Sakana AI Launches Lab for Recursive Self-Improvement, Challenging Compute Arms Race

Anthropic — sakana AI has launched a dedicated research lab for recursive self-improvement: AI that iteratively improves itself. The Japanese startup, co-founded by Transformer co-author Llion Jones, sees RSI as an alternative to the raw compute arms race among big US labs.

Japanese AI startup Sakana AI has created a new research group exploring how AI can speed up and improve the development of new AI systems. The Sakana AI RSI Lab builds on the company’s earlier work. Since its founding in 2023, Sakana has focused on evolutionary, adaptive AI systems and, more recently, on practical steps toward recursive self-improvement. In its announcement, Sakana points to several research milestones from the past two years. These include LLM-Squared, where language models design better training methods for other language models, and the Darwin Gödel Machine, which generates, tests, and iterates on variants of its own codebase.Ad With ShinkaEvolve and ALE-Agent, Sakana also highlights work on evolutionary program optimization and agents that derive new strategies from trial-and-error loops. Another key project is The AI Scientist, a system for automating parts of scientific research. A later version wrote a paper that passed peer review, according to Sakana. The underlying research was published in Nature in March 2026.AdDEC_D_Incontent-1 Taken together, Sakana AI presents these projects as evidence that recursive self-improvement is no longer purely theoretical, but is already being tested in controlled research environments. In its blog post, Sakana outlines a four-phase transition from conventional, human-led AI optimization to self-improving systems.Ad The roadmap builds partly on Sakana’s own research. It starts with models designed not as chatbots but for open-ended agent tasks from the ground up. With The AI Scientist, Sakana has already shipped a system that applies agent capabilities to automated research, from idea generation and experiments to writing scientific papers. The next step is recursive self-improvement itself: AI agents that actively work on their own technical foundations by writing, benchmarking, and verifying code for their underlying architectures.AdDEC_D_Incontent-2 The fourth phase and long-term goal is broader access to frontier AI. Sakana positions RSI as a counter to the dominant scaling paradigm: instead of training ever-larger monolithic models with ever more compute, the company bets on adaptive systems and evolutionary optimization, where AI finds better solutions in as few attempts as possible.Ad The idea is that recursive self-improvement could work with moderate compute and depend less on the massive GPU clusters that big US AI labs and cloud providers run today. It’s a plausible research direction, but still a strategic bet. There’s no proof yet that self-improving systems can actually offset the structural advantage of large-scale data centers.