No Priors Ep. 87 | With Co-CEO of Waymo Dmitri Dolgov

1. 0:00 – 0:15

   Introduction

   1. SGSarah Guo0:00

      (music plays) Hi, listeners. Welcome to No Priors. Today, we're hanging out with Dmitry Dolgov, co-CEO of Waymo. Waymo started as the Chauffeur Project within Google back in 2009, and eventually

2. 0:15 – 0:29

   History of Self-Driving at Google

   1. SGSarah Guo0:15

      spun off as its own company. Now it provides over 100,000 paid rides each week across San Francisco, LA, Austin, and Phoenix. I love taking Waymos, and I'm regularly campaigning for better South Bay coverage. We're excited to dig

3. 0:29 – 1:39

   DARPA Challenges and Early Involvement

   1. SGSarah Guo0:29

      into all things robo-taxis, self-driving, what it takes to deploy this technology on a mass scale, and what's next for Waymo.

   2. EGElad Gil0:36

      Hi, Dmitry. Thank you so much for joining us today.

   3. DDDmitri Dolgov0:37

      Thank you for having me.

   4. EGElad Gil0:38

      Yeah. Uh, maybe we can start off with just a little bit of a history of, um, self-driving at Google, how you got involved, and how things have evolved over time.

   5. DDDmitri Dolgov0:46

      I've been do- doing this for, you know, quite a few years. I think, uh, about 18 now. I got started in, uh, around 2006. Uh, this was the time of the DARPA Grand Challenges. Um, this was when, you know, DARPA organized, uh, a few competitions, uh, that they called the Grand Challenge in robotics, uh, uh, for the, uh, with the purpose of advancing research in autonomous vehicles, right? So the first competition they had was the first Grand Challenge. This was, uh, the challenge there was to create a car that could drive autonomously in a desert. I just completed this deck and why I'm gonna, you know, drive for about 100 miles. Uh, nobody succeeded, but, you know, there was a lot of great progress that was made, and then they repeated the challenge, and a few teams succeeded. So on the heels of that, they created another challenge th- called the DARPA Urban Challenge, uh, where the setup was, uh, kind of a mock,

4. 1:39 – 1:53

   Formation of Waymo

   1. DDDmitri Dolgov1:39

      uh, city that was supposed to imitate, you know, what driving on public roads is like. And that's the one, uh, that, uh, I was involved in. I was on Stanford's team. This was kind of my, you know, uh, moment where it clicked for me.

   2. EGElad Gil1:50

      Mm-hmm.

   3. DDDmitri Dolgov1:50

      I saw the, you know, the future and

5. 1:53 – 3:05

   Industry Lineage and Early Skepticism

   1. DDDmitri Dolgov1:53

      the benefits, and I've never looked back. That's what I've been doing ever since. Uh, and then, uh, we started this project at Google in 2009. Uh, it was just me and a small group of us, uh, and then that grew into what now is Waymo, uh, when we started the company, uh, in the very beginning of 2017.

   2. EGElad Gil2:09

      All right. Yeah, it seems like a lot of the, um, lineage or history of this field all traces back to a handful of labs. You know, it's like Sebastian Thrun's lab at Stanford and a few others, and it seems like the founders of a lot of the companies that ended up eventually existing in this ecosystem all came out of the same sort of cohort of people, which I always think is fascinating to think about in terms of lineages.

   3. DDDmitri Dolgov2:28

      Yeah, yeah. No, definitely a, definitely a small world. The CMU team and the Stanford team, there was, uh, a few, uh, people who came and started this project at Google-

   4. EGElad Gil2:35

      Mm-hmm.

   5. DDDmitri Dolgov2:35

      ... in, in 2009, uh, came from those teams.

   6. EGElad Gil2:37

      Mm-hmm. I think when you started working on this, this was considered a little bit of a crazy thing to do, right? It was early on. A lot of parts of the waves of deep learning hadn't really quite happened yet, um, in terms of o- applications across all sorts of areas, like AlexNet hadn't existed yet, like all these other sorts of things that-

   7. DDDmitri Dolgov2:53

      No, nothing existed, right? (laughs)

   8. EGElad Gil2:54

      Yeah, yeah, yeah.

   9. DDDmitri Dolgov2:54

      Uh, and you're, you're absolutely right. Uh, people, uh, you know, heard a lot about us being crazy and it's, you know, never going to work. In all honesty with ourselves-

   10. EGElad Gil3:04

       Mm-hmm.

   11. DDDmitri Dolgov3:04

       ... we're not exactly

6. 3:05 – 4:33

   Initial Goals and Milestones

   1. DDDmitri Dolgov3:05

      sure if, you know, we are just a little bit crazy or, you know, completely, yeah, insane, uh, when we're trying to go after this problem.

   2. SGSarah Guo3:11

      Did you treat it as an open-ended research project, or you treated it as like how you had an idea in your mind of, like, an end point date where this would be viable on public roads?

   3. DDDmitri Dolgov3:20

      More of the former, but it was not, it was not research, right? So no, you know, and then the kind of the university DARPA Challenge date, it was a research project. Uh, yeah. Uh, then when we started Google, it, you know, was u- under the belief that, you know, we can make it work. Uh, and if we can, then the impact, the positive impact of this technology on the world and the mission is, is worth it. Uh, but it was early days, so we actually, you know, uh, had very little data to, you know, go by in terms of, you know, uh, you know, thinking about how long it's gonna take and how hard the problem is going to be.

   4. EGElad Gil3:56

      How long did you think it was gonna take? Like, at the time that you started working on this?

   5. DDDmitri Dolgov3:58

      Uh ...

   6. SGSarah Guo3:59

      In a-

   7. DDDmitri Dolgov3:59

      Oh, I, I don't know if we had, you know, a specific date, but I think that was actually the first question that we posed.

   8. EGElad Gil4:03

      Mm-hmm.

   9. DDDmitri Dolgov4:03

      It's like, let's not build a product, right? In the first, you know, couple of years or so-

   10. EGElad Gil4:06

       Mm-hmm.

   11. DDDmitri Dolgov4:06

       ... we didn't have, like, a, you know, product in mind or, you know, uh, a target date 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 gonna be viable, uh, was, you know, what nowadays you would call kind of an advanced driver

7. 4:33 – 4:50

   Pivot to Full Autonomy

   1. DDDmitri Dolgov4:33

      assist system, right? And we had some expectations of, you know, a, 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.

   2. EGElad Gil4:49

      You're

8. 4:50 – 5:29

   Scaling and Deployment

   1. EGElad Gil4:50

      now doing something like 100,000 rides a week, so five million rides a year sort of annualized out, which is incredible. What was the inflection point, or what suddenly caused that sort of volume to happen or all these things to come together? 'Cause it feels like a reasonably recent phenomenon in some sense.

   2. DDDmitri Dolgov5:04

      Yeah, you're right. Uh, but th- those are discrete, you know, some discrete jumps. So yeah, kind of if, you know, we rewind the clock a little bit, I think in my mind there were a few kind of generational discontinuous steps-

   3. EGElad Gil5:13

      Mm-hmm.

   4. DDDmitri Dolgov5:14

      ... on kind of that progression from, you know, that point in 2013 when we said let's go for it to where we are today. Right, so exactly right, you know, 100,000 trips per week, more than a million miles per week and growing, you know, exponentially.

   5. EGElad Gil5:25

      Mm-hmm.

   6. DDDmitri Dolgov5:25

      So some interesting ones were, you know, in, uh, uh, 2015, that was kind of our zero to

9. 5:29 – 6:59

   Generational Breakthroughs

   1. DDDmitri Dolgov5:29

      one moment. This is when, you know, for the first time, we put a car on the road. Uh, there was that, you know, our, what we call the, you know, third generation, uh, of our system. This was the, uh, third generation of our, you know, self-driving, uh, hardware suite, you know, sensors, computer, and we put it on a custom-designed vehicle that we called the Firefly.

   2. EGElad Gil5:45

      Mm-hmm.

   3. DDDmitri Dolgov5:45

      Right? We took a few rides with nobody behind the wheel, zero to one moment. Uh, then the next, uh, evolution in that kind of generational step was our fourth generation of our driver. Those are the Pacifica minivans.

   4. EGElad Gil5:57

      Mm-hmm.

   5. DDDmitri Dolgov5:57

      Um, uh, with the fourth generation of the Waymo hardware suite. And we deployed those, uh, uh, in a full autonomous mode in Arizona i- in Chandler, and we actually opened it up to the public in 2020.Right? Uh, but at that point, uh, we... The focus was on doing it, uh, repeatedly, right? And the focus was on, uh, maturing the, you know, the technology, the building of the driver, the evaluation of the driver-

   6. SGSarah Guo6:24

      Mm-hmm.

   7. DDDmitri Dolgov6:24

      ... and of doing releases in a regular cadence, getting it out to real customers, hearing from the customers, right? Understanding the feedback, you know, and iterating. So that was the focus of that fourth generation, was not, you know, to grow and scale and capture the market, right? And then at that point, we, uh, made the decision to jump to what, you know, we now call the fifth generation of the Waymo Driver. It's on the JLR, uh, IP. This is what you see in, you know, in the fleet today, in, in those four cities, uh, uh, Phoenix, San Francisco, LA, and you know, Austin.

   8. SGSarah Guo6:52

      I thought it was very smart actually, that you started in Arizona, um-

   9. DDDmitri Dolgov6:55

      Mm-hmm.

   10. SGSarah Guo6:55

       ... versus in California. And I think, you know, for example, um-

   11. DDDmitri Dolgov6:58

       Mm-hmm.

10. 6:59 – 9:26

    Choosing Deployment Cities

    1. SGSarah Guo6:59

       ... I, 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-

    2. DDDmitri Dolgov7:10

       Mm-hmm.

    3. SGSarah Guo7:10

       ... to start that as a sort of a test bed or a place to-

    4. DDDmitri Dolgov7:13

       So I guess, you know, it depends on the different time, you know, horizons. So in, 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, as I mentioned, is to kind of go end to end.

    5. SGSarah Guo7:25

       Mm-hmm.

    6. DDDmitri Dolgov7:25

       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 a deployment. Uh, and then there's the development of the system. So for the development of the system, you wanna go after the hardest problems possible, right? You wanna go after the densest environments, you wanna go after the harshest weather.

    7. SGSarah Guo7:42

       Mm-hmm.

    8. DDDmitri Dolgov7:42

       So we've kind of in parallel been doing that. So we've made a decision to deploy, you know, in Chandler in 2020-

    9. SGSarah Guo7:47

       Mm-hmm.

    10. DDDmitri Dolgov7:47

        ... 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 wanna take to scale."

    11. SGSarah Guo8:01

        What's the hardest environment for a self-driving car?

    12. DDDmitri Dolgov8:05

        So, density matters.

    13. SGSarah Guo8:06

        Mm-hmm.

    14. DDDmitri Dolgov8:06

        Speed matters.

    15. SGSarah Guo8:07

        Yeah.

    16. DDDmitri Dolgov8:07

        Uh, weather matters.

    17. SGSarah Guo8:08

        Okay.

    18. DDDmitri Dolgov8:09

        So kind of, you know, where, you know, those come together-

    19. SGSarah Guo8:10

        Mm-hmm.

    20. DDDmitri Dolgov8:11

        ... uh, is, uh, where most complexity is, right?

    21. SGSarah Guo8:13

        Mm-hmm. So New York in the winter is really bad. And then-

    22. DDDmitri Dolgov8:16

        Yeah.

    23. SGSarah Guo8:17

        ... you know.

    24. DDDmitri Dolgov8:17

        That's right.

    25. SGSarah Guo8:18

        Yeah.

    26. DDDmitri Dolgov8:18

        That's right. Uh, but then this is why we picked San Francisco is that, you know, it's good for learning, uh, advancing the driver. It is a very interesting, you know, commercial market. Uh, we also in the same time went, uh, after downtown Phoenix. It has, you know, a different mix, more, more of the higher speed roads. And, uh, that gives us, you know, kind of, uh, the way we think about it at terms of the development and evaluation of the driver is kind of in the space of the operating domain, not necessarily areas or zip codes. And then you kind of take cities and you map that to the operating domain and, and you deploy, right?

    27. SGSarah Guo8:46

        Hm.

    28. DDDmitri Dolgov8:46

        And then looking forward is the kind of in terms of how we think about, you know, future cities. It is, you know, the, a few lenses. It is market.

    29. SGSarah Guo8:52

        Mm-hmm.

    30. DDDmitri Dolgov8:53

        Like, you know, is, is there a good, you know, uh, market from the commercial perspective? Uh, what is the technical, uh, complexity, you know, what is the, you know, regulatory environment? Uh, and so-
11. 9:26 – 11:01

    Technological Advancements

    1. DDDmitri Dolgov9:26

       that's, that's a boost, right? Really, it's all about, you know, AI as you mentioned.

    2. SGSarah Guo9:29

       And what, what specifically in AI was the, the shift or change that was important? Was it moving to sort of end-to-end DL for everything? Was it the transformer backbone? Was... I'm just sort of curious, like was there a specific thing that-

    3. DDDmitri Dolgov9:39

       So for that last jump, the models, you know, transformers, as you mentioned-

    4. SGSarah Guo9:42

       Mm-hmm.

    5. DDDmitri Dolgov9:42

       ... played a huge role, right?

    6. SGSarah Guo9:43

       Hm.

    7. DDDmitri Dolgov9:43

       Before that, you know, we had the big breakthrough before that, you know, it was, uh, ConvNets, you mentioned AlexNet that was-

    8. SGSarah Guo9:48

       Mm-hmm.

    9. DDDmitri Dolgov9:48

       ... around 2013.

    10. SGSarah Guo9:49

        Yeah.

    11. DDDmitri Dolgov9:49

        So that gave us a big boost, but was still kind of a plateaued at kind of, you know, the wrong place.

    12. SGSarah Guo9:54

        Hm.

    13. DDDmitri Dolgov9:54

        And then it was a few of those things coming together, right?

    14. SGSarah Guo9:56

        Hm.

    15. DDDmitri Dolgov9:56

        It is, you know, transformers, uh, it is, you know, bigger models, more compute coupled with kind of the whole, you know, evaluation measures. We often talk about the architectures and what's really m- more important kind of the machine surrounding the architecture. You need to ask, you know, the architecture is an enabler-

    16. SGSarah Guo10:09

        Mm-hmm.

    17. DDDmitri Dolgov10:10

        ... but really to make it work at the level that we care about, you need like everything around it, the data engine that will, you know, flywheel of, you know, training the system, evaluating it. And you kind of have to think about the problem of evaluating the driver in tandem of building it, right? And, uh, you know, you need to simulate it, you need data. So all of those coming together I think is what leads to-

    18. SGSarah Guo10:28

        Mm-hmm.

    19. DDDmitri Dolgov10:28

        ... uh, you know, the, the breakthrough and discontinuity that you're seeing-

    20. SGSarah Guo10:31

        Mm-hmm.

    21. DDDmitri Dolgov10:31

        ... with, uh, where we are today.

    22. SGSarah Guo10:32

        Yeah. Can you explain how you guys think about evaluation internally and then also, um, you know, how that might differ from how regulators evaluate this from the safety case perspective?

    23. DDDmitri Dolgov10:42

        So the in- that's a big question, uh, but in, uh, I think, I'm glad you're asking about because it is a super important and an like insanely difficult problem, right? We often talk again about the building of the driver, but there's two problems. The building of the driver and the evaluation of it. And they go, you know, hand in hand. So, you know, internally it starts with,

12. 11:01 – 14:41

    Evaluating Safety

    1. DDDmitri Dolgov11:01

       you know, figuring out what metrics you care about, then, uh, bringing the data to support the evaluation of those metrics, you know, which we, you know, have hundreds.

    2. SGSarah Guo11:09

       Hm.

    3. DDDmitri Dolgov11:09

       Uh, then you need all the infrastructure, uh, including things like the simulation. There are some things you can evaluate in open loop, you know, kind of, and some things you need closed loop simulation for. So you need to build, you know, a s- a realistic scalable simulator to support all of that, right?

    4. SGSarah Guo11:23

       Mm-hmm.

    5. DDDmitri Dolgov11:23

       There are, you know, all of these metrics that guide our, you know, development of the system that, you know, help us, uh, improve and kind of train, uh, the, the Waymo AI, the Waymo driver, and then it funnels into what we call validation and evaluation. The aggregate of the evaluation and validation methodology is what we call the readiness and safety framework.

    6. SGSarah Guo11:41

       You know, if you look at-

    7. EGElad Gil11:42

       ... uh, miles driven in an urban setting, or-

    8. DDDmitri Dolgov11:45

       Mm-hmm.

    9. EGElad Gil11:45

       ... you know, some comparison to human drivers in a given city that you operate in-

    10. DDDmitri Dolgov11:48

        Yeah.

    11. EGElad Gil11:49

        ... what is the relative safety level of what Waymo's doing versus a human driver at this point?

    12. DDDmitri Dolgov11:52

        We are pretty proud, uh, of our record. I think we have now, now that we've driven, you know, tens of millions, uh, of miles in fully autonomous, what we call rider only mode-

    13. EGElad Gil12:01

        Mm-hmm.

    14. DDDmitri Dolgov12:01

        ... uh, and driving, you know, today more than a million miles, uh, per week-

    15. EGElad Gil12:05

        Mm-hmm.

    16. DDDmitri Dolgov12:05

        ... I think we can, you know, with pretty good confidence in empirical data say that we are, you know, better than human's benchmarks.

    17. EGElad Gil12:10

        Mm-hmm.

    18. DDDmitri Dolgov12:10

        So we published some of that, you know, very recently, uh, on what we call the Safety Hub.

    19. EGElad Gil12:13

        Mm-hmm.

    20. DDDmitri Dolgov12:14

        Uh, the latest data point that we shared was based on 22-

    21. EGElad Gil12:17

        Mm-hmm.

    22. DDDmitri Dolgov12:17

        ... uh, million fully autonomous rider only miles.

    23. EGElad Gil12:19

        Mm-hmm.

    24. DDDmitri Dolgov12:19

        And, uh, we compared our performance-

    25. EGElad Gil12:23

        Mm-hmm.

    26. DDDmitri Dolgov12:23

        ... uh, versus human benchmarks, uh, by, you know, different severity levels of, you know, context of collisions, different severity levels. So we see, depending on the severity level, we see that, you know, for the lowest severity, lower severity outcomes were about, um, you know, uh, a factor of two better-

    27. EGElad Gil12:38

        Mm-hmm.

    28. DDDmitri Dolgov12:38

        ... than the human's benchmarks.

    29. EGElad Gil12:39

        Mm-hmm.

    30. DDDmitri Dolgov12:39

        And as you look at more severe outcomes-
13. 14:41 – 16:52

    Regulatory Stance and Trust

    1. EGElad Gil14:41

    2. DDDmitri Dolgov14:41

       ... at a, at a, at a good rate. Uh, but the main thing, uh, is-

    3. EGElad Gil14:44

       What is the total number of miles that are driven in the US per year?

    4. DDDmitri Dolgov14:47

       Uh-

    5. EGElad Gil14:47

       I'm sorry to put this on a fractional basis.

    6. DDDmitri Dolgov14:49

       All, all vehicles, I think it's, uh, you know, in different modalities-

    7. EGElad Gil14:51

       Mm-hmm.

    8. DDDmitri Dolgov14:51

       ... you know, large vehicles, small vehicles, I think it's, uh, just over three trillion miles.

    9. EGElad Gil14:54

       Okay.

    10. DDDmitri Dolgov14:55

        So quite a bit.

    11. EGElad Gil14:55

        Yeah.

    12. DDDmitri Dolgov14:55

        We ha- we ha- we ha- we have-

    13. EGElad Gil14:56

        (laughs) We have that on our way.

    14. DDDmitri Dolgov14:58

        (laughs) But the way, again, you know, the way we think about it is that it's important for this to be kind of an iterative process where we earn trust.

    15. EGElad Gil15:06

        Mm-hmm.

    16. DDDmitri Dolgov15:07

        It is not a thing where, you know, you-

    17. EGElad Gil15:08

        Mm-hmm.

    18. DDDmitri Dolgov15:08

        ... you- you know, build it and then you, uh-

    19. EGElad Gil15:10

        Mm-hmm.

    20. DDDmitri Dolgov15:10

        ... you know, just turn on the switch and-

    21. EGElad Gil15:12

        Sure.

    22. DDDmitri Dolgov15:12

        ... that's great, right? It needs to be something new, different. There needs to be a dialogue. It needs to be like we, you know, talked about establishing, uh, the safety-

    23. EGElad Gil15:19

        Sure.

    24. DDDmitri Dolgov15:19

        ... record, right? And we needed to build up to that, right?

    25. EGElad Gil15:21

        Mm-hmm.

    26. DDDmitri Dolgov15:21

        We collected in tens of millions miles. Now, we feel pretty good about that. So then we, you know, that gives us confidence-

    27. EGElad Gil15:26

        Mm-hmm.

    28. DDDmitri Dolgov15:26

        ... that earns us, you know, trust.

    29. EGElad Gil15:28

        Yeah.

    30. DDDmitri Dolgov15:28

        Uh, so you have to be transparent about where we are, and then we, you know, uh-
14. 16:52 – 23:19

    Future of Autonomous Driving

    1. DDDmitri Dolgov16:52

       And that's what we have today, right?

    2. EGElad Gil16:53

       Mm-hmm.

    3. DDDmitri Dolgov16:53

       So far, we, we have, you know, achieved good quality-

    4. EGElad Gil16:56

       Sure.

    5. DDDmitri Dolgov16:56

       ... of the driver. We have built all the machinery to evaluate it and know-

    6. EGElad Gil16:59

       Mm-hmm.

    7. DDDmitri Dolgov16:59

       ... what it takes. So now for us, it's an optimization.

    8. EGElad Gil17:02

       Mm-hmm.

    9. DDDmitri Dolgov17:02

       Right? It's a, it's an optimization, simplification, and if we have that, you know, that, uh, the thing that works, and you have a good mechanism-

    10. EGElad Gil17:08

        Mm-hmm.

    11. DDDmitri Dolgov17:09

        ... to evaluate it, then, you know, it, it really is drastically different-

    12. EGElad Gil17:13

        Mm-hmm.

    13. DDDmitri Dolgov17:13

        ... in terms of, you know, how fast you can move.

    14. EGElad Gil17:15

        Yeah.

    15. DDDmitri Dolgov17:15

        And I guess I've been in, in this other mode for years-

    16. EGElad Gil17:17

        Mm-hmm.

    17. DDDmitri Dolgov17:18

        ... where we have not solved the kind of the, haven't cracked the nut, right?

    18. SGSarah Guo17:20

        Yeah.

    19. DDDmitri Dolgov17:20

        And you're kind of hypothesizing what it would take and, like, you know, yeah, what is the yield of this technical breakthrough? And you're kind of climbing uphill, right? And this is, you know, old analogy of, you know, going to mountain. You see the peak.-

    20. SGSarah Guo17:30

        Mm-hmm.

    21. DDDmitri Dolgov17:30

        ... Then you get there, and you thought it was the summit, but no.

    22. SGSarah Guo17:32

        Mm-hmm.

    23. DDDmitri Dolgov17:32

        Now you see like the, you know, the, the landscape. And that's what it felt like for us, you know, for many years. That's kind of the trend that the in- industry usually follows.

    24. EGElad Gil17:39

        Mm-hmm.

    25. DDDmitri Dolgov17:39

        And I just find it that it's a qualitatively different place to be in when you've cracked the nut-

    26. EGElad Gil17:43

        Mm-hmm.

    27. DDDmitri Dolgov17:43

        ... and you have the evaluation, and now you can optimize and scale.

    28. SGSarah Guo17:45

        As part of your optimization, um, do you think of, like, reducing or simplifying the sensor suite as an important priority?

    29. DDDmitri Dolgov17:52

        Every generation of the hardware, you know, it, uh, increase capability, but also simplify-... drastically, and then, uh, the cost comes down-

    30. EGElad Gil17:59

        Mm-hmm.
15. 23:19 – 26:06

    Business Strategy and Partnerships

    1. DDDmitri Dolgov23:19

       hailing business, right? We don't build our own vehicles, right? We partner, we partner with-

    2. EGElad Gil23:22

       Mm-hmm.

    3. DDDmitri Dolgov23:22

       ... Tier One's, we partner with Williams, we partner, you know, with, uh-

    4. EGElad Gil23:25

       Mm-hmm.

    5. DDDmitri Dolgov23:25

       ... other companies who help us on our operational side, you know, we partner with companies-

    6. EGElad Gil23:28

       Mm-hmm.

    7. DDDmitri Dolgov23:28

       ... who help us on the network side, so forth and so on.

    8. EGElad Gil23:30

       How do you think car ownership will change over the next decade or two? So if, uh, you know, it's- it's very exciting to see this ramp in terms of, you know, driverless rides that are happening.

    9. DDDmitri Dolgov23:39

       Mm-hmm.

    10. EGElad Gil23:39

        And one could argue at some point some proportion of the population, just like people flip to Uber to, um, you know, ferry them around in major cities versus driving or taking taxis or other things, there could be a flip here to- to autonomous systems versus owning cars, right? You should just be able to order something on demand, have it show up at the right time and you just get in and it takes you wherever you need to go.

    11. SGSarah Guo23:59

        Do you view that as a 10% use case, a 30%? I'm just sort of curious-

    12. DDDmitri Dolgov24:03

        Yeah.

    13. SGSarah Guo24:03

        ... like, what proportion of miles do you think will convert over time?

    14. DDDmitri Dolgov24:06

        Uh, I think over time, we'll see more and more as, you know, as, uh, technology matures, as, you know, it gets deployed-

    15. SGSarah Guo24:11

        Mm-hmm.

    16. DDDmitri Dolgov24:11

        ... in more of these, uh, different products and modalities.

    17. SGSarah Guo24:14

        Yeah.

    18. DDDmitri Dolgov24:14

        I think you're starting to see, uh, some of that, uh, even today in ride hailing, right?

    19. SGSarah Guo24:17

        Mm-hmm.

    20. DDDmitri Dolgov24:17

        And it's not uniform, right? But even in kind of the densest cities, if you look at, you know, the people who live in San Francisco or, you know-

    21. SGSarah Guo24:23

        Sure.

    22. DDDmitri Dolgov24:23

        ... people who live in New York, even, uh, before, you know, autonomy, right, uh, there was a shift of, you know, fewer people wanted to... in those areas, wanted to, uh, own cars, especially the younger generations, right? It's, uh, uh, not- not what they're excited about, right? Mm-hmm. Uh, and I think with, you know, with autonomy in those areas, in kind of the densest cores, you will see, you know, more of that evolution and continued trend, right? And then- then, you know, over time, it will expand, and so on, and so on.

    23. SGSarah Guo24:45

        Yeah. Right.

    24. DDDmitri Dolgov24:45

        But, you know, the thing that I'm most excited about is in all of those modalities, we'll be bringing the safety benefits-

    25. SGSarah Guo24:49

        Mm-hmm.

    26. DDDmitri Dolgov24:49

        ... of this technology, uh, to the ecosystem.

    27. SGSarah Guo24:51

        One of the reasons I ask is I remember, um, talking with people in the self-driving world, I don't know how long ago, eight years ago, nine years ago, whatever it was. And at the time, everybody thought this wave was coming. I think a lot of people were off in terms of the timeframe-

    28. DDDmitri Dolgov25:03

        Yeah.

    29. SGSarah Guo25:03

        ... in terms of what actually happened, and there was sort of a flurry of startups all getting up and running at that time. And a lot of the conversations were around how urban environments would change over time.

    30. DDDmitri Dolgov25:12

        Mm-hmm.
16. 26:06 – 26:40

    Changing Urban Mobility Trends

    1. DDDmitri Dolgov26:06

       is the primary one that we're very focused on. You know, we've talked about as kind of the North Star, as the mission, as the vision for, you know, many years, uh-

    2. SGSarah Guo26:14

       Mm-hmm.

    3. DDDmitri Dolgov26:14

       ... but, you know, it was always kind of with this caveat, once we get to scale. So I think today, we're starting to actually earn the right to talk about, you know, starting to realize that mission, right?

    4. SGSarah Guo26:24

       Mm-hmm.

    5. DDDmitri Dolgov26:24

       You know, uh, tens of millions of miles behind umbrella, but, you know, more than a million per week.

    6. SGSarah Guo26:29

       Mm-hmm.

    7. DDDmitri Dolgov26:30

       We... And the benchmarks, the safety benchmarks that are, you know, statistically significant to seem, you know, empirically unambiguous-

    8. SGSarah Guo26:35

       Mm-hmm.

    9. DDDmitri Dolgov26:35

       ... can talk about actually, you know, real, tangible safety benefits and,

17. 26:40 – 28:43

    Challenges and Misconceptions in Self-Driving Timelines

    1. DDDmitri Dolgov26:40

       like, inducing- reducing, you know, harm and injuries that are happening on the road today. So that's our primary focus, and as, you know, I think that's the primary, uh, benefit that we'll see. And then beyond that, I think there's going to be auxiliary ones like the ones we mentioned now.

    2. SGSarah Guo26:51

       What do you think is the role of, uh, traditional OEMs in- in this world, um, uh, when, you know, I'd- I'd say functionally, like, a car takes you from point to point, ride hailing takes you from point to point with like, you know, different tiers of comfort level. But, uh, you know, very large industry has been built around people buying passenger vehicles for, like, you know, industrial design or brand or all these other things, um, that, uh- uh, you know, you really need a Ford Raptor to run around the Bay Area, right? Um, well, how do you- how do you think that evolves-

    3. DDDmitri Dolgov27:24

       Uh...

    4. SGSarah Guo27:25

       ... when- when, you know, perhaps one of the more primary, um, drivers of value is now AI and the ability to do this autonomously?

    5. DDDmitri Dolgov27:32

       Right. But I guess, you know, we think of what we're doing as building the driver.

    6. SGSarah Guo27:36

       Mm-hmm.

    7. DDDmitri Dolgov27:37

       And...

    8. SGSarah Guo27:37

       And the driver could still drive different cars.

    9. DDDmitri Dolgov27:38

       And the driver... Exactly.

    10. SGSarah Guo27:39

        Mm-hmm.

    11. DDDmitri Dolgov27:40

        You put the driver, but you still need the car.

    12. SGSarah Guo27:42

        Mm-hmm.

    13. DDDmitri Dolgov27:42

        And different form factors, you know, whether it's, uh, a car that's good for ride hailing in certain, you know, urban environments, whether it's, you know, a- a different vehicle that you need for good transport or truck or, you know, something that you want to take on longer trips with, you know, family.

    14. SGSarah Guo27:54

        Mm-hmm.

    15. DDDmitri Dolgov27:54

        You know, we will need different cars. We'll need different form factors. And, uh, I think it's very, very complementary-

    16. SGSarah Guo27:59

        Mm-hmm.

    17. DDDmitri Dolgov27:59

        ... what we're doing and what, uh, you know, the, uh, the car industry, uh, is building.

    18. SGSarah Guo28:05

        Perhaps to Elad's point about how cities will change, the answer has clearly been given the efforts to change both drivers and cars and the environment to just change the driver, right? Um, which is what- what you guys have done. Do you think there are arguments still to change the infrastructure, right? Like, you can make, for example, in the public transport space, right, there's other form factors that require the participation of the public sector in order to deploy.

    19. DDDmitri Dolgov28:31

        Uh, absolutely. Sustainability is- is, you know, very important for us. Safety is the primary thing, but, uh, I think all of those modalities can coexist. Uh, in fact, uh, just, uh, in the last couple of days, we, uh, announced,

18. 28:43 – 30:54

    The Role of Traditional OEMs in an Autonomous Future

    1. DDDmitri Dolgov28:43

       um, uh, something that we're doing where we are incentivizing people to take Waymos in, uh, the cities where we operate to public transit hubs.

    2. SGSarah Guo28:51

       Mm-hmm.

    3. DDDmitri Dolgov28:51

       And then, you know, everybody benefits.

    4. SGSarah Guo28:53

       Sure. How do you think about the form factor of the car itself? I know that there was companies like Zoox that Amazon bought where-

    5. DDDmitri Dolgov28:58

       Mm-hmm.

    6. SGSarah Guo28:58

       ... they kind of hollowed out the inside of the car because you no longer needed the steering column and everything else.

    7. DDDmitri Dolgov29:02

       Mm-hmm.

    8. SGSarah Guo29:02

       And they put seats facing each other, almost like a London cab.

    9. DDDmitri Dolgov29:05

       Mm-hmm.

    10. SGSarah Guo29:06

        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?

    11. DDDmitri Dolgov29:14

        Yeah. So designing a car, uh, around the passengers makes total sense.

    12. SGSarah Guo29:19

        Mm-hmm.

    13. DDDmitri Dolgov29:19

        Right? Like, we, you know, uh, in the past, we've designed cars around, you know, primarily the- the driver.

    14. SGSarah Guo29:24

        Mm-hmm.

    15. DDDmitri Dolgov29:24

        Right? Uh, if, you know, it's the Waymo driver, it's all about the- the rider experience.

    16. SGSarah Guo29:29

        Mm-hmm.

    17. DDDmitri Dolgov29:29

        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- with the passenger in mind, right?

    18. SGSarah Guo29:41

        Mm-hmm.

    19. DDDmitri Dolgov29:41

        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.

    20. SGSarah Guo29:53

        Mm-hmm.

    21. DDDmitri Dolgov29:53

        You know, there's different aspects of it, like, you know, we don't have cars facing each other.

    22. SGSarah Guo29:56

        Mm-hmm.

    23. DDDmitri Dolgov29:56

        I think there's... You know, it's an open question. Like, some people get, uh, you know-

    24. SGSarah Guo29:59

        Mm-hmm.

    25. DDDmitri Dolgov30:00

        ... nauseous when you do that.

    26. SGSarah Guo30:01

        (laughs)

    27. DDDmitri Dolgov30:01

        Like, you kind of want to... You know, there's benefits on, uh, facing forward. But, you know, all of that, I think, will be for us as an industry to figure out as, you know, 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-

    28. SGSarah Guo30:13

        Mm-hmm.

    29. DDDmitri Dolgov30:13

        ... not around the driver.

    30. SGSarah Guo30:14

        It seems like it could do very interesting SKUs too where, you know, I've always wanted a car with like a Peloton in the back or something.
19. 30:54 – 33:42

    Designing Cars for Autonomous Ride-Hailing

    1. DDDmitri Dolgov30:54

       calling into meetings from Waymos. Oh, yeah. So it is... (laughs) But it, it is, you know, actually, you know, like, uh, we, we joke about it, but it, uh, it gets to the point of privacy. Uh, it just becomes, you know, in this, if you don't have another human in the car, right? You can, you know, you can do a work meeting. You can do a, you know, call. You can, like, listen to your favorite music, uh, on full volume and not worry about, you know, that, that interaction of, like, having another human that you're sharing the spaces. So we are seeing, that was one of the, you know, hypotheses of the benefits, uh, of, of our product. And we are, you know, seeing very positive feedback from our Mm-hmm. ... RAs today, uh, uh, you know, along, along that specific dimension as well.

    2. SGSarah Guo31:28

       You know, as just somebody excited to see this technology expand coverage range, is the blocking factor to, let's say, you know, a billion miles a week, is it, like, putting more cars on the road from a capital perspective? Is it just operationally this can only happen so fast? Is it your view of, um, like, what you wanna see from a safety and trust perspective, like consumer trust perspective? What's the bottleneck?

    3. DDDmitri Dolgov31:53

       Primarily, it's the latter.

    4. SGSarah Guo31:54

       Okay.

    5. DDDmitri Dolgov31:55

       So we, we always, you know, our playbook has been to, uh, you know, go about it responsibly and gradually and-

    6. SGSarah Guo32:01

       Mm-hmm.

    7. DDDmitri Dolgov32:01

       ... earn trust every step of the way and have this transparent dialogue. Again, this is a very new thing, new technology, new product, you know, very different from what people are used to. I think it has to be this

    8. EGElad Gil32:11

       process. And again, you know, trust is this thing that's hard to, you know, earn-

    9. SGSarah Guo32:16

       Mm-hmm.

    10. DDDmitri Dolgov32:16

        ... but very easy to lose. So that's the main thing, right?

    11. SGSarah Guo32:19

        Mm-hmm.

    12. DDDmitri Dolgov32:19

        Uh, and you s- we see that. We see that, like, in places where, you know, we operate, uh, and we've engaged with the communities, and there's riders who have, you know, used Waymos. It is, uh, there's a lot of trust. There is, you know, people, you know, use the word magic a, a lot about the experience. Uh, and then you go to a different place where people have not experienced it, and there's more anxiety- Mm-hmm. ... and less trust. Mm-hmm. So you can't, uh, just get there in one step. You have to do it- Mm-hmm. ... kind of responsibly and iteratively. So that, that's, that's the main thing that

    13. EGElad Gil32:47

        Yeah, it sends us back when, uh, they had elevator operators, getting rid of the operator was a big deal, right? 'Cause you used to have somebody-

    14. DDDmitri Dolgov32:52

        Yeah.

    15. EGElad Gil32:52

        ... in the elevator who would close the door for you and push the button and control that experience. That's interesting to see that evolution of different types of technology over time or people's interpretation of it. Um, how do you think about generalized abilities? You mentioned you're building a general-purpose driver that could potentially port into other types of vehicles. Do you think there's other extensions into other forms of robotics with what you're building? Or do you think those are all more specialized models? Or how do you think about where this could go from that perspective?

    16. DDDmitri Dolgov33:18

        On the driving part, uh, we have kind of designed it to be generalizable, and we're v- very happy, you know, with what we're seeing, especially with the fifth-generation driver, and, like, the AI generalizes really well, you know, based on, again, we've been using, you know, data from a very broad ODD to build it, even, you know, if we're deploying- Mm-hmm. ... you know, responsibly and gradually once we, you know, believe that we've achieved the level of performance that we require for a certain ODD, which maps to, you know, certain

20. 33:42 – 35:18

    Scaling Responsibly

    1. DDDmitri Dolgov33:42

       areas and ZIP codes. And to get you the other part of the question of, you know, going beyond autonomous vehicles, some of the stuff, like the nature o- of the problem and the complexity, I think the, some of the research that we do, uh, is pretty foundational- Mm-hmm. ... uh, when we talk about, you know, perception, right? You know, you can be in a car. You can be in a different, uh, modality in a, like, operating in the physical world. Uh, a lot of the research that we've published and a lot of work we've done, I think can, you know, benefit those communities as well when we talk about, you know, AI, uh, being deployed in a kind of real-time system, in a safety-critical system. A lot of the work that we have done, I think, you know, can translate, uh, to others and, you know, so forth. When we talk about the evaluation of the system, kind of what, you know, many robotics applications, you know, beyond autonomous vehicles need a good realistic scalable simulator, you know, that, that, that, you know, fundamental work translates, so on, you know, so forth and so on. Mm-hmm. We are very focused on the- Mm-hmm. ... you know, trillions of miles where we can have the positive benefits. So for us, uh, like, I focus, I, I think focus is very, very important. So we are being very laser, you know, laser focused on, uh, driving.

    2. SGSarah Guo34:43

       Can I, um, ask, go back and ask, like, perhaps a, um, a more technical question here? Like, a while back, you said, um, you wanted to, you know, focus on the full autonomy problem. There were, uh, there are many other teams who actually, you know, have some lineage in the, like, you know, Waymo, Chauffeur, Google, um, programs that chose, uh, a use case that looked like it was going to be easier, trucking, like long-haul trucking, uh, deliveries. It's not clear that's much easier. Do you think there's a lesson to be learned here? Or, or at least,

21. 35:18 – 37:10

    Generalizability and Future Applications of AI

    1. SGSarah Guo35:18

       you, you know, there are more miles being driven autonomously on the road in passenger vehicles by Waymo than in these other applications today.

    2. DDDmitri Dolgov35:25

       Yeah, uh-

    3. SGSarah Guo35:26

       What, what lesson is there to be learned?

    4. DDDmitri Dolgov35:26

       Yeah, no, no. Uh, I think that's, you know, great question. You know, kind of the big differentiation that I would draw, and this is, like, orders of magnitude, and the difference between full autonomy-

    5. SGSarah Guo35:34

       Mm-hmm.

    6. DDDmitri Dolgov35:35

       ... verse- at scale-

    7. SGSarah Guo35:36

       Mm-hmm.

    8. DDDmitri Dolgov35:36

       ... versus, uh, you know, a driver assist system.

    9. SGSarah Guo35:39

       Mm-hmm.

    10. DDDmitri Dolgov35:40

        Uh, and th- that, that's the big, like, that's the chasm. To your question, uh, of, you know, different vehicle platforms, different operating domain. You can have, you know, uh, slower speed, uh, applications where, you know, you do local deliveries, or you can have, you know, a trucking application, uh, on, you know, uh, freeways. Um, and they're a little bit different, but if we're talking about full autonomy, maybe there's second-order differences, but the first-order complexity is still there. Mm-hmm. Right? You can, uh, like, the, if you think about, you know, the core, the heart of the problem of, you know, building a generalizable and safe driver and, you know, being able to evaluate it, and the incredibly high bar of safety, the, you know, complexity of the noisy, messy physical environment and the long tail of, you know, people, you know, doing all kinds of, you know, weird thing, uh, and, uh, the necessity of making real-time decisions where milliseconds, you know, matter, and, like, how hard that AI problem is, uh, it's, you know, the, the, the distribution, the contours change a little bit if you're, you know, talking about, you know, freeways or lower speed. But the fundamentals are, like, you, you don't get, like, there's no silver bullets. You don't get to skip the, you know, core complexity. Mm-hmm. Now, for example, freeways, on a nominal case, they're a bit more structured.

    11. SGSarah Guo36:44

        Mm-hmm.

    12. DDDmitri Dolgov36:44

        Right? Like, you know, but, uh, you still, uh, encounter with lower frequency, but at higher speeds where the severity is high, you encounter-... all kinds of things. You encounter, you know, construction zones. You encounter, you know, grilles and mattresses and all kinds of stuff falling off of the cars in front of you. You encounter cars, uh, you know, having getting into accidents and kind of spinning out in front of you. You encounter, uh, you know, people driving recklessly, you know, uh, whether they're in cars, whether they're on motorcycles. You encounter, you know, pedestrians

22. 37:10 – 42:58

    The Complexity of Achieving Full Autonomy

    1. DDDmitri Dolgov37:10

       jaywalking. You encounter, you know, uh, all kinds of things, right? And it happens much less frequently. So this is where, you know, it might be unintuitive. If it happens, you know, at once per million miles, none of us have seen, you know, examples like that in our world, so it kind of, it can lead to this, you know, early stage optimism about like, okay, there's, there's the simplification. But if you wanna do it full autonomously, and you wanna do it at scale, that complexity is still there. It's just, you know, (inaudible) .

    2. SGSarah Guo37:34

       And why is it breaking from, like, um, you know, uh, let's say, advanced driver assistance that, uh, seems to work in more and more scenarios-

    3. DDDmitri Dolgov37:42

       Mm-hmm.

    4. SGSarah Guo37:42

       ... versus, let's say, full autonomy?

    5. DDDmitri Dolgov37:45

       What's the, what's the delta?

    6. SGSarah Guo37:46

       Yeah.

    7. DDDmitri Dolgov37:47

       It's the number of nines, right? And it, it's the nature of this, this problem, right? If you think about, you know, where we started in 2009.

    8. SGSarah Guo37:53

       Mm-hmm.

    9. DDDmitri Dolgov37:54

       Uh, one of our first, uh, you know, uh, milestones, one of the goals that we set for ourselves was to drive, you know, uh, 10 routes. Each one was 100 miles long, all over the Bay Area. Um, you know, freeways, uh, Downtown San Francisco, around Lake Tahoe, you know, everything. And you had to do 100 miles with no intervention. So the car had to, you know, drive autonomously from beginning to end. That's the goal that we created for ourselves. It was, you know, about a dozen of us. Took us maybe 18 months. We achieved that.

    10. SGSarah Guo38:22

        (laughs)

    11. DDDmitri Dolgov38:22

        2009.

    12. SGSarah Guo38:23

        (laughs)

    13. DDDmitri Dolgov38:23

        No ImageNet, no Continets, no transformers, no big models, com- tiny computers, you know, all this... Right? Very easy to get started. It's always been the property. And with every wave of technology, it's been al- you know, very easy, uh, to get started. But that, the hard problem, and it's kind of like, that, that early part of the curve has been getting, like, you know, even steeper and steeper.

    14. SGSarah Guo38:41

        Mm-hmm.

    15. DDDmitri Dolgov38:42

        But that's not where the complexity is. The complexity is in the long tail of the many, many, many nines. And you don't see that if you go, you know, uh, for a prototype, if you go for, you know, a driver assist system, uh, and this is where, you know, we've been spending all of our... That's the only hard part of the problem, right? And I guess, you know, nowadays, it's always been getting easier with every technical, uh, uh, kind of, uh, cycle. So nowadays, you can take with, like, all of the advances of... in AI, and especially in the, you know, generative AI world and the LLMs and VLMs, you can take kind of an almost off the shelf... Uh, you know, Transformers are amazing.

    16. SGSarah Guo39:13

        Mm-hmm.

    17. DDDmitri Dolgov39:14

        VLMs are amazing. You can take, uh, kind of a VLM, uh, that, uh, can accept, uh, images or video, and is, you know, has a decoder where you can give it, you know, text prompts and it will output text, and you can fine-tune it, you know, with just a little bit of, you know, data to go from, let's say, camera data in a car, to instead of words, to trajectories. Or, you know, whatever decisions you wanna make. You just, you know, take the thing 'cause a black box-

    18. SGSarah Guo39:38

        Mm-hmm.

    19. DDDmitri Dolgov39:38

        ... you know, you take whatever's been trained for

    20. EGElad Gil39:40

        You find too it a little bit and like not without, you know, 'cause I think if you ask any good grist in, in computer science to build, you know, NAV today, this is what they would do.

    21. SGSarah Guo39:47

        Yeah.

    22. DDDmitri Dolgov39:47

        And out of the box-

    23. SGSarah Guo39:48

        That's amazing, yeah.

    24. DDDmitri Dolgov39:49

        ... you get something that... It- it's amazing, right?

    25. SGSarah Guo39:51

        Yeah.

    26. DDDmitri Dolgov39:51

        Like, the power of Transformers-

    27. SGSarah Guo39:52

        Yeah.

    28. DDDmitri Dolgov39:52

        ... the power of VLMs is mind-blowing.

    29. SGSarah Guo39:54

        Mm-hmm.

    30. DDDmitri Dolgov39:54

        Right? So with just a little bit of effort, you get something on road, and it works. You can, you know, drive it on tens, hundreds of miles and it just, we'll, it will blow your mind. But then is that enough? Is that enough to remove the driver and drive, you know, millions of miles and have a safety record, you know, that is demonstrably better than humans? No. Right? And I guess this is, you know, with every tech, you know, evolution of technology and any breakthrough in AI, we've seen kind of that reshaping.
23. 42:58 – 46:13

    The Importance of Data and Iteration in AI Development

    1. DDDmitri Dolgov42:58

       where we are, the momentum, and the future. Right? And I've been doing this, well, for close to two decades. Uh, the vision was always there, all right? But we had these big existential questions. Can we build the thing? Can we, you know, figure out what's good enough, and how do we evaluate it? Uh, can... will people wanna use it? Can we do it in a way that's, you know, commercially viable, you know, uh, and so forth and so on? Uh, and, you know, can we go the distance? And like now, where we are today, you know, operating at the scale we are and scaling ?1:50:03 ] we've, we've, we've demonstrated that we can build a thing. We are proud of our safety record, figured out how to evaluate it. We see that people wanna use it, and we get very positive feedback, and people are excited about it. We see that we can do it in a way that's cost-efficient and kind of likely, you know, viable. Uh, so I am super excited about, you know, what the future holds, and we're starting to talk about realizing the mission of actually, you know, making, uh, you know, realizing those safety benefits. So now it's all about, you know, optimization, scaling, and bringing this technology to more people and more places.

    2. EGElad Gil44:01

       Amazing. Yeah, very exciting. Thank you again for joining us today.

    3. DDDmitri Dolgov44:04

       Thank you for having me.

    4. SGSarah Guo44:04

       And congratulations-

    5. EGElad Gil44:05

       Thanks for the, really good-

    6. SGSarah Guo44:06

       ... on, uh, you know, the breakthrough progress over the last, you know, small amount of time.

    7. DDDmitri Dolgov44:09

       Thank you. Thank you. (instrumental music)

    8. SGSarah Guo44:12

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Episode duration: 44:30