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

Summary

Planet Money – “Asking for a Friend… Which Jobs are Safe from AI?”

Release Date: September 10, 2025
Hosts: Sally Helm & Amanda Aronczyk (NPR)

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Episode Overview

This episode tackles the growing anxiety about artificial intelligence (AI) and its impact on the future of work. Listeners and the Planet Money team ask the pressing question: Which jobs are actually safe from AI?
Through personal stories and expert interviews, the hosts examine new research frameworks that reveal not simply which positions may be “safe,” but how society’s entire relationship with work could change. The conversation moves from individual concerns to sweeping economic implications, ultimately suggesting that our response and imagination might matter as much as any list.

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Personal Anxieties: Real Stories from Listeners

Time: 00:24–04:59

• Sally and Amanda introduce Charlie Baker, a college senior who falls in love with the LSAT but hesitates to pursue law out of fear that AI might automate legal work.
  • “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.” – Charlie Baker (01:57)
• Anna Wynn, a tech product designer, is also anxious as automation threatens her field.
  • “What would AI not automate out? …anything right now, physical, maybe a plumber…” – Anna Wynn (02:20, 03:01)
  • She considers switching to hands-on work, like welding or her mother’s job as a nail tech.
• The hosts highlight a wave of similar worries from listeners—people considering yoga teaching, real estate, or legacy jobs for safety.
  • “It feels to me like we all have no idea how to think about this…” – Sally Helm (04:42)

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The Search for an Answer: Job “Exposure” to AI

Daniel Rock’s “Exposure” Framework

Time: 07:00–17:44

• Sally seeks a definitive list of jobs immune to AI and finds Daniel Rock’s research, done with OpenAI, which ranks 1,000+ occupations by “AI exposure.”
• How the list works:
  • Every job is broken into tasks (from the ONET database – 08:24).
  • Each task is scored for its exposure:
    • E0: Not exposed (AI can’t help)
    • E1: Fully exposed (AI can do the task quickly)
    • E2: Somewhat exposed (AI might help with added systems)
  • “Exposure to AI is not the same thing as this job will be automated.” – Sally Helm (12:17)
• Examples:
  • Acute care nurses: “administer blood transfusions” = E0; “document data” = E1
  • High-exposure (more likely to change): Public relations, translators, knowledge workers
    • “She had it [GPT-4] write a press release for her in her tone and she said it did an absolutely great job… this is the first few years of my career just in a machine.” – Daniel Rock (11:20)
  • Low-exposure (less likely to change): Welders, short order cooks, athletes
• Rock’s Argument:
  • Exposure means potential for change—not a hit list.
  • AI is like electricity: a “general purpose technology” that will shift work in unexpected ways (13:11).
    • “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.” – Daniel Rock (14:24)
  • Some fields may grow because of AI, not in spite of it.
    • “If you're really exposed, it could be great for you if you could use AI to make yourself a thousand times more productive… you might do really well.” – Daniel Rock (15:31)
• Key takeaway: The most-exposed jobs will see the most change, but not necessarily elimination. Demand elasticity, adoption patterns, and social choices all matter.
  • “We just can’t know.” – Daniel Rock (28:51)

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The “Humanness” Factor: The EP.O.C.H. Framework

Isabella Loisa’s “Epoch Score”

Time: 19:58–27:59

• Researcher Isabella Loisa (MIT) takes a different approach: Instead of AI’s exposure, she asks what skills humans bring that AI can’t (yet) match.
  • She responds to widespread fear:
    “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.” – Isabella Loisa (20:08)
• The “Ep.O.C.H.” Score:
  • E: Empathy (genuine human connection)
  • P: Physical Presence (need to be there in person)
  • O: Opinion/Judgement/Ethics (critical thinking, moral calls)
  • C: Creativity (inventing new things)
  • H: Hope/Vision/Leadership (inspiring, guiding, motivating)
• Each occupation is scored; those with higher Epoch values are more “human-shaped.”
• Examples:
  • Emergency management directors and managers score high (empathy, presence, judgment).
  • Construction workers score high—surprisingly, because of mentoring others.
    • “Turns out… they are mentoring or teaching less experienced construction workers.” – Isabella Loisa (24:31)
  • Clerical jobs (tax preparers, insurance appraisers) score low.
• The complexity of real jobs: Many mix “automatable” and “human” tasks. So even within one field, some tasks could be replaced by AI, some merely empowered.
  • “If you have a bunch of clerical tasks, but also some connected tasks that are highly human, then your job might be safer.” – Amanda Aronczyk (26:08)
  • For lawyers: writing briefs (AI-possible), courtroom argument (human-essential).
    • “All the different occupations that require critical thinking, judgment, even creativity, that is not going to go away.” – Isabella Loisa (27:19)
• Advice:
  • Focus on mastering the “human” skills.
  • “Like learn how to think.” – Isabella Loisa (27:56)

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Navigating the Uncertain Future

Concrete Advice & Real-Life Example

Time: 28:58–31:50

• There’s no one “safe” career, and there may never be a neat master list.
• Both Rock and Loisa agree:
  • “Learn to use AI so that you can be ready… shape it to your advantage.” (28:58)
  • But: This is hard, requires trial and error, and ethical choices.
• Example: Veterinarian Kat Reardon
  • Used AI (ChatGPT) to draft patient notes, saving time and reducing risk of injury (since both hands stay on the animal).
    • “I’ll get bit less often … I can kind of feel if they’re kind of starting to get upset … because I was distracted and not paying attention previously.” – Kat Reardon (30:44)
  • She transitions part-time into teaching AI to help other vets.
  • “It’s the AI augmentation story… and not one that I would have imagined.” – Sally Helm (31:09)
• Big Picture:
  Imagination and adaptability may be the most important skills of all. Change is likely to be surprising—and we’re writing the rules as we go.
  • “I went in looking for a list… but I learned there really is no list. Not yet. Maybe not ever. We are in for a weirder ride than that.” – Sally Helm (31:09)

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Notable Quotes and Moments

| Time | Speaker | Quote | |---------|---------------------|-----------------------------------------------------------------------------------------------------------| | 01:57 | Charlie Baker | “I don’t know if it’s worth the investment now to go to law school… if I’m potentially… replaced by AI.” | | 04:42 | Sally Helm | “It feels to me like we all have no idea how to think about this…” | | 11:20 | Daniel Rock | “She had it [GPT-4] write a press release… She said, wow, this is the first few years of my career in a machine.” | | 12:17 | Sally Helm | “Exposure to AI is not the same thing as this job will be automated.” | | 14:24 | Daniel Rock | “So much is going to change that we really can't say which jobs are going away…” | | 15:31 | Daniel Rock | “If you’re really exposed, it could be great… you might do really well.” | | 20:08 | Isabella Loisa | “I was more perhaps afraid of the fear that people were feeling.” | | 27:19 | Isabella Loisa | “All the different occupations that require critical thinking, judgment, even creativity… not going away.”| | 27:56 | Isabella Loisa | “Like learn how to think.” | | 28:51 | Daniel Rock | “We just can’t know.” | | 30:44 | Kat Reardon | “I’ll get bit less often … because I was… not paying attention previously because I was getting my notes done.”| | 31:09 | Sally Helm | “I went in looking for a list… but there really is no list. Not yet. Maybe not ever.” |

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Key Insights and Takeaways

• No job is absolutely immune—AI is a “general purpose technology” that will shape, eliminate, and create jobs in unpredictable ways.
• Exposure ≠ Elimination: Being exposed to AI mainly predicts how much your job is likely to change, not whether it will vanish.
• “Humanness” matters: Roles heavy on empathy, ethical judgment, creativity, physical presence, and hope/leadership are less likely to be fully automated.
• Augmentation, not just automation: Many jobs will be transformed, not replaced, as AI takes over certain tasks and enhances others.
• Adaptability is crucial: Those who actively learn to use AI can shape its adoption in their fields.
• Imagination and flexibility are as important as technical skills in the looming age of AI.

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Further Resources

• Daniel Rock’s research: AI “exposure” across the labor market
• Isabella Loisa’s research: The “Ep.O.C.H.” humanness framework

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For those anxious about AI’s impact on work:
There’s no “magic list” of safe jobs—but the more human your work, the more personally adaptable you are, and the more you proactively engage with new technology, the more likely you are to thrive. The future of work is not fixed; it’s ours, collectively, to invent.

Transcript

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[Sally Helm]

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[Planet Money Host]

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[Sally Helm]

Last summer, Charlie Baker was very. 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.

[Amanda Aronczyk]

And one day he's in the break room.

[Sally Helm]

I'm picturing beige. Everything beige.

[Daniel Rock]

Yeah. Or gray.

[Charlie Baker]

It's like. And it also, it has this sort of smell of like a thrift store, if that. If that makes sense.

[Sally Helm]

Yep. I do know that smell. It's not, you know, I don't love it for you, but I know it.

[Amanda Aronczyk]

And in that pungent break room, he sees on the table something that in other circumstances would not be exciting.

[Charlie Baker]

Someone had left out, like, a LSAT studying book. And I was like, oh, maybe I should check this out.

[Sally Helm]

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.

[Charlie Baker]

It'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.

[Sally Helm]

Do this meaning take the LSAT and go to law school.

[Amanda Aronczyk]

But almost immediately, it's like the soundtrack shifts in Charlie's mind.

[Sally Helm]

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, horrible horror movie where there's an evil robot on the prowl going after every last job.

[Charlie Baker]

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.

[Sally Helm]

AI. A lot of people are worried about this.

[Charlie Baker]

I have no idea how to plan for the future.

[Anna Wynn]

It's so uncertain and scary. What would AI not automate out?

[Amanda Aronczyk]

Anna Winn. Like, Charlie is worried about the robots. Anna is in her 30s. She's been doing product design in tech for 10 years.

[Sally Helm]

Do you like your job?

[Anna Wynn]

Yeah, I do. It's great. But then there's also, you know, all the looming layoffs that are happening across the industry.

[Amanda Aronczyk]

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.

[Sally Helm]

So Anna has started paging through a list of jobs in her mind, trying to imagine which of them might survive AI.

[Anna Wynn]

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 electrician next to look at.

[Sally Helm]

Or there's her mom's job, nail tech.

[Anna Wynn]

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.

[Amanda Aronczyk]

But also, who knows, maybe welding.

[Anna Wynn]

I saw, like, a video with someone who trained for it. I was like, huh?

[Sally Helm]

She's still in the research phase, but she's taking it really seriously. Getting down to brass tacks, what the.

[Anna Wynn]

School costs, how much, maybe I would be making the first year, second year, and then an apprentice. So really kind of, you know, doing the numbers before making a leap.

[Amanda Aronczyk]

Charlie is doing the numbers too. Is law school going to be worth.

[Charlie Baker]

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.

[Amanda Aronczyk]

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.

[Sally Helm]

Charlie and Anna are so, so not alone. I 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.

[Amanda Aronczyk]

Dozens of you, our listeners, have written into us about this, saying things like, maybe my yoga teacher side gig, 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.

[Sally Helm]

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? How do you figure that out?

[Amanda Aronczyk]

Hello and welcome to Planet Money. I'm Amanda Oroncic.

[Sally Helm]

And I'm Sally Helm. Asking for a friend, which jobs are safe from AI?

[Amanda Aronczyk]

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.

[Sally Helm]

I love these conversations, knowing more about the machines but also about what it actually means to be human.

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[Sally Helm]

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.

[Amanda Aronczyk]

On the list are all kinds of jobs. Midwives, detectives, pesticide handlers, sprayers and applicators, comma, vegetation.

[Daniel Rock]

Oh, there's lots of cool ones there. Dredge operators. That one's pretty cool.

[Sally Helm]

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.

[Amanda Aronczyk]

Is they took these thousand or so jobs and looked at each job as a bundle of tasks.

[Sally Helm]

Yeah, things you do if you are a midwife or a detective or a pesticide handler.

[Daniel Rock]

20,000 tasks that people do in the economy.

[Sally Helm]

The source of these task lists is an amazing government database called onet. If 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?

[Daniel Rock]

Yeah, last I counted. Yeah, that's sounds about exactly right.

[Sally Helm]

Okay. Explain economic impact of policies to the public. Supervise research projects and students study projects. Have you ever done that?

[Daniel Rock]

That sounds. Yeah, was doing that.

[Amanda Aronczyk]

Yesterday, Daniel's paper looked at 19,265 tasks listed in ONET. The paper took those tasks and evaluated how exposed each one 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.

[Daniel Rock]

E2 is. Yeah, you could get some benefits, but you have to build systems around it.

[Sally Helm]

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. They have 26 tasks, let's say administer blood and blood product transfusions.

[Daniel Rock]

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.

[Sally Helm]

So not exposed.

[Daniel Rock]

Not exposed.

[Amanda Aronczyk]

So you go task by task.

[Sally Helm]

So yeah, so here I have document data related to patient's care.

[Daniel Rock]

Yeah, that seems like something a large language model could help. So yeah, that would be like an E1 task.

[Amanda Aronczyk]

And then you give an exposure score to the job as a whole. And voila, the exposure list.

[Sally Helm]

When I first opened it, there was like a drum roll in my mind because 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.

[Daniel Rock]

Love the dredge.

[Sally Helm]

Operator, pourers and casters, comma, metal.

[Amanda Aronczyk]

Also on the low end, less exposed 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.

[Sally Helm]

We have public relations specialists. Why are they so high?

[Daniel Rock]

Oh, wow. Yeah, so I've, I've seen this one in person when a public relations specialist used GPT4 for the first time and I saw the light bulb go off. She 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. Cause she said, wow, this is the first few years of my career just in a machine.

[Sally Helm]

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.

[Amanda Aronczyk]

It feels like the jobs at the top of this list are going to disappear, killed by AI.

[Sally Helm]

I mean like, is this basically an automation hit list?

[Daniel Rock]

No, it's absolutely not an automation hit list. It's instead a what is the potential for this work to change list.

[Sally Helm]

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? Daniel 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?

[Daniel Rock]

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, like it's fine, right?

[Sally Helm]

Instagram changed it all. Just kidding. Obviously, electricity is the general purpose technology. It 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. Like, you know, before electricity, there was no job electrician or electrical engineer or lighting technician, all of which are today listed in onet.

[Daniel Rock]

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, let's cool it with all of the prognostication about how jobs are going away.

[Sally Helm]

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. They might be bigger and weirder, they might be better or worse.

[Amanda Aronczyk]

Basically, you cannot plan around them.

[Sally Helm]

But what you can do is see from this list which jobs are likely to change the most. Like, at least at first. First. And Charlie and Anna are right by that measure. Lawyers are going to see a lot of changes and welders will see fewer.

[Amanda Aronczyk]

But change in this case is a value neutral word. Daniel is adamant we should not hear this job will change and think this job will go away.

[Daniel Rock]

If you're really exposed, it could be great for you if you could use AI to make yourself a thousand times more productive. 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.

[Sally Helm]

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.

[Amanda Aronczyk]

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. Like maybe we only need so many news articles or logos. And so if news writers and graphic designers get way more productive, there are fewer of those jobs available.

[Sally Helm]

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?

[Daniel Rock]

They might think that way, but they should not think of it that way. From on the basis of our data.

[Sally Helm]

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. One 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.

[Daniel Rock]

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.

[Amanda Aronczyk]

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.

[Sally Helm]

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

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[Sally Helm]

Isabella 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.

[Isabella Loisa]

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.

[Sally Helm]

You were afraid of the fear? The only thing fear is fear itself. Isabella said, no.

[Isabella Loisa]

Yes, because I, I saw that there was a lot of anxiety in folks around me and I was like, ooh, this isn't good.

[Sally Helm]

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 sciencey questions. She teamed up with a well known MIT economist, Roberto Rigabone, 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?

[Isabella Loisa]

Let's look at what humans can do because we're here, there's billions of us in 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.

[Amanda Aronczyk]

To answer that question, Isabella and her co author talk to a lot of people. Her co author Roberto Rigabone 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 Epoch 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. P is presence. Do you need to physically be there to do the task or does your work benefit from face to face collaboration?

[Isabella Loisa]

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.

[Sally Helm]

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. H is for hope. So tell me about that one.

[Isabella Loisa]

Yes, that is also my favorite, because when it came up, I was like, really?

[Sally Helm]

Hope?

[Isabella Loisa]

Is hope something that we need for work? And then when you actually look at the data, there's a lot of occupations that require to have hope in the future.

[Sally Helm]

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 gotta have hope for your client's recovery. In fact, in some ways, that is the very thing you're hired to have.

[Amanda Aronczyk]

Next, 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 epoch score. The result is. Sally, for you. A list.

[Sally Helm]

A list.

[Amanda Aronczyk]

A list of jobs that essentially score higher or lower on humanness.

[Sally Helm]

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. In fact, managers of all kinds scored high on epoch, even something like information technology project managers. That was surprising to me. It kind of sounds like a computery 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.

[Isabella Loisa]

I was very surprised, and I was like, what's happening here? And it turns out that there's one or two tasks in their occupational description which says they are mentoring others or teaching less experienced construction workers, for example.

[Amanda Aronczyk]

Now, there were some weird things that happened in assigning epoch scores because some tasks are so obvious to us that they actually aren't explicitly written down in onet. 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 Epoch, even though absent some kind of like major robotics boom, those are jobs where you do in fact really have to be there.

[Sally Helm]

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. Now, 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.

[Amanda Aronczyk]

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?

[Sally Helm]

Isabella's paper looks at that question in an interesting way. It takes those task lists again from onet, and it zeroes in on the fact that some tasks tend to occur together. So 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.

[Amanda Aronczyk]

Like for a professor. One of their tasks might be making slides for a lecture. AI can probably do that, but a linked task might. Giving the lecture, that's pretty human. Or take lawyers.

[Isabella Loisa]

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.

[Sally Helm]

You 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.

[Isabella Loisa]

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.

[Sally Helm]

So you're kind of telling Charlie, you can go to law school and think about like the more interesting parts of the law, like try to get good at judgment, try to get good at argument. Don't worry about clerical tasks so much because they might be done by machines.

[Isabella Loisa]

Yes, exactly. Like learn how to think.

[Sally Helm]

It is kind of similar to what Daniel Rock told me.

[Daniel Rock]

It sounds like Charlie's already off to a very clever start. It sounds like Charlie is thinking about the discount rate on his expected future cash flows for being a lawyer as being a little bit higher. Right. Riskier cash flows there. Sorry, I went full guy. No, I think he is. Lawyers in particular are the group of people I'm least worried about. They will find a way to change the rules of the game that help as a field. Right.

[Sally Helm]

He did say, remember those paralegals who made their jobs more interesting? And think about how you could do that as a lawyer. Try to imagine what might be possible for a young lawyer in the future that isn't possible now.

[Amanda Aronczyk]

Daniel also had an interesting thought for Anna Wynn, the tech worker who's thinking about becoming a plumber or a welder. He pointed out if everyone decides to be a welder right now, there just might end up being too many welders. Maybe wages would go down.

[Daniel Rock]

So you're not necessarily safe and also you're not necessarily in danger no matter where you are. Along the spectrum, we just can't know.

[Sally Helm]

Daniel and Isabella both had one very concrete piece of advice, which is to learn to use AI so that you can be ready to kind of roll with what's coming, hopefully shape it to your advantage. And that's not my favorite ever piece of advice, I think, because it's just hard figuring out how to use these tools well takes work, let alone how to use them ethically. But I did talk to one person who helped me see how this could go. Well, her name is Kat Reardon. She is a veterinarian. She told me that one of her favorite zoo animals is the Coatis.

[Planet Money Host]

They have these long noses and these. And these incredible stripy tails. There was one, his name was Jacob, and his favorite thing in the world was dryer sheets. So if I, like, put dryer sheets in my pocket, he would come and, like, put his nose in my pocket and, like, get all excited about the dryer sheets.

[Sally Helm]

So why was I talking to Kat about this Coatis and dryer sheets? Because Kat has made the previously unknown veterinarian 2 AI career jump. Here's how it happened. She was posting online one day about how she'd started using ChatGPT to help her with her patient notes. Taking these notes is a huge drain on her and other veterinarians. And. And ChatGPT was making this really annoying part of Kat's job way faster.

[Amanda Aronczyk]

So she posted about this and then she heard from an AI startup saying, actually, we're trying to make a tool like that to sell to vets. Do you want to try it? She did and now she works there doing things like helping the AI learn veterinary terms that it needs to know. She also still works as a vet and she uses the tool to automate parts of her job, like listening to her appointments and doing a first draft of her notes.

[Sally Helm]

Yeah, she told me it's helped in some surprising ways.

[Planet Money Host]

I honestly I'll get bit less often because I have my hands on the animal, both hands, and I can kind of feel if they're kind of starting to get upset and I can feel the little muscles tensing or whatever that I was distracted and not paying attention to previously because I was worried about getting my notes done and because you.

[Sally Helm]

Had one hand like on a pen, literally.

[Planet Money Host]

Yeah, yeah. So I, yeah, I feel like it's a safety issue as much as anything else.

[Sally Helm]

It's the AI augmentation story that Daniel and Isabella are hoping for and not one that I would have imagined. And as I had these conversations, I kept thinking that that is in fact the trait we all need to be applying here. Imagination. I went in looking for a list, a concrete guide to help me navigate what's on going, coming. But I learned there really is no list. Not yet. Maybe not ever. We are in for a weirder ride than that.

[Amanda Aronczyk]

Today's episode was produced by Eric Metal and edited by Marianne McCune.

[Sally Helm]

It was backtracked by Sierra Juarez and engineered by Robert Rodriguez. Alex Goldmark is our executive producer. Special thanks to Arvind Karunakaran. He wrote that paper about the paralegals using AI. I'm Sally Helm.

[Amanda Aronczyk]

And I'm Amanda Aronczyk. This is NPR. Thanks for listening.

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