For me personally, research and then grantmaking at Open Phil has been excellent for my career development, and it’s pretty implausible that grad school in ML or CS, or an ML engineering role at an AI company, or any other path I can easily think of, would have been comparably useful.
If I had pursued an academic path, then assuming I was successful on that path, I would be in my first or maybe second year as an assistant professor right about now (or maybe I’d just be starting to apply for such a role). Instead, at Open Phil, I wrote less-academic reports and posts about less established topics in a more home-grown style, gave talks in a variety of venues, talked to podcasters and journalists, and built lots of relationships in industry, academia, and the policy world in the course of funding and advising people. I am likely more noteworthy among AI companies, policymakers, and even academic researchers than I would have been if I had spent that time doing technical research in a grad school and then went for a faculty role — and I additionally get to direct funding, an option which wouldn’t have been easily available to me on that alternative path.
The obvious con of OP relative to a path like that is that you have to “roll your own” career path to a much greater degree. If you go to grad school, you will definitely write papers, and then be evaluated based on how many good papers you’ve written; there isn’t something analogous you will definitely be made to do and evaluated on at OP (at least not something clearly publicly visible). But I think there are a lot of pros:
The flipside of the social awkwardness and stress that Linch highlighted in one of his questions is that a grantmaking role teaches you how to navigate delicate power dynamics, say no, give tough feedback, and make non-obvious decisions that have tangible consequences on reasonably short timeframes. I think I’ve developed more social maturity and operational effectiveness than I would have in a research role; this is a pretty important and transferrable skillset.
There is more space than there would be in a grad school or AI lab setting to think about weird questions that sit at the intersection of different fields and have no obvious academic home, such as the trajectory of AI development and timelines to very powerful AI. While independent research or other small-scale nonprofit research groups could offer a similar degree of space to think about “weird stuff,” OP is unusual in combining that kind of latitude with the ability to direct funding (and thus the ability to help make big material projects happen in the world).
Thanks Mishaal!
I think previous experience taking on operationally challenging projects is definitely the most important thing here, though it may not necessarily be traditional job experience (running a student group or local group can also provide good experience here). Beyond that, demonstrating pragmatism and worldliness in interviews (for example, when discussing real or hypothetical operational or time management challenges) is useful.
I think an important quality in a role like this is steadiness — not getting easily overwhelmed by juggling a lot of competing tasks, having the ability to get the easy stuff done quickly and make smart calls about prioritizing between the harder more nebulous tasks. And across all our roles, being comfortable with upward feedback and disagreement is key.