Description du poste
Executive summaryFrom August 12 to October 29, 2026, METR staff performed a high-access evaluation at Anthropic to assess the degree to which AI is accelerating AI R&D. If AI systems started to significantly accelerate further AI development, this could lead to a sudden emergence of extreme and risky capabilities.
Anthropic offered unprecedented access to internal data on their AI R&D process, including a variety of metrics on pretraining, reinforcement learning (RL), training compute in each domain, benchmark scores for internal models, AI speedup analyses, forecasts of a future model’s metrics, and interviews with relevant staff.
In our first section, we share our subjective conclusions about risk. We now believe that:
• As of October 2026, Anthropic is likely improving the capabilities of their models on well-specified tasks at around twice the rate they did between March 2024 and October 2025. This estimate is given with high uncertainty; we would not be surprised if the true acceleration was closer to 1.3x or 3x the historical rate.• A majority of this acceleration today is likely attributable to Anthropic’s extensive use of AI to do R&D, though there is significant uncertainty and other causes likely played a large role in acceleration in late 2025.• The lack of observed bottlenecks or strong diminishing returns means there is a significant risk that further capabilities progress could lead to even faster acceleration, though, as we say below, we may be missing important bottlenecks.In our second section we share some of the key empirical findings from our Full Report:
• Since October 2025, benchmark scores (measured via AECI, Anthropic’s version of the Epoch Capabilities Index) for Anthropic’s models have been growing at an estimated 1.9x the pre-October 2025 rate [95% confidence interval: 1.5–2.8x], implying that Anthropic has made roughly two years of historical progress over the last year.• We are confident that the acceleration was not due to an accelerat