The Federal Trade Commission has issued data demands to nine of America's most consumer-facing companies — Delta, United, JetBlue, Lyft, Uber, DoorDash, Instacart, Target, and Walmart — ordering them to disclose how their algorithms use personal data, loyalty programs, and browsing histories to set individualized prices for consumers and wages for gig workers. Chair Andrew Ferguson framed it as scrutiny of "opaque methodology" that may charge different people different amounts for identical products or services. The probe builds on a July 2024 Biden-era inquiry that targeted the supply side — companies like McKinsey and JPMorgan Chase that sell surveillance pricing tools to retailers. This round goes directly to the consumer-facing firms deploying those tools. The FTC found in January 2025 that "a person's precise location or browser history can be frequently used to target individual consumers with different prices for the same goods and services." The pattern is clear: a growing infrastructure of algorithmic price discrimination operates largely without consumer knowledge or consent. The structural problem is jurisdictional. The FTC is explicitly barred from regulating airlines or banks, which means three of the nine named companies — Delta, United, and JetBlue — face an inquiry with no enforcement backstop. The agency can collect data from them but cannot impose fines, mandate disclosures, or compel changes. For the remaining six, the Trump administration's broader deregulatory stance casts doubt on whether data collection will lead to rulemaking. Former FTC commissioner Rohit Chopra, now heading California's Business and Consumer Protection Agency, delivered the most honest assessment: the agency has "not been using these studies that produce meaningful reports that ultimately lead to any rule-making changes." This is the core diagnostic. The FTC can investigate, but its track record of converting surveillance pricing investigations into binding rules is effectively zero. The inquiry functions as a transparency exercise — valuable, but not a remedy. The extraction mechanism is straightforward. Companies harvest behavioral data — purchase history, location, loyalty tier, device type, browsing patterns — and use it to charge the maximum each individual will tolerate. Gig workers face the inverse: algorithms calculate the minimum payment each driver will accept. Both sides of the transaction are squeezed by the same optimization logic. The consumer sees a price; the driver sees a fare. Neither sees the model that generated it. Several states have begun moving independently, introducing legislation to ban surveillance pricing and restrict electronic shelf labels that enable real-time personalized pricing. This state-level activity may prove more consequential than the federal inquiry, particularly as the FTC's appetite for enforcement remains uncertain. The real regulatory action is migrating to Sacramento, not Washington. The twenty-year trajectory is clear: without binding federal rules, algorithmic price discrimination becomes the default for every consumer transaction with a digital interface. Companies that resist it face competitive disadvantage. The result is a permanent information asymmetry where the seller knows exactly what each buyer will pay and the buyer knows nothing about the model pricing them. The FTC's data demand is a flashlight in a dark room — useful for seeing, but it doesn't change what's there.
