3 Takeaways - A Smarter, More Hopeful Future of Work - If We Get Artificial Intelligence Right (#284)

Episode Date: January 13, 2026

Elon Musk and Geoffrey Hinton warn of an AI-driven job apocalypse.MIT’s David Autor, one of the world’s leading thinkers on how technology reshapes work, says the real danger lies somewhere else.The biggest risk of AI isn’t mass unemployment - it’s whether human skills and expertise will still matter.David explains how AI could expand middle-class opportunity by lowering barriers to high-value work, why past technologies created more new jobs than they destroyed, and what we need to get right to make this moment a hopeful one.

The warnings about artificial intelligence are everywhere, and they're getting scarier. Elon Musk calls AI the most disruptive force in history, warning that a day is coming when no one will need a job. Jeffrey Hinton, often called the godfather of AI, has suggested people consider going into plumbing. And surveys show three quarters of Americans believe AI will shrink the, the job market. But what if all that is wrong? What if AI's real impact isn't mass unemployment, but something completely different and maybe even transformative? Hi, everyone. I'm Lynn Toman, and this is three takeaways. On three takeaways, I talk with some of the world's best thinkers, business leaders, writers, politicians,

newsmakers, and scientists. Each episode ends with three key takeaways to help us understand the world and maybe even ourselves a little better. Today I'm excited to be with David Otter. David has spent decades studying exactly how technology reshapes jobs and wages. He's a professor at MIT and one of the world's leading experts on the future of work. His research has shaped how policymakers and business leaders think about automation, globalization, and artificial intelligence. His recent work challenges some of the scariest headlines about AI and jobs. David, welcome and thank you so much for joining three takeaways again today. Thank you very much. Pleasure to be here. It is my pleasure. David, you've made a

striking claim that AI could let more people do work that used to be reserved for top experts. You've mentioned, for example, that a nurse practitioner could take on some of a doctor's work or a legal assistant could handle tasks once done by senior partners. What's actually changing here? And why does it matter so much? What's actually changing is a lot of that work requires deep reservoirs of expertise and training and judgment. And with better tools, more people could do some of that work effectively.

without as much training, without as many years in school, doesn't mean we don't need the people who are even more expert. But there's a lot of medical care that requires skills and expertise, but it isn't at the frontier. And more people could do that work with the right tools. And this is a way of bringing more people into the middle class jobs. Over the last four years, we've seen a real hollowing out of the distribution of occupations where people in the middle, people with high school and some college education, working in offices, work in factories, specialized, knowledge-intensive work that has been displaced a lot of it by automation, some of it by trade. And those people have been pushed predominantly downward

into low-paid services, food service, cleaning, security, etc., socially valuable work, but poorly paid because it's an expert, because most people can do that work without trainer certification. The hope is we could move more people back into the middle, not into the same occupations that previous existed, but into a new set of more knowledge-intensive, more decision-intensive, more judgment-intensive set of activities. It's where a lot of the value is. And what could get them there? Well, it's a combination of the right training, foundational education, for those activities,

for medicine, for, you know, law, for software, for engineering, for construction, and better tools. Then now people to do that work, that high-stakes work more effectively because they have the supporting infrastructure, the guidance and guard rails to use their knowledge effectively. And those additional tools you're talking about are AI. AI would be central to creating those tools. We haven't had technologies for creating similar tools until now. Looking back at big technologies in history, could we have predicted the new types of jobs they created? No, not very well. And this is an important point. Something that others have emphasized that I worked on as well is a lot of the work that we do is new work. It's not

simply the same work done faster. A lot of employment is in work that didn't exist. So in a recent paper with Anna Solomon's and Carolyn Chin and Brian Segler, we estimate that about 60% of employment in 2020 is in occupational specialties that were not present in 1940. So I say, well, what does new work exactly mean? Like, it's all work. What makes it new? From our perspective, what makes work new is that it requires new expertise. It requires knowledge or skills or specialized capabilities that weren't present. That could be in software. It could be in fuel injection systems in cars.

Or could be in tattooing. There's a lot of personal services that also require new expertise. So what makes work new is it requires some specialized skill sets that some people possess, that not everyone possesses, and that it produces something of value. And this is a huge challenge because it's very hard. When we look forward to the future, It's easy to imagine what will be automated. Things we're doing that we won't be doing.

But it's much harder to say, what will we be doing that we aren't doing right now? In the turn of the 20th century, about 38% of U.S. employment was in agriculture. Now, it's under 2%. But if you had gone to people 100 years ago and say, hey, what do you think all you farmers will be doing your kids a century from now, they would not have said, oh, I don't know, search engine optimization, neural networks, malware, pediatric oncology. they wouldn't have been able to imagine it. So it's not just as we've automated or advanced our technology.

It's not that we just do a narrower and narrower set of things by humans and everything else done by machines. The variety of work, I would say, is much broader than it used to be 100 years ago. But that's because we added expert work. Technologies like tractors, cars, electricity and the Internet brought huge benefits for families, lower prices, new products. But what about the workers who's doing? jobs were disrupted, what has happened to them? You make a broadly important, relevant point.

There are broadly distributed gains to these things, right? So when prices fall, like it's all a sudden it's just, you know, it gets cheaper to translate something, for example, that's great for consumers. It's great for business that use translation. It's just not good for the workers who perform that service, right? So we shouldn't think that's just value destruction, but it's often the case that the gains are diffuse and pretty small for any individual, but the losses are very, very concentrated. And we've seen this in many domains. One place we saw it really slightly different, but very close related, is in the manufacturing sector. And especially during the 2000s, with China's very, very rapid rise as a manufacturing exporter, especially as joining the

World Trade Organization, we saw the loss of at least a million manufacturing jobs directly related to that. And workers who were displaced did not quickly rebound and find themselves in better employment. And the communities in which they were located also did not quickly rebound. And now they are regrowing again, but with a different set of workers. You know, what this underscores is that the nature of demand or demand for skills can change much more rapidly than people can reskill. And most career transitions don't occur within a career. They occur across generations.

Kids and manufacturing workers don't go into manufacturing. People don't go into administrative work because it's not available. But usually once you're at mid-career, you've invested in a skill set. You have a form of expertise that's valuable, that's specialized. And you can't just say, oh, I guess I can't do that anymore. I'll become a software engineer. I'll become a lawyer. I'll become a doctor.

Most people, in general, if they're displaced from the expert work that they do, they end up doing something less expert. When very big changes happen, that devalue skill sets that are used by a lot of people, that has a very direct and acute cost to the people whose livelihoods are hugely disrupted. And not just their livelihoods, but the whole basis on which they make a living. Absolutely. many people assume that AI will follow the same path as past technologies. In your view, how is AI genuinely different?

I don't think any two technologies have followed the same room. And whenever someone says, will this time be different, you should always say, yeah, of course, but all previous times were also different. The era of electrification was very different from the era of telecommunications. The information age was very different from the Industrial Revolution. What makes AI distinct? it doesn't just follow simple rules. For most machines, the best case scenario is they do exactly what they're supposed to do.

What's different about AI is, it'll do things it wasn't designed to do specifically. It was discovered that AI was really good at computer programming. That was never the intention of large language models. They were trying to learn English language or natural language. Because it can learn inductively from unstructured information and make inferences and recognize patterns and see things that we don't necessarily see, it allows it to function in settings where we don't really have good tools. So much of the work that could, in theory, be automated because it follows simple rules

from procedures, has been automated. So most of the work that we do doesn't look like that anymore. Most of our work is actually not simple, wrote execution of repetitive actions. That certainly was the case 100 years ago. That was the case in early factory work. That was the case in a lot of office work. Most of the work that people do now requires decision-making. And we haven't had tools that are good at working in the kind of messy environment of weighing competing objectives.

And what AI is potentially useful for is supporting that type of work. In many, many cases, AI is better as a collaborator. And so increasingly, the work that we do are decision making is important. The stakes are very high. You know, in medicine, in law, in construction, in child care, in skilled repair. So having tools that help us do that well would be great. For years, people predicted that AI would replace professions like radiology. What has actually happened so far?

And what does that tell us about AI and expertise? Radiology is a good example. It's now very widely discussed one. You know, Jeffrey Hinton, who's the inventor of neural nets, one of the inventors and won the Nobel Prize for that, you know, said about 10 years ago, oh, you know, within five years, perfectly obvious, we don't need radiologists anymore, machines will just be better at this than people. And there is now a ton of AI in radiology,

and it's a very good tool, but it has not displaced radiologists. They're busier than ever. Why is that? Well, reading X-rays is part of what they do, but a lot of what they do is they integrate other information. They care for pages more holistically. They talk with other experts,

and they also read much more supporting information. It's not just looking at X-rays. So this tool definitely makes them better of what they do, their limitations as well. But so far, the job of radiologists is much, much broader than just reading scans. Now, to be clear, this could change. You know, it may be at the moment, it's a very strong complement and collaborator with radiologist, maybe at some point.

It really will be an autonomous actor. I don't want to say that won't happen, right? So if you think about, like, the progress of taxi and chauffeur services, we got Uber and Lyft, they provide, you know, navigation services and they tell the drivers where to go and so on. but the next wave, there will be no drivers. It's always a moving frontier. And one shouldn't say it's forever, you know, one or the other, and it's moving fast. So a lot of things could change.

But at the moment in radiologists, it's definitely the case that this tool is a better collaborator than is a pure automator. And I suspect that will be true for quite a while. You've said that AI could help build middle income jobs. How exactly could that happen? How we could use AI. It's really saying, like, let's target a set of activities where we think we could create openings, where we could give better tools to allow people to do more valuable work. This could be in software, many times in healthcare, could be in law, could be in skilled

trades in construction. And the idea is to try to reduce barriers to entry, give people foundational training and better tools. So there's an easier way in that uses foundational expertise more effectively. It doesn't mean you do it overnight. It means you get the right training. and then you're the right tools. And there's still layers of expertise.

Again, this doesn't eliminate lawyers, doesn't eliminate doctors. It just says there are sets of activities that can be done by capable, qualified people, the right tools who aren't the most expensive people in the room. This is not a frictionless process. The adjustments are painful. And it's possible that we'll get more competition for the professions. And I don't think that would be a bad thing. The professions are highly paid.

They're highly secure. They're very expensive. And we are dependent upon them for health care, for our children's education, for, you know, our legal services, for the code that they write for us, for the buildings they built. And so, you know, we have a collective interest in making those things more productive, more affordable, but also, ideally, having more people engaged in the middle of that who can do that work and where it creates opportunity. How can we prepare workers for an AI-driven economy?

I think the fundamentally valuable human skills are going to remain, you know, one, specialized domain knowledge in some area where they can, and two, is really the sort of judgment and flexibility to work in a very noisy environment where you have all kinds of inputs, all kinds of data of unknown quality, and you have to make high stakes decisions. To do that well, people need to be able to communicate. They need to be able to think analytically. They need to actually understand statistics, sadly. and make sound decisions in this very complicated world. AI offers great tools for that. It also offers great risk because people can use it in a way that interferes and undermines the learning process. And that, I think, it's a totally separate discussion, but a very legitimate concern. You know, you should recognize, you know, even more in the labor market, AI has great potential and great risk.

Some worry that if AI makes workers much more productive, will simply need fewer of them. And there are other areas where productivity, gains and lower prices could actually increase demand for workers. Where do you see both of those situations? What kind of industries? Will we need fewer workers? And where might we have lower prices and need more workers? Productivity growth is not the problem that most people should be worrying about. This was also the case in the 1960s during the Kennedy administration, the Johnson administration, the Blue Ribbon Commission on automation and says, if we keep having this much productivity growth, there just won't be enough demand for us to use up all this stuff and people run out of work

because we're just making too much of everything. This has never occurred. People's consumption desires rise at least as fast as productivity. The concern should be about whether the expertise that people have is somehow commodified such that it doesn't matter how much we don't need the people or we don't have to pay them much because everyone can do it. That should be our concern. So I'm not worried about us getting more productive.

that would be really good news, really good news. I'm worried about us devaluing labor. Right now, six out of every $10 in the economy is first paid to workers before it goes into the rest of the economy. The other four is paid to capital. I'm worried about that fraction falling a lot. That would be a very serious problem. And what happens if expertise essentially becomes a commodity?

We would need a new form of income. I mean, if we were in a world without labor scarcity, then we would need a totally different system of income distribution. There would still be scarcity. There's only so much prestige and esteem. There's going to be scarcity. It's going to be competition. But if labor itself, which is the asset most of us possessed from which we draw our incomes,

if all of a sudden that were zeroed out, then we would have incredible productive abundance, and yet most people wouldn't have a claim on it. What would they buy it with? So, again, I think that's a much, much longer discussion, but I think it's a scenario that if we could avoid it, we should. I think that would be a very serious societal challenge, profound one. What's the optimistic version of the future of work? Work is much, much better than it was 100 years ago for almost everybody.

It's less precarious, it's less dangerous, people work fewer hours, and they have higher standards of living. And many people think, oh, we're all still working so hard, even though we have all this technology, doesn't that prove that Keynes was wrong? But in fact, the average American works under 2,000 hours a year. They worked over 3,000 hours a year 100 years ago. We entered labor force later in life. We retire when we have decades of healthy years ahead of us. We have not squandered all this productivity growth by just turning ourselves into work slave.

That's a public misconception. If we had a world where we were much more productive, we could have more leisure. We could have better health care. We'd still hopefully have to work. We'd still be needed. But with the same amount of work, we could have more leisure, better health, probably the most important, and enjoyment of our time. So that would be a great future. It's not out of reach. In fact, if we're as productive as we think we will be with AI, then it's much more accessible than

it's ever been. The challenges that we face are much more ones, I think, about the distribution of that productivity growth, because so much of that depends on the labor market. For people feeling anxious about their jobs or young people starting out on their careers, what's the single most important thing you want them to understand about AI and their future? AI is a tool. It's not a force onto itself and that you should try to figure out how to use it effectively to complement what you do. And whatever you do, you should be saying, well, how can I use this tool to increase what I can do? The quality, the quantity. I think that's the opportunity. But the way to think about it is I want to use it to collaborate, to take what I can do and make me more effective with it.

David, what are the three takeaways you'd like to leave the audience with today? The first is when people are worrying about the quantity of jobs are worrying about the wrong thing. They should be worried about the value of human expertise. We could have lots of employment, but at lower wages, we work even harder. But that wouldn't be a good world. The world we want is one where human expertise remains valuable, complemented by our technology. So we have higher standards of living for our work, not so we have to work harder. The second thing to recognize is, even if this works out well, let's say the world gets 5% better because of AI, those benefits will be very unevenly distributed. And people whose expertise is devalued can be very damaged by that. And

others will ride this wave successfully. And we've seen this with other transitions. So no matter how well we think it's going to go, we need to invest in and support the people who are going to bear the brunt of those costs. We should be preparing for that. The third thing I would say is so much of the discussion of AI is catastrophes. It's just focused on the worst case scenario. And those who are not focused on the worst case scenario, by the way, are doing the opposite, which is just like, it's going to be utopia. And both of those perspectives are very distortionary. We should be optimistic and pessimistic simultaneously. We should recognize there's going to be. be real transition costs and we're going to make some terrible mistakes. Simultaneously,

there's an incredible opportunity that we've never had. We've never had tools that could do things like this and they're going to be so important. And that's something to focus on because there's constructive opportunity there. David, this has been great. Thank you. Pleasure to be here. If you're enjoying the podcast and I really hope you are, please review us on Apple Podcasts or Spotify or wherever you get your podcasts. It really helps get the word out. If you're interested, you can also sign up for the Three Takeaways newsletter at Three Takeaways.com, where you can also listen to previous episodes.

You can also follow us on LinkedIn, X, Instagram, and Facebook. I'm Lynn Toman, and this is Three Takeaways. Thanks for listening.