Erik Brynjolfsson
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Erik Brynjolfsson is an American economist who is among the most influential scholars studying the economics of information technology and artificial intelligence. He is the Jerry Yang and Akiko Yamazaki Professor and a Senior Fellow at the Stanford Institute for Human-Centered AI (HAI), where he founded and directs the Stanford Digital Economy Lab. [1][2][3] Brynjolfsson spent three decades on the faculty of MIT, from 1990 to 2020, before moving to Stanford University, and across that career he has been one of the first economists to measure the productivity contributions of computing and, more recently, of generative AI. [1][2]
To a general audience Brynjolfsson is best known as the co-author, with Andrew McAfee, of the bestseller "The Second Machine Age" (2014) and its companion volumes "Race Against the Machine" (2011) and "Machine, Platform, Crowd" (2017). [1][2] Within economics he is known for early work on the information technology "productivity paradox," for the "Productivity J-Curve" and "GDP-B" frameworks for measuring the digital economy, and for the 2022 essay "The Turing Trap," which warns against building AI that merely imitates and replaces people. In 2016 he co-founded the AI Index, the widely cited annual report on the state of the field, and he is one of the most-cited authors on the economics of information. [1][3]
Brynjolfsson was born in Roskilde, Denmark, and is a United States citizen of Icelandic descent. [1] He earned a bachelor's degree, magna cum laude, in applied mathematics in 1984 and a master's degree in decision sciences, both from Harvard University. [1][2] He went on to MIT for doctoral study, completing a Ph.D. in managerial economics at the MIT Sloan School of Management in 1991 under the supervision of Thomas W. Malone. [1] His training combined formal mathematics with the economics of organizations, a pairing that shaped his lifelong interest in how technology, firms, and productivity interact.
Brynjolfsson served on the MIT faculty from 1990 until 2020. [1][2] He was a professor at MIT Sloan, where he held the Schussel Family Professorship of Management, and he built and led two of the school's main research centers on technology and the economy: he directed the MIT Center for Digital Business and then the MIT Initiative on the Digital Economy. [1][2] Throughout this period, and continuing today, he has been a research associate at the National Bureau of Economic Research (NBER). [2][3]
In 2020 Brynjolfsson moved to Stanford, where he assembled his research group as the Stanford Digital Economy Lab and was named the Jerry Yang and Akiko Yamazaki Professor and a Senior Fellow at Stanford HAI. [2][3] He is also the Ralph Landau Senior Fellow at the Stanford Institute for Economic Policy Research (SIEPR) and a professor by courtesy at the Stanford Graduate School of Business and in the Stanford Department of Economics. [2][3] His lab studies how digital technologies, and AI in particular, reshape productivity, labor markets, and economic measurement.
Brynjolfsson has written nine books and more than 100 academic articles, and he holds five patents. [2] His most widely read books, several of them co-authored with Andrew McAfee, popularized the argument that digital technologies are entering a phase of rapid, compounding capability that will transform work and the economy.
| Year | Book | Co-author |
|---|---|---|
| 2010 | Wired for Innovation: How Information Technology Is Reshaping the Economy | Adam Saunders [1] |
| 2011 | Race Against the Machine | Andrew McAfee [1] |
| 2014 | The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies | Andrew McAfee [1] |
| 2017 | Machine, Platform, Crowd: Harnessing Our Digital Future | Andrew McAfee [1] |
Brynjolfsson came to prominence in the 1990s by tackling the "productivity paradox," the puzzle, summarized by Robert Solow, that the computer age was visible everywhere except in the productivity statistics. [1] His research showed that the gains from information technology appear only once firms make large complementary investments in reorganizing work, retraining people, and building new business processes. He argued that this organizational capital and other intangible assets are systematically undercounted, which obscures the true contribution of computing to the economy. [1] This complementarities view, that technology pays off only alongside changes in how organizations operate, became a foundation for much of his later work.
In a 2021 paper in the American Economic Journal: Macroeconomics, Brynjolfsson, with Daniel Rock and Chad Syverson, formalized this idea as the Productivity J-Curve. [4] General purpose technologies such as AI require heavy complementary investments that are largely intangible and poorly captured in national accounts. As a result, official productivity growth is first understated, while firms are quietly building the intangible capital needed to use a new technology, and later overstated, when those investments are harvested. The trajectory traces a J shape. The framework offered an explanation for why the measured productivity benefits of AI can lag years behind the technology's apparent capability.
Brynjolfsson has also argued that standard statistics miss much of the value created by free digital goods, because gross domestic product counts spending rather than consumer welfare. With Avinash Collis, W. Erwin Diewert, Felix Eggers, and Kevin J. Fox, he proposed an alternative metric, GDP-B, that uses large incentive-compatible choice experiments to estimate how much people value goods they do not pay for. [5] In one application, the team estimated that including the welfare gains from Facebook would have added between 0.05 and 0.11 percentage points per year to United States GDP-B growth, value that conventional GDP records as roughly zero. [5] The work has fed directly into debates among statistical agencies about how to measure economic growth in a digital age.
In 2022 Brynjolfsson published "The Turing Trap: The Promise and Peril of Human-Like Artificial Intelligence" in Daedalus, the journal of the American Academy of Arts and Sciences. [6] The essay draws a sharp distinction between two uses of AI: automation, in which machines substitute for and replace human workers, and augmentation, in which machines extend what people can do. He argues that pursuing human-like AI, the kind that can pass Alan Turing's 1950 imitation game, creates excess incentives to automate, which concentrates wealth and power and erodes workers' bargaining position. [6] Augmentation, by contrast, tends to create new capabilities and lets more people share in the gains. Brynjolfsson calls for shifting incentives toward augmentation and for replacing the Turing Test as a goal with new benchmarks that reward AI for accomplishing things no human could do alone. The "Turing Trap" has become a frequently cited frame in policy discussions about the direction of AI development.
Since the release of large language model systems such as ChatGPT in late 2022, Brynjolfsson's lab has produced some of the most cited empirical evidence on how generative AI affects real workers.
In "Generative AI at Work," first circulated as an NBER working paper in April 2023 and published in the Quarterly Journal of Economics in 2025, Brynjolfsson, Danielle Li, and Lindsey R. Raymond studied the staggered rollout of a generative AI conversational assistant to 5,179 customer-support agents at a software company. [7][8] Access to the tool raised productivity, measured as customer issues resolved per hour, by about 14 percent on average. [7] The gains were highly uneven: novice and lower-skilled agents improved by roughly 34 percent, while the most experienced and highly skilled agents saw little change. [7] The authors found that the AI worked in part by capturing and spreading the tacit knowledge of the firm's best workers, helping newcomers move down the experience curve faster, and that it also improved customer sentiment and employee retention. [7] The study became an early and influential data point in arguments that generative AI may compress skill gaps rather than simply replace labor.
In 2025 Brynjolfsson turned to economy-wide labor-market effects. In a Digital Economy Lab paper titled "Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of Artificial Intelligence," he, with Bharat Chandar and Ruyu Chen, analyzed high-frequency payroll microdata from ADP, the largest United States payroll processor. [9] They reported that since the widespread adoption of generative AI in late 2022, early-career workers aged 22 to 25 in the occupations most exposed to AI had experienced roughly a 13 percent relative decline in employment, even after controlling for firm-level shocks, while employment for older workers and in less-exposed jobs held steady or grew. [9] The authors framed the result not as evidence of mass layoffs but as a sign that AI was beginning to change who gets hired, with particular consequences for entry-level work and the traditional career ladder. [9]
Brynjolfsson is one of the most-cited scholars on the economics of information and digital technology, and he has received numerous honors and best-paper awards over his career. [1][2]
| Year | Honor |
|---|---|
| 2011 | Distinguished Fellow, Information Systems Society [1] |
| 2016 | Co-founder, AI Index (continuing member of its Steering Committee) [1][3] |
| 2020 | Honorary doctorate, University of Turku [1] |
| 2022 | Inaugural Fellow, Schmidt Sciences AI2050 initiative [10][11] |
He has also taken on public-service roles connecting his research to policy. He co-chaired National Academies committees on automation and the future of work, including a 2017 study on automation and the United States workforce and a later study, with Tom Mitchell, on artificial intelligence and the future of work, and he has testified before Congress on the economic effects of AI. [1] As director of the Stanford Digital Economy Lab and a co-founder of the AI Index, he remains, as of 2026, one of the most prominent academic voices on how AI will reshape productivity, jobs, and economic measurement. [2][3]