How Close Is Recursive Self-Improvement?

AI companies increasingly say that they may soon achieve recursive self-improvement (RSI). With RSI, current AI systems help develop future AI systems in a recursive loop. RSI could dramatically accelerate AI capability progress. Importantly, this acceleration could happen inside AI companies before outsiders can notice.

AI companies now say that this loop is starting. In an update to its IPO filing, Anthropic claimed that, based on expected future models, it now meets its definition of “dramatically accelerated progress attributable to AI.” OpenAI said that it reached its goal of an “automated AI research intern” in September 2026, and it targets an automated AI researcher by March 2028.

The data reported by AI companies show that AI does a large and fast-growing share of AI R&D.

At Anthropic, Claude wrote over 90% of contributions to the codebase in September 2026. At OpenAI, AI agents did 3.1 workdays of work for each human workday in August 2026, up from about 0.5 in May 2026.

However, humans still supply most of the research judgment, such as choosing which research areas to focus on. For instance, no measured part of Anthropic’s AI R&D is fully autonomous . Anthropic also states that “large performance gaps persist when it comes to Claude exercising judgement in choosing goals in both engineering and research.” At OpenAI , AI completed 57% of 2–4-hour tasks with no human intervention in July 2026 (up from 28% in January) and 35% of 8–16-hour tasks (up from 10%).

On PostTrainBench , where agents turn small base models into useful assistants, the best agent scores 45.6, against 51.1 for the official instruct models. On WeirdML , where agents build ML models for small, unusual problems, GPT-6 Astra scored 42.2 (out of 100) in September 2026, up from 15.0 for GPT-5.6 Sol in July 2026. On TasteVal , where a model chooses and interprets AI R&D experiments, Claude Opus 5.5 reaches the score of human experts with 2.3× less compute.

However, AI agents struggle with open-ended research. On InnovationEval , where agents try to reinvent a published ML innovation they have not seen, GPT-5.6 Sol recovered 15% of the innovation’s gain. (GPT-6 Astra scored 63% but had likely memorized the original paper.) In the CRUX 2 open-world evaluation, AI agents got the research question of an unpublished AI paper and had to write their own paper on it. The original paper’s authors rejected all four AI-written papers, including two from a follow-up study with newer models, more time, and a larger budget.

In a report published after our November 3 data cutoff, METR now estimates that capability progress at Anthropic is about 2× its earlier rate, and that around 80% of this acceleration is due to AI. This estimate applies to Anthropic only and includes unreleased models. In addition, part of the acceleration may reflect Anthropic catching up to the frontier, and Anthropic redacted much of the evidence that METR found most informative.

On a simple extrapolation of current trends, mostly using company-reported metrics, AI could take over most AI R&D tasks by 2027–2028, in line with OpenAI’s internal goal of an automated AI researcher by March 2028. A key uncertainty is whether these trends continue, as the remaining non-automated tasks increasingly require research judgment. Forecasts of full automation of AI R&D range from 2027 (insiders at AI companies and the AI Futures Project’s AI 2027 scenario) to 2052 (the AI Impacts survey of ML researchers).

In our report, we cover the key points of debate, including the views of researchers who expect RSI very soon and those who are more skeptical. For example, the AI Futures Project’s AI 2027 scenario depicts a superhuman AI researcher in 2027. In contrast, Arvind Narayanan and Sayash Kapoor expect gradual automation of AI development instead of “a singular, discontinuous moment when recursive self-improvement is achieved.”

The ensemble forecasts about a 40% chance of a fast year by 2030. If a fast year happens, the ensemble gives about a 70% chance that AI automation of AI R&D caused it. The four LLMs disagree substantially on the probability of a fast year. For a fast year by the end of 2030, their forecasts range from 35% (Claude Sonnet 5.5) to almost 56% (GPT-6 Astra).