However, I'm quite skeptical of this type of consideration making a big difference because the ML industry has already varied the compute input massively, with over 7 OOMs of compute difference between research now (in 2025) vs at the time of AlexNet 12 years ago, (invalidating the view that there is some relatively narrow range of inputs in which neither input is bottlenecking) 

Seems like this is a strawman of the bottlenecks view, which would say that the number of near frontier experiments, not compute, is the bottleneck and this quantity didn't scale up over that time

ETA: for example, if the compute scale up had happened, but no one had been allowed to run experiments with more compute than AlexNet, it seems a lot more plausible that the compute would have stopped helping because there just wouldn't have been enough people to plan the experiments

Plus the claim that alg progress might have been actively enabled by the access to new hardware scales

Agree with those updates. 

Though a small update as I don't think a default gov-led project would be much better on this front. (Though a well designed one led by responsible ppl could be way way better of course.)

And I've had a few other convos that made me more worried about race dynamics. 

Still think two projects is prob better than one overall, but two probbetter than six

I meant at any point, but was imagining the period around full automation yeah. Why do you ask?

I'll post about my views on different numbers of OOMs soon

Sorry, for my comments on this post I've been referring to "software only singularity?" only as "will the parameter r >1 when we f first fully automate AI RnD", not as a threshold for some number of OOMs. That's what Ryan's analysis seemed to be referring to. 

I separately think that even if initially r>1 the software explosion might not go on for that long

Obviously the numbers in the LLM case are much less certain given that I'm guessing based on qualitative improvement and looking at some open source models,

Sorry,I don't follow why they're less certain? 

based on some first principles reasoning and my understanding of how returns diminished in the semi-conductor case

I'd be interested to hear more about this. The semi conductor case is hard as we don't know how far we are from limits, but if we use Landauer's limit then I'd guess you're right. There's also uncertainty about how much alg progress we will and have met

Why are they more recoverable? Seems like a human who seized power would seek asi advice on how to cement their power

I agree the easy vs hard worlds influence the chance of AI taking over. 

But are you also claiming it influences the badness of takeover conditional on it happening? (That's the subject of my post)

So you predict that if Claude was in a situation where it knew that it had complete power over you and could make you say that you liked it then it would stop being nice? I think would continue to be nice in any situation of that rough kind which suggests it's actually nice not just narcissistically pretending