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.

EDIT: I believe I've found the "plan" that Politico (and other news sources) managed to fail to link to, maybe because it doesn't seem to contain any affirmative commitments by the named companies to submit future models to pre-deployment testing by UK AISI.

I've seen a lot of takes (on Twitter) recently suggesting that OpenAI and Anthropic (and maybe some other companies) violated commitments they made to the UK's AISI about granting them access for e.g. predeployment testing of frontier models.  Is there any concrete evidence about what commitment was made, if any?  The only thing I've seen so far is a pretty ambiguous statement by Rishi Sunak, who might have had some incentive to claim more success than was warranted at the time.  If people are going to breathe down the necks of AGI labs about keeping to their commitments, they should be careful to only do it for commitments they've actually made, lest they weaken the relevant incentives.  (This is not meant to endorse AGI labs behaving in ways which cause strategic ambiguity about what commitments they've made; that is also bad.)

Huh, that went somewhere other than where I was expecting.  I thought you were going to say that ignoring letter-of-the-rule violations is fine when they're not spirit-of-the-rule violations, as a way of communicating the actual boundaries.

Yeah, there needs to be something like a nonlinearity somewhere.   (Or just preference inconsistency, which humans are known for, to say nothing of larger organizations.)