Claude's Evolution Speed Doubles, 80% of Increment Comes from AI

Editor | Zenan

AI has finally provided concrete numbers on the speed of developing the next generation of AI.

On November 5, the third-party evaluation agency METR released a report on Anthropic, stating that the benchmark capability progress made by the company over the past year is roughly equivalent to the progress made in the previous two years.

From August 12 to October 29, the agency spent about 11 weeks reviewing Anthropic's internal model performance, training computing power, research and development efficiency, and interviewing researchers.

Their analysis utilized Anthropic's comprehensive capability index, AECI. The data shows that since October 2025, the progress rate of large models on this index has been approximately 1.9 times that of the previous period, with a 95% confidence interval of 1.5 to 2.8 times.

Since October 7, 2025, this score jumped from about 15 points per year to about 29 points per year, completing what previously took about two years in just one year, roughly equivalent to the transition from Claude 2.1 to Sonnet 4.5. Anthropic's own risk report had previously assumed that Mythos Preview was a one-time leap, after which it would revert to the original trend; however, new models have emerged, and the new slope continues.

Looking at the entire industry, the acceleration of cutting-edge models is only 1.4 times.

More critically, METR is quite certain that this acceleration does not come from the growth rate of training AI computing power. The main reason for the improvement in AI capabilities comes from AI's own assistance.

In interviews, a recurring phenomenon was that researchers can finally try more ideas.

In the past, an experiment might take one or two days to implement, and the time and computational costs were insurmountable, leading many ideas to be shelved before they could be validated. Now, researchers can hand multiple ideas to an Agent to see which ones are worth pursuing further.

One researcher described that their work is now primarily limited by ideas and computing power, with coding no longer being a bottleneck. Others feel that using Claude makes it seem as if they have a postdoc and a graduate student working together on the project. Experiments and implementations have become cheaper, making researchers more willing to experiment, and many small experiments that were previously considered not worth the effort now have the opportunity to become new research directions.

METR's report also unveiled some internal progress that was previously unseen by the outside world. Their analysis included three models from Anthropic that have not yet been publicly disclosed, referred to as Model 2, Model 3, and Model 4. These were trained between July and October of this year.

The strongest of these is Model 4 (some speculate it is Claude 6), which has already exceeded 90% on many common benchmarks. Anthropic employees subjectively estimated its effectiveness in October, voting that it improved their work efficiency by about 12 times on average.

These models have not yet been released and may not necessarily become public products, but their existence reminds the outside world that merely looking at release events and public rankings may not provide a complete picture of what is happening internally at AI giants. From about 1 time at the beginning of 2025, to about 2.5 times for Opus 4.5, about 5 times for Mythos 5, and jumping to 12 times for Model 4, the exponential fit suggests an annual increase of 3.9 times.

METR estimates that as of October this year, about 80% of the acceleration in current AI research is estimated to come from AI. The remaining 20% comes from non-AI factors, details of which Anthropic does not allow to be disclosed.

So, is AI already capable of independently developing its next generation?

Currently, there is still a gap in achieving complete recursive self-improvement (RSI) for AI to autonomously develop the next generation of models. Anthropic researchers themselves believe that in the most critical part of model training, AI has not yet made highly significant and fully autonomous contributions, and the main benefits still come from AI assisting human development.

However, Anthropic researchers have already described a significant amount of AI's autonomous contributions as "signs of life," and there are quantitative analyses that statistically account for the proportion of tasks completed by AI versus humans in various stages, but the data has been removed by Anthropic.

As long as stronger models can help researchers develop the next generation faster, the feedback process may continue to enhance. How far the path of RSI can go depends on the still non-automated stages and whether resources such as computing power can keep up.

In terms of predictions for the near future, METR has not seen any significant bottlenecks that would hinder rapid progress in the next 12 months, but they also admit that they have changed their judgments multiple times as information increases, and their current confidence is limited.

Additionally, although Anthropic allowed the third-party agency METR to conduct an internal investigation, the 100-page report provided has undergone significant redactions. Most of the most convincing evidence has been removed by Anthropic itself.

The accompanying review released on the same day also presented the differences in risk statements between the two parties: Anthropic had described a certain leading indicator as having a progress rate of "less than twice," while METR calculated it to be about 1.97 times based on its internal data, believing that a more accurate statement should be "about twice." The twofold acceleration involves Anthropic's early warning threshold.

METR believes that the possibility that the relevant threshold was crossed by July 15 cannot be ruled out, but most relevant researchers still tend to believe that it had not yet been crossed at that time. Both parties also agree that the direct catastrophic risk of the relevant models at that time was low.

As the speed of model involvement in development increases, whether people can timely recognize changes and whether companies can fully disclose uncertainties is becoming a question that must be answered before the arrival of the next generation of AI.

Reference content:

https://metr.org/blog/2026-11-05-anthropic-ai-rd-acceleration-assessment/#executive-summary

https://x.com/METR_Evals/status/2118412183860351084

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