By Zershaaneh Qureshi |  Watch on Youtube  |  Listen on Spotify  |  Read transcript

Episode summary

The average career is 80,000 hours long. With AI advancing so rapidly, the hours you have left in your career matter more than ever.

Some leading AI researchers think there’s a 10% chance that AI systems begin automating AI research itself this year — and a 60% chance by the end of 2028. This could introduce aggressive feedback loops that completely reshape every industry, institution, and career.

If these predictions are right, the window for influencing the direction of the future could be closing fast. As 80,000 Hours cofounder Benjamin Todd argues in his new book, that makes thinking carefully about your career more important than ever.

Fortunately, there are lots of ways to use your career to make the AI transition go well.

In today’s conversation with host Zershaaneh Qureshi, Ben lays out three scenarios — from AGI by 2029 to a decades-long plateau in AI progress — and explains why not everyone needs to bet on the shortest timeline. A fresh graduate and a senior government official have wildly different leverage, so timing your impact well means weighing where you are in your career against the urgency of the risks.

Ben also addresses the obvious anxieties:

• Will AI come for all the jobs he’s recommending?
• What’s the point in following his advice if the job market is about to collapse?
• Which skills are actually worth building right now?

His new book, 80,000 Hours: How to Have a Fulfilling Career That Does Good, provides a surprisingly concrete framework for making career decisions in these radically uncertain times.

This episode was recorded on May 7, 2026.

Our production team includes:

• Video editors: Josh Alward, Dominic Armstrong, Jasper Luithlen, Milo McGuire, Luke Monsour, and Simon Monsour
• Producers: Elizabeth Cox and Nick Stockton
• Coordination and support: Katy Moore and Lou Moran
• Camera operator: Jeremy Chevillotte
• Music: CORBIT

Highlights

AI safety needs much more than just technical research

Benjamin Todd: I think historically it’s easy to feel like AI means ‘the alignment challenge’ and that we just need technical researchers solving that challenge and there’s not that much else that other people can do. But I really want people to think: it’s not just about AI misalignment — what we’re facing is something more like the complete transformation of every aspect of society. It’s like the Industrial Revolution, but happening 10 times faster and going to some place that we don’t yet know. I do still think misalignment risks and AI loss of control risks are probably the risks I would say are the most important and neglected, but there’s now a lot of other things.

So something like concentration of power, that’s a much more social science-y, geopolitical-elements-type cause which needs a lot of nontechnical people working on it.

There are still pandemic risks, and engineered pandemic risks, and that actually mainly requires company-building and engineering-type skills. But there’s a lot we could do to make the world a lot safer from pandemics, which would just be good in general but also the risks could be accelerated if technological progress speeds up.

And in the book I also briefly talk about this range of emerging challenges which are also super diverse, from the philosophical questions of what do we do about AI sentience and legal questions around that, to even things like space governance and gradual disempowerment, which is again more of a social structure type of challenge, rather than a technical challenge.

I would also say even if you want to focus on global health, you should probably be thinking a lot about how you could use AI to help with that, or how AI might affect various aspects of it.

So yeah, there’s a much broader range of issues, and then there’s also a very broad range of roles these days. More technical researchers would still be great, but we’ve done surveys asking organisations that we think are impactful what their biggest talent bottlenecks are — and a lot of them are saying operations stuff; so people to do management, HR, accounting, finding offices, hiring people, and all the things you need to do to run an organisation. So anyone who’s worked in business, or pretty much any other field, might have those skills.

Another huge bottleneck is communications: spreading the word about these problems, doing PR and media for these different companies, or even being an individual writer talking about these things. And then there’s also still a huge need for people in policy and government. It’s very possible to get into these types of roles in just a couple of months, even if you don’t have an AI background already.

The selfish case for helping transformative AI go well

Benjamin Todd: Even in many of the good [transformative AI] scenarios, the world could still end up kind of totally alien to us. And yeah, there’s something very sad about that. I think people whose reaction to AI is just pure excitement, that feels very off base to me. It is a scary thing we’re facing. It’s not that helpful to dwell in the fear, but that’s not to deny that it’s scary. This is an insane thing to be happening and we might not be able to handle it — but then moving from that into, “So what can I actually do?”

Ultimately it’s about focusing on the things you can change and accepting things you can’t. Although in some ways it’s an overwhelming situation to be in — and I think it actually will get more overwhelming because the pace of change will increase — I can sometimes feel some gratitude for: “Isn’t it amazing that I get to play any part in this at all?” One of the most important things to happen in history, how is it that we’re here at this moment and might be able to do anything about it? It’s a feeling of gratitude or almost like amazement at this situation.

Zershaaneh Qureshi: I think it’s very easy to fall into these pessimistic styles of thinking. But I think that one thing that really does help with that is remembering all the stuff that we’ve talked about so far about the things that people can actually do to help. Yes, this might be a really critical time in history, but it’s sort of like a critical opportunity that people can take. There is tonnes of work still to be done, and as you’ve said, there are ways that people can pretty quickly shift into roles or start indirectly helping in a way that could actually set the future on a more positive path. And I find that encouraging and motivating.

Benjamin Todd: I think I find for myself, just even on a selfish level, I feel a bit less stressed if I feel like I’m doing my part, like I figured out what I can do and I’m doing my best. We can’t guarantee this will go well, but there is a lot that can be done at the margin to make it significantly more likely.

The possibility of an imminent intelligence explosion

Benjamin Todd: Right now, AI is actually still quite narrow in what it can do. It’s very good at multi-hour software engineering tasks, but it’s still a long way from being able to do most jobs, and it still can’t even play Pokemon better than most children. But if we suddenly had another five years like the last five years of progress in one year, you could get to a point where suddenly something much more general, much more like a true digital worker, you could say, started to work. And this would be an AI that you could literally ask to do almost any job that can be done virtually.

Zershaaneh Qureshi: Yeah, and the idea then is that once that happens, society changes very rapidly, right? You’re deploying AIs in all sorts of different fields, progress in all sorts of fields goes crazy, things are moving faster than we can keep track of, and that throws up a bunch of risks?

Benjamin Todd: Yeah, there’ll be these 100 million copies of Claude — which is almost like the whole working population of the US — it will be almost like a whole country’s worth of new people doing whatever those Claude things are doing at that point, and they’ll be able to think very fast and act very autonomously.

So that’s where you also start to get these loss of control risks coming in. And more dangerous scenarios, like single companies might end up with a workforce that’s bigger than the entire human workforce now, which could give those companies huge amounts of power — and starts all kinds of novel risks and things to think about.

Zershaaneh Qureshi: Yeah. OK, so you now think that AI R&D could be automated pretty soon, basically? That’s your feeling?

Benjamin Todd: I don’t know, but when I was in the Bay Area in February talking to people about this, I was pretty shocked to find many of the people who seem to have the best track records of forecasting AI to me were saying it could be a 10% chance we reach this point this year. I wasn’t really thinking it literally could be this year that an intelligence explosion would start.

And then the chance a couple of years later is even greater still. So Jack Clark recently published a piece arguing that there’s a 60% chance that AI R&D is automated by the end of 2028, which I think reflects the views of many people actually in the labs themselves doing this research.

Zershaaneh Qureshi: He’s at Anthropic, right?

Benjamin Todd: Yeah, he’s at Anthropic. But it’s not just people at Anthropic. I’ve heard this from people at DeepMind as well and OpenAI, that they just really feel like these tools are really helping them and doing more and more independently and autonomously. And they can just kind of see this process, they just draw the trendlines forward a few years and it seems like it could be really speeding up our research.

AI was supposed to replace radiologists. They now make $500K a year

Benjamin Todd: I do think people — especially technologists — are often a bit too quick to assume there will be lots of unemployment due to AI. The key way of seeing it is that partial automation of a job often increases wages and employment for that job.

An empirical example is people have been saying radiologists are going to be made unemployed for ages because AI can do image recognition really well. But actually radiologist employment is up and I think average wages in the US are something like $500,000 a year, so it’s even still a super highly paid job.

I think there’s actually a pretty straightforward explanation of this, which is that only about a third of their time is spent analysing images. That means all the other stuff they’re doing is stuff that AI can’t really do yet: coordinating with other employees at the hospital, talking to the patients about how to understand the results, and “the machine is broken,” and “we need to figure out whether to trust these scans,” and stuff like that. Even if the routine bit that AI can do becomes much more efficient, it’s only actually increasing their productivity by like 20% or 30%.

And then if you increase the productivity of a job by 20%, that might even mean you hire more of them. It depends on the job. But say, like with a sales team, if each of your salespeople can sell 50% more — because they can use AI to find lots of leads and help draft emails and now they’re going a lot faster — why wouldn’t you just get 50% more revenue? Or even more, you might even be like, “Previously I had to pay this marketer $50,000, but they would only bring in $30,000 of revenue, so there was no point in doing it.” But if now they bring in $60,000 of revenue, then you might even hire more people.

Why college students shouldn't retrain as plumbers

Benjamin Todd: You don’t want to just look at what [skills] will increase in value; you also want to look at what’s valuable now. And so start with that as your base point, and then you can imagine some will go up and some will go down.

This is one reason why I think it doesn’t make that much sense to tell a college student who could do consulting to become a plumber instead, because the difference between those white-collar jobs and plumbers is still quite large now. So, even if white-collar jobs come down in wages and plumbers go up a bit in wages, it might be quite a long time before they actually cross.

So instead that person should probably be looking at a white-collar job, but in one of these parts that’s harder for AI to do. So maybe it’s more social skills heavy, or it’s a more messy thing, or it uses management skills. That’s probably where that type of person should be focusing first.

If you were very on the fence and not sure whether to go to college or learn a trade, and it’s a very borderline case for you, then I think — compared to the past — there’s a stronger argument for doing the complex physical skills, which will take a bit longer to automate.

Though of course eventually there will be robotics, so all of these things are about at the time frame. [In the book] I talk about riding the wave of the things that are most valuable at the time, rather than trying to come to some permanent solution.