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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 [email protected] for sponsorship information.
By Jon Krohn4.6
295295 ratings
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 [email protected] for sponsorship information.

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