Training ML models requires LABELED data examples. Nobody has ever trained a ML model just showing it the world and having it figure everything out. Show a 6 year old a 10 minute video of a basketball game with no other information and they will tell you that it's a game and the primary goal is to put the ball in a basket, and there are two teams. Show a ML model video from every single basketball game ever played but with no labels or rules, and it will return nothing. However, tell a ML to learn to play pong, and tell it that making the score go up is "good" and here is the one control it has, and in 10 minutes it will be the best pong player ever.
This is why Tesla can only learn from FSD being active, because it's the only time there is a "score". They can't afford to have humans review arbitrary videos, so all they learn from is negative reinforcement when a driver overrides. FSD goes to do something, and the driver cancels. So now you know at least SOMETHING that happened was bad. You at least have some chance of learning from that. But you learn nothing from when humans just dive the car because you have no idea what the human wanted to do, so you have no way to judge your ML model's theoretical behavior against it.
And now you can see why this process will take forever to learn what a hand wave means. You need a car on FSD to be at that intersection, with multiple other cars, decide not to go, and have a driver override it. Then you need about 100M examples of this, since 99% of those will be for some other reason than a hand wave, and you need 1M of them to even start hinting to the model that the wave allows you to ignore the right of way rules that apply 99% of the time you are at a stop, given there is zero labeling of this data.
Assume this override on FSD happens once every 5 seconds in your fleet, 24/7. Will only take you 16 years to get 100M examples.
And yet we think a 1 month free trial makes a big difference in data collection.