METR: Independent Assessment of AI R&D Acceleration at Anthropic in October 2026

Executive summary

From 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 acceleration in the scaling of training compute.
• Interviews and surveys are consistent with AI usage causing a doubling in the pace of capabilities progress. Anthropic staff self-report dramatic uplift from use of AI systems in interviews and surveys, although these are challenging to interpret. We were told that R&D is accelerated primarily via AI assistance rather than fully autonomous contributions, but there are some signs that fully autonomous contributions will play a larger role soon.
• We did not see evidence for significant bottlenecks which would prevent further capabilities progress due to limits on algorithmic progress, training compute, experiment compute, or data in the next 12 months. However, our understanding of the status of different bottlenecks changed several times as we received more detailed information, so we are not confident in our current assessment.

Some of the key findings that most informed our conclusions about risk were redacted by Anthropic.In our third section, we describe the assessment process:

• The scope, setup, and timeline of this assessment.
• Details of the information shared with METR.
• Details of what information and evidence we were not able to share given Anthropic’s redactions.

Finally, we describe the biggest limitations of our analysis:

• We could be underestimating capability acceleration if we did not assign enough importance to (1) evidence that some growth rates have further increased recently; (2) further automation of AI R&D tasks leading to a further acceleration in progress; (3) evidence that AECI growth has sometimes understated growth in real-world impact.
• We could be overestimating capability acceleration if we did not assign enough importance to (1) non-AI factors in Anthropic’s research progress; (2) AECI’s focus on verifiable tasks; (3) reasons why Anthropic’s early growth rate (2023–2025) was unusually low.

The remainder of the report goes into detailed discussion of each of the key components. We include appendices with supplemental material.

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