It would be amazing if we always knew ahead of time which of the people pursuing a fat-tailed career path would end up on the fat end of that tail…
If you limit your impact considerations to AI risks (rather than cause neutral), a simple heuristic would be to ask orgs how valuable their recent hire is to them, top candidate vs second best (some 80k articles on this, let me know if you can’t find them yourself). Additionally, AI risk nonprofits usually have higher total employee cost per person than 80k/year so you can assume that a great fit devoting their time is more valuable than receiving this sum in donations.
1. Yes, of course we don’t completely know. However 80000 hours has written in their research that even if we are talking on “ex-ante” expected distribution of people, it’s probably still fat- tailed distribution. Therefore, it’s possible we “often” know who’s going to be in fat tailed and who’s probably not.
2.I’ve heard of this heuristic. However in my case, I have to predict in advance. (I can’t work in a non-profit now since I’m only 19). Also, it’s probable you reduce AI risks in the non-EA world. In that case, your marginal impact isn’t the gap of you and the second best applying the job.
We cannot infer from knowing it’s a fat-tailed distribution who’s going to be in the impactful fat tail and who’s gonna be average (or do I misunderstand you here?). We need lots of people making informed bets, and we likely need an ecosystem. We can however give recommendations based on some heuristic—e.g., if you have an easy time taking advanced ML classes, you’re more likely to have an impact in a technical field than someone who hasn’t—those are cheap tests. I recommend applying to speak with 80,000 hours advising team if you haven’t!
I think it’s reasonable to use past numbers as heuristic for future hires. I agree many impactful opportunities will be outside of EA orgs, but my hunch is most people who’ll be very impactful in those roles (e.g. as a civil servant) would’ve also been quite successful inside an EA org (depending on different levels of “absorbency” between those at a given time—see Joey’s post), depending on personal fit. Another consideration is how abundant funding in that cause is—does everything that’s reasonable get a grant anyways, or are the competitive? Again, it’ll matter if you want to do cross-cause comparison.
I wonder why this particular question that you want answers to seems to be your crux, however—it seems the most urgent question for you is which major to choose, and for that, dentistry doesn’t seem like the strongest earning to give option for the vast majority of people (or even a decision you could delay by 4 years?) - I’d encourage you to brainstorm more options and choose paths that allow you to learn more about your skill set as well as staying flexible—employment is likely going to look quite different in 4 years.
It would be amazing if we always knew ahead of time which of the people pursuing a fat-tailed career path would end up on the fat end of that tail…
If you limit your impact considerations to AI risks (rather than cause neutral), a simple heuristic would be to ask orgs how valuable their recent hire is to them, top candidate vs second best (some 80k articles on this, let me know if you can’t find them yourself). Additionally, AI risk nonprofits usually have higher total employee cost per person than 80k/year so you can assume that a great fit devoting their time is more valuable than receiving this sum in donations.
Thanks for your answering a lot.
1. Yes, of course we don’t completely know. However 80000 hours has written in their research that even if we are talking on “ex-ante” expected distribution of people, it’s probably still fat- tailed distribution. Therefore, it’s possible we “often” know who’s going to be in fat tailed and who’s probably not.
2.I’ve heard of this heuristic. However in my case, I have to predict in advance. (I can’t work in a non-profit now since I’m only 19). Also, it’s probable you reduce AI risks in the non-EA world. In that case, your marginal impact isn’t the gap of you and the second best applying the job.
We cannot infer from knowing it’s a fat-tailed distribution who’s going to be in the impactful fat tail and who’s gonna be average (or do I misunderstand you here?). We need lots of people making informed bets, and we likely need an ecosystem. We can however give recommendations based on some heuristic—e.g., if you have an easy time taking advanced ML classes, you’re more likely to have an impact in a technical field than someone who hasn’t—those are cheap tests. I recommend applying to speak with 80,000 hours advising team if you haven’t!
I think it’s reasonable to use past numbers as heuristic for future hires. I agree many impactful opportunities will be outside of EA orgs, but my hunch is most people who’ll be very impactful in those roles (e.g. as a civil servant) would’ve also been quite successful inside an EA org (depending on different levels of “absorbency” between those at a given time—see Joey’s post), depending on personal fit. Another consideration is how abundant funding in that cause is—does everything that’s reasonable get a grant anyways, or are the competitive? Again, it’ll matter if you want to do cross-cause comparison.
I wonder why this particular question that you want answers to seems to be your crux, however—it seems the most urgent question for you is which major to choose, and for that, dentistry doesn’t seem like the strongest earning to give option for the vast majority of people (or even a decision you could delay by 4 years?) - I’d encourage you to brainstorm more options and choose paths that allow you to learn more about your skill set as well as staying flexible—employment is likely going to look quite different in 4 years.