00:39:46 · Aditya Agarwal, Dmitri Dolgov
[00:39:46] S2: Those are both really good, especially tying that back to what you said in the beginning, which was when you started at Waymo, you weren't trying to build a product. You were just like, we were trying to figure out feasibility and we set out some very concrete milestones and goals. And I'm assuming you must have seen that you were able to make progress without it kind of shifting from underneath you to kind of give you confidence to keep on laddering up from there. [00:40:12] S0: We were not in that stage. We weren't in end to end. As I said, this was about, these goals that we picked, this was not in service of building a product. This was in service of us learning. [00:40:25] S2: This learning. [00:40:25] S0: Okay. And I guess this is where I would make a clear distinction. Right? In some ways, if you take my own advice, you're working a hard problem, you start for the fundamentals before you start specializing and thinking about the product or an application. That was not the very first thing we did. But that thing was not in service of... That integration of all the different components at the very, very early stage was not because we thought we're gonna be that close and we're gonna tweak it and have a product in six months. It was because we needed to bring everything together to really deeply understand the complexity of the task ahead of us. Then once we achieved that, we kind of went back to the lab and started looking at the fundamentals. Got it.
00:41:05 · Aditya Agarwal
[00:41:05] S2: One of the cool things that happens in Silicon Valley is that as a new architecture, as a new almost modality of company comes about, that informationknowledge tends to diffuse out into a bunch of different organizations, like whether it's Google Brain or OpenAI that figures out how to build transformers, now we have 10 companies that know how to kind of do that if not more. And so do you think it's a reasonable bet that a few years from now a lot of what the shape and structure of how to create a company that has like a rigorous infrastructure for evals, for testing, for rolling out kind of things that operate in the real world, do you think we will know how to do this as an industry, like call it five years from now, in a way that perhaps entrepreneurs here can maybe take that bet that that is much more, that will become a thing that we know how to do as an industry? Because I would argue that there are not that many. I mean, there are some companies doing robots kind of in the real world, but nowhere close to the scale of like Waymo today. And so is that a reasonable hypothesisbet in the coming kind of, call it, three to five years?
00:42:15 · Dmitri Dolgov
[00:42:15] S0: Look, the way things are going, the AI nowadays, I want to be careful. Not to predict what's gonna happen in five years. As if you asked me this question five years ago, I think I would not have gotten it right. Fair. But I think you're absolutely right that the tools of today and the rate at which they're proving are so powerful that we will be able to share some of those fundamentals. Right? And what you can do with AI agents nowadays and how quickly you can, yeah, kind of give you that foundation is absolutely amazing and mind blowing. So I think it will but my intuition is that it will just kind of how we spoke about the complexity curve as a function of effort in our domain, I think you will see that some of the easier applications and, you know, some of the easier products to deploy, there's gonna be an amazing proliferation of those. I think you will see magic. I think you will see, you know, like, there's, you know, things that, you know, in the past would take, you know, a large company a year. Now you'd be able to do with a team of five people in a month. Right? So something like that. There's some things that I would not expect to fully commoditize, and I think it's, you know, the things where the bar is just excruciatingly high, right, when we're talking about, you know, something like an R domain where the cost, you know, it's multiple components that have to come together. Right? It's the reliability of the vehicle platform. It's the reliability and the performance of all the different components. Right? You have to think about, you know, your power domain, about redundancy and sensing, like everything. The performance of the software in that extremely high number of nines, that, you know, there's a lot of people and a lot of great companies working on this problem. So I'm excited about the progress we're gonna make there. But I think there's naturally is gonna be some applications and some domains that emerge where it's a combination of very multiple multidisciplinary things that have to come together, where the cost, like, the the margin for error is so extremely slim that I don't think that will be fully commoditized.
00:44:30 · Ruchi Sanghvi, Dmitri Dolgov, Aditya Agarwal
[00:44:30] S1: I think you've already answered this question, but it's crushing my dreams a little bit. Does the Waymo Driver ever generalize? Like, is there some sort of general physical agent hiding in your foundation model that can have different embodiments, not in the form of a car? [00:44:47] S0: Yeah. Well, there's... Does the Waymo Driver generalizes when we talk about generalizability... [00:44:53] S2: How to build robots? [00:44:55] S1: Or, like, delivery bots? Or, like, know... [00:44:57] S0: Are we still on record? [00:44:58] S2: Not on this.
00:45:01 · Dmitri Dolgov
[00:45:01] S0: Let me answer it maybe at different levels. There is the most specific version of generalizability, where we think about the Waymo Driver generalizing to different cities, different countries, different vehicles, different sensor configuration. That is done. It generalizes extremely well. We're in 15 cities across The US right now. We're operating the fleet on the fifth generation and the sixth generation of the driver. It has different sensors, different vehicles, the JLR base and the Ojai, and it's the same driver. That will continue. We are focusing on ride hailing today. There's you know, north of 13,000,000,000,000 miles being driven worldwide, predicted to be about 20 by the end of the decade, so why wouldn't you want to have the benefits of autonomous vehicles in all of the driving modalities, whether it's ride hailing, whether it's personally owned vehicles, whether it's trucking and deliveries. So that will, you know, same product, slight modifications, will generalize extremely well, something that, you know, we're working towards. Then there's the question of, you know, will it generalize to other versions of physical AI agents? And there I think my answer is a little bit more nuanced. The perceptual parts I would expect to generalize really well. Sensing and understanding the environment. I think the fundamentals that we talked about, like training recipes, like how do you properly do, you know, close loop eval and RL in those domains, the fundamental infrastructure that supports all of that, that will generalize pretty well. The specifics of, you know, how do you build a simulator for a humanoid robot versus how you build it for a car, there's gonna be a lot of work there. And the actual generative part, if your, you know, physical kind of embodiment of your agent is very different, that planning generative part, that will require some adaptation as well.
00:47:16 · Ruchi Sanghvi, Dmitri Dolgov
[00:47:16] S1: I have one on behalf of my son, who is here on the first row. He's nine years old. He's a huge fan of robotics and cars as well. And he wanted to know whether he would still need to learn to drive by the time he's 16 or whether he'll be able to afford an autonomous vehicle by the time he's 16. [00:47:36] S0: Would you like to drive? Yes. Yeah. So then you can. Right? I I think it's gonna be a [00:47:43] S1: little Maybe of questions for me. Can I buy him an autonomous vehicle by the time he's 16? [00:47:49] S0: I get the question from my wife quite a bit. Like, when can I autonomously send my kids in Waymo without needing to be in the car with them? So I think we're in 15 cities today. We're doing about half a million trips every week. Driving about 5,000,000 fully autonomous miles, and it's scaling exponentially and very fast. So last year we were operating fully autonomously in five cities. Right now we're in 15 in The US, expanding internationally. The driver is generalizing pretty well. So I think it's not that far where you'll be in a place where you, you know, any major city across the globe, you get in there and you take a Waymo and it takes you anywhere you want to go. So I have three small kids. We are definitely kind of a Waymo primary family. Which is good. Okay. [00:48:46] S1: But that's not the same as owning one. When can I buy one?