With each of these breakthroughs, Musk presented a well-reasoned view that was highly contrarian against the authority consensus; and in each case, his view turned out to be not only correct, but so correct that it became the new authority consensus.
Maybe this time he is wrong and you are right. But if you are wondering why the market and the masses believe him, it's because he continues to provide reasonable explanations for what he's proposing, and he has a track record to back them up. If history predicts the future, then in a few years, we'll add "authorities said you needed LIDAR for self-driving" to the list above. This is already starting to play out in the SDC space, with Hotz of Comma.ai sharing Musk's views on vision, and Levandowski sharing his more generalized concern about LIDAR (the latter is more recent if you're not familiar):
> Levandowski contends that this “race” to deploy autonomous vehicles has yet to start in earnest largely due to shortcomings from “crutch technologies,” a descriptor he uses for hardware like LiDAR and HD maps. His position is that while these provide sensing and localization for the vehicle in the present moment, they have serious compromises and don’t produce the level of predictive ability required to commercially deploy autonomous vehicles.
https://techcrunch.com/2018/12/18/anthony-levandowski-pronto...
Karpathy was able to explain in very simple and understandable terms this week why Tesla's approach is advantageous. If the critics understand this domain so well, how come they can't provide a straightforward retort? If Feinman could do it with quantum physics, surely a machine learning expert can do it with neural networks. I would love to read/hear/watch such a retort if anybody is interested in presenting one.
Edit: fwiw I've actually taken a look at a number of waymos internal systems, and sat down with people who worked on autopilot.
Talk to anyone with research experience who are building world scale ML systems that are used by real people, and it's unambiguous that Elon Musk is a con man.
It comes down to small details, like the nature of the training data, the nature of the customizations, the nature of the business problems, the nature of the regulatory problems, the difference between highway driving and city driving. The enormous amount of special casing, the nature of the QA infrastructure, the capital on hand, the routing and mapping problems...
What Tesla has is a decent lane finding system. What Google has is an end to end system. It won't take them long to expand to all the major cities; solving the geofencing problem is easy, they already made street view.
And they were doing L4 successfully years ago. They're keeping drivers in the cars right now mostly because something going wrong is a PR risk, not because they need them. And L5 isn't a particularly important goal.
My guess is I'd have to write up 20 pages of detailed analysis at minimum to convince folks. In general if you don't have 5 to 10 years in depth experience in this space, you probably shouldn't consider qualified to have an opinion.