@rohanpaul_ai: “If you hit recursive self-improvement, that curve will go to hyperexponential, and that is a key part of the investmen…
Summary
Google DeepMind CSO Jasjeet Sekhon discusses how AI infrastructure spending is driven by hopes for recursive self-improvement, citing AlphaEvolve's bounded gains like reducing Gemini training time by 1%.
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“If you hit recursive self-improvement, that curve will go to hyperexponential, and that is a key part of the investment thesis, the scientific thesis, and a key part of why society’s investing what it’s currently investing in.”
Google DeepMind Chief Strategy Officer Jasjeet Sekhon says AI infrastructure spending is financing a hoped-for self-improvement loop.
In its strongest form, recursive self-improvement (RSI) will let AI design, evaluate, and train more capable successors with progressively less human supervision.
The current evidence is still narrower: e.g. Google’s AlphaEvolve proposes algorithms, then automated evaluators run and score them against human-defined objectives.
Google says its resulting kernel changes reduced Gemini training time by 1%, an example of bounded improvement rather than autonomous successor design.
From “Berkeley RDI” YouTube channel, (full video link in comment)
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