At an Aug. 4 Senate Judiciary subcommittee hearing titled “Your Data, Their Profit: The Consumer Cost of AI Surveillance Pricing,” lawmakers and witnesses grappled with the growing use of artificial intelligence to tailor prices to individual consumers. The hearing’s title, noted one witness, captures a real fear: that companies are using data to infer vulnerability and extract maximum profit. But Z. John Zhang, a marketing professor at the University of Pennsylvania’s Wharton School, offered a counterintuitive defense: personalized pricing, he argued, can actually benefit the consumers who need it most.
Zhang, who testified at the hearing, acknowledged that the term “surveillance pricing” has become a powerful political cudgel. Yet he warned that the phrase obscures a central paradox: different prices can look unfair, but a single price for everyone can be even less fair. A uniform price feels neutral because everyone sees the same number, but in practice it can exclude people who would buy at a lower price, preserving access mainly for wealthier or less price-sensitive consumers. The visible unfairness of price variation, he argued, must be weighed against the invisible unfairness of exclusion.
Zhang drew on history to illustrate his point. In the early 1980s, airlines developed “yield management,” a system that charged passengers different fares based on timing, restrictions, and flexibility. Consumers disliked the complexity, and two passengers sitting side by side might have paid wildly different prices for the same flight. Yet if regulators had banned the practice as unfair, air travel might have remained a luxury reserved for the rich and the expense-account traveler. Instead, variable pricing allowed airlines to fill empty seats, offer lower fares to flexible travelers, and preserve capacity for those who valued convenience more.
The airline industry’s collective profits have been negligible—according to Airlines for America, cumulative net profits from 1979 to 2024 totaled just $28 billion over 45 years, essentially zero after inflation. But consumers gained broader access to air travel. That history matters, Zhang said, because price variation often looks unfair before its full effects are understood. A firm that must charge one price faces a margin-volume trade-off: set the price high, and price-sensitive consumers are priced out; set it low, and the firm sacrifices revenue from those who would have paid more.
Personalized pricing, Zhang argued, can make mutually beneficial transactions possible. A consumer who would not buy at the uniform price gets a lower offer, the firm gains a sale, and consumer access expands. But he was careful to say that not every form of personalized pricing is defensible. Pricing built on deception, illegal discrimination, privacy abuse, exploitation of sensitive data, or monopoly power deserves scrutiny and, where appropriate, prohibition. AI can make these risks more serious, he said, if a company infers that a consumer is desperate, confused, isolated, sick, or unlikely to comparison shop, and then raises the price in ways the consumer cannot see or challenge.
The hard task, Zhang said, is to distinguish harmful practices from access-expanding personalization. Student discounts, off-peak fares, targeted coupons for a price-sensitive shopper, lower subscription tiers, and flexible travel fares all involve price variation and consumer data. Consumers may accept them because they preserve agency and help people qualify for a better price. A hidden surcharge imposed because an algorithm concludes that a loyal or vulnerable customer will not leave is different. The economics may be related, but the legitimacy is not.
Zhang proposed a practical framework for regulators, asking five questions: Was the data collected legally, transparently, and within reasonable consumer expectations? Are consumers being deceived about the price or the basis for it? Are protected classes being harmed directly or through proxies? Is the firm using market power to prevent switching or competition? Does the pricing practice expand access, improve matching, or intensify competition—or does it merely extract more from consumers who cannot protect themselves? If the answers point to deception, discrimination, privacy abuse, or coercive market power, intervention is warranted. If not, policymakers should be careful not to outlaw price variation merely because it is unfamiliar or uncomfortable.
The debate is not hypothetical. As states target AI surveillance pricing as a new form of “poor tax,” and as lawmakers like Rep. Pallone demand airlines disclose their AI pricing practices, the pressure to act is mounting. Zhang’s testimony offers a cautionary tale: in trying to protect consumers from abusive pricing, Congress should not overlook the invisible unfairness of uniform pricing—products priced too high for marginal consumers, innovation chilled by regulatory fear, and markets less able to serve heterogeneous demand. If we had banned yield management in the early 1980s, he concluded, we might still think of flying as a luxury. We should not make the same mistake with AI-enabled personalized pricing.
