Grantmaking is more like a skill than a path

Epistemic status: Moderately strongly held based on anecdotal data, but I could easily imagine learning about broader patterns and withdrawing this claim.


TL;DR: Grantmaking, in my view, is best viewed as a set of skills or a stage in a career path than a career path itself. To be excellent at grantmaking, I think you need to be an excellent generalist in a specific field first.

This post is mostly a critique of the 80,000 Hours career profile ‘Grantmaker focused on pressing problems’. I agree with most of the article but object to the framing (grantmaking as a career path). 80,000 Hours staff reviewed this post and might make some changes to their article, but I’ll let them comment on that if they want to.

My perspective

Without intending to, I have spent a good chunk of my career trying to be a good grantmaker. I spent ~1/​3rd of ~1.5 years at what is now Longview Philanthropy making grants to support projects focused on protecting future generations[1] and I now make grants as part of my role as Community Events Manager at CEA.

I don’t claim to be a good or an experienced grantmaker but I have been asked by a few people who are interested in grantmaking what they should do now to become a grantmaker focused on pressing problems.

I find myself explaining to people who ask this question what I wish I knew before I tried grantmaking: to be excellent at grantmaking you need to be an excellent generalist[2] in a specific field first.

More specifically, I think you need at least the following:

  • A “bird’s eye view” of a field—Which projects already exist? What is the track record of work in the space? Who’s who?

  • A theory of change—What needs to happen in your field to solve an important problem? Why hasn’t that happened already? What’s the best path to the solution?

  • A strong network—You often need to be able to talk to someone working in the field to understand whether a new project might work. Who do you know who could pick up the phone to explain something to you?

  • Technical know-how—Can you quickly understand and vet proposals for projects in your field? Can you spot errors?

I think the best way to attain this set of skills is to pursue a generalist path in a specific field, rather than pursue a “grantmaker path”. Examples of more generalist paths might be researcher, policy analyst or manager.

Much of this is covered in the 80,000 Hours article and I agree with most of that article. But I think that the framing of grantmaking as a “path,” rather than a “skill” or “stage,” is confusing for two reasons:

  • Framing grantmaking as a path encourages people to try to learn generalist skills outside the context of a specific problem.

  • Framing grantmaking as a path encourages people early in their career to apply for an extremely narrow set of jobs (e.g. research analyst at Open Phil).

Framing grantmaking as a path encourages people to try to learn generalist skills outside the context of a specific problem.

I agree with the contents of the section “How to assess your fit” but I worry that people early in their career will try to work on things like ‘judging people accurately’ or ‘thinking of grant ideas’ before they have built a birds-eye view of a field or theory of change for a problem.

For example, I spoke to a student at EA Global who was interested in becoming a grantmaker in AI safety. I asked them what they thought of various lines of attack on the alignment problem and they said something along the lines of “I’m not sure I want to do that kind of research directly, I’m more of a people person which is why I’m focused on becoming a grantmaker.”[3] This sounds like it could be a big career misstep—it would be very hard to be a good grantmaker in the field of AI safety without a deep understanding of the alignment problem and the current research projects in the field.

Framing grantmaking as a path encourages people early in their career to apply for an extremely narrow set of jobs (e.g. research analyst at Open Phil)

Open Philanthropy hires only a few people per year to these roles (5 in their latest round)[4] - I expect that’s because there are very few excellent researchers who aren’t already focused on a specific problem. These jobs also aren’t simply a step towards being a grantmaker—I think Open Philanthropy also (mostly?) hires research analysts to conduct research that informs their cause prioritisation and strategy, rather than make grants.

When Open Philanthropy hires grantmakers, they usually hire grantmakers from the fields they make grants in.[5] In fact, I expect a very small number of people could be strong grantmakers in important and technical fields like AI safety and biosecurity[6] and ~all of those people will be leading researchers in the field already.

I worry that some promising young generalist will read the 80,000 Hours article and, instead of starting a career focused on a specific problem, will try to learn grantmaking skills independently (by e.g. reading the EA Funds reports and generating ideas), apply for one of a very small number of jobs, get rejected because of inexperience and then decide that this path isn’t for them.[7]

Instead, I think we should encourage people to consider grantmaking as a skill (a set of skills is probably more accurate) or a stage in their career. This framing makes it clear that it is something you’re more likely to be able to do well if you become an excellent generalist in a specific field and usually not something you can start working on as soon as you graduate.[8] I’d then hope to see more paths like:

  • Train in computer science > Become an expert in ML > Work for an AI safety research group > Become a manager > Move to a foundation to make grants in the space (A bit like Paul Christiano, although I think he mostly advises on grants vs. makes them).

  • Train in philosophy or economics > Become an expert on global priorities research > Earn a PhD > Move to a foundation to make grants in the space (A bit like Tyler John).

Closing thought

I find a useful analogy to “grantmaker as a career path” is “academic professor as a career path”. It doesn’t really make sense to have “professor” as your goal—instead, it makes more sense to try to become an excellent academic obsessed with a problem. Being a professor is then just a step on that path you might take later in your career.

To be clear, there are reasons to think some people might be good at being a professor (e.g. good at teaching) or being a grantmaker (e.g. good judgement). These skills are real and worth building. My claim is that they will be more useful if they’re developed alongside domain-specific expertise; much like being a great teacher is more useful if you’re also an expert on a particular topic.


My thanks to Arden Koehler, Luisa Rodriguez, Tyler John, Kit Harris, Simran Dhaliwal and Lizka Vaintrob for their feedback. All errors are my own.

  1. ^

    Ultimately, I proved not to be a good fit for this work at the time and moved to a more operational role. This is mostly because I was too inexperienced to properly assess proposals in most longtermist cause areas. I think I was okay at other aspects of the role (e.g. communicating with grantees and managing my time).

  2. ^

    “Expert” could also replace this term but I think it’s worth emphasising that being good at grantmaking requires more of a “generalist” set of skills (vs. being very knowledgable).

  3. ^

    I want to emphasise that this person seemed very smart and reasonable otherwise and I’m paraphrasing here to highlight a misunderstanding I think they had.

  4. ^

    According to the 80k article. There might have been a more recent round.

  5. ^
  6. ^

    And others, like basic science, global development, nanotechnology, progress studies etc.

  7. ^

    This is just a toy example of the thing I’m worried about, I haven’t heard of this actually happening.

  8. ^

    There are exceptions, of course. A recent graduate might be able to support community-builders via grantmaking or run a fellowship programme where the criteria allow for reasonable guesses without a lot of context on the object-level problem.