The phrase recursive self-improvement erupted on social media following an Anthropic report that tracked AI-driven gains in the company’s internal software-engineering productivity.
What’s new: 80 percent of Anthropic’s code is authored by Claude, up from less than 5 percent before the preview release of Claude Code, the company wrote in a blog post, adding that the trend points toward AI systems that “design and refine themselves.” Anthropic’s report thrust the theoretical notion of recursive self-improvement (RSI) into the spotlight, further dividing the AI community between those who call for drastic measures to forestall a dystopian future and those who caution that unrealistic fears will severely undermine the good that AI can do.
Rising productivity: Anthropic measured rising software-development productivity attributable to AI and extrapolated a few scenarios for the future.
- Today, tool-using agents like Claude Code not only suggest code but verify it in a terminal and merge it directly via step-by-step human review or nearly autonomously. This has accelerated engineers’ production and acceptance of code, contributing to Claude’s authorship or co-authorship of 80 percent of the company’s code as of May 2026. (In April, OpenAI president Greg Brockman said that OpenAI models were authoring or co-authoring a similar percentage of that company’s code.)
- AI agents and improved coding models have made engineers even more efficient, enabling them to multiply the number of lines of code they contribute quarterly. In the second quarter of 2026, after Claude Mythos Preview launched, each engineer contributed eight times more lines of code than they had in the first quarter of 2023, after Claude launched. Moreover, in April 2026, the company shipped more than 800 API fixes, reducing API errors 1,000-fold, that engineers estimated would have taken humans working alone four years to complete without AI.
- AI-written code is steadily improving. Anthropic asked an LLM to classify code issues as (i) trivial, (ii) routine, (iii) substantial, or (iv) open-ended, meaning the solution and criteria for success were unclear. In September 2025, Claude Code could solve less than 80 percent of trivial problems, a portion that had risen to about 90 percent by May 2026. Its ability to solve routine tasks also rose from 65 to 90 percent, substantial tasks from under 40 percent to over 80 percent, and open-ended problems from less than 20 percent to 76 percent.
- Based on this data, Anthropic imagines three scenarios for the future of AI and software development. In the first, AI remains less capable than the best human engineers. In the second scenario — which Anthropic judges the most likely — AI-aided software engineering continues to accelerate, but humans maintain control of model research and development. In the third, AI becomes capable of improving itself.
Bandwagons and skeptics: Anthropic isn’t the only organization in the AI community thinking about RSI, but responses range from skeptical to bullish.
- A number of companies and prominent engineers took advantage of RSI’s sudden high profile. OpenAI wrote, “We also see early signs of recursive self-improvement (RSI) in today’s systems.” The Japanese research organization Sakana AI launched its RSI Lab, a research group devoted to building self-improving AI.
- Many observers noted the great distance between agentic coding, in which agents respond to requests from human engineers who organize, direct, and evaluate broad efforts, and ongoing self improvement, in which agents manage the entire endeavor. UCLA Adjunct Professor Arun Rao said, “I think it will be a longer journey than Anthropic expects,” while AI policy researcher Miles Brundage said “I am personally not that RSI pilled compared to some of y’all.” Matthew Barnett, co-founder of MechanizeWork, noted that “data and compute bottlenecks” stand in the way.
- Others noted the strong flavor of marketing in Anthropic’s framing of AI-driven productivity. “There is a bit of navel-gazing, some marketing, and a lot of very sincere beliefs about what Anthropic thinks is likely,” Wharton professor Ethan Mollick posted on X. Tech analyst Michael Spencer observed that “the latest batch of huge seed [funding] rounds were in AI startups focused on this trend.”
Behind the news: Recursive self-improvement traces back to early ideas about “intelligence explosions,” most famously articulated by I. J. Good in 1965, who argued that a sufficiently advanced machine intelligence could improve its own design and rapidly surpass human intelligence. In the 2000s and 2010s, Eliezer Yudkowsky at UC Berkeley’s Machine Intelligence Research Institute formalized RSI as a central concern in AI alignment research. The idea re-entered mainstream AI research with the rise of large language models and AI-assisted coding. A team at Chinese Information Processing Laboratory and elsewhere recently proposed a benchmark, Meta-Agent Challenge, to evaluate AI systems’ capacity for RSI.
Why it matters: The potential for RSI, like the potential for artificial general intelligence (AGI), is distant. A great deal of work and likely a number of breakthroughs stand between present systems, which increasingly multiply human productivity in software development and other fields, and systems will oversee, design, and engineer their own improvements in a recursive loop that, once it begins, continues ad infinitum. Meanwhile, the AI community is divided over exaggerated notions of AI-related danger and here-and-now risks to ongoing innovation and the benefits it can bring. Science-fiction scenarios may be effective if the goal is to scare people or persuade them to give you money, but realistic visions of possible futures are necessary to make real progress.
We’re thinking: In its blog post, Anthropic revived the idea of a global, temporary pause in AI research. Although it doesn’t advocate stopping all research — as the Future of Life Institute did a few years ago — it put this idea back on the table. It’s a bad one, and it empowers doomsayers whose fears are at best unrealistic and at worst self-serving. We wholeheartedly support regulation of dangerous applications, but we should continue to improve fundamental technology as quickly as possible.