"AI Answers Are Random, You Can't Measure Them" — A Polite, Data-Backed Rebuttal
The most frequent objection to AI visibility tracking is also the most defensible-sounding one: if a language model produces a different answer every time you ask, what exactly are you measuring? The objection is not wrong, it is incomplete — and the incompleteness is recoverable with standard sampling statistics. This post takes the strongest version of the argument seriously, then walks through the statistics that convert the apparent randomness into a stable signal. No hand-waving, no marketing-speak, just the arithmetic that explains why daily-sampled LLM measurement is roughly as reliable as Nielsen television measurement was in 1975.