There is a significant overlap between EA and AI safety, and it is often unclear whether people supposedly working on AI safety are increasing/decreasing AI risk. So I think it would be helpful if you could point to some (recent) data on how many people are being introduced to global health and development, and animal welfare via 80,000 Hours.
We actually have a post on this forthcoming, but I can give you the figures for 80,000 Hours specifically now.
Global Poverty:
80,000 Hours:
37.3% rated Global Poverty 4⁄5
28.6% rated Global Poverty 5⁄5
All respondents:
39.3% rated Global Poverty 4⁄5
28.7% rated Global Poverty 5⁄5
So the difference is minimal, but this also neglects the fact that the scale of 80K’s recruitment swamps any differences in % supporting different causes. Since 80K recruits so many, it is still the second highest source of people who rate Global Poverty most highly (12.5% of such people) after only personal contacts.
Animal Welfare:
80,000 Hours:
26.0% rated Animal Welfare 4⁄5
9.6% rated Animal Welfare 5⁄5
All respondents:
30.1% rated Animal Welfare 4⁄5
11.8% rated Animal Welfare 5⁄5
Here the difference is slightly bigger, though 80,000 Hours remains among the top sources of recruits rating Animal Welfare highest (specifically, after personal contact, the top sources are ‘Other’ (11.2%), ‘Book, article or blog’ (10.7%), 80,000 Hours (9%).
These are numbers from the most recent full EA Survey (end of 2022), but they’re not limited only to people who joined EA in the most recent year. Slicing it by individual cohorts would reduce the sample size a lot.
My guess is that it would also increase the support for neartermist causes among all recruits (respondents tend to start out neartermist and become more longtermist over time).
That said, if we do look at people who joined EA in 2021 or later (the last 3 years seems decently recent to me, I don’t have the sense that 80K’s recruitment has changed so much in that time frame, n=1059), we see:
Hi David,
There is a significant overlap between EA and AI safety, and it is often unclear whether people supposedly working on AI safety are increasing/decreasing AI risk. So I think it would be helpful if you could point to some (recent) data on how many people are being introduced to global health and development, and animal welfare via 80,000 Hours.
Thanks Vasco.
We actually have a post on this forthcoming, but I can give you the figures for 80,000 Hours specifically now.
Global Poverty:
80,000 Hours:
37.3% rated Global Poverty 4⁄5
28.6% rated Global Poverty 5⁄5
All respondents:
39.3% rated Global Poverty 4⁄5
28.7% rated Global Poverty 5⁄5
So the difference is minimal, but this also neglects the fact that the scale of 80K’s recruitment swamps any differences in % supporting different causes. Since 80K recruits so many, it is still the second highest source of people who rate Global Poverty most highly (12.5% of such people) after only personal contacts.
Animal Welfare:
80,000 Hours:
26.0% rated Animal Welfare 4⁄5
9.6% rated Animal Welfare 5⁄5
All respondents:
30.1% rated Animal Welfare 4⁄5
11.8% rated Animal Welfare 5⁄5
Here the difference is slightly bigger, though 80,000 Hours remains among the top sources of recruits rating Animal Welfare highest (specifically, after personal contact, the top sources are ‘Other’ (11.2%), ‘Book, article or blog’ (10.7%), 80,000 Hours (9%).
Thanks, David! Strongly upvoted.
To clarify, are those numbers relative to the people who got to know about EA in 2023 (via 80,000 Hours or any source)?
Thanks Vasco!
These are numbers from the most recent full EA Survey (end of 2022), but they’re not limited only to people who joined EA in the most recent year. Slicing it by individual cohorts would reduce the sample size a lot.
My guess is that it would also increase the support for neartermist causes among all recruits (respondents tend to start out neartermist and become more longtermist over time).
That said, if we do look at people who joined EA in 2021 or later (the last 3 years seems decently recent to me, I don’t have the sense that 80K’s recruitment has changed so much in that time frame, n=1059), we see:
Global Poverty
80,000 Hours
35.8% rated Global Poverty 4⁄5
34.2% rated Global Poverty 5⁄5
All respondents
40.2% rated Global Poverty 4⁄5
33.2% rated Global Poverty 4⁄5
Animal Welfare
80,000 Hours
26.8% rated Animal Welfare 4⁄5
8.9% rated Animal Welfare 5⁄5
All respondents
29.7% rated Animal Welfare 4⁄5
13.7% rated Animal Welfare 5⁄5