The concept of recursive self-improvement (RSI) for AI has been a polarizing topic in the technology industry since Irving John Good explored it in his 1966 paper, Speculations Concerning the First Ultraintelligent Machine. For much of the time since then, RSI has been a more appropriate topic for science fiction rather than a serious issue for technology business leaders or economists to consider; indeed, Good is also famous for being the technology consultant (along with Marvin Minsky) for Stanley Kubrick during the production of 2001: A Space Odyssey. The science-fiction value of the concept was certainly clear.
Given the rapid pace of AI development and the fact that some of the frontier labs have explicitly defined automated AI researchers as a goal, it seems appropriate to explore RSI as a real possibility. In particular, the potential impact of RSI on the AI industry’s competitive landscape and the broader economy will likely be top of mind as we progress through 2026. Whether or not the reader believes in the possibility of RSI, it’s important to understand the concept to decipher the research directions of the frontier labs and potential considerations of policy makers.
“…when I look at all the publicly available information I reluctantly come to the view that there’s a likely chance (60% +) that no-human-involved AI R&D – an AI system powerful enough that it could plausibly autonomously build its own successor – happens by the end of 2028.”
Jack Clark, Co-founder of Anthropic, May 2026
