Because of "resume driven engineering". Even simple problems that you can solve with PID controllers or Kalman Filters but everyone wants to throw ML at it instead, so they can put "ML experience" on their LinkedIn because that's what's hot right now as recruiters probably never heard of Kalman filters or PID controllers.
A lot of technical decisions aren't based on "what's the quickest, cheapest and easiest solution to the problem?" but "what solution is most likely to get me hired at a pay bump when I jump ship?"
The thing is, to train a NN to estimate your output from your input, you need input-output pairs. KFs are a way of measuring that output in the first place. So they are not even the same class of solutions.
