AI Pricing Raises Alarm Over Shopper Fairness

U.S. shoppers are angry and confused about how restaurant chains and major retailers decide what to charge. Digital shelf labels are spreading, while businesses increasingly use artificial intelligence to predict how customers will respond to a price. For many people, that's where the trouble starts.

On social media, the unease has a name. Terms like "dynamic pricing" and "surveillance pricing" have fueled anxiety about whether different shoppers pay different amounts for the same product. Consumers already expect prices to shift for airline seats, hotel rooms and rideshare trips. What feels new is the possibility that algorithms and personal data now shape ordinary purchases, including groceries and clothing.

Who Holds the Power

Katie Thomas, who leads an internal consumer behavior think tank at management consulting firm Kearney, laid out the stakes. "There are major tensions happening right now between who's really going to have the power with some of these algorithms and usage of bots and AI: the companies or the shopper." That question sits at the heart of the debate, and most shoppers can't answer it by themselves. They can see a price. They can't see the data behind it.

Companies say some of the alarm stems from misunderstanding. Last month, Walmart CEO John Furner wrote in a blog post that digital tags save workers time and keep prices current. He said the labels aren't used to raise prices based on weather, time of day or an individual shopper. "We price the product, not the person," Furner wrote. Yet companies' limited transparency about the customer data they collect and how they use it has fueled suspicion. Thomas said social media posts falsely claim that electronic shelf labels Walmart plans to install in all its U.S. stores scan shoppers' faces or cellphones to set individualized prices. Those claims spread quickly because the systems behind them are hard to see.

The Policy Gap

Lawmakers and regulators are trying to close that gap. Legislators in at least 30 states have considered pricing bills this year, including proposals on algorithmic and personalized pricing, according to the National Conference of State Legislatures. The Federal Trade Commission has proposed an enforcement policy warning that failing to disclose the use of personal data to set individualized prices could violate consumer protection law.

Maryland, New Jersey and Connecticut have already passed restrictions on using shoppers' personal data to determine what they pay. The Maryland and New Jersey laws focus on groceries, with exceptions for certain loyalty discounts and other promotional offers. Connecticut's law covers a broader range of retail purchases. That difference matters: shoppers in one state may have protections that shoppers just a few miles away don't.

Past enforcement shows why regulators can't rely on voluntary good behavior. California prosecutors sued Target in 2022, alleging its app showed lower prices before shoppers entered stores and raised them once they were inside. Target agreed to a $5 million settlement and a ban on using geofencing technology to increase prices. Target said it used the app feature to "provide guests with accurate pricing, which can vary across stores and channels," and that it didn't personalize prices based on guests' data or behavior.

Evidence about price changes is more mixed than online outrage suggests. Decodo, a web data company, tracked price changes in 2025 across electronics, clothing and home-improvement supplies. About half of the U.S. changes it recorded were decreases, said Gabriele Vitke, a senior product marketing manager, challenging the assumption that dynamic pricing only pushes prices up. Researchers who tracked prices at an unnamed grocery chain found "virtually no surge pricing" after digital labels arrived. Asa Farquhar of retail technology company Relex Solutions said retailers usually update electronic shelf prices on a regular schedule, often weekly, because shoppers value consistency.

But some of the strongest evidence points the other way. Last year, an investigation by Consumer Reports, Groundwork Collaborative and More Perfect Union found that Instacart shoppers saw different prices for identical products at the same stores. Instacart later ended what it described as tests that randomly assigned shoppers to groups, rather than setting prices based on personal information. Different prices don't always mean a company used personal data. But they do mean shoppers had no way to know why they paid what they paid.

Official Response

The antitrust debate carries higher stakes. In a 2023 antitrust lawsuit against Amazon, the FTC challenged, among other practices, an algorithmic tool codenamed Project Nessie. The agency alleged Amazon used it to identify products whose prices it could raise with competitors likely to follow, generating more than $1 billion in excess profits. A trial is scheduled for next year. Amazon said the FTC allegations "grossly mischaracterize" the tool. It said the tool aimed to prevent retail price-matching from creating unsustainably low prices and that Amazon scrapped it before the lawsuit.

A consumer lawsuit against McDonald's raises a similar question. It alleged the company's pricing system used nonpublic sales data from competing locations to coordinate prices. The Chicago-based company called the lawsuit "filled with inaccuracies" and said it makes the tool available to U.S. franchisees to help them set prices at their individual restaurants. The National Retail Federation, which represents large and small stores, argued that algorithmic pricing gives retailers a new way to do what they've long done. That's a fair description of the mechanics. It doesn't answer whether the new tools' scale and opacity call for new rules.

Last year, global consulting company Simon-Kucher surveyed more than 2,200 business leaders in 28 countries and 39 industries. Forty-one percent said they used AI software to gather market intelligence, including competitors' prices, and 28% said they used it for "real-time price optimization." The figures suggest these tools are spreading well beyond a handful of early adopters, even if the extent of U.S. retailers' use of AI for pricing remains unclear.

Why This Matters:

For ordinary shoppers, the question is whether the price on the shelf reflects the product or the person standing in front of it. The answer depends on data most consumers can't see, under rules that differ from state to state and remain unfinished. Reporting on Walmart, Target, Instacart and Amazon has already shown how quickly pricing practices can stray from what customers expect. Grocery bills and household budgets are where that gap hits hardest. People with the least room to absorb a surprise are often the ones paying it. Democratic oversight, clear disclosure rules and enforceable limits on personal data use can keep these systems accountable to the public. Without those safeguards, shoppers must trust companies to police themselves, and the record so far gives them little reason to.