Fair if you don't want to watch the entire video. The VLA stuff is early in the video but I can summarise it for you (if you trust me to do so): basically none of the current deep learning based approaches to autonomy generalise. Not RL, not transformers, not anything else. They all tend to work fine in environments and tasks within their training set but outside of it, pffft, performance evaporates. That's my summary.
It's really not about 99,9% correct. Rather that's the error you can expect when you deploy such systems to the real world. You can start building an intuition about this if you consider the combinatorial space that these systems must search to find the right action in a given situation. Most of those systems are trained on images i.e. sets of pixels (some also have more sensors like lidar which just blows up the combinatorics even more). Specifically what these systems learn is a function mapping a set of pixels to actuator commands, essentially mapping input images to actions. For an RBG image of 128 x 128, there's (256^3)^(127^2) = 16,777,216 ^ 16,384 = 6.468074e+118369 unique combinations of pixels. Each of those has to be mapped to one of k actuator commands where k is sometimes an integer, sometimes a real. As you can see, that's an insanely high number and there is no way to make a dent in it even with millions of examples of images-to-actions.
Now of course deep learning approaches have shown remarkable, stunning ability in powering through gigantic combinatorial spaces - but all those successful applications are in domains where it is not necessary to map the entire space of inputs to outputs, not even a big chunk of it. Consider the success in board games like chess and Go both of which have huge search spaces, as often pointed out with cardinalities larger than the number of atoms in the universe etc. Yes, but an automated system doesn't have to search that entire space to beat a human: like the joke with the two guys trying to outrun a bear, all the deep learning system need do is search further than a human can search. Or, consider image classification or language generation: even 20% error (which is what you get in real world situations, as opposed to "in the lab" testing on specific datasets like ImageNet) is OK a lot of the time. So what if your image classifier thinks a cat is an impala? Nobody dies. Mostly.
That's not the case in the real world where a system can be expected to continuously find itself in situations that are either subtly or wildly different than the ones in its training set and the stakes are very high. 20% error in that setting doesn't work. If the robot can't deal with a previously unseen situation it can very well destroy itself, and cause an unknown amount of damage besides. The real world is as unforgiving as it is unpredictable; and non-ergodic (i.e. trying stuff at random until you find the thing that works, doesn't work).
Self-driving cars are ultimately made to work with very careful, good, old-fashioned manual engineering, including detailed mapping of an area where the system is restricted etc. That is why e.g. you see Waymo rolling out its cars slowly from one city to the next: if their systems were really autonomous they could just deploy them anywhere a human driver can drive. From New York to Mumbai and from Athens to London. The fact they can't is the strongest evidence that whatever proprietary secret sauce moat they have... doesn't really work.
The "really" I keep dropping in there all the time is suspicious, I know, but that's not entirely my fault. Waymo and all the others claim that their systems are "autonomous". I have to bat that back by saying, no, they're not autonomous. Then there's going to be a lengthy, pedantic discussion about what is "autonomous". So I try to avoid that, in my opinion unproductive, exchange with the "really" hedge. Maybe not such a good idea. I'm open to suggestions to improve the clarity of my language.