AI threatens to end fixed pricing, enabling retailers to charge personalized prices

The price tag as we know it may be approaching obsolescence. For more than 150 years, since John Wanamaker hung fixed prices in his Philadelphia department store during the 1860s, the posted price has functioned as a democratic anchor—the same number for every customer, every day. That stability is now under threat from artificial intelligence systems that retailers are quietly testing and refining, systems designed to charge different people different amounts for identical goods based on personal data harvested from their shopping habits, income levels, ZIP codes, and browsing histories.

The shift builds on dynamic pricing, a practice companies have deployed for decades to adjust costs based on supply, demand, weather, and time of day. Airlines and hotels have long used this approach. But AI introduces a new dimension: the ability to process vast troves of consumer data and calculate, for each individual shopper, the maximum price they might be willing to pay. The result is what some call surveillance pricing—a system in which the price you see depends not on what something is worth, but on who you are.

Lindsay Owens, CEO of Groundwork Collaborative and author of "Gouged: The End of a Fair Price," has become a leading voice warning about this shift. She told CBS News that predictability matters enormously to people managing tight budgets. "The posted price has been paramount for consumers, and I do think the price tag is at risk of extinction," she said. "Technology is allowing companies to stop setting prices for products and start setting prices for you."

The clearest evidence of this emerging practice surfaced in December when Groundwork Collaborative and Consumer Reports published findings from an Instacart trial. The grocery platform, which had acquired Eversight—a software company built specifically to enable AI pricing—ran experiments showing different prices to different customers shopping from the same store. The investigation found that identical grocery baskets cost up to 23 percent more for some shoppers than others. For a family of four, that gap could amount to roughly $1,200 per year. Instacart disputed the findings, characterizing the price differential as reflecting an "atypical" basket and asserting that it does not use protected personal characteristics like race or gender to set prices. In December, the company announced it would stop using AI to charge different prices for identical items on its platform.

But Instacart is not alone. Groundwork research released this month revealed that Walmart has applied for patents on technology designed to build comprehensive shopper profiles and use them to set individualized prices. One patent application describes technology that could adjust prices based on items already in a customer's cart—if someone has tuna fish, they might be offered a different price for mayonnaise. Electronic shelf labels could theoretically display different prices to different people standing in the same aisle. Walmart has denied developing such tools, with executive vice president Dan Bartlett stating in a public letter that the company "does not and will not use an individual customer's personal information, income, shopping history, urgency or willingness to pay to set an individualized price."

Jay Zagorsky, a senior lecturer at Boston University's Questrom School of Business, expressed skepticism about such denials. He told CBS News that he believes retailers are developing personalized pricing capabilities, and that they are unlikely to announce these practices publicly. "Nothing positive is gained by a merchant stating they are doing this," he said. "The days when the price you saw stayed fixed for a relatively long period of time appear to have passed."

Analysts at Cornerstone Research, an economic consulting firm, noted in a 2025 report that these technologies "may empower firms to collect, process and analyze vast amounts of consumer data, leading to more granular, dynamic, and individualized pricing than previously observed." The shift represents a fundamental departure from a pricing norm that has governed retail for generations—one in which transparency and uniformity were taken for granted. As AI systems grow more sophisticated, that assumption is eroding, largely out of public view.