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YouTube Creator AI Production Workflow: What Actually Survives the Slop Crackdown
YouTube's monetization policy in 2026 doesn't ban AI tool use — it targets "mass-produced," "repetitive," and "inauthentic" content patterns, distinguishing output characteristics from the production method behind them. AI-generated videos remain eligible for monetization when the creator adds original value — research, commentary, storytelling, or editorial judgment — and toggles the "altered or synthetic content" disclosure YouTube Studio requires. What gets caught isn't AI use itself; it's mass-produced, verbatim text-to-speech-over-stock-footage content with no editorial layer, enforced under a three-strike system: warning, 90-day suspension, then permanent removal from the YouTube Partner Program. This piece walks through what an AI-assisted production pipeline actually looks like for solo creators who've adapted to this policy, the tools real developers have built for specific pipeline stages, and where the crackdown has hit hardest.
Last updated Aug 21 · 17 min read
What actually counts as "AI slop" under the policy
Coverage of YouTube's policy describes it as targeting "mass-produced," "repetitive," and "inauthentic" content patterns specifically — not AI tool use as a category. The distinguishing question the policy asks is about output characteristics (is this content genuinely distinct, or a near-identical repeat of a template with minor variations) rather than about the production method used to make it. In practice, this means the same AI video-generation tool can produce content that's fine under the policy or content that gets flagged, depending entirely on what the creator does with the output — add research, commentary, and editorial judgment, and it's treated as legitimate; publish raw, unedited text-to-speech over stock footage at high volume, and it's treated as the specific "mass-produced" pattern the policy targets.
The "Altered or Synthetic Content" disclosure requirement, which went into full enforcement in January 2026, adds a separate, related obligation: any content using AI-generated or AI-altered media that could be mistaken for real footage must be disclosed via a toggle in YouTube Studio, independent of whether the content otherwise meets the originality bar. Non-compliance with the disclosure requirement itself — regardless of the underlying content's quality — can trigger demonetization, reduced reach, or channel termination.
Are faceless channels specifically targeted?
Not by design, but disproportionately by effect. Coverage from outlets including The Hollywood Reporter and The Next Web reports faceless creators being hit harder by the crackdown even when their specific content isn't actually "slop," because the policy's enforcement mechanisms struggle to reliably distinguish content format (no on-camera host) from content intent (mass-produced manipulation). A faceless channel built around genuinely original research, scripting, and editorial judgment is, per the policy's own stated targeting logic, not what's supposed to be caught — but because faceless production is also the format most associated with the mass-produced pattern the policy targets, faceless creators as a group have absorbed more collateral impact than on-camera creators using similar AI tools for editing assistance.
The scale of the crackdown
Tech Times reported that YouTube "wiped 35 million subscribers" in connection with the AI slop crackdown — a concrete, if striking, indicator of how broadly the enforcement action has reached across the platform. Separately, Kotaku specifically reported small creators "feeling the crunch" under the updated monetization rules, distinct from the broader subscriber-count figure, pointing to real economic impact concentrated among smaller channels rather than just a platform-wide statistical adjustment.
Small creators vs. large studios
The available coverage points toward small and solo creators bearing a disproportionate share of the crackdown's impact relative to larger studios and production teams. This tracks with a structural explanation: larger studios more often already have the editorial review layer, original scripting, and production polish that the policy is designed to distinguish as "authentic," while solo creators leaning heavily on fully automated pipelines (script generation, voice synthesis, stock footage assembly, with no editorial pass) are structurally closer to the exact pattern the policy targets — even when their intent isn't manipulative.
Other platforms followed YouTube's lead
YouTube wasn't alone in tightening AI content rules around this period. Meta/Facebook and Snapchat both introduced parallel restrictions limiting monetization or full-AI generation for creators around the same window, suggesting this is a platform-wide industry response to a shared concern about AI-generated content volume and quality, rather than a YouTube-specific policy quirk. Coverage from Digiday frames the resulting split bluntly: creators are concerned, marketers are cheering — reflecting that advertisers, who ultimately fund the creator economy through ad spend, generally welcome tighter content-quality standards even as the creators subject to those standards experience real revenue disruption.
It's also worth noting the policy has not been static: YouTube had to clarify its AI slop policy multiple times across 2025 and again in 2026 after creator confusion about exactly where the line sits — a signal that even the platform's own enforcement criteria are still being actively refined, not a fixed, fully-settled rulebook.
What an AI-assisted production pipeline actually looks like
Real developer-built tools spotted in Hacker News discussion cover distinct, separable stages of an actual AI-assisted YouTube production pipeline, rather than one all-in-one black box:
1. Niche and CPM research. A channel-analysis tool spanning 400,000+ channels helps creators identify which niches carry favorable ad rates (CPM) and competitive dynamics before committing to a content direction — a data-driven alternative to guessing at a niche.
2. Script-to-video generation. Tools that take a written script and assemble a video around it, handling the mechanical assembly work while leaving the actual writing (where the "original value" the policy cares about lives) to the creator.
3. AI stock footage generation. Rather than relying on generic, overused stock footage libraries, some creators generate custom AI visuals matched specifically to their script's content — a way to differentiate visually from other channels using the same public stock sources.
4. Thumbnail CTR optimization. A purpose-built AI thumbnail generator reportedly helped one builder move click-through rate from 1.2% to 2.3% on a faceless niche channel — a concrete, developer-reported performance improvement from optimizing a specific pipeline stage rather than the whole pipeline at once.
The throughline across all four stages: AI tools are handling the mechanical, repeatable parts of production (research aggregation, visual assembly, format optimization), while the specific layer the policy cares about — original scripting, editorial judgment, commentary — remains where creators are reportedly investing their own effort, precisely because that's the layer that determines which side of the "slop" line the finished video lands on.
Practical examples
Example 1 — A creator building a research-heavy faceless channel. A solo creator uses a channel-analysis tool to identify an underserved niche with decent CPM, writes original scripts incorporating genuine research and a distinct point of view, uses AI tools only for voice synthesis and visual assembly, and discloses AI-altered content per YouTube's toggle requirement — a structure aligned with the policy's stated distinction between authentic and mass-produced content.
Example 2 — A creator at risk under the same tools. A different solo creator uses the same category of AI tools but skips the scripting-and-research step entirely, generating scripts algorithmically from trending topics and publishing at high volume with no editorial review — structurally the "mass-produced," "repetitive" pattern the policy is designed to catch, regardless of how capable the underlying AI tools are.
Example 3 — Optimizing one pipeline stage without changing the whole pipeline. Following the cited thumbnail-CTR case, a creator keeps their existing scripting and production process unchanged but adopts a dedicated AI thumbnail tool specifically for the format-optimization stage, isolating that single variable to measure its effect on click-through rate.
*Illustrative only:* Examples 1 and 2 are constructed to illustrate the policy's stated distinction, not confirmed accounts of specific named creators; Example 3 reflects the general pattern of the cited developer's reported 1.2%→2.3% CTR result without claiming it as anyone's guaranteed outcome.
Data and evidence
– Policy targets "mass-produced," "repetitive," "inauthentic" content patterns, not AI tool use itself: reporting via [Engadget, Aug 5 2026 and TechCrunch, Jul 9 2025/Jul 20 2026], aggregated via Google News search.
– Faceless creators disproportionately affected even when not producing "slop": [The Hollywood Reporter, Jun 13 2026; The Next Web, Jun 15 2026].
– YouTube "wiped 35M subscribers" in connection with the crackdown: [Tech Times, Jul 15 2026].
– Small creators "feeling the crunch" under updated rules: [Kotaku, Aug 11 2026].
– Meta/Facebook and Snapchat introduced parallel AI-content restrictions around the same period: [Tubefilter, Jul 15 2025; Business Insider, Jul 31 2026].
– Real developer tools for niche/CPM research (400K+ channels), script-to-video generation, AI stock footage generation, and thumbnail CTR optimization (1.2%→2.3% CTR reported): Hacker News search.
– "Creators concerned, marketers cheering" framing: [Digiday, Jul 18 2025].
– Policy required multiple clarifications in 2025 and 2026 after creator confusion: [TechCrunch, Jul 20 2026; Metricool, Jul 22 2026].
– Some cheaply-produced AI content still earning meaningful revenue despite policy changes, suggesting uneven enforcement: [AI CERTs, Mar 1 2026].
– 2026 policy detail: AI-generated videos remain monetization-eligible with original value and the synthetic-content disclosure toggle; three-strike enforcement (warning, 90-day suspension, permanent removal): corroborated across Miraflow.ai, Eliro, and AITuber.
Evidence not sufficiently verified: the underlying news-aggregator source URLs cited in the original research brief point to Google News search results rather than the specific individual articles themselves; the outlet names, headlines, and dates are reported as cited in the brief, but this guide cannot independently re-verify each individual article's exact wording beyond what the brief's citations describe.
Comparisons
AI video generator vs. traditional editing for YouTube. AI generation tools (script-to-video, AI stock footage) compress production time dramatically compared to traditional filming and editing, but the policy risk is specifically concentrated in AI-assisted pipelines that skip the editorial/research layer entirely — traditional editing doesn't carry the same disclosure or "mass-produced pattern" risk simply because it's inherently slower and harder to mass-produce at the same volume.
Faceless channels vs. on-camera creators, under the current policy. Both are eligible for monetization under the same underlying originality standard; faceless channels have absorbed disproportionate collateral impact from enforcement specifically because the format overlaps heavily with the mass-produced pattern the policy targets, not because the policy treats facelessness itself as disqualifying.
Real-world use cases
Developers on Hacker News have built and shared tools addressing specific, narrow pipeline stages — niche/CPM research across 400,000+ channels, script-to-video assembly, AI stock footage generation, and thumbnail optimization — reflecting a broader pattern of solo creators and indie developers treating AI-assisted YouTube production as a set of separable tooling problems rather than a single end-to-end black box. The reported 1.2%→2.3% CTR improvement from a dedicated thumbnail tool illustrates the value of measuring and optimizing individual pipeline stages in isolation, rather than changing an entire production process at once and being unable to attribute the resulting performance change to any specific variable.
Common mistakes
– Treating "AI slop" avoidance as a checkbox rather than a substance question. The policy targets output characteristics (mass-produced, repetitive, inauthentic), not the mere presence of AI tools — creators who assume avoiding a specific tool is enough, without adding genuine editorial value, misunderstand the actual bar.
– Skipping the synthetic-content disclosure toggle. This is a separate compliance obligation from the "originality" question, and non-compliance can trigger demonetization independent of the underlying content's quality.
– Publishing at a volume that structurally resembles mass production, even with individually well-researched scripts, since velocity itself is part of the pattern the policy's enforcement appears to weigh.
– Assuming faceless format alone is the risk factor. The disproportionate impact on faceless creators is a byproduct of format-intent overlap, not evidence that facelessness itself is disqualifying — genuinely original faceless content is still described as eligible.
– Not tracking which specific pipeline stage is driving performance changes. Changing scripting, visuals, and thumbnails simultaneously makes it impossible to know which change (if any) actually improved results, unlike the isolated thumbnail-tool test cited above.
Best practices
– Invest creator time specifically in the scripting, research, and commentary layer — this is the layer the policy's stated distinction (authentic vs. mass-produced) actually turns on, regardless of which AI tools handle the mechanical production stages.
– Use the "Altered or Synthetic Content" disclosure toggle in YouTube Studio whenever content includes AI-generated or AI-altered media that could be mistaken for real footage — treat this as a separate, non-negotiable compliance step distinct from content-quality decisions.
– Pace publishing volume in a way that doesn't structurally resemble the "mass-produced" pattern the policy targets, even if individual videos are genuinely well-researched.
– Isolate and test individual pipeline stages (thumbnails, scripting approach, visual style) separately, following the pattern of the cited CTR-optimization case, rather than changing the whole pipeline at once.
– Stay current on policy clarifications — the rules have been updated multiple times across 2025-2026, so a compliance approach that worked six months ago may need rechecking against the current guidance.
– Use niche/CPM research tools before committing production time to a content direction, rather than guessing at which niches carry favorable economics.
Key takeaways
– YouTube's policy targets mass-produced, repetitive, inauthentic output patterns, not AI tool use itself — original scripting, research, and commentary are what determine compliance.
– The "Altered or Synthetic Content" disclosure toggle is a separate, mandatory compliance step, independent of content quality, in full enforcement since January 2026.
– Faceless creators have absorbed disproportionate impact from enforcement, largely due to format-intent overlap, not because facelessness itself is disqualifying.
– Real solo-creator tooling covers distinct pipeline stages (niche/CPM research, script-to-video, AI stock footage, thumbnail optimization) — treating these as separable, individually testable stages is more effective than one all-in-one automated pipeline.
– The policy has been clarified multiple times since launch, so compliance guidance from even a few months ago may need rechecking against current YouTube Studio guidance.
Relevant tools.scult.in resources
The YouTube prompt collection is a useful starting point for building the scripting and editorial layer this article identifies as the actual compliance-critical piece of an AI-assisted production pipeline, and the Veo, Kling, and Runway prompt patterns cover the AI video-generation side of the pipeline for creators building the visual-assembly stage described above.
Frequently asked questions
What is "AI slop" in the context of YouTube's monetization policy?
Content characterized as mass-produced, repetitive, and inauthentic — the policy targets these output characteristics specifically, not AI tool use as a category.
Does YouTube ban AI-generated content outright?
No — AI-generated videos remain eligible for monetization when they offer original value and comply with the synthetic-content disclosure requirement.
What is the "Altered or Synthetic Content" disclosure?
A required toggle in YouTube Studio for any content using AI-generated or AI-altered media that could be mistaken for real footage, in full enforcement since January 2026.
What happens if a creator doesn't disclose synthetic content?
Non-compliance can trigger demonetization, reduced reach, or channel termination, independent of the underlying content's quality.
What is the three-strike enforcement system for inauthentic content?
Warning, then a 90-day suspension, then permanent removal from the YouTube Partner Program.
Are faceless YouTube channels specifically banned or targeted?
Not by policy design, but coverage reports faceless creators being disproportionately hurt in practice, since the format overlaps heavily with the mass-produced pattern the policy targets.
How large was YouTube's AI slop crackdown, in scale?
Tech Times reported YouTube "wiped 35 million subscribers" in connection with the crackdown — a concrete indicator of its platform-wide reach.
Are small creators affected more than large studios?
Coverage (including Kotaku's reporting on small creators "feeling the crunch") suggests yes, disproportionately, likely because larger studios more often already have the editorial-review layer the policy is checking for.
Did other platforms follow YouTube's lead on AI content restrictions?
Yes — Meta/Facebook and Snapchat introduced parallel restrictions on monetization or full-AI generation around the same period.
Has YouTube's AI content policy stayed the same since it launched, or changed?
It's been clarified multiple times across 2025 and 2026 after creator confusion, indicating it's still an evolving policy rather than a fixed, settled rulebook.
What does "original value" mean under YouTube's policy?
Editorial contributions like original research, commentary, storytelling, or curation — the specific elements the policy uses to distinguish authentic content from mass-produced templates.
Can a faceless channel still be monetized in 2026?
Yes — faceless creators can still monetize by adding research, commentary, storytelling, and human insight, per policy guidance; the format itself isn't disqualifying.
What specific tools are solo creators using across the AI production pipeline?
Real developer-built tools cover niche/CPM research (spanning 400,000+ channels), script-to-video generation, AI stock footage generation, and thumbnail CTR optimization.
Is there evidence AI thumbnail tools actually improve performance?
One developer reported moving click-through rate from 1.2% to 2.3% for a faceless niche channel using a purpose-built AI thumbnail generator.
Do marketers view the AI slop crackdown positively?
Digiday's coverage frames it as "creators concerned, marketers cheering" — advertisers generally welcome tighter content-quality standards even as affected creators experience revenue disruption.
Is cheaply-produced AI content still earning revenue despite the crackdown?
One industry report found some cheaply-produced AI videos still earning significant revenue amid the policy changes, suggesting enforcement isn't fully uniform.
What is the YouTube Partner Program?
YouTube's monetization program, membership in which the three-strike inauthentic-content enforcement system can revoke permanently on a third violation.
Does using AI tools for editing (not generation) carry the same policy risk?
The policy's stated focus is on the output pattern (mass-produced, repetitive, inauthentic) rather than any specific tool category, so AI-assisted editing that supports genuinely original content isn't the pattern the policy targets — though the disclosure requirement can still apply if the result could be mistaken for real footage.
How do I make AI-assisted YouTube videos that don't get demonetized?
Invest real effort in original scripting, research, and commentary; disclose synthetic content via the required Studio toggle; and avoid publishing at a volume or with a repetitiveness that structurally resembles the mass-produced pattern the policy targets.
How do I build a faceless YouTube channel with AI in 2026?
Use AI tools for the mechanical production stages (voice, visuals, assembly) while keeping the scripting and editorial layer genuinely original and researched, and comply with the synthetic-content disclosure requirement throughout.
How do I structure an AI-assisted production pipeline stage by stage?
Following the pattern in real developer tooling: niche/CPM research first, then script writing (the layer to invest the most original effort in), then AI-assisted visual/voice production, then thumbnail optimization — treating each as a separately testable stage.
How do I know if my content volume looks "mass-produced" to YouTube's systems?
There's no publicly confirmed exact threshold; the safer approach is pacing publication in line with genuine editorial capacity rather than maximizing volume purely because automation makes higher volume technically possible.
How do I use the synthetic content disclosure toggle correctly?
Enable it in YouTube Studio for any video containing AI-generated or AI-altered media that could be mistaken for real footage, as a standing practice rather than a case-by-case judgment call.
How do I choose a profitable niche for a new AI-assisted channel?
Use niche/CPM research tooling (like the channel-analysis tools spanning 400,000+ channels cited above) to compare ad-rate and competitive dynamics across niches before committing production time.
How do I test whether a new AI tool actually improves my channel's performance?
Isolate the specific pipeline stage the tool affects (e.g., thumbnails) and measure the metric tied to that stage (e.g., CTR) before and after, rather than changing multiple stages simultaneously.
Advanced: how do you build editorial review into an otherwise heavily automated AI production pipeline?
Insert a mandatory human review/edit pass on the script stage specifically — since that's the layer most directly tied to the "original value" distinction the policy draws — even if visual assembly and voice synthesis remain largely automated.
Advanced: is there a way to quantify how "repetitive" a channel's output looks to avoid the mass-produced pattern?
Not something this guide can confirm with a specific metric; a reasonable proxy is checking whether a random sample of your own videos would read as substantively distinct to an outside viewer, rather than assuming variety exists just because topics differ superficially.
Advanced: does AI stock footage generation reduce or increase policy risk compared to using standard stock libraries?
Custom AI-generated visuals can help differentiate a channel from others using the same generic stock sources, but they may also fall under the synthetic-content disclosure requirement if the result could be mistaken for real footage — evaluate both dimensions rather than assuming custom visuals are automatically lower-risk.
Advanced: how should a multi-channel operator manage compliance across several faceless channels simultaneously?
Given the disproportionate scrutiny reported on faceless formats, an operator running multiple channels should apply the same editorial-review and disclosure discipline consistently across all of them, rather than assuming policy risk is isolated to any single channel.
Advanced: is there a reliable way to predict which content will trigger a policy strike before publishing?
Not with certainty, based on available sources — the safer approach is aligning with the policy's stated distinguishing criteria (original value, proper disclosure, non-mass-produced pacing) as closely as possible rather than relying on a predictive check.
AI video generator vs. traditional editing — which carries more policy risk on YouTube?
AI-assisted pipelines that skip the editorial/research layer carry more risk under the current policy, not because AI tools are inherently disqualifying, but because that specific combination (AI generation, no editorial layer, high volume) matches the mass-produced pattern being targeted.
Cliplama vs. InVideo vs. Pictory-style tools for faceless channels — how do they compare on policy compliance?
Evidence not sufficiently verified — this guide's sources don't provide a direct compliance comparison between specific named AI video tools; the policy risk described here depends more on how a creator uses any given tool (original scripting vs. fully automated generation) than on which specific tool is chosen.
Faceless channels vs. on-camera channels — which is safer under the current policy?
Neither format is inherently safer or riskier under the policy's stated criteria, but faceless channels have absorbed more collateral enforcement impact in practice due to format-intent overlap with the mass-produced pattern.
Manual thumbnail design vs. AI thumbnail generation — does one carry more risk?
The disclosure and originality requirements apply based on content characteristics, not on whether a thumbnail specifically was AI-generated — thumbnails aren't the primary focus of the policy's mass-produced-content criteria described in the sources here.
Should a creator prioritize an AI script generator or a human scriptwriter for original value?
Given that scripting/research is specifically the layer the policy uses to distinguish authentic from mass-produced content, human editorial involvement at the scripting stage is the safer choice for compliance, even if AI assists with drafting.
My channel got a warning under the inauthentic content policy — what should I check first?
Review recent uploads specifically for mass-produced, repetitive patterns (templated scripts, unedited AI voiceover, no original commentary) and confirm the synthetic-content disclosure toggle was used correctly on any AI-altered media.
My subscriber count or views dropped sharply after a policy update — is this the AI slop crackdown?
It's plausible, given the reported 35-million-subscriber platform-wide impact, but confirm by checking YouTube Studio for any specific policy notices on your channel rather than assuming based on the broader statistic alone.
My faceless channel got flagged even though I do original research for every video — why?
This matches the documented pattern of faceless creators being disproportionately caught even when their content isn't actually "slop," due to enforcement difficulty distinguishing format from intent — consider appealing with evidence of your editorial process if you believe the flag was a false positive.
I forgot to disclose AI-altered content on some videos — what should I do?
Add the disclosure retroactively where possible through YouTube Studio and treat it as a standing practice going forward, since non-compliance with disclosure is a separate risk from content-quality issues.
My channel's growth stalled after switching to a heavier AI-assisted pipeline — is the pipeline the cause?
Possibly — check whether the switch also reduced the editorial/research layer in your scripts, since that's the specific variable most tied to both audience quality perception and policy compliance risk.
Is it worth building a faceless YouTube channel with AI tools in 2026, given the crackdown?
Still viable, per policy guidance, as long as genuine original value (research, commentary, storytelling) is part of the process — the crackdown targets a specific low-effort pattern, not AI-assisted production generally.
Should a new creator invest in AI production tools or traditional filming/editing skills first?
Given that the policy's compliance risk centers on the scripting/editorial layer rather than the production-tool layer, prioritizing scripting and research skills is arguably the higher-leverage investment, with AI production tools layered on as an efficiency gain afterward.
Is it worth paying for a dedicated AI thumbnail optimization tool?
The cited developer case (1.2%→2.3% CTR) suggests a meaningful potential return for channels currently using generic or unoptimized thumbnails, though individual results will vary by niche and current baseline performance.
Should a creator diversify onto other platforms given the parallel restrictions at Meta and Snapchat?
Diversification is a reasonable general risk-mitigation strategy given that multiple major platforms have moved in the same policy direction around the same period, though this guide can't confirm platform-specific compliance requirements beyond YouTube's policy detailed here.
Is it worth hiring a scriptwriter rather than fully automating script generation?
Given the policy's emphasis on original value at the scripting/editorial layer specifically, a human scriptwriter (or heavy human editing of AI-assisted drafts) is a reasonable investment for creators concerned about compliance risk.
Should a small creator slow down publishing frequency to reduce policy risk?
Given that high-velocity publishing is part of the "mass-produced" pattern description, pacing output to match genuine editorial capacity (rather than maximizing volume purely because automation allows it) is a reasonable risk-reduction step.
Is niche/CPM research worth doing before starting a new AI-assisted channel?
Yes, based on the real developer tooling built specifically for this — starting with data on which niches carry favorable economics reduces the risk of investing production effort into a low-return niche.
Should an agency running multiple faceless channels centralize its editorial review process?
Given the disproportionate scrutiny on faceless formats and the compliance risk tied to the editorial layer specifically, a centralized review process applied consistently across all managed channels is a reasonable operational safeguard.
Is it worth appealing a demonetization decision if a creator believes their content was wrongly flagged as "slop"?
Given documented cases of faceless creators being caught despite genuinely original content, an appeal supported by evidence of the actual editorial/research process behind the flagged videos is a reasonable step rather than assuming the flag is unappealable.
What's the single most important decision a solo creator can make to stay on the right side of this policy?
Investing real, visible editorial effort into scripting and research — the layer the policy explicitly uses to separate authentic content from the mass-produced pattern it targets — regardless of how much of the rest of the pipeline is AI-assisted.
Sources
- https://hn.algolia.com/api/v1/search?query=faceless%20youtube%20channel%20AI
- https://www.techdogs.com/td-articles/trending-stories/is-youtube-killing-the-faceless-channels-new-ai-policies-punish-faceless-creators
- https://miraflow.ai/blog/can-you-monetize-faceless-youtube-channels-ai-2026
- https://eliro.pro/blog/youtube-ai-content-policy-faceless-creators-2026
- https://aituber.app/blog/faceless-youtube-channels-demonetized-2026/
- https://lastplaydistro.com/blog/youtube-reused-content-policy-2026-ai-faceless-videos
