California Looks to Crack Down on Algorithmic Pricing and Clarify Antitrust Pleading Standards

Background

Over the past two years, California has become increasingly enthusiastic about antitrust enforcement. During that period, the state assembly introduced new legislation to ratchet up penalties for Cartwright Act violations. Attorney General Paula Blizzard announced that the State would resurrect criminal enforcement of Cartwright Act violations after a decades-long hiatus. And Governor Gavin Newsom appointed a veteran antitrust prosecutor to oversee a newly-created agency tasked with monitoring the petroleum markets for perceived misconduct.

California’s antitrust momentum coincides with another enforcement trend that has been building in other states and in the class action bar — scrutiny of algorithm-based pricing and data services. Typically, so-called “algorithmic pricing” involves a consulting service or industry participant aggregating price, supply, and other relevant data from market participants and then using that data to recommend prices for goods or services. Proponents of the practice argue that algorithmic pricing helps firms gather market data more efficiently and timely adjust to market dynamics, while critics worry that the practice can (in at least some instances) lead to reductions in competition or even foster collusion between market participants.

Algorithmic pricing has become an area of intense interest for antitrust hawks in legislatures across the country. In Arizona, for example, legislators introduced H.B. 2847, which proscribes using algorithmic pricing to set rental rates for residential real estate. In Ohio, the state legislature went further by introducing S.B. 79, which would apply to all industries and ban any pricing algorithms trained on nonpublic competitor data.

Restrictions on Algorithmic Pricing in California

California’s AB 325 picks up where Ohio left off. The bill would prohibit the distribution and use of a common pricing algorithm¹ if either: (1) the firm distributing the algorithm distributes it to two or more buyers with the intent that “it be used to set or recommend prices or commercial terms of the same or similar products or services and the [distributor] coerces any person to set or adopt a recommended price or commercial term of the same or similar products or services”; or (2) a person uses a common pricing algorithm to set or recommend prices or commercial terms of products or services and either (a) “[k]nows or should know that they are adhering to or participating in a scheme to fix the price or commercial term of the same or a similar product or service” in California; or (b) “[c]oerces any person to set or adopt a recommended price or commercial term for the same or similar products or services” in California.

Notably, the bill does not proscribe the use of pricing algorithms in isolation. A firm could, in theory, develop and use a pricing algorithm for its own private use. Similarly, a developer might aggregate market data and build a bespoke algorithm for the exclusive use of one buyer. AB 325 would apply only where multiple competitors in the same market (or similar markets) use the same algorithm to recommend prices or terms.

Clarifying California’s Cartwright Act Pleading Requirements

The second portion of AB 325 addresses pleading standards under the Cartwright Act. It provides that a plaintiff bringing a claim under the Cartwright Act need only allege sufficient facts “demonstrating that the existence of a contract, combination in the form of a trust, or conspiracy to restrain trade or commerce is plausible.” It continues that plaintiffs need not also allege facts “tending to exclude the possibility of independent action.”

This language may be solving a problem that does not exist. To sustain a cause of action under the Cartwright Act, California courts have held that Cartwright Act claims must plausibly allege: “The formation and operation of the conspiracy; the illegal acts done pursuant thereto; a purpose to restrain trade; and the damage caused by such acts.”² California courts generally have not held that plaintiffs must also allege facts that tend to exclude the possibility of independent action at the pleading stage.

It is thus unclear what the drafters of AB 325 were intending to accomplish. Most likely this is a response urged by plaintiffs’ attorneys to clarify that the higher standard of evidence required at the summary judgment phase — “evidence that tends to exclude, although it need not actually exclude, the possibility that the alleged conspirators acted independently rather than collusively” — is not required in complaint allegations in response to a demurrer.³

Whether needed or not, the clarified pleading standard for Cartwright Act may be intended to bring California state court procedure into alignment with a wide swath of federal antitrust jurisprudence. While courts at the federal level expect evidence excluding independent decision-making at the summary judgment and trial stages,⁴ they have typically declined to apply this requirement at the pleading stage.⁵

Conclusion

If passed, AB 325 would have much more impact on algorithmic pricing than pleading standards in the Golden State. It is too early to conclude whether the bill will pass, and the bill has seen several significant amendments, but the new legislation appears to have momentum. An earlier version of the bill passed the Assembly Standing Committee on Judiciary on April 8, 2025 by a vote of 9-3. The current version has also passed the Assembly Standing Committee on Privacy and Consumer Protection by a vote of 11-3, as well as the Committee on Appropriations by a vote of 10-2. These developments suggest there is significant support for the bill, at least in one house of California’s bicameral legislature. Firms involved in the development, distribution, or purchase of pricing algorithms should be aware of this new legislation — as well as the hawkish attitude towards such products that is ascendant in Sacramento.

¹ The bill defines “common pricing algorithm” to mean “any process or rule, including a process derived from machine learning or other artificial intelligence techniques, that processes the same or substantially similar data to recommend or set a price or commercial term using the same or performing a substantially similar function.”

² G.H.I.I. v. MTS, Inc., 147 Cal. App. 3d 256, 265 (1983); see also In re California Gasoline Spot Mkt. Antitrust Litig., No. 20-CV-03131-JSC, 2022 WL 3215002, at *1 (N.D. Cal. Aug. 9, 2022).

³ See, e.g., Aguilar v. Atl. Richfield Co., 25 Cal. 4th 826, 852 (2001) (“Therefore, in addition, the plaintiff must present evidence that tends to exclude, although it need not actually exclude, the possibility that the alleged conspirators acted independently rather than collusively.”).

⁴ See Matsushita Elec. Indus. Co. v. Zenith Radio Corp., 475 U.S. 574, 588 (1986) (at  summary judgment stage, plaintiffs must present evidence that “tends to exclude the possibility” of independent action).

⁵ See, e.g., SmileDirectClub, LLC v. Tippins, 31 F.4th 1110, 1118 (9th Cir. 2022) (stating that “Rule 12(b)(6) does not require” that plaintiffs offer “evidence that tends to exclude the possibility” of independent conduct at the pleading stage, but only “at the summary judgment stage”); SD3, LLC v. Black & Decker (U.S.) Inc., 801 F.3d 412, 425 (4th Cir. 2015) (“There is no authority for extending the Monsanto/Matsushita standard to the pleading stage.” (alterations adopted; citation omitted)); Anderson News, L.L.C. v. Am. Media, Inc., 680 F.3d 162, 184 (2d Cir. 2012) (“[T]o present a plausible claim at the pleading stage, the plaintiff need not show that its allegations suggesting an agreement are more likely than not true or that they rule out the possibility of independent action, as would be required at later litigation stages such as a defense motion for summary judgment[.]”).