People sometimes simplify the differences between Waymo autonomous driving and Tesla FSD as Lidar-based solution vs. a vision-based solution, especially as Tesla has been saying it doesn’t use any lidar.
But there are several other important distinctions.
For example, the production & scale is different. Tesla owns the mfg and has been selling cars to consumers, a lot of cars. Meanwhile, Waymo deploys the solution on other carmakers’ cars. As Waymo’s fleet is much smaller than Tesla’s annual delivery, the cost structure can be very different. Tesla can enjoy better economy of scale vs. Waymo, even if the hardware is the same.
Secondly, the responsibility is different, which is a key difference and debating point for robotaxi going forward. Tesla is reluctant to take on responsibility for its FSD solutions as the cars are sold, but Waymo owns the car and operates the ride-hailing service. Waymo takes the responsibility if there is accident due to the autonomous software. On a separate note. if you think about Uber, that’s actually is a very good business model. It can gain from the value creation of autonomous driving potentially, but because it’s just a platform, it’s just matching cars and passengers and take a cut from fares, so it doesn’t need to take responsibility for a autonomous driving software failure. Uber is not a provider of the traveling service but is just providing the matching services.
There is a third element, which I am not hundred percent sure about. It is said that Waymo relies on hard-coded rules and local data vs. Tesla currently more like a blackbox. So the programmers write specific instructions to tell Waymo cars what would do in different (extreme) scenarios. Instead, in Tesla’s current version (end-to-end), it is using a lot of data the train the AI mode without human specifying what to do in each case. Tesla is only feeding data to the AI, and let AI learn from human drivers. This is also why some argue that Waymo is much harder to scale.
However, I do think Waymo is underestimated –
1/ it can actually take a similar approach as Tesla as well, because Waymo also has a lot of data – maybe not as much as Tesla, but Waymo for example has a lot of data in San Francisco. Waymo probably already has the best driver (or at least very good) for San Francisco. If you have the “best driver” in San Francisco and all the related data, you can probably train an AI model with what you have – Waymo can train an AI driver w/o specific rules, but based on data from its current fleet in San Francisco, which is running w/ handwritten rules by human programmers. And from that you are also replicating what Tesla was doing – basically this AI driver for San Francisco is just a learning the best practices of how to drive in San Francisco from the existing Waymo cars. I don’t think Tesla has significant data advantage if we are just talking about San Francisco.
2/ and here comes another bold assumption, which is that if you are the best driver (AI version, not specific-rules based version) in San Francisco (plus Phoenix), you are probably not a bad driver in other cities. Of course you don’t know about the specifics about other cities and other countries, so you are not as good in NYC as you are in SF, but you also won’t be as bad as people expected. And over time maybe in just a few weeks this “San Francisco guy” can do a decent job in New York City as well. If that’s true, and that’s probably a big if, then Waymo’s solution can actually be more scalable than people would have expected.
What I’m trying to say is that, at current stage, I don’t think Tesla’ choice technology has already won this autonomous driving competition with huge data advantages. The jury is still out.