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I'm worried that "pause all AI development" is like the "defund the police" of the alignment community. I'm not convinced it's net bad because I haven't been following governance-- my current guess is neutral-- but I do see these similarities:

  • It's incredibly difficult and incentive-incompatible with existing groups in power
  • There are less costly, more effective steps to reduce the underlying problem, like making the field of alignment 10x larger or passing regulation to require evals
  • There are some obvious negative effects; potential overhangs or greater incentives to defect in the AI case, and increased crime, including against disadvantaged groups, in the police case
  • There's far more discussion than action (I'm not counting the fact that GPT5 isn't being trained yet; that's for other reasons)
  • It's memetically fit, and much discussion is driven by two factors that don't advantage good policies over bad policies, and might even do the reverse. This is the toxoplasma of rage.
    • disagreement with the policy
    • (speculatively) intragroup signaling; showing your dedication to even an inefficient policy proposal proves you're part of the ingroup. I'm not 100% this was a large factor in "defund the police" and this seems even less true with the FLI letter, but still worth mentioning.

This seems like a potentially unpopular take, so I'll list some cruxes. I'd change my mind and endorse the letter if some of the following are true.

  • The claims above are mistaken/false somehow.
  • Top labs actually start taking beneficial actions towards the letter's aims
  • It's caused people to start thinking more carefully about AI risk
  • A 6 month pause now is especially important by setting anti-racing norms, demonstrating how far AI alignment is lagging behind capabilities, or something
  • A 6 month pause now is worth close to 6 months of alignment research at crunch time (my guess is that research at crunch time is worth 1.5x-3x more depending on whether MIRI is right about everything)
  • The most important quality to push towards in public discourse is how much we care about safety at all, so I should endorse this proposal even though it's flawed

It's incredibly difficult and incentive-incompatible with existing groups in power

Why does this have to be true? Can't governments just compensate existing AGI labs for the expected commercial value of their foregone future advances due to indefinite pause? 

This seems good if it could be done. But the original proposal was just a call for labs to individually pause their research, which seems really unlikely to work.

Also, the level of civilizational competence required to compensate labs seems to be higher than for other solutions. I don't think it's a common regulatory practice to compensate existing labs like this, and it seems difficult to work out all the details so that labs will feel adequately compensated. Plus there might be labs that irrationally believe they're undervalued. Regulations similar to the nuclear or aviation industry feel like a more plausible way to get slowdown, and have the benefit that they actually incentivize safety work.

I'm planning to write a post called "Heavy-tailed error implies hackable proxy". The idea is that when you care about  and are optimizing for a proxy , Goodhart's Law sometimes implies that optimizing hard enough for  causes  to stop increasing.

A large part of the post would be proofs about what the distributions of  and  must be for , where X and V are independent random variables with mean zero. It's clear that

  • X must be heavy-tailed (or long-tailed or something)
  • X must have heavier tails than V

The proof seems messy though; Drake Thomas and I have spent ~5 person-days on it and we're not quite done. Before I spend another few days proving this, is it a standard result in statistics? I looked through a textbook and none of the results were exactly what I wanted.

Note that a couple of people have already looked at it for ~5 minutes and found it non-obvious, but I suspect it might be a known result anyway on priors.

Doesn't answer your question, but we also came across this effect in the RM Goodharting work, though instead of figuring out the details we only proved that it when it's definitely not heavy tailed it's monotonic, for Regressional Goodhart ( Jacob probably has more detailed takes on this than me. 

In any event my intuition is this seems unlikely to be the main reason for overoptimization - I think it's much more likely that it's Extremal Goodhart or some other thing where the noise is not independent