AB 325 inserts two provisions into the Business and Professions Code. Section 16729 makes it unlawful to use or distribute a “common pricing algorithm” as part of a contract, trust, or conspiracy that restrains trade, and also outlaws use or distribution of such an algorithm when a person coerces another to set or adopt the algorithm’s recommended price or commercial term in California.
The text defines “common pricing algorithm” broadly (any methodology, including software, used by two or more persons that uses competitor data to recommend, align, stabilize, set, or otherwise influence price or a commercial term) and expressly treats distribution to include licensing, subscriptions, and similar access models.
Section 16756.1 changes pleading requirements for Cartwright Act claims: a complaint need only allege factual matter showing that the existence of an agreement (contract, combination, or conspiracy) is plausible and does not have to plead facts that tend to exclude independent action. Together these changes expand the Cartwright Act’s prohibited conduct into algorithm-mediated activity and lower the early-dismissal threshold for antitrust litigation—shifting enforcement risk onto software vendors, platforms, and businesses that rely on shared pricing technologies.
At a Glance
What It Does
Creates a statutory prohibition on using or distributing a “common pricing algorithm” in any collusive contract, combination, or conspiracy under the Cartwright Act, and forbids use/distribution where coercion leads another to adopt algorithm-recommended prices or terms in California. Separately, it lowers the pleading standard for Cartwright Act complaints to require only plausible allegations of an agreement.
Who It Affects
SaaS pricing vendors, algorithm providers, marketplaces and retailers that use shared pricing tools, pricing consultants, and companies that license or subscribe to pricing services. It also affects plaintiffs’ counsel and the Attorney General by changing how antitrust claims are pleaded.
Why It Matters
The bill targets algorithm-facilitated coordination that can produce cartel-like outcomes without traditional communications. By criminalizing certain algorithm distribution and easing pleading rules, the statute increases enforcement leverage and litigation risk while leaving open hard questions about what tools fall inside the ban.
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What This Bill Actually Does
AB 325 adds two targeted changes to California’s Cartwright Act framework. First, it makes the use or distribution of so-called “common pricing algorithms” unlawful when used as part of a contract, combination, or conspiracy that restrains trade.
The statute goes further and makes distribution unlawful when one party coerces another to set or adopt a price or commercial term recommended by the algorithm for the same or similar products or services within California. The prohibition is framed to catch both direct use of a shared algorithm and business models that provide identical or aligned recommendations to multiple competitors.
The bill defines “common pricing algorithm” expansively: any methodology—software, computer code, or other technology—used by two or more persons that relies on competitor data to recommend, align, stabilize, set, or otherwise influence prices or commercial terms. “Distribute” covers selling, licensing, or providing access (including subscriptions). The statute explicitly says a “person” does not include end consumers and defines “price” to include money or other value, explicitly including compensation to employees or independent contractors, so the prohibition can reach algorithmic tools that coordinate wages or contractor pay.Second, AB 325 alters the pleading requirement for Cartwright Act claims.
Instead of demanding detailed allegations that exclude the possibility of independent action, a complaint now needs only factual allegations that make the existence of an agreement plausible. That change makes it easier for plaintiffs—private or government—to get past early motions to dismiss and proceed to discovery, where algorithmic evidence often resides.Taken together, the new prohibition and the lower pleading threshold shift the balance toward enforcement against algorithm-aided coordination but leave numerous boundary questions for courts and regulators: what exactly counts as coercion, how to treat price-recommendation tools with mixed uses, and how discovery will be structured when key evidence is embedded in vendor code or platform logs.
Companies that build, license, or subscribe to pricing technology should reassess contractual terms, data practices, and litigation exposure.
The Five Things You Need to Know
The bill defines “common pricing algorithm” as any methodology used by two or more persons that uses competitor data to recommend, align, stabilize, set, or otherwise influence a price or commercial term.
“Distribute” is broad: selling, licensing, providing access, or otherwise making an algorithm available—including subscription or service sales—can trigger liability.
The statute makes it unlawful where a person coerces another to set or adopt a price or commercial term recommended by a common pricing algorithm for the same or similar products or services in California.
“Price” is explicitly broad and includes money or other things of value, including compensation paid to employees or independent contractors, bringing wage-setting tools within scope.
A Cartwright Act complaint now only must allege facts showing an agreement is plausible; plaintiffs are no longer required to plead facts that tend to exclude the possibility of independent action.
Section-by-Section Breakdown
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Prohibits use or distribution of common pricing algorithms and forbids coercive adoption
Subsection (a) creates a per se prohibition on using or distributing a common pricing algorithm as part of a contract, combination, or conspiracy that restrains trade. Subsection (b) adds an independent prohibition: even absent a classic agreement, a person violates the statute if they use or distribute a common pricing algorithm and coerce another to adopt its recommended price or commercial term for the same or similar goods or services in California. Practically, this exposes both algorithm operators and parties who pressure others to accept algorithmic recommendations to Cartwright Act liability.
Definitions – scope and technical reach of the prohibition
The bill supplies several operative definitions: “common pricing algorithm” (uses competitor data to influence pricing/terms and must be used by two or more persons); “distribute” (explicitly covers licensing and subscription access); “commercial term” (service level, availability, output); and a broad meaning of “price” that includes employee/contractor compensation. Those definitions intentionally sweep beyond classic price lists to encompass software-as-a-service, vendor-hosted recommendation engines, and other technology-mediated coordination mechanisms, increasing exposure for vendors and downstream users.
Lower pleading standard for Cartwright Act claims
This provision requires only plausible factual allegations of an agreement for a Cartwright Act complaint to proceed. Plaintiffs no longer must plead facts that tend to exclude independent action. The practical effect is to make early dispositive motions harder to win and to push more cases into discovery, where algorithmic metadata and vendor records will be central to proving coordinated behavior.
Fiscal and enforcement framing
The bill notes that it creates or changes a criminal definition under state law and therefore declares no state reimbursement to local agencies for costs. That language signals criminal exposure for conduct covered by the Cartwright Act and places enforcement responsibilities (and related costs) with state and local prosecutors and agencies as they implement the new provisions.
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Who Benefits
- California consumers—potentially: by deterring algorithmic coordination that raises prices, consumers may see more competitive pricing if enforcement succeeds.
- Small businesses and independent sellers coerced by dominant platforms—those forced to adopt platform-recommended prices or terms gain explicit statutory protection against algorithm-driven coercion.
- State and private enforcers—attorneys general and plaintiff firms gain easier access to discovery because complaints need only plead plausible agreement, improving their ability to investigate opaque algorithmic conduct.
- Workers and contractors—because “price” explicitly can mean compensation, employees and gig workers may gain protection against coordinated wage suppression mediated by shared algorithms.
Who Bears the Cost
- Algorithm vendors and SaaS pricing providers—licensing, subscription, or data-sharing business models now carry antitrust risk and may require product changes, contractual carve-outs, or compliance controls.
- Marketplaces and large platforms—companies that provide uniform recommendations or standardize seller pricing face exposure under the coercion clause and will likely need new contractual safeguards and monitoring.
- In-house legal and compliance teams—must reassess vendor contracts, data practices, and pricing strategies and may incur higher legal and operational expenses to limit exposure.
- State and local prosecutors—may absorb increased investigative and enforcement costs, including technical resources to subpoena and analyze vendor code and logs, despite the bill’s reimbursement language.
Key Issues
The Core Tension
The bill pits two legitimate aims against each other: the need to deter and remediate stealthy, algorithm-driven collusion that can replicate cartel outcomes without explicit communications versus the risk of criminalizing or chilling broadly useful pricing tools and legitimate data-driven business practices. Lowering the pleading bar helps catch sophisticated secrecy, but it also raises the prospect of costly discovery and criminal exposure for firms whose algorithmic products have legitimate, procompetitive uses.
The statute’s core terms are broad but undefined in key respects, creating immediate implementation and litigation questions. “Competitor data” and “coerce” are not operationally defined, so courts will interpret whether routine data-sharing (e.g., price indexes, market intelligence, or anonymized benchmarks) or recommended prices from analytics platforms amount to illegal coordination. Similarly, the phrase “used by two or more persons” could sweep in multi-tenant SaaS that provides individualized, data-driven recommendations; distinguishing procompetitive personalization from coordination will be fact-intensive.
Lowering the pleading standard to plausibility transfers pressure from pre-discovery motions to discovery itself. That helps enforcers access algorithmic evidence but also invites litigation that may turn on expensive technical discovery into proprietary systems and trade secrets.
Criminal exposure for distribution amplifies stakes: vendors accustomed to licensing code or providing hosted services face potential criminal liability even where the same tools have legitimate operational uses (dynamic pricing, yield management, individualized offers). Finally, the law’s territorial language—tying coercion to prices for products or services “in the jurisdiction of this state”—raises complex questions about multi-jurisdictional markets and cross-border platforms that sell both inside and outside California.
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