The longtermist AI governance landscape: a basic overview

Abstract

On a high level, I find it helpful to consider there being a spectrum between foundational and applied work. On the foundational end, there’s strategy research, which aims to identify good high-level goals for longtermist AI governance; then there’s tactics research which aims to identify plans that will help achieve those high-level goals. Moving towards the applied end, there’s policy development work that takes this research and translates it into concrete policies; work that advocates for those policies to be implemented, and finally the actual implementation of those policies (by e.g. civil servants). There’s also field-building work (which doesn’t clearly fit on the spectrum). Rather than contributing directly to the problem, this work aims to build a field of people who are doing valuable work on it. . Of course, this classification is a simplification and not all work will fit neatly into a single category. You might think that insights mostly flow from the more foundational to the more applied end of the spectrum, but it’s also important that research is sensitive to policy concerns, e.g. considering how likely your research is to inform a policy proposal that is politically feasible. We’ll now go through each of these kinds of work in more detail.