Algorithmic Pricing and Antitrust Scrutiny: Competitor Data, Surveillance Pricing, and the Emerging State Patchwork

This CLE course will cover antitrust and consumer protection risks associated with algorithmic and AI-driven pricing. The panel will analyze recent case law, DOJ and FTC efforts, and developing state laws addressing common pricing algorithms, competitor information, automated pricing recommendations, individualized pricing, and surveillance pricing.

Companies use algorithms, AI, and automated decision-making technology (ADMT) to set, recommend, and adjust consumer prices. Federal and state enforcers are scrutinizing these practices on two fronts: algorithms generating prices based on ingested competitor data and those that set individualized prices based on consumer data. For antitrust counsel, the key question is whether using a pricing tool is independent competitive conduct or evidence of concerted action, information exchange, delegation, or coordination among competitors that gives rise to legal liability. 

Recent litigation illustrates the emerging and sometimes differing approaches taken by the courts and regulators, as seen in Gibson v. Cendyn Group, Duffy v. Yardi, and Segal v. Amadeus. The cases will be discussed, highlighting how they should inform how to evaluate pricing vendors, how competitively sensitive information is handled, the ways pricing recommendations are determined and followed, and the evidentiary record that could lead to violations under Sec. 1 of the Sherman Act. Presentation time will include analysis of how technology operates, what information technology receives and then shares, party communications, vendor agreements, and more.

The DOJ's and FTC's emerging positions on algorithmic coordination, information exchange, hub-and-spoke theories, and third-party pricing vendors will be discussed, including their Statements of Interest. The panel will also analyze the DOJ's RealPage case and the March 2026 Greystar final judgment and related settlements.

State activity proliferates. California has taken a significant antitrust approach with Assembly Bill 325 (AB 325), amending the Cartwright Act to address pricing algorithms and coercion involving algorithmic pricing recommendations, and lowering Cartwright's pleading thresholds. New York bars algorithmic rent setting and mandates pricing disclosures. Colorado's recently announced draft ADMT and Conversational AI Service Rules provide expedited guidance under its amended AI Act. Colorado's new rules are expected to heighten compliance, and more states will follow. 

In addition to algorithms, AI, and ADMT, businesses use dynamic, demand-based, and revenue management systems to set prices. Faculty time will outline how AI, algorithmic pricing, and ADMT differ from personalized and surveillance pricing, and the legal significance of competitor data, consumer data, vendor-facilitated recommendations, and delegated pricing authority. Liability theories under Sec. 1 will be covered, including per se vs. rule of reason; hub-and-spoke liability; unlawful information exchange vendor issues; tacit collusion; proof problems; and more. We will examine existing and pending state legislative efforts and when business pricing practices cross legal boundaries. We will also discuss recent enforcement efforts and risk mitigation strategies to align pricing practices with emerging legal standards.

Listen as our panel analyzes pricing tools, recent algorithmic pricing litigation, federal enforcement efforts, evolving state legislative frameworks, and practical strategies for evaluating pricing tools, identifying potential information exchange and coordination risks, how to structure vendor arrangements and agreements, and best practices for advising clients on compliance requirements of algorithmic pricing.