Academics choose to work on things when they’re doable, important, interesting, publishable, and fundable. Importance and interestingness seem to be the least bottlenecked parts of that list.
The root of the problem is difficulty in evaluating the quality of work. There’s no public benchmark for AI safety that people really believe in (nor do I think there can be, yet—talk about AI safety is still a pre-paradigmatic problem), so evaluating the quality of work actually requires trusted experts sitting down and thinking hard about a paper—much harder than just checking if it beat the state of the art. This difficulty restricts doability, publishability, and fundability. It also makes un-vetted research even less useful to you than it is in other fields.
Perhaps the solution is the production of a lot more experts, but becoming an expertise on this “weird” problem takes work—work that is not particularly important or publishable, and so working academics aren’t going to take a year or two off to do it. At best we could sponsor outreach events/conferences/symposia aimed at giving academics some information and context to make somewhat better evaluations of the quality of AI safety work.
Thus I think we’re stuck with growing the ranks of experts not slowly per se (we could certainly be growing faster), but at least gradually, and then we have to leverage that network of trust both to evaluate academic AI safety work for fundability / publishability, and also to inform it to improve doability.