The “fleet learning” pipeline Elon described at Autonomy Day doesn’t actually exist in practice. They have some hardcoded heuristic triggers that capture snippets of data to be annotated by human labelers to train perception models off of. The bottleneck is labeling/engineering.

Autonomous vehicles by and large aren’t a data problem anyways - it’s a robotics problem conditioned on the output of ML models for perception and prediction. The majority of the work is just implementing all of the bizarre edge cases inside of the robotics stack.

The idea of tackling this with an e2e ML model is a pipe dream touted by people that aren’t familiar with the space or trying to hype up their approach. Whenever this approach is attempted teams will very quickly realize how untenable it is and return to implementing individual robotics modules.

tl;dr there’s nothing special about Tesla’s approach to AVs or machine learning and you shouldn’t expect them to leapfrog the competition because of some nebulous ML-based advantage.

Coming from someone who has seen the interiors of both the FSD beta and the stack at an actual AV company.