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

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.

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.

The acceleration in late 2025 was likely influenced by non-AI factors. We investigated a variety of factors which could have contributed to the observed acceleration in late 2025, but details of many of these factors have been redacted.

We guess that a majority (perhaps ≈80%) of the current (October 2026) acceleration is due to AI. This judgment is given with substantial uncertainty, but is based on a variety of lines of evidence on contributions from AI and other causes. We believe AI’s contribution has grown relatively more important over time, and do not think the other factors we investigated would explain the sustained acceleration. The evidence of speedup from the usage of AI we describe below also seems compatible with a large contribution.

A key concern is that acceleration from the use of AI could start to compound, as the development of more capable models leads to further acceleration. As we describe below, we did not identify any clear bottlenecks (limited inputs that would prevent further growth) or steep diminishing returns to further scaling that would get in the way of such compounding acceleration, though our understanding evolved several times over the course of this assessment, and we have significant uncertainty. Because of this, we believe there is a significant risk that further capabilities progress could lead to even faster acceleration over the coming year.

We received convincing evidence that the acceleration was not due to an acceleration in the scale of training compute. Further details on this evidence were redacted.

Third, 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 in maturity depending on the area.

We did not see evidence for significant bottlenecks in algorithmic progress, training compute, experiment compute, or data that will take effect in the next 12 months. However, our understanding of the status of different bottlenecks changed several times as we got more detailed information, so we are not confident in our current assessment. Anthropic redacted our analysis of a number of specific bottlenecks.

If we take our main AECI results and plot all frontier models instead of just Anthropic models, we estimate a lower degree of acceleration.

Prior Anthropic system cards have posited the hypothesis that there was a one-time jump in capabilities at Mythos Preview after which there was a return to the previous trend. Recent models make this hypothesis look less likely than a permanent trend change.

Nevertheless, we observe consistent growth in these self-reports, shown in the chart below. For the most capable internal model in October 2026, average self-reported uplift was ≈12x.