The Exchanges

Q for example, um, I think crews ran into some issues in San Francisco where there was activists like putting cones on the cars and trying to stop them and doing other things. And so it seems smart to, to start in Arizona. I was just sort of curious, what are the criteria that led you to, to start that as a sort of a test bed or a place to?

A So I guess, you know, it depends on the different time horizon. So, you know, in the fourth, on the fourth generation, we picked, uh, a deployment area that was kind of medium complexity, uh, and the goal there was, I mentioned, is to kind of go end to end. So we picked an environment where we thought it was, you know, we check enough of the boxes to, you know, help us learn the most important things that we wanted to learn and de-risk, right? While, and that was the deployment. Uh, and then there's the development of the system. So for the development of the system, you want to go after the hardest problems possible, right? You want to go after the densest environments. You want to go after the harshest weather. So we've kind of in parallel been doing that. So we've made a decision to deploy, you know, in Chandler in, you know, to learn from the end to end system while, you know, pushing on developing the system. Then when we, we've, uh, you know, learned enough and we made that discontinuous jump to the fifth generation of our driver. And then we said, okay, like that's the platform we believe that we want to take to scale.

AI assessment note: “we picked a deployment area that was kind of medium complexity”

Q forward a year or two, like now, you know, Crack the nut. Waymo is looking at scale and, um, probably more about the business. Like, do you think of robo taxis at scale as the, the near term business plan? Are there other modalities or like deployment? Like, if I think about this as just like a CapEx problem, like other avenues that are important for you guys to explore.

A That's the main one. Right hailing is the main one that we're focusing on. So we're, you know, focused on technology. We're focused on the product. We are learning from our users. We're every day, we're earning trust. Uh, and we're setting up the ecosystem of, you know, partnerships in that space. So that's our primary focus, right? We are very excited about, uh, the commercial opportunity there. Uh, it's not the only one. I always, you know, think of Waymo as, you know, a technology company. We're building a generalizable Waymo driver, right? With the mission to build the world's most trusted driver. And we want to deploy that driver, not just in ride hailing, right? There's, as I said, there's more than three trillion miles in the US. There's more than 10, you know, trillion miles driven, you know, worldwide. Uh, so the vision and the mission is to deploy the Waymo driver in, you know, different commercial products and different applications and maybe different, you know, modalities across all of that spectrum. So that includes, uh, you know, things like, uh, deliveries, things like, you know, long haul trucking. It includes, uh, you know, things like personally owned vehicles. Uh, but right now we're very focused on retailing.

AI assessment note: “That's the main one. Right hailing is the main one that we're focusing on.”

Q that there was companies like Zoox that Amazon bought, where They kind of hollowed out the inside of the car because you no longer needed the steering column and everything else, and they put seats facing each other, almost like a London cab. Uh, do you have any thoughts on what that experience will look like in the future as more and more things move to autonomous self-driving ride-hailing systems?

A Yeah, so designing a car, uh, around the passengers makes total sense to me. Like we, you know, in the past, we've designed cars around, you know, primarily the driver. Right. Uh, if, you know, it's the Waymo driver, it's all about the, the rider experience. Right. So we have, uh, done, you know, quite a bit of work on the sixth generation of the, you know, the Waymo driver and the car, and the car is designed, uh, around with the passenger in mind. Right. So it is more spacious. Uh, it is all about the user experience. You know, you have, you know, flat floors, you have, you know, lower, uh, floor for entry. You have doors that slide to the side. So it's all about, you know, getting in. So absolutely, you know, there's different aspects of it. Like, you know, we don't have cars facing each other. I think there's, you know, it's an open question. Like some people get, uh, you know, nauseous when you do that. Like you kind of want to, you know, there's benefits on facing forward. But, you know, all of that, I think will be for us as an industry to figure out as soon as we move forward. But I think the, the key, the key point, it becomes, you know, much more like the design is around the rider, not around the driver.

AI assessment note: “we don't have cars facing each other. I think there's, you know, it's an open question.”

Q How long did you think it was going to take? Like at the time that you started doing that?

A Well, I don't know if we had any specific data, but I think that was actually the first question that we posed. We said, you know, let's not build a product, right? In the first, you know, couple of years or so, we didn't have like a product in mind or a target data in mind. The first order of business was to explore the space. Right. So we, you know, towards that end, we created some milestones for ourselves, uh, with the goal of prototyping and learning and just understanding. So after those two years, we said, ah, okay, you know, there's something there. Let's start talking about, you know, what the product could be. And actually our first product that we thought was going to be viable, uh, was, you know, what nowadays you would call kind of an advanced driver system, right? And we had some expectations of, you know, a small number of years that it would take for us to get there. Uh, when, you know, we, after working on it for a while and making more progress on kind of the core of the technology, We decided that was not the, you know, right path for us, that we want to go after full autonomy. That was, you know, that made that pivot around 2013.

AI assessment note: “we didn't have like a product in mind or a target data in mind”

Q think of the, um, the iteration cycle for Waymo now still like many other AI companies where in eval some, some set of cases comes up that you don't handle as well as you want, and then you collect more data and you put it into the pipeline, you retrain and you deploy, or are there still architectural changes that are happening even past this point of cracking the nut?

A Both. Uh, so the first thing you mentioned where it's, you know, the, the data collection and, uh, you know, understanding where it Performance is not good enough kind of building the whole, you know, data flywheel and evaluation flywheel. That's at the heart of it, right? But, uh, I think there's this, and this is where it gets, you know, a bit nuanced, you know, what do you do? What is the architecture and what is the training methodology, right? Uh, in particular, uh, kind of the simplest thing you can do is, you know, an end-to-end model that is trained just on imitative, you know, kind of imitating human drivers. So, you know, very easy sensors, you know, pixels go in, Driving, you know, behavior that you have examples of, and you just train it to imitate human drivers. And you can run this, you know, flywheel and kind of run the circle that you described while operating, you know, under, you know, that paradigm.

AI assessment note: “Both. Uh, so the first thing you mentioned... That's at the heart of it”

Q It's amazing. Um, given that, what do you think the regulatory stance should be?

A I think we want to make sure that, you know, we enable, Uh, this, uh, technology and service of the mission of making roads safer. Uh, and, you know, we've been engaged with regulators, you know, for many years and have that dialogue. And, you know, so far we've, uh, had good success, uh, getting all the necessary, you know, permits and all of, uh, to a lot of scale. The way we think about it, uh, internally, and that's how kind of we have the dialogues with the regulators, with communities, with writers, it needs to be based on transparency and it needs to be in responsible, iterative Gradual process, right? Because this thing is very new. The technology is very new. The product system are very different.

AI assessment note: “it needs to be based on transparency and it needs to be in responsible, iterative Gradual process”