New laws regulating algorithmic pricing enacted in New York and California

Introduction

The last few years have seen a surge in litigation focusing on pricing algorithms.  These cases involve allegations that competitors’ use of revenue management software facilitates price-fixing and coordination by providing pricing recommendations to software users.  The earliest of these cases involved residential rentals—but cases have focused on other sectors as well, including hotels and health insurance.  None of these cases has yet proceeded to trial and there are only a few decisions on motions to dismiss (and even fewer summary judgment decisions). The outcomes of the cases have also diverged, with some courts dismissing the complaints and others allowing the cases to proceed to discovery[1], and with some courts finding that pricing algorithms should be treated as per se unlawful and others finding that they should be evaluated using the rule of reason.[2]  Evidence regarding whether the algorithm used competitors’ information has been important to how the cases to date have been decided, and that is also the focus of the recent laws enacted in two of the nation’s most populous states.[3]

Against this backdrop, we have also seen a focus by state—and even local—legislatures nationwide on competitor coordination through the use of pricing algorithms.  Federal legislators have been less active in this area.[4]  California Assembly Bill 325 (CA AB 325) and New York Assembly Bill A1417B (NY AB 1417B) are two recent noteworthy laws:

CA AB 325

In summary, CA AB 325 prohibits agreements to use or distribute a “common pricing algorithm,” defined as any software or other technology that two or more people use which ingests competitor data[5] to recommend, align, stabilize, set, or otherwise influence a price or commercial term (including terms as related to both upstream vendors and downstream customers), and lowers the pleading standard under the Cartwright Act (California’s antitrust statute, Cal Bus. & Prof. Code § 16720) for certain civil claims.

Specifically, CA AB 325 amends the Cartwright Act in three ways:

1. Clarifies that, under the Cartwright Act, it is unlawful to “use or distribute a common pricing algorithm as part of a contract, combination in the form of a trust, or conspiracy to restrain trade or commerce in violation of the [Cartwright Act].”  Prior to this law, the Cartwright Act already prohibited contracts, combinations, or conspiracies that restrain trade, and this new law makes clear that activities that previously violated the Cartwright Act still violate the Cartwright Act even when a common pricing algorithm is used.

2. Makes it unlawful “to use or distribute a common pricing algorithm if the person coerces another person to set or adopt a recommended price or commercial term recommended by the common pricing algorithm for the same or similar products or services.”  A “person” is defined as “corporations, firms, partnerships and associations existing under or authorized by the laws.”[6] “Coerces” is not defined in the law and can be given its common meaning.

   This provision is broader than the first part of CA AB 325, in that it does not require a “contract, combination in the form of a trust, or conspiracy to restrain trade or commerce” but would allow proof of coercion to suffice.

3. CA AB 325 also lowers the pleading standard under the Cartwright Act for civil collusion or coordination allegations (not specific just to algorithms). The current standard requires plaintiffs to plead a person intentionally conspired to fix prices.  Under CA AB 325, it is sufficient for plaintiffs to plead only that the existence of a contract, combination in the form of a trust, or conspiracy to restrain trade or commerce is plausible.  Plaintiffs will not be required to allege facts tending to exclude the possibility of independent action.  This change does not expand the scope of activities prohibited under the Cartwright Act but eases the pleading standard.  It is likely that summary judgement decisions in many cases will focus on whether competitors acted independently.

NY AB A1417B

The New York statute is more narrowly aimed at the housing market.  In summary, NY AB A1417B bans residential rental property landlords from colluding via the use of algorithmic rental pricing software and from using algorithmic software to set rental terms.

Specifically, NY AB A1417B:

1. Makes it a violation to facilitate an agreement between two or more landlords not to compete with respect to residential apartments including by using an algorithmic pricing software to coordinate; and
2. States it is an unlawful agreement for a landlord to set rental terms based on recommendations from an algorithmic pricing software.

NY AB A1417B also bans a landlord’s use of an algorithm to set not only rental prices, but also the “lease renewal terms, occupancy, levels, or other lease terms and conditions.”  This provision’s scope is untested but plaintiffs will likely attempt to define “other lease terms and conditions” broadly, in particular to include any form or type of payment a renter may make.

CA AB 325 and NY AB 1417B are recent and prominent examples of algorithmic pricing bills that became law, but legislators continue to be active even where those laws were passed as well as in other states.  For example:

1. In California, the California Preventing Algorithmic Collusion Act of 2025, a bill that would have prohibited the usage of certain pricing algorithms incorporating confidential, nonpublic, competitively sensitive information of two or more competitors, recently failed to pass.  The 2024 version of this bill was much broader, and established illegality for use or distribution of any pricing algorithm that used, incorporated, or was trained with nonpublic competitor data, without any regard as to whether the information was competitively sensitive.
2. On the same day that CA AB 325 was signed into law, CA SB 763 was also signed into law.  CA SB 763 is not specific just to algorithms and increases maximum criminal penalties for corporate violators of the Cartwright Act from $1 million to $6 million per violation and for individual violators from $250,000 to $1 million per violation.  CA SB 763 adds a provision to the Cartwright Act allowing courts to impose additional civil penalties of up to $1 million per violation.  CA SB 763 also makes clear that the remedies and penalties under the Cartwright Act are “cumulative” to other Cartwright Act remedies and penalties as well as remedies and penalties available under other state causes of action.  This clarification may increase potential exposure for defendants.
3. In New York, the Algorithmic Pricing Disclosure Act, which imposes disclosure requirements for businesses using pricing algorithms that incorporate certain personal information, was signed into law in May 2025.[7]
4. Many other state legislatures have introduced bills; some examples include New Jersey (NJ SB 3657) and Pennsylvania (PA HB 1779).
5. In the rental pricing context, cities have also been active.  Although New York was the first state to enact a ban on the use of algorithmic pricing for rental pricing, cities including Jersey City, Philadelphia, Minneapolis, and Seattle, have also enacted bans.

Takeaways

Algorithmic pricing is an area in which legislation—and litigation—has moved quickly and will likely continue to do so.  For companies using pricing algorithms, especially those that operate in multiple states, it will be important to monitor for new laws and developments at the federal, state, and city levels and evaluate how these proposals may impact current or proposed pricing strategies or other business operations.

Even with this uncertainty, some trends have started to emerge:

1. A legislative focus on collusion:  The bills that have so far become law (as well as the developing case law in this area) focus on collusive, anti-competitive behavior facilitated by the use or distribution of pricing algorithms.  Bills that have focused on outlawing the usage of all pricing algorithms have generally gained less traction.
2. Changing standards:  As CA AB 325 demonstrates, some legislation has focused on changing the legal standards that apply to the usage and distribution of pricing and other algorithms. Just as plaintiffs in litigations focused on pricing algorithms have argued for evaluation under a per se standard (generally understood to mean that the use or distribution is inherently illegal, without the need for further proof of harmful effects on the market), some critics of pricing algorithms have pushed for new laws that will require courts to evaluate pricing and other algorithms under a per se standard.  So far, no state has enacted legislation to this effect.
3. Users and providers are both potentially on the hook:  While sale or use of pricing algorithms is currently not per se unlawful, under some current and proposed laws, users of pricing algorithms developed by a third-party provider can be found liable. Users—or potential users—of pricing algorithms should proactively work with antitrust counsel to ensure compliance with antitrust laws when considering whether to implement new algorithmic pricing tools.  Some potential suggestions include establishing internal policies for approving any such tools before they are licensed or implemented, requiring vendors of algorithmic tools to explain what inputs their tools ingest and act upon, and scrutinizing claims from vendors that specific tools are used by competitors or influence pricing behavior.  Implementers may also consider the appropriate use of compliance with laws and indemnification provisions in license agreements.  Users should work closely with antitrust counsel in situations involving communications with competitors about the use of pricing software, including in actual or virtual meetings of user groups.