State laws governing AI‑driven algorithmic pricing are beginning to form a coherent regulatory category focused on preventing price‑fixing, collusion through shared algorithms, and opaque personalized pricing. New York and California have taken the lead with statutes that directly regulate how businesses may use pricing algorithms, especially when those algorithms incorporate competitor data or personalize prices using consumer information.
California – AB 325 (Shared Pricing Algorithms and Antitrust Liability)
California’s AB 325 is one of the most sweeping state laws addressing algorithmic pricing. It was signed on October 6, 2025, and takes effect January 1, 2026. The law targets the use of shared or coordinated pricing algorithms that incorporate competitor information, even when that information is publicly available.
Core requirements
- Prohibits shared pricing algorithms that contain competitor data, regardless of whether the data is public.
- Creates liability for coercing or pressuring others to adopt algorithm‑recommended prices.
- Strengthens antitrust enforcement, including enhanced penalties and lower pleading standards under the Cartwright Act.
Policy rationale
California lawmakers responded to a surge in litigation alleging that revenue‑management software and algorithmic pricing tools facilitate tacit collusion by enabling competitors to coordinate prices indirectly.
New York – A3008 (Personalized Algorithmic Pricing Disclosure Act)
New York’s A3008, signed on May 9, 2025, establishes one of the first U.S. disclosure regimes for personalized algorithmic pricing. It requires companies to inform consumers when prices are set using algorithms that rely on personal data.
Core requirements
- Mandates transparency when businesses use personalized or dynamic pricing algorithms.
- Defines dynamic pricing as pricing that fluctuates based on conditions, including consumer‑specific factors.
- Requires clear disclosure to consumers when algorithmic pricing is used.
Policy rationale
New York’s law is designed to address concerns about hidden price discrimination, where AI systems adjust prices based on personal data, purchasing behavior, or inferred willingness to pay. It is the first law to require a prominent disclosure that a pricing algorithm was used.
How these laws fit into the broader landscape
- Antitrust and competition concerns
California’s AB 325 focuses on preventing algorithmic tools from becoming vehicles for coordinated pricing, even unintentionally. This reflects a growing belief among regulators that AI‑driven pricing can create collusive outcomes without explicit agreements.
- Consumer‑protection and transparency
New York’s A3008 addresses the other side of the problem: personalized pricing that may be opaque or unfair to consumers. By requiring disclosure, the law aims to give consumers visibility into when AI is influencing the price they see. Similar consumer‑protection themes appear in AI insurance regulation and AI advertising law.