U.S. shoppers feel more than just irritation over elevated costs. They show clear anger and puzzlement regarding the advanced technological approaches restaurant groups and large chains now employ to establish those costs.

Electronic shelf tags are quickly taking the place of printed versions along supermarket rows. AI systems assist firms in choosing charges by forecasting likely customer reactions. Phrases such as dynamic pricing and surveillance pricing spark worry across social platforms, where people question whether individuals receive varying charges for identical items.

Shoppers have accepted fluctuating costs for air travel, lodging and transport services, yet the idea that routine purchases could be guided by algorithms they cannot grasp and personal details they might not know are being shared has hit a sensitive spot.

Major frictions exist now over who holds real control with these algorithms along with bot and AI applications: the firms or the buyer, noted Katie Thomas, head of an internal consumer behavior unit at consulting firm Kearney.

Firms that apply the systems note some worries arise from misunderstandings about how their pricing tools operate. Walmart chief John Furner stated in a recent post that electronic tags free staff time and maintain accurate costs. He added the labels do not raise charges according to weather, time or any single buyer.

We set the product price, not the individual, Furner stated.

Yet firms often lack clarity on the buyer data gathered and its application, which breeds suspicion and fuels speculation. Social posts wrongly suggest electronic shelf labels Walmart intends for all U.S. locations scan faces or phones to create custom charges, Thomas observed.

Officials and regulators have begun to respond. Lawmakers in no fewer than 30 states reviewed pricing measures this year, including steps on algorithmic and custom pricing, per the National Conference of State Legislatures.

The Federal Trade Commission offered an enforcement approach that cautions firms failing to reveal use of personal data for custom charges could breach consumer protection rules.

Below are several pricing methods and tools now under review.

What it covers: Dynamic pricing involves shifting charges based on shifting market factors such as supply and demand. An airline that alters ticket costs as departure nears applies dynamic pricing. Uber raising fares when demand exceeds available drivers, like during peak hours, represents a form called surge pricing.

Why relevant: Regular cost shifts now reach far past food items. Decodo, a web data firm, followed cost shifts in 2025 across goods from electronics to apparel and home supplies. Roughly half the U.S. shifts tracked were reductions, against buyer assumptions that dynamic pricing only lifts costs, said Gabriele Vitke, a senior product marketing manager.

What they involve: Electronic screens that allow stores to change shelf costs digitally rather than swap paper labels.

Why relevant: Digital shelf tags, common for years in Europe and Asia, gain notice as they reach major U.S. chains like Walmart and Whole Foods. Consumer groups argue the tags might later support surge or custom pricing. Labor groups have also resisted the approach.

The tags ease frequent updates but do not always produce higher costs in high-demand times. Stores usually refresh electronic shelf costs on set schedules, often weekly, since buyers prefer steadiness, said Asa Farquhar of Relex Solutions, a retail tech firm. Studies that followed costs at one grocery chain found almost no surge pricing after the tags appeared.

Altering costs during the day as airlines or ride services do remains very uncommon in grocery and general retail today, Farquhar said.

What it covers: Personalized pricing offers different charges and deals based on locations, incomes, browsing patterns and other details about single buyers or groups. Some groups and regulators apply the term surveillance pricing to this same method.

Why relevant: The extent of personal data use for custom charges stays unclear. An FTC staff report from January 2025 showed intermediaries providing tools that can shape prices and offers worked with at least 250 clients, including grocery and apparel retailers.

Lawmakers in Maryland, New Jersey and Connecticut passed limits on using buyer data to set charges. The Maryland and New Jersey rules target groceries, with allowances for loyalty deals and similar offers. Connecticut rules span a wider set of retail buys.

California officials sued Target in 2022, claiming the retailer app showed lower costs before entry and raised them inside. Target settled for 5 million dollars and accepted a ban on geofencing tech to lift charges.

Target noted the app feature aimed to give accurate pricing that can differ by store and channel. The chain said it did not customize charges on guest data or actions and stays focused on clear pricing.

Varying charges do not always signal personal data use. An inquiry last year by Consumer Reports, Groundwork Collaborative and More Perfect Union found Instacart users saw different costs for the same items at identical stores. Instacart later stopped what it called tests that randomly grouped shoppers rather than using personal details.

What it covers: Algorithmic pricing relies on software to suggest or set charges automatically. Firms have applied such systems for years. AI helps the software spot patterns in sales, rival costs and other data to forecast demand and gauge how charges affect sales and profits. These tools can back both dynamic and custom pricing.

Why relevant: The scale of AI use by retailers for pricing stays unclear, though studies indicate firms in many sectors now adopt it.

Simon-Kucher, a global consulting firm, polled over 2,200 business leaders across 28 countries and 39 sectors last year, with 41 percent reporting AI software use for market data including rival costs. Another 28 percent said they used it for real-time price optimization, meaning selecting charges to meet goals like higher profits, more buyers or clearing stock.

The National Retail Federation, which speaks for stores of all sizes, said algorithmic pricing offers a fresh way to do what retailers have long done: track and adjust costs based on supply, demand, season, rival prices and other elements.

Some legal actions have claimed firms used pricing algorithms to weaken competition.

A 2023 FTC suit against Amazon challenged, among other items, an algorithmic tool called Project Nessie. The agency said Amazon used the tool to spot items whose costs it could raise with rivals likely to match, creating over 1 billion dollars in extra profits.

A trial is set for next year. Amazon called the FTC claims a gross mischaracterization of the pricing tool.

Project Nessie aimed to prevent retail price-matching from producing unsustainably low costs, the company said. Amazon added it tested the tool for a few years but it failed to perform as planned and was dropped before the agency suit.

A recent buyer suit against McDonald alleged its pricing system used private sales data from rival sites to align charges. The Chicago-based chain called the suit filled with errors and noted it offers the tool to U.S. franchisees to aid price setting at their own locations.