Surveillance Pricing: Is AI Charging Your Customers Different Prices?
Two customers open the same online store at the same time. One sees a product for $89. The other sees it for $109.
The difference is not inventory. It is not a weekend promotion. It is not increased demand.
Instead, software has analyzed information about the second customer and concluded that the customer may be willing to pay more.
That is the emerging business and legal issue known as surveillance pricing, also called personalized pricing or personalized algorithmic pricing.
Artificial intelligence (AI), sophisticated data analytics, customer tracking, location information, browsing history, purchasing behavior, and third-party data can make individualized pricing increasingly feasible. The legal environment around those practices is now changing quickly.
In August 2026, the Federal Trade Commission (FTC) issued a proposed enforcement policy statement specifically addressing personalized pricing. The FTC emphasized that it does not have authority to prohibit personalized pricing in every circumstance. But it also warned that businesses may violate Section 5 of the Federal Trade Commission Act when consumers reasonably expect a generally available price and the business fails to adequately disclose that personal data is being used to personalize that price. FTC Proposed Enforcement Policy Statement on Personalized Pricing
The FTC has extended the public comment period on that proposal through September 25, 2026. FTC Extension of Comment Period
For businesses, the issue is larger than whether an AI system technically “sets” a price. Management and General Counsel should be asking what information enters the pricing system, whether prices differ among customers, what customers are told, what outside vendors are doing with company and consumer data, and what laws apply in each state where the company does business.
What Is Surveillance Pricing?
Surveillance pricing generally refers to using information about an individual consumer, or a particular group of consumers, to determine the price that person or group will be offered.
That should be distinguished from ordinary dynamic pricing.
A hotel charging more during a convention, an airline adjusting fares as seats disappear, or a retailer discounting excess inventory may be responding to market conditions that affect customers generally.
Personalized pricing is different. The price can change because of information about you.
The FTC's proposed policy provides examples involving information such as prior purchases, disposable income, shopping habits, location, whether competing apps are installed on a consumer's phone, and even circumstances suggesting that a consumer urgently needs a particular service. The FTC draws the distinction between ordinary supply-and-demand pricing and pricing based upon personal information used to estimate a particular consumer's willingness to pay. FTC Proposed Enforcement Policy Statement on Personalized Pricing
That distinction is becoming particularly important as businesses deploy AI systems that can combine information from customer relationship management systems, advertising platforms, loyalty programs, online behavior, data brokers, mobile applications, and other sources.
The FTC Is Putting Personalized Pricing on the Compliance Agenda
The FTC's August 2026 proposal is significant because it addresses a common assumption: that personalized pricing is permissible so long as no statute expressly prohibits the practice.
The agency is taking a more nuanced position.
The FTC acknowledges that Congress has not authorized it to prohibit personalized pricing categorically. But under the proposed policy, a company could face Section 5 scrutiny when its representations, omissions, data practices, or pricing practices are unfair or deceptive.
The proposed policy says that where consumers reasonably expect prices not to vary based upon personal data, businesses using personalized pricing should clearly and conspicuously disclose that the price is personalized, the basis for the personalization, and the type of data being used. FTC Proposed Enforcement Policy Statement on Personalized Pricing
The FTC also expressly addresses the underlying data practices. Collection or use of personal information for personalized pricing without adequate disclosure or appropriate consent could itself create Section 5 concerns. FTC Proposed Enforcement Policy Statement on Personalized Pricing
That changes the compliance discussion.
A business may need to understand not merely what price its system produces, but why the system produced it and what data generated that result.
New York Already Requires an Algorithmic Pricing Disclosure
For businesses operating in New York, this is not only a proposed federal policy.
New York General Business Law § 349-a already regulates personalized algorithmic pricing.
The statute defines personalized algorithmic pricing as dynamic pricing set by an algorithm using personal data. An entity subject to the law that advertises or displays a personalized algorithmic price to a New York consumer must provide a clear and conspicuous disclosure stating:
“THIS PRICE WAS SET BY AN ALGORITHM USING YOUR PERSONAL DATA.”
The statute also authorizes enforcement by the New York Attorney General and permits civil penalties of up to $1,000 per violation after the statutory enforcement process. New York General Business Law § 349-a
New York is also considering going substantially further.
Assembly Bill A9349-B, commonly referred to as the One Fair Price Act, has passed both the New York Assembly and Senate. As of this writing, the official legislative page continues to list its current status as “Passed Senate & Assembly,” rather than enacted law. New York A9349-B – One Fair Price Act
If enacted in its present form, the bill would move beyond the existing disclosure model and prohibit specified uses of personalized algorithmic pricing and the collection, use, retention, or disclosure of personal data for surveillance pricing, while preserving defined exceptions. It would also require disclosures concerning automated pricing systems that use non-personal inputs. New York A9349-B – One Fair Price Act
Businesses therefore should distinguish carefully between the New York law that is already in effect and the broader prohibition that has passed the Legislature but has not yet become law.
Pennsylvania Is Considering Its Own Surveillance Pricing Act
Pennsylvania has entered the discussion as well.
House Bill 1942, introduced as the Surveillance Pricing Act, would generally prohibit surveillance pricing while creating exceptions for certain cost-based pricing, publicly disclosed discounts, broadly available group discounts, loyalty programs, insurance-related activity, and specified credit-related uses.
The proposed statute defines surveillance pricing to include customized pricing based, in whole or in part, on personally identifiable information gathered through electronic surveillance technologies, including device tracking, biometric monitoring, sensors, cameras, location information, and other consumer characteristics or behavior. Pennsylvania House Bill 1942
The bill remains pending in the Pennsylvania House Consumer Protection, Technology and Utilities Committee. Pennsylvania General Assembly – HB 1942 Status
Pennsylvania's Joint State Government Commission has also addressed surveillance pricing in its broader work on AI. Its 2026 report distinguishes traditional dynamic pricing based on market conditions from individualized prices generated from personal and behavioral information. Pennsylvania Joint State Government Commission – Surveillance Pricing Discussion
For Pennsylvania businesses, HB 1942 is not current law. But it is another indication that state legislatures are looking closely at how businesses combine AI, personal data, and pricing.
Surveillance Pricing Is Not the Same as Algorithmic Price Fixing
There is another pricing issue that businesses should keep separate: antitrust law.
An algorithm that examines one company's own costs, inventory, demand, and historical sales presents a very different legal issue from a system that uses competitors' competitively sensitive nonpublic information to coordinate prices across a market.
The Department of Justice (DOJ) has been actively pursuing the latter theory.
On September 4, 2026, the DOJ announced a proposed consent decree with Pinnacle Property Management Services as part of its ongoing RealPage litigation. Among other restrictions, the proposed decree would bar specified uses of algorithms incorporating competitors' competitively sensitive information and restrict the exchange of sensitive pricing information among competitors. DOJ – Pinnacle Algorithmic Coordination Settlement
That is an antitrust case, not a surveillance-pricing case.
But there is a common governance lesson: a business should know what its pricing technology is actually doing.
Calling a product “AI-powered,” “revenue optimization,” or “automated pricing” does not answer the legal questions. The relevant issues include what information is fed into the system, where that information originates, what the algorithm is designed to optimize, whether competing businesses contribute data, and whether management remains capable of exercising independent pricing judgment.
Why Third-Party Pricing Vendors Deserve Legal Review
Many companies will not build their own pricing algorithms.
They will purchase software.
That can create a dangerous gap between operational responsibility and legal understanding. Marketing may procure the platform. Information technology may connect the data. A software vendor may determine the model. Sales personnel may simply receive the resulting price.
Meanwhile, senior management may have no clear answer to a basic question:
Why did Customer A receive a different price from Customer B?
Outsourcing the technology does not eliminate the need for corporate oversight.
Vendor contracts should be examined for data rights, confidentiality, permitted uses of customer information, compliance obligations, representations concerning data sources, audit rights, indemnification, information security, subcontractors, retention practices, and the company's ability to understand or challenge how pricing recommendations are generated.
Surveillance Pricing Questions General Counsel Should Be Asking Now
A business does not need to wait for an enforcement action or a new statute to understand its exposure. A practical review should include:
Identify every automated pricing tool. Determine whether pricing software, AI systems, e-commerce tools, advertising platforms, loyalty systems, or outside vendors can change customer pricing.
Map the inputs. Determine whether the system uses inventory and market conditions or personal information such as purchase history, browsing behavior, location, device information, demographics, inferred income, loyalty data, or third-party information.
Determine whether customers actually receive different prices. Discounts, recommendations, advertising, and price displays should be analyzed separately.
Review consumer disclosures and privacy notices. The business should compare what it actually does with what it tells consumers it does.
Review vendor contracts and data flows. A company should know whether its vendor combines the company's information with information from other customers, data brokers, competitors, or unrelated third parties.
Analyze the jurisdictions involved. New York already has a specific disclosure statute. Pennsylvania has proposed legislation. Other states are pursuing their own approaches, creating the potential for another state-law compliance patchwork.
Keep antitrust analysis separate but included. If a pricing platform receives nonpublic information from competing companies or produces coordinated market recommendations, the issue may extend beyond consumer protection and privacy into federal and state antitrust law.
The Larger Business Lesson
The legal question is not simply whether a company uses AI.
The more useful questions are:
What data is the AI using?
What decision is it making?
What does the customer believe is happening?
What has the company disclosed?
And could the same technology create different legal risks in different states?
Surveillance pricing illustrates a broader problem companies will increasingly encounter with artificial intelligence. A technology may be commercially attractive, widely available, and technically sophisticated while still creating legal consequences that the people purchasing or operating it have never considered.
That is where legal review should occur before deployment rather than after a customer complaint, regulatory inquiry, or lawsuit.
About Todd B. Nurick
Todd B. Nurick is a Pennsylvania and New York business attorney with approximately 30 years of experience advising businesses on contracts, transactions, corporate governance, investigations, compliance, employment-related business issues, risk management, and disputes. Through the Law Office of Todd B. Nurick, he also serves businesses as Fractional General Counsel/Outside General Counsel, providing experienced legal oversight without requiring a full-time in-house legal department.
Sources
Federal Trade Commission, Proposed Enforcement Policy Statement Regarding Personalized Pricing, Aug. 19, 2026. FTC source
Federal Trade Commission, Extension of Public Comment Period Regarding Personalized Pricing, Sept. 3, 2026. FTC source
New York General Business Law § 349-a, Pricing. New York statutory text
New York Assembly Bill A9349-B, One Fair Price Act/current legislative status. New York legislative source
Pennsylvania House Bill 1942, Surveillance Pricing Act. Pennsylvania bill text
Pennsylvania General Assembly, HB 1942 history/status. Pennsylvania legislative status
Pennsylvania Joint State Government Commission, 2026 AI report – Surveillance Pricing. Pennsylvania legislative research source
U.S. Department of Justice, proposed Pinnacle consent decree regarding algorithmic coordination, Sept. 4, 2026. DOJ source
This article is for general informational purposes only and does not constitute legal advice or create an attorney-client relationship. Laws and regulatory guidance concerning artificial intelligence, consumer data, personalized pricing, and algorithmic pricing continue to develop, and businesses should obtain legal advice concerning their particular circumstances.
