I'm cross-posting my guest post on Epoch's Gradient Updates newsletter, in which I describe some new research from my team at UChicago's XLab — roughly, the algorithmic improvements that most improve capabilities at scale are the ones that require the most compute to find and validate.

This week's issue is a guest post by Henry Josephson, who is a research manager at UChicago's XLab and an AI governance intern at Google DeepMind.

In the AI 2027 scenario, the authors predict a fast takeoff of AI systems recursively self-improving until we have superintelligence in just a few years.

Could this really happen? Whether it's possible may depend on if a software intelligence explosion — a series of rapid algorithmic advances that lead to greater AI capabilities — occurs.

A key crux in the debate about the possibility of a software intelligence explosion comes down to whether key algorithmic improvements scale [...]

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Outline:

(01:53) Are the best algorithmic improvements compute-dependent?

(07:48) Can Capabilities Advance With Frozen Compute? DeepSeek-V3

(08:55) What This Means for AI Progress

(12:50) Limitations

(14:10) Conclusion

The original text contained 7 footnotes which were omitted from this narration.

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Narrated by TYPE III AUDIO.

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