Well put thesis thank you. Do you believe Tesla is abandoning the multiple NNs they have used currently and instead going for one single NN for everything?
I think when Karpathy joined, they restarted the NN side of thing. The training data was likely still usable in some form.
My guess: the NN exists as one object. However, the internals of the NN (lane detection, sign detection, vehicle gap adjusting) could still be tested (and thus trained) individually. What the suspected SW 2.0 approach removes is having separate groups working on separate aspects with separate NNs and then trying to merge them at the end.
General can also mean that they are using some more typical, research paper supported, methods, but they do everything over the complete data set. Whereas some people may start at a neighborhood, then region, then city, then country, then another country, then worldwide with changes each time, Tesla could be going for all or nothing.
Analogy: training a robot to walk, progressive: flat, high mu ground, bumpy ground, hill, ruts, mountains, ice. General: all topology cases at once.
Similar to the Lidar debate: Lidar gets to a working system sooner, but Tesla is going for pure vision, which Lidar system will need also for completeness. So, in terms of apparent progress, Lidar system start out ahead, but then run into many of the same problems.
End results: the Tesla approach may look poor until it works, then it looks good (perhaps great).
Just my thoughts.