This seems to be arguing that the big labs are doing some obviously-inefficient R&D in terms of advancing capabilities, and that government intervention risks accidentally redirecting them towards much more effective R&D directions.  I am skeptical.

1. If such training runs are not dangerous then the AI safety group loses credibility. 
2. It could give a false sense of security when a different arch requiring much less training appears and is much more dangerous than the largest LLM. 
3. It removes the chance to learn alignment and safety details from such large LLM

1. I'm not here for credibility.  (Also, this seems like it only happens, if it happens, after the pause ends.  Seems fine.)
2. I'm generally unconvinced by arguments of the form "don't do [otherwise good thing x]; it might cause people to let their guard down and get hurt by [bad thing y]" that don't explain why they aren't a fully-general counterargument.
3. If you think LLMs are hitting a wall and aren't likely to ever lead to dangerous capabilities then I don't know why you expect to learn anything particularly useful from the much larger LLMs that we don't have yet, but not from those we do have now.

This seems non-reponsive to arguments already in my post:

If we institute a pause, we should expect to see (counterfactually) reduced R&D investment in improving hardware capabilities, reduced investment in scaling hardware production, reduced hardware production, reduced investment in research, reduced investment in supporting infrastructure, and fewer people entering the field.

We ran into a hardware shortage during a period of time where there was no pause, which is evidence that the hardware manufacturer was behaving conservatively.  If they're behaving conservatively during a boom period like this, it's not crazy to think they might be even more conservative in terms of novel R&D investment & ramping up manufacturing capacity if they suddenly saw dramatically reduced demand from their largest customers.

For example, suppose we pause now for 3 years and during that time NVIDIA releases the RTX5090,6090,7090 which are produced using TSMC's 3nm, 2nm and 10a processes.

This and the rest of your comment seems to have ignored the rest of my post (see: multiple inputs to progress, all of which seem sensitive to "demand" from e.g. AGI labs), so I'm not sure how to respond.  Do you think NVIDIA's planning is totally decoupled from anticipated demand for their products?  That seems kind of crazy, but that's the scenario you seem to be describing.  Big labs are just going to continue to increase their willingness-to-spend along a smooth exponential for as a long as the pause lasts?  What if the pause lasts 10 years?

If you think my model of how inputs to capabilities progress are sensitive to demand for those inputs from AGI labs is wrong, then please argue so directly, or explain how your proposed scenario is compatible with it.

Yeah, "they're following their stated release strategy for the reasons they said motivated that strategy" also seems likely to share some responsibility.  (I might not think those reasons justify that release strategy, but that's a different argument.)

Yeah, I agree that it's too early to call it re: hitting a wall.  I also just realized that releasing 4o for free might be some evidence in favor of 4.5/5 dropping soon-ish.

Vaguely feeling like OpenAI might be moving away from GPT-N+1 release model, for some combination of "political/frog-boiling" reasons and "scaling actually hitting a wall" reasons.  Seems relevant to note, since in the worlds where they hadn't been drip-feeding people incremental releases of slight improvements over the original GPT-4 capabilities, and instead just dropped GPT-5 (and it was as much of an improvement over 4 as 4 was over 3, or close), that might have prompted people to do an explicit orientation step.  As it is, I expect less of that kind of orientation to happen.  (Though maybe I'm speaking too soon and they will drop GPT-5 on us at some point, and it'll still manage to be a step-function improvement over whatever the latest GPT-4* model is at that point.)

It's not obvious to me why training LLMs on synthetic data produced by other LLMs wouldn't work (up to a point).  Under the model where LLMs are gradient-descending their way into learning algorithms that predict tokens that are generated by various expressions of causal structure in the universe, tokens produced by other LLMs don't seem redundant with respect to the data used to train those LLMs.  LLMs seem pretty different from most other things in the universe, including the data used to train them!  It would surprise me if the algorithms that LLMs developed to predict non-LLM tokens were perfectly suited for predicting other LLM tokens "for free".

EDIT: looks like habryka got there earlier and I didn't see it.

https://www.lesswrong.com/posts/zXJfH7oZ62Xojnrqs/#sLay9Tv65zeXaQzR4

Intercom is indeed hidden on mobile (since it'd be pretty intrusive at that screen size).

Ah, does look like Zach beat me to the punch :)

I'm also still moderately confused, though I'm not that confused about labs not speaking up - if you're playing politics, then not throwing the PM under the bus seems like a reasonable thing to do.  Maybe there's a way to thread the needle of truthfully rebutting the accusations without calling the PM out, but idk.  Seems like it'd be difficult if you weren't either writing your own press release or working with a very friendly journalist.

I hadn't, but I just did and nothing in the article seems to be responsive to what I wrote.

Amusingly, not a single news source I found reporting on the subject has managed to link to the "plan" that the involved parties (countries, companies, etc) agreed to.

Nothing in that summary affirmatively indicates that companies agreed to submit their future models to pre-deployment testing by the UK AISI.  One might even say that it seems carefully worded to avoid explicitly pinning the companies down like that.