Erik Brynjolfsson is the Jerry Yang and Akiko Yamazaki Professor and Senior Fellow at the Stanford Institute for Human-Centered AI (HAI), and Director of the Stanford Digital Economy Lab. He also is the Ralph Landau Senior Fellow at the Stanford Institute for Economic Policy Research (SIEPR), Professor by Courtesy at the Stanford Graduate School of Business and Stanford Department of Economics, and a Research Associate at the National Bureau of Economic Research (NBER).
Tom Mitchell is Founders University Professor in the Machine Learning Department in Carnegie Mellon University’s School of Computer Science.
by Matty Smith
Communications
November 22, 2024
7 min read
How can humans shape a future where AI benefits everyone? The National Academies of Sciences, Engineering, and Medicine have released a new report called “Artificial Intelligence and the Future of Work,” which takes an in-depth look at the relationship between AI and the workplace.
Co-chairs Erik Brynjolfsson, director of the Stanford Digital Economy Lab, and Tom Mitchell, Founders University Professor at Carnegie Mellon University, answered questions about the report.
What was the purpose of the report?
Tom Mitchell: Congress requested this report to provide a study by the U.S. National Academies of the “current and future impact of artificial intelligence on the workforce of the United States across sectors.” It builds on an earlier 2017 report on the same topic, which Erik and I co-chaired. A lot has happened in AI and the economy since 2017!
What were your hopes going into the report, and were they met?
Erik Brynjolfsson: We started working on this report in 2022, before ChatGPT was released. At the time, I knew that we were in the early stages of a technology revolution that was vastly increasing the power of AI—I’ve been using LLMs for a while already.
We assembled some of the absolute top experts from academia and industry to put together a report on the implications for the workforce in the economy. We hoped to provide the definitive source that people could turn to to better understand not only the technology, but also the implications for productivity, the workforce, education, and measurement issues. I’m delighted with the results and believe we delivered on that goal.
What findings surprised you?
TM: [When we began in 2022] I was assuming that physical robots would be one of the AI technologies that would have the greatest impact on jobs—things like self-driving vehicles, and assembly line work. When ChatGPT appeared, and other LLMs, we had to go back to square one in our thinking about where AI was headed, and what it meant for jobs.
As it turns out, LLMs operate in the mental world of knowledge work, in contrast to the physical world where robots work. Therefore, the impact on jobs is very different from what I expected when we got started.
What are the biggest changes since the 2017 study?
TM: The appearance of LLMs, and the impact the pandemic had on work habits such as remote work and the economy (for example, a big increase in online retail and services).
EB: One of the real pleasures of being able to work with Tom Mitchell again on a National Academies report was that we could compare what we learned this time to what we did in 2017. One of the biggest changes is what happened with measurement. Perhaps the most prominent conclusion of the earlier report was that we were flying blind due to a lack of good data on AI and its effects on the economy. In fact, we used the phrase ”flying blind” in an article for the journal Nature that we wrote to summarize our findings.
However, this time around the data is much improved. Not only has the government stepped up and helped and funded detailed research on AI technology adoption by over 800,000 firms but the private sector also has much more detailed and often real-time data about technology, wages, job postings, and other changes in the economy. There’s a real opportunity to combine these improved sources of data in a public-private partnership to get an even better understanding of AI and its effects.
What hasn’t changed in the way you’d like?
TM: In our 2017 report, we predicted that self-driving cars might be widespread by now—too optimistic a prediction—but we never predicted that by 2024 you’d be able to have intelligent conversations with computers.
What excites you most about the potential for AI to enhance education?
TM: I believe this is the decade when AI can change education for the better. Why?
First, we have known for a while that human tutoring significantly improves student learning.
Second, we now have multiple online education platforms that have taught millions of online students, who therefore have more experience teaching than a person could get even if they taught for a hundred years. We now have machine learning methods that use that data to learn and teach better.
Third, we have just begun to create new computer tutors that use LLMs like ChatGPT to teach in new ways, like having conversations with students about their confusions, and like co-writing essays with students. The opportunity is great, but we need to organize and fund this research if we want to turn this potential into reality.
EB: As an educator myself, I’m acutely aware that my industry has been badly lagging when it comes to using technology–after all, I often use a chalkboard that Aristotle himself would have been comfortable with. But with generative AI, we have the potential to transform education in a fundamental way. In particular, we know from research that students who get individualized tutoring can learn up to two standard deviations faster than students in a classroom where 20 or 30 kids are all instructed at the same pace. Of course, individual tutors have been far too expensive to provide for all kids.
Now LLMs like ChatGPT have the potential to change that. I already know a lot of kids, and for that matter, adults, including a Nobel Laureate, who use these tools daily for individualized tutoring to learn new subjects and to dive deeper into existing topics. What’s more, LLMs can be incredibly engaging and entertaining. In the coming years, I expect a revolution as developers create even better versions of these tools and millions of stu dents gain their benefits.
Is there anything in particular you’d like readers to focus on?
TM: One of the most important things we can do for the workforce is give them a clear, real-time picture of how the demand for different worker expertise is shifting over time, and what education opportunities are available to them to chart their own career path. A key part of this is actually collecting this real-time information. That will require public-private data partnerships that combine data from different companies that have real-time information about job openings, resumes, salary scales, and more.
We can do this, but it will require imaginative thinking about how to build these partnerships while respecting the legitimate privacy and competitive concerns of the organizations that have these data.
What would you hope to see if you did another report down the line?
EB: One thing I expect will happen if we do a report like this in the future is that we’re going to not just talk to talk but walk the walk. AI will be a much bigger contributor: helping us with the literature review, gathering the relevant data, analyzing it, and even assisting with the writing. We are acutely aware of some of the weaknesses and risks of AI, but also the real strengths, especially when it’s combined with human oversight.
Lastly, what do you hope people take away from the report?
TM: We strongly expect continuing technical progress in AI, and expect it to significantly impact the economy and the workforce. I hope readers of the report get that the future we’ll live in is not destined by technology—really, it will be determined by the decisions we make about how to use it. That includes decisions made by politicians, technologists, workers, and all of us.
EB: As we worked on this report, it became clear that there are some real opportunities for an amazingly better world when it comes to improvements in science, healthcare, and widely shared prosperity, but also a real risk in areas like privacy, bias, democracy, national defense–even catastrophic risk. We laid out different scenarios and discussed some of the choices that we can make individually and collectively.
Our most important conclusion was that there is no predetermined future, but rather our choices will determine our future. In fact as the tools become more powerful, our choices become ever more consequential.
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