1004: Recursive Self-Improvement
About this episode
Jon Krohn discusses the concept of recursive self-improvement in AI and its implications for future AI development.
Could an AI get good enough at AI research to build its own, more capable successor and kick off a compounding loop? That’s recursive self-improvement (RSI) and it surged into the conversation after Anthropic revealed that, as of May 2026, Claude wrote more than 80% of the code merged into its production codebase. In this Five-Minute Friday, Jon Krohn separates today’s AI-assisted coding from true RSI, walks through the accelerating evidence - METR’s shrinking task “time horizon,” Google DeepMind’s AlphaEvolve, Andrej Karpathy’s overnight training-tuner, weighs Jack Clark’s 60% bet that AI builds its own successor by 2028 against the compute, data and “marketing” skeptics. As ever, Jon lands in the optimistic middle. Additional materials: www.superdatascience.com/1004 Interested in sponsoring a SuperDataScience Podcast episode? Email natalie@superdatascience.com for sponsorship information.
More episodes of Super Data Science: ML & AI Podcast with Jon Krohn
- 1011: The Math Still Matters: Deep Skills in the Age of AI, with Dr. Catherine Williams
- 1010: Fable 5 as Advisor: Anthropic's Two-Model Pattern for Smarter, Cheaper Agents
- 1009: How AI Is Quietly Saving Lives, with Steve Mock
- 1008: The AI-Native Startup Playbook
- 1007: How to Find Solid Career Ground in the AI Era, with 80,000 Hours Founder Ben Todd
- 1006: In Case You Missed It in June 2026
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