Overview
Takeaways
- Maryland was the first state to pass surveillance pricing legislation, but it will not be the last.
- The regulatory focus is likely to expand beyond groceries and food delivery into other sectors
- Companies using surveillance or algorithmic pricing tools should monitor state and federal developments and ensure compliance with privacy and disclosure requirements.
- Companies should assess their tools across jurisdictions and ensure they understand inputs and avoid use of competitors’ confidential pricing data.
Although many states and the federal government have proposed legislation to curb surveillance pricing, Maryland recently became the first to pass such legislation. On April 28, 2026, Maryland passed the Protection From Predatory Pricing Act, a law expressly restricting food retailers and food delivery services from using surveillance pricing.[1] A violation will constitute a deceptive trade practice under the Maryland Consumer Protection Act and will be subject to a $10,000 fine per violation after the law goes into effect on October 1.
Surveillance Pricing vs. Dynamic Pricing
The FTC has described surveillance pricing as the use of "advanced algorithms, artificial intelligence and other technologies, along with personal information about consumers — such as their location, demographics, credit history, and browsing or shopping history — to categorize individuals and set a targeted price for a product or service."[2] One example might be a clothing retailer using AI to analyze a customer’s shopping history and generating a coupon for a product she has not purchased in months. Another example is a retail or grocery app charging more for groceries if a customer’s location data indicate that the customer is in a more affluent area.
Surveillance pricing is distinct from dynamic pricing. Dynamic pricing involves the use of artificial intelligence and algorithms to adjust the prices of goods and services in real-time based on market forces of supply and demand — without using a customer’s personal data. Hotels and airlines have used dynamic pricing for years to charge higher prices during peak travel times. More recently, rideshare applications like Uber and Lyft, as well as sporting and concert events, have also employed dynamic pricing to reflect demand. For the airline ticket example, under dynamic pricing, two travelers would see price fluctuations based on supply and demand over time, but the same price if they searched at 10 a.m. on Tuesday for the same itinerary. Under surveillance pricing, travelers from different locations and with different browsing histories would see different prices if they searched at 10 a.m. on Tuesday for same itinerary.
While the act of using algorithms and artificial intelligence for dynamic pricing typically does not run afoul of antitrust laws in and of itself,[3] there is a risk that the use of such software could be considered collusive conduct if it allows competitors to access competitively sensitive pricing information. For example, the DOJ investigated and sued RealPage, a technology platform that provides data analytics for the real estate industry, alleging that the company’s algorithm that recommends apartment rental prices based on competitors’ pricing information amounted to a hub-and-spoke price-fixing conspiracy. The DOJ reached a consent decree with RealPage in November 2025 requiring that the company use only historical rental data at least twelve months old — taking the data out of the “real-time” dynamic pricing realm — and that it not report rental pricing information more narrowly than at the statewide level.[4]
The DOJ also recently stated that it would consider criminal probes of companies that use algorithmic pricing software if the companies know that nonpublic data are used by the software to set prices. Acting Deputy Assistant Attorney General Daniel Glad said that "[t]he rim, the agreement among competitors, is the element that determines whether this is a vertical arrangement subject to the rule of reason, or a horizontal conspiracy subject to the per se rule . . . And where the rim is present — where competitors have understood that their sensitive nonpublic data will be used to set prices for competitors and have participated on that understanding — the door to the per se rule, and therefore to criminal enforcement, is open."
Why is Surveillance Being Targeted?
As with dynamic pricing, surveillance pricing creates a risk for collusion among competitors if they use the pricing information obtained through a common pricing algorithm. Furthermore, the risk of predatory pricing is greater with surveillance pricing because a monopolist can use the information to lower prices below cost only when a particular customer is a likely target of a competitor.
Additionally, there is heightened concern about surveillance pricing due to its use of personal data. Privacy concerns about what personal data are being collected and how it is being used appear to be driving much of the proposed legislation. Some of the concerns about lack of transparency could be addressed at the outset of the purchasing experience through clear statements about collection and use of personal data. Detractors also argue that the use of surveillance pricing can increase costs for certain consumers by extracting a higher price for a product if the data suggest that the consumer desires the product and is willing to pay more to obtain it. These concerns may be particularly acute when surveillance pricing is used to extract higher prices for essentials like food.
On the other hand, proponents of surveillance pricing argue that the practice helps increase firm efficiency and expands access to price-sensitive customers by bringing people into the market who otherwise would not have been customers. In the example above about a business generating a coupon for a product a customer had not purchased in several months, if successful, the surveillance pricing increases total economic surplus by stimulating an additional purchase at a discounted price that the marginal customer is willing to pay.
Beyond Maryland – Other Actions on Surveillance Pricing
Maryland's Protection From Predatory Pricing Act is the first law targeting surveillance pricing, but lawmakers in other states such as Arizona,[5] Florida,[6] Hawaii,[7] Illinois,[8] Kentucky,[9] Nebraska,[10] Vermont,[11] Virginia,[12] and Washington[13] have proposed similar legislation. New York requires disclosures of any surveillance pricing that was set using personal data.[14] Others are still attempting to regulate surveillance pricing through laws already on their books. For example, California recently launched a probe into whether retailers, grocers, and hotels are using personal data in a way that consumers do not expect in violation of the California Consumer Privacy Act.[15]
While states are actively policing surveillance pricing, federal efforts to this end appear largely stagnant. The FTC under Chair Lina Khan initiated a market study pursuant to Section 6(b) of the FTC Act regarding surveillance pricing and released preliminary findings showing "that retailers frequently use people’s personal information to set targeted, tailored prices for goods and services — from a person's location and demographics, down to their mouse movements on a webpage."[16] However, current Chairman Andrew Ferguson dissented from releasing the preliminary findings, reasoning that they were premature.[17] The current status of the study is unknown, though the Trump administration generally has taken a hands-off approach to regulating artificial intelligence technology.[18]
Members of Congress have also introduced legislation aimed at surveillance pricing such as the Stop Price Gouging in Grocery Stores Act[19] and the Stop AI Price Gouging and Wage Fixing Act of 2025.[20] The bills target the use of surveillance pricing in food retail stores but appear to have stalled in their respective committees. Most recently, on May 19, 2026, a bipartisan bill was introduced appearing to target the same conduct as Maryland’s Protection From Predatory Pricing Act, though the text of the bill has not yet been released.[21]
[1] See generally 2026 Md. Laws ch. 154 (H.B. 895) (amending Md. Code Ann., Tax–Gen. § 11‑206(c) and codifying Md. Code Ann., Com. Law § 13‑321). “Food retailers” are limited to stores that are larger than 15,000 square feet and stores that offer food that is consumed off-site, meaning the Act is targeted towards grocery stores and not restaurants. Id., at § 1(a)(4).
[2] FTC Issues Orders to Eight Companies Seeking Information on Surveillance Pricing, FTC (July 23, 2024), https://www.ftc.gov/news-events/news/press-releases/2024/07/ftc-issues-orders-eight-companies-seeking-information-surveillance-pricing.
[3] In certain circumstances, the use of algorithms to set pricing to resellers may run afoul of the Robinson-Patman Act and, if not properly restrained, could result in predatory pricing.
[4] United States v. RealPage, Inc., No. 1:24-cv-00710-WO-JGM (M.D.N.C. May 19, 2026) (final judgment) (Doc. 194).
[5] H.B. 2489, 57th Leg., 2d Reg. Sess. (Ariz. 2026).
[6] S.B. 1749, 2026 Reg. Sess. (Fla. 2026).
[7] H.B. 2458, 2026 Reg. Sess. (Haw. 2026).
[8] H.B. 4248, 104th Gen. Assemb., Reg. Sess. (Ill. 2026).
[9] H.B. 33, 2026 Reg. Sess. (Ky. 2026).
[10] L.B. 1006, 109th Leg., 2d Reg. Sess. (Neb. 2026).
[11] S.207, 2026 Reg. Sess. (Vt. 2026).
[12] H.B. 121, 2026 Reg. Sess. (Va. 2026).
[13] S.B. 6312, 2025-2026 Reg. Sess. (Wash. 2026).
[14] Attorney General James Warns New Yorkers About Algorithmic Pricing as New Law Takes Effect, Office of the New York State Attorney General (Nov. 5, 2025), https://ag.ny.gov/press-release/2025/attorney-general-james-warns-new-yorkers-about-algorithmic-pricing-new-law-takes.
[15] On Data Privacy Day, Attorney General Bonta Focuses on Surveillance Pricing, Compliance with California Consumer Privacy Act, State of California Department of Justice (Jan. 27, 2025), https://oag.ca.gov/news/press-releases/data-privacy-day-attorney-general-bonta-focuses-surveillance-pricing-compliance.
[16] FTC Surveillance Pricing Study Indicates Wide Range of Personal Data Used to Set Individualized Consumer Prices, FTC (Jan. 17, 2025), https://www.ftc.gov/news-events/news/press-releases/2025/01/ftc-surveillance-pricing-study-indicates-wide-range-personal-data-used-set-individualized-consumer.
[17] Dissenting Statement of Commissioner Andrew N. Ferguson Joined by Commissioner Melissa Holyoak Regarding the Surveillance Pricing 6(b) Staff Research Summaries, FTC (Jan. 17, 2025), https://www.ftc.gov/system/files/ftc_gov/pdf/surveillance-pricing-6b-research-summaries-ferguson-dissent-final.pdf.
[18] See Exec. Order No. 14179, 90 Fed. Reg. 8741 (Jan. 31, 2025).
[19] Stop Price Gouging in Grocery Stores Act of 2025, H.R. 4966, 119th Cong. (2025).
[20] Stop AI Price Gouging and Wage Fixing Act of 2025, H.R. 4640, 119th Cong. (2025).
[21] H.R. 8895, 119th Cong. (2026).
