Calling it "AI" is marketing sugar. It is closer to an inference-only state machine where gradient descent did the wiring instead of an engineer, and the annoying part is that once the detector setup or noise profile moves, retraining and redeploy stop being normal ML chores and turn into hardware respins, validation, and a lot of waiting. That distinction stops sounding pedantic the first time a bug fix means touching silicon instead of pushing to a repo.