Research Interests

  • Innovation, Entrepreneurship, Economics of AI, and Economics of Information Systems
    • My research interests are innovation and entrepreneurship driven by disruptive technologies such as algorithms, AI, and Quantum Computing.

Peer-Reviewed Publications

  • The Quantum Tortoise and the Classical Hare: When Will Quantum Computers Outpace Classical Ones and When Will They Be Left Behind? (with W. Moses and N. Thompson)
    Proceedings of the IEEE, 2025. (IF: 25.9; a top-tier journal in CS) Paper Link: Paper
    Abstract
    Quantum computing promises transformational gains for solving some problems, but little to none for others. For anyone hoping to use quantum computers now or in the future, it is important to know which problems will benefit. In this paper, we introduce a framework for answering this question both intuitively and quantitatively. The underlying structure of the framework is a race between quantum and classical computers, where their relative strengths determine when each wins. While classical computers operate faster, quantum computers can sometimes run more efficient algorithms. Whether the speed advantage or the algorithmic advantage dominates determines whether a problem will benefit from quantum computing or not. Our analysis reveals that many problems, particularly those of small to moderate size that can be important for typical businesses, will not benefit from quantum computing. Conversely, larger problems or those with particularly big algorithmic gains will benefit from near-term quantum computing. Since very large algorithmic gains are rare in practice and theorized to be rare even in principle, our analysis suggests that the benefits from quantum computing will flow either to users of these rare cases, or practitioners processing very large data.
  • The Dual Edges of AI: Advancing Knowledge While Reducing Diversity (with H. Kang, N. Kim, and J. Kim)
    PNAS NEXUS, 2025. Paper Link: Paper
    Abstract
    We study how the interaction between human professionals and AI in advancing knowledge, using professional Go matches from 2003 to 2021. In 2017, an AI-powered Go program (APG) far surpassed the best human player, and professional players began learning from AI. Such human–AI interaction paved a new way to reassess historical Go knowledge and create new knowledge. We analyze standard patterns (defined as a sequence of the first eight alternating moves) in about 15 million moves by over 1,700 players in nearly 70,000 professional Go games and find that, after APG, professional players significantly changed how they adopted different sets of moves. However, new knowledge catalyzed by AI comes at the expense of a reduced diversity in moves. Further, AI's impact on knowledge creation is greater for highly skilled players; since AI does not explain, learning from AI requires the absorptive capacity of the top professionals.
    • Finalist for the Best Paper of the 2023 Conference on Information Systems and Technology (CIST)
      • (Top 5 out of 208 papers accepted from 337 submissions)
    • Media Cover: MIT IDE Blog
  • How Does AI Improve Human Decision-Making? Evidence from the AI-Powered Go Program (with H. Kang, N. Kim, and J. Kim)
    Strategic Management Journal, 2025. Paper Link: Paper
    Abstract
    Firms increasingly utilize AI to assist or replace human tasks. However, AI can also train humans and make them better. We study how the AI’s instructional role improves human decision-making in the professional Go games where an AI-powered Go program (APG) unexpectedly surpassed the best human player, surpassing the best human knowledge and skill accumulated over thousands of years. To isolate the learning-from-AI effect, we compare the quality of human moves to that of AI’s superior solutions, before and after the initial public release of an APG. Our analysis of 750,990 moves in 25,033 games suggests that APG’s training significantly improved the players’ move quality—reducing the number of errors and the magnitude of the most critical mistake. The improvement is most prominent in the early stage of a game when uncertainty is higher. Further, younger players benefit more than older players, suggesting generational inequality in learning from AI.
  • Practical Quantum Computing is about More Than Just Hardware (with N. Thompson, P. Shukla, and C. Dukatz)
    California Management Review, 2024. Paper Link: Paper
  • When Does AI Payoff?: AI-Adoption Intensity, Complementary Investments, and R&D Strategy (with T. Kim, W. Kim, and Y. Lee (Equally contributed))
    Technovation, 2022. Paper Link: Paper

  • International Alliance Formation: The Effect of Technology Competition Networks (with N. Kim and W. Kim)
    Journal of Business Research, 2022. Paper Link: Paper

  • Are Social Entrepreneurs More Risk-Averse? (with N. Kim and W. Kim)
    Applied Economics Letters, 2019. Paper Link: Paper

Working Papers

  • VC Ownership and Innovation in Newly Public Firms (Revise and Resubmit) (with T. Kim and H. Woo)

  • Competition and Firm innovation (Under Review) (with T. Kim, G. Park, and N.Kim)

    • Winner of the Best Paper Award in the Korean Society for Innovation Management and Economics (KOSIME) Summer Conference 2024

Work-in-progress

  • Breaking Digitial Addition in the Age of AI: The Roles of Reward Sensitivity, Goal Setting, and Incentives (with J. Kuem) Data Analysis

  • How Does AI Improve Human Collaboration and Performance? Evidence from AI-powered X-ray Triage in University Hospitals (with H. Kang and N. Kim)
    Data Analysis After Passing IRB Review
    • Grant: Co-principal investigator, USC Marshall Institute for Outlier Research in Business Grant Program, “How Does AI Improve Human Decision-Making and Performance? Evidence from AI-powered X-ray Triage in University Hospitals” (with H. Kang and N. Kim) (2022-2023) ($19,400)
  • Algorithm Patents (with N. Thompson)
    Data Collection