The average person works 80,000 hours over the course of their career. Ideally, that time should be fulfilling, well-paid, and spent doing things that make the world a better place.

Of course that’s much, much easier said than done. In an increasingly fragile job market made still more fraught by AI, there’s no longer such a thing as a safe bet.

According to Benjamin Todd, most people lack a systematic approach to thinking about their career choice. Todd is the co-founder and president of 80,000 Hours, a nonprofit dedicated to helping people move into careers focused on tackling the “world’s most pressing problems” — issues that include AI safety, biosecurity, global health, and animal welfare. 80,000 Hours uses the effective altruism framework of importance, neglectedness (how many resources are devoted to the problem), and tractability (or solvability) to decide which causes to prioritize.

In his new book 80,000 Hours: How to Have a Fulfilling Career That Does Good, which was released this week, Todd pulls together more than a decade of research and advising into a guide for making career decisions. It’s aimed at people just starting out as well as more experienced workers looking to make a switch, providing a framework to make career choices.

I spoke with Todd about careers and skill sets that are more resistant or adaptable to AI job disruptions, why “going with your gut” (usually) isn’t good advice, tips for landing a high-impact job offer, and other topics.

Our conversation below has been lightly edited for length and clarity.

There’s a lot of anxiety around advances in AI and job displacement, how that affects people’s job prospects and how they should think about career choices.

Yeah, I feel like when I talk to people about their careers these days, that’s the main thing that’s on their mind. … I think a lot of the simple answers about which jobs will be best [in the age of AI] are too simple.

How have the last few years — thinking about AI but also other disruptions and changes to the job market — changed your core assumptions about how people should choose their careers?

The main thing that comes to mind is we seem to be getting more and more evidence that far more capable AI will be here soon.

Then I think that just has a lot of implications for which problems are most pressing, and then potentially also which skills are most valuable. If there’s going to be a lot of change and things will be more unpredictable 10 years from now, then it makes sense to focus on shorter-term plans than to spend 10 years training to do something. Starting medical school now seems a lot more risky than it would have been 10 or 20 years ago.

When you say AI is coming and going to change things, are you talking about artificial general intelligence (AGI) specifically?

I mean there’s multiple levels. I think [where the technology is now], if it just froze here, would be kind of similar to the internet and how important it was. But the big-picture thing that seems most important is the idea that you could get to some kind of AI that can do a lot of remote work jobs at roughly a human level. That seems like it could bring the economy and science into a significantly different regime.

I’m probably a bit more skeptical than most technologists of mass near-term unemployment from AI, though I also think that most economists are still underrating how big a deal it could eventually be.

You mention in the book that managing AI agents is a skill less likely to be replaced by AI. Why is that?

I talk about four things that could make skills become more valuable in the future given technology and automation. And the second one is complementarity to AI. So it’s not that AI won’t be able to do that, it’s that it’s a skill where as AI gets better, that skill becomes more valuable. Because if AI is more useful and being used to do more things, and you can make it like 1 percent or 10 percent more efficient, then the value of that additional efficiency increases as AI becomes more useful.

Right now, AI is pretty bad at these messy, nebulous, long-horizon things where you need to coordinate between lots of people and decision-makers. I think in an intermediate future there will be a lot of the more routine work tasks that are being done by AI agents, but then there’s human managers who are needed to stitch them together.

That seems to me like that might be a very lucrative job, but that might not add up to a lot of jobs.

That comes down to how much more stuff can get done in total. And those people would be way more productive than people have been in the past, because everyone is running a team of 10 AIs. So we would want many more people doing that type of thing.

One way to think about it is that a lot of things that in the past would have been too expensive to do would become economically feasible because now you don’t need a team of 30 people to start this new nonprofit. You can do it with a team of three people and a bunch of AI. So then a lot of people could start new projects and you just get a lot more total things being done with [the aid of] AI rather than, “Oh, we have to do the same stuff as before, but with only 10 percent as many people employed.”

I think that’s maybe good for people at a mid- or senior level in their career, but it could make things harder for more entry-level people.

I think that’s a little bit too early to say. So there is some research that finds that skilled human managers are also better at managing AI agents, and there’s a kind of correlation in that skill set. There is research about the most junior software engineers, [that finds] their jobs are down 20 percent. But in some ways young people are just much more adaptable to new technology, and I find a lot of college students seem to be significantly more sophisticated at using AI.

So in some ways, and because it’s changing so fast as well, young people might be better placed to learn how to use these tools faster and adapt as they keep changing. I’m a bit less confident it’s going to be bad for the younger workers.

That’s interesting because I’ve seen quite a lot of headlines and quite a lot of anxiety from younger people around their job prospects.

I think it’s very understandable to be anxious because they’re facing far more change to the job market than any recent generation has had to face. No one really knows exactly how it’s going to shake out. I would say one point for optimism is in theory it will mean that many projects are possible that weren’t possible before. That does also open up a lot of extra opportunities for young people who I think in some ways are better placed to take on these more risky and novel things because they’re less set in their ways.

Because better or worse, AI is a force multiplier.

Totally. We were talking about this skill [at managing AI agents] being lucrative. It would also be applicable to a lot of social problems as well.

What does effective altruism get right about career choice — and wrong?

I think most people just aren’t thinking enough about the impact of their career at all, and they actually have this amazing opportunity to at a minimum save people’s lives and maybe do a lot more by helping prevent the next pandemic or being one of the only people working on AI risks.

When people are thinking about choosing a career, that should really be one of the first things they say: “The world’s facing massive problems. You could do something about them. Wouldn’t that be fulfilling and interesting? Why not do it?”

But people within effective altruism can think too much about their impact. I think people naturally compare themselves to others, but then people who get into effective altruism will tend to compare themselves based on impact. That’s better than comparing it based on how many yachts you have, but there’s still always someone who has more impact than you, and it’s easy for people to have this sense they’re not doing enough. They can potentially go into careers where they think there’s an intellectual case for being really impactful, but it’s not actually a good day-to-day lifestyle for them and they can end up getting pretty demoralized several years down the line. Those are some of the more common pitfalls.

I think you make a very compelling case that when people go with their gut, when they try to make career choices based on intuition, they aren’t always very good at that. You recommend a more systematic approach to thinking this through. Do you think people usually benefit from an outside observer acting as a sounding board?

I do encourage people to work through a systematic approach, especially when it comes to assessing personal fit. A lot of the advice is really about getting out of your head. I think oftentimes the most useful thing people can do is just apply to lots of positions and see what they get.

Often the best way to assess your fit is to speak to someone who has experience hiring in [that] area, they’re the people who’ve done the most assessing of who is going to succeed in a path.

In general, getting an outside perspective is super useful. That’s part of one of the big benefits of the one-on-one advice we offer on the 80,000 Hours website. … You can not consider enough options or factors, so getting an outside perspective is one of the best ways to help broaden your frame and make sure you haven’t missed something.

The key is to have a mixture of a more systematic approach and not do something your gut is actively worried about without understanding the reasons. There’s lots of research that shows that guts are bad at stock picking or predicting which person is going to succeed in some 10-year career path. But your gut is really good at things like, “Do I trust this person?” because that’s what we’ve evolved to be really good at guessing, and it’s something you have had a decent amount of practice about over your life. So if your gut is worried about a path, that might be picking up on something that actually you’re not excited about. The advice I give is don’t go with your gut, but do check with it. So I also wouldn’t say to totally ignore your gut either.

I think some people will chafe at the idea that some career paths are far more impactful than others. What would you say to more skeptical readers? People who would be reluctant or unable to retrain?

In the introduction, I mention this study where people were surveyed on how much they thought different charities more effectively save lives than others. They thought the best charity would be about 50 percent more effective than an average one at saving lives. Our intuitions are very bad at grasping big differences in scale. … When you ask experts in global health, they say there’s a hundred times difference between the most effective charity and the average for saving lives. It seems like no one knows about these differences even though it’s a huge deal. It means you could work for 10 years on a path and then retire and do whatever you most enjoy for the remaining 30 years and still achieve what would have taken hundreds of years working in one of the less effective charities.

I would actually advocate that people keep working rather than retire, but because there’s these huge differences in impact, it actually means it should be possible to find something that is both better for you personally and more impactful for the world.

There is a chapter in the book about what you can do that’s the most impactful thing without changing jobs if you’re already in a career. I talk about donating 10 percent of your income [to effective charities], political advocacy, and even just “slacktivism.” When most people do that they just tweet into their echo chamber … but if you’re talking about something that actually is a huge deal that no one knows about, [it can be effective.]

Another example I use is if you can help someone else find a really impactful job, then that has just as much impact as doing the job yourself. … I talk about being a multiplier.

How can people realistically transition into higher-impact careers, especially if those paths come with greater uncertainty in the age of AI?

It depends a lot where someone is starting from. … There’s more and more fellowships that are designed to help people transition [into higher-impact careers] quickly. You did the Horizon Institute for Public Service fellowship, which I would say is in this genre.

For more experienced people, if you’re an accountant or something like that, lots of organizations need people doing operations and accounting so they might sometimes hire people from outside the field pretty quickly. If that doesn’t work, it’s more of a case of thinking over one or two years, asking, “How can I best position myself to get one of these jobs?”

For that, you could look at the list of skill sets in the guide and think about whether you could learn any of these skills. There’s also a chapter on types of jobs that are really good for gaining skills quickly. One example is working at smaller, rapidly growing organizations, because you can advance faster and those roles tend to be more generalist. That type of generalist skill set is really useful in a lot of social impact organizations, and it means you can do things with AI earlier and get stuff done using those tools. Whereas if you go to a larger organization instead where the work tends to be more routine, that’s closer to something that AI is going to be able to do sooner.

What advice do you have for people with financial constraints that require them to secure a role right away, even if it may not be the highest impact or greatest fit?

I see impact as one important factor, but your own well-being matters too. You might also have dependents as well. Ultimately, you have to make your list of options and then choose the one that’s best given your goals. If money is a priority for you right now, then I think you should focus on that. There’s no shame in it.

I also talk about the idea of having a plan Z, [if your plan A and B don’t work out] that on some level you’re okay with. If you can’t do that, then you should focus on getting yourself into a stronger position first. Maybe you need to focus more on things like building skills or saving money which will mean you can take bigger risks later.

There’s this axiom that the best time to get a job is when you have a job, so you have more leverage or experience. How true do you think that is?

What most helps in getting a job is doing something as close as possible to the actual work. Obviously being in a job already is a very good way to demonstrate that you can do the work. But people who don’t have jobs already can often find ways to do that, like a portfolio project.

I talk about the “pre-interview” project, where you come to the interview with a specific proposal [to the company you’re applying] for how you would help them with some challenge the organization is facing … most jobseekers don’t have that level of understanding of a position. So you’re already standing out just by having thought about it.

If you’ve ever used an online patient portal to message your doctor in the middle of the night, you won’t be surprised to learn that responding to those messages takes an increasingly big bite out of clinicians’ workdays.

So in recent years, hospitals have begun adopting an AI tool that can draft responses for them. The tool was supposed to make a time-consuming task go more quickly and smoothly, said Philip Barrison, an MD-PhD student at the University of Michigan Medical School who studies AI in healthcare.

Instead, the tool has given doctors and nurses a new to-do list. First they have to read the AI-generated response and decide if it “is actually something that they think they would say,” Barrison said. Humans are suggestible, and looking at something and deciding whether you would have thought of it on your own is a cognitively complex task.

Even if the message looks correct, the clinician still needs to “edit it to the point where they think it’s acceptable” to send to a patient, Barrison said. The AI tool introduces a totally new set of complicated judgment calls into what used to be a relatively straightforward process. As a result, many clinicians have chosen not to use it at all.

They’re fortunate to have the choice. Buoyed by expectations of cost savings and skyrocketing productivity, companies are increasingly asking (and sometimes requiring) employees to use AI to make their work more efficient. Meta, for example, last year instructed some workers to use AI to “go 5X faster by eliminating the frictions that slow us down.” The CEO of Shopify told employees they’d need to prove they “cannot get what they want done using AI” before the company would approve new hires. Some companies are even evaluating or ranking employees based on how much they use AI tools.

Workers in some sectors have found major time savings from AI. But for others, the tools just change the work rather than making it faster. Workers might be spending less time writing patient portal messages, for example, but more time editing the releases the AI tool writes.

At best, this mismatch between employer expectations and employee reality can be an annoyance. In other cases, however, it can result in workers being laid off for failing to meet unrealistic efficiency demands. Some critics say the overzealous adoption of AI in high-stakes settings like healthcare even puts people’s lives at risk. Now workers, unions, and experts are increasingly calling for guardrails to protect employees from inflated expectations around AI — and customers, students, patients, and the general public from mistakes that can happen when managers put AI adoption above all else.

The hidden costs of AI use

Corporations are increasingly presenting employees with a choice: Use AI to be more productive or “you’re going to be automated out of a job,” said Aiha Nguyen, director of the labor futures program at the research organization Data & Society.

But the effects of AI on productivity aren’t as straightforward as some CEOs have claimed. In one 2025 study, software developers believed AI made them faster, but in fact they took 19 percent longer to complete tasks. (The researchers tried to repeat the experiment this year but had trouble recruiting developers who would agree to work without AI.) And in a recent survey of 5,000 white-collar workers, 40 percent of rank-and-file employees said AI saved them no time at all.

Workers across heavily AI-exposed fields point to hidden timesucks that come with using the technology. Julie, an art teacher, wrote in a response to a Vox reader survey that her school’s administrators routinely suggest using AI for lesson-planning, emails, and progress report comments. She’s tried AI-generated lesson plans, but they don’t account for the fact that kids may work through an activity at different speeds.

“First, I am checking what AI suggests, then I am editing them,” she said. “Why add a step I can accomplish on my own?”

For an employee at an East Coast communications agency, an internal AI tool was supposed to speed up the process of drafting press releases and other documents about the pharmaceutical industry.

“The goal is, I think, to be able to plug and chug into this machine and be able to turn a lot of materials around a lot quicker than we already do,” said the employee, who asked to remain anonymous for fear of career repercussions.

But when the employee tried to use it for basic research, it made too many mistakes. Double-checking its work erased any time savings. When the employee tried using it for communications with clients, its people-pleasing tendencies became a problem, as the tool put a “weird happy spin” even on messages warning of bad news.

“Part of the reason we take a human speed to turn things around is because there is so much nuance behind everything that we do,” the employee told me. “AI is just not going to be able to catch it.”

It’s not just that AI makes errors. With the advent of agentic AI, workers are increasingly being asked to edit and oversee the output of multiple AI tools, a new kind of work that can have unexpected costs.

One recent study of 1,488 workers across industries, for example, found that excessive oversight of AI agents could lead to what the researchers called “AI brain fry,” a kind of cognitive fatigue. “Participants described a ‘buzzing’ feeling or a mental fog with difficulty focusing, slower decision-making, and headaches,” the researchers wrote in Harvard Business Review. Brain fry was also associated with an increased number of errors and an increased desire to quit one’s job.

The researchers also found that while using one or two AI tools increased productivity, adding additional tools produced diminishing returns, and after four tools, productivity actually declined.

What workers really want from AI

Despite such findings, companies continue to pressure employees to use AI, and to cite AI investment as a rationale for layoffs, even as companies that try to link staff reductions to AI adoption tend to struggle on the stock market.

Some workers and organizations, however, are beginning to push back. National Nurses United, the country’s largest nurses’ union, has criticized the use of AI tools in hospitals to estimate staffing needs or to recommend treatment protocols for patients.

There’s no guarantee that these tools will take into account a patient’s individual profile, including underlying medical conditions, the way human clinicians can, Cathy Kennedy, the union’s president, told me. AI is supposed to “help us do our work more efficiently, but at the end of the day, it makes it even more burdensome,” she said.

Hospitals need to evaluate, with nurses at the table, whether AI tools really work as advertised, Kennedy said. “We have to stop — we have to go back and really see if this is truly doing what it needs to do,” she said.

The same is true across industries, Barrison, the healthcare researcher, told me. “Organizations need to be prepared to say when, if they were seeking a return on investment, if they were seeking value in a technology — how do you define what that value is? And if there’s not value there anymore, how do you turn it off?”

Some workers have found ways that AI actually helps them do their work — just not the ones management expected. Julie, the art teacher, likes to use Claude to learn more about topics she’s less familiar with, like kiln-firing ceramics.

Meanwhile, researchers have found that AI can actually reduce employee burnout, if it’s used to complete tasks employees find burdensome. “Everybody in every job has a list of things that they procrastinate on,” said Julie Bedard, a managing director and partner at Boston Consulting Group who led the AI brain fry study. “Those are the places I get, unsurprisingly, a lot of enthusiasm to try AI with.”

But employers won’t find out what those burdensome tasks are unless they listen to rank-and-file employees. “Worker standards and worker rights should continue to be at the heart of all of this,” Nguyen said, “rather than just focusing too much on the AI.”