About

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 holds the position of Ralph Landau Senior Fellow at the Stanford Institute for Economic Policy Research (SIEPR), is a Professor by Courtesy at the Stanford Graduate School of Business and the Stanford Department of Economics, and serves as a Research Associate at the National Bureau of Economic Research (NBER). His research focuses on the effects of information technologies on business strategy, productivity and performance, digital commerce, and intangible assets. As a best-selling author, Brynjolfsson writes and speaks to global audiences about these topics, contributing significantly to the understanding of how digital technologies transform economies and societies.

Research topics

• Computer Science
• Economics
• Business
• Sociology
• Political Science
• Engineering
• Artificial Intelligence
• Statistics
• Demography
• Economic growth
• Geography
• Engineering ethics
• Psychology
• Mathematics
• Econometrics
• Law
• Data science
• Demographic economics
• Medicine
• Social psychology
• Macroeconomics
• Physics
• Neoclassical economics
• Management science

Selected publications

• A Research Agenda for the Economics of Transformative AI

  National Bureau of Economic Research · 2025-09-01 · 10 citations

  reportOpen access1st authorCorresponding

  As we approach Transformative Artificial Intelligence (TAI), there is an urgent need to advance our understanding of how it could reshape our economic models, institutions and policies.We propose a research agenda for the economics of TAI by identifying nine Grand Challenges: economic growth, innovation, income distribution, decision-making power, geoeconomics, information flows, safety risks, human well-being, and transition dynamics.By accelerating work in these areas, researchers can develop insights and tools to help fulfill the economic potential of TAI.

  PublisherDOI

• Experiment Registration for The Consumer Welfare Effects of Online Ads: Evidence from a 9-Year Experiment

  AEA Randomized Controlled Trials · 2025-06-02

  datasetSenior author
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• Gains from Product Variety: Evidence from a Large Digital Platform

  Information Systems Research · 2025-02-06 · 4 citations

  article1st authorCorresponding

  Digital platforms can increase product variety and consumer choice by facilitating the discovery and availability of new products. In this paper, we document the massive growth of new products on the largest digital platform in China and quantify the welfare implications for consumers. Using sales data on three categories of books from 2015 and 2019, we find that the number of product titles almost doubles, whereas prices fall somewhat. Most of the new products are niche offerings that exhibit less elastic demand. Accounting for the niche nature of new products generates welfare gains 40 times larger than those from lower prices and 30% higher than existing estimates that do not distinguish between mass and niche products. We also examine the geographic variation in these gains and find that consumers in rural and low-income regions enjoy greater benefits from increased variety. The findings emphasize that expanding niche product offerings may outperform price reductions in generating consumer benefits. Policymakers are encouraged to support e-commerce development in underserved regions to harness the inclusive growth enabled by digital platforms.

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• Experiment Registration for The Consumer Welfare Effects of Online Ads: Evidence from a 9-Year Experiment

  AEA Randomized Controlled Trials · 2025-06-02

  datasetSenior author
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• Generative AI at Work

  The Quarterly Journal of Economics · 2025-02-04 · 445 citations

  article1st authorCorresponding

  Abstract We study the staggered introduction of a generative AI–based conversational assistant using data from 5,172 customer-support agents. Access to AI assistance increases worker productivity, as measured by issues resolved per hour, by 15% on average, with substantial heterogeneity across workers. The effects vary significantly across different agents. Less experienced and lower-skilled workers improve both the speed and quality of their output, while the most experienced and highest-skilled workers see small gains in speed and small declines in quality. We also find evidence that AI assistance facilitates worker learning and improves English fluency, particularly among international agents. While AI systems improve with more training data, we find that the gains from AI adoption are largest for moderately rare problems, where human agents have less baseline experience but the system still has adequate training data. Finally, we provide evidence that AI assistance improves the experience of work along several dimensions: customers are more polite and less likely to ask to speak to a manager.

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• A Definition of AGI

  ArXiv.org · 2025-10-21

  preprintOpen access

  The lack of a concrete definition for Artificial General Intelligence (AGI) obscures the gap between today's specialized AI and human-level cognition. This paper introduces a quantifiable framework to address this, defining AGI as matching the cognitive versatility and proficiency of a well-educated adult. To operationalize this, we ground our methodology in Cattell-Horn-Carroll theory, the most empirically validated model of human cognition. The framework dissects general intelligence into ten core cognitive domains-including reasoning, memory, and perception-and adapts established human psychometric batteries to evaluate AI systems. Application of this framework reveals a highly "jagged" cognitive profile in contemporary models. While proficient in knowledge-intensive domains, current AI systems have critical deficits in foundational cognitive machinery, particularly long-term memory storage. The resulting AGI scores (e.g., GPT-4 at 27%, GPT-5 at 57%) concretely quantify both rapid progress and the substantial gap remaining before AGI.

  PublisherOA PDFDOI

• LLM Time Machines: Valuing Digital Goods Over Time

  SSRN Electronic Journal · 2025-01-01 · 1 citations

  preprintOpen accessSenior author
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• LLM Social Simulations Are a Promising Research Method

  ArXiv.org · 2025-04-03 · 4 citations

  preprintOpen access

  Accurate and verifiable large language model (LLM) simulations of human research subjects promise an accessible data source for understanding human behavior and training new AI systems. However, results to date have been limited, and few social scientists have adopted this method. In this position paper, we argue that the promise of LLM social simulations can be achieved by addressing five tractable challenges. We ground our argument in a review of empirical comparisons between LLMs and human research subjects, commentaries on the topic, and related work. We identify promising directions, including context-rich prompting and fine-tuning with social science datasets. We believe that LLM social simulations can already be used for pilot and exploratory studies, and more widespread use may soon be possible with rapidly advancing LLM capabilities. Researchers should prioritize developing conceptual models and iterative evaluations to make the best use of new AI systems.

  PublisherOA PDFDOI

• The Rise of Industrial AI in America

  SSRN Electronic Journal · 2025-01-01 · 9 citations

  preprintOpen access
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• Toward understanding the impact of artificial intelligence on labor

  UNC Libraries · 2025-07-18

  articleOpen access

  Rapid advances in artificial intelligence (AI) and automation technologies have the potential to significantly disrupt labor markets. While AI and automation can augment the productivity of some workers, they can replace the work done by others and will likely transform almost all occupations at least to some degree. Rising automation is happening in a period of growing economic inequality, raising fears of mass technological unemployment and a renewed call for policy efforts to address the consequences of technological change. In this paper we discuss the barriers that inhibit scientists from measuring the effects of AI and automation on the future of work. These barriers include the lack of high-quality data about the nature of work (e.g., the dynamic requirements of occupations), lack of empirically informed models of key microlevel processes (e.g., skill substitution and human-machine complementarity), and insufficient understanding of how cognitive technologies interact with broader economic dynamics and institutional mechanisms (e.g., urban migration and international trade policy). Overcoming these barriers requires improvements in the longitudinal and spatial resolution of data, as well as refinements to data on workplace skills. These improvements will enable multidisciplinary research to quantitatively monitor and predict the complex evolution of work in tandem with technological progress. Finally, given the fundamental uncertainty in predicting technological change, we recommend developing a decision framework that focuses on resilience to unexpected scenarios in addition to general equilibrium behavior.

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Frequent coauthors

• Avinash Collis

  85 shared

• Marshall W. Van Alstyne

  Boston University

  66 shared

• Lorin M. Hitt

  University of Pennsylvania

  42 shared

• Felix Eggers

  University of Groningen

  41 shared

• Sinan Aral

  Massachusetts Institute of Technology

  37 shared

• Yu Jeffrey Hu

  Purdue University West Lafayette

  36 shared

• W. Erwin Diewert

  UNSW Sydney

  30 shared

• Daniel Rock

  University of Pennsylvania

  27 shared

Labs

• Stanford Digital Economy LabPI

Education

• Ph.D., Economics

  Massachusetts Institute of Technology (MIT)

  1987

• B.A., Economics

  Harvard University

  1982

Awards & honors

• Ralph Landau Senior Fellow at SIEPR