This is more speculative and confusing than my typical posts and I also think the content of this post could be substantially improved with more effort. But it's been sitting around in my drafts for a long time and I sometimes want to reference the arguments in it, so I thought I would go ahead and post it.

I often speculate about how much progress you get in the first year after AIs fully automate AI R&D within an AI company (if people try to go as fast as possible). Natural ways of estimating this often involve computing algorithmic research speed-up relative to prior years where research was done by humans. This somewhat naturally gets you progress in units of effective compute — that is, as defined by Epoch researchers here, "the equivalent increase in scale that would be needed to match a given model performance absent innovation". [...]

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Outline:

(04:09) The standard deviation model

(10:59) Differences between domains and diminishing returns

(13:02) An alternative approach based on extrapolating from earlier progress

(18:14) Takeaways

The original text contained 10 footnotes which were omitted from this narration.

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First published:
June 24th, 2025

Source:
https://www.lesswrong.com/posts/hpjj4JgRw9akLMRu5/what-does-10x-ing-effective-compute-get-you

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Narrated by TYPE III AUDIO.

“What does 10x-ing effective compute get you?” by ryan_greenblatt

LessWrong (30+ Karma)

“What does 10x-ing effective compute get you?” by ryan_greenblatt