The FTC’s August 19 draft policy statement targets the use of personal data in AI-driven pricing, marking a shift from policing marketing claims to regulating the operational outcomes of algorithmic systems. Where the Commission’s enforcement record has focused on companies that overclaim their AI capabilities, this new framework goes after the machines themselves—or more precisely, the companies that deploy them to charge different prices to different consumers.
The draft, which passed with a 2-0 vote, defines personalized pricing as the use of personal data to set prices according to the amount a company believes an individual consumer is willing to spend. The practice, often powered by machine learning, enables granular consumer segmentation and rapid A/B price testing that remains largely invisible to the average shopper. As FTC Chairman Andrew Ferguson noted,
When consumers see a listed price, they expect it to be the same price that everyone else sees, not the retailer’s estimate of how much they are willing to pay based on their personal data.
The policy’s core mechanism is mandatory disclosure. Under the draft, where consumers have a reasonable expectation of uniform pricing, firms must disclose three elements: that the price is personalized, the basis for that personalization, and the specific types of data used. The FTC leverages its existing authority under Section 5 of the FTC Act, which prohibits unfair or deceptive acts or practices, to enforce these requirements. The Commission cannot ban personalized pricing—it lacks the statutory authority—but it can require transparency where silence would be deceptive.
This expands the FTC’s oversight of AI. For the past two years, the Commission’s enforcement effort—Holland & Knight’s August 18 analysis calls it a record that “catches companies for overclaiming AI capabilities but hasn’t addressed autonomous agent behavior”—has focused on marketing claims. The personalized pricing policy targets the operational layer: the algorithms that segment consumers, test price sensitivity, and optimize for maximum extraction. The investigation traces back to July 2024, when the FTC ordered eight companies to produce information about their surveillance pricing practices.
The timing aligns with growing congressional attention. On August 5, the Senate Judiciary Committee held a hearing titled “Your Data, Their Profit: The Consumer Cost of AI Surveillance Pricing,” which revealed bipartisan support for federal action. A House bill, the “Stop AI Price Gouging and Wage Fixing Act,” has been introduced. New York already requires algorithmic-pricing disclosure at the state level. The FTC is not acting in isolation—it is filling a gap that state legislatures and Congress have been circling.
But the policy introduces a complication the Commission may not have fully resolved. On July 1, the FTC released a separate policy statement asserting implied preemption of state laws that require the “suppression of accurate outputs” for ideological or political objectives. That statement, published as Federal Register 91 Fed. Reg. 41,638, directly targets Colorado’s AI-driven decision-making law (SB 26-189, effective January 1, 2027). The personalized pricing policy, by contrast, pushes toward more disclosure—a direction some states have already taken. The two policies pull in different directions: one constrains state regulation, the other expands federal oversight of the same class of systems.
What the policy cannot do matters as much as what it does. The FTC cannot ban personalized pricing. It cannot create a comprehensive framework for all AI-driven commerce. And its effectiveness depends on a comment period that closes September 18, 2026, and a finalization process that could soften the draft’s requirements. The 30-day window is short; industry responses will shape whether the disclosure framework becomes a genuine constraint or a compliance formality.
Internationally, the policy aligns with a broader convergence. The EU’s Article 50 transparency obligations, active since August 2, require disclosure when consumers interact with AI systems. The FTC’s personalized pricing framework addresses a parallel concern from the American regulatory tradition: not a right-to-know rooted in data protection, but a deception-prevention standard rooted in consumer expectations. Two different legal foundations, a similar structural move—forcing visibility into systems that operate behind the listed price.
Businesses deploying AI for pricing now face a straightforward regulatory reality: their algorithmic outputs are subject to Section 5 scrutiny where consumers reasonably expect uniform pricing. The question is not whether the FTC will enforce this standard, but how aggressively, and whether the preemption tension with state laws will be resolved before companies have to comply with both.