There would be, it seems, two possibilities: 1 - Large scale fleet learning of the kind Tesla is doing is the only way to iron out the corner cases and build a robust self driving system. 2 - Large scale fleet learning is not necessary to build a robustly safe system. If #1 is the truth - then Tesla still has no competition and its lead is only growing. If #2 is the truth, then fleet learning's value is somewhat hyped up, and competing automakers can jump in the market over the next few years - going from zero autonomous cars to selling them off the shelf. If in fact #1 is the truth, as Musk claims, and the other automakers know it - then why in the world are we going into the third year of Tesla being the only automaker with a large scale fleet test/learning project of autonomous driving? The other makers could safely implement the exact same thing with zero liability risk if they just ran their fleets in a pure shadow mode - but they are not doing so. Why not? Instead we are seeing small scale deployments of test bed cars bristling with many more sensors than Tesla actually ships in the real world - what seem to amount to never-ending big dollar science projects. In terms of real world autonomous driving Tesla is competing with - itself. To play devil's advocate for a moment, let's remind ourselves that these other automakers also employ many phd artificial intelligence researchers. Let us presume these researchers are not stupid and that they are not living under a rock - and are thus aware of Tesla's fleet scale approach. Let's also assume these engineers could design and start shipping a fleet-wide learning system in actual vehicles which are for sale - today. So why aren't they doing it? Is it in fact, not necessary? If it isn't necessary then what, if anything, does that imply about Tesla's firm public claims that fleet learning is the only viable way to build out a robust neural network that is safe in the real world?