# Template-and-scale sameness Part of [The AI Tells Index](https://feedsquad.com/ai-tells). A tell signals low effort. It does not identify an author. Skilled writers produce every shape listed here, some of them daily, and automated detectors misread those writers for it at rates measured above 60 percent on non-native English prose. Nothing in this index proves that a machine wrote anything. Read an entry as one piece of evidence to weigh against the false-positive notes printed beside it. ## Facts - Id: template-scale-sameness - Category: Platform-specific (platform) - Subcategory: aggregate - Also known as: interchangeable output across a body of work, inauthentic content, every post the same shape - Status: Active. Currently signals low-effort writing. - Severity: high - Evidence grade: primary-doc (platform policy, vendor documentation, or a model card) - Languages: en - Added: 2026-08-15 - Updated: 2026-08-15 - Page: https://feedsquad.com/ai-tells/template-scale-sameness ## Description Output that is interchangeable across one author's or one channel's entire body of work. YouTube renamed its repetitious-content monetization policy to inauthentic content on 15 July 2025 and describes what it covers: content that is mass-produced or templated, and channels where the material feels interchangeable from video to video. The same page says AI-assisted work with an original voice remains monetisable, which makes this the clearest authorship-indifferent definition of slop that any platform has written down. This entry merges the uniform-body-of-work reading with the channel-sameness reading, which cited the same policy page and made the same judgment. ## Why it reads as machine-written Reinhart and colleagues measured model prose as more uniform than human prose at population level, with large effect sizes on specific grammatical features. That is a fact about corpora and not about any one video. Pudasaini and colleagues found the other half of the story: detectors that keyed on uniformity were learning corpus artefacts and misfired badly on short human text. So this ships as a judgment over a body of work, with no per-document statistic behind it, and the rubric refuses to score a single item. ## Detection Type: judge (a rubric for a lightweight model judge) Rubric for a lightweight model judge: ```text Sample ten items from the same account or channel, spread across at least a month. For each, write down the opening move, the order of the sections and the closing move. Return no-tell if the account is a format show whose repetition is the product, such as a daily brief or a quiz, and each item still carries its own facts, names or footage. Return tell when the ten items share one skeleton and any of them could be swapped for another without a reader noticing, listing the shared skeleton beat by beat. Never score a single item with this rubric; if fewer than ten are available, return insufficient-evidence. ``` ## Examples Constructed specimens. Written for this index. Never quoted from anyone's posts. ### Specimen 1 Before, exhibiting the tell: > Video 3 opening: Have you ever wondered why the ocean is blue? Today we explore five surprising facts about the ocean. Video 4 opening: Have you ever wondered why the sky is blue? Today we explore five surprising facts about the sky. After, repaired: > Video 3 opening: The ocean looks blue because water absorbs red light in the first ten metres, which is also why everything below 30 metres films grey without a filter. Video 4 opening: We shot the same reef with and without a red filter at 18 metres, and the difference starts at 00:40. Note: Both repairs keep the format. What changes is that each video now contains something the other one does not. ## False positives Who legitimately writes this way. Format shows repeat their shape on purpose and are supposed to. A daily market brief, a quiz channel, a recipe series and a liturgy all have a fixed skeleton, and YouTube's own policy says such work monetises fine when a voice is added. Non-native writers and technical writers also work from templates because a template is how you keep a second language or a compliance requirement under control. The check is whether swapping two items would go unnoticed, and that is a question about the body of work rather than about any sentence in it. ## Model attribution The uniformity finding is population-level and covers instruction-tuned models generally, with the effect reported as larger for instruction-tuned than for base models. No vendor documents it as a known behaviour, and it supports no claim about a single document. ## Platform notes - youtube: The monetization policy renamed on 15 July 2025 covers content that is repetitive or mass-produced, content made with a template, and channels whose material feels interchangeable from video to video. The same policy states that AI-assisted content with an original voice can monetise, which is the clearest available statement that the platform is judging effort rather than authorship. ## Sources 1. YouTube, inauthentic content monetization policy (renamed 15 Jul 2025) https://support.google.com/youtube/answer/1311392 (tier: primary-doc; accessed 2026-08-14) 2. Social Media Today, YouTube clarifies monetization update on inauthentic repeated content https://www.socialmediatoday.com/news/youtube-clarifies-monetization-update-inauthentic-repeated-content/752892/ (tier: press; accessed 2026-08-14) 3. Reinhart et al., Do LLMs write like humans? PNAS 122(8) (arXiv:2410.16107) https://arxiv.org/abs/2410.16107 (tier: peer-reviewed; accessed 2026-08-14) 4. Pudasaini et al., Why AI-Generated Text Detection Fails: Evidence from Explainable AI Beyond Benchmark Accuracy (arXiv:2603.23146) https://arxiv.org/abs/2603.23146 (tier: primary-doc; accessed 2026-08-14) ## Status history Ids are permanent. A retired tell keeps its id and its page. - 2026-08-15, Active: A written platform policy defines the pattern, states the consequence in money, and explicitly permits AI-assisted work with an original voice. Two peer-reviewed papers set the boundaries: one showing that model prose is more uniform at population level, the other showing that uniformity-keyed detectors were learning artefacts. High severity because monetisation is at stake, and judged over ten items because nothing smaller is defensible. ## License CC BY 4.0. https://creativecommons.org/licenses/by/4.0/ Attribution: The AI Tells Index, feedsquad.com/ai-tells Reuse the data, including commercially. Keep the attribution line. --- Dataset version 1.0.0. Schema version 1. Part of [FeedSquad](https://feedsquad.com). Built by [Herman Foundry](https://hermanfoundry.com) from Levi, Finnish Lapland.