The questions in FTC v. Qualcomm are consequential in setting competitive norms in an economy anxious about the exercise of market power. Like many other antitrust cases, this one shows symptoms of antitrust law’s inherent vulnerability to ideology stampeding facts and data. Seen as an algorithm, antitrust has had patches and updates over the years. Still, few have recognized the breadth and depth of transformation artificial intelligence (“AI”) can bring to antitrust adjudication. AI enables courts to better render evidence-based decisions. As a tool, it is non-ideological and enables courts to minimize ideological stampeding. As a powerful new partner in making sense of the complex, dynamic, and fast-moving licensing markets many businesses operate in, courts and agencies can harness its ability to model price and innovation effects more precisely. There are challenges to implementing AI with data accountability, data availability, and data bias. These challenges can be addressed. The time to retool antitrust is now.

By Daryl Lim 1

 

I. INTRODUCTION

Many antitrust stakeholders will remember 2020 as the year of its rebirth. Congressional politics, network economics, missteps on privacy, and corporate hubris converged to catalyze a profound reassessment of antitrust law. While much of the attention over excessive private power has focused on Facebook, Google, Amazon, and Apple, it remains to be seen whether current efforts to rein them in will generate any

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