I'm not sure I understand the distinction that you're drawing here. (It seems like my scenarios could also be interpreted as failures where AI don't know enough human values, or maybe where humans themselves don't know enough human values.) What are some examples of what your claim was about?

Examples:

1. Your AI thinks it's acceptable to inject you with heroin, because it predicts you will then want more heroin.
2. Your AI is uncertain whether you'd prefer to explore space or stay on Earth. It randomly guesses that you want to stay on Earth and takes irreversible actions on your behalf that force you to stay on Earth.

In contrast, something like a threat doesn't count, because you know that the outcome if the threat is executed is not something you want; the problem comes because you don't know how to act in a way that both disincentivizes threats and also doesn't lead to (too many) threats being enforced. In particular, the problem is not that you don't know which outcomes are bad.

As in, the total expected value lost through such scenarios isn't as large as the expected value lost through the risk of failing to solve intent alignment?

No, the expected value of marginal effort aimed at solving these problems isn't as large as the expected value of marginal effort on intent alignment.

(I don't like talking about "expected value lost" because it's not always clear what does and doesn't count as part of that. For example I think it's nearly inevitable that different people will have different goals and so the future will not be exactly as any one of them desired; should I say that there's a lot of expected value lost from "coordination problems" for that reason? It seems a bit weird to say that if you think there isn't a way to regain that "expected value".)

Can you give some ballpark figures of how you see each side of this inequality?

Uh, idk. It's not something I have numbers on. But I suppose I can try and make up some very fake numbers for, say, AI persuasion. (Before I actually do the exercise, let me note that I could imagine the exercise coming out with numbers that favor persuasion over intent alignment; this probably won't change my mind and would instead make me distrust the numbers, but I'll publish them anyway.)

To change an existentially bad outcome from AI persuasion, I'd imagine first figuring out some solutions, then figuring out how to implement them, and then getting the appropriate people to implement them; seems like you need all of these steps in order to make any difference. (Could be technical or governance though.) It feels especially hard to do so at the moment, given how little we know about future AI capabilities and when each potential capability arrives. Maybe:

1. Alignment is 100x more likely to be an existentially risky problem at all (think of this as the ratio between probabilities of existential catastrophe by the given problem assuming no intervention from longtermists).
2. A piece of alignment work now is 10x more likely to target the right problem than a similar piece of work for persuasion.
3. A piece of alignment work now is currently 10x harder to produce than a similar piece of work for persuasion.

So I guess overall I'm at ~100x, very very roughly?

How? How would you train an AI to distinguish between philosophy optimized for persuasion, and correct or well-intentioned philosophy that just happens to be very persuasive?

Putting on my "what would I do" hat, I'm imagining that the AI doesn't know that it was specifically optimized to be persuasive, but it does know that there are other persuasive counterarguments that aren't being presented, and so it says that it looks one-sided and you might want to look at these other counterarguments. Or it says that there are other counterfactual letters you could have received, such that after you read them you'd be convinced of the opposite position, and then it asks whether you still want to read the letter.

If your AI doesn't know about the counterarguments, and the letter is persuasive even to the AI, then it seems like you're hosed, but I'm not sure why to expect that.

A lot of people are playing status games where faith/loyalty to their cause/ideology gains them a lot of status points. Why wouldn't they ask their AI for help with this? Or do you imagine them asking for something like "more faith", but AIs understand human values well enough to not interpret that as "lock in values"?

I totally expect them to ask AI for help with such games. I don't expect (most of) them to lock in their values such that they can't change their mind.

I'm not entirely sure why you do expect this. Maybe you're viewing them as consciously optimizing for winning status games + a claim that the optimal policy is to lock in your values? But what if the values rewarded by the status games (as they already seem to, e.g. moving from atheism to anti-racism)? In that case it seems like you don't want to lock in your values, to better play the status game in the future.

The former seems to just require that the AI is good at reasoning about mathematical/empirical matters (e.g., are there many simulations of me actually being run in some universe or set of universes) which I think AIs will be good at by default, whereas dealing with the threats seems to require reasoning about hard philosophical problems like decision theory and morality.

If you've determined a set of universes to care about, then shouldn't at least decision theory reduce to mathematical / empirical matters about which decision procedure gets the most value across the set of universes? I do agree that moral questions are still not mathematical / empirical, but I don't find that all that persuasive. I expect AIs will be able to do the sort of philosophical reasoning that we do, and the question of whether we should care about simulations seems way way easier than the question about which simulations of me are being run, by whom, and what they want.

I'm guessing you feel better about mathematical / empirical reasoning because there's a ground truth that says when that reasoning is done well. I don't particularly find the existence of a ground truth to be all that big a deal -- it probably helps but doesn't seem tremendously important.

I expect that AIs (or humans) who are less cautious or who think their values can be easily expressed as utility functions will do this first, and thereby gain an advantage over everyone else and maybe forcing them to follow.

Fair enough -- I agree this is plausible (though only plausible; it doesn't seem like we've sacrificed everything to Moloch yet).

I don't think it's so much that the coordination involving humans is a lot of work, but rather that we don't know how to do it in a way that doesn't cause a lot of waste, similar to a democratically elected administration implementing a bunch of policies only to be reversed by the next administration that takes power, or lawmakers pursuing pork barrel projects that collectively make almost everyone worse off, or being unable to establish and implement easy policies (see COVID again). (You may well have something in mind that works well in the context of intent aligned AI, but I have a prior that says this class of problems is very difficult in general so I'd need to see more details before I update.)

If we're imagining coordination between a billion AIs, that seems less obviously doable. I still think that, if you've solved intent alignment, it seems much easier. Democratic elections are not just about coordination -- they're also about alignment, since politicians have to optimize for being re-elected. It seems like you could do a lot better if you didn't have to worry about the alignment part.