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YouTube clarifies inauthentic content policy with three monetization categories

YouTube clarified its inauthentic content policy on July 16, naming three categories that can make a channel ineligible for Partner Program monetization: generic or repetitive video, off-putting or emotionally manipulative video, and AI personas discussing sensitive topics.

Media & internetAnalysisGrace OkonkwoPublished: 6 November 20263 min readSources 15
YouTube clarifies inauthentic content policy with three monetization categories

The updated guidance, rolled out on July 16, affects all YouTube Partner Program members. Matt Halprin, vice president of trust and safety, set out the categories in a Creator Insider video, the company's official channel for creator updates. "AI can actually allow people to make a lot of videos," Halprin said. "Sometimes those videos are great, and it really enhances creativity."

The three categories

Halprin said the first category covers generic, repetitive or template-based video, including material that can be made easily with AI, computer-generated imagery or templates with little variation from upload to upload. Tutorial videos could fall under the policy if they reproduce content already prevalent on the platform instead of offering something original, he said.

He said the second category covers "off-putting" content designed to be distressing or emotionally manipulative to chase views. His example was a channel that shows an animal in distress and then a rescue. Channels dedicated to that content will be removed from the Partner Program regardless of whether the footage is AI-generated. Yahoo Tech reported that news reports, documentaries, fictional series and other substantive work can still cover tragedy, violence or distress when the material serves a larger story. Gizmodo said the rule will likely cause issues for channels that discuss the news.

The third category targets AI personas, which TechCrunch described as representations of real people made with AI, used to discuss sensitive topics such as healthcare, finance, law and politics. Yahoo Tech cited examples of an AI-generated doctor diagnosing symptoms and an AI-generated podcast host recommending investments. YouTube's concern is that viewers might mistake the characters for credible human experts. Halprin said any channel with too much of any of the three types will not be able to monetize.

Clarification, not a new policy

A YouTube spokesperson told Business Insider the policy is not new. It is a clarification of the company's long-standing "inauthentic content" policy, intended to reduce ambiguity for creators who had been demonetized for repetitive, low-effort or emotionally manipulative videos. YouTube first moved against mass-produced and repetitive video under the inauthentic label in 2025, when it announced it was stemming revenue from such content.

How large the AI slop problem is

Kapwing created a new YouTube account and analyzed the first 500 Shorts the platform recommended. It classified 104, or 21%, as "AI slop"; another 165, or 33%, fell into a broader "brainrot" category that includes AI slop and other low-quality, engagement-optimized content. The 21% figure is for the first 500 Shorts served to a new account, not all YouTube videos.

In a separate sample, Kapwing examined 15,000 popular channels and identified 278 that published nothing but AI-generated content. Those channels had 63 billion views, 221 million subscribers and an estimated $117 million in annual ad revenue as of October 2025. YouTube said the findings did not necessarily reflect the platform as a whole; it terminated one channel identified in the study and removed several others from its monetization programs, Yahoo Tech reported.

Why the categories matter to YouTube's ad business

The Partner Program pays creators through advertising and subscriptions. YouTube now generates more ad revenue than rival streamers and has overtaken Netflix in average daily views worldwide, TechCrunch reported. Business Insider reported that YouTube has spent years pitching itself to advertisers as television's future, and that a surge of AI-generated spam threatens that message. In January 2026, the platform removed more than a dozen popular channels that had amassed millions of views with AI-generated videos featuring cats and Jesus. Chief executive Neal Mohan used the term "AI slop" in his January 2026 annual letter and pledged to build on the platform's spam and clickbait detection systems.

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Sources

15
  1. 01YouTube clarifies policies around AI slop and upsetting videosEN
  2. 02YouTube Cracks Down on 'Off-Putting Content' and AI SlopEN
  3. 03YouTube's AI Slop Problem And How Marketers Can CompeteEN
  4. 04YouTube reveals which AI slop videos can't make moneyEN
  5. 05YouTube cracks down on low-effort content with new monetization rulesEN
  6. 06YouTube is outsourcing its AI slop problem to you, and that's a terrible ideaEN
  7. 07From Substack to YouTube, here are the social platforms cracking down on AI slopEN
  8. 08AI Slop vs UGC: As synthetic content speeds up, can it win the trust gameEN
  9. 09The winners and losers of YouTube’s view count and monetization overhaulEN
  10. 10YouTube adds tools and ramps up protection for creatorsEN
  11. 11YouTuber ortet Videoaufnahmen anhand von FlugzeuggeräuschenEN
  12. 12Russia and US seek to block plan for binding rules on autonomous weaponsEN
  13. 13Learn the rules: The hacker mindsetEN
  14. 14Launch HN: Rivet (YC W23) – Scale and Orchestrate Agents on S3EN
  15. 15What happened to McSkillet? CSGO skins YouTuber’s death in tragic crash explained - DexertoEN

All figures and quotations in this text come from the sources listed below.

Content prepared by the editorial team with AI assistance.

Grace Okonkwo

Grace Okonkwo

AI, models and technology

Grace Okonkwo covers AI, models and technology for FLASH24, working from primary sources such as model cards, API documentation and benchmark papers rather than vendor summaries. She checks training data provenance, evaluation conditions and reported scores against the underlying datasets before any figure reaches print. She interviews researchers and engineers directly, tracks release calendars from major labs, and compares successive model versions on the same tests. Her own self-hosting, home-network and documentation-reading habits feed straight into that desk, since she tests tools on her own hardware first. She does not publish benchmark claims without a reproducible method.

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