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[Linkpost] “Thoughts on Toby Ords AI Scaling Series” by Srdjan Miletic

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I've been reading Toby Ord's recent sequence on AI scaling a bit. General notes come first, then my thoughts.

Notes

  • The Scaling Paradox basically argues that the scaling laws are actually pretty bad and mean progress will hit a wall fairly quickly unless the next gen or two of models somehow speed up AI research, we find a new scaling paradigm etc...
  • Inference Scaling and the Log X Chart says that inference is also not a big deal because the scaling is again logarithmic. My intuition here is that this is probably true for widespread adoption of models. It's probably not true if there are threshold effects where a single $100'000 query can be drastically better than a $100 query and allow you to, say, one shot open research problems. I'm not sure which world we live in.
  • Inference Scaling Reshapes Governance talks about the implications of inference being a big part of models. One of the implications is that instead of getting a big bang of new model trained => millions of instances, we get a slower gradual wave of more inference = stronger model with a gradual rightward [...]

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    Outline:

    (00:20) Notes

    (00:26) Takeaways

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    First published:

    February 4th, 2026

    Source:

    https://forum.effectivealtruism.org/posts/rDBLQFQzviW78eRk5/thoughts-on-toby-ords-ai-scaling-series

    Linkpost URL:
    https://www.dissent.blog/notes-on-toby-ords-ai-scaling-series/

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    Narrated by TYPE III AUDIO.

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