U.S. consumers are not merely frustrated by high prices; they are angry and confused about the increasingly high-tech ways in which restaurant chains and major retailers set those prices.

Digital price tags are rapidly supplanting paper ones in grocery aisles.

Artificial intelligence tools assist businesses in determining what to charge by anticipating how customers might respond.

Terms such as “dynamic pricing” and “surveillance pricing” are provoking anxiety on social media, where users speculate whether different people are being charged different prices for the same product.

Americans have grown used to fluctuating prices when booking flights, hotel rooms and rides, yet the prospect of everyday spending being shaped by algorithms they do not understand and personal data they may not realize they are sharing has struck a nerve.

“There are major tensions unfolding right now over who will ultimately hold real power with some of these algorithms and the use of bots and AI: the companies or the shopper,” said Katie Thomas, who heads an internal consumer behavior think tank at the management consulting firm Kearney.

Companies utilising the technology maintain that certain concerns arise from misconceptions regarding how their pricing tools operate.

Walmart CEO John Furner stated in a blog post last month that digital price tags save workers’ time and keep prices up to date.

He said the labels are not employed to raise prices on the basis of the weather, time of day or individual shopper.

“We price the product, not the person,” Furner wrote.

Yet companies are not invariably transparent regarding the customer data they collect and the manner in which it is used, which has rendered consumers suspicious and given rise to speculation.

For instance, social media posts falsely imply that electronic shelf labels Walmart intends to install in all its U.S. stores scan shoppers’ faces or cellphones in order to set individualized prices, Thomas said.

Lawmakers and regulators are taking action.

According to the National Conference of State Legislatures, legislators in at least 30 states have considered pricing bills this year, including measures addressing algorithmic and personalized pricing.

The Federal Trade Commission has proposed an enforcement policy warning businesses that failing to disclose their use of personal data to set individualized prices could violate consumer protection law.

The following are some of the pricing practices and technologies that are attracting scrutiny.

What it is: Dynamic pricing entails adjusting prices in response to fluctuating market conditions, including supply and demand.

An airline that adjusts ticket prices as departures approach is employing dynamic pricing.

Uber’s practice of raising fares when demand outstrips the supply of available drivers, such as during rush hour, constitutes a type of dynamic pricing known as surge pricing.

Why it matters: Frequent price changes extend far beyond food.

Decodo, a web data company, tracked price changes in 2025 across products ranging from electronics to clothing and home improvement supplies.

Roughly half of the U.S. changes it recorded were decreases, contrary to consumers’ assumptions that dynamic pricing only raises prices, said Gabriele Vitke, a senior product marketing manager.

What they are: digital displays that enable stores to update shelf prices electronically, rather than replacing paper tags.

Why it matters: Digital price labels, long established in Europe and Asia, are attracting growing scrutiny as they extend to major U.S. retailers such as Walmart and Whole Foods.

Consumer advocates have contended that digital labels could ultimately be deployed for surge pricing or personalized pricing.

Labor unions have likewise opposed the technology.

Electronic shelf labels facilitate more frequent price adjustments but do not necessarily result in higher prices during periods of strong demand.

Retailers typically update electronic shelf prices on a regular schedule, often weekly, because shoppers value consistency, said Asa Farquhar of Relex Solutions, a retail technology company.

Researchers who tracked prices at an unnamed grocery chain found “virtually no surge pricing” after the labels were introduced.

“Changing prices throughout the day, as airlines or rideshare apps do, is exceedingly rare in grocery and general merchandise today,” Farquhar said.

Personalized pricing means offering customers different prices and discounts based on their locations, incomes, web-browsing habits and other information about individuals or groups of shoppers.

Some advocacy groups and regulators use the term “surveillance pricing” for the same practice.

Why it matters: It remains unclear how extensively businesses deploy personal information to customize prices.

An FTC staff report released in January 2025 found that intermediaries offering tools capable of tailoring prices and promotions worked with at least 250 clients, including grocery and clothing retailers.

Lawmakers in Maryland, New Jersey and Connecticut have recently enacted restrictions on the use of 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, whereas Connecticut’s legislation covers a broader range of retail purchases.

In 2022, California prosecutors sued Target, alleging that the retailer's app displayed lower prices before shoppers entered stores and then raised them once they were inside.

Target agreed to a $5 million settlement as well as a ban on employing so-called geofencing technology to raise prices.

Target said the app functionality at issue was used to “provide guests with accurate pricing, which can vary across stores and channels.”

The retailer said it didn’t personalize prices based on a guest’s data or behavior and remains “committed to clear, transparent pricing.”

Differing prices do not necessarily mean that personal data was used.

Last year, an investigation by Consumer Reports, Groundwork Collaborative and More Perfect Union found that shoppers on Instacart were shown different prices for identical products at the same stores.

Instacart subsequently ended what it described as tests that randomly assigned shoppers to groups rather than setting prices based on their personal information.

Algorithmic pricing refers to the use of software to recommend or automatically determine prices.

Companies have employed such systems for decades.

AI can assist the software in identifying patterns in sales, competitors’ prices and other data in order to predict demand and estimate how different prices would affect sales and profits.

These tools can support both dynamic and personalized pricing.

Why it matters: It remains unclear how widely retailers deploy AI to assist in price-setting, yet research indicates that businesses across industries are adopting it.

Last year, Simon-Kucher, a global consulting firm, surveyed more than 2,200 business leaders across 28 countries and 39 industries, and 41% said they used AI software to gather market intelligence, including competitors’ prices.

Another 28% said they used it for “real-time price optimization.”

Price optimization means choosing prices that advance business goals, such as increasing profits, attracting shoppers or clearing unsold goods.

The National Retail Federation, which represents both large and small retailers, said algorithmic pricing is a new way of doing what retailers have always done: monitoring and adjusting prices according to supply, demand, seasonality, competitors’ prices and other factors.

Some lawsuits have alleged that companies used pricing algorithms to undermine competition.

In a 2023 antitrust lawsuit against Amazon, the FTC challenged, among other practices, an algorithmic tool codenamed Project Nessie.

The agency alleged that Amazon used the tool to identify products whose prices it could raise, with competitors likely to follow, thereby generating more than $1 billion in excess profits.

A trial is scheduled for next year.

Amazon asserted that the FTC allegations “grossly mischaracterize” the pricing tool.

According to the e-commerce giant, Project Nessie was meant to keep retail price-matching practices from producing unsustainably low prices.

Amazon said it had tested the tool for several years, but it did not work as intended and was scrapped before the federal agency's lawsuit.

A recent consumer lawsuit against McDonald’s alleged that its pricing system uses nonpublic sales data from competing locations to coordinate prices.

The Chicago-based burger giant said the lawsuit was “filled with inaccuracies” and that the company makes the tool available to U.S. franchisees to help them set prices at their individual restaurants.