This article explores the implications of personalized pricing within U.S. antitrust law.
The confluence of extensive digital data and advancements in artificial intelligence (AI) and machine learning (ML) is significantly influencing pricing strategies. These technologies may empower firms to collect, process, and analyze vast amounts of consumer data, leading to more granular, dynamic, and individualized pricing than previously observed. This evolution, driven by the speed and scale of AI/ML data processing, signifies a qualitative shift from static, segment-based pricing to dynamic, highly individualized, and often automated mechanisms. Historically, information and transaction costs constrained effective price discrimination. Big Data has potentially lowered these information costs, while AI/ML may further reduce the expense of data analysis and implementing sophisticated pricing rules. This combination enables personalization and dynamism previously confined to theoretical models.
In this data-rich environment, pricing surveillance, price discrimination, and algorithmic pricing are distinct yet intertwined phenomena. Pricing surveillance provides data for personalized pricing, and algorithmic pricing executes these strategies. Their synergy creates complex challenges for antitrust economics, as isolating each component may not adequately address concerns. The efficiency of data collection, algorithmic processing, and personalized pricing could amplify both pro-competitive benefits and anti-competitive risks.
In this article, coauthors Gabriela Antonie, J. P. Bruno, Mariella Gonzalez and Esperanza Johnson explore these implications within U.S. antitrust law, drawing on economic theory, enforcement trends, and technological developments.
This article was originally published by Competition Policy International’s Antitrust Chronicle in July 2025.
Chicago 
