Algorithmic Pricing: Navigating Antitrust and Consumer Protection Risks

As artificial intelligence (AI) becomes increasingly available to support business operations, companies may consider incorporating AI-driven tools, such as those used for algorithmic pricing, into their marketing strategies. Algorithmic pricing refers to the use of automated systems or artificial intelligence to determine, recommend, or adjust prices, and spans a wide range of applications, from adjusting prices based on inventory levels or demand patterns to more sophisticated tools that incorporate competitor or consumer data. Before adopting these tools, companies should consider that regulators have intensified their scrutiny of algorithmic pricing across multiple enforcement regimes.

Recent regulatory and litigation activity has focused on two distinct uses of algorithmic pricing, each raising a different set of potential legal concerns, specifically:

• Algorithms that incorporate or share pricing information from multiple competing companies to set or recommend prices, which antitrust enforcers have scrutinized as a potential vehicle for coordination among competitors.
• Algorithms that set individualized prices based on personal consumer data, often called “surveillance” or “personalized” pricing, which has become the subject of new state disclosure requirements and substantive restrictions under consumer protection law.

This Advisory provides an overview of the evolving legal landscape in both areas — from federal antitrust enforcement and litigation to a growing patchwork of state consumer protection laws — and offers practical considerations for companies that use, or are considering using, AI-driven pricing tools.

Antitrust Considerations

Key Cases

The Ninth Circuit’s August 2025 decision in Gibson v. Cendyn Group, LLC is the first federal appellate opinion to address the antitrust implications of algorithmic pricing. The court affirmed dismissal of claims that Las Vegas Strip hotels had violated Section 1 of the Sherman Act by separately licensing a common pricing software.¹ The court held that competitors’ independent decisions to use the same software vendor — without an agreement among themselves to do so or to follow its recommendations — did not, without more, give rise to antitrust liability, noting that the license agreements at issue did not raise antitrust concerns because they did not restrain any hotel’s ability to price its own rooms independently.²

At the same time, the court identified circumstances in which use of a common pricing software could pose competitive concerns, including where competitors agree among themselves to use the software and adhere to its pricing recommendations, or where the software pools and shares confidential pricing information among competitors.³

The Ninth Circuit’s decision in Gibson v. Cendyn builds on a body of district court decisions that have reached differing outcomes despite similar fact patterns. For example, in 2023, the Middle District of Tennessee allowed the claims to proceed in a multidistrict litigation against RealPage and multi-family building owners and manager clients, but applied a more defendant-friendly rule of reason analysis rather than per se liability because the case did not involve a direct agreement among competitors or a complete delegation of pricing authority to the software.⁴ In Duffy v. Yardi (W.D. Wash., 2024), by contrast, the court held that plaintiffs adequately alleged a per se unlawful price-fixing agreement where competitors allegedly shared nonpublic information through a common software provider.⁵ Most recently, in Segal v. Amadeus IT Group (N.D. Ill., 2026), another case involving hotel software, the court again focused on what it found was a lack of alleged agreement among competitors to coordinate their pricing, rather than mere allegations of sharing aggregated and anonymized information, in dismissing plaintiff’s third amended complaint.⁶

The U.S. Department of Justice (DOJ) and the Federal Trade Commission (FTC) filed Statements of Interest in multiple of these private-plaintiff district court cases, arguing among other things that using a pricing algorithm to set benchmark or “starting point” prices may constitute unlawful concerted action regardless of any differences in final pricing, exchanging pricing information through an algorithm may violate antitrust law in the same way as direct information sharing, and that an invitation proposing collective action, followed by conduct demonstrating acceptance of the invitation — such as contracting with a common software provider — can establish an antitrust violation even without a direct agreement among competitors. DOJ also filed an amicus brief before the Ninth Circuit in Gibson, advancing the same arguments.

Recent Developments

In May 2026, a North Carolina federal court approved a final judgment resolving the DOJ’s 2024 civil suit against RealPage, a provider of revenue management software used by multi-family building owners and managers.⁷ Among other terms, the judgment requires RealPage to cease using nonpublic data from competing properties in the “runtime” operation of its products, refrain from sharing competitively sensitive information (CSI) among users, modify certain product features (including auto-accept functionality), implement an antitrust compliance program, and submit to a three-year independent compliance monitor.⁸ The settlement allows RealPage to continue using CSI that is at least 12 months old to train its algorithm.⁹ RealPage did not admit any wrongdoing as part of the settlement.

While DOJ has yet to bring a criminal antitrust case based on algorithmic pricing software, Acting Deputy Assistant Attorney General Daniel Glad recently stated that criminal antitrust liability can arise where competitors knowingly agree to use software that relies on their nonpublic data to set prices, observing that such an arrangement could supply the elements of a potentially criminal per se antitrust violation. He noted that the distinction turns on whether competitors have agreed to share nonpublic information with the understanding that it will be used to set prices for other competitors.¹⁰

State and Municipal Measures

In 2025, two states — California¹¹ and New York¹² — passed statutes governing the use of algorithmic pricing software. The primary California antitrust law is the Cartwright Act, which is generally consistent with Section 1 of the federal Sherman Act. On October 6, 2025, Governor Gavin Newsom signed Assembly Bill 325, amending the Cartwright Act to make it unlawful to (1) 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 and (2) 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.¹³ The act applies to all industries that operate in California.

In contrast to California’s approach, New York’s law is specifically focused on the residential rental industry. The law prohibits the use of algorithms to set rental rates, making it unlawful to “set or adjust rental prices, lease renewal terms, occupancy levels, or other lease terms and conditions … based on recommendations from a software, data analytics service, or algorithmic device performing a coordinating function.”¹⁴ Between 2024 and 2025, multiple municipalities also passed ordinances to prohibit the use of algorithms in setting rents.

Consumer Protection Considerations

On the consumer protection side, state legislatures and attorneys general have increasingly focused on “surveillance pricing” or “personalized pricing” as a key enforcement priority. Much of the activity to date has centered on grocery retail and online food delivery industries. In May 2026, for example, a bipartisan coalition of state attorneys general submitted comments to the FTC in connection with its rulemaking on fees in online food delivery services, urging the agency to require disclosure of personalized pricing.

States have taken varying approaches to address their concerns regarding algorithmic pricing, ranging from disclosure requirements to outright bans. New York and Maryland recently became the first states to enact laws directly addressing personalized pricing, with their respective approaches reflecting that spectrum. New York requires businesses to disclose their use of personalized pricing, while Maryland, though limited to the food sector, goes further by prohibiting covered retailers and delivery platforms from setting individualized prices based on consumer data. We have outlined the key requirements for both laws below. Notably, as New York’s disclosure law illustrates, states’ scrutiny is not necessarily confined to any single sector and may extend to consumer-facing businesses generally.

New York’s Algorithmic Pricing Disclosure Act

Applying a transparency-based approach, New York’s Algorithmic Pricing Disclosure Act requires any business in the state that uses a consumer’s personal data to set an algorithmic price to display, alongside that price, a clear and conspicuous statement that “THIS PRICE WAS SET BY AN ALGORITHM USING YOUR PERSONAL DATA.”¹⁵ The law applies across industries and defines “personal data” broadly to include “any data that identifies or could reasonably be linked, directly or indirectly, with a specific consumer or device.”¹⁶ The law took effect on November 10, 2025, after surviving a First Amendment challenge brought by the National Retail Federation, with the court rejecting the argument that the mandated disclosure was unconstitutional compelled speech.¹⁷ Enforcement rests with the New York Attorney General, who must allow an opportunity to cure before pursuing civil penalties of up to $1,000 per violation; there is no private right of action.¹⁸

While she has yet to take any formal enforcement actions under the law, the Attorney General’s recent inquiry into Instacart shows what compliance will be measured against in practice. After a December 2025 study reported that shoppers were quoted prices as much as 23% higher for the same items, the Attorney General issued a demand letter to Instacart questioning its compliance with the newly enacted law. In particular, the Attorney General took the position that a disclosure buried in linked fine print — and missing from the pages where prices actually appeared — did not meet the law’s “clear and conspicuous” standard.¹⁹

Notably, while New York’s law stops at disclosure, the state has introduced legislation that would go further and ban “surveillance pricing” outright, a reminder that today’s disclosure regime may be a floor rather than a ceiling.²⁰

Maryland’s Protection From Predatory Pricing Act

Maryland has gone further than disclosure. Its Protection from Predatory Pricing Act (HB 895), signed in April 2026 and effective October 1, 2026, makes Maryland the first state to prohibit — rather than merely require disclosure of — personalized pricing, though only in the grocery sector.²¹ The act bars large food retailers (grocery-style establishments of at least 15,000 square feet) and third-party food delivery providers from using “dynamic pricing” — defined as setting a price specific to an individual consumer based on that consumer’s personal data — to charge higher prices. It separately prohibits covered businesses from using “protected class data,” such as race or gender, in a way that denies a consumer a good, service, or advantage. The act carves out ordinary commercial practices, including loyalty programs, promotional discounts, and price differences attributable to objective costs like shipping or taxes. As with New York, enforcement sits exclusively with the state Attorney General rather than private plaintiffs, though Maryland’s law carries steeper civil penalties of up to $10,000 per violation, with more for repeat offenders.

Beyond New York and Maryland, algorithmic pricing continues to attract attention from lawmakers in other states and at the federal level. Connecticut has since become the third state to act, enacting an omnibus privacy law — effective October 1, 2026 — that employs a hybrid approach pairing a New York-style disclosure requirement with a prohibition on “surveillance pricing” by retail sellers and third-party food delivery services.²² A number of other states appear poised to follow, with the legislatures in Illinois and California considering outright bans on “surveillance pricing.”²³ Federal lawmakers have also begun to engage on this issue, introducing the Stop Price Gouging in Grocery Stores Act of 2026, which would bar surveillance-based pricing in food stores.²⁴ Additionally, members of the House Oversight Committee and Energy and Commerce Committee have launched separate investigations requesting information from companies regarding their surveillance pricing practices and use of personal data to set individualized prices.²⁵

Key Takeaways

Across both the antitrust and consumer protection contexts, the regulatory and enforcement landscape for algorithmic pricing is developing quickly and may vary by jurisdiction and industry. Companies that use, or are considering using, algorithmic or AI-driven pricing tools may wish to keep the following considerations in mind:

• Understand the tools and their inputs. Companies should understand how any pricing tool works, what data it relies on (including any competitor or consumer data), and how its pricing recommendations are used, so they can assess the tool against the applicable legal frameworks. Vendor agreements should describe these matters in writing.
• Retain meaningful control over pricing. Courts and enforcers have distinguished tools that merely recommend prices, subject to user discretion, from arrangements that delegate pricing authority or constrain the user’s ability to set prices independently. Avoid communications with competitors about the use of pricing software.
• Evaluate disclosure obligations for personalized pricing. Where pricing relies on consumers’ personal data, companies should evaluate whether disclosure requirements such as New York’s or Connecticut’s apply and, if so, ensure that any disclosures are clear, conspicuous, and presented where consumers encounter the relevant prices.
• Monitor developments and seek counsel. Given the pace of legislative and enforcement activity across jurisdictions (including bans of certain strategies, such as the ban on dynamic grocery pricing in Maryland), companies should monitor developments in the regions where they operate and consult antitrust and consumer protection counsel before deploying new pricing tools.

Arnold & Porter regularly advises companies on their pricing practices and disputes relating to them. Our Antitrust/Competition and Consumer Protection & Advertising practices would be happy to assist with any questions you have regarding compliance with algorithmic pricing laws.

© Arnold & Porter Kaye Scholer LLP 2026 All Rights Reserved. This Advisory is intended to be a general summary of the law and does not constitute legal advice. You should consult with counsel to determine applicable legal requirements in a specific fact situation.