This is a column about AI. My fiancé works at Anthropic. See my full ethics disclosure here.

Last week, I sat down with Aaron Levie, the CEO of Box, who made what I thought was a pretty strong case that jobs are just harder to automate away than AI companies keep telling us. Aaron's argument went something like: the last mile of human work — judgment, context, the messy parts — doesn't actually get automated anytime soon, and companies are about to have many more humans and agents working together, which means we can keep our jobs. I left that conversation pretty optimistic.

This week, I wanted to put that optimism in front of somebody who has spent his whole career trying to measure — sometimes at the level of whole economies — what new technology actually does to work. James Manyika is a senior vice president at Google and Alphabet, where he runs Google's research and labs operations, along with a team the company calls technology and society — an effort to consider the broader consequences of AI systems like Gemini and develop Google's strategy around them.

There's much to consider. As I discuss with Manyika in this conversation, seven in 10 Americans now oppose data center construction in their communities. And the message that many Americans have gotten about AI from the industry itself — first we'll take your job, and eventually we might kill you — clearly hasn't rallied many people to Big Tech's banner.

Given his role, it's no surprise that Manyika takes a more optimistic view: jobs are harder to automate than Silicon Valley often gives them credit for, he says, and the process will unfold more slowly than some of more aggressive predictions that radiate out of other frontier AI companies. (This is a view shared by some of Manyika's fellow Google executives: DeepMind CEO Demis Hassabis warned Wired today that it may be a mistake to replace software developers with AI tools. “I think it's a lack of imagination—and a lack of understanding of what's really going to happen,” he said.)

But unlike most Big Tech executives, Manyika developed his views during a long career outside Silicon Valley. A longtime McKinsey executive, Manyika co-authored a paper about the potential effects of automation on labor nearly a decade ago. He has since co-chaired the UN Secretary-General's high-level advisory body on AI and served as vice chair of the National AI Advisory Committee under President Biden.

And so when leaders at rival companies like Microsoft and Anthropic insist that a significant portion of white-collar work is about to disappear, Manyika is skeptical.

"Some of those predictions were made two years ago — that in two years, 50% of jobs would be wiped out," Manyika told me. "Well, two years is up. Let's take a look. And anybody who makes that prediction for two years from now, I'm willing to take the bet."

Highlights of our conversation follow. Highlights of our conversation are below, edited for clarity and length. We also hope you’ll listen to the entire conversation wherever you get your podcasts — just search for Platformer — or watch it on YouTube at youtube.com/caseynewton.

And let us know what you think — we’re new to podcast production, and welcome your feedback at casey@platformer.news. 

Casey Newton: You did your PhD in AI and robotics at Oxford decades before AI became the biggest story in the world. What did you believe or see back then that most people were missing?

James Manyika: I'll take you back even before that. I did my undergraduate degree at the University of Zimbabwe, and my undergraduate thesis was actually the first paper I ever published. Guess what it was on? AI — training and modeling an artificial neural network. There was a postdoc visiting from Canada who had worked as part of the Montreal–Toronto Geoff Hinton crew, and he suggested I should build a neural network for my undergraduate thesis. That was the very first thing I ever published, in 1993.

Newton: Well before folks like me were spending every waking hour reading and writing about this. What captured your interest?

Manyika: Two things. I grew up on Star Trek, so the idea of AI fascinated me. I watched 2001: A Space Odyssey. But I was also just intrigued by the idea that it would be possible to build systems that can do advanced cognitive tasks. So when I went to Oxford, I did my PhD in AI and robotics to continue pursuing this.

Newton: You've since spent a good portion of your career trying to measure how technologies change economies. You spent a long while at McKinsey, where you wrote a paper called "Jobs Lost, Jobs Gained" almost a decade ago. Now you're inside Google, where you can see what happens when these tools actually land in workplaces. When you look at the debate we're having right now about the future of AI and jobs, where do you land?

Manyika: It's such an exciting moment. The technology and its capabilities are expanding at an incredible pace. But when you try to translate that into what it might mean for work and jobs and occupations, I get a very mixed view. It's roughly what that paper said 10 years ago, which I think is still correct: there will be some jobs that decline, there will be jobs that grow, and most importantly — a third aspect — a lot more jobs will change.

Whether you're looking at the aggregate economy, the sectoral level, or by occupation, you get a different mix of those three things happening. But all three things will happen. The research hasn't changed very much. The debate that people have is, what's the mix of those three things? As opposed to, are these three things going to happen.

Newton: Let me name a dynamic that may be on some listeners' minds. You are now employed by one of the biggest beneficiaries of the current AI boom. How do you tell when you're hearing the labor economist in your head and when you're hearing the SVP at Google?

Manyika: I hear both things. Less the SVP at Google — more so the AI researcher and computer scientist in me is extraordinarily excited about the pace of the technology. That part of me thinks, "Oh my goodness, this is going to be extraordinary, and it's going to happen very, very quickly."

The labor economist part of me says, "Hang on a second — these things don't actually play out that quickly in the economy, and the dynamics are more mixed." So I almost hear two speeds proceeding here. I often think that as AI researchers, our community tends to overstate what happens in the labor markets based on what we're seeing on the technology frontiers. These are two very different conversations.

Newton: At the McKinsey Global Institute, you found that about 50% of tasks would be automatable through AI, but only about 10% of occupations would be fully automatable. A few generations of AI later, does that ratio still sound right?

Manyika: All of the pieces have been moving. 

At the task level, many more tasks are now possible to automate — that picture's moved pretty quickly. But if you look at the composition of occupations — the Bureau of Labor Statistics tracks somewhere between 850 and a thousand real occupations — and ask how many existing occupations have the majority, call it 90%, of their constituent tasks automatable, that number is still under 10%. Most researchers will still say that.

How many tasks look like they're going to be hard to automate? Partly because AI can't do them yet, or because of coupled tasks where the weak link slows down the combination. If you take two tasks and can automate one of them, but they need to be done in a coupled way, you'll only go at the speed of the weakest link. Most jobs have these couplings that make full automation very difficult.

One other thing that's moved is task duration. If you had asked in 2017, of the tasks possible to automate, some were very short — 30 seconds or a minute is about as long as you could predictably do a task in an automated way. Now we can do some of those tasks for up to four-plus hours. The task duration with reasonably predictable completion has made tremendous progress.

Newton: So what I'm hearing is that if you measured the tasks that are automatable now, that number is trending much higher than 50%. But at the same time, the number of jobs you could fully automate is stubbornly holding in roughly the same place it was 10 years ago. What is your best explanation for that divide?

Manyika: Part of the divide is that we now understand more fully that whole jobs have a much more complex mix of tasks, and this idea of weak links or coupled tasks matters a great deal in most occupations. If you look across the whole economy at most complex tasks, we can't automate most of them. So the question of which whole jobs you can automate more than 90% is still a relatively small number.

Most of the debate among labor economists is whether in the next decade that number is more like 2 or 3% or more like 9 or 10%. I don't think anybody who's looked at whole-job automation would say it's 50% or any of these extraordinarily large numbers.

That's why I come back to the view that three things will happen. Yes, there will be some job declines. But there'll also be jobs that grow — that's a function of existing jobs that grow in demand because the technology often changes the demand picture, and new jobs get created. We forget that David Autor and others have shown that if you went back to 1945 and compared to today, something like over 60% of the jobs we have in the economy today didn't exist back then, and many were introduced as a result of technological shifts that created the category.

But the biggest effect is the jobs-changed part. The nature of the job itself shifts. This is what happened with bank tellers. This is what happens with radiologists. We still have the category "bank teller," but I can guarantee what a bank teller does today is not what a bank teller in 1970 did.

Newton: I want to press on that optimistic picture. If that was all that happened in the next two or three years, I'd be breathing this deep sigh of relief. On the other hand, you have folks like Mustafa Suleyman at Microsoft saying he thinks that within 18 months, all white-collar work will be automatable. Dario Amodei at Anthropic is predicting very high unemployment as a result. When you look at statements like that, do you think those guys are wrong? Are they missing something?

Manyika: I'll just say: let's take the bet. Some of those predictions were made two years ago — that in two years, 50% of jobs would be wiped out. Well, two years is up. Let's take a look. And anybody who makes that prediction for two years from now, I'm willing to take the bet.

There's an extraordinary unevenness when it comes to things playing out in the labor markets that we forget. I live in San Francisco, and we've had driverless cars here for three years. But somebody in another city like Chicago has no idea what we're talking about. We often talk about the jaggedness of the technology — I think that's true. There's also jaggedness in the economy in terms of how this plays out. Don't get me wrong — I think it'll be faster than the Industrial Revolution, but it won't be as fast as the technology often suggests.

I'm happy to have this conversation a decade from now.

Newton: Let's bring it to Google. Since you've been there starting in 2022, what have you seen in terms of jobs lost, jobs created, jobs changed?

Manyika: A lot of jobs are changing. What software developers do is changing a lot. People now work with agents, they manage agents, they pose questions, they spend less time doing bug fixes.

Keep in mind that in this whole jobs conversation, we often forget what I'll call the demand elasticity of things. There are some activities where there's so much more we want to consume and do — we've just been limited by the ability to do that. Software development is one of those. The amount of software that could still be developed to build extraordinary things is very, very large. We haven't built all the software we're going to build. We haven't designed all the systems we're going to design.

That won't be true for every activity. There are some activities where the demand is quite frankly limited — there's only so much of something the economy needs. In those cases, you'll see trade-offs between jobs lost and jobs gained play out in a demand sense quite differently.

Newton: Even folks who agree with you on the big picture tend to worry a lot about the entry level. If you're talking to somebody graduating this June who says, "James, how do I approach the first part of my career?" — what are you telling them?

Manyika: The future is actually pretty exciting. The economies are going to grow, and there's going to be lots of opportunities. But what it will take to prepare for those opportunities is dramatically changing.

A decade ago, when people asked me what their kid should do, I'd ask how old the kid was. If they told me the kid was 18, I'd say they should learn to code. If they told me the kid was two, I'd say, "Hang on a second — you should think about what kind of skills are going to be important, because this AI thing is going to make a lot of progress."

What we said at the time was correct but may no longer be true — that coding was going to be important in the mechanical sense of churning out lines of code. Now the systems are able to do that. That doesn't mean computer science as a field has gone away. When I studied computer science as an undergraduate, the coding part was just one slice of what I had to learn. I had to learn algorithm design and so much more. We may need to go back to that, because we're finding that it's the more broadly educated, skilled computer scientists who are a lot more interesting than the ones whose only claim is the ability to generate lines of code.

One more thing. I was looking at the data the other day — the demand for software development jobs is actually going up. It's not that these jobs are going away, even in this moment. But the skills required are changing.

Newton: This is another tension in Silicon Valley I'm quite interested in. Just within the past week, I've talked with you, Aaron Levie from Box, and Nikesh Arora, the CEO of Palo Alto Networks. Both Aaron and Nikesh said, "Please send me more engineers. I don't have nearly enough engineers for what I want." At the same time, it's earnings call season, and other CEOs get on the call and say, "Well, we're getting rid of 5% of the workforce to prepare for the AI future."

Manyika: There's so much more going on than the AI effect. As somebody who's super excited about AI's impact on the economy, I'll say: not much has happened yet. I'm saying that both on the positive side and the negative side. On the positive side, at the economy level we've yet to see the productivity gains, which I'm looking forward to and excited about. But we also haven't seen much of the AI-driven labor impacts everybody's talking about.

There was a paper — I think it might have been the "Canaries in the Coal Mine" paper — and what I found interesting was that the sharp declines they showed happened around October 2022. ChatGPT didn't come out until November 2022, and adoption didn't really happen in the enterprise space until maybe 2023. So if the sharp declines in entry-level hiring happened in October 2022, you'd have to believe something else was going on. There's now been analysis showing a whole bunch of monetary effects in the labor markets, plus leftover hangover stuff from COVID. There may be a tiny sliver that's AI-driven, but a lot more of it is driven by other macroeconomic effects.

That's not to say we shouldn't worry about AI's labor market effects. We should. I just don't think they've happened yet at the scale anybody's concerned about.

Newton: Let's move further into speculation. When I talk with folks whose jobs are beginning to change due to AI, it seems like an increasingly large part of their job is reviewing AI output. Whereas once they spent a lot of time manually writing code, now they spend more time reviewing it. The thing about reviewing work is it doesn't always scratch that same creative itch. Could job change wind up being a different kind of job loss, because some jobs that once felt very creative now just feel like tedious drudgery?