METR Report Finds Anthropic's AI R&D Loop Running at About 2x Pace
METR, drawing on Anthropic's internal R&D data and researcher interviews, estimates the company is now improving its models at roughly twice its 2024-2025 pace, with about 80% of the acceleration attributed to Anthropic's own AI use rather than faster growth in training compute. The report also says Anthropic's public August risk report understated the speed, and finds no algorithm, compute or data bottleneck likely to slow the loop within 12 months.
Full Report
Rohan Paul (@rohanpaul_ai) · 2026-11-06 00
So much revelation from METR's latest report.
Recursive self-improvement (RSI) at Anthropic has moved from theory to solid measurement, and the early loop already runs at about 2x.
After they spent 11 weeks going through Anthropic’s internal R&D data and interviewing its key researchers.
Anthropic is now improving its models at about twice its 2024 to 2025 pace.
Claude itself is the main engine. METR’s best guess puts about 80% of current acceleration on Anthropic’s own AI use, and it rules out faster growth in training compute as the cause.
Anthropic’s own public August risk report understated how fast things were moving. METR reached that conclusion in a separate review that compared the public report with the private version and other nonpublic data.
METR saw nothing likely to slow this loop within 12 months, with no bottleneck in algorithms, compute or data. Redacted evidence also hints that acceleration may already exceed 2x and keep climbing.
Staff self-report roughly 12x productivity gains from Model 4, and researchers see “signs of life” for AI doing key training work alone. One called Mythos Preview the first model useful for autonomous experiments.
Anthropic redacted most of the evidence METR found most telling, saying it could help rivals go faster. It also canceled midway a planned check of how its compute splits between capabilities and safety.
↳ 引用 @METR_Evals (METR): Since August, a team within METR has worked with internal Anthropic information to assess whether AI itself is accelerating AI development.
We are now publishing a report summarizing the results of this risk assessment.
