AI Safety's Biggest Talent Gap Isn't Researchers. It's Generalists.

By Topaz, Agustín Covarrubias 🔸, Alexandra Bates, Parv Mahajan, Kairos @ 2026-04-13T14:46 (+27)

This post was cross posted to LessWrong

TL;DR: One of the largest talent gaps in AI safety is competent generalists: program managers, fieldbuilders, operators, org leaders, chiefs of staff, founders. Ambitious, competent junior people could develop the skills to fill these roles, but there are no good pathways for them to gain skills, experience, and credentials. Instead, they're incentivized to pursue legible technical and policy fellowships and then become full-time researchers, even if that’s not a good fit for their skills. The ecosystem needs to make generalist careers more legible and accessible.

Kairos and Constellation are announcing the Generator Residency as a first step. Apply here by April 27.

Epistemic status: Fairly confident, based on 2 years running AI safety talent programs, direct hiring experience, and conversations with ~30 senior org leaders across the ecosystem in the past 6 months.

The problem

Over the past few years, AI safety has moved from niche concern toward a more mainstream issue, driven by pieces like Situational Awareness, AI 2027, If Anyone Builds It, Everyone Dies, and the rapidly increasing capabilities of the models themselves.

During this period, over 20 research fellowships have launched, collectively training thousands of fellows, with 2,000-2,500 fellows anticipated this year alone[1]. The talent situation for strong technical and policy researchers is far from solved, but meaningful progress has been made.

The story for non-research talent is very different. By our count, there are roughly 7 fellowships for non-research talent (producing around 300 fellows this year[2]), spread thin across an array of role types. As a result, many critical functions within AI safety remain acutely talent-constrained.

More broadly, the ecosystem has a lot of people who are great at thinking about ideas. We need more people who are great at thinking about people and projects. Read more about this here.

The consistent feedback we hear from senior people across the ecosystem is that the hardest roles to fill are not research roles. They are:

Based on our experience and anecdotes from organizations in our networks[3], many organizations trying to hire find that research postings attract dozens of qualified applicants, while non-research postings often surface only 0-5 applicants who meet the core requirements (strong mission alignment, meaningful AI safety context, and general competence) despite receiving hundreds of applications.

Why the pipeline is broken

The fellowship landscape is massively skewed toward research. 

Around 20 research fellowships together produce 2,000-2,500 fellows per year. For fieldbuilding, the current options are essentially Pathfinder (where the vast majority of fellows still intend to pursue research careers) and a few dedicated fieldbuilding spots at Astra. These produce an estimated 5-10 fieldbuilding generalists hired per year. This asymmetry signals that the primary route into full-time AI safety work runs through research. And, while research is a core part of safety, it is also necessary to find and develop people who can manage research projects, run organizations, and implement and communicate research ideas.

There is no clear career ladder for generalists. 

A research-oriented person has a well-worn trajectory: BlueDot → ARENA → SPAR → MATS → junior researcher → senior researcher. And while this path isn't perfect, nothing comparable exists for generalists. The typical route involves running a strong university group, then hope you get hired directly at a fieldbuilding org, with no intermediate steps or clear progression path afterwards. The risk discourages people who might otherwise be excellent generalists from committing to the path.

There is no credentialing or proving ground. 

Unlike research, where fellowship participation provides a track record and hiring signal, aspiring generalists have no equivalent way to demonstrate competence. Organizations won't hire untested junior talent for critical operational roles, but there's nowhere for junior talent to get tested[4].

There is no routing infrastructure. 

Matching people to opportunities happens through ad hoc referrals and personal networks. This doesn't scale, and it means we regularly miss promising candidates. As the field has matured and institutional structure has grown, coordination overhead and established networks make it harder for aspiring generalists to self-start projects and stand out the way that was possible a few years ago. 

Why this matters now

We believe that there are now more good policy and technical ideas ready for implementation than there is coordination ability and political will to implement them in governments and AI companies. On the margin, we think we're receiving smaller returns from additional researchers entering the field, especially outside the top 10% of research talent. It’s also plausible that AI safety research will be automated more quickly during takeoff than most other types of work.

Many expect the funding landscape for AI safety will expand significantly over the next two to three years, which makes this bottleneck more urgent. More capital will be available, but without the people to deploy it effectively, that capital will stay inert. This already appears to be a bottleneck for current grantmakers, and it could get much worse.

Naively, we expect the world to get a lot weirder as capabilities progress. In a world where the demands on the AI safety ecosystem rapidly increase and evolve, training people with strong thinking, agency, and executional abilities, rather than narrow technical skills, seems highly leveraged.

This is particularly important because it enables us to diversify our bets and cover a large surface of opportunities for impact. There’s no shortage of project ideas for growing the field of AI safety, scaling up our policy efforts, or communicating to the public, but we simply don’t have enough talent to plan, design, and execute on all of them. Our bottleneck isn’t funding or ideas, it’s people.

Counter-Arguments

"You said hundreds of people are applying to these roles. Why can't some of them be good fits? Aren't there many people who could fill operations positions?"

We draw a distinction between "hard ops" and "soft ops." Hard ops roles (finance, legal, HR, etc.) benefit from  expertise, and hiring experienced professionals without AI safety context is typically sufficient. Soft ops roles (program management, talent management, generalist positions, etc,) are different. Domain expertise matters less than having strong inside-view models of the field and generalist competency. Succeeding in these roles requires real mission alignment and enough context to spot high-EV opportunities that someone without that background would miss.

"I'm not sure I agree that research talent is less important than generalist talent."

We're deliberately not making a strong comparative claim about the impact of generalists versus technical and policy researchers. What we are saying is that generalist talent is currently the binding constraint. It is harder to source than research talent and, in our models, represents the tighter bottleneck for the ecosystem's ability to convert funding and ideas into impact.

"How important is generalist talent in shorter timelines worlds?"

Our sense is that generalist talent is crucial across all timelines. While shorter timelines do compress the window for upskilling, our experience is that motivated junior people can skill up relatively quickly and help add urgently needed capacity, making the counterfactual value of pipeline-building here quite high even in shorter timeline worlds (sub 3 years).

"You argue there are all these research fellowships and no programs for non-research talent. But couldn't those programs just produce generalists?"

The existing research fellowships are well-optimized and have a strong track record of producing researchers who get placed into AI safety roles. Some fellows have gone on to non-research roles, but anecdotally this is rare. These programs seem to have a much stronger track record of taking talent who are open to different career paths and funneling them toward research, than of producing researchers who are open to different career paths.

"Aren't there a lot of non-research roles currently in AI safety?"

A few hundred people do this work today versus a few thousand researchers. There used to be a steadier stream of talent aiming for these roles, but short-timelines anxiety, the expansion of research programs, and the disappearance of some entry points that used to exist have contracted the pipeline considerably.

The Generator Residency

As a first step toward addressing these problems, Constellation and Kairos are announcing the Generator Residency: a 15-30 person, 3-month program focused on training, upskilling, credentialing, and placing generalists. The program runs June 15 through August 28, 2026 and applications close April 27.

Learn more and apply here

How it works:

Residents will work out of Constellation and receive ideas, resources (funding, office space), and mentorship from successful generalists at organizations like Redwood, METR, AI Futures Project, and FAR.AI.

For the first few weeks, residents will write and refine their own project pitches while meeting the Constellation network and building context in the field. They will then create and execute roughly 3-month projects, individually or in groups, with generous project budgets. Throughout the program, we’ll provide seminars, 1:1s, and other opportunities for residents to deeply understand current technical and policy work, theories of change, and gaps in the ecosystem.

During and after the program, we’ll support residents in finding roles at impactful organizations, spinning their projects into new organizations, or having their projects acquired by existing ones. Selected residents can continue their projects for an additional three months (full-time in-person or part-time remote), with continued stipend, office access, and housing.

We hope to place a majority of job-seeking residents into full-time roles at impactful organizations within 12 months of the program ending.

Examples of projects we’d be excited about hosting include:

  1. ^

    This estimate draws on a separate analysis that projected the number of fellows using both publicly and privately available information, as well as extrapolations from actual data through late 2024. The fellowships included in this analysis were: AI Safety Camp, Algoverse (AI Safety Research Fellowship), Apart Fellowship, Astra Fellowship, Anthropic Fellows Program, CBAI (Summer/Winter Research Fellowship), GovAI (Summer/Winter Research Fellowship), CLR Summer Research Fellowship, ERA, FIG, IAPS AI Policy Fellowship, LASR Labs, PIBBSS, Pivotal, MARS, MATS, SPAR, XLab Summer Research Fellowship, MIRI Fellowship, and Dovetail Fellowship.

  2. ^

    The programs included in this analysis were: Tarbell (AI Journalism), Catalyze Impact Incubator (AI Safety Entrepreneurship), Seldon Lab (AI Resilience Entrepreneurship), Horizon Institute for Public Service Fellowship (US AI Policy/Politics), Talos Fellowship (EU AI Policy/Politics), Frame Fellowship (AI Communications), and The Pathfinder Fellowship. Fellow counts were derived primarily from publicly available data.

  3. ^

    We're deliberately vague about which organizations we're referring to here since we haven't asked permission to disclose the outcomes of recent hiring rounds. For research roles, we're mainly referring to technical AI safety nonprofits, policy nonprofits, and think tanks. For non-research roles, we're mainly referring to fieldbuilding nonprofits and technical and policy nonprofits that have recently tried hiring non-research talent requiring meaningful AI safety context beyond a BlueDot course.

  4. ^

    Several years ago, aspiring generalists could more easily test their fit by self-starting projects in an ecosystem with minimal infrastructure and ample white space. As the field has grown, more institutional structure exists, and with it, more coordination overhead. The blank slate is gone, and the ecosystem's complexity now deters people without strong inside-view models, reputations, or existing connections from trying ambitious projects. We're not sure this is net negative in most cases, but it does mean fewer people gain the experience needed to position themselves for these roles.