/linkedin-humanizer

AI-Tell Rules: Tier-Classified Reference

Fifteen rules from the linkedin-humanizer package, sorted by what kind of evidence each one actually represents.

Tiers:

• Forensic - real AI signal, undefendable. The model or its template leaked.
• Strict - real human pattern, but the user banned it for taste. Defending it inside this brand voice is pointless.
• Aesthetic - pattern flagged because LLMs use it, not because it signals AI. Famous human writers built careers on these.

Defense strength: how well the rule survives a "but a human wrote that" challenge. Low = the rule wins. High = the writer wins.

Contents

• Tier 1 - Forensic (real AI signals)
• Tier 2 - Strict (corporate-speak, easy ban)
• Tier 3 - Aesthetic (overreach, defendable)
• Summary table
• Key citations

Tier 1 - Forensic (real AI signals)

Rule 1. oaicite / contentReference / turn0search0 markers

• Tier: forensic
• Why flagged: These are internal tokens from OpenAI's tool-use scaffold (citation pills, search-result handles). They appear when someone copy-pastes from ChatGPT without cleaning the output. No human types :contentReference[oaicite:0]{index=0} by hand.
• Famous human user: none. Zero recorded cases.
• Defense strength: zero
• Citation: Wikipedia, "Signs of AI writing" - https://en.wikipedia.org/wiki/Wikipedia:Signs_of_AI_writing

Rule 2. Knowledge-cutoff disclaimers

• Tier: forensic
• Why flagged: Phrases like "As of my last update in January 2022..." or "I don't have access to real-time information..." are GPT-3.5/4 training-cutoff boilerplate. A human would write "as of last year" or just give the date.
• Famous human user: none.
• Defense strength: zero
• Citation: Wikipedia "Signs of AI writing"; TechCrunch on OpenAI's discontinued classifier - https://techcrunch.com/2023/07/25/openai-scuttles-ai-written-text-detector-over-low-rate-of-accuracy/

Rule 3. Phrasal templates left unfilled

• Tier: forensic
• Why flagged: Visible scaffolding like [Your Name], 2025-XX-XX, [Describe section X], [Insert metric here]. These are prompt-template artifacts where the human forgot to fill the slot.
• Famous human user: none.
• Defense strength: zero
• Citation: Wikipedia "Signs of AI writing"

Rule 4. Mad-Libs blanks

• Tier: forensic
• Why flagged: Adjacent to rule 3. Sentences like "I [verb] the [noun] every [time period]" or "The result was a [adjective] [outcome]." These come from instruction-tuned outputs where the model echoed the prompt structure instead of resolving it.
• Famous human user: none.
• Defense strength: zero
• Citation: Wikipedia "Signs of AI writing"

Rule 5. Em dash overuse - above ~1 per 100 words (3+ in a short post)

• Tier: forensic (at the overuse threshold)
• Why flagged: A single em dash is a stylistic choice (see rule 11). But three or more em dashes in a 200-word LinkedIn post was one of the strongest stylometric signals GPT-4 emitted: the model glued clauses where a human would split into two sentences. V3 keeps the density cap (~1 per 100 words, 1-2 per post) and replaces only the excess, with a comma, colon or parentheses, never a period.
• Famous human user: Emily Dickinson is the famous defense, but Dickinson used em dashes in poetry across hundreds of poems - not three in a single 200-word business post. Density matters.
• Defense strength: low (at the overuse threshold). The single-use defense (rule 11) is high; the overuse case is forensic.
• Citation: Wikipedia "Signs of AI writing"; GPT-5.4 corpus rate 1.43 per 1,000 words vs. human 3.23 (2026)

Tier 2 - Strict (corporate-speak, easy ban)

Rule 6. AI vocabulary: leverage, utilize, harness, delve, foster, cultivate

• Tier: strict
• Why flagged: Each of these has a one-syllable Anglo-Saxon equivalent (use, use, use, look, build, grow). LLMs over-use the Latinate version because RLHF training samples skewed corporate. Humans use them too - but the user has banned them in his own voice for taste.
• Famous human user: any McKinsey deck, any HBR article from 1995-2015. "Leverage" was the management-consulting verb of the 1990s.
• Defense strength: medium in the abstract, zero inside this brand voice - the user explicitly rejected this register.
• Citation: Wikipedia "Signs of AI writing" lists all six under AI vocabulary

Rule 7. Filler adverbs: fundamentally, essentially, ultimately, crucially

• Tier: strict
• Why flagged: These are sentence-opener crutches that add no information. "Fundamentally, the issue is X" reduces to "the issue is X." LLMs use them as soft hedges; the user wants them deleted.
• Famous human user: academic philosophy papers (Daniel Dennett uses "fundamentally" constantly). Academic register is fine in academia, not in a LinkedIn post.
• Defense strength: medium in academic prose, zero in this voice.
• Citation: Wikipedia "Signs of AI writing"

Rule 8. Filler openers: "In today's fast-paced world", "In the age of AI"

• Tier: strict
• Why flagged: These are pure throat-clearing. The post hasn't started yet. LLMs deploy them because the training data is full of corporate blog intros that did the same thing.
• Famous human user: every LinkedIn ghost-writer from 2015-2022. The pattern predates GPT.
• Defense strength: low. Even before AI, copywriting style guides killed these openers.
• Citation: Wikipedia "Signs of AI writing"; Ann Handley, Everybody Writes (2014) on opener filler

Rule 9. Cliché closers: "What do you think?", "Tag someone who needs this"

• Tier: strict
• Why flagged: Generic engagement bait. LinkedIn's algorithm explicitly penalizes engagement bait under its 2024+ heuristics, and these closers signal the post wasn't written for a specific reader.
• Famous human user: every LinkedInfluencer 2016-2022. Pre-dates AI.
• Defense strength: low. Even pre-AI, the algorithm hated them.
• Citation: LinkedIn engagement-bait policy (in-app community guidelines); Wikipedia "Signs of AI writing"

Rule 10. Negative parallelism: "X isn't Y, it's Z"

• Tier: strict (Sergey's hard ban)
• Why flagged: "It's not a bug, it's a feature" / "It's not what you say, it's how you say it." LLMs over-deploy this because RLHF reward models favor it as quotable. The user has explicitly banned it as a personal pattern - too clean, too pat, no friction.
• Famous human user: every TED talk 2010-2020. Tony Robbins, Simon Sinek. The pattern is real human rhetoric, but the user rejected it.
• Defense strength: medium in oratory, zero in this voice (hard ban).
• Citation: Wikipedia "Signs of AI writing" under "negative parallelism"

Tier 3 - Aesthetic (overreach, defendable)

Rule 11. Em dashes - single use

• Tier: aesthetic
• Why flagged: Leftover 2023-24 folklore. In 2026 the frontier models emit fewer em dashes than humans (GPT-5.4: 1.43 per 1,000 words vs. human 3.23) and The Economist called the dash "no longer a reliable sign." The signal only exists above ~1 per 100 words (rule 5). Zero dashes across a long post is now itself the tell of someone trying to look human.
• Famous human users:
  • Emily Dickinson - built her entire poetic style on em dashes. "Because I could not stop for Death - / He kindly stopped for me -" (1863). Roughly 1,800 poems, em dashes throughout.
  • Cormac McCarthy - uses em dashes in Blood Meridian, The Road, No Country for Old Men. McCarthy famously refuses quotation marks; em dashes do dialogue work.
  • Joan Didion, The Year of Magical Thinking (2005) - em dashes for parenthetical grief.
• Defense strength: high (single use). The overuse threshold (3+ in a short post) flips to forensic - see rule 5.
• Citation: Stanford HAI / Liang et al. (2023) on detector bias - https://hai.stanford.edu/news/ai-detectors-biased-against-non-native-english-writers ; TechCrunch on OpenAI classifier shutdown for low accuracy - https://techcrunch.com/2023/07/25/openai-scuttles-ai-written-text-detector-over-low-rate-of-accuracy/

Rule 12. Rule of three

• Tier: aesthetic for the one natural triad; strict for stacked / perfectly parallel triads and any third triad in a post
• Why flagged: Triadic structure ("X, Y, and Z") runs at 2x expert-human density across 2026 frontier models (arXiv 2604.19768). The tell is the density and the interchangeable items, not the form: 26% of top human tweets contain exactly one.
• Famous human users:
  • Lincoln, Gettysburg Address, 1863: "of the people, by the people, for the people."
  • Julius Caesar, 47 BCE: veni, vidi, vici - "I came, I saw, I conquered."
  • Winston Churchill, House of Commons, 13 May 1940: "blood, toil, tears and sweat" (technically four, but the cadence is built on threes throughout the speech).
  • Thomas Jefferson, Declaration of Independence, 1776: "life, liberty, and the pursuit of happiness."
  • Aristotle, Rhetoric, 4th century BCE - formally identified the rule of three as a foundational rhetorical device.
• Defense strength: high. This is 2,400 years of human rhetoric. Flagging it as AI is detector overreach.
• Citation: Aristotle, Rhetoric, Book III; Stanford HAI on detector false positives

Rule 13. Passive voice

• Tier: aesthetic
• Why flagged: GPT-4 over-uses passive constructions. Humanizers strip them by default. But passive voice has legitimate uses - agent-obscuring, formal register, scientific neutrality.
• Famous human users:
  • Watson & Crick, Nature, 25 April 1953: "It has not escaped our notice that the specific pairing we have postulated immediately suggests a possible copying mechanism for the genetic material." Pure passive understatement - the most famous sentence in 20th-century biology.
  • Joan Didion, Slouching Towards Bethlehem (1968) - uses passive deliberately for narrative distance.
  • The entire scientific literature - passive voice is journal house style for a reason. "The samples were treated with..." is correct; "We treated the samples with..." reads as informal.
• Defense strength: high in technical/scientific contexts, medium in business writing. Don't strip passive in a research summary.
• Citation: Watson & Crick, Nature 171:737-738 (1953); Wikipedia "Signs of AI writing" notes passive voice as flagged but contested

Rule 14. AI vocabulary: "robust"

• Tier: aesthetic
• Why flagged: Lumped in with leverage/utilize/harness in OriginalityAI's vocabulary list.
• Famous human users:
  • Every epidemiologist for a century - "robust" has a precise statistical meaning: insensitive to assumption violations. "A robust estimator" is a 1960s term of art (Peter J. Huber, Robust Statistics, 1964).
  • Software engineers - "robust system" means tolerant of edge cases. Replacing it with "solid" loses meaning.
  • Immunologists - "robust immune response" is standard vocabulary in Nature and Cell.
• Defense strength: high in technical writing, medium in business writing. Keep "robust" if it's doing technical work; replace with "solid" only when it's generic praise.
• Citation: Peter J. Huber, "Robust Estimation of a Location Parameter," Annals of Mathematical Statistics (1964); Stanford HAI on detector bias against technical English

Rule 15. Curly quotes ("smart quotes")

• Tier: aesthetic
• Why flagged: Some detectors weight " " ' ' as AI signal because LLM outputs preserve them and human typing usually produces straight " and '.
• Famous human users:
  • Microsoft Word, Google Docs, Apple Pages - all auto-convert straight quotes to curly by default. Anyone typing in those tools produces curly quotes without thinking.
  • The New Yorker - house style since 1925 mandates curly quotes. Every published piece uses them.
  • Every traditionally typeset book since the invention of moveable type - curly quotes are correct typography. Straight quotes are an ASCII compromise.
• Defense strength: high. Flagging curly quotes as AI is detector incompetence - it's flagging Microsoft Word's defaults.
• Citation: The Chicago Manual of Style, 17th ed., §6.115 on quotation marks; Adelphi University lawsuit illustrating cost of false positives - https://www.plagiarismtoday.com/2025/10/14/adelphi-university-sued-over-ai-allegation/

Summary table

Key citations

• Stanford HAI / Liang et al. (2023) - AI detectors are biased against non-native English writers. Single most-cited paper for "detectors over-fire on aesthetic patterns." https://hai.stanford.edu/news/ai-detectors-biased-against-non-native-english-writers
• TechCrunch (25 July 2023) - OpenAI shut down its own AI-text classifier, citing low rate of accuracy. The company that built GPT couldn't reliably detect GPT. https://techcrunch.com/2023/07/25/openai-scuttles-ai-written-text-detector-over-low-rate-of-accuracy/
• Wikipedia, "Signs of AI writing" - community-maintained taxonomy. Source for forensic markers (oaicite, knowledge-cutoff) and the strict vocabulary list. https://en.wikipedia.org/wiki/Wikipedia:Signs_of_AI_writing
• Adelphi University lawsuit (Oct 2025) - student sued the university after a false-positive AI accusation. The legal cost of trusting detectors on aesthetic signals. https://www.plagiarismtoday.com/2025/10/14/adelphi-university-sued-over-ai-allegation/

Last Updated: 2026-04-25 Maintained By: Claude Code and Codex, for Sergey Bulaev Purpose: Educational backbone for the controversial post arguing that AI-writing rules are forensic in some cases and aesthetic overreach in others.