The word “recursion” is the latest buzzword in AI circles. Two separate startups have taken on the name, and many more have started referencing Recursive self-improvement (RSI) in their roadmaps. Like AGI before it, RSI has become a three-letter byword for a cataclysmic AI takeoff – even if there’s still a little disagreement about exactly what it means. In basic terms, RSI refers to an AI system that can continuously upgrade itself. Once AI systems can manage the upgrade cycle better than humans, the process can become a closed loop, limited only by the compute power they can access, and humans no longer necessary or even helpful. Scary or not, that’s a vision that a lot of AI labs are eager to chase. Earlier this month, well-known AI researcher, Richard Socher, launched the aptly named Recursive Superintelligence launched with RSI as an explicit goal. “Our main focus is to build truly recursive, self-improving superintelligence at scale,” Socher told TechCrunch at launch, “which means that the entire process of ideation, implementation, and validation of research ideas would be automatic.” A number of other prominent researchers are already chasing that same goal, hoping for a breakthrough that will make recursive self-improvement possible. One of the most prominent is Alex Karpathy, a legendary figure from Tesla and OpenAI, who is using agent swarms to train LLMs on simple tasks for a project he calls Auto-Research. Karpathy has been unusually open about the project, tweeting about milestones regularly and making the building blocks available through a public GitHub repo. So far, the work has mostly been confined to making minor improvements on a GPT-2 scale model – as Karpathy noted in March, “It’s not novel, ground-breaking ‘research’ (yet)” – but it’s been enough to convince lots of other researchers to follow the RSI dream. And with Karpathy now working on pre-training at Anthropic, he will have plenty of opportunity to apply the idea at a larger scale. Adaption – founded by Cohere and Google alum Sara Hooker – recently launched a similar tool called AutoScientist in an effort to automate frontier training. Like Karpathy’s auto-researchers, the system trains agents to make incremental improvements – but for Adaption, the goal is to make it easier to train a full-scale frontier model. If those same researchers start to push the frontier forward, the system could quickly spiral into something very much like RSI. Disarray founder Doris Xin drew more specific RSI interest when her self-trained machine learning agent took home 28 medals in a recent Kaggle competition, beating out many human-trained agents. As she sees it, the major challenge is reliability. “I would argue, given infinite compute and infinite time horizon, we are already there,” Xin told me. “I want to make an argument that this is not a creative endeavor, really. It’s just a lot of meat-and-potatoes engineering.” Not there yet There’s also plenty of evidence that the AI industry isn’t very close to recursive systems in any meaningful way — and is still grappling with talking to a wary public about its progress. So Google CEO Sundar Pichai basically admitted in a recent podcast interview. “It’s a continuum, and we are all definitely making progress,” Pichai said. “But in the way people describe R.S.I., that would represent a next level of acceleration and would have a lot of implications, but we aren’t quite there yet.” But the continuum includes an awful lot of self-improving AI systems. In January, one of Anthropic’s lead programmers for Claude Code estimated that “close to 100%” of his team’s code was written by the tool – a frank admission that Claude Code was literally writing itself. Just because engineers are using an AI tool doesn’t mean the tool can replace them – but Anthropic seems to be getting close to replacing engineers too. In a recent survey tied to the Mythos preview, five out of 18 Anthropic engineers believed that, with harness improvements, this version of Mythos could soon substitute for an L4 engineer – a mid-level programmer who can take on involved projects without supervision. Still, there were some of the same weaknesses you might expect. “Some of Claude’s major reported weaknesses compared to an L4 include: self-managing week-long ambiguous tasks, understanding org priorities, taste, verification, instruction-following, and epistemics,” the report reads. In other words, its weaknesses are everything involved with self-direction, which is the cornerstone for RSI. But sure, for everything else, Claude is ready to step right in. Just like the AGI term before it, the AI industry also can’t tell us how far away it is from showcasing a meaningful recursive system. When Georgetown’s Center for Security and Emerging Technology assembled a group of experts to study RSI last year, the group found a major split in assessments – some expecting an immine
