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[[{“value”:”In this episode, I break down Anthropic’s research on recursive self-improvement—AI systems that can design and train the next generation with less human help—and why the key battleground is “taste” (choosing goals and next steps). I compare this to evolutionary algorithms and newer examples like DeepMind’s AlphaEvolve, Sakana’s Darwin Gödel Machine, and Karpathy’s AutoResearch, then cover METR Task Horizon and how task length has been doubling. I go through Anthropic’s internal results (Claude writing most merged code, speedup experiments, bug fixes, and a study where models sometimes pick better research next steps), plus the main skepticism: bad productivity metrics, internal-only models, and Goodhart’s Law/reward hacking. I end with an open safety problem where Claude agents closed the gap far faster than humans, and what this means for specifying and checking work.
LINKS:
https://www.anthropic.com/institute/recursive-self-improvement
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TIMESTAMP:
00:00 Self Improvement Basics
01:30 Evolutionary Loops Today
03:50 Task Horizon Doubling
05:18 Claude Productivity Claims
08:11 Goodhart’s Law
10:30 Agents as Researchers
12:22 What It Means for You”}]] Read More Prompt Engineering
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