How to get into AI safety in 3 months (article reading by career advisor Matt Beard)

Matt Beard (0:00)

Hi, listeners. My name is Matt. I'm a former advisor and writer here at 80,000 Hours. This is a narration of my article, How to Get Into AI Safety in Three Months. You can find the full piece, including the many links that I mentioned, plus a few memes on the 80,000 Hours Substack.

As an advisor, I've spoken with hundreds of people who want to use their careers to mitigate AI risks, but don't know where to start. While the most efficient route to enter depends on your background, there's some foundational advice for breaking into AI safety that I find myself repeating on almost every advising call. These steps are hard, but they're not particularly complicated. Almost everyone I've advised who found work in AI safety followed them, and most experts or hiring managers that I've spoken with have endorsed similar ideas. If you're efficient and driven, I think it's possible to pivot within three months. So consider this a crash course for breaking into AI safety. It covers many steps, like how to build foundational knowledge, identify your comparative advantages, test your fit, develop side projects, tighten your feedback loops, network in AI safety, and start applying.

One, build your foundational knowledge and AI safety context. There are a couple dozen hours of foundational reading that I think everyone trying to enter the field should do, but it might be more approachable than you think. If you take 30 minutes each day to read while drinking your morning coffee, listen to podcasts and article narrations on your commute, or make a reading group with your friends, you can build up your knowledge quickly. When my advisees have enthusiastically jumped into the AI safety rabbit hole, I've seen them get a better sense of where to focus and how to improve their fellowship or job applications. What should you read or listen to? Our AI Risk Essential Reading Lists and Problem Profiles are great places to start. Then you might take a Blue Dot course or read foundational papers like the Alignment Problem from a deep learning perspective. Once you have a sense of what you want to specialize in, which the next section will cover, you can also review more specific resources like our Collections for Technical Work, Policy Work, etc.

Your goal should be to reach the point where you can have a 30-minute conversation at an AI Safety Conference, and the other person would come away thinking that you understand the core problems and a few potential solutions. Research-heavy roles will require more specialized expertise, but for example, most jobs won't require you to implement the transformer architecture. It's often enough to know what the transformer is and why it's significant. Specifically, you should have an answer for how fast you think that progress might be over the next few years, which problems you think are most pressing based on this, and a few ideas for which organizations' theories of change you think make the most sense. Your views might evolve over time, but to ensure you're efficient with your job search, you should know some organizations might expect to hear different kinds of answers from competitive applicants. You might think, who am I to form opinions about this? I'm not an expert, and while I understand that concern, I'd say you don't have to be certain, but you should have something to say. It's fine to keep your views loosely held and to refer to experts. Deferral is a skill, after all. But you should take ownership over forming your own views. Hiring managers will want to hear them, and they'll be interested in your reasoning for how you got there. Some people refer to all of this as developing your AI safety context. The EA forum post, Why Experienced Professionals Fail to Land High Impact Roles, explains why some otherwise promising candidates can get rejected too early. Some don't communicate that they understand AI safety issues and an organization's approach to solving them. In other words, they haven't developed sufficient context about the field. In other industries, this isn't as necessary. But in most jobs tackling catastrophic risk from AI, organizations want to find candidates who share their mission and are willing to think carefully about problems in a scope-sensitive way. This can also include non-research jobs. These organizations aim to solve big problems that many other people haven't quite grasped as possibilities yet, so it's important that you're eager to think about them directly. Of course, you don't have to agree with everything a hiring manager or an organization thinks. Thoughtful pushback can show that you've developed your own views and aren't just parroting the right phrases. It's helpful to show your reasoning.

Two, identify the in-demand skills you have or could build. And in the article here, there's a picture of three ladders. They're quite precariously perched, and it says with a caption, it's fine if your career looks like this for a while. You should build a ladder of cheap tests to discover what you're good at.