Dmitri Dolgov: evaluation

Transcript context

…Was it the case… My impression, not knowing anything is that in the early days, there was maybe a lot of San Francisco-specific work or Phoenix-specific work in the early markets, whether it be mapping or something else and that you guys seem to either have solved that in generalizing it or just scaled up your ability to do the city-specific work. What enabled the rapid-city expansion? We usually think about it, the capability of the Waymo Driver as well as deployment, not primarily and directly in that space of cities or zip codes. I think about the operating domain. Then the freeways, cold weather, snow, rain, fog, density, et cetera. That's what we are building. That's where we're evaluating, and then that maps to a particular city, be it within the operating domain or outside of it. If we rewind history a little bit, our initial deployment in where we started offering a fully autonomous commercial service for the first time was in 2020 in Chandler, Arizona. That was on what we called the fourth generation of the Waymo Driver. This was, if you remember, the Pacifica minivans with different hardware, different software. There, we were super focused on doing the whole thing end-to-end: learn how to build the driver, evaluate it, deploy regularly, operate it end-to-end 24/7 with customers, learn from the customers. Then we were very focused on that operating domain of mostly Chandler, which is a medium, low-complexity one. Then, when we made the jump to the fifth generation of our system, this is what's on the highways today, we really wanted to take a huge bite out of that operating domain. We collected data all over the United States, all different states, different cities. When we chose to deploy in the hardest parts of San Francisco, hardest parts of Phoenix, we made a big jump on the hardware side, and most importantly, on the software, the AI side. I would say that was the big discontinuous jump. That's what you're seeing now after we've scaled up and iterated all of the aspects of building and deploying the driver. This is now why you're seeing us go in parallel and scaling in the US and globally. So driver v5 was just a much more generalizable stack than v4? What was it about it? Was it just that it had been trained on a much wider data set?…

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