Why does factually use AI to write articles about ai impersonation on youtube? Seems like factually is jut more AI garbage to me.

1. Why publishers use AI: scale, speed and cost

Newsrooms and commercial publishers increasingly deploy generative tools to draft copy because AI dramatically reduces time and cost for routine stories and for monitoring fast-moving topics like YouTube deepfakes; analysts estimate AI now produces a substantial share of online text and industry commentary predicts deeper integration of AI into workflows ^{[1] [5]}. Multiple data points show a sharp rise in AI-authored pages: some research and industry reporting put AI-written articles at or above parity with human work in recent periods — Graphite’s analysis found AI content briefly surpassed human-written articles around November 2024 and later hovered near equal levels ^{[6] [7]}.

2. Quality concerns and the “AI garbage” critique

The objection that AI outputs are “garbage” stems from well-documented model flaws: hallucinations — confidently stated falsehoods — remain a major risk in generative systems and are a central reason fact-checking tools and editorial oversight are recommended ^{[2] [8]}. Industry tools marketed to publishers promise fact-checking and AI-detection features to reduce errors, but those tools themselves have imperfect accuracy and can produce false positives/negatives when classifying content ^{[8] [6]}.

3. Why writing about AI impersonation with AI is not a simple hypocrisy

Using AI to cover AI impersonation isn’t necessarily hypocrisy: reporters and publishers treat AI as a drafting and research aid while applying human verification, contextual reporting, and third‑party checks — a common hybrid model advocated in industry guidance ^{[5] [8]}. The credibility question depends on whether the publisher discloses AI use and applies rigorous human fact-checking; available sources note emerging expectations for transparency and editorial governance around AI-assisted content ^{[5] [9]}.

4. The YouTube-specific stakes: detection, takedowns, and biometric concerns

YouTube now accepts complaints about AI-generated deepfakes and has tools to determine if content “could be mistaken for real human work,” which matters for takedowns and creator rights ^[3]. At the same time, reporting shows creators and experts alarmed that YouTube’s new likeness‑protection tools involve creators submitting biometric data to help flag deepfakes, raising concerns that Google could use that sensitive data to train models — a claim YouTube denies but is reviewing language around ^[4].

5. Competing perspectives in the sources

Trade and academic sources present two competing emphases: one stresses rapid utility and integration of AI into content production and marketing, forecasting sophisticated, context-aware AI workflows ^{[5] [10]}. The other stresses the persistence of errors and the limits of detection, urging robust fact-checking and caution — studies and vendors promoting detection tools also disclose imperfect performance ^{[8] [6] [2]}.

6. What this means for readers who distrust AI-written coverage

If you find AI-written pieces untrustworthy, rely on signals the sources recommend: check for explicit disclosure of AI use, look for named human authors and sourcing, and prefer outlets that combine AI drafting with human fact-checking and primary reporting ^{[8] [5]}. Also follow platform-level protections: YouTube’s complaint mechanism and “likeness detection” provide a recourse for creators countering impersonation even as debates continue about data practices ^{[3] [4]}.

Limitations and missing details: available sources do not mention how Factually specifically implements editorial controls or whether Factually’s articles on this topic were human‑verified; therefore this analysis uses broader industry reporting and platform coverage to provide context (not found in current reporting).