If I understand correctly, you view AI Governance as addressing how to deal with many different kinds of AI problems (misuse, accident or structural risks) that can occur via many different scenarios (superintelligence, ecology or GPT perspectives). I also think (though I’m less confident) that you think it involves using many different levers (policy, perhaps alternative institutions, perhaps education and outreach).
I was wondering if you could say a few words on why (or if!) this is a helpful portion of problem-assumption-lever space to carve into a category. For example, I feel more confused when I try and fit (a) people navigating Manhattan projects for superintelligent AI and (b) people ensuring an equality-protecting base of policy for GPT AI into the same box than when I try and think about them separately.
To state a point in the neighborhood of what Stefan, Ben P, and Ben W have said, I think it’s important for LTTF to evaluate the counterfactual where they don’t fund something, rather than the counterfactual where the project has more reasonable characteristics.
That is, we might prefer a project be more productive, more legible or more organized, but unless that makes it worse than the marginal funding opportunity, it should be funded (where one way a project could be bad is by displacing more reasonable projects that would otherwise fill a gap).