Signs of AI Writing
A Combined Reference

01 The Statistical Foundation

Perplexity and Burstiness

These are the two measurable properties that underpin almost every other tell on this list.

Perplexity measures how predictable each word choice is given the preceding context. AI models select the most probable next token, making their output statistically forecastable. Human writers make more varied, sometimes unexpected word choices that raise the perplexity score.

Burstiness measures variance in sentence length. Human writing has high burstiness; some sentences are four words, others are forty. AI text maintains a remarkably uniform medium length throughout. This consistency is itself the tell. The LegitWrite framework uses burstiness as a core signal.

A related concept: uniform quality. Human writers produce work of uneven quality. Some paragraphs are stronger than others. Some sentences come easily; others are awkward. An argument that starts strong may lose steam by page three. AI maintains a remarkably consistent level of polish from the first sentence to the last. Every paragraph is equally finished, every transition equally smooth. That evenness, paradoxically, is what makes it feel wrong.

Regression to the Mean

LLMs use statistical algorithms to predict what should come next based on massive training corpora. The result tends toward the most statistically likely output that applies to the widest variety of cases. The Wikipedia guide uses a vivid analogy: it is like shouting louder and louder that a portrait shows a uniquely important person, while the portrait itself is fading from a sharp photograph into a blurry, generic sketch. The subject becomes simultaneously less specific and more exaggerated.

This smoothing produces text that says true things which are also true of basically everything else. A total absence of personal detail. It explains. It generalizes. But it never sounds like a real person has been through anything.

02 Structural and Rhetorical Tells

The Negation Pivot

The single most prominent AI writing tell, per multiple sources. Takes several forms:

• "It's not X, it's Y": "It's not just a product launch, it's a movement." "This isn't about efficiency, it's about transformation."
• Two-sentence version: Stating something, then immediately pivoting with "however" to reframe it.
• "Not just X, but also Y": "Not only a work of self-representation, but a visual document of her obsessions."
• "No X. No Y. Just Z.": The fiction cousin. False drama through negation.

The structure performs the appearance of nuance while actually being a single rhetorical move repeated endlessly. The Kriss NYT piece identifies it as perhaps the most recognizable single pattern. Worth noting: LLMs didn't invent this construction. L'Oréal used it on a Barcelona bus stop ad before ChatGPT existed. The tell isn't the construction itself; it's the frequency.

The Trailing Participle Clause

AI models use present participial constructions ("-ing" phrases stapled to the end of already-complete sentences) at two to five times the rate found in human-written text, per research published in PNAS. These -ing words introduce unnecessary evaluations.

The Rule of Three

LLMs overuse the rule of three compulsively. Adjective stacks ("comprehensive, actionable, strategic"), noun stacks ("keynote sessions, panel discussions, and networking opportunities"), parallel phrases. AI adds a third element even when only two are relevant, or when the third is weirdly broad compared to the first two.

Importance Inflation

LLM writing puffs up the importance of the subject matter by adding statements about how arbitrary aspects of the topic represent or contribute to a broader topic. There is a distinct and easily identifiable repertoire of ways it does this.

LLMs do this even for the most mundane subjects. Population data. Etymology. They sometimes hedge first, acknowledging the subject is relatively unimportant, then talk about its importance anyway. When writing about biology, they over-emphasize ecosystem connections, belabor conservation status, and speculate about preservation efforts even when the status is unknown.

Notability Obsession and Source-Listing

LLMs act as if the best way to prove a subject is notable is to hit readers over the head with claims of notability. They list sources that have covered the topic without additional context. They inaccurately attribute their own superficial analyses to named sources. They exaggerate the quantity of sources, presenting views from one or two as widely held. They mention multiple "reviewers" or "scholars" while only citing one person. They imply lists are non-exhaustive when the sources give no indication other examples exist.

They also frequently note social media presence: "maintains an active social media presence." This wording is particularly idiosyncratic to AI text and uncommon before ~2024.

The "Challenges and Future Prospects" Formula

Many LLM-generated articles include a section beginning with "Despite its [positive words], [subject] faces challenges..." and ending with a vaguely positive assessment or speculation about future initiatives. This rigid formula appears for everything from the Panama Canal to pyroelectric materials to Afghan television stations.

Compulsive Summaries

"Overall." "In conclusion." "To sum up." "In summary." AI does this after four paragraphs. The LegitWrite framework identifies this: conclusions that summarize main points without adding anything new, introductions that define terms and state what the article will cover. Intro-body-conclusion applied to every output regardless of length.

The Hedging Cascade

Where human writers might confidently assert a position, AI presents multiple perspectives even when unnecessary, creating text that feels perpetually balanced to the point of being wishy-washy. This reflects training to be helpful and harmless, but strips writing of conviction.

Sean Kernan tested this directly: he prompted ChatGPT with "Why don't some marriages last?" and found phrases like "some marriages struggle" throughout. An unwillingness to commit to anything. Human writers vary their certainty based on context. AI maintains uniform cautiousness.

This can also produce contradictions in longer pieces. The prediction engine maintains local coherence but not a thesis. Paragraph N is coherent. Paragraph N+5 is coherent. They may not agree with each other.

03 Vocabulary and Lexical Tells

The AI Vocabulary Fingerprint

Multiple studies demonstrate that LLMs overuse specific words. These started appearing far more frequently after 2022. They co-occur: where there is one, there are likely others. One or two may be coincidence. An edit introducing lots of them, lots of times, is one of the strongest tells.

2023 – MID-2024 (GPT-4)

MID-2024 – MID-2025 (GPT-4o)

MID-2025 – PRESENT (GPT-5)

Each model has a distinct fingerprint, termed an "aidiolect" by researchers. Certain models favor "intricate" and "underscore"; others prefer "palpable" and "continuation." The word "delve" was ChatGPT's signature in 2023 and dropped off sharply by 2025. On PubMed, "delve" spiked dramatically after ChatGPT's first full year. The Helsinki study (April 2025) confirmed surges in specific words in student essays post-ChatGPT, along with increased word count and sentence length.

Content marketing variants: Unlock, Empower, Elevate. Title structures: "from X to Y," "X Things You Should Know," "Master X in X Days."

Avoidance of Basic Copulatives

AI substitutes simple "is" or "are" constructions for fancier alternatives: "serves as a," "marks the," "stands as a." One study documented over a 10% decrease in "is" and "are" usage in academic writing in 2023. Similarly, AI prefers "features," "offers," and "boasts" to the neutral "has." Sometimes it gets elaborate: "ventured into politics as a candidate" instead of "was a candidate."

Elegant Variation

AI has a repetition-penalty that discourages reusing words. A character's name gets replaced by "protagonist," then "key player," then "eponymous character." Guardian editors have mocked this as "POVs" (popular orange vegetables), after a draft about carrots that kept finding new ways to avoid saying "carrots."

04 Punctuation and Formatting

Em Dash Overuse

The em dash appears with remarkable frequency in AI text, serving as a universal connector. Some call it the "ChatGPT dash." Models lack an intuitive sense of rhythm or pause. The em dash becomes a probabilistic shortcut; a way to create syntactic complexity without navigating semicolons, colons, or commas.

With GPT-4.0, engineers fixed comma overuse but shifted behavior toward even more em dashes. GPT-5.1 attempted to suppress the pattern after it became notorious. AI also skips en dashes entirely, using hyphens for ranges where en dashes belong.

Collateral damage: human writers who used em dashes for years before ChatGPT now face accusations of AI use. Reddit's r/FanFiction community has had heated arguments about this.

Curly Quotation Marks

ChatGPT and DeepSeek use curly quotes instead of straight ones, sometimes inconsistently mixing both. They also use curly apostrophes. Note: Microsoft Word's smart quotes do the same thing, as does macOS by default. Gemini and Claude typically do not produce curly quotes.

Formatting Overkill

Excessive bolding of key terms. Every bulleted list as "Term: Definition." Numbered lists where paragraphs would work. Headers where none are needed. AI reaches for structural formatting reflexively. The SSRN paper identifies "overly symmetrical bullet points" as a distinct tell.

Inline-Header Vertical Lists

A specific pattern: ordered or unordered lists where each item begins with an inline boldfaced header, separated by a colon from descriptive text. The "Key Takeaways" format applied to everything.

Emoji Insertion

AI decorates section headings or bullet points with emoji, most noticeable when conversational output gets pasted into formal contexts.

Title Case in Headings

AI strongly capitalizes all main words: "Global Context: Critical Mineral Demand" instead of "Global context: Critical mineral demand."

05 Tone and Register

The Promotional Register

LLMs have serious problems with neutral tone. Even when prompted for encyclopedic style, output tends toward advertisement or travel-guide prose. Towns become "vibrant" with "rich cultural heritage." Landscapes are "scenic." Views are "breathtaking." Facilities are "clean and modern." When writing about cultural heritage (even Japan's electronics industry), they constantly remind readers of its importance.

Older LLMs (GPT-4) are more blatantly positive. Newer ones are subtly positive instead.

Forced Sass / The Ta-Da Problem

The Hunting the Muse author calls this "Mean Girls energy." Phrases like "but here's the truth" or "but here's what nobody's saying" create manufactured conflict. On LinkedIn it's epidemic. "Hot take:" and "The result?" and "Then I realized:" perform revelation without revealing anything. Placeholder drama.

Sycophantic Openings

"Great question!" "That's a really interesting point!" "I hope this finds you well." In article form: intro paragraphs that describe what the piece is about to do instead of just doing it.

The Absent Voice

The default tone is formal, neutral, emotionally detached. Grammatically correct but lacking subtle variations that convey personality. AI generates text that represents a statistical average of millions of voices. Instruction-tuning further flattens variation, pushing output toward a generic persona. No idiosyncrasy. No tics. No preferences that don't serve the argument.

Overly Formal Tone

AI avoids contractions and leans into stiff phrasing. Instead of "You'll love this cozy home," it writes "You will appreciate the comfort this home provides." Human writers code-switch between registers constantly. AI maintains one register throughout.

06 Cliché Phrases and Templates

Cliché Openers

If it sounds like the first line of a high school essay, that's usually the signal.

Structural Templates

• "From X to Y": "From bustling cities to serene landscapes," "From beginners to experts"
• Main clause + comma + -ing phrase: "The system analyzes the data, revealing key insights"
• Rigid paragraph template: Topic sentence + supporting evidence + summary statement, uniformly applied
• Challenges formula: "Despite its [positive thing], [subject] faces challenges..."

Weasel Words and Vague Attribution

AI attributes opinions to vague authorities: "Observers have cited," "Experts argue," "Industry reports suggest." Often only one source exists. Sometimes none. The Wikipedia guide calls this "weasel wording."

Knowledge-Cutoff Disclaimers

"While specific details about [X] are not extensively documented..." or "As of my last knowledge update..." These are the AI admitting it doesn't know something but filling space. When the missing info is about a person's private life, the disclaimer often claims they "maintain a low profile." Entirely speculative.

07 Technical and Markup Tells

Markdown in Non-Markdown Contexts

AI chatbots default to Markdown formatting. When told to generate for platforms that don't use Markdown, the result mixes both markup languages. Asterisks for bold instead of platform-native formatting. Hash symbols for headings. Fenced code blocks (```wikitext) are a very strong indicator.

ChatGPT-Specific Artifacts

• turn0search0: Placeholder reference markers from ChatGPT's web search
• contentReference[oaicite:0]{index=0}: Bug-produced reference markup
• oai_citation: Another reference rendering bug
• utm_source=chatgpt.com: UTM parameters added to cited URLs
• utm_source=copilot.com: Microsoft Copilot's equivalent
• referrer=grok.com: Grok's equivalent
• grok_card XML tags: Grok-specific citation formatting

Hallucinated Citations

AI generates references to non-existent articles with DOIs that lead to completely unrelated papers. Book citations lack page numbers. Even real books may be cited with pages that don't verify the text. Multiple broken external links in a new article are a strong indicator, especially if dead links aren't found in the Internet Archive.

Placeholder Text

Fill-in-the-blank templates that users forget to complete. Placeholder dates like "2025-XX-XX" in citation fields. Placeholder URLs like "INSERT_SOURCE_URL_30."

08 Behavioral and Contextual Tells

Sudden Style Shifts

A sudden change in writing quality, like unexpectedly flawless grammar compared to previous work. A mismatch between user location and English variety (an Indian user writing about an Indian university in American English) since many models default to American English.

Verbose Edit Summaries

AI-generated edit summaries are unusually long and formal: "I revised the content to provide a neutral and informative description... The tone was adjusted to be more encyclopedic and less promotional."

Hallucinated Specificity

Oddly precise data delivered with false confidence: "the 2023 Pew survey that found 73.4% of Gen Z prefer analog watches." A real writer citing a real number would link it. A fake number gets a decimal point and no source trail.

Collaborative Communication Leaked into Content

Text meant as chatbot correspondence pasted into published content: "Would you like me to..." or "Here's a template for your wiki user page" or "If you plan to add this information... ensure that the content is presented in a neutral tone."

The Consistency Paradox

Every paragraph equally polished. Every transition equally smooth. No peaks and valleys. No sections where the writer got excited and overran, no sections where they were clearly tired. AI doesn't have bad days.

09 What's NOT a Reliable Indicator

False accusations of AI use drive away contributors and foster suspicion. These indicators are ineffective and produce false positives:

• Perfect grammar: Many human writers are skilled or come from professional backgrounds.
• "Bland" or "robotic" prose: LLM output actually skews positive and verbose, not robotic.
• "Fancy" or "academic" vocabulary: AI favors specific words, but not all formal prose is AI.
• Letter-like formatting: People wrote formal letters for centuries before LLMs.
• Transition words (in isolation): Only specific transitions are overused by AI. Many style guides accept them.
• Unsourced content: Most unsourced content predates LLMs. Modern AI also includes citations (they're just often wrong).
• Mixing casual and formal registers: Indicates code-switching, youth, neurodivergence, or multiple contributors.

10 Real-World Incidents (2025–2026)

The NYT Modern Love Controversy

November 2025. Kate Gilgan's essay "I Was Deemed Unfit to Be a Mother" was flagged by readers. Becky Tuch of Lit Mag News: "I don't want to falsely accuse writers of AI-use. But this reads EXACTLY like AI slop." The essay included constructions like "Not hate. Not anger. Just the flat finality of a heart too tired to keep trying." Gilgan admitted using ChatGPT, Claude, and Gemini for "inspiration and guidance and correction," insisting on "collaborative editor" rather than "content generator."

The NYT Book Review Plagiarism

Early 2026. Freelance writer Alex Preston's review of "Watching Over Her" bore similarities to a Guardian review. He admitted using an AI tool and failed to catch sections pulled from the Guardian. The NYT called it "a serious violation" of standards and cut ties.

The Ars Technica Fabricated Quotes

A senior reporter was fired after including AI-fabricated quotes. He claimed the chatbot summarized his notes and hallucinated quotes slipped through.

The Pangram Labs Study

Detection software found opinion sections of the NYT and Wall Street Journal were six times more likely to contain AI-generated prose than news articles. Freelance contributors and op-ed writers face less editorial oversight.

The Romance Novel Boom

February 2026 NYT article ("The New Fabio Is Claude") covered romance authors using Claude at industrial scale. One author created 21 pen names. AI-forward publisher Future Fiction declared its mission to "pioneer the use of AI in every aspect of the writing and publishing process."

11 Model-Specific Patterns

Each model has a distinct fingerprint, termed an "aidiolect" by researchers.

• ChatGPT (GPT-4): Heavy em dashes, "delve," "intricate," "tapestry," curly quotes, sycophantic openings, turn0search artifacts, utm_source=chatgpt.com
• ChatGPT (GPT-4o): Reduced "delve," more "enhance," "fostering," "showcasing"
• ChatGPT (GPT-5): Suppressed em dashes, notability-inflation vocabulary, "emphasizing," "enhance"
• Claude: Avoids curly quotes, longer sentences, less em dash reliance, different hedging patterns
• Gemini: Different vocabulary preferences, occasionally uses profanity, fewer ChatGPT-specific artifacts
• DeepSeek: Uses curly quotes like ChatGPT
• Grok: grok_card XML tags, referrer=grok.com on URLs

12 Quick Reference: Words to Watch

Sources

1. Wikipedia:Signs of AI writing (WP:AISIGNS), ~15,000 words, last edited April 9, 2026
2. Sam Kriss, "Why Does A.I. Write Like ... That?" — NYT Magazine, December 3, 2025
3. Thomas Cox, "How to spot when writing is AI: 6 elements of a robot's style" — Hunting the Muse, December 30, 2025
4. Michael G Wagner, "The Ten Telltale Signs of AI-Generated Text" — The Augmented Educator (Substack), October 2, 2025
5. Sean Kernan, "13 Signs You Used ChatGPT To Write That" — Substack, April 21, 2025
6. Gayanthi Gunawardhana, "How to Spot AI-Generated Text: Telltale vs. Subtle Signs" — SSRN, August 24, 2025
7. LegitWrite 2025 Detection Framework — legitwrite.com
8. EyeSift, "How to Tell if Something Was Written by AI: 7 Signs (2026)" — eyesift.com, March 2026
9. SAGE Perspectives, "AI detection for peer reviewers" — June 11, 2025
10. East Central College Faculty Resource — Updated February 17, 2025
11. Florida Realtors, "Spotting the Signs of AI Writing" — October 10, 2025
12. Wandering Educators, "Top Signs Your Writing Was Generated by AI" — November 21, 2025
13. John Lande, "What the NYT Gets Right (and Wrong) About AI Writing" — Indisputably, December 3, 2025
14. Futurism coverage of NYT incidents and Pangram Labs study — March–April 2026
15. Helsinki study on student essays (April 2025) — arxiv.org
16. Kobak et al., "Delving into LLM-assisted writing" — Science Advances, July 2025
17. Reinhart et al., "Do LLMs write like humans?" — PNAS, February 2025
18. Juzek & Ward, "Why Does ChatGPT 'Delve' So Much?" — ACL 2025 Findings
19. Russell, Karpinska & Iyyer, "People who frequently use ChatGPT..." — ACL 2025