# "It's not X, it's Y" 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: replacive-contrastive-negation - Category: Rhetorical and discourse (rhetorical) - Subcategory: contrastive-negation - Also known as: contrastive negation, replacive subtype, epanorthosis, correctio, negative parallelism, antithesis, contrastive reframe, X rather than Y, The danger isn't X. It is Y., Most people think X. The reality is Y., Forget X. Focus on Y., uncontracted contrast, strawman consensus opener, corrective anaphora - Status: Active. Currently signals low-effort writing. - Severity: medium - Evidence grade: peer-reviewed (a published study measures the pattern) - Languages: en - Added: 2026-08-14 - Updated: 2026-08-17 - Page: https://feedsquad.com/ai-tells/replacive-contrastive-negation ## Description A clause denies one description of the subject and immediately substitutes a larger one, with no connective between the halves. The denied term was never asserted by anyone. Wikipedia's editors file the family under negative parallelisms and give three templates. Linguists separate the substituting form, called replacive contrastive negation, from the additive form built on not only X but also Y. The additive form is catalogued separately, because the two constructions do different work. This entry covers the replacive one only, and only the asyndetic version with no but between the halves. That narrowing is deliberate. A sentence like the report is not the fastest option, but it is the cheapest is ordinary English and stays out of scope. ## Why it reads as machine-written The construction promises a correction and delivers an upgrade. Classical rhetoric names the genuine move correctio: amending a first thought to make it stronger. Here nothing is amended. The rejected term was never claimed, so the sentence performs the shape of insight at no cost. Models produce it because the shape reads as emphasis to a human rater and the second half accepts any abstraction. The Economist reported models reaching for it repeatedly to carry emphasis. One instance means nothing. Three in a short post means the writer had a cadence and went looking for content to fill it. ## Detection Type: deterministic (a regular expression, run as written) Scope: sentence ECMAScript regular expression. The build validator compiles it and tests it against this entry's own specimens. ```regex \b[A-Za-z][\w'’-]*(?:\s+[\w'’-]+){0,3}(?:\s+(?:is|are|was|were)(?:\s+not|n['’]t)|['’](?:re|m)\s+not)\b(?![^.;!?]*\bbut\b)[^.;!?]{0,80}?[,;]\s*(?:it|they|this|that|these|those)(?:['’]s|['’]re|\s+(?:is|are|was|were))\b ``` Flags: gi ## Examples Constructed specimens. Written for this index. Never quoted from anyone's posts. ### Specimen 1 Before, exhibiting the tell: > Onboarding isn't a checklist, it's a relationship. That distinction sounds slight on the page and it changes very nearly everything about how the first conversations go. Companies arrive at this understanding eventually, though usually after losing a handful of accounts they had counted as safe, and starting from the relationship is mostly a way of skipping that part. After, repaired: > Our onboarding is five steps over eleven days. The only one that moves retention is the day-three call with a person. We cut the other four to two and retention held. Note: The repair is not a synonym swap. The second version names the change and what happened after. ### Specimen 2 Before, exhibiting the tell: > This isn't a pricing change, it's a statement about who we build for. Price has always been the clearest thing a company says about itself, whether or not anyone means it that way. The realisation usually arrives some time after the invoice does, which is why the explanation afterwards is so much harder than the decision was. After, repaired: > We raised the entry tier from 12 to 19 dollars a seat and kept the old price for accounts opened before June. Two thirds stayed. The accounts that left were each using one feature. Note: Figures in this repair are invented for the specimen. ### Specimen 3 Before, exhibiting the tell: > They're not customers, they're partners. That distinction shapes everything about how the relationship actually works, and most companies say it long before they have earned it. The good ones let the word do some work. The rest put it on a slide and move on. After, repaired: > Four of our accounts test releases before we ship them, and two have written docs we now publish. We give those four a direct line to the engineer who owns the area. ## False positives Who legitimately writes this way. Writers of myth-busting and common-misconceptions pieces use this form as their organising device, and Wikipedia's own guide says so in the same section that names the tell. Speechwriters and preachers use it deliberately, where it is a real correctio rather than filler. Sports commentary runs on it. Sales copy has used it for a century, and the software joke about a bug and a feature predates the whole discourse. Writers whose first language is German or Spanish have a dedicated corrective conjunction, sondern and sino, and reach for the English equivalent more readily than an English monolingual would. One occurrence is not a finding. The signal is repetition inside one short document, plus a second half that names nothing the first half did not. ## Model attribution Not tied to a vendor. Wikipedia lists it as a general LLM sign. The Washington Post counted not just X but Y variants in about six percent of shared ChatGPT chats in July 2025, and that figure covers the additive template rather than this one, so it does not transfer. One research pass found no peer-reviewed source assigning the construction to any model family. ## Platform notes - wikipedia: Catalogued under negative parallelisms, shortcut WP:AIPARALLEL, with three named templates. The same section states that human writers use the form commonly, especially in listicles about myths. - linkedin: LinkedIn's May 2026 policy suppresses out-of-network distribution for content with no unique perspective. It names no sentence construction, and neither does any other LinkedIn policy text. - x: X applies an llm_slop_post label that routes to spam handling for thirty days. The classifier prompts are withheld from the public repo, so nothing published shows this or any other construction being scored. ## Sources 1. Wikipedia:Signs of AI writing, section Negative parallelisms (WP:AIPARALLEL) https://en.wikipedia.org/wiki/Wikipedia:Signs_of_AI_writing (tier: community; accessed 2026-08-14) 2. Silvennoinen, Not only apples but also oranges: Contrastive negation and register, VARIENG vol. 19 (2017) https://varieng.helsinki.fi/series/volumes/19/silvennoinen/ (tier: peer-reviewed; accessed 2026-08-14) 3. Silva Rhetoricae, Epanorthosis (correctio), ed. Gideon O. Burton, Brigham Young University https://rhetoric.byu.edu/Figures/E/epanorthosis.htm (tier: primary-doc; accessed 2026-08-14) 4. The Economist, How to spot AI writing (2026-07-30) https://www.economist.com/culture/2026/07/30/how-to-spot-ai-writing (tier: press; accessed 2026-08-14) 5. slop-lint, social-reply-register molds https://github.com/eric-sabe/slop-lint (tier: community; accessed 2026-08-14) 6. vale-ai-tells, 111 machine-checkable rules https://github.com/tbhb/vale-ai-tells (tier: community; accessed 2026-08-14) 7. Reuters Institute, How AI-generated prose diverges from human writing and why it matters https://reutersinstitute.politics.ox.ac.uk/news/how-ai-generated-prose-diverges-human-writing-and-why-it-matters (tier: press; accessed 2026-08-15) ## Status history Ids are permanent. A retired tell keeps its id and its page. - 2026-08-14, Active: Opened as active. Named by Wikipedia's editors as a current sign with three templates, reported by The Economist as a repeated model habit in a 2026 rewrite study, and given a precise linguistic taxonomy by Silvennoinen. The human base rate is real and the entry says so, which is why severity is moderate rather than strong. ## 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.