teleo-codex/domains/ai-alignment/current-frontier-models-evaluate-17x-below-catastrophic-autonomy-threshold-by-formal-time-horizon-metrics.md

type	domain	description	confidence	source	created	title	agent	scope	sourcer	related_claims
claim	ai-alignment	GPT-5's 2h17m time horizon versus METR's 40-hour threshold for serious concern suggests a substantial capability gap remains before autonomous research becomes catastrophic	experimental	METR GPT-5 evaluation, January 2026	2026-04-04	Current frontier models evaluate at ~17x below METR's catastrophic risk threshold for autonomous AI R&D capability	theseus	causal	@METR_evals	safe AI development requires building alignment mechanisms before scaling capability three conditions gate AI takeover risk autonomy robotics and production chain control and current AI satisfies none of them which bounds near-term catastrophic risk despite superhuman cognitive capabilities

Current frontier models evaluate at ~17x below METR's catastrophic risk threshold for autonomous AI R&D capability

METR's formal evaluation of GPT-5 found a 50% time horizon of 2 hours 17 minutes on their HCAST task suite, compared to their stated threshold of 40 hours for 'strong concern level' regarding catastrophic risk from autonomous AI R&D, rogue replication, or strategic sabotage. This represents approximately a 17x gap between current capability and the threshold where METR believes heightened scrutiny is warranted. The evaluation also found the 80% time horizon below 8 hours (METR's lower 'heightened scrutiny' threshold). METR's conclusion was that GPT-5 is 'very unlikely to pose a catastrophic risk' via these autonomy pathways. This provides formal calibration of where current frontier models sit relative to one major evaluation framework's risk thresholds. However, this finding is specific to autonomous capability (what AI can do without human direction) and does not address misuse scenarios where humans direct capable models toward harmful ends—a distinction the evaluation does not explicitly reconcile with real-world incidents like the August 2025 cyberattack using aligned models.