ARTICLE
23 March 2026

California Trains Its Sights On Algorithmic Collusion

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A&O Shearman

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A&O Shearman was formed in 2024 via the merger of two historic firms, Allen & Overy and Shearman & Sterling. With nearly 4,000 lawyers globally, we are equally fluent in English law, U.S. law and the laws of the world’s most dynamic markets. This combination creates a new kind of law firm, one built to achieve unparalleled outcomes for our clients on their most complex, multijurisdictional matters – everywhere in the world. A firm that advises at the forefront of the forces changing the current of global business and that is unrivalled in its global strength. Our clients benefit from the collective experience of teams who work with many of the world’s most influential companies and institutions, and have a history of precedent-setting innovations. Together our lawyers advise more than a third of NYSE-listed businesses, a fifth of the NASDAQ and a notable proportion of the London Stock Exchange, the Euronext, Euronext Paris and the Tokyo and Hong Kong Stock Exchanges.
As courts and lawmakers across the U.S. figure out how to handle AI's rapid growth, California has sent a clear message...
United States California Antitrust/Competition Law
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As courts and lawmakers across the U.S. figure out how to handle AI's rapid growth, California has sent a clear message: algorithm-driven collusion is fair game for antitrust enforcement. Starting January 1, 2026, California law prohibits using a shared pricing algorithm as part of an agreement to limit competition.1 Some see this update to the state's Cartwright Act as mostly restating what the law already covered. But a new ban on algorithmic pricing coercion could push some businesses to rethink how they are deploying AI into their businesses. Whether this amendment causes a small ripple or a major wave may come down to how courts read the law.

The amendment addresses algorithmic pricing in two ways. First, it bans using or distributing a "common pricing algorithm"—which the statute defines as technology that two or more entities use to recommend prices or commercial terms based on competitor data—as part of an agreement to restrain trade under the Cartwright Act. Second, it makes it illegal to use or distribute such an algorithm while coercing someone else to follow the algorithm's recommendations for the same or similar products or services.

One important wrinkle: the legislative history makes clear that these provisions apply whether the algorithm uses private or public competitor data.2 As a result, companies looking to use pricing algorithms need to scrutinize not just the data that users contribute, but also any data the algorithm provider pulls from public sources. While the statute creates liability for "coercing" another to adhere to recommended prices, it doesn't define coercion—leaving courts to sort out what counts.

The new law also clarifies what plaintiffs need to allege to bring a Cartwright Act claim. Plaintiffs must include "factual allegations demonstrating that the existence of a contract . . . is plausible" but don't need to "allege facts tending to exclude the possibility of independent action." Most California courts already applied this plausibility standard at the motion to dismiss stage, so this may just reinforce existing practice rather than break new ground.

California isn't the only state paying attention to algorithmic pricing, though it's taking a collusion-focused approach. Other states have passed or are considering laws that tackle this issue differently, some focusing on specific markets like housing. For example, New York recently banned landlords from using algorithmic software that analyzes other property managers’ information to set rental rates, lease terms, and occupancy levels.3 Connecticut passed a similar ban that specifically prohibits the use of competitors’ non-public data as an input for algorithmic rental rate recommendations.4 New York also now requires businesses to disclose when prices are set by “surveillance pricing”—a tool that analyzes consumers’ data to set personalized prices—with other states like Illinois and Pennsylvania considering similar measures.5

The new California law, combined with the proliferating patchwork of state regulations, adds another layer of complexity to decisions companies face when adopting AI. While federal courts are developing a consensus around how algorithmic pricing providers collect and analyze user data, these new state laws may shift the risk profile. We will continue to monitor these developments using our multidisciplinary team, which includes experienced practitioners in antitrust, privacy, IP, and technology transactions. A&O Shearman helps companies navigate this fast-moving landscape as they bring AI into their operations.

Footnotes

1. Cal. Bus. & Prof. Code § 16729 (2026).

2. Assemb. Floor Analysis of A.B. 325, 2025-2026 Reg. Sess., at 2 (Cal. Sept. 5, 2025) (“The bill applies regardless of whether the underlying data is public or private, reflecting the understanding that even public data can enable collusion with processed similarly across competitors.”)

3. N.Y. Gen. Bus. Law § 340-b (2025).

4. 2025 Conn. Pub. Acts 25-1, § 32 (Nov. Spec. Sess.)

5. N.Y. Gen. Bus. Law § 349-a (2025).

The content of this article is intended to provide a general guide to the subject matter. Specialist advice should be sought about your specific circumstances.

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