Generative AI: Text, Image, Video & Audio Synthesis

Current landscape

The FTC's existing rules already ban fake reviews "by someone who does not exist," reaching AI-generated fake testimonials, and its Endorsement Guides require disclosure when AI-generated content would matter to a reasonable consumer. But in December 2025, the Commission reopened and vacated its own 2024 consent order against an AI writing tool — the case most often cited as the model AI-fake-content enforcement action — explicitly citing the administration's AI policy agenda and reasoning that banning a technology over potential misuse "unduly burdens AI innovation." That's a deregulatory reversal, not an escalation, and it means the FTC's current AI focus has shifted toward a different question (AI accuracy and alleged ideological suppression) rather than commercial-content labeling. Multiple marketing and SEO sites circulate claims of a "dedicated FTC AI enforcement unit" and specific per-violation penalty figures for AI-content disclosure; none of this appears on the Commission's own site or in law-firm client alerts, and it directly contradicts the documented reversal above. Treat those claims as unreliable.

Progress instead came from a completely different source: negotiated collective bargaining. The entertainment performers' union's agreements distinguish a "digital replica" (a specific, identifiable performer's voice or likeness) from a "synthetic performer" (a non-identifiable AI character), requiring informed consent and compensation for the former. A 2025 interactive-media agreement, ratified after a industry-wide strike, tightened this further: consent must be separately signed, not bundled into a general hiring contract, with per-use compensation and mandated usage reporting. The union has also used labor-practice complaints to police how consent is obtained — a 2025 complaint against a video-game maker's use of an AI-recreated deceased actor's voice wasn't about the estate's consent, which had been obtained, but about the company's failure to bargain with the union over displacing working voice-match actors who would otherwise have been hired for the role.

Market impact, held to an honest standard of evidence

Several creative professions show measurable, if self-reported, disruption. A 2025 survey found 70% of freelance translators reporting decreased work volume, and one individual case reported a 60% income decline; a major international organization's translation staff shrank from 200 to 50. Yet the overall language-services market kept growing — a bifurcated market, not a uniformly collapsing one. Illustrators surveyed in 2024 reported comparable effects: roughly a quarter had lost work directly to generative AI, and over a third reported declining income. Voice-actor surveys found about a fifth reporting a lost job to a synthetic voice, with the large majority never having consented to any licensing of a synthetic version of their own voice. These are self-reported, self-selected survey samples, not population-representative measurements; they're treated here as worth acting on, not as precise, generalizable statistics. On the commercial side, major stock-image libraries have earned substantial revenue licensing their libraries directly to AI developers for training — precisely the kind of large-rights-holder deal AI-10 already argues leaves individual contributors out — while a major proposed merger between two stock libraries was terminated in mid-2026 after a competition regulator demanded a divestiture, evidence of financial strain in that market even if not solely AI-driven.

Disclosure infrastructure that's working

Content-provenance credentialing has reached scale: thousands of organizations now participate in a shared standard, with major creative-software providers auto-attaching credentials across billions of generated assets, and at least one phone manufacturer now hardware-signing photos at the point of capture. New York's 2025 law requiring disclosure when a paid advertisement reaching its residents features an AI-generated "synthetic performer" is the first of its kind and already in force; California's parallel law requires large providers to offer free detection tools, with a labeling requirement phasing in later in 2026. Platforms have responded too: a major streaming service overhauled its AI-disclosure and voice-cloning policy after discovering that a fully AI-generated "band" had accumulated over a million monthly listeners before revealing its synthetic origin — a directly analogous concern to a fully AI-generated "actress" whose 2025 debut drew formal objection from the performers' union over training use without permission or compensation.

The volume problem this issue can't scope its way out of

Everything above is scoped to deception about a specific work's authorship or a specific person's likeness, a narrower question than the most mainstream, most viral criticism of generative AI's real-world deployment: the sheer volume of low-effort synthetic content now degrading the ordinary experience of using the internet, independent of whether any single piece of it deceives anyone about who made it. This has its own widely recognized name — "AI slop" — precisely because it's become a mainstream, bipartisan-in-its-annoyance complaint, not a niche policy concern. A February 2026 study of one Amazon book category found roughly three-quarters of listings were likely AI-generated, several apparently repackaging public- domain texts under new covers; those listings average a small fraction of the reviews human-authored books receive, evidence the volume itself degrades buyer trust even before any individual case of impersonation is proven. YouTube's own CEO named reducing "slop" a stated 2026 priority after an investigation found a large share of videos recommended to children were AI-generated filler; "pink slime" AI-generated local-news sites — mimicking the form of journalism without the reporting — are, per journalism-industry analysis, expanding rather than contracting. This issue's proposals on named-individual consent and paid-content disclosure do not, on their own, touch this problem, and it would be a gap to imply otherwise. Proposal 6 is the response: platform-level provenance and quality enforcement at the scale this problem exists, not just the narrower consent-and-disclosure framework the rest of this issue focuses on.

Trade-offs and opposing views

Disclosure and consent requirements impose compliance cost on legitimate creative-tool use, and the current administration's own stated position — that restricting AI tools "over potential misuse" burdens innovation — is a direct, live counter-argument to this issue's proposals, not a hypothetical one. This issue's answer is that its proposals target disclosure and likeness-consent specifically, a narrower and more targeted intervention than the blanket enforcement action the FTC itself pulled back from. On the other side, some creative-industry and union advocates would prefer categorical restrictions on fully synthetic performers rather than a consent-and-compensation framework — the union's own objection to a 2025 AI-generated "actress" reflects that instinct. This issue's position extends AI-10's framing rather than departing from it: negotiate consent and payment for use of a real person's contribution, don't ban the underlying capability.