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Remark in guaranteed safe AI newsletter:
Niplav writes
So, a proposal: Whenever someone claims that LLMs will d/acc us out of AI takeover by fixing our infrastructure, they will also have to specify who will pay the costs of setting up this project and running it.
I’m almost centrally the guy claiming LLMs will d/acc us out of AI takeover by fixing infrastructure, technically I’m usually hedging more than that but it’s accurate in spirit.
If transformative AI is developed soon, most open source projects (especially old ones relevant to internet infrastructure) are going to be maintained by humans with human response times. That will significantly increase the time for relevant security patches to be reviewed and merged into existing codebases, especially if at the time attackers will submit AI-generated or co-developed subtle exploits using AI systems six to nine months behind the leading capabilities, keeping maintainers especially vigilant.
I usually say we prove the patches correct! But Niplav is correct: it’s a hard social problem, many critical systems maintainers are particularly slop-phobic and won’t want synthetic code checked in. That’s why I try to emphasize that the two trust points are the spec and the checker, and the rest is relinquished to a shoggoth. That’s the vision anyway– we solve this social problem by involving the slop-phobic maintainers in writing the spec and conveying to them how trustworthy the deductive process is.
Niplav’s squiggle model: Median $~1b worth of tokens, plus all the “setting up the project, paying human supervisors and reviewers, costs for testing infrastructure & compute, finding complicated vulnerabilities that arise from the interaction of different programs…” etc costs. I think a lot’s in our action space to reduce those latter costs, but the token cost imposes a firm lower bound.
But this is an EA Forum post, meaning the project is being evaluated as an EA cause area: is it cost effective? To be cost effective, the savings from alleviating some disvalue have to be worth the money you’ll spend. As a programming best practices chauvinist, one of my pastimes is picking on CrowdStrike, so let’s not pass up the opportunity. The 2024 outage is estimated to have cost about $5b across the top 500 companies excluding microsoft. A public goods project may not have been able to avert CrowdStrike, but it’s instructive for getting a flavor of the damage, and this number suggests it could be easily worth spending around Niplav’s estimate. On cost effectiveness though, even I (who works on this “LLMs driving Hot FV Summer” thing full time) am skeptical, only because open source software is pretty hardened already. Curl/libcurl saw 23 CVEs in 2023 and 18 in 2024, which it’d be nice to prevent but really isn’t a catastrophic amount. Other projects are similar. I think a lot about the Tony Hoare quote “It has turned out that the world just does not suffer significantly from the kind of problem that our research was originally intended to solve.” Not every bug is even an exploit.
While this is a good argument against it indicating governance-by-default (if people are saying that), securing longtermist funding to work with the free software community over this (thus overcoming two of the three hurdles) still seems to be a potentially very cost-effective way to reduce AI risk to look into, particularly combined with differential technological development of AI defensive v. offensive capacities.
That's maybe a more productive way of looking at it! Makes me glad I estimated more than I claimed.
I think governments are probably the best candidate for funding this, or AI companies in cooperation with governments. And it's an intervention which has limited downside and is easy to scale up/down, with the most important software being evaluated first.