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Law Limits Companies Seeking To Set Prices Based On Data Collected, Your Profile, Spending Habits, Demographics
By David Carlucci
Have you ever searched for a flight, decided to sleep on it, and woken up to find the price had jumped overnight?
Most people assume that happens because demand increased. More people want the seat, so the price goes up. That is how markets work. Prices respond to conditions. That is a principle as old as commerce itself.
But what if the price changed not because more people wanted the seat, but because an algorithm determined that you personally were willing to pay more for it? What if the company already knew, based on your browsing history, your past purchases, the device you were using, and dozens of other data points, that you were unlikely to shop elsewhere?
That was the question behind a bill that generated significant debate in Albany. The legislation prohibit what lawmakers are calling surveillance pricing and could reshape how businesses across New York are permitted to use personal data when setting prices. It is one of the most consequential consumer protection proposals to pass the Legislature in years, and it arrives at a moment when artificial intelligence is changing the economics of nearly every industry.
The legislation was delivered to Governor Kathy Hochul on June 4, 2026. If signed into law, it will take effect 180 days after enactment.
What Is Surveillance Pricing?
Surveillance pricing occurs when a company uses information about a specific consumer to determine what price to show that person for a good or service.
That information can include browsing history, purchase history, location data, device type, search behavior, and the amount of time a consumer has spent looking at a particular product. Artificial intelligence systems can process all of that data in seconds and produce a price calibrated to what they believe a particular customer is willing to pay.
The result is that two people shopping for the exact same product at the exact same moment could be shown two different prices. Not because supply changed. Not because demand shifted across the broader market, but because an algorithm assessed each customer differently and concluded that one of them would pay more.
Most consumers have no way of knowing when this is happening. The price appears on the screen like any other price. There is no disclosure, no notification, and no indication that the number reflects anything other than the standard market rate.
What the Bill Would Do
The legislation draws a fundamental distinction between two types of price movement.
Prices that respond to market conditions would remain fully permitted. A hotel that raises rates on a busy holiday weekend is responding to real demand. A retailer that discounts inventory at the end of a season is managing supply. A ride-share company that charges more when fewer drivers are available reflects market conditions at that moment. An airline that raises fares as seats fill up is doing what airlines have always done. None of that would be affected by this legislation.
What the bill targets is something different. It prohibits businesses from using personal consumer data to produce a price that differs from what another customer would see for the same good or service. The price a consumer is shown must be grounded in market conditions. It cannot be grounded in what an algorithm believes about that specific individual.
The law goes further than just the moment of sale. It also prohibits businesses from collecting, sharing, or retaining personal data for the purpose of enabling individualized pricing in the first place. That provision addresses the data infrastructure behind the practice, not just the price that ultimately appears on screen. For companies that have built systems designed to gather and analyze consumer behavior at scale, that is a significant requirement.
Where the Debate Gets Complicated
This is where reasonable people disagree, and where the legislative debate was most active.
Many companies use customer data in ways that consumers experience as beneficial. Loyalty programs, membership discounts, targeted coupons, and personalized promotions are all built on an understanding of customer behavior. A grocery chain that offers lower prices to rewards card members is using data. A retailer that sends a returning customer a discount based on past purchases is using data. A subscription service that offers a better rate to long-term members is using data.
Supporters of these programs argue that data-informed pricing is not inherently exploitative. It can mean a loyal customer receives a better deal than a new one. It can mean a consumer receives a coupon for something they actually want rather than something they would never buy. The argument from the business community is that restricting data use in pricing could limit these benefits and create compliance uncertainty for companies that have invested in customer relationship programs in good faith.
The legislation attempts to carve out space for these practices. Discounts offered uniformly to all members of a loyalty or rewards program would generally be allowed. Discounts for broadly defined groups such as veterans, seniors, or teachers would also be permitted. A business could still reward a returning customer based on their purchase history with that specific business, provided that data is not combined with broader personal data to build a more comprehensive individual profile.
The question that concerns business groups is whether those definitions are clear enough and whether the exceptions are broad enough to protect common practices that consumers value. Consumer advocates respond that the ambiguity in existing law is precisely what has allowed individualized pricing to expand without public awareness or meaningful accountability. That tension drove multiple rounds of amendment to the bill and is likely to shape how the law is interpreted and enforced if it signed.
Online marketplaces face particular scrutiny under the bill. A platform that hosts third-party sellers and simultaneously collects data across all of those transactions would face restrictions on how that combined data can be used to set prices for any individual consumer. That provision is aimed at the largest technology and commerce platforms, where the accumulation of consumer data across an entire ecosystem is most pronounced.
Why This Conversation Is Happening Now
Twenty years ago, a business knew relatively little about any individual customer. A store could track what sold well and what did not. A catalog company could note which products a customer had ordered before. But the depth of individual consumer intelligence available today is categorically different.
Smartphones, connected devices, and the architecture of the modern internet have made it possible to collect detailed behavioral data from consumers continuously and often without their active awareness. Artificial intelligence has made it possible to act on that data at a scale and speed that was not previously imaginable. A system can now assess thousands of individual signals about a consumer and produce a customized price in the time it takes a page to load.
For businesses, that capability represents a meaningful investment in understanding customers and optimizing operations. Knowing more about who is buying, when they are buying, and what they are willing to pay is genuinely useful information for pricing decisions, inventory management, and competitive strategy. The business case for data-driven pricing is real.
For consumer advocates, the same capability represents an information imbalance that the consumer cannot see, cannot challenge, and often cannot even detect. The consumer brings their preferences and their budget to a transaction. The company brings an algorithmic profile of that specific consumer. The question is whether that asymmetry is something the law should address.
New York would be among the first states to directly regulate this practice at a statutory level. What the Legislature ultimately decided is how to draw the line between legitimate data-driven business practice and a form of pricing that operates invisibly and may not reflect what the market would otherwise produce.
The Broader Significance
This bill does not exist in isolation. It is part of a growing national conversation about artificial intelligence, data privacy, and the rights of consumers in a digital economy. Federal regulators have begun examining surveillance pricing practices as well. Several other states are watching what New York does.
Whatever the outcome of this specific legislation, the underlying questions it raises are not going away. As artificial intelligence becomes more capable and more affordable, the tools that currently exist only at the largest technology companies will become accessible to businesses of every size. The debate Albany is having today is one that every state and eventually every market will need to have.
The central question is a straightforward one.
When a business knows a great deal about you, how much of that knowledge is it permitted to use in deciding what to charge you?
New York my have an answer. The conversation is worth following closely, regardless of which side of the counter you happen to be standing on.
David Carlucci consults organizations on navigating government and securing funding. He served for ten years in the New York Senate.
