There’s a refrain in the field that AI safety needs more generalists, and more people asking how to get in as one. To which I’ve been asking: what even is a generalist?
I agree with the overall sentiment that the field needs a greater range of talent than existing talent pipelines supply. Most fellowships support technical research talent (e.g. MATS, Astra, Pivotal, LASR) and policy talent (e.g. Horizon, GovAI, Talos). Only a handful of programs support other kinds of talent (e.g. Generator Residency, BlueDot’s Incubator week, plzdontkillus).
This “other” talent is what I think people mean when they say generalist — people beyond the broad buckets of research or policy. It’s the “everything else” bin, where everyone from creators to serial entrepreneurs to operators to recruiters get mushed together. They’re all treated as though they face the same blockers and have the same opportunities.
This category is too broad to be useful. The field can’t design support around it, and the people who identify with it can’t figure out how to contribute.
I call myself a generalist too.
“Generalist” is a convenient shorthand for being adaptable and having a wide range of skills. My colleagues sometimes describe me as a “Swiss Army knife” or having a high “adaptability quotient”.
However, saying I’m adaptable conveys a strength I have rather than describing what I actually do well.
When I was applying for roles back in 2022, I found that generalist-esque roles with job titles like “ops”, “community” or even “executive assistant” often required very different skills and context on AI safety.
I filtered for responsibilities that were more in my wheelhouse (e.g. designing effective learning experiences rather than doing finance or HR) and required little familiarity with the field (e.g. running a program rather than designing one).
Today, I still think of myself as a generalist. But I would filter for very different roles that still have the same job titles because I have far more context on the field and more specialised skills.
“Generalist” is a label that covers many archetypes and each has a different pathway into AI safety.
I give different advice to self-described generalists depending on: (1) depth of expertise, and (2) how much context they have on AI safety.
The classic generalist has shallow expertise and is new to AI safety. The best are sharp, adaptable, agentic and pick things up quickly. They have a broad range of skills that don’t fit neatly into buckets like technical or policy.
Their pathway starts with the first ~100 hours of deep engagement with the field — understanding what the problem actually is, what success looks like, the challenges with making progress on it and getting to know the orgs. Alongside that, it’s making their growth trajectory visible. They don’t have a track record in AI safety, so the evidence of their potential will be the times they’ve picked things up quickly or effectively solved problems.
The deep-context generalist is past those first 100 hours and has more refined takes on how to make AI go well. Their skills don’t fit the technical or policy bucket. They might be good fits for leading programs, mentoring, advising, research management, communications, and many other roles.
Their pathway is developing good takes and making them legible to potential collaborators. This might mean doing the real thing — making attempts at solving concrete problems in AI safety. For example, building useful tools for AI safety or writing publicly.
The specialist generalist brings skills that are hard to develop within the field, like scaling company operations from 5 to 50, navigating legal structures in specific jurisdictions or recruiting senior talent. They are often lumped in with generalists because their speciality isn’t research or policy. Most AI safety orgs are small (<20 people) and young (<5 years old). Very few people within the field would have had the chance to build this expertise, so the field needs to import this rather than promote into it.
Their pathway is focused on demonstrating they care about AI safety (because orgs are highly mission-driven and often select hard for culture fit), and that their skills translate to the specific problems the role is tackling.
Each archetype has different blockers. Even the one thing they all seem to need — “more context” — isn’t one thing. The classic generalist needs foundations. The generalist with deeper context needs sharper takes. The specialist needs to build conviction.
The same split runs through everything else. A structured course might serve the classic generalist best, while the generalist with deeper context might get more from an intensive, in-person cohort. The specialist’s next step is applying to roles, while the classic generalist’s is doing projects.
If you design for a clear archetype, all of this falls out naturally. If you design for the average, you won’t serve any one well.
Don’t say generalist, say what you actually mean
Whenever someone wants feedback on a program they are designing for generalists or advice on contributing to AI safety as one, I have to ask them: what do you mean “generalist”?
Don’t ask: how can I contribute to AI safety as a generalist? Ask how someone with your particular skills and context can contribute. You’ll get more specific advice and relevant opportunities.
Don’t design programs for generalists. Design programs for one archetype with concrete blockers, so you know where to find them and what will actually move them into the field.
Don’t hire a “generalist”. Define the aptitudes, expertise and context the role needs, so that you can find exceptional candidates and pitch them on it.
The field is right: we need a wider range of talent in AI safety than most pathways supply. The way to get more talent in is by making these pathways concrete enough for people to walk them.
i’ve also harbored the same confusion, much-needed double-clicking! thanks for the writeup Li-lian.
This really resonates with me. Having worked across different sectors, in both the private and public spaces, I’ve gradually become what I’d describe as a generalist.
Not because I know everything, but because each experience has given me a different set of skills, perspectives and context. Over time, I’ve learned to connect the dots between technology, business, government, operations, community and people.
I think that’s one of the underrated strengths of a generalist, the ability to see the bigger picture and connect things that may not seem connected at first.
The challenge, as you rightly point out, is that “generalist” can mean very different things depending on the depth of expertise and context someone has.