How to spot AI writing patterns: a practical signal guide

Elevated frequency of specific abstract or formal words that appear disproportionately in AI-generated text.

Repeating structural patterns in how sentences and paragraphs are built. Cadence signals persist even when vocabulary is varied.

Paragraph-level and document-level architectural patterns: how ideas are organized, opened, and closed.

Predictable first-line constructions that AI models default to when opening posts or paragraphs.

Filler connective phrases that organize text without carrying substantive content.

Signal avoidance is easy

Any system (or writer) aware of the signal list can avoid the most obvious vocabulary and cadence markers. Absence of signals does not indicate human writing.

Human writers produce AI-signal-dense writing

Trained professional writers in certain contexts — business communication, academic abstracts, strategic memos — produce elevated signal density. Context matters.

False-positive risk increases with specificity

Individual words (like "navigate") are high-false-positive signals. Multi-word phrases and compound patterns are more reliable. Single-word signals should not be used alone.

Signal lists become stale

AI model training evolves. Models trained with explicit avoidance of known signals will produce cleaner output. Signal lists require ongoing maintenance to remain calibrated.

How do you spot AI writing patterns?

Look for stacking signals across five dimensions: vocabulary (elevated frequency of words like "delve," "showcase," "underscore"), cadence (repeating structural progressions like rhetorical contrast or resolution closers), structural symmetry (balanced paragraphs, consistent clause lengths), hook patterns (predictable opening constructions like "Most people..." or "Here's the thing:"), and transitions (filler connectives that carry no information: "It's important to note," "Furthermore," "In today's landscape"). No single signal is conclusive. The density of co-occurring signals is what makes AI writing identifiable.

What are the most reliable signs of AI writing?

The most reliable signs appear when signals stack. Individually, words like "pivotal" or "leverage" are common human words. When they appear alongside smooth filler transitions, a resolution closer at the end of every paragraph, symmetrical sentence lengths, and a hook that begins with a generalization, the compound pattern becomes a meaningful signal. Signal stacking is more diagnostic than any individual word or phrase.

Can you tell if writing is AI just from vocabulary?

Not reliably. Vocabulary signals are useful starting indicators — Kobak et al. (2025) showed post-ChatGPT frequency increases for words like "delve" reaching 28× baseline in academic abstracts — but vocabulary alone produces false positives. Human writers use elevated vocabulary, especially in professional contexts. Vocabulary analysis is most useful when combined with cadence analysis, structural analysis, and hook pattern review.

What does it mean when AI writing signals co-occur?

Co-occurring AI writing signals suggest a compound pattern that is harder to explain by style preferences alone. A human writer might habitually use one AI-associated pattern. Finding four or five co-occurring patterns in a single piece — elevated vocabulary, smooth transitions, a Generic Opener hook, balanced paragraphs, and a Resolution Closer — moves the signal from coincidence to compound pattern. Bloomberry's AI Sentence DNA corpus was built around this stacking principle.

Is high signal density proof that writing was AI-generated?

No. Signal density cannot be used to make authorship determinations. High AI signal density indicates writing that shares structural and vocabulary characteristics with AI-generated content. It does not confirm who or what wrote the text. Human writers can produce high-signal-density writing, and AI systems can be configured to avoid these signals. The value of signal identification is in improving writing quality and voice clarity, not in making authorship claims.