Most "AI detectors" are not magic classifiers that recognise a model's handwriting. Many of them reduce a document to a handful of statistics about how predictable and how uniform it is, then put a threshold on the result. That design has a well-documented failure mode: it penalises people who write in a second language.

This post walks through the main signals at a conceptual level, shows why simpler prose drifts toward the "AI" side of the threshold, and ends with what that means if you build or deploy these tools.

Signal 1: perplexity

Take a language model and feed it a text one token at a time. At each step the model assigns a probability to the token that actually comes next. Perplexity is, roughly, the exponentiated average negative log-probability across the sequence:

PPL(x) = exp( -(1/N) * Σ log p(x_i | x_<i) )

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Low perplexity means the model was rarely surprised by what came next.

Why does that correlate with machine text?…