Asking for a friend … which jobs are safe from AI?

0:00Support comes from our 2025 lead sponsor of Planet Money, Amazon Business. How can you grow your business from idea to industry leader? Bring your vision to life with smart business buying tools and technology from Amazon Business. Simplify how you stock up to get ahead. Go to AmazonBusiness.com for support. This is Planet Money from NPR. Last summer, Charlie Baker was very bored. He was a rising college senior, had an internship at the New Jersey Department of Community Affairs. Entering data into spreadsheets, that was what he did on an exciting day. And one day, he's in the break room. I'm picturing beige, everything beige.

0:42Yeah, or gray. It's like, and it also, it has this sort of smell of like a thrift store, if that makes sense. Yep, I do know that smell. It's not, you know, I don't love it for you, but I know it. And in that pungent break room, he sees on the table something that, in other circumstances, would not be exciting. Someone had left out, like, an LSAT studying book. And I was like, oh, maybe I should check this out. He starts working through this book, doing practice questions for the law school admissions exam for fun. And it is, like, the perfect law school meet-cute. LSAT book and Charlie run into each other in the break room, and the rest is history.

1:23It's all weird little, like, puzzles. The most convoluted riddles, like, anyone has ever written. And I was like, oh, I should really do this. Do this meaning take the LSAT and go to law school. But almost immediately, it's like the soundtrack shifts in Charlie's mind. Maybe he's not in a fun law school rom-com. Maybe he and everyone he knows is actually living in some kind of dystopian technological horror movie where there's an evil robot on the prowl going after every last job. I don't know. I don't know if it's worth the investment now. to go to law school for three years if I'm potentially going to just be replaced by an AI chatbot.

2:08AI. A lot of people are worried about this. I have no idea how to plan for the future. It's so uncertain and scary. What would AI not automate out? Anna Wynn, like Charlie, is worried about the robots. Anna is in her 30s. She's been doing product design and tech for 10 years. Do you like your job? Yeah, I do. It's great. But then there's also, you know, all the looming layoffs that are happening across the industry. She suspects that some of these layoffs are already driven by AI. And she's worried that she could be next. There's already software that can make designers like her a lot faster. She thinks it's possible that AI could eventually cut her out of the loop entirely.

2:51So Anna has started paging through a list of jobs in her mind, trying to imagine which of them might survive AI. And it's anything right now physical, maybe a plumber. I mean, it's going to take a while before you could automate that. I was thinking maybe an electrician next to look at. Or there's her mom's job, nail tech. She's always like, you know, you can come back here. There's a lot of work. Like, I'm ready to start a business with you if you want. But also, who knows? Maybe welding. I saw like a video with someone who trained for it. I was like, huh. She's still in the research phase, but she's taking it really seriously, getting down to brass tacks.

3:30What the school costs, how much maybe I would be making the first year, second year as an apprentice. So really kind of, you know, doing the numbers before making a leap. Charlie is doing the numbers too. Is law school going to be worth it? If I'm going to graduate with, say, whatever,$100 ,000 in debt to a legal field where they're decreasing the jobs, I mean, that's a really bad situation. So despite his love affair with the LSAT, he has decided to delay law school for now. Wait and see. Anna is also in a sort of holding pattern, both of them just bracing for the AI future. Charlie and Anna are so, so not alone.

4:14I mean, I am worried about this. And so are lots of my friends. Worried about which jobs they should steer towards or away from. worried about what direction their kids should go or not go. Dozens of you, our listeners, have written into us about this, saying things like, maybe my yoga teacher side gig is actually my safest bet now, and my parents were in real estate, and I never thought I'd say it, but maybe that's what I should do. It feels to me like we all have no idea how to think about this. Like, even if you can really quickly remember all the jobs that exist, which of them might be your safe harbor?

4:53How do you figure that out?

4:58Hello and welcome to Planet Money. I'm Amanda Aronchik. And I'm Sally Helm. Asking for a friend, which jobs are safe from AI? Today on the show, we talk to two researchers who have come up with some first drafts of the future. Some potential blueprints for people like Charlie and Anna. And me. And Sally. Two frameworks for thinking about how AI will affect jobs. which might disappear, which might be more likely to stay, and which will change in ways we haven't even imagined. I love these conversations knowing more about the machines, but also about what it actually means to be human.

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7:11I had kind of a secret mission as I set out to report this story, one that I hadn't even fully articulated to myself. What I really wanted to find, if I'm being honest, is a list of jobs that are just going to be immune to AI. The super intelligent thinking machines will not be able to do them. And for a moment, I thought I'd found it. I have the list pulled up in front of me right now. It takes almost a thousand jobs and ranks them by something called AI exposure. And before I understood precisely what that means, I was like, jackpot. This list is going to tell me and Anna and Charlie everything we need to know about our AI future.

7:55On the list are all kinds of jobs. Midwives, detectives, pesticide handlers, sprayers, and applicators, comma, vegetation. Oh, there's lots of cool ones there. Dredge operator, that one's pretty cool. This is Daniel Rock. He is the man behind the list. His co-authors on his paper about this were researchers at OpenAI. They actually used AI as a tool in this study. And what they did is they took these thousand or so jobs and looked at each job as a bundle of tasks. Yeah, things you do if you are a midwife or a detective or a pesticide handler. 20 ,000 tasks that people do in the economy. The source of these task lists is an amazing government database called O-Net.

8:41If you go look at it, which I recommend you do, I also recommend that you set a timer or you may find yourself, as I did, looking up after like half an hour and realizing that you have just read the entire task list for baristas. Daniel and I looked at the task list for him, an economist. According to this, you have 16 tasks. Is that right? Yeah, last I counted. Yeah, that sounds about exactly right. Okay. Explain economic impact of policies to the public. supervise research projects and students' study projects. Have you ever done that? That sounds, yeah, I was doing that yesterday. Daniel's paper looked at 19 ,265 tasks listed in O-Net.

9:20The paper took those tasks and evaluated how exposed each one is to AI. Daniel's measuring exposure, which means basically how much these large language models can help us do our tasks. If the AI can help a human complete a task in at least half the time, Daniel labels it E1. If AI can't really help at all, it's E0. Then there's E2, which is a sort of in-between score. E2 is, yeah, you could get some benefits, but you have to build systems around it. Like, AI can't just do this one out of the box. It'd need some extra software or something tacked on in order to help. So, we pulled up the task list for an acute care nurse.

10:03They have 26 tasks. Let's say administer blood and blood product transfusions. Right. So in the horrifying future world nightmare where AI systems do this, it's probably not a large language model or like this vintage of technology is doing that. So we're going to call that an E0. So not exposed. Not exposed. So you go task by task. So yeah, so here I have document data related to patients' care. Yeah, that seems like something a large language model could help. So yeah, that would be like an E1 task. And then you give an exposure score to the job as a whole. And voila, the exposure list. When I first opened it, there was like a drum roll in my mind.

10:47Because it is a concrete way to look at this big question about the future. About how AI is going to start reaching into the labor market and shaking things up. I'm looking at this list. I've put it in order. Down at the bottom, we've got wellhead pumpers. Yeah. Our favorite dredge operators. Love the dredge operator. Pourers and casters, comma metal. Also on the low end, less exposed to AI, there were athletes, dancers, short order cooks, and as Anna Wynn suspected, a lot of physical blue collar jobs. Meanwhile, at the top, a lot of knowledge workers, translators, writers. We have public relations specialists.

11:30Why are they so high? Oh, wow. Yeah. So I've seen this one in person when a public relations specialist used GPT-4 for the first time and I saw the light bulb go off. She, you know, had it write a press release for her in her tone. And she said it did an absolutely great job. Now, there was a little bit of fear in there, too, because she said, wow, this is like the first few years of my career, like just in a machine. Yeah. So this is the thing that feels scary about Daniel's list. The idea that this machine has read up on everything we've ever done and now maybe it doesn't need us. It feels like the jobs at the top of this list are going to disappear, killed by AI.

12:16I mean, like, is this basically an automation hit list? No, it's absolutely not an automation hit list. It's instead of what is the potential for this work to change list? That is admittedly not as catchy a way to describe it. But that is really the big point that Daniel wanted to stress to me. Exposure to AI is not the same thing as this job will be automated. So Anna, Charlie. And you, Sally. We are not looking at a list of safe and unsafe jobs. The main question that Daniel Rock is asking in this paper is not, can I come up with a list of jobs that are safe from AI so that Sally Helm can sleep easier at night?

13:00Daniel has a much bigger question about AI as a whole. He wants to figure out, how far reaching is the change we're talking about here? Is AI the kind of technology that will seep into basically every corner of the economy? Economists call that a general purpose technology. So is it that or is it something more limited? Is this like electricity or is it, you know, like Instagram? They're very different, right, in terms of the implications. Instagram is obviously a general purpose technology and electricity was okay. Right. Instagram changed it all. Just kidding. Obviously, electricity is the general purpose technology.

13:42It changes life and work so much that almost no job today doesn't have something to do with electricity. At least it feels that way to me. And when you look at these exposure scores, it's really clear. AI is going to touch a lot of sectors. Daniel and his co-authors find, yeah, seems like a general purpose technology. And what that means for us is something that I found simultaneously sort of deflating and kind of hopeful. Daniel told me that because AI appears to be this big new general purpose technology, the changes to the economy will be so vast that they are very hard to imagine from where we stand now.

14:24Like, you know, before electricity, there was no job electrician or electrical engineer or lighting technician, all of which are today listed in O-Net. So much is going to change that we really can't say which jobs are going away, which jobs are going to become more important. We're kind of saying, you know, let's cool it with all of the prognostication about how jobs are going away. And this is frustrating because it means I don't have something very concrete to bring back to Charlie and Anna. But this is one of the big takeaways from Daniel's paper. Like if you take really seriously that we're talking about something on the order of electricity here, you have to admit that the changes to the labor market might not be what you first imagine.

15:07They might be bigger and weirder. They might be better or worse. Basically, you cannot plan around them. But what you can do is see from this list which jobs are likely to change the most, like at least at first. And Charlie and Anna are right. By that measure, lawyers are going to see a lot of changes and welders will see fewer. But change in this case is a value neutral word. Daniel is adamant. adamant. We should not hear, this job will change and think, this job will go away. If you're really exposed, it could be great for you. If you could use AI to make yourself a thousand times more productive.

15:51Let's say you're an AI researcher, right? They're highly exposed. If you can use these tools to be a really high quality AI researcher, you might do really well and companies are going to be really excited to hire you at higher wages. Yeah, if workers get more productive and companies and consumers want more of what they are producing, then everyone wins. So it could be that some of these highly exposed fields see an explosion of growth, that they're a really good place to be. Economists would say that demand is elastic. Of course, if workers get more productive and the world doesn't want even more of what they're producing, demand is inelastic, that leads to job loss.

16:34Like maybe we only need so many news articles or logos. And so if newswriters and graphic designers get way more productive, there are fewer of those jobs available. Like, if I'm a company and I look at this list and I think, okay, well, it looks like various people that I employ are pretty high on this list. Maybe I should think about automating those jobs. Like, does that make sense? They might think that way, but they should not think of it that way on the basis of our data. Daniel thinks that organizations will need to experiment. He gave me an example of a study where a company gave an AI tool to two groups of paralegals.

17:17One group was told, just use these tools to get more productive. And the other group was told, use these tools to do the parts of your job that you hate. The office where they said use this tool to get rid of the things you don't like doing, the paralegal role changed. They really flourished. They started working on some work that even seemed like junior attorney work. In the other office, there was limited adoption. It didn't really make as much of a dent. So what Daniel's paper does tell us is which jobs AI might change. What it doesn't tell us is which jobs will live and which will die. But in my quest to answer that question, I did find another paper that gave me a whole new way of looking at all of this.

18:05using that same list of 19 ,000 plus tasks that workers are doing all across the economy. That's after the break.

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19:43Isabella Loisa is a researcher at MIT. And when she started to hear her friends talk about AI a couple of years ago, she had a Franklin Delano Roosevelt moment. I don't think I was ever afraid of AI taking over my job. I was more perhaps afraid of the fear that people were feeling, right? You were afraid of the fear. The only thing to fear is fear itself, Isabella said. No, yes, because I saw that there was a lot of anxiety in folks around me. And I was like, ooh, this isn't good. She wanted to figure out, are those fears justified? Isabella is a computational social scientist, meaning basically that she incorporates computer science techniques to help answer social science-y questions.

20:25She teamed up with a well-known MIT economist, Roberto Rigobone, and what they ended up doing was kind of turning Daniel's O-Net research inside out. Instead of looking at what tasks AI can help do, they asked, what are humans good for? Let's look at what humans can do because we're here. There are billions of us on the planet right now. And even if AI came and automated all the jobs that exist, then what are we going to do, right? So that's what really sparked that kind of question of like, hey, let's look at what is complementary that humans can do very well that machines still can't do that well, at least for now.

21:07To answer that question, Isabella and her co-author talked to a lot of people. Her co-author, Roberto Rigobone, has actually been thinking about this for years. They consulted psychologists and philosophers, and they ended up condensing things down into a single score. It's called the EPOC score. It's an acronym, and each letter stands for an area where they think that humans will be especially needed to complement AI. It's kind of a humanness score. E stands for empathy, pretty human trait. AI can maybe simulate it, but arguably the whole point of empathy is that another human is feeling your pain.

21:47P is presence. Do you need to physically be there to do the task? Or does your work benefit from face-to-face collaboration? Then we have O for opinion, judgment, critical thinking. But here we also really want to emphasize all the moral and ethical judgments that humans have to do, right? So it's kind of like ethics, but you didn't want another E. Yes. C is for creativity. Isabella emphasizes that AI is trained on a bunch of existing data. So even if they can, like, write a poem, they're arguably not as good as humans at imagining entirely new possibilities. And then H is one of my favorites, actually.

22:27H is for hope. So tell me about that one. Yes, that is also my favorite. Because when it came up, I was like, really? Hope is hope something that we need for work? And then when you actually look at the data, there is a lot of occupations that require to have hope in the future. The full name of this category is hope, vision, and leadership. So it's things that involve planning and like envisioning a goal and rallying people to get there. One example might be a substance abuse counselor. You got to have hope for your client's recovery. In fact, in some ways, that is the very thing you're hired to have.

23:04Next, Isabella wanted to figure out how much of these various skills are involved in any given occupation. So she used essentially a computer program to read all of those O-Net tasks. And then she would assign each job an overall EPOC score. The result is, Sally, for you, a list. A list. A list of jobs that essentially score higher or lower on humanness. Yes. Imagine my excitement. A list. And near the top of the list, we have, for example, emergency management director. These are people who prepare for disasters and then come in after disasters to help manage the fallout. The job requires lots of judgment, lots of empathy, lots of presence.

23:48In fact, managers of all kinds scored high on EPOC. Even something like information technology project managers. That was surprising to me. It kind of sounds like a computer-y job. But if you look at their list of tasks, it's a lot of planning, a lot of leading teams, managing people. And in general, more jobs than you might think have a lot of epoch going on. Construction workers, for example, scored higher than Isabella expected on empathy. I was very surprised. And I was like, hmm, what's happening here? And it turns out that there is one or two tasks in their occupational description, which says they are mentoring others or teaching less experienced construction workers, for example.

24:33Now, there were some weird things that happened in assigning EPOC scores, because some tasks are so obvious to us that they actually aren't explicitly written down in O-Net. Like the task list for barber doesn't say, I have to physically be at the salon holding the scissors in my hands. So a lot of physical, manual labor jobs actually scored pretty low on EPOC. Even though, absent some kind of major robotics boom, those are jobs where you do in fact really have to be there. But the other thing that sticks out is that clerical jobs tended to score low. Tax preparers, insurance appraisers. So it's possible that those jobs could be most at risk from AI.

25:18Now, of course, all of this is just a theory. Maybe AI will get a lot more human-like, or maybe we just won't care that it's only simulating empathy. But importantly, unlike Daniel and his co-authors, Isabella and her co-author actually did try to break down the risk for different jobs based on these humanness scores. The question is basically, is AI likely to swoop in and steal this job? Or is the job still going to exist, but AI is just going to help humans out? Is the job likely to be automated or augmented? Isabella's paper looks at that question in an interesting way. It takes those task lists again from O-Net, and it zeroes in on the fact that some tasks tend to occur together.

26:05So if you have a bunch of clerical tasks, but also some connected tasks that are highly human, then your job might be safer. AI might end up augmenting you, not replacing you. Think of it not as a robot taking your job, but as your own personal bionic arm. Like for a professor, one of their tasks might be making slides for a lecture. AI can probably do that. But a linked task? Giving the lecture. That's pretty human. Or take lawyers. The task that is delivering the argument in front of the judge requires a lot of presence, so it's really hard to automate that task. But then writing the brief about it, you know, that task might be very automatable.

26:49You know, this is actually making me think of a listener who wrote into us. His name is Charlie. I told Isabella about Charlie Baker, our listener who has decided to delay law school. And Isabella agrees with Daniel Rock. The legal field is likely to be affected by AI. The more clerical type of jobs can be more easily automated. But there's another great number of occupations in the legal field which are not going to be as impacted. All the different occupations that require critical thinking, judgment, even creativity, that is not going to go away. So you're kind of telling Charlie you can go to law school and think about, like, the more interesting parts of the law.