Pod Insights a16z Podcast Are Autonomous Vehicles Finally Here? Buckle up! About this episode Self-driving cars have been on the horizon for quite some time. But, they might actually be here.
We got to ride around in one all over San Francisco with Waymo’s Chief Product Officer, Saswat Panigrahi.
We discussed so much, including the five levels of autonomy, the infamous LIDAR vs video debate, regulation, UX, and the role of AI in fine-tuning these models. But most importantly, if autonomy is here… now what?!
Topics Covered:
00:00 - A first look at Waymo
02:06 - 5 levels of autonomy
04:20 - Technology challenges
06:55 - The technology behind Waymo
11:58 - LiDAR vs video debate
16:21 - How Waymo differentiates
19:01 - Technological unlocks on the horizon
21:26 - The role of AI in autonomous vehicles
24:17 - How Waymo views safety
31:25 - Collaborating with regulators
37:12 - Learnings from the first 2m miles
39:48 - Driving user retention
44:16 - Waymo’s expansion strategy
47:55 - Changing regulation
48:42 - Societal unlocks enabled by autonomy
53:47 - Will self-driving cars replace humans?
54:26 - Closing thoughts
Resources:
Check out Waymo: https://waymo.com/
Check out Waymo’s Youtube channel: https://www.youtube.com/@Waymo
Find Saswat on Twitter: https://twitter.com/saswat101
Check out a16z's 8-part series on the autonomous vehicle ecosystem: https://a16z.com/2018/02/03/autonomy-ecosystem-frank-chen-summit/
Find a16z on Twitter: https://twitter.com/a16z
Find a16z on LinkedIn: https://www.linkedin.com/company/a16z
Subscribe on your favorite podcast app: https://a16z.simplecast.com/
Follow our host: https://twitter.com/stephsmithio
Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures.
Transcript 11,131 words · 3 speakers
Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.
0:00 Speaker 00 We are in San Francisco. We're not taking you to some, you know, little test facility in a desert to show you that this
0:08 Steph Smith? We are on the road.
0:09 Speaker 00 We are in one of the most vibrant cities on the planet. That's where we are in the journey. We're fully autonomous where we need to be.
0:17 Steph Smith? Self-driving is here. I think if I was a human driving there, like, I don't know what would have happened.
0:22 Speaker 00 And you're asking, does it get boring for you afterward?
0:25 Steph Smith? Oh, I love seeing your excitement. And we are not the only ones who have noticed.
0:29 Speaker 00 That you're just noticing that we don't have a driver.
0:32 Steph Smith? I love seeing so many people when they look inside. Look at them, just like, what's going on there? Today, I got to ride fully autonomously with Waymo's Chief Product Officer, Saswat Paniwah, who's actually been on this journey since 2016. We discussed so much, including the five levels of autonomy.
0:52 Speaker 00 Fully autonomous, as you can see, nobody in the front seat, no expectation of a human to take over.
0:57 Steph Smith? The infamous LiDAR versus video debate.
1:00 Speaker 00 Saying you love LiDARs and hate cameras, or vice versa, as saying you love one wavelength versus the other wavelength, right? It's not a fundamental thing.
1:09 Steph Smith? Regulation, user experience, the role of AI in all this, but especially the question, if autonomy is truly here,
1:21 Steph Smith? now what?
1:24 Steph Smith? As a reminder, the content here is for informational purposes only, should not be taken as legal, business, tax, or investment advice, or be used to evaluate any investment or security, and is not directed at any investors or potential investors in any A16Z fund. For more details, please see a16z.com/disclosures.
1:44 Steph Smith? All right, so before we jump in, if you are hearing this, you are listening to our RSS feed, which is great. We love that you're here. But for this episode, this episode is such a special one. I would really recommend you go watch on our YouTube channel. I honestly feel so lucky that I got to ride around in this self-driving car for an hour. And we have so much good footage. We literally saw the full gamut of possibilities. We saw someone running a red light. We saw a group of bicyclists, literally like 20 or 30 bicyclists. We saw a construction zone. We even saw a two-year-old kid who saw that there was no driver in the front seat. And you could just see the confusion on this kid's face. So really, it was an incredible ride.
2:28 Steph Smith? And if you watch on YouTube, you can see all the footage from inside the car, outside the car, and from the car itself. You can literally see how the software is interpreting the world around it and transforming that into this representation, this simulation on screen. It's honestly so cool. I actually just hit my one-year as the A16Z podcast host. And this by far was my highlight. This is so cool. I hope you all get to experience this one day. And again, if you want to go see what I'm talking about and not just hear about it, go to our YouTube channel at youtube.com/at, that's the at sign, A16Z, or just search A16Z on YouTube. Really, this is the one episode. If you're going to go on over to our YouTube channel, this is it.
3:13 Steph Smith? All right, we will see you on the road.
3:20 Steph Smith? Wow.
3:24 Steph Smith? Very clean. So what do I do? Do I just? Oh, yeah, the KB up there. Nice. Oh, I like don't even know how to interact with it. All right. We just jump in?
3:39 Speaker 02 Hey there, Catherine.
3:40 Steph Smith? Thank you.
3:47 Steph Smith? This is cool.
3:50 Steph Smith? We just click start?
3:51 Speaker 00 Yeah, do you want to? Go ahead.
3:55 Steph Smith? Oh wow.
3:58 Steph Smith? This is wild.
4:02 Steph Smith? This is cool, so you can play music.
4:03 Speaker 00 Yeah.
4:04 Steph Smith? And you can just ask it to pull over if you want.
4:06 Speaker 00 Yeah, anytime.
4:07 Steph Smith? At any time.
4:08 Speaker 00 And you can call support. And there was an audio cue to remind you in case folks get in and they, you know, into a minute thinking like, this is a...
4:14 Steph Smith? Yeah, like this is kind of scary.
4:16 Speaker 00 But it turned out, actually, that wasn't...
4:18 Steph Smith? Really?
4:19 Speaker 00 Folks just got eased into it pretty quick. Somehow, the smoothness with which it drives.
4:23 Steph Smith? Yeah.
4:24 Speaker 00 There's an instant trust.
4:25 Steph Smith? Totally.
4:26 Speaker 00 Very early on, so...
4:27 Steph Smith? I mean, so I get carsick pretty easily, but to your point, like, it doesn't feel jerky at all.
4:32 Speaker 00 Right.
4:32 Steph Smith? It feels really smooth. But I mean, I'm so curious, because you've been working in this space for so long. Does knowing, kind of like, how the sausage is made, does that make it any less magical? Like, when you got into this car for the first time and there was no driver sitting there?
4:46 Speaker 00 Yeah.
4:47 Steph Smith? Were you also like, oh my gosh, like, what's going on here?
4:49 Speaker 00 Absolutely, I'm totally like a kid on this one. So 2017 was probably the first time there was a person in the front there. But they didn't have actual control, so that was my first.
4:59 Steph Smith? Yeah.
5:00 Speaker 00 And 2019 was the first time I was in a car with truly nobody on a public street, just driving around.
5:05 Steph Smith? Yeah.
5:05 Speaker 00 And every single time, even if, of course, the year before, the month before, the day before, and the hour before, I was in anticipation and preparing for it.
5:13 Steph Smith? Yeah.
5:14 Speaker 00 Still being inside it, it's truly special.
5:16 Steph Smith? I mean, it's so cool to just see it navigating.
5:18 Speaker 00 Yeah.
5:19 Steph Smith? The slight turns on the wheel. Something I have to ask about is, it's felt like this is coming for a while. Like, I almost feel like the future is here. Like, we're seeing this, we're sitting in a car with no driver, but a lot of people would kind of say, this has been promised every two years, kind of thing. And it's like, we're finally at the two years, in a way.
5:35 Speaker 00 Yeah.
5:36 Steph Smith? And so maybe you could kind of map out where we are in that kind of arc, the five levels of autonomy.
5:42 Speaker 00 Yep.
5:42 Steph Smith? And also where we still have left to go.
5:45 Speaker 00 Yeah, certainly. We are in level four right now. So, fully autonomous, as you can see, nobody in the front seat, no expectation of a human to take over. And in level two and three, it's really, really crucial to communicate the expectations to the driver.
5:59 Steph Smith? Yep.
6:00 Speaker 00 Because it's very easy that during a normal situation, the driver feels, hmm, the car is kind of driving well, so I can pick up a book and start reading. No, that's serious, and there is an expectation to take over. So we are in that level four with a certain scope. So right now, if we were to begin heavily snowing in SF,
6:16 Steph Smith? Yeah.
6:16 Speaker 00 which had has, I believe around last season, there was a little bit of snow. We wouldn't operate it.
6:21 Steph Smith? We couldn't.
6:21 Speaker 00 But Level 5 is truly defined as anywhere, anytime.
6:24 Steph Smith? Right. I mean, and even the idea of autonomy, people kind of view in this binary way, right? Like, the whole idea of Level puts that into perspective. Like, Level 2 is lane sensing, automatic braking.
6:34 Speaker 00 Right.
6:34 Steph Smith? Is Level 3, where basically what we're doing now, but there would be a human in the front? Or how would you define it?
6:39 Speaker 00 No, no, I would say a vast difference even between Level 3 and Level 4. Huge. It's almost like difference between driving and flying, I would say.
6:48 Steph Smith? Okay, yeah.
6:48 Speaker 00 It's massive difference. Because this concept still, that within seconds you need to take over, versus now we can have this conversation.
6:56 Steph Smith? Yeah, we're not doing anything.
6:57 Speaker 00 Right? That would be challenging, right? But the assurance level you need to get to for Level 4 is just a universe different, I would say.
7:04 Steph Smith? And if we wanted to, let's just imagine, we're rolling down a hill for some reason. It feels like the car is not stopping. Could I take this over if I wanted to?
7:12 Speaker 00 So if you did, the car would say, hmm, I'm being interfered with. I'm a fully autonomous car. I'm not supposed to be interfered with in this manner. So it'll pull over, basically.
7:21 Steph Smith? Okay, got it. So we've talked about the kind of arc of innovation. You've also worked in this industry for a long time. Since 2016, is that correct?
7:29 Speaker 00 Yeah, that's right, that's right.
7:30 Steph Smith? Okay, so tell me a little bit more about the barriers along the way to Level 4. What would you say, was it the technology, the regulations, some combination? Are there major factors that have really delayed us from getting to this point where we're now at Level 4?
7:44 Speaker 00 Yeah, yeah. The one thing, you mentioned the innovation arc. I always reflect that on any journey, when you're trying to do something that has never been done before, there'll always be ups and downs, some challenges you foresaw, some you didn't.
7:56 Steph Smith? Yeah.
7:56 Speaker 00 The key is, are you clear on how massive the price or the benefit to society is on the other side of it? Because then that makes it all worth it. So we were super clear from the get-go that a fully autonomous car that does not get browsy. That we even have physical constraints, right? So even when you are alert, let's say you were looking for parking on this side of the street. Your face would be turning towards that constantly looking for that parking spot. So you wouldn't be able to see.
8:23 Steph Smith? You don't have your sensor.
8:24 Speaker 00 Yeah, yeah, exactly. You can't keep turning back and forth. And so we're fundamentally convinced that a fully autonomous driver is gonna be safer. So, once we started that, yes, the largest challenge I would say was largely, I would call it technology, but I mean two different things. One is building the driver itself that can drive under these conditions and having the high grade of performance. But also measuring that is pretty hard. This smooth sort of early-stage drive under very tight constraints is now relatively with today's technology, not terribly hard to build. But to be able to do that at the scale of 24/7, busy intersections at slow speed, but also high-speed intersections of Phoenix.
9:07 Speaker 00 So in Phoenix, for example, the streets are wider, so you don't deal with these narrow situations.
9:12 Steph Smith? Yep.
9:13 Speaker 00 But the driving speed is 45, people are sometimes going 60 on that. That means you gotta see a lot further. So very different sets of challenges, very diverse ones.
9:22 Steph Smith? Yeah.
9:23 Speaker 00 And the technology, it really required the full stack, right? We build the hardware, we build the software. Because if you built just the software and waited for somebody else to deliver the hardware, the speed of learning, the speed of iteration that was necessary to build something like this was so steep that it was not feasible. So we had to build the lasers, the cameras, the radars, and the software on top, and the massive simulation infrastructure as well.
9:45 Steph Smith? I was gonna say, there's so many moving parts, and actually, maybe let's talk about that technology. So this is my first time in a fully autonomous vehicle, but I've seen them around my area. They're driving around, I see no driver, but I also see the swirling thing on top. I see a bunch of different kind of appendages to the car. So maybe you could just break that down, like what is happening? How is all this technology coming together? And what are the bits and pieces that you've added on to the car that allow it to be autonomous?
10:13 Speaker 00 So fundamentally, you can think of it like, is the car aware of what's happening around it? And then can it anticipate what the things around it are gonna do?
10:22 Steph Smith? Yeah.
10:22 Speaker 00 And then reasoning on what it should do. These are sort of the three components. In perceiving what's around us, think of the example we were just discussing, you're trying to look for a parking. So you're focused on that task. This car with those appendages, as you mentioned, can see three football fields away. Three hundred sixty degree and it's getting a snapshot multiple times a second.
10:44 Steph Smith? Okay.
10:44 Speaker 00 And it's relying on a combination of the state of the art laser, camera, radars, all strategically positioned.
10:52 Steph Smith? Okay.
10:53 Speaker 00 So to give you an idea, lasers give you a very precise understanding of everything around you. It's the smallest detail. So if there was a child an inch out of this pole, it would be able to mark, oh, this is a demarcated child away from that pole. It'll get to see that. But, you know, the cameras are needed to distinguish between the red light and the green light.
11:13 Steph Smith? Oh, yeah.
11:14 Speaker 00 And the radars can almost see around corners, even when the laser and the camera or our human eyes can't.
11:20 Steph Smith? Okay.
11:21 Speaker 00 Because they can sense objects coming in.
11:22 Steph Smith? Okay.
11:23 Speaker 00 And so we took an approach that we want to combine the best strengths of each of these modalities to create the best picture of what you can see around the world that you're just incredibly better than a human possibly could, both due to their attention span, the range, the fidelity, and the combination of these sensors coming together. So that's what we see. But then there's a harder challenge of anticipating what the person will do. Take a look at that pedestrian. They're standing pretty close to the crosswalk, right?
11:51 Steph Smith? We don't know if they're gonna move, right?
11:52 Speaker 00 Exactly. Are they gonna jump in or are they gonna stay? Are they gonna jaywalk or are they gonna obey the light?
11:57 Steph Smith? Yeah.
11:57 Speaker 00 This requires a deep, deep understanding and tremendous amount of machine learning. Each stage here, by the way, requires an insane amount of machine learning.
12:06 Steph Smith? And it's not the type of thing that you can just like put on the road and say like,
12:08 Speaker 00 No, no.
12:09 Steph Smith? Oh, let's see if it makes sense.
12:10 Speaker 00 Yeah, we're not trying to tell you is this a cat image or a dog image. Like it's, so for example, for that pedestrian, in addition to seeing that they're there, acknowledging which was this problem one, you got to look at even their gait, their hand movement, their leg movement to anticipate, are they about to take motion? And if you're always conservative, which was, say, four years ago, it's like we couldn't detect the pedestrians. We could totally detect them. It was this nuance of are we being over conservative, assuming they may jump in hence let's not move.
12:37 Steph Smith? Right.
12:37 Speaker 00 Versus confidently moving forward.
12:40 Steph Smith? Ooh, so are we?
12:42 Speaker 00 At our first stop of the multi-stop.
12:45 Steph Smith? Amazing.
12:47 Speaker 00 So yeah, that anticipation of what a car is gonna do, what a pedestrian is gonna do, what a child is gonna do, because they can erratically jump through.
12:54 Steph Smith? Yeah.
12:54 Speaker 00 What a motorcyclist is gonna do. Are they gonna lane split on speed and get around you?
12:58 Steph Smith? Mm-hmm.
12:58 Speaker 00 So all these motion models and understanding of how people behave, how this gentleman is walking, and what their gait and motion tells you about where they're gonna go. That is the second. And third and final, what should the car do? This feeling that you had that it gently accelerated, but not harshly.
13:13 Steph Smith? Yes.
13:13 Speaker 00 That takes into account not just look at this gentleman. He is, almost has his feet almost on the crossroad, but he's not intending to cross.
13:21 Steph Smith? Yeah.
13:21 Speaker 00 Because he has a stop sign. A very conservative system would just come to a stop. But here we sort of asserted ourselves a little bit.
13:28 Steph Smith? Yeah, we went for it. That's such a great point because... So I learned to drive in the last year. I was actually waiting for self-driving and it took a little too long, but as part of that...
13:38 Speaker 00 You wanna hit resume, right?
13:40 Steph Smith? Yeah, absolutely. Let's go.
13:42 Speaker 00 You can see here, for example, we're showing...
13:44 Steph Smith? Oh yeah, they're jaywalking.
13:45 Speaker 00 Yeah. And you see, within the last minute, you saw an example in which we looked and we noticed that this pedestrian is not gonna cross, so we didn't come to an abrupt halt, we went through smoothly. And later, we just noticed that somebody is jaywalking and we eared it to them.
13:57 Steph Smith? Yeah.
13:57 Speaker 00 That delicate nuance is...
13:59 Steph Smith? And like this lady, you actually don't know. She kinda looks like, oh, she's went back.
14:02 Speaker 00 And you can see here, we are tracking them. So you know, if there was anxious passenger, is this car seeing it? We give them the feedback that we are. We're seeing this pedestrian crossing right here on the screen. We tell them why we're slowing. Because what happens is also, people zone out, they take their space, they speak to their kid if they're picking them up after soccer practice, or take a phone call. And then they notice when the car has stopped. And they're like, hmm, why are we stopped? And here, we try to give them the feedback, why we're stopped? It's for a stop sign now. Or we're eeling for this truck that passed by.
14:32 Steph Smith? So I wanna get to the comfort, like how do you, past autonomy, evolve this product? Because it really is a product.
14:37 Speaker 00 Yep.
14:37 Steph Smith? But first, I feel like the rivalry in self-driving is LiDAR versus Tesla Vision or the video processing. How has Waymo thought about that decision of what some people pose it as, expensive hardware, more simple software? Because you have so much fidelity from LiDAR versus a bunch of cameras and just like a lot heavier processing? So what went into that decision? And also, how are you thinking about that moving forward? Like, is there a future where maybe actually you don't need all of the same sensing systems?
15:06 Speaker 00 Yeah, great question. So personally, I have found the LiDAR and video debate almost takes like an ideological sense. Mm hmm. You know, for hard problems, the innovation arc that we're talking about. Yeah. The best approach is taking a first principles approach, right? Mm hmm. Without neither love nor hate for a specific technology, that is a technology. Yeah. You shouldn't get to that level. It's a tool. And LiDAR clearly has strengths that a camera doesn't. For example, at nighttime, even the best cameras will have some challenges. And camera clearly has strengths that the LiDAR doesn't. The red, green example that we mentioned. And similarly, LiDAR has strengths that LiDARs and cameras don't.
15:44 Speaker 00 So saying you love LiDARs and hate cameras, or vice versa, is saying you love one wavelength versus the other wavelength, right? It's not a fundamental thing. Okay. But there is a practical question. So on the first principles, does the combination of these sensors position you better than individual? Mm hmm. The answer is yes. It can show you that there are situations in which camera will be insufficient. Now, the question is a practical economic one. Is your ability to bring this public good to a large number of individuals hindered by the fact that these things are expensive? Yes. And LiDARs, twenty years ago, if somebody told you that all these cars are gonna have radars, you'd be like, no, radars are expensive. Guess what? Most cars have radars today. Cameras on cell phones were a novelty.
16:29 Speaker 00 Now, cell phones have better cameras than dedicated cameras of six years ago. Yep. LiDAR is going through the same transformation. Okay. Right now, the iPhones have LiDARs, right? The amount we have been able to cost down these LiDARs in the last two years is incredible. Okay, so you're not concerned about that? No. And four, five years ago, we had that belief. Now we have that proof. Because hardware generations, there's multiple examples outside of WiMo to see something that began. Chips are a great example. Cameras are a great example. So it would be surprising if you had hardware that you were able to package. And then with focused effort, you weren't able to. So we had that belief and now we have the proof.
17:08 Steph Smith? Right, and Waymo actually manufactures LiDAR, correct? And I feel like Waymo in a way has chosen to, to your point, like manufacture the hardware, work on its own software simulation technology. Give me the thought process there in terms of, there's always this question in business. Do I build? Do I borrow? Do I buy?
17:26 Speaker 00 Yeah.
17:27 Steph Smith? And given that this is a capital intensive business, how do you think about that? Where should you outsource and where do you really need that fundamental technology yourself?
17:35 Speaker 00 Great question. The first thought we had, let's build this all ourselves. That was not the first thought. But we said, okay, let's see what's the best out there, absolute best, even easing a little bit the cost requirement. Say we were willing to pay.
17:48 Steph Smith? Yeah, we'll pay for it.
17:49 Speaker 00 Yeah, what's the best out there? And what we found is that the absolute best LiDAR out there, absolute best radar out there, was not optimized for the task of autonomous driving.
18:04 Steph Smith? Okay.
18:05 Speaker 00 And hence, we had to build it. Each hardware generation, we do evaluate that. We try to see, okay, hmm, have radars evolved to the point that we could use something off the shelf? So that builders' why is a pretty practical choice each single time.
18:19 Steph Smith? And as we look forward, the question becomes, where do you build a moat? Because now you're not the only company that has achieved some level of autonomy. And you could imagine, like, let's just say a future where we've just achieved level five in many places with many companies. How do you think about what differentiates? Is it really a data moat based on the amount of training that a company has been able to do? Is it owning like the proprietary LiDAR that is just 10x cheaper than the competitor? Trying to think ahead in terms of where does value really accrue in that future?
18:49 Speaker 00 So first, just thinking of the space we're operating in.
18:52 Steph Smith? Yep.
18:52 Speaker 00 We're talking about a space with trillion, you know, miles, right? So it's vast. Imagine when, let's say, the first cars were being built. Folks said, hey, is there space for only one provider or two providers, right? When you're talking about a space of a trillion miles of today, and then you think about what is the potential value add when you have a driver that drives itself? Because partly, if you think about the miles today, we talk about a truck driver shortage, for example.
19:20 Steph Smith? Yep.
19:21 Speaker 00 In fact, even those commercial miles are kind of stunted by the lack of availability of drivers.
19:26 Steph Smith? Sure, yeah.
19:26 Speaker 00 So really, we're talking about a space that is all cars, trucks, and all transportation. When something is that vast, and the number of autonomy players today are much smaller than three years ago, if you look at it. Because it is a pretty challenging problem still. So I think the universe is different. But still a valid question. We do believe that there is a knowledge curve. So by having driven 20 million plus miles in testing, by having done billions of miles of simulation, we become aware of problem spaces that others may not have discovered yet. And that goes into hardware design and software design and simulation design. Hardware design in particular has long lead times. So that becomes a flywheel effect. Your question about how did you know you had to build your own laser?
20:11 Speaker 00 Because by that time, we'd already driven 10 million miles and we're like, hmm, we're gonna need that.
20:16 Steph Smith? Yeah.
20:16 Speaker 00 And that thing's, whoops. That was a person, you saw that that person basically ran a red light, it was red for them. And we can talk if you and/or I were driving.
20:25 Steph Smith? I was gonna say, like, I think if I was a human driving there, like, I don't know what would have happened.
20:30 Speaker 00 And you would definitely not have been able to carry a conversation when that was happening, right?
20:34 Steph Smith? No, I can barely, I can't even talk to someone next to me most of the time when I'm driving. But, okay, so talking about the technology, I want to talk about safety next, but first, are there any important technological unlocks that you still see on the horizon that not just Waymo, but the industry of autonomy is still trying to solve? Is it really that cost curve? Or is there something else that is still in the way of us really rolling this out more broadly?
20:56 Speaker 00 I mean, look, we are definitely, the rate of innovation even just within Waymo, which I can speak to most confidently, is massive. I'll give you a concrete example. When we came from Phoenix to SF, we did have some work to do to adapt the assertiveness in the driving here is different from the Phoenix driving. But when we went to Los Angeles. The driver worked shockingly good from the get go.
21:18 Steph Smith? Really?
21:18 Speaker 00 Yeah, shockingly. And now, there's a portion of Scottsdale, which is the northeastern part of our Phoenix territory. It's a much denser area, lots of restaurants, lots of shops. There, we were able to go in like within two months. We just went there, we decided we're gonna open up Scottsdale within two months. And the reason for that is we're truly, the driver's generalizing very well.
21:38 Steph Smith? Okay.
21:39 Speaker 00 And the concept is pretty intuitive. And this is advancement in AI that's enabling it. But think of a driver that's capable of this kind of tight traffic navigation.
21:48 Steph Smith? Yes.
21:48 Speaker 00 Lots of pedestrians and cyclists. But low speed of travel, that's where we are right now.
21:53 Steph Smith? Yes.
21:53 Speaker 00 Now imagine in Phoenix, forty-five miles per hour, three-lane, four-lane streets, lots of oncoming traffic and being able to navigate that. Pretty much every good weather city is like a linear combination of those two things, right? So in Los Angeles.
22:07 Steph Smith? You kinda got the best of both worlds.
22:09 Speaker 00 Exactly. So you go to West Hollywood, you're much more like a S of style driving, lots of pedestrians, cyclists, and so on. You go to the LA's more faster boulevards, it's a lot like Phoenix. So once you have solved these two, you're just, the AI is just much more generalizable. The second area, I would say, you already mentioned cost down, is making the simulator a lot better. I'll give you an example. We have billions of miles of simulation in good weather.
22:35 Steph Smith? Yeah.
22:36 Speaker 00 If you want to test how would we do in rain, imagine being able to simulate rain. So that you can take all the learnings in good weather, all the tough situations you encountered, and now test yourself in rain. What if rain was a complicating factor on top of that? What if I add a cyclist into that tough situation more? What if all these combinatorial questions?
22:55 Steph Smith? Yeah.
22:55 Speaker 00 Being able to realistically simulate that, that's also a huge area.
22:59 Steph Smith? Yeah, I mean, it's a little foggy today, but I also wonder, you know, you talked about AI, and obviously this is running off of an algorithm that's been trained on all these miles. Is it one algorithm? Or let's say it is an extremely foggy day. It's a rainy day. You're in a new environment. Is it a different, slightly fine tuned model based on different situations? Or is it all one aggregate? That's just ingesting all of this information?
23:22 Speaker 00 It's definitely many, many deep models. Some very general extremely deep learning models and some specialized models to make them really good at some very hard tasks. Like, for example, understanding pedestrians' intent is such a vast space. It's like understanding humans, right?
23:39 Steph Smith? I know, and we're pretty hard to understand.
23:41 Speaker 00 It takes us a lifetime to understand ourselves. So, understanding human behavior and motion, there could be specific models. There could be end to end models on just driving like a good citizen, polite to other riders. That can be a more end to end model. Being comfortable to riders' preferences, that can be a very end to end model as well. So it's a mix of this. And there's AI at every layer of the stack, from perceiving the world to predicting other people's behavior, to the driving, to the testing. So, for example, you asked about fog. So what we try to do is we both observed how other people drive in fog. We're also trying to reason about how well can we see in fog? So if this fog were to get a lot denser, the appropriate thing to do is I can't see that far,
24:24 Speaker 00 so I shouldn't be driving as fast as I would normally do. So that kind of learning is built in into multiple layers of the stack as well. But also some general things that the AI surprises you. You asked about fine tuning. One of the powerful things that deep models are telling us, as well as generative AI is telling us, is that you actually don't need to hand tune every single thing. It works. So that kind of learning, we are doing. And one thing I will say, you asked about moat a little bit earlier. You know, the high level concept of AI may be easy to understand, but really, the breakthrough engineering that you sometimes need in AI is just having the raw infrastructure to intake all this data. The amount of data you have to learn to handle to build a really well learned algorithm. It's pretty hard.
25:08 Speaker 00 And that's where Google's infrastructure that we have worked with.
25:12 Steph Smith? Yep.
25:12 Speaker 00 It's just immense. And the machine learning investments that Waymo and Google did twelve years ago, thirteen years ago, is beginning to pay off in a manner that's pretty hard to just get there.
25:23 Steph Smith? Yeah.
25:23 Speaker 00 So that's a modus.
25:24 Steph Smith? That's actually a great point because, again, we're in a space where you need to react in milliseconds.
25:29 Speaker 00 Right.
25:29 Steph Smith? Right? And so you need to not just be able to train this algorithm, but to interpret live.
25:34 Speaker 00 Yeah.
25:34 Steph Smith? And process that information live. Let's talk about safety, right? I mean, that is like the foundational piece of whether we can get these cars on the road.
25:41 Speaker 00 Right, right.
25:42 Steph Smith? And something I'd love to hear from you on is how different parties interpret safety. Being on the road, you could say, just inherently isn't safe. You can get in a crash, you can die. Unfortunately, that happens every day. So how would you say, between the regulators?
25:59 Speaker 00 Yep.
25:59 Steph Smith? The technologists, like yourself, who are building this, and then the consumers, the people, the riders, how do each of them view this, like, concept of safety? How are you designing the product with that in mind?
26:11 Speaker 00 There is the analytical and quantitative version of safety. You can say the nature of collisions you have, the probability of entering into a certain collision under certain circumstances, lot of complications in there. And good news is many regulatory bodies do have their teams that study crashes, and there are great databases of crashes and so on. There's the element of risky behavior. You may have gotten lucky that you didn't get into a collision, but you undertook risky behavior. So, for example, in that situation we were in before, we could see that those two individuals were gonna jaywalk.
26:44 Steph Smith? Yes.
26:45 Speaker 00 Now, you could have argued that, well, the car has the right of way, we were driving right. No, but that's risky behavior, right? It's preventative behavior. So you can't see that solely with the presence or absence of collisions. Were you a good driver? And third and finally, do you make the person feel safe? So we could be breaking hard anytime we sense a risk.
27:06 Steph Smith? Yeah.
27:07 Speaker 00 Or we could
27:08 Steph Smith? Wouldn't feel very safe.
27:09 Speaker 00 Exactly, you wouldn't feel very safe. And then the fourth layer I would add on top of that is just because you've figured out how to drive smoothly, meeting the expectations of rider, you shouldn't falsely promise
27:21 Steph Smith? Hmm.
27:21 Speaker 00 the true analytical safety either. So for example, designing an algorithm whereby you drive smooth on a street could be easy, but you shouldn't over promise that you can detect a pedestrian jumping out of a car. That takes real heavy engineering that consumer may begin believing that, oh, just because it can drive smoothly, it can probably protect me from that. And I think being truthful about your capabilities is really, really important and what Waymo has tried to do.
27:49 Steph Smith? See their reaction?
27:50 Speaker 00 Yeah, yeah. And you can see how tight the space is, truck on the right, a car just went by, a pedestrian just crossed, and there's somebody just going in. And noticing that this fire truck didn't have its sirens on, so it didn't try to rush out of the way.
28:03 Steph Smith? Yeah, to get out.
28:04 Speaker 00 So slight change into that situation, which we can test in simulation, we would say, hmm, right now, the politest thing to do is let that pedestrian pass and, you know, wait for this turn. But when the sirens are on, it's a different environment.
28:16 Steph Smith? Uh huh.
28:16 Speaker 00 So anyhow.
28:17 Steph Smith? And that's another data point, right? That you need to take in, it's sound, it's not just visual.
28:21 Speaker 00 Oh, yeah, that's the sensor we didn't discuss. There are microphones that can not only hear that there is a siren, but also point at where the siren is coming from to know.
28:29 Steph Smith? Directionally.
28:29 Speaker 00 Yeah.
28:30 Steph Smith? That's fascinating. What does the data say, though? Can you kind of ground us in there is a certain number of crashes that humans engage with every single year? And then where the technology is at relative to that?
28:42 Speaker 00 Yeah, so both the methodology by which we evaluate our safety, which is a combination of many, many.
28:47 Steph Smith? This will be interesting, there's a bunch of uh.
28:50 Speaker 00 Ocean of cyclists here. Look at that, it went around a double parked truck.
28:55 Steph Smith? And look at them, they're like, they don't know what to.
28:57 Speaker 00 Yeah, look at all of them. We're telling you who are we waiting for.
29:01 Steph Smith? See, they just pointed inside.
29:04 Speaker 00 Look at that, we can detect all of them, we're giving you feedback that we can see all of them.
29:08 Steph Smith? You can literally sense every single one.
29:09 Speaker 00 Yep.
29:09 Steph Smith? Oh, and them coming around the corner.
29:11 Speaker 00 Yeah, yeah.
29:11 Steph Smith? Oh.
29:12 Speaker 00 And we're turning. Because we can see that their handles are turning leftward, so we can understand that they're likely gonna go that way. So we don't have to come to a stop. So balance between making progress, because that's what a rider expects.
29:26 Steph Smith? Yeah, exactly.
29:27 Speaker 00 And ultra-conservative behavior that may or may not be warranted. To your earlier question, so what we did is, I'll give you just two examples from many safety methodologies we employ. East Valley of Phoenix. We have been operating for a while. There, one thing we did is we took every fatal crash that had occurred and resimulated and showed that we could avoid that.
29:48 Steph Smith? Okay.
29:48 Speaker 00 That was one very specific data set. What we also did is we were the first company ever to cross 1 million fully autonomous miles, miles like this.
29:58 Steph Smith? Yes.
29:58 Speaker 00 Nobody in the front seat. So there's no debate about what the car could have done. It was real miles. In that, we published our full crash tags.
30:06 Steph Smith? Okay.
30:07 Speaker 00 And there was not a single collision with injury.
30:09 Steph Smith? Really?
30:10 Speaker 00 In 1 million miles.
30:10 Steph Smith? Not a single one?
30:10 Speaker 00 Not a single one.
30:11 Steph Smith? Okay.
30:12 Speaker 00 And only two of them would meet the standards of a reportability called CISS. And, yeah. All right.
30:20 Steph Smith? You're here. Oh, we're here.
30:22 Speaker 00 Yeah, and look at that, how it stopped because it saw that they wanted to cross.
30:25 Steph Smith? I love seeing it, so many people when they look inside. Look at them, just like, what's going on there? All right. Oh, and she's been telling us, yeah. Okay, so I'm gonna click resume ride.
30:36 Speaker 00 And the reason, by the way, we're switching a little bit from safety to user experience design. The reason it told you cyclist approaching is what we know is even when the car is stopped, what happens is people open the door and the cyclist will take...
30:47 Steph Smith? I mean, I've seen videos of that.
30:49 Speaker 00 So this element of safety, it's not just about when it's driving, but it's about caring about safety every second.
30:55 Steph Smith? I love the analogy you used about even a driver. We only have two eyes, they're right up in the front of our face. If we're looking a certain direction, if we're focused on music or a podcast in the car, our senses are, as much as we like to believe as humans, we're all special and we're the best drivers. We are variable in ourselves in terms of what we're focused on.
31:13 Speaker 00 And if your two kids in the back start screaming at each other or something. By the way, I know you're relatively new to SF as well. I hope you're enjoying the view as well.
31:21 Steph Smith? I know, it's so cool to see around the city. Absolutely. I love to think about these like second, third order effects, like people taking city tours in these cars. I mean, we've basically turned this into a recording studio, which you would never think of in the past.
31:35 Speaker 00 I can take confidential work calls. That was not a thing I could do before, right?
31:39 Steph Smith? It's such a good point. I was looking through the reviews on the app and I saw this one. You get a sense of the user that wants to be in a car like this and they called it basically Uber and Lyft for introverts. And you know, there's just these little things that you don't think about because again, we're fixated on safety and that is so foundational. But then you really do, once you have covered safety, once it becomes safe in the eyes of the regulators, the consumers, it opens up all these doors.
32:04 Speaker 00 Oh, absolutely.
32:05 Steph Smith? You know, we work with so many founders in nascent spaces, AI, Web three, space. I would say those three industries also have a fair amount of pushback, which autonomous vehicles do as well, right? And in many cases, rightfully so, especially autonomous vehicles, like we're talking about people's lives on the line. And so have there been any learnings since you've been working in this space for a while where you're trying to meet this safety need? But you're also trying to push regulation, welcome this technology into different cities.
32:33 Speaker 00 Yeah, yeah. It may sound like an idealistic answer, but I do believe that there are applications that have a fundamentally different use case or a promise. And then they're trying to make sure they're not harming the public or unintended consequences. The reason we exist is to make driving safer. So we have deep fundamental alignment with what sometimes the regulators are trying to achieve. Now, we may have different inputs, we may have different data, we may be approaching it from a different angle. But I believe that every conversation I have had with anybody who is in state, federal or local government, or even outside of regulators, just firemen, local law enforcement, we very quickly in that conversation,
33:18 Speaker 00 I begin to appreciate, our team begins to appreciate, and they begin to appreciate that we're trying to do the same thing here.
33:24 Steph Smith? Okay.
33:25 Speaker 00 And that is a powerful.
33:28 Speaker 00 baseline to begin constructive conversations out of. But if, for example, the goal was something else, and by the way, what about safety? Right? That would be a very challenging conversation. We try to say, this is what we're trying to do, folks. This is what we have measured.
33:42 Steph Smith? Finding common ground.
33:43 Speaker 00 Yeah, and just transparently sharing the data. Safety, oh, is it collisions? All right, well, in collisions, we have published our accident reconstruction. We have done twenty million miles of testing, billions of miles of simulation, and we're telling you every single contact we have had in one million autonomous miles. By the way, this week, we're about to cross two million. First company again. First company again, twenty-four seven, including daytime, not filtering out any of the challenging situations, dense downtowns, twenty-four seven, two million miles. More than hundred sixty years of human driving worth of data we'll just share with the world. And then they can see for themselves that we're clearly a safer driver. And if ever there was an event about which they asked us, we would transparently share with them.
34:27 Speaker 00 So I think that gives a fundamentally good basis, and I genuinely believe that even the word pushback, all right, it's almost like internal debates at Waymo. When we debate, how should we design this thing? We come at it from different angles. Somebody may see the user's expectation of smoothness of drive. Somebody may see more, hmm, what's that scooter's intent? You see how the scooter swung in from the right, kind of came in between a bus and themselves? What are their expectations? What's the expectation of a pedestrian if they were to jump out on this side? We may approach the problem from different angles, but our core mission of safety is so deeply drilled into every Waymo nut that we believe that every person who meets us will see that.
35:10 Steph Smith? Yeah, and I mean, one aspect I love that it's ingesting all this data from, like you said, almost two million miles now. And when you think about us as human drivers, like no one, like I certainly have not driven two million miles. I'm not even really processing exactly what happens when a scooter is coming up on the right here, and if I've spent my whole life driving in SF, which is not the case for me, but some people that is, and then you drop them in Phoenix, they are new to that road, just like you training in a new city.
35:39 Speaker 00 And by the way, the two million thing that you mentioned, that's just the fully autonomous miles.
35:42 Steph Smith? Mm hmm.
35:43 Speaker 00 We have billions in simulation.
35:44 Steph Smith? Yep.
35:45 Speaker 00 But yeah, here there was a lot of experimentation on how much is the appropriate amount of detail. So the vehicle and the sensors are seeing a lot more detail than what's being shown here, right? We're seeing many, many points per square inch of detail here. So we tried to experiment with how much detail we put in here. And there are folks who, when in the early days, we had a version here that would show a lot more detail. And they would engage like this, right? They were, in fact, they would look more inside than outside the window and keep crushing. Did it see that goal? Did it see that thing?
36:18 Steph Smith? Yeah.
36:19 Speaker 00 And what we came to a balance with is we want to put people at ease and invite them to use this space. And it pops up. So when the vehicle stops, you will notice it'll try to explain why it's stopping. Like, is it a stop sign or is it yielding to pedestrian? Because we realized through lots of experimentation that that's when people want to take a look at the screen.
36:38 Steph Smith? Okay.
36:39 Speaker 00 So for example, if you're by yourself, we want you to get lost in the beauty of San Francisco around you. If you are checking emails, it's all right, go do that. And you will take your head off your phone or from viewing the painted ladies. When you see, hmm, why are we stopped? And we will tell you, well, there are two pedestrians on the right, there is a car crossing on the left. There's a car parking in the front, we'll explain that to you. But then you can see here, who are we slowing down for? You see that little gentle highlight? Lots of design experimentation goes into that. Because we don't want to be in your face. We want to be gentle, soft, and you'll notice that in the night, this will go into dark mode. Because the ambient lighting is reduced. So we don't want to be too bright, because if you want to just take a nap, we want you to take a nap.
37:25 Steph Smith? That's a great point, and I'm curious if there's other learnings from, again, you've rolled this out. The number one thing is safety, but then from there, it's like, how do you create a great product? That people want to engage with, that people want to come back to, so it's not just a novelty, right? Where you're like, oh, I sat in an autonomous vehicle. Where they want to use this for their commute every day or for their daily lives. So how do you think about that, other than maybe the screen? Are there other things within the car?
37:48 Speaker 00 Thousands, thousands. And by the way, speaking of modes, that's another powerful flywheel advantage, right? If you have served. The first 10,000 humans who have been in a fully autonomous car. The feedback they give you and the time you have to incorporate that into your learning is a great positive flywheel, approaching 100,000 fully autonomous rides in a month. So that feedback does help. I'll give you just a couple of examples. We could speak for hours just on that part. One example I'll tell you is imagine a residential street like this one that we're passing. Remember when we were getting into the car, you were asking, hmm, which side of the street should I be on?
38:25 Steph Smith? Yep.
38:25 Speaker 00 Now imagine you're just getting out of home.
38:27 Steph Smith? Yep.
38:28 Speaker 00 Going to work. Some riders, you would imagine, expect the vehicle to pull up right on their side of the street.
38:35 Speaker 00 Actually, it turns out to be largely incorrect. Because the way you would achieve that is, let's say you're coming from this direction, you would make a U-turn.
38:42 Steph Smith? Yes.
38:43 Speaker 00 And come back to them. But on narrow residential streets, folks are like, that was not necessary. It takes me a second to walk across the street. I could have entered on the other street. You didn't need to turn around.
38:52 Steph Smith? Yeah, you're like, I gotta get to work.
38:54 Speaker 00 Yeah, I gotta get to work. But that same reasoning does not hold on a street like this one.
38:59 Steph Smith? Yes, yep.
39:00 Speaker 00 Because you're like, why are you making me cross two lanes of traffic? I was going to the coffee shop on the other side. Just drop me in front of the coffee shop. So you can see how it's not just a single rule that you code in. You don't say, hey, always park on the side that the pedestrian is on. No. It depends on the context. If you're on a busy street like that and somebody is going to a business, they would like to be dropped very likely in front of the business they're going to. Whereas if they are getting out of home, going to work, and it's a narrow residential street, just go to whichever side is closest.
39:30 Steph Smith? Yep.
39:31 Speaker 00 That's just a thing, just that rule, training it, required speaking to many riders, what they expect, learning that, and then being able to articulate that from a machine learning standpoint and overall rules-based standpoint. That's just one example. Many more.
39:45 Steph Smith? Are there any other learnings about what makes people feel comfortable or want to come back? Like, one thing that's coming up is like, I can see the driving.
39:51 Speaker 00 Yeah.
39:52 Steph Smith? Wheel, right? And I can imagine, especially once. L5 is hit, then we don't really need the steering wheel, right? You don't need the same car design because in the past, for a hundred years, cars have been designed around the driver.
40:06 Speaker 00 Right.
40:06 Steph Smith? And now, we don't have a driver. So, you know, it introduces all these questions. But I'm curious, I know we haven't removed the steering wheel per se, but are there other dynamics that just from us growing up in cars designed a certain way that we expect certain things? And then are there other things where you're actually like, oh, no, we can start to get rid of some of this?
40:24 Speaker 00 Yeah, yeah. Specifically about the steering wheel. It's blocked currently by regulations beyond a certain number of vehicles and so on. And our next generation vehicle that we did design with CVT and Geely is a pretty powerful platform thought with the rider in mind. So we spent months and years with designers on all teams trying to visualize that. I personally do believe that, thinking of all the screen and software aspects.
40:48 Steph Smith? He's like waving the car.
40:51 Speaker 00 Did you see that? And, and that was beautiful, wasn't it? Like, it's polite, but it's also responsive. And that, that, you know, today we spoke about everything from safety to artificial intelligence, to design, to user understanding. In that three seconds there, all of that got exercised, right? Because we were confident that there's enough gap, there's no imminent contact. We saw a collaborative fellow occupant of the road, not an adversarial one, because sometimes it could be the other kind.
41:18 Steph Smith? Have you seen that, by the way, where people will start yelling at?
41:21 Speaker 00 No, I mean, yelling is expression of emotions, that's okay. The physical demonstration of turning in or like that example that we saw right now of somebody who ran a red light, if you recall.
41:31 Steph Smith? Yes.
41:32 Speaker 00 Those are the ones that are truly could result in danger, which is what the vehicle positions it. It thinks about many small details. Where is it positioning itself? How much gap is it leaving? What's its velocity so that it has the greatest optionality if somebody were to behave recklessly?
41:46 Steph Smith? Okay.
41:47 Speaker 00 So when we are crossing that sign, we're not assuming that everybody's going to obey the light. We're trying to monitor, hmm, their light is red, which means they should be slowing down. Why aren't they slowing down? That's an anomaly. Let's prepare for this anomaly. Let's protect, let's be defensive against that anomaly.
42:03 Steph Smith? Like, something that comes to mind is just as a rider, you wanting to feel confident in the car and seeing it be a little more assertive is actually really reassuring at points, when it makes sense. Because to your point, if it's constantly stopping, if it's constantly pulling over, then I don't have confidence that this thing is going to know what to do and a kind of.
42:20 Speaker 00 Yeah, and you have a busy life. You want to get where you're going, right? So, by the way. Iconic place coming up.
42:26 Steph Smith? Oh, yeah. Are we at the painted ladies?
42:27 Speaker 00 Yeah.
42:29 Steph Smith? I think I see them up there.
42:30 Speaker 00 Yeah, yeah. By the way, that's the other thing you were asking early on about where are we in the journey? We are in San Francisco. We're not taking you to some little test facility in a desert to show you that.
42:43 Steph Smith? We are on the road.
42:44 Speaker 00 You are in one of the most vibrant cities on the planet. And if you wanted to come by to Los Angeles, we would take you to the most important part of Los Angeles. And in Phoenix, we would take you to Scottsdale. And when you land at the airport, we will pick you up at the airport. That's where we are in the journey. We're fully autonomous where we need to be.
43:03 Steph Smith? I love that it tells you finding a spot to pull over.
43:05 Speaker 00 Yeah, look at that. Right? And it sees all those little kids there. Do you see that?
43:10 Steph Smith? Yep.
43:11 Speaker 00 And it's more cautious because it understands that kids can jump out more.
43:16 Steph Smith? They're unpredictable.
43:17 Speaker 00 Yes.
43:18 Steph Smith? Oh, and vehicle approaching. So that's what you were saying earlier about so you know not to open your door.
43:23 Speaker 00 Exactly, exactly.
43:24 Steph Smith? And now you can continue. All right, so those are the painted ladies. Beautiful. But yeah, I love the point that we're just, we're fully on the road. We're not in the middle of nowhere practicing.
43:35 Speaker 00 And you should see the eyes of that kid just noticing that we don't have a driver. Look at that.
43:39 Steph Smith? Like, he's young enough where he can't even articulate, but he's like, this isn't, my pattern recognition is off.
43:44 Speaker 00 And by the time he grows up, this will be the more common route.
43:47 Steph Smith? Well, I mean, you mentioned you're in SF, you're in Phoenix.
43:49 Speaker 00 Yeah, Los Angeles.
43:51 Steph Smith? L.A. How have you decided which markets to address first? Is it just a matter of what cities will welcome this technology? What goes into that calculus?
43:59 Speaker 00 In the very, very early days, we tried to make sure that we're picking a city that challenged the system in many different directions. Because we were in development stage, so we tested in 20 cities just to make sure that from the very early days, we're building a generalizable driver. Not one that just works in one location, but generalizes double parked truck, by the way.
44:17 Steph Smith? And so is it, it's even sensing maybe the lights blinking?
44:21 Speaker 00 When, for example, somebody's unparking, we notice their noses starting to jerk out.
44:26 Steph Smith? It is so bizarre to see a wheel moving on its own like that. Like, it truly does look kind of fake in a way. But yeah, so you're trying to find cities that kind of test the system, push it forward.
44:37 Speaker 00 Yeah, and then what we found is that we have tested, for example, in Miami as well for the heavy rain. We have tested in Death Valley for extreme temperatures. We have tested in Tahoe for snow. So there was a testing phase in which we went to 20 cities just to make sure we've enough of a diverse dataset to be building off of. Look at that gentle, because this car was coming, that tram was coming. Anyhow, it's beautiful to see.
44:58 Steph Smith? I know, I love it. It really is something that it's like, I'm watching every little thing that's happening.
45:04 Speaker 00 You can see this pedestrian crossing, the car going, another one approaching with green light. So turn right after the pedestrian went off. Anyway, sorry. And you were asking, does it get boring for you after working?
45:15 Steph Smith? Oh, I love seeing your excitement, because I mean, this is my first time. So I really am, like, as they say, like, taking it all in. How many times do you think you've been in a...
45:24 Speaker 00 Yeah, like, just the first year in 2019, you spent a ton of hours in it.
45:28 Steph Smith? Actually, were you scared at all when you were first testing it? Because now, I mean, I guess I've seen some of the data, I've seen these on the road. So there's a level of like, oh, I know these work. But when you were first getting into the vehicle, was there any, like, fear, apprehension?
45:47 Speaker 00 Not fear, the closest feeling I can describe is when you have prepared for an exam for six years. Whatever is the biggest exam you've given, this little feeling that you got that you have prepared as best as you possibly could for it. You left nothing on the table, but it is exam day.
46:04 Steph Smith? You're like, oh gosh, I hope I can show up, where I hope this works. Well, it's funny, because a lot of people, they'll see the stats and they'll be like, that's the average human. But they think for whatever reason, they outperform the average human on the road. But I'm the opposite, I'm like you, where I'm like, I want this.
46:18 Speaker 00 The fundamental attribution error, by the way, is that more than 50% of people think they're better than average. You're just not supposed to be feasible, right? So maybe on the city selection, just to close that out. So yeah, LA, 2 billion plus TAM. Huge diversity of use cases, everything from commute trips all the way to sports events, to sightseeing tours that you were mentioning. San Francisco, similarly, both SF and LA are top two among the top five ride-hailing markets in the US and among the top in the world. Phoenix is the fastest-growing city in the United States. Phoenix Airport is among the top ten busiest airports in the world. So when we commit to a city for a launch,
46:56 Steph Smith? Right.
46:57 Speaker 00 that's different than going to a city for data collection and testing.
47:00 Steph Smith? Something else that's coming to mind is, I wonder, we are in the early stages, we're only in a few cities. But as consumers do see these on the roads, like this is something that some people really will want to see in their cities. And so have you seen any shift in terms of regulators maybe being a little against it to actually being like, this is a competitive advantage if my city offers this?
47:21 Speaker 00 We definitely see that in the place where we have been the longest is Phoenix East Valley. And everybody from passengers to neighbors who even haven't taken a ride. They noticed that it's a much more polite driver to law enforcement, all the way to city and mayor level, all the way to state level, absolutely.
47:41 Steph Smith? I love thinking about just what does this unlock?
47:43 Speaker 00 Yeah.
47:43 Steph Smith? And there are few industries that can unlock so much because people do spend so much time
47:49 Speaker 00 Oh, yeah.
47:49 Steph Smith? in cars. I think that was an amazing, beautiful thing we saw from remote work. It's just the second, third order effects, like what do we have when we get that commute back? But then there still are people spending a lot of time on the roads. And so I'd love to hear from you, like, what are some of those impacts, the wider impacts? Maybe it's on the way the insurance industry works, maybe it's on trucking,
48:10 Speaker 00 Yeah.
48:10 Steph Smith? maybe it's on city design, now that we have data, like, that's another aspect. We now have data about how people really move around a city and interact and how things are designed, parking, right? So maybe pick and choose what excites them most.
48:23 Speaker 00 Pretty vast, yeah, right? So I do believe it's pretty profound and only some of those aspects we can see and some we will be shocked by what it'll do. Because again, like you said, not only is the car designed around a driver, life is designed around driving, right? Like, look at how much parking space. You know, that house right there costs insanely higher amount in dollar per square mile. And I'm sure as a new SF resident, you were aware of it.
48:50 Steph Smith? So I'm well aware.
48:52 Speaker 00 So there are so many things in our cities that are designed around assumption of not only a human driver, but also of a highly underutilized expensive asset just sitting there. All these vehicles just add up the cost of these vehicles. And think of how much space in the city they may be consuming, while not adding to the productivity of the city. Because at this instant, I'm sure each of these vehicles added to the mobility and freedom of individuals. That's great. But at this instant, they're not utilized. They're not adding value, neither to their rider. They're giving a promise that when the person who's gone in for an eight-hour workday, when they come out, it'll still be there.
49:32 Steph Smith? I heard this quote the other day. It was like, "We dedicate in cities more space to sleeping cars than sleeping humans." Which is kind of crazy when you think about it.
49:38 Speaker 00 And first and foremost, there are people who are traveling today that we believe over time, just the roads will get safer. And that in itself, I know I'm saying safety so many times, but that truly is central, right? 1.35 million people are killed on the streets. How is that acceptable, right? So that's first and foremost for all kinds of, for society at large. It's a pandemic almost, right? And this is an antidote to that. And then there are classes of individuals for whom this freedom is not available. Above 65, many folks can't drive anymore or drive while taking risk or would have the freedom of mobility. There are folks with visual impairments.
50:22 Steph Smith? Yes.
50:22 Speaker 00 Then vulnerable populations being able to take jobs that they otherwise wouldn't have been. That nighttime use case that we said, giving economic opportunity, which they simply would not be comfortable. Or you shouldn't ask somebody to own a car before they can take their entry-level job. Like, that's a challenging economic catch-22, right? So there's that. Then the third is, yes, how much pollution idling cars in city centers are causing? How much city real estate is being lost to idle assets? So yeah, layers and layers upon that. And yes, that's just in passenger vehicles. Then you consider in-city delivery. Then you consider long-haul trucking, where already tremendous amount of economic opportunity in the United States is being lost to lack of drivers.
51:07 Speaker 00 And by the way, when we do have drivers, we have mandatory breaks, because fatigue is a real thing.
51:12 Steph Smith? We recently moved. I drove from near San Diego up to San Francisco. And we happened to do it at night. And just the number of truck drivers that we saw on the route, I just, I knew this already, but seeing it, I was like, "Something feels wrong here." And I just imagine a future where some people might not like this, but that's automated. That sounds beautiful to me.
51:32 Speaker 00 Yeah, and there's a beautiful transition points as well in the sense that... Truck driving could become a local job, which would be powerful in many, many different ways in the sense that.
51:42 Steph Smith? Oh, I see it sends the pylon.
51:45 Speaker 00 And you see that?
51:47 Steph Smith? Oh my gosh, that's incre- because see, I was wondering actually if it could tell if that's a hu- so they can tell that those are humans.
51:52 Speaker 00 Yeah.
51:53 Steph Smith? But then these are pylons and it can send every single one.
51:56 Speaker 00 And there was like a roughly written keep right and a should going there?
52:00 Steph Smith? Yeah, because you could imagine how it might think to go left.
52:02 Speaker 00 I think there's enough space there. So I think construction zones, by the way, are interesting change to an otherwise structured world, right?
52:10 Steph Smith? Because there are no construction zone is the same, right?
52:12 Speaker 00 Yeah, exactly. Each one is unique and the degree of intelligence required to figure that out is pretty substantial as well. Because it's suddenly you're breaking the prior structure of the world.
52:22 Steph Smith? Yeah, so I mean, you mentioned how these vehicles can actually make our world safer. I mean, maybe I'm getting a little ahead of myself, but do you imagine a future where actually, right now, it's like most humans drive? Where actually it becomes illegal or the minority of people who are able to drive? Because we get the technology so far along where it's just, again, it's a no-brainer for us to have the tech drive us around instead of vice versa.
52:49 Speaker 00 The mission we have is being able to provide this option, safe and easy for people and things to move around. I think it's good to have the choice.
52:58 Steph Smith? All right, so maybe to close things off, I'd love to hear, you've been working in this space for a while. What gets you excited?
53:04 Speaker 00 Seeing the riders the first time, the fifth time, and what they say about us, that gets me excited.
53:10 Steph Smith? Amazing. Well, thank you so much. This was an excellent first ride. I'm so excited to do this more in San Francisco. I love the music too that they're, they're welcoming us to explain this out.
53:19 Speaker 00 By the way, we had lots of music as well. We were having a conversation, so I didn't show you. But yeah, a lot of effort has gone into this one as well.
53:25 Steph Smith? Yeah, and I can't wait. I imagine like this being personalized as well in the future. The climate control, the music, hooking up to Spotify.
53:31 Speaker 00 From your app, we did both of these screens as well because there would be cases in which somebody would be in front, synchronizing all of that, but for another time.
53:39 Steph Smith? Well, this was great and I guess we just hop out?
53:41 Speaker 00 Yeah, yeah.
53:42 Steph Smith? We're here.
53:53 Steph Smith? This is great. I guess I shouldn't, it knows I'm here.
53:56 Speaker 00 It knows.
53:56 Steph Smith? I'm imagining myself on this screen as the little guy.
54:03 Speaker 00 So how did you find the ride?
54:04 Steph Smith? I really enjoyed it. I mean, I feel like... It's funny because I only really noticed the lack of driver for maybe two minutes. And then, as you saw, I was so involved in the conversation. You don't even notice there's no driver there.
54:19 Speaker 00 And that's what we want.
54:21 Steph Smith? What's that quote where it's like, "Any sufficiently advanced technology is indistinguishable from magic"? And it really does like, wow, we're here.
54:28 Speaker 00 Yeah.
54:28 Steph Smith? We're here. We just did it.
54:30 Speaker 00 Well, I'm glad to have been there when you had your first experience.
54:32 Steph Smith? Yeah, well, thank you so much. This was so cool.
54:35 Speaker 00 Yeah.
54:40 Steph Smith? Alright, if you made it to the end here, I just wanted to say thank you. This episode in particular was actually really special to me. I got my first learner's permit when I was 16 in Canada. And I actually waited until I was 29 to get my driver's license because, quite frankly, it scared me and I was waiting for self-driving. And it's finally, at least in some places like San Francisco, it has arrived. And I am so excited, I am so happy that I could share this with you. And if you are just as excited as I am to see how this whole thing unfolds, let us know in the comments what you are most excited about, how you think autonomous vehicles might most reshape society.
55:23 Steph Smith? Because there really are so many implications, whether it's public infrastructure, energy, finance, shopping. I'm so interested to see how this technology finally comes into play. Alright, on that note, thank you again so much for joining me. We'll see you next time, and we'll see you on the road.
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