Key Takeaways
- AI text tends toward smooth, generic, evenly-structured prose.
- Detector tools are unreliable and produce false accusations.
- Context and verification beat any single “AI or not” signal.
- What matters is accuracy and value, not who typed it.
The common tells
AI-generated writing has a texture. It is smooth, grammatically clean, and evenly paced — often too even. Watch for hedging phrases, tidy three-part lists everywhere, a lack of specific detail or lived experience, and confident claims with no sources. Individually these mean little; together they form a recognisable pattern.
Why detector tools fail
AI-detection tools promise a percentage and deliver false confidence. They regularly flag human writing as AI (especially from non-native speakers) and clear obviously-AI text. Because they cannot reliably tell the difference, using them to accuse someone — a student, a writer, an applicant — is genuinely harmful. Do not treat a detector score as evidence.
What to do instead
Shift the question from “was this AI?” to “is this accurate and useful?” Check the specifics: are the facts right, the sources real, the examples concrete? AI-generated errors — invented citations, plausible-sounding but wrong details — are far easier to catch than the writing style, and they are what actually matter.
Why it matters at all
Origin matters in a few real contexts: academic honesty, disclosure in journalism, and trust in reviews. In most others, it does not. A useful, correct, well-made piece is valuable regardless of how it was drafted.
The bottom line
You can often sense AI writing by its smooth, generic texture, but you cannot prove it, and detector tools should not be trusted to. Judge content by whether it is true and useful, not by guessing who or what produced it.