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What Ryan Greenblatt thinks about scaling laws

@ryan-greenblatt · 26 positions · 0 changes of mind

Chief scientist at Redwood Research, where he works on technical AI safety and AI control.

Ryan Greenblatt did not write this page.

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5 dated positions, 2026, in their own words. Our reading of what Ryan Greenblatt has said — not written or endorsed by them.

  1. Our readingGetting five years of AI progress out of one year takes roughly eight years of algorithmic progress, because the compute you would have had must be made up too.

    Their wordsBasically, the story would end up being that to get five years of AI progress, you're probably going to need around I would say like maybe eight years of algorithmic progress very roughly. Um, which is a lot a lot of algorithmic progress.

    ↗Ryan Greenblatt – What happens once AI can automate AI research?youtube.com 4th of 26 in this recording

    Interesting

  3. Our readingScaling up spending on expert human data has not been a major driver of progress in AI research.

    Their wordsSo my sense is that scaling up the amount of effort spent on getting expert human data has not been hugely important for AI R&D in general.

    ↗Ryan Greenblatt – What happens once AI can automate AI research?youtube.com 5th of 26 in this recording

    Interesting

  5. Our readingThe least verifiable part of AI research is the judgement call about what goes into the one big training run.

    Their wordsLike the thing that I think is most likely to be sort of the bottleneck in terms of like the AI are really good at verifiable domains but not not at doing the actual thing is just like big experiments. You only get a few tries um well a few is maybe a bit understated but like basically like historically R&D has been driven by doing near frontier scale experiments and that has been pretty important and like actually doing the one big training run where you decide exactly what to include in that.

    ↗Ryan Greenblatt – What happens once AI can automate AI research?youtube.com 8th of 26 in this recording

    Interesting

  7. Our readingToken prices have stayed flat because labs deliberately kept models smaller than expected in order to get more experimental cycles.

    Their wordsI think one reason why um the the AIs have been scaled up less than you would have otherwise expected and like for example cost of of per token hasn't increased as much as you might have thought is because there's a benefit to doing more of your um work at small scale where you can run more training runs and get more cycles in

    ↗Ryan Greenblatt – What happens once AI can automate AI research?youtube.com 9th of 26 in this recording

    Interesting

  9. Our readingDeep properties of a model are inherited through the previous generation's data, which is why AI systems from different companies end up correlated with one another.

    Their wordsAnd so there's some like deep underlying properties of the model that are being sort of transferred between model generations because basically you you train your AI on data from the prior generation and keep going.

    ↗Ryan Greenblatt – What happens once AI can automate AI research?youtube.com 25th of 26 in this recording

    Interesting