I agree with you: data is not so much of a moat as a fixed expense for anyone wanting to join in. And for the big automakers or Uber, it is not such a big cost

I agree with you: data is not so much of a moat as a fixed expense for anyone wanting to join in. And for the big automakers or Uber, it is not such a big cost to pay. You did miss in your cost estimate scaling the compute costs of ML training to ingest that data. Also test courses and simulation to amplify and elaborate on tricky cases found in the data. Adding those things in adds 10's of millions to the estimate but doesn't change the fundamental analysis. I think the real moat is in fleet networked data. If you have a lot of cars on the road and they are sharing info about strange situations to expect, it could be a big advantage for how "smart" the driving seems. I am thinking things like broken stoplights or badly placed traffic cones. A networked solution can alert other cars of the puzzling condition and the interpretation. Then later cars passing that location can proceed more confidently than if each vehicle has to work out an interpretation itself.