FTC Investigations & the Accountability Test for Generative AI
Regulators aren’t grading your press releases. They’re reading your logs.
The U.S. Federal Trade Commission is shifting from “watching AI” to examining how it’s actually built, tested, and monitored—especially when the fallout hits consumers. In September 2025, the FTC sent compulsory orders to seven companies behind consumer chatbots, asking for proof of how they test, track, and mitigate harms (particularly to kids and teens), what data they keep, and how they monetize the conversations. That’s not a headline; it’s a checklist.
This isn’t happening in a vacuum. In early 2024 the FTC launched a 6(b) market study into the money and control behind AI—those tight partnerships between cloud giants and model developers. The staff’s 2025 report details how investment terms can confer privileged access, exclusivity, and influence—the kind of structural leverage that shapes who gets to compete and who merely rents compute. Translation: accountability isn’t just about model outputs; it’s about market power.
And enforcement is already here. Remember the NGL case? The agency banned the anonymous messaging app from marketing to minors and called out false “AI moderation” claims. The message to founders: if you invoke AI to promise safety or accuracy, you’re on the hook when reality doesn’t match the pitch.
Two more signals worth noting:
Fake reviews rule (2024): the FTC now has a civil-penalty tool aimed squarely at AI-generated review pollution. “We didn’t write the reviews; the model did” won’t fly.
Impersonation & voice cloning: the agency has moved to tighten rules and affirmed that the Telemarketing Sales Rule covers AI-generated calls and clones. Your growth loop can’t include synthetic trust.
The real question for builders
If the FTC asked tomorrow, could you show (not tell) that you:
Define foreseeable misuse and test against it (red-team plans, child-safety evals, jailbreak metrics).
Monitor in the wild with transparent, auditable incident workflows (e.g., safety regressions, abuse spikes, model updates tied to fixes).
Document your data practices—what’s collected, how it’s retained, when it’s used to improve models, and where users can opt out. “It’s hashed” is not a magic cloak.
Align your claims with reality. If you market “AI moderation,” show precision/recall, coverage limits, and failure modes—before a regulator asks you to.
Surface conflicts and dependencies in your stack and deals (who holds compute, who sees customer telemetry, who can veto roadmaps). Structure is strategy.
Guard the demand side—no synthetic reviews, endorsements, or “AI-made me say it” excuses in growth experiments.
Protect identity: detect and deter voice/face impersonation pathways in features and APIs. (“We’re just the platform” is not a defense.)
The operating system of accountability
Before launch: publish a plain-English model card for consumers (not just researchers): training sources in broad strokes; known limitations; where human review sits; what you log and why.
During growth: make safety and truth a product metric, not a policy slide. Report it like latency.
At scale: tie incentives to prevented harm, not just usage. If the board only sees retention, the product will only optimize retention.
Compliance is the floor. Trust is the strategy.
The perfect ending isn’t a slogan; it’s a decision:
Build the thing you’d be proud to demo—under oath.
Because when the knock comes, the question won’t be “Do you believe in responsible AI?” It will be, “Show us.” And if you can’t show it, you didn’t build it.
Sources you can take to your GC:
FTC orders on child/teen impacts of consumer chatbots (Sept 11, 2025).
FTC 6(b) inquiry into AI partnerships (Jan 25, 2024) + Staff report (Jan 17, 2025).
NGL settlement: banned from marketing to minors; deceptive AI moderation claims (July 9, 2024).
Final rule banning fake reviews & testimonials (Aug 14, 2024).
AI impersonation & voice-cloning actions/affirmations (Feb–Mar 2024).