3 Takeaways™
A Smarter, More Hopeful Future of Work - If We Get Artificial Intelligence Right (#284)
13 Jan 2026
Chapter 1: What are the alarming predictions about AI and jobs?
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. Geoffrey 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 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 Thoman, 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.
Chapter 2: How could AI actually transform the job market?
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.
Chapter 3: What role does expertise play in the future of work?
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, working 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 forward.
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 training or certification the hope is we could move more people back into the middle not into the same occupations that previously 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 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 guardrails to use their knowledge effectively.
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Chapter 4: How can AI create more middle-class job opportunities?
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 Solomons 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 you might say, well, what does new work exactly mean? 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.
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Chapter 5: What historical lessons can we learn from past technological advancements?
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 set 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.
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Chapter 6: What happens to workers when their jobs are automated?
But what about the workers whose 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 if all of a sudden it's just, you know, it gets cheaper to translate something, for example, that's great for consumers. It's great for businesses 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 closely related is in the manufacturing sector.
And especially during the 2000s with China's very, very rapid rise as a manufacturing exporter, especially as it joined 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. What this underscores is that the nature of demand or demand for skills can change much more rapidly than people can reskill.
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Chapter 7: How should we prepare for an AI-driven economy?
And most career transitions don't occur within a career. They occur across generations. Kids in 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.
Chapter 8: What is the optimistic vision for the future of work with AI?
In your view, how is AI genuinely different?
I don't think any two technologies have followed the same route. 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 and 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 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 where decision making is important.
The stakes are very high 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 a very widely discussed one. Jeffrey Hinton, who's the inventor of neural nets, one of the inventors and won the Nobel Prize for that, said about 10 years ago, oh, within five years, it's perfectly obvious we won'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.
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