About

Arun Sundararajan is the Harold Price Professor of Entrepreneurship and Professor of Technology, Operations, and Statistics at New York University’s Stern School of Business. He is also the Director of the Fubon Center for Technology, Business and Innovation and an affiliated faculty member at various interdisciplinary research centers, including the Center for Data Science. His expertise encompasses the economics of digital goods, network effects, regulation and governance of artificial intelligence, and digital platforms. His research focuses on artificial intelligence and intellectual property, generative AI, platform-enabled change, the convergence of digital and biosciences, market power, data sovereignty, antitrust policy for technology companies, and the digital future of work. Sundararajan has published extensively in academic journals and outlets such as The New York Times, The Financial Times, The Guardian, Wired, and Harvard Business Review, and has received numerous awards for his scholarship. He has provided expert testimony to various governmental bodies, including the U.S. Congress, the European Parliament, and the United Nations, and is a member of several global forums and initiatives related to AI governance and digital economy policy. Additionally, he advises organizations across sectors on issues of strategy, regulation, and AI governance, and teaches in executive education programs worldwide.

Research topics

• Economics
• Political Science
• Marketing
• Computer Science
• Business
• Psychology
• Advertising
• Development economics
• Microeconomics
• Finance
• Industrial organization
• Medicine
• Database
• Pathology
• History
• Virology
• Physics

Selected publications

• COVID-19 and Digital Resilience: Evidence from Uber Eats

  SSRN Electronic Journal · 83 citations

  • History
  • Virology
  • Medicine

  Using order-level data from Uber Technologies, we study how the COVID-19 pandemic and the ensuing shutdown of businesses in the United States in 2020 affected small business restaurant supply and demand on the Uber Eats platform. We find evidence that small restaurants experience significant increases in activity on the platform following the closure of the dine-in channel. We document how locality- and restaurant-specific characteristics moderate the size of the increase in activity through the digital channel and explain how these increases may be due to both demand- and supply-side shock. We observe an increase in the intensity of competitive effects following the economic shock and show that growth in the number of providers on a platform induces both market expansion and heightened inter-provider competition. Higher platform activity in response to the shock does not only have short-run implications: restaurants with larger demand shocks had a higher on-platform survival rate one year after the lockdown, suggesting that the platform channel contributes towards long-run resilience following a crisis. Our findings document the heterogeneous effects of platforms during the pandemic, underscore the critical role that digital technologies play in enabling business resilience in the economy, and provide insight into how platforms can manage competing incentives when balancing market expansion and growth goals with the competitive interests of their incumbent providers.

  DOI

• Experiment-Share-Scale: A Crowd-Based Framework for Getting Started with Artificial Intelligence in Business School Classrooms

  SSRN Electronic Journal · 2026-01-01

  preprintOpen accessSenior author
  PublisherDOI

• Platform Design, Earnings Transparency and Minimum Wage Policies: Evidence from A Natural Experiment on Lyft

  Open MIND · 2026-02-09

  preprintSenior author

  We study the effects of a significant design and policy change at a major ridesharing platform that altered both provider earnings and platform transparency, examining how it affected outcomes for drivers, riders, and the platform, and providing managerial insights on balancing competing stakeholder interests while avoiding unintended consequences. In February 2024, Lyft introduced a policy guaranteeing drivers a minimum fraction of rider payments while increasing per-ride earnings transparency. The staggered rollout, first in major markets, created a natural experiment to examine how earnings guarantees and transparency affect ride availability and driver engagement. Using trip-level data from over 47 million rides across a major market and adjacent markets over six months, we apply dynamic staggered difference-in-differences models combined with a geographic border strategy to estimate causal effects on supply, demand, ride production, and platform performance. We find that the policy led to substantial increases in driver engagement, with distinct effects from the guarantee and transparency. Drivers increased working hours and utilization, resulting in more completed trips and higher per-hour and per-trip earnings, with stronger effects among drivers with lower pre-policy earnings and greater income uncertainty. Increased supply also generated positive spillovers on demand. We also find evidence that greater transparency may induce strategic driver behavior. In ongoing work, we develop a counterfactual simulation framework linking driver supply and rider intents to ride production, illustrating how small changes in driver choices could further amplify policy effects. Our study shows how platform-led interventions present an intriguing alternative to government-led minimum pay regulation and provide new strategic insights into managing platform change.

  DOI

• Platform Design, Earnings Transparency and Minimum Wage Policies: Evidence from A Natural Experiment on Lyft 

  SSRN Electronic Journal · 2026-01-01

  preprintOpen accessSenior author
  PublisherDOI

• Generative AI Governance

  2025-08-06

  book-chapterSenior author

  Given the significant barriers to creating high-quality foundation models (cost of collection of training data, need for access to immense computing power), a small number of primarily closed-source foundation models are establishing leadership in the generative AI market. Applications based on these foundation models are being deployed by a number of firms across multiple sectors.

  PublisherDOI

• How Corporate Boards Must Approach AI Governance

  SSRN Electronic Journal · 2025-01-01 · 3 citations

  preprintOpen access1st authorCorresponding
  PublisherDOI

• Naive Algorithmic Collusion: When Do Bandit Learners Cooperate and When Do They Compete?

  SSRN Electronic Journal · 2025-01-01

  preprintOpen accessSenior author
  PublisherDOI

• Fairness principles across contexts: evaluating gender disparities of facts and opinions in large language models

  AI and Ethics · 2025-12-05

  articleOpen accessSenior author

  Abstract This paper examines how fairness principles differ when evaluating large language model (LLM) outputs in fact-based versus opinion-based contexts, focusing on gender disparities in responses related to notable individuals. Using prompts designed to elicit either factual information (identifying Nobel Prize winners) or subjective judgments (identifying the most accomplished figures in a field), we analyze responses from GPT-4, Claude, and Llama-3. For fact-based tasks, fairness is assessed through correctness and refusal rates, revealing minimal gender disparities when models achieve high accuracy, although refusal patterns can vary by model and gender. For opinion-based tasks, where no single correct answer exists, fairness is operationalized through representational metrics such as demographic parity and disparate impact. Results show substantial gender disparities in opinion-based outputs across all models, with representation shaped by prompt wording (e.g., “important” vs. “prestigious”), subject domain, and inclusion of secondary answers. However, the highly skewed context makes the final assessment about fairness challenging. Our findings highlight that fairness metrics and interpretations must be contextualized by output type. Performance parity is an appropriate goal for fact-based outputs, whereas representational inclusivity is central for opinion-based outputs. Representational inclusivity alone may not be sufficient when the context for the LLM’s task differs from the population. We discuss theoretical implications for fairness evaluation, noting that high performance can mitigate disparities in factual contexts but that opinion-based contexts require more nuanced, values-driven approaches.

  PublisherOA PDFDOI

• Incentives for Digital Twins: Task-Based Productivity Enhancements with Generative AI

  ArXiv.org · 2025-09-10

  preprintOpen accessSenior author

  Generative AI is a technology which depends in part on participation by humans in training and improving the automation potential. We focus on the development of an "AI twin" that could complement its creator's efforts, enabling them to produce higher-quality output in their individual style. However, AI twins could also, over time, replace individual humans. We analyze this trade-off using a principal-agent model in which agents have the opportunity to make investments into training an AI twin that lead to a lower cost of effort, a higher probability of success, or both. We propose a new framework to situate the model in which the tasks performed vary in the ease to which AI output can be improved by the human (the "editability") and also vary in the extent to which a non-expert can assess the quality of output (its "verifiability.") Our synthesis of recent empirical studies indicates that productivity gains from the use of generative AI are higher overall when task editability is higher, while non-experts enjoy greater relative productivity gains for tasks with higher verifiability. We show that during investment a strategic agent will trade off improvements in quality and ease of effort to preserve their wage bargaining power. Tasks with high verifiability and low editability are most aligned with a worker's incentives to train their twin, but for tasks where the stakes are low, this alignment is constrained by the risk of displacement. Our results suggest that sustained improvements in company-sponsored generative AI will require nuanced design of human incentives, and that public policy which encourages balancing worker returns with generative AI improvements could yield more sustained long-run productivity gains.

  PublisherOA PDFDOI

• The Rise of Recommerce: Ownership and Sustainability with Overlapping Generations

  arXiv (Cornell University) · 2024-05-15 · 2 citations

  preprintOpen accessSenior author

  The emergence of the branded recommerce channel - digitally enabled and branded marketplaces that facilitate purchasing pre-owned items directly from a manufacturer's e-commerce site - leads to new variants of classic IS and economic questions relating to secondary markets. Such branded recommerce is increasingly platform-enabled, creating opportunities for greater sustainability and stronger brand experience control but posing a greater risk of cannibalization of the sales of new items. We model the effects that the sales of pre-owned items have on market segmentation and product durability choices for a monopolist facing heterogeneous customers, contrasting outcomes when the trade of pre-owned goods takes place through a third-party marketplace with outcomes under branded recommerce. We show that the direct revenue benefits of branded recommerce are not their primary source of value to the monopolist, and rather, there are three indirect effects that alter profits and sustainability. Product durability increases, a seller finds it optimal to forgo marketplace fees altogether, and there are greater seller incentives to lower the quality uncertainty associated with pre-owned items. We establish these results for a simple two-period model as well as developing a new infinite horizon model with overlapping generations. Our paper sheds new insight into this emerging digital channel phenomenon, underscoring the importance of recommerce platforms in aligning seller profits with sustainability goals.

  PublisherOA PDFDOI

Frequent coauthors

• Gal Oestreicher-Singer

  Tel Aviv University

  18 shared

• Abraham Seidmann

  Boston University

  13 shared

• Anindya Ghose

  New York University

  12 shared

• Sinan Aral

  Massachusetts Institute of Technology

  12 shared

• Vasant Dhar

  New York University

  9 shared

• Lauren Rhue

  8 shared

• Lev Muchnik

  Hebrew University of Jerusalem

  7 shared

• Eyal Carmi

  6 shared

Labs

• Learning Science LabPI

Awards & honors

• Google Faculty Research Award