Erik Brynjolfsson, director of the Stanford Digital Economy Lab, has been researching and writing about the impact of digital technologies on work for decades. He has published some of the biggest studies on genAI and jobs, including early research showing the technology provided a meaningful productivity boost for less experienced customer-service workers, and a recent study suggesting that it has worsened job prospects for some groups of entry-level workers.
We recently spoke with Brynjolfsson, who is a cofounder of the startup Workhelix and whose books include The Second Machine Age, about where the AI transformation stands today and what capabilities workers should be building for what comes next. Here are highlights from that discussion, edited for length and clarity:
In your 2000 paper ‘Beyond Computation,’ you argued that companies need to make complementary investments and change their business processes to really capture the benefits of information technology. Where do you think we are today with those kinds of changes around AI?
It’s like rolling waves, it happens one after another. That said, I do think we’re still very early. There are new business models that are going to be invented—that are being invented—that we haven’t seen yet.
That ‘Beyond Computation’ paper was written in the year 2000. The internet was hot and everybody was excited about it. Jeff Bezos had said, ‘Let’s take bookstores and we can reinvent how they work.’ Thankfully, when he said that, he didn’t mean take the cashier and replace them with a robot. That would have been kind of a lame way to automate a bookstore. He came up with a whole new business model.
But it’s interesting that now we’re in 2025 and most retailing is still done in physical stores. Books, software, a lot of that’s moved to the cloud. But total retail sales in the United States—every year, more and more of it is done online, but it takes decades for this transition to happen in the economy. So we can look at the current situation and see how the internet is taking time to transform industries like retail. It’s happening, nobody would say it was a dud. But it takes a long time.
Likewise, AI will play out over some number of years or decades and continue to transform the economy. The tech-industry people are very enthusiastic about these [models’] capabilities, and I agree, they’re breathtaking. They can win the Math Olympiad gold medal, etc. That’s awesome. But then people in the rest of the world are like, ‘Well, where’s the productivity? Is it really changing my insurance company? Is automobile manufacturing completely different now?’ And in most cases you have to squint really hard to see any difference at all.
That gap between capabilities and actual implementation is what I talked about in ‘Beyond Computation,’ and it’s self-evident that we are a long way from closing that gap. I believe it’s going to happen a lot faster than it did with electricity or with the internet, but it will still take years.
You’ve written about all of the wealth that digital technologies have created, but also the inequality they’ve unleashed. How do you see AI impacting inequality?
It is a great question. There’s evidence going in both directions in different places. In my study of call centers with Lindsey Raymond and Danielle Li, it was striking how it was the least experienced workers and also the least productive workers—the ones who had been doing worse in advance—who got the biggest [productivity] boost. The top workers actually got basically zero boost. AI was really helping the less skilled workers.
That was the opposite of what happened through most of the past 20, 30 years with other kinds of IT [information technology], where it tended to be [what] economists call ‘skill-biased technical change.’ It tended to help the more skilled workers get even more productive and not do as much for less skilled workers. So previous technologies increased the gap. In the call center study, we saw a reduction. Other people have found similar patterns among coders and among management consultants, where the gap tightened a little bit. That could be encouraging.
At the same time, the capital-labor effect goes the other way. AI is shifting more value towards capital and away from labor. That will tend to increase inequality, partly because capital owners already tend to be wealthier, but also because capital income is way more skewed than labor income. So you’re just moving to a much more extreme set of concentration and inequality.
Your 2014 book The Second Machine Age makes it clear that it’s hard to predict which skills are ‘future-proof.’ But it’s likely easier to identify skills that complement current technologies. What skills do you think complement AI?
On your first point, I agree. A lot of the things that we said in The Second Machine Age held true for a while, but now some of them are flipping around. At the time, I didn’t think computers could be that creative, but LLMs and generative AI do have a kind of creativity, or [they] help with creativity.
On your second question, I would say leadership and management of agents, whether they’re human or non-human. I think increasingly we’re all going to be CEOs of teams of workers, and a lot of them will be non-human workers. Having the skills to identify a goal and specify it clearly and allocate it with clear instructions to the agents and have them work on that, and then have them come back and verify whether they’re doing the right performance—arguably even [with] some incentives and motivation—those are all skills that are super valuable when dealing with agents and with dealing with humans. Almost all of us are going to become CEOs.
Read the full transcript of our interview with Brynjolfsson.
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