Survey of EA org leaders about what skills and experience they most need, their staff/​donations trade-offs, problem prioritisation, and more.

N.B. You may be interested to read our follow-up post: Many EA orgs say they place a lot of financial value on their previous hire. What does that mean, if anything? And why aren’t they hiring faster?


For the third year running we’ve surveyed leadership at EA organisations about a range of issues where their views might be relevant to EAs’ career decisions:

What are the most pressing talent gaps in professional effective altruism in 2018? And which problems are most effective to work on? New survey of organisational leaders.

It complements the 2018 EA Survey which aims to collect information about everyone who says “they can, however loosely, be described as an effective altruist.”

We asked leaders about:

  • what skills and experience they most need;

  • what skills and experience they think the community as a whole will need in the future;

  • how many donations they’d be willing to forego for their latest hires;

  • their view on the relative cost-effectiveness of the different EA Funds, and which new funds they’d like to see;

  • how urgent their need for extra donations and staff is;

  • and various other issues.

We also surveyed people who identify as members of the EA community and work directly on problems like animal welfare and poverty, to see how their views on some of these questions would compare.

Here are some of the findings:

  • EA organisation leaders said experience with operations or management, and generalist researchers are what their organisations will need most of over the next five years.

  • They said the community as a whole will most need more government and policy experts, operations experience, machine learning/​AI technical expertise, and skilled managers.

  • Most EA organisations continue to feel more ‘talent constrained’ than funding constrained, rating themselves as 2.8/​4 talent constrained and 1.5/​4 funding constrained.

  • Leaders thought the key bottleneck for the community is to get More dedicated people (e.g. work at EA orgs, research in AI safety/​biosecurity/​economics, ETG over $1m) converted from moderate engagement. The second biggest is to increase impact of existing dedicated people through e.g. better research, coordination, decision-making.

  • We asked leaders their views on the relative cost-effectiveness of donations to four funds operated by the community. The median view was that the Long-Term Future fund was twice as effective as the EA Community fund, which in turn was 10 times more cost-effective than the Animal Welfare fund, and twenty times as cost-effective as the Global Health and Development fund. Individual views on this question varied very widely, though 1828 respondents thought the Long-Term Future fund was the most effective.

  • In addition, we asked several community members working directly on animal welfare and global development for their views on the relative cost-effectiveness of donations to these funds. About half these staff thought the fund in their own cause area was best, and about half thought either the EA Community fund or Long-Term Future fund was best. The median respondent in that group thought that the Animal Welfare fund was about 33% more cost-effective than the Long-Term Future fund and the EA Community fund—which were rated equally cost-effective—while the Global Health and Development fund was 33% as cost effective as either of those two. However, there was also a wide range of views among this group.

  • The organisations surveyed were usually willing to forego over a million dollars in additional donations to get the right person in a senior role 3 years earlier, or several hundred thousand dollars for a junior hire.

Continue reading for details of the method and results...

Most answers were similar to what we found in 2017, so next year we expect to either ask different questions or interview a smaller number of people in greater depth and see whether their responses change after further reflection.