Antitrust and Algorithmic Pricing

As artificial intelligence (AI) technology advances, algorithmic pricing is transforming how firms interact and compete in the marketplace. The increased use of sophisticated algorithms to set prices in real time based on user data  raises important and urgent concerns about fairness, transparency, and market power. Many scholars warn that algorithmic pricing tools will facilitate unlawful cooperation between firms that may lead to unsustainably high prices, harming consumers.

Since their passage over a century ago, the Sherman Act and the Clayton Act still offer the main statutory tools for addressing anti-competitive behavior in the United States. These laws are enforced by the U.S. Department of Justice and the Federal Trade Commission (FTC), which independently investigate and prosecute cases involving monopolization, price-fixing, and other forms of collusion. While the Justice Department, which has exclusive jurisdiction on certain industries, focuses on civil and criminal enforcement of antitrust law, the FTC specializes in civil enforcement and consumer protection.

Regulators in the United States have acknowledged the challenges posed by algorithmic pricing, with related issues being raised in recent federal lawsuits. In a 2024 statement, the FTC indicated an interest in bringing more enforcement actions targeting the use of AI algorithms to evade antitrust laws. The Justice Department followed suit in 2025 with its submission of a Statement of Interest related to an ongoing litigation, in which the agency argued that the use of algorithms to set benchmark pricing and exchange pricing information could be violations of federal antitrust law.

European regulators have also responded to the increased use of algorithms in the marketplace on both regional and national levels. The primary antitrust regulatory body for the European Union is the European Commission, which has jurisdiction over European Union member states. The Commission, along with the United Kingdom’s Competition and Markets Authority, have acknowledged the need to expand their prohibition of cartels to encompass AI-driven algorithmic collusion. Recently, both Italian and German legislators enacted provisions to address algorithmic price collusion outside the scope of cartels.

The rise of AI-driven algorithmic pricing raises several fascinating, albeit perplexing, questions. For example, scholars consider whether antitrust law should be more stringent or relaxed in response to AI technology, and whether and how AI technology will transfer the way anticompetitive harm is measured.

In this week’s Saturday Seminar, scholars debate the implications and regulatory responses to advances in AI-driven algorithmic pricing.

• In an article, Herbert Hovenkamp of the University of Pennsylvania Carey Law School explains that the brevity of the Sherman Act and the Clayton Act has led courts to fill in statutory gaps with their own interpretations. Hovenkamp describes how courts interpret the statues as free market policy, developing sweeping rules with little textual basis. Hovenkamp notes that the definition of a “person” to include ‘corporations and associations’ likely excludes AI algorithms, maintaining that people that use algorithms to achieve illegal ends may still be held liable. Hovenkamp emphasizes the need for clearer legal guidance, given that algorithms may soon be able to emulate anticompetitive conspiracies without human intervention.
• In a chapter in Cambridge Handbook on Price Personalization and the Law, Haggai Porat of Harvard Law School assesses various legal frameworks that might be used to regulate algorithmic personalized pricing in the United States. Regulation through antitrust law, Porat acknowledges, is limited because it focuses on preserving competition, not policing high prices or unfairness. Porat explains that price discrimination itself is not illegal and does not inherently reduce competition or harm consumers. For example, Porat notes that the FTC and Justice Department are hesitant to intervene unless a given pricing scheme facilitates anticompetitive conduct. Nonetheless, Porat concludes that antitrust should be used when algorithmic pricing accompanies the harmful exercise of market power, such as predatory pricing or collusion.
• In an article in the NYU Law Review, Christopher Leslie of the University of California, Irvine School of Law argues that AI enables previously unworkable predatory pricing practices. Leslie explains that predatory pricing is a form of monopolistic conduct because it uses below-cost prices to push out competitors, gaining market control. He notes this can be illegal under antitrust law when it helps a firm build monopoly power. Leslie acknowledges that predatory pricing was traditionally deemed irrational because firms could not target rivals or recover losses. Leslie contends, however, that AI—which allows firms to target specific consumers, limit losses, and later raise prices—makes predatory pricing economically rational and, thus, worthy of renewed antitrust scrutiny.
• In an forthcoming article, Roman Inderst of Goethe University Frankfurt and Stefan Thomas of the University of Tübingen argue that algorithmic pricing challenges existing understandings  of cartel conduct. The authors contend that shared algorithmic pricing services that base recommendations on publicly unavailable pricing data constitutes explicit and illegal collusion. Inderst and Thomas emphasize the need for regulators to revise counterfactual assessments, which estimate pricing conditions in the absence of collusive behavior, to reflect the capabilities of AI-based algorithmic pricing. The authors recommend that policymakers focus on regulating markets that are prone to coordinated pricing while requiring firms to independently develop algorithms without sharing data amongst themselves.
• In an article in the University of Pennsylvania Journal of Business Law, Thomas Cheng and Julian Nowag of The University of Hong Kong Faculty of Law examine how individualized algorithmic targeting enables precise predatory pricing, exclusionary rebates, and customer-specific tying and bundling. Cheng and Nowag demonstrate that algorithms allow firms to minimize predation losses and more effectively recoup costs, posing novel challenges for competition regulators. The authors argue that regulators should adapt merger-review protocols and continuously monitor markets to detect algorithm-driven anti-competitive behaviors. Cheng and Nowag suggest strengthening competition law with proactive enforcement tools to identify emerging risks, preserve market and industry competition, and protect consumer welfare.
• In an essay in the University of Chicago Law Review, Edward M. Iacobucci of the University of Toronto Faculty of Law identifies an inverse relationship between the strictness of the law and the sophistication of AI pricing. The author explains that while although algorithmic pricing facilitates tacit cooperation between firms, it may stabilize prices by detecting cheating and punishing firms that cheat with lowered prices. Iacobucci argues that anti-monopolistic merger policy should become less strict as AI pricing becomes more advanced. He reasons that algorithms will drive anticompetitive outcomes regardless of whether mergers are cleared before self-correcting, and that regulators will intervene inefficiently . Iacobucci recommends that, following the advent of AI pricing, antitrust law should shift its focus away from high prices toward prioritizing innovation and efficiency.