%2Ftech-giants-are-set-to-spend-200-billion-in-2024-chasing-ai-1.1730471888620","publisher":"Gulf News (Bloomberg)","event_date":"2024","available_at":"2024-11-01","explanation":"Shows spending in the hundreds of billions tied to the AI boom in 2024 alone. It is total capex, not spending on LLM scaling specifically, so it bears on the size of the claim only as context."},{"id":"e2","relation":"supports","excerpt":"Much of the investment going into AI has been based on the belief that the scaling law “would hold,” he said.","locator":"paragraph beginning 'There’s a very real shift'","url":"https://www.theverge.com/2024/11/22/24303470/ai-model-llm-progress-hitting-scaling-wall","publisher":"The Verge","event_date":"2024-11-20","available_at":"2024-11-22","explanation":"An industry CEO's statement that much AI investment rested on belief in scaling. This is consistent with the rationale part of the claim, but says nothing about how much went to alternatives."}],"what_would_resolve":"A credible breakdown of realized AI investment from 2020 to November 2024 that separates LLM and generative-AI scaling from non-LLM approaches such as neurosymbolic, cognitive-architecture, or hybrid research, and shows both the dollar total for LLM scaling and the relative shares.","retrospective":true,"plain_reading":true},"payload_hash":"441c0f3ab0fd2748088d8f233886200a54e1424ba666770205e70612e589a7ed","policy_version":"2026-10-28.7","recorded_at":"2026-10-29T11:51:53Z","drafter":{"provider":"anthropic","model":"claude-opus-5-5"},"reviewers":[{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-satya-nadella-and-the-three-stages-1c0969-4x-review-anthropic-1"},{"provider":"openai","model":"gpt-6-astra","run_id":"marcus-satya-nadella-and-the-three-stages-1c0969-4x-review-openai-1"}],"final_text":{"provider":"openai","model":"gpt-6-astra","run_id":"marcus-satya-nadella-and-the-three-stages-71fb79-1x-final-openai-1"},"status":"current","notice":null,"superseded_by":null,"evidence_meta":[{"id":"e1","source_type":"news_report","audited":null,"derived":null,"metric":{"company":"Amazon, Microsoft, Meta, Alphabet (combined)","metric":"capital expenditures","currency":"USD","units":"billions","accounting_basis":"estimate","period_start":"2024-01","period_end":"2024-12"}},{"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":"2026-10-29T11:51:53Z","next_review_due_at":null,"horizon_now":"not_applicable"},"event_ids":["marcus-satya-nadella-and-the-three-stages-71fb79-assessment-published-a2"],"last_substantive_change_at":"2026-10-29T17:00:18Z"}},{"url":"https://punditclaims.ai/claims/marcus-satya-nadella-and-the-three-stages-a08185","standing":"current","assessment_label":"Can’t be checked","versions":[{"assessment_id":"marcus-satya-nadella-and-the-three-stages-a08185-a1","status":"current","url":"https://punditclaims.ai/claims/marcus-satya-nadella-and-the-three-stages-a08185#assessment-marcus-satya-nadella-and-the-three-stages-a08185-a1"}],"claim":{"claim_id":"marcus-satya-nadella-and-the-three-stages-a08185","subject_id":"gary-marcus","revision":1,"content_hash":"2327234b2e3f0ea5027fccab654d38cc3c97c365205cdf3f4eca0519252ad757","normalized_proposition":"Resistance to criticism of scaling slowed scientific progress in AI.","kind":"causal","target":"resistance to new ideas in AI","conditions":[],"original_horizon_text":null,"topic_tags":["scaling","other"],"concerns_providers":[],"selection_stratum":"declared-lead","occurrences":[{"id":"marcus-satya-nadella-and-the-three-stages-a08185-o1","statement_date":"2024-11-23","statement_date_precision":"day","excerpt":"What’s at stake? 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I assess the reaffirmed forecast from November 9, 2024.","Neither an isolated stock decline nor continued use before the qualifying downturn establishes the conditional outcome."],"support_condition":"Both parts of the antecedent are independently established, followed by evidence that AI and LLMs retain practical uses through that downturn, including probabilistic prediction or generation.","contradiction_condition":"Both parts of the antecedent are independently established, followed by evidence that AI or LLMs have disappeared from practical use, or that LLMs no longer retain the stated practical role."},"axis":"forecast_outcome","outcome":"unresolved","horizon_state":"open_horizon","evaluation_as_of":"2026-10-28","rationale":"Sherlund Partners measured October 9–23, 2026 declines of 18.2% in its Datacenter Enablers basket and 11.9% in Silicon & Semis, supporting the valuation component [e1]. 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These items establish neither both conditions during a qualifying downturn nor continued practical AI and LLM use through that episode [e1, e2, e3].","limitations":["The market and sentiment measurements cover different populations and periods [e1, e2].","Concerns about financing the AI buildout do not independently establish declining enthusiasm for GenAI [e1].","The usage survey predates the October selloff and measures broad AI use, not subsequent LLM use for probabilistic prediction or generation [e3]."],"evidence":[{"id":"e1","relation":"supports","excerpt":"The Hyperscalers fell 5.9%.","locator":"Section “Two weeks, two different moves,” first paragraph","url":"https://sherlundai.com/insights-ai-sell-off-october-2026","publisher":"Sherlund Partners","event_date":"2026-10-23","available_at":"2026-10-25","explanation":"Its market baskets show a substantial October 9–23 selloff, including declines of 18.2% for datacenter enablers and 11.9% for silicon and semiconductors. This supports the valuation component, but the report describes financing concerns while saying inference demand remains strong; it does not establish both antecedents."},{"id":"e2","relation":"supports","excerpt":"excitement and hopefulness have dropped.","locator":"Opening paragraph; survey dates in “Survey Methods”","url":"https://news.gallup.com/poll/708224/gen-adoption-steady-skepticism-climbs.aspx","publisher":"Gallup","event_date":"2026-03-04","available_at":"2026-04-08","explanation":"The February 24–March 4 survey establishes waning enthusiasm among US respondents aged 14–29. Its population and timing do not establish a general decline in GenAI enthusiasm during the October market downturn."},{"id":"e3","relation":"context","excerpt":"A majority (56%) of Americans report weekly AI use","locator":"Section “A Majority of Americans Report Frequent AI Use and Primarily Use It For Research,” first paragraph","url":"https://navigatorresearch.org/americans-unsure-on-ai-but-few-approve-of-trumps-handling-of-the-issue","publisher":"Navigator Research","event_date":"2026-10-05","available_at":"2026-10-20","explanation":"The registered-voter survey shows continued AI use and reports improved favorability since September. 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and many others, including Melanie Mitchell, Subbarao Kambhampati, Emily Bender and Ernie Davis, have long emphasized that there are principled limits with LLMs.","kind":"observation","target":"LLMs","conditions":[{"text":"many of us, including named researchers","type":"scope","from_source":true}],"original_horizon_text":null,"topic_tags":["capabilities"],"concerns_providers":[],"selection_stratum":"declared-lead","occurrences":[{"id":"marcus-confirmed-llms-have-indeed-reached-ce65c9-o1","statement_date":"2024-11-09","statement_date_precision":"day","excerpt":"Many of us, including Melanie Mitchell, Subbarao Kambahapati, Emily Bender, Ernie Davis, etc. have been emphasizing for ages that there are principled limits with LLMs.","locator":"paragraph 12","document":{"url":"https://garymarcus.substack.com/p/confirmed-llms-have-indeed-reached","title":"CONFIRMED: LLMs have indeed reached a point of diminishing returns","publisher":"Marcus on 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It does not establish that their arguments were correct.","Read 'principled limits' as arguments connecting limitations to training, representation, understanding, or reasoning mechanisms, rather than isolated mistakes.","Read 'for ages' colloquially as a pre-existing body of criticism, not a specified minimum number of years for every person.","The historical period has no stated beginning and ends immediately before publication."],"support_condition":"Dated publications and independent contemporary reporting document such arguments across the named researchers before November 9, 2024, including substantial history before 2024.","contradiction_condition":"Reliable historical records refute the attributions or show that the supposed longstanding arguments only arose contemporaneously with or after the post."},"axis":"historical_observation","outcome":"supported","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"The record supports the ordinary historical reading. Bender, Marcus, and Davis made relevant arguments in 2020; Mitchell discussed mechanism-based reasoning limitations in 2023; and Kambhampati's pre-post paper explicitly argued for planning limitations. This establishes an existing body of criticism. It does not establish that every researcher claimed immutable limits or agreed with Marcus's broader conclusions.","limitations":["'For ages' is colloquial, and the evidence does not establish an identical duration for every researcher.","Mitchell's position is more qualified than a claim of permanent impossibility.","The outcome depends on reading 'principled limits' as mechanism-based concerns.","The ACL source supplies a publication month, so its availability date is a conservative month-end bound."],"evidence":[{"id":"e1","relation":"supports","excerpt":"In this position paper, we argue that a system trained only on form has a priori no way to learn meaning.","locator":"Abstract","url":"https://aclanthology.org/2020.acl-main.463","publisher":"Association for Computational Linguistics","event_date":"2020-07","available_at":"2020-07-31","explanation":"Bender and Koller's paper makes an explicit conceptual-limit argument more than four years before the post. The metadata gives July 2020; July 31 is used as a conservative availability bound."},{"id":"e2","relation":"supports","excerpt":"Whether LLMs have such general abstract-reasoning capacities, elicited through prompting tricks, scratchpads, or other external enhancements, still needs to be systematically demonstrated.","locator":"Final paragraph","url":"https://aiguide.substack.com/p/can-large-language-models-reason","publisher":"Melanie Mitchell, AI: A Guide for Thinking Humans","event_date":"2023-09-10","available_at":"2023-09-10","explanation":"Mitchell discusses memorization, pattern matching, and failures of generalization more than a year before the post. 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I use the period from 2023 to the post date (2024-10-30), when the AI data-centre boom was visible in utility plans.","'Even if GenAI never becomes reliable' is a hedge, not a condition to test."],"support_condition":"Evidence that AI data-centre demand was keeping fossil generation in use or expanding it, and that this benefits oil- or gas-exporting countries.","contradiction_condition":"Evidence that AI power demand was met without sustaining fossil fuels, or that fossil exporters did not benefit."},"axis":"causal_assessment","outcome":"unresolved","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"The claim has two parts. First, AI power demand is keeping fossil fuels in use. Second, this benefits oil-rich countries. For the first part there is partial evidence. DeSmog reported in December 2025 (e1) that Dominion Energy had announced non-binding plans in 2020 to retire its Clover coal plant by 2025. Dominion reversed that in 2023, when its plan projected that data-centre demand would nearly quadruple by 2038. That is one utility, and it concerns data centres generally, not generative AI specifically. An IEA report summarised in April 2025 (e2) projects that renewables and natural gas will lead in meeting data-centre demand. That is a later projection and serves only as context. For the second part, the packet has no evidence that oil- or gas-exporting countries benefited. The claim as a whole is not established.","limitations":["The evidence covers data-centre demand in general, not generative AI specifically.","Only one utility decision within the target period is documented in the evidence items.","The IEA material is a secondary summary of a 2025 projection, published after the post.","The packet has no evidence on fossil-fuel export demand or revenue."],"evidence":[{"id":"e1","relation":"supports","excerpt":"It reversed course just three years later. Under its 2023 plan, Dominion projected that its energy demand from data centers would nearly quadruple by 2038.","locator":"section Data Centers Changed Coal's Trajectory, paragraph 2","url":"https://www.desmog.com/2025/12/12/15-coal-plant-retirements-delayed-ai-data-centers-trump-doe-orders","publisher":"DeSmog","event_date":"2023","available_at":"2025-12-12","explanation":"Before the post, Dominion reversed its plan to retire the Clover coal plant, citing data-centre demand growth. This supports part (1) of the claim."},{"id":"e2","relation":"context","excerpt":"renewables and natural gas are set to take the lead due to their cost-competitiveness and availability in key markets.","locator":"paragraph 3","url":"https://www.hydrocarbonengineering.com/gas-processing/10042025/iea-ai-set-to-drive-surging-electricity-demand","publisher":"Hydrocarbon Engineering (reporting IEA)","event_date":"2025-04-10","available_at":"2025-04-10","explanation":"The IEA projects natural gas as a lead supplier for AI data-centre demand, alongside renewables. 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Only the implied forecast is graded.","Early resolution rule: the forecast is contradicted early if, before the deadline, credible evaluations show an AI system able to do essentially any task a human can do, including physical and long-horizon work. It can be supported only once the deadline passes."],"support_condition":"At 2026-10-30, AI systems still cannot do many tasks humans can do, as shown by public capability evaluations.","contradiction_condition":"By 2026-10-30, credible evidence shows an AI system that can do anything any human can do."},"axis":"forecast_outcome","outcome":"unresolved","horizon_state":"not_due","evaluation_as_of":"2026-10-28","rationale":"Fortune reported (e1) that on 29 October 2024 Musk predicted AI would be able to do anything a human can do within the next year or two. Marcus's implied forecast is that this will not happen. Under the contract, the window ends on 30 October 2026, two days after the evaluation date. The contract allows early contradiction only if credible evidence shows an AI system matching humans on essentially all tasks. The packet has no such evidence. In July 2026 the Center for AI Safety reported (e2) that the best automation rate on the Remote Labor Index was 15.8%. It also said AIs still fell short of professional quality on most projects. That does not meet the early-contradiction rule. 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digits."}],"what_would_resolve":null,"retrospective":true,"plain_reading":true},"payload_hash":"c2ac2331d64f90e7a420a23438a5ca2136e6dc21d016ec331f89ab5fbe01fe6f","policy_version":"2026-10-28.7","recorded_at":"2026-10-29T07:40:09Z","drafter":{"provider":"anthropic","model":"claude-opus-5-5"},"reviewers":[{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-llms-dont-do-formal-reasoning-and-204262-4x-review-anthropic-1"},{"provider":"openai","model":"gpt-6-astra","run_id":"marcus-llms-dont-do-formal-reasoning-and-204262-4x-review-openai-1"}],"final_text":{"provider":"openai","model":"gpt-6-astra","run_id":"marcus-llms-dont-do-formal-reasoning-and-204262-4x-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":"peer_reviewed","audited":null,"derived":null,"metric":null},{"id":"e3","source_type":"independent_measurement","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":"2026-10-29T07:40:09Z","next_review_due_at":null,"horizon_now":"not_applicable"},"event_ids":["marcus-llms-dont-do-formal-reasoning-and-204262-assessment-published-a1"],"last_substantive_change_at":"2026-10-29T17:00:18Z"}},{"url":"https://punditclaims.ai/claims/marcus-llms-dont-do-formal-reasoning-and-269833","standing":"current","assessment_label":"Mixed","versions":[{"assessment_id":"marcus-llms-dont-do-formal-reasoning-and-269833-a1","status":"current","url":"https://punditclaims.ai/claims/marcus-llms-dont-do-formal-reasoning-and-269833#assessment-marcus-llms-dont-do-formal-reasoning-and-269833-a1"}],"claim":{"claim_id":"marcus-llms-dont-do-formal-reasoning-and-269833","subject_id":"gary-marcus","revision":2,"content_hash":"68d17a9a241b3da8d0b9ae246510ae1500c456a662cc80caf17d83020ff731a1","normalized_proposition":"Marcus strongly believes that the current results on LLMs' reasoning failures, including the Apple study and the other work he cites, are robust.","kind":"observation","target":"findings that LLMs lack formal reasoning","conditions":[{"text":"strongly believe","type":"qualifier","from_source":true}],"original_horizon_text":null,"topic_tags":["capabilities"],"concerns_providers":[],"selection_stratum":"declared-lead","occurrences":[{"id":"marcus-llms-dont-do-formal-reasoning-and-269833-o1","statement_date":"2024-10-11","statement_date_precision":"day","excerpt":"I strongly believe the current results are robust.","locator":"paragraph 23","document":{"url":"https://garymarcus.substack.com/p/llms-dont-do-formal-reasoning-and","title":"LLMs don’t do formal reasoning - and that is a HUGE problem","publisher":"Marcus on AI","published_at":"2024-10-11T18:44:22Z","date_uncertain":false,"date_provenance":"JSON-LD 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problem","observed_at":"2026-10-28T23:59:05Z"},"criteria":{"from_speaker":["I strongly believe","the current results","are robust"],"our_interpretation":["The literal claim is a report of his own belief, which cannot be checked. The checkable part is that the results are robust.","Plain reading of 'robust': the reasoning-failure findings he cites, led by the Apple GSM-Symbolic/GSM-NoOp results, hold up when others re-test or re-analyse them.","Target period: from the Apple paper's release (2024-10-07) to the evaluation date. Robustness can only be shown by replications after the post, so the period covers the scrutiny window; this choice is flagged.","The test is replication of the findings on comparable setups, not whether newer models later improved. Later model improvement alone does not make the original findings fragile."],"support_condition":"Independent studies reproduce the main effects, such as sensitivity to irrelevant context and drops on altered problems, with sound statistics.","contradiction_condition":"Independent re-analyses or replications find the main effects largely vanish under proper statistics or cleaner test data, even for the original models."},"axis":"historical_observation","outcome":"mixed","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"Read plainly, the claim is that the cited reasoning-failure findings, led by Apple's GSM-Symbolic study, would hold up. The record is split. An independent 2025 benchmark (GSM-DC) confirmed that LLMs are significantly thrown off by irrelevant context, which matches the core NoOp idea. But a peer-reviewed 2026 re-analysis found the GSM-Symbolic drop significant for only 8 of 20 open models, and partly explained by larger numbers in the new problems. A 2026 independent replication reproduced the NoOp drop with raw distractors. The drop mostly vanished once ambiguous distractors were removed. So the general sensitivity has replicated, but the size and meaning of the headline Apple results have not held up cleanly.","limitations":["'I strongly believe' is a statement of belief; only the robustness of the results is graded.","There is no exact NoOp replication, because the dataset was not released.","Only the math results were checked for replication."],"evidence":[{"id":"e1","relation":"supports","excerpt":"Our experiments demonstrate that LLMs are significantly sensitive to IC, affecting both reasoning path selection and arithmetic accuracy.","locator":"Abstract","url":"https://arxiv.org/abs/2505.18761","publisher":"arXiv (Yang et al.)","event_date":"2025-05-24","available_at":"2025-05-24","explanation":"An independent team built a controlled benchmark with irrelevant context (IC) and found LLMs significantly sensitive to it. This reproduces the core GSM-NoOp-style finding."},{"id":"e2","relation":"contradicts","excerpt":"we find that only 8 exhibit statistically significant performance changes under the original prompt format. Moreover, we identify a previously unacknowledged factor: the distribution of integers in problem texts of the main GSM-Symbolic dataset is systematically shifted towards larger values","locator":"Abstract","url":"https://arxiv.org/abs/2605.28700","publisher":"arXiv (Długosz, Oliveira, Díaz-Rodríguez; accepted EMNLP 2026)","event_date":"2026-05-27","available_at":"2026-05-27","explanation":"A peer-reviewed re-analysis found the GSM-Symbolic drop significant for only 8 of 20 models, and partly explained by larger numbers in the new dataset. That cuts against the robustness of that headline result."},{"id":"e3","relation":"contradicts","excerpt":"we precisely replicate the original findings only when we do not audit out examples that may actually be ambiguous for the model. When we carefully remove samples that may genuinely impact the calculation, the effect is drastically reduced.","locator":"TL;DR","url":"https://www.lesswrong.com/posts/Ze4C99Dasj74YKCFh/revisiting-gsm-symbolic-do-2026-frontier-models-still-fail","publisher":"LessWrong (author 'Sturb')","event_date":"2026-03","available_at":"2026-06-05","explanation":"An independent replication, including the original GPT-4o snapshot, reproduced the NoOp drop with unfiltered distractors. The drop largely disappeared once ambiguous distractors were removed, so the NoOp effect is sensitive to test construction. This is a non-peer-reviewed blog post, and it had to rebuild the NoOp data because Apple did not release it."}],"what_would_resolve":"An exact replication with Apple's released GSM-NoOp data, and replication checks on the planning and arithmetic results, would sharpen this verdict.","retrospective":true,"plain_reading":true},"payload_hash":"4a325128b4cb9858ce690b0890716ea17bca8b4441aa18510a456b190a460661","policy_version":"2026-10-28.7","recorded_at":"2026-10-29T07:40:09Z","drafter":{"provider":"anthropic","model":"claude-opus-5-5"},"reviewers":[{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-llms-dont-do-formal-reasoning-and-204262-4x-review-anthropic-1"},{"provider":"openai","model":"gpt-6-astra","run_id":"marcus-llms-dont-do-formal-reasoning-and-204262-4x-review-openai-1"}],"final_text":{"provider":"openai","model":"gpt-6-astra","run_id":"marcus-llms-dont-do-formal-reasoning-and-204262-4x-final-openai-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},{"id":"e3","source_type":"independent_measurement","audited":null,"derived":null,"metric":null}],"public_from":null,"public_until":null},"history":[],"review":{"review_due":false,"triggers":[],"last_completed_review_at":"2026-10-29T07:40:09Z","next_review_due_at":null,"horizon_now":"not_applicable"},"event_ids":["marcus-llms-dont-do-formal-reasoning-and-269833-assessment-published-a1"],"last_substantive_change_at":"2026-10-29T17:00:18Z"}},{"url":"https://punditclaims.ai/claims/marcus-llms-dont-do-formal-reasoning-and-4a617e","standing":"current","assessment_label":"Supported","versions":[{"assessment_id":"marcus-llms-dont-do-formal-reasoning-and-4a617e-a1","status":"current","url":"https://punditclaims.ai/claims/marcus-llms-dont-do-formal-reasoning-and-4a617e#assessment-marcus-llms-dont-do-formal-reasoning-and-4a617e-a1"}],"claim":{"claim_id":"marcus-llms-dont-do-formal-reasoning-and-4a617e","subject_id":"gary-marcus","revision":1,"content_hash":"3879f420cdf4d839d26f483883002a7bdbfae0eec187886eb6f443bc422751d8","normalized_proposition":"o1 does acceptably on small planning problems, but its performance drops quickly as problems get larger.","kind":"observation","target":"OpenAI o1","conditions":[],"original_horizon_text":null,"topic_tags":["capabilities","reliability"],"concerns_providers":["openai"],"selection_stratum":"declared-lead","occurrences":[{"id":"marcus-llms-dont-do-formal-reasoning-and-4a617e-o1","statement_date":"2024-10-11","statement_date_precision":"day","excerpt":"Performance is ok on small problems, but quickly tails off.","locator":"paragraph 11","document":{"url":"https://garymarcus.substack.com/p/llms-dont-do-formal-reasoning-and","title":"LLMs don’t do formal reasoning - and that is a HUGE problem","publisher":"Marcus on AI","published_at":"2024-10-11T18:44:22Z","date_uncertain":false,"date_provenance":"JSON-LD datePublished"},"observed_at":"2026-10-28T23:59:05Z","observed_via":"live","archive_url":null,"access_state":"full_public","snapshot_hash":"4ce2827448bf13d9b13f0eb4e4a847fc94bc58cbce1d9ee875eb79423aec7088","receipts":1}],"first_statement_date":"2024-10-11","related":[],"current":{"assessment_id":"marcus-llms-dont-do-formal-reasoning-and-4a617e-a1","payload":{"claim_id":"marcus-llms-dont-do-formal-reasoning-and-4a617e","subject_id":"gary-marcus","normalized_proposition":"o1 does acceptably on small planning problems, but its performance drops quickly as problems get larger.","kind":"observation","conditions":[],"original_horizon_text":null,"statement":{"excerpt":"Performance is ok on small problems, but quickly tails off.","locator":"paragraph 11","url":"https://garymarcus.substack.com/p/llms-dont-do-formal-reasoning-and","date":"2024-10-11","document_title":"LLMs don’t do formal reasoning - and that is a HUGE problem","observed_at":"2026-10-28T23:59:05Z"},"criteria":{"from_speaker":["recent analysis of GPT o1 by Subbarao Kambhapati's team","Performance is ok on small problems","but quickly tails off","as problems are made bigger"],"our_interpretation":["Read as: in the Valmeekam, Stechly and Kambhampati PlanBench evaluation of o1 (Sept 2024), o1 scores well on small Blocksworld problems but much lower on larger ones.","'GPT o1' is read as the o1-preview model that the paper tested."],"support_condition":"The paper reports high o1 accuracy on small planning problems and a steep drop on larger problems.","contradiction_condition":"The paper reports that o1 accuracy holds up on larger problems, or is poor even on small ones."},"axis":"historical_observation","outcome":"supported","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"Marcus summarized the Kambhampati team's o1 analysis as 'ok on small problems, but quickly tails off.' The paper reports that o1-preview scored 97.8% on 3-5 block Blocksworld problems. On 110 larger problems with 6 to 20 blocks, it scored only 23.63%, and most of those correct answers were the shorter plans. The authors themselves write that performance 'quickly degrade[s]'. This matches his description. The paper also calls o1 a 'quantum improvement' over earlier models, which Marcus does not mention, but that does not conflict with his statement.","limitations":["The model tested was o1-preview.","This is one benchmark domain (Blocksworld)."],"evidence":[{"id":"e1","relation":"supports","excerpt":"Without any obfuscation, we see performance quickly degrade from the 97.8% reported earlier. In fact, over these 110 instances, o1-preview only manages 23.63%, and most of this accuracy comes from correctly solving problems which require fewer than 28 steps.","locator":"Section 'Accuracy with Increased Problem Size'","url":"https://arxiv.org/html/2409.13373v1","publisher":"arXiv (Valmeekam, Stechly, Kambhampati)","event_date":"2024-09-20","available_at":"2024-09-20","explanation":"o1-preview scored 97.8% on small problems but 23.63% on 6-20 block problems, and its correct answers on the larger set were mostly the shorter plans. That is good performance on small problems with a quick fall-off."},{"id":"e2","relation":"context","excerpt":"while o1's performance is a quantum improvement on the benchmark, outpacing the competition, it is still far from saturating it.","locator":"Abstract","url":"https://arxiv.org/abs/2409.13373","publisher":"arXiv (Valmeekam, Stechly, Kambhampati)","event_date":"2024-09-20","available_at":"2024-09-20","explanation":"The authors also call o1 a large improvement over earlier models. This is fair context: 'ok on small problems' if anything undersells the 97.8%."}],"what_would_resolve":null,"retrospective":true,"plain_reading":true},"payload_hash":"2b69fb76289a262b0921907ac89ebbd15d8c1036bf83a39cc208b7a72403f49e","policy_version":"2026-10-28.7","recorded_at":"2026-10-29T07:40:09Z","drafter":{"provider":"anthropic","model":"claude-opus-5-5"},"reviewers":[{"provider":"anthropic","model":"claude-opus-5-5","run_id":"marcus-llms-dont-do-formal-reasoning-and-204262-4x-review-anthropic-1"},{"provider":"openai","model":"gpt-6-astra","run_id":"marcus-llms-dont-do-formal-reasoning-and-204262-4x-review-openai-1"}],"final_text":{"provider":"openai","model":"gpt-6-astra","run_id":"marcus-llms-dont-do-formal-reasoning-and-204262-4x-final-openai-1"},"status":"current","notice":null,"superseded_by":null,"evidence_meta":[{"id":"e1","source_type":"preprint","audited":null,"derived":null,"metric":null},{"id":"e2","source_type":"preprint","audited":null,"derived":null,"metric":null}],"public_from":null,"public_until":null},"history":[],"review":{"review_due":false,"triggers":[],"last_completed_review_at":"2026-10-29T07:40:09Z","next_review_due_at":null,"horizon_now":"not_applicable"},"event_ids":["marcus-llms-dont-do-formal-reasoning-and-4a617e-assessment-published-a1"],"last_substantive_change_at":"2026-10-29T17:00:18Z"}},{"url":"https://punditclaims.ai/claims/marcus-llms-dont-do-formal-reasoning-and-577dc0","standing":"current","assessment_label":"Can’t be checked","versions":[{"assessment_id":"marcus-llms-dont-do-formal-reasoning-and-577dc0-a2","status":"current","url":"https://punditclaims.ai/claims/marcus-llms-dont-do-formal-reasoning-and-577dc0#assessment-marcus-llms-dont-do-formal-reasoning-and-577dc0-a2"}],"claim":{"claim_id":"marcus-llms-dont-do-formal-reasoning-and-577dc0","subject_id":"gary-marcus","revision":2,"content_hash":"835d56d9e6adf99690127a85a800ef0eb3962cd602c83ffbf64a9a187d1f5179","normalized_proposition":"As Marcus argued in The Algebraic Mind in 2001, symbol manipulation, in which some knowledge is represented truly abstractly in terms of variables and operations over them as in algebra and traditional programming, must be part of the mix.","kind":"causal","target":"AI systems","conditions":[{"text":"as part of the mix","type":"qualifier","from_source":true}],"original_horizon_text":null,"topic_tags":["capabilities","other"],"concerns_providers":[],"selection_stratum":"declared-lead","occurrences":[{"id":"marcus-llms-dont-do-formal-reasoning-and-577dc0-o1","statement_date":"2024-10-11","statement_date_precision":"day","excerpt":"What I argued in 2001, in The Algebraic Mind, still holds: symbol manipulation, in which some knowledge is represented truly abstractly in terms of variables and operations over those variables, much as we see in algebra and traditional computer programming, must be part of the mix.","locator":"paragraph 24","document":{"url":"https://garymarcus.substack.com/p/llms-dont-do-formal-reasoning-and","title":"LLMs don’t do formal reasoning - and that is a HUGE problem","publisher":"Marcus on AI","published_at":"2024-10-11T18:44:22Z","date_uncertain":false,"date_provenance":"JSON-LD datePublished"},"observed_at":"2026-10-28T23:59:05Z","observed_via":"live","archive_url":null,"access_state":"full_public","snapshot_hash":"4ce2827448bf13d9b13f0eb4e4a847fc94bc58cbce1d9ee875eb79423aec7088","receipts":1}],"first_statement_date":"2024-10-11","related":[],"current":{"assessment_id":"marcus-llms-dont-do-formal-reasoning-and-577dc0-a2","payload":{"claim_id":"marcus-llms-dont-do-formal-reasoning-and-577dc0","subject_id":"gary-marcus","normalized_proposition":"As Marcus argued in The Algebraic Mind in 2001, symbol manipulation, in which some knowledge is represented truly abstractly in terms of variables and operations over them as in algebra and traditional programming, must be part of the mix.","kind":"causal","conditions":["as part of the mix"],"original_horizon_text":null,"statement":{"excerpt":"What I argued in 2001, in The Algebraic Mind, still holds: symbol manipulation, in which some knowledge is represented truly abstractly in terms of variables and operations over those variables, much as we see in algebra and traditional computer programming, must be part of the mix.","locator":"paragraph 24","url":"https://garymarcus.substack.com/p/llms-dont-do-formal-reasoning-and","date":"2024-10-11","document_title":"LLMs don’t do formal reasoning - and that is a HUGE problem","observed_at":"2026-10-28T23:59:05Z"},"criteria":{"from_speaker":["What I argued in 2001, in The Algebraic Mind, still holds","symbol manipulation","variables and operations over those variables","must be part of the mix"],"our_interpretation":["The claim says symbol manipulation 'must be part of the mix' but does not say for what goal (reliable reasoning, AGI, some benchmark) or by when.","It is a theoretical necessity claim about AI design with no deadline or test. Present-day systems either use symbolic tools (code execution, proof checkers) or do not. Neither fact settles whether symbol manipulation is necessary.","No plain reading yields a definite test, so the claim is treated as not adjudicable."],"support_condition":"Not applicable: no stated goal or test would show the necessity claim true.","contradiction_condition":"Not applicable: no stated goal or test would show the necessity claim false."},"axis":"not_applicable","outcome":"not_adjudicable","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"In a post dated 2024-10-11, Marcus says that symbol manipulation, with knowledge represented abstractly as variables and operations over them as in algebra and programming, 'must be part of the mix.' He does not say what it is needed for, such as reliable reasoning or a benchmark. He gives no deadline and no test. Without a stated goal, no dated evidence could show the necessity claim true or false. For that reason the claim is not graded. The evidence packet cites no outcome evidence. The two sources it considered were left unused because they could not settle a necessity claim that names no goal.","limitations":["The packet cites no evidence about whether current AI systems use symbolic components, or how well systems without them perform.","'Part of the mix' is vague. It is unclear whether external tools such as code interpreters would count as symbol manipulation.","A narrower version of the claim, naming a goal and a date, could be checked. 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That is the latest state public evidence could describe before the post.","The 'formal reasoning' label is his interpretation of the failure. Only the factual part, that LLMs make illegal moves, is graded here."],"support_condition":"A documented evaluation from 2024, before the post, shows that current leading LLMs (including the strongest available ones) make illegal chess moves at a non-trivial rate.","contradiction_condition":"Evaluations from the same period show that leading LLMs reliably made only legal moves, so illegal moves were rare or absent."},"axis":"historical_observation","outcome":"supported","horizon_state":"not_applicable","evaluation_as_of":"2026-10-28","rationale":"Marcus said that failing to follow chess rules was a continuing failure of LLMs. The Register covered a 2024 benchmark of 1,000 chess puzzles. It reported that even GPT-4o, the best model tested, made illegal moves 12.7% of the time, and that most other LLMs made more illegal moves than legal ones. The benchmark's author counts these illegal moves as a failure to internalize the board and the rules. So before the post, leading LLMs did still break chess rules at a meaningful rate. Calling this a failure of 'formal reasoning' is Marcus's interpretation and is not graded.","limitations":["The evidence is one puzzle benchmark with a single prompt format. Other formats or full games may give different illegal-move rates.","The benchmark repository's current table is from 2025, so the 2024 figures come from the news report."],"evidence":[{"id":"e1","relation":"supports","excerpt":"Even GPT-4o made an illegal move 12.7 percent of the time, and most of the other LLMs made more illegal moves than legal ones.","locator":"paragraph beginning 'Calculating Elo wasn't exactly straightforward'","url":"https://www.theregister.com/2024/06/04/chess_puzzle_benchmark_llm","publisher":"The Register","event_date":"2024-05","available_at":"2024-06-04","explanation":"On a 1,000-puzzle benchmark in mid-2024, the strongest model tested (GPT-4o) made illegal moves 12.7% of the time, and most other LLMs made more illegal moves than legal ones. 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