Erik Brynjolfsson

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
• Artificial Intelligence
• Business
• Political Science
• Engineering
• Sociology
• Macroeconomics
• Medicine
• Neoclassical economics

Selected publications

• On the Opportunities and Risks of Foundation Models

  arXiv (Cornell University) · 2021 · 2169 citations

  AI is undergoing a paradigm shift with the rise of models (e.g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks. We call these models foundation models to underscore their critically central yet incomplete character. This report provides a thorough account of the opportunities and risks of foundation models, ranging from their capabilities (e.g., language, vision, robotics, reasoning, human interaction) and technical principles(…

  PublisherOA PDFDOI

• The Productivity J-Curve: How Intangibles Complement General Purpose Technologies

  American Economic Journal Macroeconomics · 2020 · 535 citations

  1st authorCorresponding

  General purpose technologies (GPTs) like AI enable and require significant complementary investments. These investments are often intangible and poorly measured in national accounts. We develop a model that shows how this can lead to underestimation of productivity growth in a new GPTs early years and, later, when the benefits of intangible investments are harvested, productivity growth overestimation. We call this phenomenon the Productivity J-curve. We apply our method to US data and find that…

  DOI

• 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-skille…

  PublisherDOI

• A causal test of the strength of weak ties

  Science · 2022 · 198 citations

  The authors analyzed data from multiple large-scale randomized experiments on LinkedIn's People You May Know algorithm, which recommends new connections to LinkedIn members, to test the extent to which weak ties increased job mobility in the world's largest professional social network. The experiments randomly varied the prevalence of weak ties in the networks of over 20 million people over a 5-year period, during which 2 billion new ties and 600,000 new jobs were created. The results provided e…

  PublisherDOI

• Artificial Intelligence Index Report 2024

  arXiv (Cornell University) · 2024-05-29 · 158 citations

  preprintOpen access

  The 2024 Index is our most comprehensive to date and arrives at an important moment when AI's influence on society has never been more pronounced. This year, we have broadened our scope to more extensively cover essential trends such as technical advancements in AI, public perceptions of the technology, and the geopolitical dynamics surrounding its development. Featuring more original data than ever before, this edition introduces new estimates on AI training costs, detailed analyses of the resp…

  PublisherOA PDFDOI

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

Similar researchers at Stanford University

• Brian Batemanh-105
• Melissa Bondyh-96
• Jud Campbellh-78