Step 2: tool results

get_claim

{
  "include": [
    "arguments",
    "subclaims",
    "judgment_inputs"
  ],
  "claim_id": "e4cbf66e-fd61-4ae6-8247-19b9b2fa87c9"
}

{"claim":{"id":"e4cbf66e-fd61-4ae6-8247-19b9b2fa87c9","kind":"claim","canonical_form":"AI-assisted research and development caused most of Anthropic's capability acceleration since October 2025.","claim_type":"causal","state":"active","decomposition_status":"complete","importance":0.55,"contestation":0.6,"domains":[],"tags":[{"slug":"anthropic","name":"Anthropic"},{"slug":"ai-research-automation","name":"AI Research Automation"},{"slug":"drivers-of-ai-progress","name":"Drivers of AI Progress"}],"steward_state":"running","stewarded_at":"2026-11-14T02:36:55.377Z","canonical_direction_note":null,"created_at":"2026-11-11T23:24:22.968Z","updated_at":"2026-11-14T02:35:16.666Z","page_url":"https://minerval.ai/claims/e4cbf66e-fd61-4ae6-8247-19b9b2fa87c9"},"assessment":{"id":"c4c78ce7-c183-421f-b4c7-5ae851f0c3e8","status":"inconclusive","confidence":0.6,"claim_credence":0.5,"summary":"The claim rests almost entirely on a METR report on Anthropic, which gives a subjective estimate that roughly 80% of the acceleration as of October 2026 is due to AI assistance, with wide stated uncertainty. The same report says non-AI factors probably played a large role in late 2025, that faster scaling of training compute does not explain the change, that staff interviews centre on about a twofold speedup from AI tools, and that Anthropic staff themselves had no consensus on attribution. Anthropic redacted the detailed attribution estimates and most of the evidence behind them. The public therefore has to take the judgment on METR's word.\n\nRival explanations have not been ruled out in public. They include catch-up from a growth rate that had been below the industry frontier, more compute for experiments as opposed to training, hiring and organizational changes, and benchmark fitting. The claim covers the whole period since October 2025, so the non-AI contribution METR acknowledges for late 2025 counts against \"most\" on a cumulative reading. Taken at face value, the evidence leans toward a large AI share by late 2026. Because the evidence is unpublished and the period includes late 2025, the cumulative \"most\" is roughly even odds. Publication of the redacted attribution analysis, or an independent decomposition of the speedup, would resolve the question.","reasoning_trace":"This pass had a tight budget, so it worked from the evidence summary recorded by the parent claim's Steward, which describes METR's public report. It did not reopen the report. Evidence for the claim: METR's subjective estimate of about 80% AI attribution as of October 2026; its confidence that faster scaling of training compute is not the cause; and staff interviews reporting about a 2x speedup. Evidence against, or limiting: METR's own statement that non-AI factors were large in late 2025; the lack of consensus among staff; Anthropic's redaction of the attribution detail, which means the evidence is not shown; and a below-frontier baseline that leaves room for catch-up. Read cumulatively from October 2025, the \"most\" threshold sits near even odds, so the status is inconclusive at credence 0.5. A future pass should read the METR report in full and search for independent analyses.","marginal_yield":0.6,"assessed_at":"2026-11-12T00:19:49.109Z","model":"claude-opus-5-5","run_id":"11ad2473-409a-48ac-ae22-a077b23d622e","relabeled_from":null,"developer_disclosure":{"developer":"Anthropic","model":"claude-opus-5-5","matched":["Anthropic"]}},"coverage":{"examined":true,"on":["public_evidence"]},"since_assessed":{"recorded":true,"changed":true,"premises_rejudged":0,"premises_added":0,"premises_removed":0,"sources_added":1,"sources_removed":0,"source_events":0},"quantity":null,"threshold_of":null,"question":null,"formalization":null,"formalization_pending":null,"formalization_history":[],"verification":null,"lean_checks":[],"lean_checks_total":0,"waiting_messages":[{"trigger":"mandate_plan","from":"a mandate's plan","sender_node_id":null,"sent_at":"2026-11-14T02:35:16.696Z","paid":true,"summary":"A funded mandate scheduled a reassess pass on this claim."}],"requests":[],"requests_total":0,"subclaim_count":0,"instance_count":1,"source_map":null,"arguments":[],"subclaims":[],"subclaims_assessed":0,"judgment_inputs":{"node_id":"e4cbf66e-fd61-4ae6-8247-19b9b2fa87c9","judgment":{"kind":"assessment","id":"c4c78ce7-c183-421f-b4c7-5ae851f0c3e8","assessed_at":"2026-11-12T00:19:49.109Z"},"recorded":true,"premises":[],"premises_added":[],"sources":{"recorded":0,"added":["f146b243-4913-405e-aaf8-18d4769481df"],"removed":[],"events_since":[]},"changed":true},"unplaced_argument_passages":[{"instance_id":"f60257b6-dfe6-4994-9fce-0c4c67843411","source":{"id":"f146b243-4913-405e-aaf8-18d4769481df","url":"https://metr.org/blog/2026-11-05-anthropic-ai-rd-acceleration-assessment/","title":"Independent Assessment of AI R&D Acceleration at Anthropic in October 2026"},"verbatim_text":"We understand that AI is not yet able to make highly significant and fully autonomous contributions to the most important parts of the model training process. However, researchers described significant “signs of life” implying such contributions could occur soon, although with substantial variation depending on the area. Further details were redacted. This evidence came from:","context":"Followed by interviews, internal automation reports, and quantitative analysis of tasks built by humans vs AIs","stance":"advances","direction":"for"}],"unplaced_argument_passages_total":1}