Cool post!

I'm a co-author on a recent blog post from METR about the NanoGPT speed-run here [1]. I think it'd be of interest to anyone who enjoyed the original post. Appreciate the good beefy runs and spend here, it's a (from my experience) not super easy to do!

(Also: just to label this comment clearly: it's written hastily from a car, and based on lighter skim of the original blog post [2] than would be ideal. Please correct any mistakes or misinterpretations I have here!)

A few callouts:

1. If I understand the experiment correctly, they start the models at the original baseline. If this is true, I have some worries about contamination. Appendix C [3] has some notes on likely contamination we observed in recent models. This makes interpretation a bit harder.

2. If you look at the token scaling plots in the original post: not all models are hitting a performance plateau. This is an important point: we shouldn't treat these results as a full upper-bound on capabilities, but rather some bound on model performance @ cost (assuming good scaffolding, etc).

3. Our post is mostly about how to _interpret_ the results given here. Quoting from our post: "If we can estimate performance as a function of cost for both humans and agents, we can measure the “expenditure horizon” as the point at which those curves cross: the budget at which humans become more cost-effective than AIs. "

Feedback appreciated. I think you can see expenditure horizon as a sibling methodology (that is much less validated) to METR's time horizon work [4] - roughly, instead of baselining against the time it takes humans to complete tasks, you baseline against cost. This may be better suited to some types of problems similar to NanoGPT.

[1] https://metr.org/blog/2026-07-21-expenditure-horizon/ (Most of this work was my coauthors listed on the post, not me. I'll claim credit for any mistakes though :) ).

[2] https://www.primeintellect.ai/blog/measuring-autonomous-rese...

[3] https://metr.org/blog/2026-07-21-expenditure-horizon/#append...

[4]https://metr.org/time-horizons/

(Edit: METR is hiring. Email is in bio if you're interested in helping AI companies and wider society understand the capabilities and risks of AI.)