Glad to see this piece, thank you for writing it! One aspect you could additionally cover is programs that support organizations in the field. For example, what we do with RAISEimpact, our women’s program, or the consulting and coordination work Future Matters is doing.
For mid-career and senior professionals, another way to get started is to apply for our career advising at Successif. We've helped several people transition into field-building roles.
I am so happy to see this career profile. If we want to scale the field, we really need more great fieldbuilders, and this type of work has not been getting enough attention from career advisory organisations. Thank you, 80 k!
Note from Aaron Gertler: Hi, Forum! We just published this career review on one of the toughest bottlenecks for AI safety. We hope it's helpful.
"Fieldbuilding" is a gigantic category and we couldn't cover all of it. If you have suggestions to improve the piece, we'd be happy to hear them.
Read this career review on the 80,000 Hours website.
In a nutshell: Fieldbuilding — developing talent and creating infrastructure for AI safety — is a high-leverage way to reduce catastrophic risk from advanced AI. An hour of good fieldbuilding can enable many hours of direct work. And yet, fieldbuilding is badly neglected; many qualified people pursue direct work, making it hard to fill some of the most promising roles.
Pros:
Cons:
Key facts on fit:
In 2017, there were only a few dozen people working full time to reduce risks from advanced AI. By 2025, there were over a thousand.1
How did this nascent academic field grow by 3000% over eight years?
Some of the growth was organic, driven by public interest in AI. But much of it came from active fieldbuilding: recruiting and training skilled people to work on AI safety and creating infrastructure to accelerate their progress.2
An hour spent on fieldbuilding may enable many hours of direct work: for example, the small team at BlueDot Impact has helped thousands of people learn about AI safety and pursue jobs in the field. We’ve seen enough stories like this to believe that fieldbuilding is often extremely impactful.
It is also badly neglected. Most people with the right skills gravitate towards more direct roles in research or policy. This creates serious bottlenecks across the AI safety ecosystem, slowing work on priority problems.
Because of this dynamic, we think that AI safety fieldbuilding is one of the most important career paths for reducing catastrophic risk from advanced AI.
This is true even for people whose skills make them a strong fit for other AI safety roles. Fieldbuilding is a flexible career path that rewards many forms of talent, and we’ve seen many people who left promising technical research or policy careers and went on to do even more good as fieldbuilders.3 Whatever your background, we think this path is worth considering.
Fieldbuilding isn’t just one activity; there is no ‘standard’ fieldbuilding role. You might:
Real-world examples:
Before 2017, the field of AI safety barely existed. Only a handful of organisations and individuals were doing serious research on ways that advanced AI could pose a catastrophic risk to humanity.
The field looks very different now. AI safety research regularly appears in top AI journals and at major conferences like NeurIPS. Hundreds of notable academics, politicians, and other public figures have expressed concern about catastrophic risks. AI safety issues have drawn the attention of governments across the world, driving policy efforts like export controls, international summits like the International Dialogues on AI Safety (IDAIS), and the creation of institutions like the US Center for AI Standards and Innovation and the UK’s AI Security Institute.
We attribute some of this growth to the rise of advanced LLMs, which produced a surge of interest in all things AI. But we don’t think AI safety would have grown nearly as much without active fieldbuilding. When Coefficient Giving, the largest funder of AI safety work, surveyed several hundred people in the field, over 60% cited a specific fieldbuilding project as one of the most important factors in how they chose their path.4
Putting the numbers aside, it makes intuitive sense that fieldbuilding would be effective. AI is progressing rapidly, and many people are already worried about it, so anyone making the case for working on AI risk won’t be starting from scratch.5 If you present the arguments well and provide the right resources, you could inspire many people to shift the focus of their careers.
Right now, the AI industry is growing even faster than the field of AI safety (in terms of both funding and staff). As we approach an era of transformative change from advanced AI, and the catastrophic risks that era could bring, we’ll need more people across the board — from researchers and entrepreneurs to policy experts and podcasters.6
What’s more, talent is often ‘heavy-tailed‘: top performers are considerably more productive than their peers, so we benefit disproportionately from enabling highly skilled people to get involved. AI firms can pay employees millions of dollars to accelerate progress, and AI safety can’t match those financial resources. To compete for talent, we’ll need compelling arguments and ideas delivered by excellent fieldbuilders.
Some AI safety jobs draw hundreds of applicants, but only a small fraction of those typically have what employers are looking for, and those people tend to receive multiple offers. As a result, there’s often a significant gap between the best- and second-best candidates, and organisations may struggle to hire anyone for a position (especially if it’s relatively senior). If you help someone with strong abilities enter the field — or help someone develop those abilities — you could make a big difference.
Beyond finding and developing talent for existing roles, fieldbuilding can also expand the talent pool by opening paths for a wider variety of people. The Tarbell Fellowship trains writers in the fundamentals of AI journalism and provides a stipend to support their work in professional newsrooms. This creates new opportunities for people with a writing background to contribute to AI safety, rather than just growing the pool of people competing for the same opportunities.
You could also create opportunities by starting something new. Like Tarbell with journalism, new fieldbuilding projects could help people join other neglected areas — like content creation, entrepreneurship, or public policy.
We’ve spoken to leaders in organisations throughout the field, and they broadly agreed with the following points:
For example:
In any career, your impact is limited by your time. Even if you work long hours, you probably won’t be as productive as ten people doing similar work.
Fieldbuilders take advantage of this fact. If you help 10 people find highly impactful AI safety roles, you’ll be responsible for a fraction of their impact. This could sum to more than you’d accomplish through direct work (depending on what they would have done without you).
Ten people is a realistic figure, and you could do even better. Take BlueDot Impact, which runs courses on AI safety and helps its students find careers in the field. It has reached more than 7,000 people, nearly 1,000 of whom now have full-time AI safety roles. Even if BlueDot’s courses only made each of those people around 10% more likely to join the field, that’s the equivalent of being directly responsible for nearly 100 careers (and counting — the organisation has only been around since 2021).7
BlueDot’s team is just seven people. By providing education and information to so many others, it seems likely that each BlueDot staffer has made a greater contribution to AI safety than they would have by working in roles without this multiplier effect.
That’s not the only multiplier effect worth noting; fieldbuilding also lets you leverage your top skills across a wider range of opportunities. A skilled researcher who transitions to research management can use their experience and judgment to shape dozens of projects instead of running a single project. An 80,000 Hours advisor might talk to a journalist, lawyer, and political staffer in a single day — applying the same sharp thinking on career choice across three different fields.
While fieldbuilding is one of the most impactful paths we’ve found, it’s worth understanding these tradeoffs before you dive in.
“I’m a technical researcher who studies the leading AI models.”
“I work in Senator Blake’s office as an expert on cutting-edge technology issues.”
“I’m helping to organise an AI policy fellowship.”
The first two answers may sound better on a CV or at a party. The importance of fieldbuilding work won’t be clear to someone who isn’t familiar with AI safety — so it won’t open the same doors as published research or a letter of recommendation from Senator Blake. In general, people have an easier time transitioning from direct work to fieldbuilding than vice versa.
Direct work offers stronger career capital (on average) in a few other ways:
However, fieldbuilding isn’t as disadvantaged as people often think. More than most direct roles, it offers:
There are reliable ways to explore careers in AI safety research or policy.
A budding researcher might take classes in machine learning, read AI safety papers, or apply for research fellowships like MATS or SPAR.
A policy hopeful might study economics and political science, read relevant publications, make connections as a campaign volunteer, or apply for policy fellowships like those offered by Horizon or IAPS.
Fieldbuilding lacks this kind of ‘one-size-fits-most’ pipeline. There are too many kinds of roles, and few programmes focused on training fieldbuilders. But we still have plenty of advice for getting started.
The median technical role (research, engineering) pays more than the median fieldbuilding role. However, fieldbuilding pay isn’t necessarily low; many organisations offer highly competitive salaries, comparable to what you’d earn from a similar role in the private sector with the same experience and seniority.
You can use our job board to check this for yourself. We don’t have a single ‘fieldbuilding’ filter, but most of these roles fit that category.
Direct work can have its own ‘fieldbuilding’ effects. As an academic, you could spend most of your time on research and still teach or train dozens of students. As a policy expert, you could draft an influential bill or white paper; SB-1047 catalysed widespread public discourse and likely served as many policymakers’ first exposure to core topics in AI governance.
If your skills or background position you to have an exceptional impact through direct work, that could also become your best fieldbuilding opportunity — if you actually focus on those aspects of the job. (An academic who cares little for mentorship or management won’t do much to develop talent.)
Fieldbuilding often involves trying to influence what other people work on. This can have harmful consequences:
While we’ve seen these outcomes before, we think they are significantly rarer than the positive outcomes we’ve highlighted, for a few reasons:
Some people think the field’s impact has been mostly negative so far — for example, speeding AI progress by highlighting its economic potential, or slowing political coordination by associating AI safety with liberal politics.
If you think that most current AI safety work is harmful, that’s a valid reason to avoid fieldbuilding. However, you could also support the specific work you think is beneficial; we’ve seen many critics of ‘mainstream’ AI safety do valuable fieldbuilding.
There’s no single profile for a successful fieldbuilder, but the experts we’ve met tend to highlight a few broadly useful traits.
Versatility: Most fieldbuilding organisations are small and fast-moving; the people who thrive in them tend to be adaptable and broadly competent.
As Jake McKinnon, who led Stanford’s effective altruism group before joining 80,000 Hours, puts it: “If you think you’d be good at almost anything, do fieldbuilding.”
Technical fluency: While you don’t need to be an engineer or an expert on any one topic within AI safety, you need to understand the basics — especially in areas like research management or curriculum design. Going beyond the basics is even better; being technically skilled makes it easier to reach other technical people.
It also helps to be well-informed on recent developments. We recommend Don’t Worry About the Vase or Transformer’s Weekly Briefing for wide-ranging updates.
Mentorship: Many fieldbuilding roles involve teaching, coaching, or training. Whether you’re mentoring a researcher or advising a student on career paths, it helps to be a good listener who can find the right advice for the right person. One expert we interviewed called out the importance of raising others’ aspirations and inspiring them to pursue ambitious goals.
Social intelligence and networking: Fieldbuilding often entails chatting with strangers, pitching ideas to an audience, and creating connections. Many great fieldbuilders have a strong mental Rolodex — a sense of what people in their network are looking for, and an eye for matching opportunities. You don’t need to be the most outgoing person in the room, but it helps to be ‘good with people’.
Passion: If we had to pick one trait successful fieldbuilders have in common, it would be their sincere dedication to AI safety — and the way they convey that dedication to people who are still exploring the field. Fieldbuilding involves working hard on something whose impact you may never fully see; genuine belief in the mission helps you sustain your efforts, and inspire that same commitment in the people you’re trying to reach.
That said, fieldbuilding is far from thankless! Your work will change people’s lives, and you’ll probably get to meet those people and experience their gratitude firsthand.
People management: AI safety has a lot of early-career people and few seasoned managers. Experienced staff are sometimes forced to manage out of necessity, even if it isn’t their comparative advantage. If you can thrive in a managerial role, you’ll help your reports develop faster and reduce strain on your senior colleagues.
Project management: AI safety evolves quickly, and fieldbuilding projects often pivot to keep up. They also tend to involve multiple stakeholders — events and fellowships aren’t solo efforts. These conditions demand coordination, flexibility, and rapid iteration — classic ‘PM’ skills.
Entrepreneurship and organisation building: AI safety has a lot of room to grow; there are many promising ideas to expand the field that no one has seriously tried.8 We need founders and builders to fill those gaps. This includes recent graduates; we’ve seen many student organisers thrive in other fieldbuilding roles.
Because you don’t need any specific experience to become a fieldbuilder, our strongest recommendation is to check the AI fieldbuilding roles on our job board and consider applying if you see a role that fits.
Our other top recommendation is to consider fieldbuilding fellowships. Any AI safety generalist can apply to the Generator Residency (due April 27), which helps you launch a new idea within three months. The Pathfinder Fellowship offers training and mentorship to help students develop campus AI safety groups.
More ideas, if you’re new to the field:
The following opportunities seem especially strong to us:
There are many other impactful roles within the field. We don’t have a single ‘fieldbuilding’ filter, but most of these roles fit the category.
Thanks to Arden Koehler, Asya Bergal, Benjamin Todd, Jake McKinnon, Lou Moran, Neav Topaz, Sudhanshu Kasewa, and Zershaaneh Qureshi for feedback on earlier drafts of this article, and many others for helpful conversations.
One fieldbuilding gap I’d like to see discussed more is how people from non‑English or non‑US backgrounds can enter the AI safety space. A lot of AI safety material assumes not just the technical ideas, but also familiarity with EA or LW culture, US/UK career norms, and a specific style of public reasoning. For people with relevant skills from other countries or languages, the challenge isn’t only translation - it’s also making the field’s norms, expectations, and opportunities understandable. I wonder if localized ‘on‑ramps’ for skilled mid‑career people could be especially valuable.