I noticed that trend in comments, yes. Unfortunatly, the real issue with what Uber or Waymo (or anyone else) are doing it's with the limitations inherent in the technology itself, specifically, machine learning for object recognition and identification and for the learning of complex beheaviours.
The limitation is -it's a bit technical, but basically, in principle, machine learning is possible under certain assumptions, as laid out by Valiant in his PAC-learning paper (A Theory of the Learnable), especially the assumption that a training sample will have the same distribution as unseen data. Under this condition, machine learning can be said to work and we can look at performance metrics and be happy they look good.
Well, except that the real world has no obligation to operate under our experimental assumptions, so once you deploy machine learning systems in the real world, their performance goes down, because you haven't seen nearly enough of the data you really need to see, in the lab.
And, if you attach such assumptions to safety-critical systems, then you're taking an unknown and unquantifiable risk. Or in other words, you're putting peoples' lives in danger.
And that's everyone who uses machine learning to train cars to drive in real-world conditions. Not just Uber.