This is also why you saw a bunch of AV startups with < 50 employees that got big initial funding rounds but folded after a couple of years - it's pretty easy to grab a few dozen grad students, slap some LIDAR on a car, encode basic road rules, and get a demo working that's compelling to investors. Once you get past that initial pitch though, you get into the weeds of trying to progress without causing regressions elsewhere.
Also, what makes you think those expectations were grounded in linearity? Waymo went from an initial project at Google in early 2009 to driving 10 complex 100 mile routes without intervention in mid 2010 - on the surface, it would have seemed they were 95% of the way there. Improvement has been sublinear for a very long time and everyone in the AV industry is aware of it.
https://jperla.medium.com/tesla-saves-lives-waymo-gambles-pa...
Before you bring up Tesla's "Fleet Learning", it's not used at all outside of gathering perception training data for labelers. greentheonly got access to a dev build of the FSD beta - ML is just used to generate a representation of the current scene - all of the driving and planning logic is rules-based and hardcoded by engineers.