AI detectors feel like magic — paste text, get a verdict. Underneath, they're statistics engines measuring how predictable your writing is. Once you understand the three measurements, both the false positives and the evasions make sense.
Perplexity: the predictability score
A language model can calculate how "surprised" it is by each next word. "The cat sat on the ___" → "mat" is unsurprising (low perplexity). AI text is generated by picking probable words, so it scores consistently low perplexity. Human writing is messier — odd word choices, idioms, small tangents — and that unpredictability reads as human.
Burstiness: the rhythm test
Humans vary wildly: a 40-word sentence, then a 5-word one. AI settles into a monotone of 15–25 word sentences, every paragraph the same shape. Detectors compute the variance of sentence lengths — low variance is the single most reliable AI tell.
Vocabulary fingerprints
Models overuse a known word set — "delve", "tapestry", "furthermore", "seamlessly", "in today's fast-paced world". Detectors keep lists. Three "moreover"s on one page is practically a confession.
Why detectors get it wrong
Non-native speakers and technical writers naturally produce low-perplexity, low-burstiness text — which is why false accusations happen constantly. Detector verdicts are probabilities, not proof.
What this means for your content
To pass, text needs the statistical texture of human writing: varied sentence lengths, diverse vocabulary, zero AI-tell phrases. You can edit that in by hand — or use the Humanizer inside Automizer (under its Writing tools), which sits next to an AI Detector so you can verify the result. For the practical workflow, see our step-by-step humanizing guide.
