The Federal Trade Commission closes its window for public comment today, September 25, on a proposed enforcement policy that redraws the agency’s regulatory perimeter. The proposal, docketed as FTC-2026-1057, marks a pivot from the agency’s prior focus on broad AI deception toward a disclosure-based framework for personalized pricing.
This proposal follows a sequence of regulatory resets since early 2025. In February and March of that year, the FTC, under Chair Andrew Ferguson, removed more than 300 business guidance blog posts from the Biden administration, including AI consumer protection guidance and enforcement action details against major tech companies, as reported by Wired. In December 2025, the agency set aside the 2024 final consent order against Rytr LLC, which had banned the company from providing AI-enabled review generation services. The FTC’s decision cited the America’s AI Action Plan, which directed the agency to review and remove orders that unduly burden AI innovation. Having cleared the old enforcement deck, the agency is now building a new one around how personal data shapes the prices consumers see.
The proposed policy defines personalized pricing broadly: any use of consumer personal data to set individualized prices based on estimated willingness to pay. While the FTC acknowledges that AI dramatically expands the potential for such practices, the policy applies to any data-driven pricing model. The agency states explicitly that it lacks authority to ban personalized pricing outright. Instead, it is leveraging Section 5 of the FTC Act to pursue cases where disclosure is inadequate. Under the proposal, companies must provide three disclosures when consumers reasonably expect static prices: that the price is personalized, the basis for that personalization, and the types of data used. Data tied to consumer vulnerability — health conditions, family circumstances, lack of alternatives — carries the highest enforcement risk.
While the federal government settles on disclosure, states are moving toward prohibition. A federal surveillance-data pricing bill died in the Senate in March 2026, leaving state law as the primary regulatory mechanism. New York’s Algorithmic Pricing Disclosure Act, effective since November 2025, requires retailers to display notice that their price was set by an algorithm using personal data. Maryland’s Protection from Predatory Pricing Act, effective October 1, will be the first state law to ban personalized pricing outright, though only in the grocery sector. New Jersey’s Fair Price Protection Act, signed in July 2026, carries penalties up to $50,000 per violation and creates a private right of action. Connecticut’s HB8002, also effective October 1, restricts revenue management and pricing algorithms. More than 40 bills are active across 24 states.
This regulatory environment gains urgency as AI agents take on purchasing and pricing decisions on behalf of consumers. As agents from companies like Meta and SpaceX negotiate, book, and buy — sometimes without the consumer seeing the final price — the gap between static expectations and data-driven reality widens. The FTC’s disclosure framework asks companies to make that gap visible. The state-level prohibitions ask them to close it. For the companies building agents that make real purchasing decisions, the question is no longer theoretical: which framework governs the price your agent accepts on your behalf?