How AI Text Detection Works Under the Hood
Detectors do not read for meaning. They measure statistical fingerprints of machine writing. Knowing the method makes you a better user. AI text detection sounds like magic until you look at how it actually works. The tools do not understand meaning the way a human reader does. They measure statistical properties of the text and compare those properties against patterns typical of machine-generated writing. Once you understand the method, the strengths and the limitations both make sense. The Two Signals Detectors Rely On Most detectors lean on two signals. The first is perplexity, which estimates how predictable each word is given the words before it. Language models generate text by picking high-probability words, so their output tends to be low perplexity. Human writing mixes predictable and surprising choices, so it tends to be higher perplexity. The second signal is burstiness, which measures variation in sentence length and structure. Human writing is bursty, with short sentences next to long ones. AI output is usually more uniform. A detector combines these signals into a score. Low perplexity plus low burstiness pushes the score toward AI-generated. Higher values push it toward human. The score is a statistical guess, not a fingerprint. Two different authors writing in the same careful, regular style can produce text that looks statistically similar to machine output. How Perplexity Is Calculated Perplexity comes from the language model itself.