Sakana AI launches research lab for recursive self-improvement, positioning the approach as an alternative to compute-intensive scaling

Sakana AI has created a new research group, the Sakana AI RSI Lab, focused on recursive self-improvement—the idea that AI systems can iteratively redesign and improve themselves. The lab builds on earlier Sakana projects including LLM-Squared (where language models design better training methods for other language models), the Darwin Gödel Machine (which generates, tests, and iterates on variants of its own codebase), and The AI Scientist (a system that automates parts of scientific research; a later version wrote a paper that passed peer review and was published in Nature in March 2026).

Sakana outlines a four-phase roadmap from conventional human-led AI optimization to self-improving systems. The company positions recursive self-improvement as a counter to the dominant scaling paradigm: instead of training ever-larger models with massive GPU clusters, the bet is on adaptive systems and evolutionary optimization where AI finds better solutions in as few attempts as possible.

Sakana frames recursive self-improvement as a path toward more efficient and accessible frontier AI. However, Anthropic has warned that once full recursive self-improvement is achieved, AI systems could drive their own development faster than institutions can keep up; there is no proof yet that self-improving systems can offset the structural advantage of large-scale data centers.