Earlier quoted context omitted.
"Fundamental architectural failure" is a very bold claim to make given that absolutely no other approaches have worked as well as Google's for urban driving.
There's a serious problem at the heart of "throw a bunch of data at it" strategy - driving has an extremely long tail that's relatively fat. You can hit the "average case" relatively quickly (you can assume that Google's done that for the SoCal environment), but being able to extend that dataset to other, less likely scenarios, like "heavy downpour in SF" or "freak snowstorm" or "sudden construction due to water main…
You're making a point about end-to-end trained ML systems; if anything, that's an indictment of Tesla's approach, not Google's more traditional sensor-based-approach.