The Road to Autonomous Vehicles: Are We There Yet?

23:22It'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. For example, understanding pedestrians intent is such a vast space. It's like understanding humans. I know, and we're really hard to understand. 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 writers preferences, that can be a very end -to -end model as well.

23:59So 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 and fog. We're also tried 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, 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.

24:35You 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 you in every single thing. It alerts. So that kind of learning we are doing, and one thing I'll say you asked about more to a little bit earlier, you know, the high level concept of AI, maybe 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 is pretty hard.

25:09And that's where Google's infrastructure that we have worked with, is just immense and the machine learning investments that Waymo and Google did 12 years, so beginning to pay off in a manner that's pretty hard to just get there. So that's a more just more. That's actually a great point because again, we're in a space where you need to react in milliseconds, right? And so you need to not just be able to train this algorithm, but to interpret live 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 right, right.

25:42And 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 dive, unfortunately, that happens every day. So how would you say between the regulators, the technologists, like yourself, we're building this, and then the consumers, the people, the riders, how do each of them view this concept of safety? How are you designing the product with that in mind? 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.

26:24And good news is many regulatory bodies do have their teams that study crashes and they're a 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 going to j walk. 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.

26:57Will you have good driver? And third and finally, do you make the person feel safe? So we could be breaking hard anytime we sense a risk. Yeah. All we could feel very safe. Exactly. You wouldn't feel very safe. And then the fourth layer I would add on top of that is just because you figured out how to drive smoothly, meeting the expectations of a rider, you shouldn't falsely promise 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 you jumping out of a car.

27:33That 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 Wemo has tried to see their reaction. Yeah, yeah. And you can see how tight this space is struck on the right. A car just went by a pedestrian just crossed and there somebody just going in and noticing that this fire truck didn't have it siren on. So it didn't try to rush out of the way. So slight change into that situation which we can test in simulation. We would say, Right now the politest thing to do is let that pedestrian pass and wait for this turn.

28:14But when the sirens are on, it's a different environment. So anyhow. And that's another data point, right? Yes. That you need to take in, it's sound, it's not just visual or. Or yeah, that's the sensor we didn't discuss. There are microphones that can not only hear that there's a siren, but also wait at where the siren is coming from to do. Yeah. 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. Yeah, so both the mythology by which we evaluate our safety, which is a combination of many many this will be in Jersey there's a bunch of ocean of cyclists here Look at that.

28:53It went around a double park truck And you see it can detect it. Yeah, look at all of them. We were telling you who are we waiting for See, they just pointed inside. Look at that. We can detect all of them were giving you feedback that we can see all of them. Yeah, you can literally sense every single one. Oh, and them coming around the corner. Yeah, yeah. Oh. And we're turning. Because we can see that their handles are turning leftward, so we can understand that they're likely going to 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.

29:26Yeah, exactly. 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 employed. 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 were going to void that. That was one very specific data set. What we also did is we were the first company ever to cross one million fully autonomous miles. miles like this. Yes. Nobody in the front seat. So there's no debate about what the car could have done.

30:02It was real miles. In that we published our full crash tags. Okay. And there was not a single collision with injury. Really? Not a single one. Not a single one. Okay. And only two of them would meet the standards of a reportability called CISS. And yeah. All right. You're here. Oh, we're here. Yeah. And look at that. How it stopped because it's so that they wanted to cross. I love seeing it so people when they look inside. Just like what's going on there. All right. You're telling us, yeah. Okay, so I'm going to click resume ride. And the reason by the way we're switching a little bit from safety to user experience design.

30:40The reason it told you cyclists approaching is what we know is even when the car is stopped what happens to people open the door of the cyclist watching. I mean, I see videos about the car. So this element of safety it's not just about when it's driving, but it's about caring about safety every second. I love the analogy you used about even a driver. We only have two eyes. They're right at the front of our face. If we're looking in 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.

31:10We are variable in ourselves in terms of what we're focused on. And if you're two kids in the back, start screaming at each other or something. By the way, I know you were relatively new to us. I hope you're enjoying the view as well. I know. I know it's so cool to be 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. I can take confidential work calls. That was not a thing I could do before, right? It's such a good point.

31:40I 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 left for introverts And you know there's just these little things that you don't think about because again And 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. Oh, absolutely. You know, we work with so many founders in nascent spaces, AI, Web3, space. I would say those three industries also have a fair amount of pushback, which autonomous vehicles do as well, right?

32:16And 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 learning since you've been working in this space for a while, where you're trying to meet the safety need, but you're also trying to push regulation and welcome this technology into different cities? 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.

32:53So 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 have been 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, I begin to appreciate our team begins to appreciate and they begin to appreciate that we're trying to do the same thing here. And that is a powerful baseline to begin constructive conversations out of.

33:31But 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. This is the left ground. Yeah, and just transparently sharing the data. Yeah, safety. Oh, is it collisions? All right. Well, and collisions we have published our accident reconstruction. We have done 20 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.

34:02First company again, 24 -7 including daytime, not filtering out any of the challenging situations, dense down downs, 24, 7, 2 million miles, more than 160 years of human driving worth of data will 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. So I think that gives a fundamentally good basis. And I genuinely believe that even the word pushback, right, it's almost like internal debates at WEMO. When we debate, how should we design this thing? We come at it from different angles.

34:41Somebody 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? Whatever they're 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 driven to every way or not, that we believe that every person who meets us will see that. Yeah, and I mean, one aspect I love that it's ingesting all this data from, like you said, almost two million miles now.

35:17And 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 an 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. And by the way the two million thing that you mentioned that's just the fully autonomous miles. Not the billions in simulation. But yeah here there was a lot of experimentation on how much is the appropriate amount of detail.

35:51So 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. They would engage like this right? In fact they would look more inside than outside the window and keep crushing and didn't see that cone, didn't see that thing. And what we came to a balance with is we were to put people at ease and invite them to use this space and it pops up.

36:26So when the vehicle stops you will notice it will try to explain why it's stopping. Like is it a stop sign or is it yielding to a pedestrian? Because we realized through lots of experimentation that that's when people want to take a look at the screen. Okay. So for example, if you're a goat by yourself, we want you to get lost in the beauty of San Francisco. 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 them. Why are we stopped and we will tell you, well, there are two pedestrians on the right.

36:57There 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, watch you today again. That's a great point. And I'm curious if there's other learnings from, again, you've rolled this out.

37:30The 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 a 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, or there are other things with cars? Oh, yeah, thousands, thousands. And by the way, speaking of mortes, that's another powerful flywheel advantage, right? If you have served the first 10 ,000 humans who have been in a fully autonomous court, 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.

38:09So 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, which side of the street should I be on? Now imagine you're just getting out of home going to work. Some riders you would imagine expect the vehicle to pull up right on their side of the street. Actually, it turns out to be largely incorrect. Because the way you would achieve that is let's say you were coming from this direction, you would make a U -turn and come back to them.

38:44but 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. I got to get down there. It didn't work. But that same reasoning does not hold on a street like this one. Yes. 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. And you don't say, hey, always park on the side that the pedestrian is on.

39:16No. 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. That's just a thing, just that rule, training it, required speaking to many providers what they expect, learning that and then being able to articulate that from a machine learning stand point and overall rules based stand. That's just one example many more. Are there any other learnings about what makes people feel comfortable or want to come back?

39:49Like one thing that's coming up is like, I can see that driving wheel, right? And I can imagine, especially once Al -5 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 100 years cars have been designed around the driver. And now we don't have a driver. So 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 we were actually like, oh, no, we can start to get rid of some of this.

40:24Yeah, yeah. Specifically about the steering wheel it's blocked, accordingly, by regulations beyond a certain number of vehicles and so on. And our next generation vehicle that we did design with C, VT, and Gili is a pretty powerful platform from thought with the writer and mind. So we spend 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. I'm just waving the car. Just do that. And that was beautiful, wasn't it? Like it's polite, but it's also responsive. And that, you know, today we spoke about everything from safety to artificial intelligence, to design, to user understanding.

41:05In that three seconds there, all of that garbage is right. Because we were confident that there's enough gap. There's no imminent contact. We saw a collaborative fellow occupant of the Rudnar in a vursary, or one because sometimes they can be the other guys. I've seen that by the way, where people start yelling. Oh my god. 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, somebody who ran a red light, if you recall. Well, those are the ones that are truly good result in danger, which is what the vehicle positions of it thinks about many small details.

41:39Where 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? So when we are crossing that sign, we're not assuming that everybody is 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 formally. 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.

42:11When it makes sense, because it's your point, if it's constantly stopping, if it's constantly pulling over, then I don't have confidence that this thing is gonna know what to do in a kind of... Yeah, and you have a busy life, you wanna get where you're going, right? So by the way, iconic place coming up right? Oh yeah, are we at the painted ladies? Yes! I think I see them up there. 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 this car.

42:43We are in the heart, everyone. We are in the heart, everyone. Most vibrant cities in 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 landed the airport, we would pick you up at the airport. That's where we are in the journey, or fully autonomous where we need to be. I know that it tells you you're finding a spot to pull over. Look at that. Right? And it sees all those little kids there. Do you see that? Yep. And it's more cautious because it understands that kids can jump out more at the airport.

43:16They're unpredictable. Oh, and vehicle approaching. So that's what you were saying earlier about, so you know not to open your door. Exactly. Exactly. Now you can continue. Please make sure. 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. And you should see the eyes of that kid just noticing that we don't have a driver. Look at that. Like he's young enough, he can't even articulate, but he's like, this isn't, my pattern recognition is on the road. And by the time he grows up, this will be the more beautiful road.

43:47Well, I mean, you mentioned you're an SF, you're in Phoenix. Yeah, Los Angeles. LA. 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? In the very, very early days, we tried to make sure that we're picking a city that challenged the system in very different directions. We were in development stages, 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 -part truck by the way. And so is it, even sensing maybe the lights blinking?

44:20Yeah, when for example somebody's unparking, we notice their noses starting to jet out. It's just so bizarre to see a wheel moving on its own like that. Like it truly does look kind of fake, you know what I mean. But yeah, so you're trying to find cities that kind of test the system, push it forward. 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 a Destadentaho for snow. So there was a testing phase in which we went to 20 cities just to make sure with enough of our diverse Data set to be building off of look at that gentle because this kind of was coming that tram was coming Anyhow this beauty to see I know I know it really is something that it's like I'm watching This pedestrian crossing the car going another one approaching but green light so turn right after the pedestrian We're off anyway, sorry And you were asking, does it get boring for you after working?

45:15I 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... Oh, I can't. And like just the first year in 2019, you're spending a ton of hours in it. 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. Yeah. So there's a level of like, oh, I know these work. Yeah. But when you were first getting into the vehicle, was there any like fear apprehension? Not fear. The closest feeling I can describe is when you have prepared for an exam for six years, whatever you use the biggest exam you've given, the little feeling that you got, that you have prepared as best as you possibly could for it.

46:01You left nothing on the table, but it is exactly. You're like, oh, I hope I can show up. Right, right, right. Well, it's funny because a lot of people, they'll see the stats and be like, test 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. The fundamental attribution error, by the way, is that more than 50 % of people think they're better than average. We'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 10.

46:30Huge diversity of use cases, everything from commute trips, all the way to sports events, to sightseeing tours that you're mentioning San Francisco similarly, both SF and LA are top two among the top five right -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 10 busiest airports in the world. So when we commit to a city for a launch, that's different than going to a city for data collection and testing. 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.

47:11And 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? We definitely see that in the place where we have been the longest as 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 mayoral level, all the way to state level, absolutely. I love thinking about just what is this unlock? And there are a few industries that can unlock so much because people do spend so much time in cars.

47:50I think that was an amazing, beautiful thing we saw from remote work. It's 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. 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 picking shoes is what the site is.

48:22Yeah, and yeah, pretty vast, yeah. So I do believe it's pretty profound and only some of those aspects we can see and some we will be shocked by when 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 I'm sure as a new S .F. resident you will wear it But think of the parking. 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.

49:03Just 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, not neither to their rider. They're giving a promise that when the person who's gone in for a eight hour work day, when they come out, it'll still be there. I heard this quote the other day. It was like, we dedicate in cities more space to sleeping cars and sleeping humans, which is kind of crazy.

49:39And first and foremost, there are people who are traveling today that we believe over time, the 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 the first and foremost for all kinds of, for society at large, it's a pandemic, 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 are drive while taking risk or would have the freedom of mobility.

50:19There are folks with visual impairments. Yes. 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. So, 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, and 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, along how it's tracking, where already tremendous amount of economic opportunity in the United States is being lost to lack of drivers.

51:07And by the way, when we do have drivers, we have mandatory breaks because fatigue is a real thing. We recently moved. I drove from near San Diego 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. 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.

51:42Oh, you see it sends the pylon. And you see that? Oh my gosh that's incorrect because see I was wondering actually if it could tell if that's a human, so they can tell that those are humans. Yeah, but then these are pylons. And there was like a roughly written keep right and and the should going there. Yeah, because you could imagine how it might think there's enough space there. So I think construction zones, by the way, are interesting changed to a otherwise structured world, right? Because there are no construction zone is the same, right? 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.

52:22Yeah, 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, 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. 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.

52:58All 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? Seeing the writers the first time the fifth time and what they say about us that gets me excited. 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 welcome to say. 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. Yeah and I can't wait. I imagine like this being personalized as well in the future.

53:29The climate control, the music, cooking up to Spotify. From your app, we did both of these screens as well, because there were be cases in which somebody would be in front synchronizing all of that, but for another time. Well, this was great, and I guess we just hopped out. Yeah, yeah. We're here.

53:53This is great. I guess I shouldn't. It knows I'm here. It knows. I'm imagining myself on this screen. Yes. School little thought.

54:02So how did you find the ride? 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, so it involved in the conversation. You don't even notice. You're right. And that's what we want. 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. We're here. We just did it. Well, I'm glad to have been there when you had your first experience. Yeah, well thank you so much. It was so cool. Yeah.

54:40All right, 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 learners 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.

55:23because 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. All right, 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.