I’m not involved in hiring at OpenAI, so I’m going to answer more in the spirit of “advice I would give for people interested in pursuing a career in EA AI policy generally.”
In short, I think actually trying your hand at the research is probably more valuable on the margin, especially if it yields high-quality research. (And if you discover it’s not a good fit, that’s valuable information as well.) This is basically what happened to me during my FHI internship: I found out that I was a good fit for this work, so I continued on in this path. There are a lot of very credentialed EAs, but (for better or worse), many EA AI policy careers take a combination of hard-to-describe and hard-to-measure skills that are best measured by actually trying to do it. Furthermore, there is unfortunately a managerial bottleneck in this space: there are far more people interested in entering it than people that can supervise potential entrants. I think it can be a frustrating space to enter; I got very lucky in many ways during my path here.
So, if you can’t actually try the research in a supervised setting, cultivating general skills or doing adjacent research (e.g., general AI policy) is a good step too. There are always skills I wish I had (and which I am fortunate to get to cultivate at OpenAI during Learning Day). Some of the stuff I studied during Learning Day which might guide your own skill cultivation include:
I’m not involved in hiring at OpenAI, so I’m going to answer more in the spirit of “advice I would give for people interested in pursuing a career in EA AI policy generally.”
In short, I think actually trying your hand at the research is probably more valuable on the margin, especially if it yields high-quality research. (And if you discover it’s not a good fit, that’s valuable information as well.) This is basically what happened to me during my FHI internship: I found out that I was a good fit for this work, so I continued on in this path. There are a lot of very credentialed EAs, but (for better or worse), many EA AI policy careers take a combination of hard-to-describe and hard-to-measure skills that are best measured by actually trying to do it. Furthermore, there is unfortunately a managerial bottleneck in this space: there are far more people interested in entering it than people that can supervise potential entrants. I think it can be a frustrating space to enter; I got very lucky in many ways during my path here.
So, if you can’t actually try the research in a supervised setting, cultivating general skills or doing adjacent research (e.g., general AI policy) is a good step too. There are always skills I wish I had (and which I am fortunate to get to cultivate at OpenAI during Learning Day). Some of the stuff I studied during Learning Day which might guide your own skill cultivation include:
Machine Learning
Economic Development
Financial Accounting
Basic Economics