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In particular, I don't expect either (any?) lab to be able to resist the temptation to internally deploy models with autonomous persuasion capabilities or autonomous AI R&D capabilities

I agree with this as stated, but don't think that avoiding deploying such models is needed to mitigate risk.

I think various labs are to some extent in denial of this because massively deploying possibly misaligned systems sounds crazy (and is somewhat crazy), but I would prefer if various people realized this was likely the default outcome and prepared accordingly.

More strongly, I think most of the relevant bit of the safety usefulness trade-off curve involves deploying such models. (With countermeasures.)

or is seriously entertaining the idea that we might need to do a lot (>1 year) of dedicated safety work (that potentially involves coming up with major theoretical insights, as opposed to a "we will just solve it with empiricism" perspective) before we are confident that we can control such systems.

I think this is a real possibility, but unlikely to be necessarily depending on the risk target. E.g., I think you can deploy ASL-4 models with <5% risk without theoretical insights and instead just via being very careful with various prosaic countermeasures (mostly control [LW · GW]).

<1% risk probably requires stronger stuff, though it will depend on the architecture and various other random details.

(That said, I'm pretty sure that these lab's aren't making decisions based on carefully analyzing the situation and are instead just operating like "idk human level models don't seem that bad, we'll probably be able to figure it out, humans can solve most problems with empiricism on priors". But, this prior seems more right than overwhelming pessimism IMO.)

Also, I think you should seriously entertain the idea that just trying quite hard with various prosaic countermeasures might suffice for reasonably high levels of safety. And thus pushing on this could potentially be very leveraged relative to trying to hit a higher target.

I'm surprised at people who seem to be updating only now about OpenAI being very irresponsible, rather than updating when they created a giant public competitive market for chatbots (which contains plenty of labs that don't care about alignment at all), thereby reducing how long everyone has to solve alignment. I still parse that move as devastating the commons in order to make a quick buck.

I mostly agree with premises 1, 2, and 3, but I don't see how the conclusion follows.

It is possible for things to be hard to influence and yet still worth it to try to influence them.

(Note that the $30 million grant was not an endorsement and was instead a partnership (e.g. it came with a board seat), see Buck's comment [LW(p) · GW(p)].)

(Ex-post, I think this endeavour was probably net negative, though I'm pretty unsure and ex-ante I think it seems great.)

Is the field advanced enough that it would be feasible to have a guaranteed no-zero-day evaluation and deployment codebase that is competitive with a regular codebase?

As far as I know (I'm far from being an expert), such absolute guarantees are too hard right now, especially if the AI you're trying to verify is arbitrary. However, the training process ought to yield an AI with specific properties.
 

"Or is the claim that it's feasible to build a conservative world model that tells you "maybe a zero-day" very quickly once you start doing things not explicitly within a dumb world model?" 

I think that's closer to the idea: you {reject and penalize, during training} as soon as the AI tries something that might be "exploiting a zero-day", in the sense that the world-model can't rule out this possibility^[1]. That way, the training process is expected to reward simpler, more easily verified actions.

Then, a key question is "what else you do want from your AI ?": of course, it is supposed to perform critical tasks, not just "let you see what program is running"^[2], so there is tension between the various specifications you enter. The question of how far you can actually go, how much you can actually ask for, is both crucial, and wide open, as far as I can tell.

1. ^{^}

   Some of the uncertainty lies in how accurate and how conservative the world-model is; you won't get a "100% guarantee" anyway.

2. ^{^}

   Otherwise, a sponge would do.

Why focus on the $30 million grant?

What about large numbers of people working at OpenAI directly on capabilities for many years? (Which is surely worth far more than $30 million.)

Separately, this grant seems to have been done to influence the goverance at OpenAI, not make OpenAI go faster [LW(p) · GW(p)].

For me, the key question in situations when leaders made a decision with really bad consequences is, "How did they engage with criticism and opposing views?"

If they did well on this front, then I don't think it's at all mandatory to push for leadership changes (though certainly, the worse someones track record gets, the more that speaks against them).

By contrast, if leaders tried to make the opposition look stupid or if they otherwise used their influence to dampen the reach of opposing views, then being wrong later is unacceptable.

Basically, I want to allow for a situation where someone was like, "this is a tough call and I can see reasons why others wouldn't agree with me, but I think we should do this," and then ends up being wrong, but I don't want to allow situations where someone is wrong after having expressed something more like, "listen to me, I know better than you, go away."

In the first situation, it might still be warranted to push for leadership changes (esp. if there's actually a better alternative), but I don't see it as mandatory. 

The author of the original short form says we need to hold leaders accountable for bad decisions because otherwise the incentives are wrong. I agree with that, but I think it's being too crude to tie incentives to whether a decision looks right or wrong in hindsight. We can do better and evaluate how someone went about making a decision and how they handled opposing views. (Basically, if opposing views aren't loud enough that you'd have to actively squish them using your influence illegitimately, then the mistake isn't just yours as the leader; it's also that the situation wasn't significantly obvious to others around you.) I expect that everyone who has strong opinions on things and is ambitious and agenty in a leadership position is going to make some costly mistakes. The incentives shouldn't be such that leaders shy away from consequential interventions.

They would not know if others have signed the SAME NDAs without trading information about their own NDAs, which is forbidden.

The Norfolk, VA meetup everywhere today at 3pm has been cancelled due to rain & not receiving any rsvps.

I would be happy to defend roughly the position above (I don't agree with all of it, but agree with roughly something like "the strategy of trying to play the inside game at labs was really bad, failed in predictable ways, and has deeply eroded trust in community leadership due to the adversarial dynamics present in such a strategy and many people involved should be let go").

I do think most people who disagree with me here are under substantial confidentiality obligations and de-facto non-disparagement obligations (such as really not wanting to imply anything bad about Anthropic or wanting to maintain a cultivated image for policy purposes) so that it will be hard to find a good public debate partner, but it isn't impossible.

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