2026 Algorithmic Pricing Liability: California AB 325 and the State-Federal Regulatory Crackdown

Algorithmic and dynamic pricing models have become a primary target for regulators, state legislators, and private plaintiffs in 2026. Armed with a mix of traditional antitrust/privacy statutes and newly enacted pricing-specific legislation, enforcement bodies are launching major initiatives that raise significant compliance and litigation risks for consumer-facing enterprises.

California's AB 325: A Game-Changer for Algorithmic Antitrust

Effective January 1, 2026, California's Assembly Bill 325 (AB 325) and Senate Bill 763 (SB 763) have fundamentally reshaped the legal landscape for algorithmic pricing. AB 325 explicitly targets the use of pricing algorithms that leverage competitor data, removing common defenses previously relied upon by enterprises.

Under the new law:

• Prohibition of Common Pricing Algorithms: The law adds Section 16729 to the Business and Professions Code, prohibiting the use or distribution of a "common pricing algorithm" as part of an anticompetitive agreement or conspiracy to restrain trade. The law defines a common pricing algorithm as "any methodology, including a computer, software, or other technology... that uses competitor data to recommend, align, stabilize, set, or otherwise influence a price or commercial term." Crucially, there is no exception for shared tools containing only publicly available data.
• Liability for Coercion: It establishes liability for coercing others to adopt algorithm-recommended prices, even without a formal agreement.
• Lowered Pleading Standards: In a major departure from federal jurisprudence, AB 325 adds Section 16756.1, which establishes a relaxed pleading standard for Cartwright Act claims in state courts, rejecting the heightened federal standard set by the U.S. Supreme Court in Bell Atlantic Corp. v. Twombly (2007).

As noted by Alston & Bird:

"According to the Assembly Judiciary Committee’s analysis, revising the pleading standard was a key feature of the bill, intended to reject the heightened federal standard set by the U.S. Supreme Court in Bell Atlantic Corp. v. Twombly, 550 U.S. 544, 565-66 (2007)... Under this new standard, a complaint will be sufficient to survive dismissal on the pleadings if it alleges facts that make a conspiracy plausible. A plaintiff does not need to allege facts that tend to exclude the possibility of independent action." — Alston & Bird LLP, "California’s AB 325 Prohibits Shared Pricing Algorithms" (November 2025)

To deter violations, SB 763 significantly raises corporate criminal penalties to $6 million (up from $1 million) and creates new civil penalties of up to $1 million per violation in actions brought by state enforcers, cumulative with existing Cartwright Act remedies.

State and Federal Enforcement: The Affordability and Surveillance Pricing Agenda

Outside of antitrust, regulators are aggressively targeting "surveillance pricing"—the practice of using highly granular personal data to set individualized prices.

• Federal Trade Commission (FTC): Following up on its January 2025 6(b) surveillance pricing study, the FTC has escalated its inquiry by opening a formal probe into the use of AI-driven tools to generate different prices for different customers.
• California Attorney General: Attorney General Rob Bonta recently announced an investigation into how businesses use personal data to set targeted prices. The office has begun issuing inquiry letters to grocers, hotels, and retailers requesting detailed information on their use of consumer data, pricing experiments, and compliance measures.
• New York's Algorithmic Pricing Disclosure Act: Signed by Governor Kathy Hochul in May 2025 and effective November 2025, this first-of-its-kind law requires retailers using personal data to set individualized prices to post a conspicuous, all-caps disclosure: “THIS PRICE WAS SET BY AN ALGORITHM USING YOUR PERSONAL DATA.” Other states, including Pennsylvania, Texas, and New Mexico, are actively considering similar legislation.

As Freshfields highlights:

"Regulators are making it clear that 2026 will be a big year for scrutiny of algorithmic pricing models, the use of personal data and algorithmic tools to set individualized prices. Armed with both preexisting consumer protection and privacy laws, as well as new algorithmic-pricing-specific legislation, state and federal regulators are opening inquiries that highlight the risk of regulation by enforcement." — Freshfields Bruckhaus Deringer, "2026 Enforcement Priority: Algorithmic Pricing" (February 9, 2026)

Compliance Implications for Enterprises

Enterprises using algorithmic or dynamic pricing must immediately:

1. Conduct a coordinated review of public-facing materials and dynamic pricing disclosures, ensuring clear consumer awareness and, where necessary, obtaining explicit opt-in consent.
2. Audit pricing algorithms to ensure they do not ingest competitor data (even if public) and are free from discriminatory biases.
3. Verify data provenance and compliance practices of third-party vendors supplying pricing data.