Platform Competition in an Age of Machine Learning
DISRUPTIVE INCUMBENTS: PLATFORM COMPETITION IN AN AGE OF MACHINE LEARNING C. Scott Hemphill * Recent advances in machine learning have reinforced the competi- tive position of leading online platforms. This Essay identifies two im- portant sources of platform rivalry and proposes ways to maximize their competitive potential under existing antitrust law. A nascent competitor is a threatening new entrant that, in time, might become a full-fledged platform rival. A platform’s acquisition of a nascent competitor should be prohibited as an unlawful acquisition or maintenance of monopoly. A disruptive incumbent is an established firm—often another plat- form—that introduces fresh competition in an adjacent market. Anti- trust enforcers should take a more cautious approach, on the margin, when evaluating actions taken by a disruptive incumbent to compete with an entrenched platform. INTRODUCTION The leading online platforms—Google in search, Facebook in social network services, and Amazon in e-commerce—benefit from economies of scale and access to user data that are difficult for rivals to replicate. These barriers are reinforced by advances in machine learning, a set of artificial intelligence (AI) techniques1 that use models to “learn” desired behavior from “examples rather than instructions.”2 This Essay considers how competition might be enhanced, notwithstanding these advantages, under existing antitrust law.3 * Moses H. Grossman Professor of Law, New York University School of Law. I thank John Asker, Adam Cox, Harry First, Jacob Gersen, Jeannie Gersen, Bert Huang, Avery Katz, Benedict Kingsbury, Bhaven Sampat, Tim Wu, and audiences at Columbia, ETH Zurich, and NYU, for helpful comments. Tim Keegan, Ryan Knox, Ina Kosova, Alison Perry, and David Stein provided outstanding research assistance.
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