1 00:00:10,200 --> 00:00:13,640 Speaker 1: Hello, and welcome to another episode of the Odd Lots Podcast. 2 00:00:13,720 --> 00:00:16,000 Speaker 2: I'm Joe Wisenthal and I'm Tracy Alloway. 3 00:00:16,640 --> 00:00:19,680 Speaker 1: Tracy, remember the hype about self driving cars from like 4 00:00:19,680 --> 00:00:21,320 Speaker 1: ten years ago, Like that really died out? 5 00:00:21,640 --> 00:00:24,279 Speaker 2: Can I tell you something? Yeah, I'm still holding out 6 00:00:24,320 --> 00:00:27,560 Speaker 2: hope for the self driving cars because I can't drive, 7 00:00:28,160 --> 00:00:31,080 Speaker 2: and it was kind of I forgot. It was kind 8 00:00:31,080 --> 00:00:34,080 Speaker 2: of acceptable when I was in my twenties, but now 9 00:00:34,120 --> 00:00:36,400 Speaker 2: it's starting to get a little embarrassing. So I really 10 00:00:36,440 --> 00:00:40,040 Speaker 2: need the self driving cars to become viable options. 11 00:00:40,200 --> 00:00:43,080 Speaker 1: So when we go on the road and like do podcasts, 12 00:00:43,200 --> 00:00:44,120 Speaker 1: like in another. 13 00:00:43,920 --> 00:00:46,199 Speaker 3: City, you have to drive. I have to drive all 14 00:00:46,200 --> 00:00:48,360 Speaker 3: the time, don't It hadn't because I think I've asked you. 15 00:00:48,400 --> 00:00:49,960 Speaker 3: I'm like, oh, Tracy, you're renting a car. 16 00:00:50,159 --> 00:00:52,159 Speaker 1: And then you like sort of change the discussion or 17 00:00:52,240 --> 00:00:54,280 Speaker 1: you bring up something else, like oh, I think there's 18 00:00:54,400 --> 00:00:55,800 Speaker 1: Ubers in that town or something. 19 00:00:55,880 --> 00:00:57,320 Speaker 3: But this is the real reason, isn't it. 20 00:00:57,360 --> 00:00:59,040 Speaker 2: Well, I mean there are Ubers everywhere, you know. What 21 00:00:59,080 --> 00:01:02,040 Speaker 2: we should talk about whether or not Ubers have decreased 22 00:01:02,200 --> 00:01:04,880 Speaker 2: enthusiasm for self driving cars as a business model. 23 00:01:05,400 --> 00:01:07,560 Speaker 1: Remember when people used to say Uber would never make 24 00:01:07,600 --> 00:01:09,640 Speaker 1: any money if they still had to pay humans, but. 25 00:01:09,560 --> 00:01:10,679 Speaker 3: Now they're making a little money. 26 00:01:11,080 --> 00:01:11,720 Speaker 2: It's true. 27 00:01:12,280 --> 00:01:15,119 Speaker 1: But I do think like generally, like when people talk 28 00:01:15,200 --> 00:01:17,759 Speaker 1: about like tech that didn't live up to the hype 29 00:01:18,400 --> 00:01:20,440 Speaker 1: and now you see it now with like chatbots and 30 00:01:20,480 --> 00:01:22,720 Speaker 1: stuff like that, and whether they're legan chain like people 31 00:01:22,760 --> 00:01:24,760 Speaker 1: go back to the self driving cars, Like to me, 32 00:01:24,880 --> 00:01:28,520 Speaker 1: that's the sort of quintessential example of the like modern times, 33 00:01:28,760 --> 00:01:30,920 Speaker 1: maybe three D printing. You don't really hear that much 34 00:01:30,920 --> 00:01:31,520 Speaker 1: about it. 35 00:01:31,520 --> 00:01:34,000 Speaker 2: Yes, but don't you also find it weird to imagine 36 00:01:34,040 --> 00:01:36,880 Speaker 2: a future in two or three hundred years where there 37 00:01:36,920 --> 00:01:40,520 Speaker 2: wouldn't be self driving cars. It feels at once both 38 00:01:40,640 --> 00:01:45,280 Speaker 2: inevitable and like hype, if that makes sense, like an artificiality. 39 00:01:45,760 --> 00:01:48,960 Speaker 1: No, I mean I definitely, I definitely agree two hundred 40 00:01:48,960 --> 00:01:51,840 Speaker 1: and three hundred years, that's like that's a long time, 41 00:01:51,920 --> 00:01:53,160 Speaker 1: like fifty years from now. 42 00:01:53,280 --> 00:01:55,040 Speaker 2: I've kind of you think fifty years from now. 43 00:01:55,120 --> 00:01:57,240 Speaker 1: Well, so this is the question, which is like my 44 00:01:58,240 --> 00:02:00,680 Speaker 1: is not an area I know that well, but my 45 00:02:00,720 --> 00:02:03,360 Speaker 1: impression is it's like look sort of classic thing where 46 00:02:03,360 --> 00:02:05,720 Speaker 1: like tech got us like ninety five percent of the 47 00:02:05,720 --> 00:02:08,360 Speaker 1: way there, and then there are some edge cases that 48 00:02:08,400 --> 00:02:10,640 Speaker 1: make self driving cars difficult. I don't know exactly what 49 00:02:10,680 --> 00:02:13,280 Speaker 1: they are, but that getting that last five percent or 50 00:02:13,280 --> 00:02:16,320 Speaker 1: whatever is so hard that it renders the whole thing 51 00:02:16,440 --> 00:02:19,760 Speaker 1: very difficult, and that whatever that last percent is is 52 00:02:20,040 --> 00:02:23,840 Speaker 1: the difference between the tech being like wow, versus actually 53 00:02:23,919 --> 00:02:24,560 Speaker 1: changing the world. 54 00:02:24,720 --> 00:02:26,440 Speaker 2: We are so close and yet so far. 55 00:02:26,639 --> 00:02:27,639 Speaker 3: Yeah, it's one of those things. 56 00:02:27,639 --> 00:02:30,080 Speaker 1: And I feel like again with chatbots and some of 57 00:02:30,120 --> 00:02:33,760 Speaker 1: these other like current like artificial intelligence applications, it comes 58 00:02:33,800 --> 00:02:36,040 Speaker 1: back to this question of like, yes, it's really great 59 00:02:36,080 --> 00:02:37,960 Speaker 1: and it sort of blows your mind, but there are 60 00:02:37,960 --> 00:02:42,400 Speaker 1: these hallucinations another thing, and like if it's not it's 61 00:02:42,400 --> 00:02:44,960 Speaker 1: not one hundred percent reliable, does that mean it really 62 00:02:45,000 --> 00:02:46,680 Speaker 1: won't be as disruptive as people expect. 63 00:02:46,880 --> 00:02:49,200 Speaker 2: Well, the other thing I'm curious about is whether or 64 00:02:49,240 --> 00:02:52,000 Speaker 2: not that sort of last five percent that you're describing, 65 00:02:52,000 --> 00:02:53,840 Speaker 2: whether that's on the software or the hardware. 66 00:02:54,080 --> 00:02:56,120 Speaker 3: Oh yeah, because that has. 67 00:02:55,919 --> 00:02:58,920 Speaker 2: Implications for you know, if we do make huge leaps 68 00:02:58,960 --> 00:03:02,720 Speaker 2: and artificial intell leigence, maybe that solves a software problem. 69 00:03:02,800 --> 00:03:06,680 Speaker 2: But maybe the issue is actually that the sensors are 70 00:03:06,760 --> 00:03:10,640 Speaker 2: too basic or too expensive, that sort of thing. 71 00:03:10,680 --> 00:03:12,160 Speaker 3: I don't know the answer to any of these questions. 72 00:03:12,200 --> 00:03:14,080 Speaker 1: The one other thing I'll say too, is like there 73 00:03:14,200 --> 00:03:15,800 Speaker 1: is a lot of car talk these days. 74 00:03:15,800 --> 00:03:17,519 Speaker 3: We've been doing more and more on the podcast. 75 00:03:17,840 --> 00:03:22,280 Speaker 1: It's entirely like on the sort of EV charging side, 76 00:03:22,280 --> 00:03:24,959 Speaker 1: and how are EV's and how EV production and batteries, 77 00:03:24,960 --> 00:03:27,320 Speaker 1: like how are they going to reshape the industry? Or 78 00:03:27,400 --> 00:03:30,240 Speaker 1: Chinese exports, how are they going to reshape the industry? 79 00:03:30,360 --> 00:03:32,959 Speaker 1: It does not seem like again ten years ago the 80 00:03:32,960 --> 00:03:35,240 Speaker 1: big question was like who's ahead in the self driving 81 00:03:35,280 --> 00:03:39,600 Speaker 1: car race? Google, GM or Ford. There was much less 82 00:03:39,600 --> 00:03:41,720 Speaker 1: talk then about EV's is the big disruptor? 83 00:03:42,000 --> 00:03:44,560 Speaker 2: Right, So I think it is time for a checkup, 84 00:03:44,720 --> 00:03:46,640 Speaker 2: right on what's going on with self driving cars? 85 00:03:46,720 --> 00:03:47,760 Speaker 3: It is time for a checkup. 86 00:03:47,800 --> 00:03:50,800 Speaker 1: And our guests says they're back, that they're happening for real. 87 00:03:50,880 --> 00:03:53,320 Speaker 1: And I do believe him to some extent because I 88 00:03:53,400 --> 00:03:56,160 Speaker 1: follow some people who live in San Francisco and they're 89 00:03:56,160 --> 00:03:57,920 Speaker 1: tweeting about it more and more that they see them 90 00:03:57,960 --> 00:04:00,440 Speaker 1: on the road. And sometimes when I'm up but four 91 00:04:00,440 --> 00:04:03,200 Speaker 1: in the morning to read the Internet in the dark 92 00:04:03,240 --> 00:04:05,520 Speaker 1: and drink coffee, I see like people who are still 93 00:04:05,520 --> 00:04:08,480 Speaker 1: out at night in San Francisco talking about all the 94 00:04:08,480 --> 00:04:10,840 Speaker 1: self driving cars around them, So there might actually be 95 00:04:10,920 --> 00:04:12,120 Speaker 1: it may not be totally over. 96 00:04:12,240 --> 00:04:13,360 Speaker 3: There might be they might be back. 97 00:04:13,840 --> 00:04:16,560 Speaker 2: Well, the things I see on the internet about self 98 00:04:16,640 --> 00:04:19,240 Speaker 2: driving cars are those edge cases where it's like a 99 00:04:19,320 --> 00:04:21,920 Speaker 2: car flum mixed by a traffic cone in the middle 100 00:04:21,960 --> 00:04:27,200 Speaker 2: of the street, which they're simultaneously like impressive and amusing 101 00:04:27,360 --> 00:04:30,120 Speaker 2: and disappointing all at the same time. If that makes sense. 102 00:04:30,400 --> 00:04:31,040 Speaker 3: It's interesting. 103 00:04:31,080 --> 00:04:34,359 Speaker 1: You're very pro self driving cars. It hadn't clicked, like 104 00:04:34,400 --> 00:04:35,640 Speaker 1: you really want this to happen. 105 00:04:35,760 --> 00:04:39,599 Speaker 2: I have a personal self interest in not having to 106 00:04:39,680 --> 00:04:43,040 Speaker 2: learn how to drive. I figure if I'm super optimistic, 107 00:04:43,120 --> 00:04:45,080 Speaker 2: maybe maybe if I just hang on for like another 108 00:04:45,160 --> 00:04:48,400 Speaker 2: ten years, Yeah, maybe, I don't know. Let's ask our guests. 109 00:04:48,440 --> 00:04:51,520 Speaker 1: Let's ask our guests. We have the perfect guest, longtime 110 00:04:51,680 --> 00:04:55,640 Speaker 1: tech journalists, a tech understander, someone who really deep delves 111 00:04:55,680 --> 00:04:58,880 Speaker 1: deep into technology to understand like how things work and 112 00:04:58,920 --> 00:05:01,719 Speaker 1: what's really happening. I followed his work for a long time. 113 00:05:01,760 --> 00:05:05,560 Speaker 1: We're actually like we're colleagues together, like eighteen years ago, 114 00:05:06,000 --> 00:05:08,159 Speaker 1: I think at a side called tech dirt. 115 00:05:08,400 --> 00:05:08,880 Speaker 3: Tim Lee. 116 00:05:09,000 --> 00:05:13,080 Speaker 1: He is the author of the understandingai dot org newsletter 117 00:05:13,279 --> 00:05:15,719 Speaker 1: longtime tech journalist, and he recently wrote a piece the 118 00:05:15,760 --> 00:05:18,880 Speaker 1: death of self driving cars is greatly exaggerated. 119 00:05:18,920 --> 00:05:20,720 Speaker 3: So, uh, Tim, great to have you on the show. 120 00:05:21,279 --> 00:05:22,839 Speaker 4: Hey, I'm great to be and I'm a fan of 121 00:05:22,839 --> 00:05:23,480 Speaker 4: the podcast. 122 00:05:23,720 --> 00:05:25,279 Speaker 3: Thank you very much, appreciate that. 123 00:05:25,600 --> 00:05:28,279 Speaker 1: Let's start ten years ago, and you know, I think 124 00:05:28,279 --> 00:05:30,080 Speaker 1: ten years ago there was a lot of self driving 125 00:05:30,160 --> 00:05:33,640 Speaker 1: car hype and my impression was, and this is so 126 00:05:34,160 --> 00:05:36,440 Speaker 1: vague and fuzzy, it's like, oh, most of it solved, 127 00:05:36,440 --> 00:05:37,800 Speaker 1: but this last part's really hard. 128 00:05:38,640 --> 00:05:40,239 Speaker 3: Is that true? What was that. 129 00:05:40,200 --> 00:05:44,719 Speaker 1: Last part that has proven to be very challenging to 130 00:05:44,880 --> 00:05:47,560 Speaker 1: like turn these from like prototypes onto track or a 131 00:05:47,640 --> 00:05:51,240 Speaker 1: very like organized grid like suburb in Arizona to something 132 00:05:51,279 --> 00:05:52,640 Speaker 1: that could actually be used on the road. 133 00:05:54,080 --> 00:05:56,200 Speaker 5: So it is true that about ten years ago, Google 134 00:05:56,360 --> 00:05:59,799 Speaker 5: was the leading company and they had vehicles that could 135 00:06:00,800 --> 00:06:04,000 Speaker 5: go on certain routes with a fair amount of kind 136 00:06:04,000 --> 00:06:08,599 Speaker 5: of preparation. And about six years ago Google rebranded itself 137 00:06:08,640 --> 00:06:11,440 Speaker 5: as Weimo, it's self driving cart projects as Wemo, and 138 00:06:11,640 --> 00:06:16,240 Speaker 5: started testing a taxi service in Phoenix, and they've been 139 00:06:16,320 --> 00:06:18,000 Speaker 5: plugging away at that ever since. There were a bunch 140 00:06:18,040 --> 00:06:21,640 Speaker 5: of other startups that were started between about twenty fourteen 141 00:06:21,839 --> 00:06:25,520 Speaker 5: and twenty eighteen, say, and a lot of those failed 142 00:06:25,720 --> 00:06:28,120 Speaker 5: or were forced to sell to some of the tech giants, 143 00:06:28,120 --> 00:06:31,080 Speaker 5: And so there's many fewer companies operating in this space 144 00:06:31,120 --> 00:06:34,000 Speaker 5: than the word five or six years ago. In terms 145 00:06:34,000 --> 00:06:36,240 Speaker 5: of what the last little bit is, it's just a 146 00:06:36,279 --> 00:06:37,839 Speaker 5: lot of little things. I mean, that's the thing about 147 00:06:37,880 --> 00:06:39,480 Speaker 5: a long tail is there's a lot of stuff out 148 00:06:39,520 --> 00:06:42,119 Speaker 5: in the long tail. One thing, for example that Weimo 149 00:06:42,200 --> 00:06:44,840 Speaker 5: and Cruz the kind of industry leaders have been struggling 150 00:06:44,920 --> 00:06:47,640 Speaker 5: with is when you deal with first responders. For example, 151 00:06:47,680 --> 00:06:50,400 Speaker 5: if we hope to an active fire site, you're not 152 00:06:50,400 --> 00:06:53,160 Speaker 5: supposed to dive over the hoses that firefighters are using. 153 00:06:53,360 --> 00:06:55,440 Speaker 5: I mean, that's something you might only encounter every one 154 00:06:55,480 --> 00:06:56,800 Speaker 5: hundred thousand miles or something. 155 00:06:57,040 --> 00:06:58,800 Speaker 4: And so there's just lots of it's a really big 156 00:06:58,920 --> 00:06:59,840 Speaker 4: deal to do it. 157 00:07:00,640 --> 00:07:02,800 Speaker 5: Yes, absolute there's another case where a crew is vehicle 158 00:07:02,880 --> 00:07:05,680 Speaker 5: like drove through a police tape in a crime scene. 159 00:07:05,760 --> 00:07:07,520 Speaker 5: So there's lots of little things that I saw a headline. 160 00:07:07,520 --> 00:07:09,159 Speaker 5: I haven't actually looked into this yet, but apparently a care 161 00:07:09,279 --> 00:07:12,240 Speaker 5: is like drove into web codcrete. So the real world 162 00:07:12,320 --> 00:07:15,480 Speaker 5: is complicated and there's just lots of weird situations that 163 00:07:15,480 --> 00:07:17,280 Speaker 5: a human being because we kind of understand how the 164 00:07:17,280 --> 00:07:19,720 Speaker 5: world works. You see, Oh that looks like web concrete. 165 00:07:19,720 --> 00:07:21,800 Speaker 5: I shouldn'tdrive on that. But you just have to, Like 166 00:07:21,800 --> 00:07:23,440 Speaker 5: it's like whack mole. You have to like hit every 167 00:07:23,480 --> 00:07:26,000 Speaker 5: single like bad thing a vehicle can do it. That 168 00:07:26,080 --> 00:07:27,680 Speaker 5: just takes a lot, a lot, a lot of work. 169 00:07:28,040 --> 00:07:29,760 Speaker 2: Yeah, I don't know why, but I find all the 170 00:07:29,800 --> 00:07:34,680 Speaker 2: stories of like robotic self driving cars behaving badly absolutely hilarious. 171 00:07:34,840 --> 00:07:37,200 Speaker 2: And not the ones where they hurt people I should 172 00:07:37,280 --> 00:07:39,640 Speaker 2: just caveat that, but the ones where, you know, something 173 00:07:39,640 --> 00:07:41,880 Speaker 2: that we wouldn't even think about, you know, there's an 174 00:07:41,920 --> 00:07:44,160 Speaker 2: object in the road, just go around it, and they 175 00:07:44,200 --> 00:07:46,280 Speaker 2: seem to really struggle with I want to ask you 176 00:07:46,320 --> 00:07:49,040 Speaker 2: more about why that seems to be an issue and 177 00:07:49,080 --> 00:07:51,320 Speaker 2: sort of get into some of the edge cases that 178 00:07:51,760 --> 00:07:54,320 Speaker 2: Joe mentioned in the intro. But before we do, why, 179 00:07:54,360 --> 00:07:57,400 Speaker 2: here's a basic question. Why have a lot of these 180 00:07:57,480 --> 00:08:02,600 Speaker 2: self driving car companies struggled Because on the face of it, 181 00:08:02,600 --> 00:08:04,680 Speaker 2: it would appear that there is a lot of money 182 00:08:04,720 --> 00:08:07,720 Speaker 2: floating out there in venture capital land that often goes 183 00:08:07,760 --> 00:08:12,680 Speaker 2: into unrealistic or unprofitable unprofitable projects. So why has this 184 00:08:12,760 --> 00:08:15,680 Speaker 2: been an issue for self driving cars in particular? 185 00:08:16,360 --> 00:08:18,600 Speaker 5: I mean, I think on some level the basic issue 186 00:08:18,600 --> 00:08:21,280 Speaker 5: with safety a lot of other areas of tech. You 187 00:08:21,360 --> 00:08:23,120 Speaker 5: kind of build them in a viable product and you 188 00:08:23,120 --> 00:08:24,880 Speaker 5: put it out in the world and you know it 189 00:08:24,880 --> 00:08:28,240 Speaker 5: breaks sometimes, but that's fine, Like that gets you more feedback. 190 00:08:28,360 --> 00:08:31,640 Speaker 5: And because you can kind of iterate rapidly, you can 191 00:08:31,680 --> 00:08:34,280 Speaker 5: like scale up very quickly and get to a profitable 192 00:08:34,559 --> 00:08:37,720 Speaker 5: scale pretty quickly. That obviously doesn't work if if the 193 00:08:37,920 --> 00:08:40,640 Speaker 5: moving fast at breaking things is like literally breaking things 194 00:08:40,679 --> 00:08:43,160 Speaker 5: and killing people. And so you have to be very 195 00:08:43,160 --> 00:08:45,959 Speaker 5: close to perfect before you can launch a commercial service 196 00:08:46,000 --> 00:08:47,440 Speaker 5: and start making money. And so you had a bunch 197 00:08:47,480 --> 00:08:49,520 Speaker 5: of startups that were trying to do this. They had 198 00:08:49,559 --> 00:08:51,839 Speaker 5: all starts of strategy to do that. Some were trying 199 00:08:51,880 --> 00:08:55,199 Speaker 5: to operate in retirement communities or do like package delivery. 200 00:08:55,200 --> 00:08:58,840 Speaker 5: They try to find kind of less demanding applications than 201 00:08:58,920 --> 00:09:01,800 Speaker 5: like drive anywhere any time. But it's just really really hard. 202 00:09:01,840 --> 00:09:04,520 Speaker 5: And so the companies that have sustained are the ones 203 00:09:04,559 --> 00:09:08,200 Speaker 5: that have Amazon, Google, GM like big companies behind them 204 00:09:08,240 --> 00:09:09,839 Speaker 5: who are willing to put like a billion dollars a 205 00:09:09,960 --> 00:09:11,800 Speaker 5: year behind them for several years in a row while 206 00:09:11,800 --> 00:09:14,920 Speaker 5: they kind of try to iron out these final little wrinkles. 207 00:09:16,200 --> 00:09:19,480 Speaker 1: So zooming forward today and that makes a lot of sense. 208 00:09:19,520 --> 00:09:20,520 Speaker 3: I hadn't really thought about that. 209 00:09:20,600 --> 00:09:22,959 Speaker 1: It's like for many tech it's okay if there are 210 00:09:23,040 --> 00:09:25,640 Speaker 1: edge cases where it doesn't work, because you just sort 211 00:09:25,679 --> 00:09:27,360 Speaker 1: of like, well, you put out in the world and like, yeah, 212 00:09:27,360 --> 00:09:31,120 Speaker 1: it's not a perfect product, but into minimum via, it's free. 213 00:09:30,920 --> 00:09:31,959 Speaker 2: And we're refining it. 214 00:09:31,960 --> 00:09:33,400 Speaker 3: It's free and we're iterating. 215 00:09:33,559 --> 00:09:37,280 Speaker 1: But you cannot do that when there's big safety issues 216 00:09:37,480 --> 00:09:40,760 Speaker 1: and if it's a threat to other drivers or pedestrians, 217 00:09:41,040 --> 00:09:44,720 Speaker 1: it's not really an acceptable way to do product. Going 218 00:09:45,200 --> 00:09:48,320 Speaker 1: to today and you are more optimistic and we'll get 219 00:09:48,320 --> 00:09:51,679 Speaker 1: to that about the prospects for their existence. But has 220 00:09:51,720 --> 00:09:54,679 Speaker 1: there been a breakthrough in recent years or has it 221 00:09:54,840 --> 00:09:58,560 Speaker 1: just been this slow, iterative grinding away at the edge 222 00:09:58,600 --> 00:10:01,679 Speaker 1: cases that it makes it so that there are fewer 223 00:10:01,679 --> 00:10:02,640 Speaker 1: and fewer edge cases. 224 00:10:03,600 --> 00:10:06,880 Speaker 5: I would say the second one. I mean, Weymo's technology 225 00:10:06,880 --> 00:10:09,800 Speaker 5: has worked pretty well. They started doing fully driver less 226 00:10:09,800 --> 00:10:13,720 Speaker 5: operations in Phoenix in the fall of twenty twenty, and 227 00:10:14,320 --> 00:10:18,200 Speaker 5: I have just very gradually expanded that service now weimo 228 00:10:18,320 --> 00:10:20,640 Speaker 5: I just a week or two ago they got permissioned 229 00:10:20,640 --> 00:10:23,520 Speaker 5: from California regulators to begin operating commercially in San Francisco 230 00:10:23,600 --> 00:10:27,000 Speaker 5: after a year or two of doing practice driving there, 231 00:10:27,480 --> 00:10:30,280 Speaker 5: and so yeah, they've they've just been plugging away at it. 232 00:10:30,360 --> 00:10:32,559 Speaker 5: And it's hard to tell from the outside because they're 233 00:10:32,600 --> 00:10:35,960 Speaker 5: not super transparent about all the details of you know, 234 00:10:36,000 --> 00:10:38,120 Speaker 5: how many incidents they have or how much work they 235 00:10:38,120 --> 00:10:39,360 Speaker 5: have to do on the back end. But yeah, it 236 00:10:39,360 --> 00:10:41,800 Speaker 5: seems like they're just very gradually making the technology better. 237 00:10:42,160 --> 00:10:44,360 Speaker 5: And they seem to think because they're now talking about 238 00:10:44,559 --> 00:10:46,480 Speaker 5: scaling up much more quickly, they seem to think that 239 00:10:47,000 --> 00:10:48,880 Speaker 5: the companies seem to think that they're that this is 240 00:10:48,920 --> 00:10:50,440 Speaker 5: ready for it to be a commercial product. 241 00:10:50,640 --> 00:10:53,120 Speaker 1: Just a really simple question. If I were to go 242 00:10:53,160 --> 00:10:56,600 Speaker 1: to San Francisco right now, could I go there and 243 00:10:56,640 --> 00:10:59,360 Speaker 1: download an app or whatever and get in a self 244 00:10:59,440 --> 00:11:00,160 Speaker 1: driving tax. 245 00:11:01,320 --> 00:11:03,480 Speaker 5: You know, I haven't checked that recently, So it was 246 00:11:03,520 --> 00:11:06,040 Speaker 5: literally like last week or the week before the California 247 00:11:06,120 --> 00:11:08,480 Speaker 5: regulators gave them permission to do that, I think, And 248 00:11:08,600 --> 00:11:10,160 Speaker 5: so till like last week, I think it was a 249 00:11:10,160 --> 00:11:12,000 Speaker 5: waiting list. But it's definitely case, you go to certain 250 00:11:12,040 --> 00:11:16,200 Speaker 5: parts of Phoenix, including the Phoenix Airport, you can hail 251 00:11:16,240 --> 00:11:18,520 Speaker 5: a taxi and it's just like Uber left. 252 00:11:18,520 --> 00:11:19,559 Speaker 4: You can try it. 253 00:11:19,640 --> 00:11:21,320 Speaker 3: I want to do it. I want to do it, Tracy. 254 00:11:21,400 --> 00:11:21,680 Speaker 3: Let's go. 255 00:11:22,280 --> 00:11:24,160 Speaker 1: Let's go to Phoenix, just so that for the one 256 00:11:24,240 --> 00:11:25,120 Speaker 1: ride then fly back. 257 00:11:25,760 --> 00:11:27,959 Speaker 4: Sure. Yeah, I talk to people there. I mean it works, 258 00:11:28,080 --> 00:11:29,480 Speaker 4: it works quite well. I mean the people. 259 00:11:29,559 --> 00:11:31,840 Speaker 5: I've talked to several people who've written in those vehicles 260 00:11:32,000 --> 00:11:34,880 Speaker 5: and at least in both rides they say it's flawless, 261 00:11:34,880 --> 00:11:36,280 Speaker 5: that drives very comfortably. 262 00:11:36,440 --> 00:11:39,160 Speaker 4: And yeah, the service they just aren't that many refuges. 263 00:11:39,880 --> 00:11:42,880 Speaker 2: Can we talk a little bit more about the edge stuff, 264 00:11:42,880 --> 00:11:47,000 Speaker 2: because my my impression is that, Okay, computers learn from 265 00:11:47,360 --> 00:11:52,160 Speaker 2: repetition and from modeling out various scenarios, but driving is 266 00:11:52,280 --> 00:11:57,320 Speaker 2: such an infinitely unpredictable experience, especially if you're in New York. 267 00:11:59,240 --> 00:12:01,600 Speaker 1: You could get you get it, Like if this technology 268 00:12:01,600 --> 00:12:03,720 Speaker 1: you'd never taken off, I have confidence you could do. 269 00:12:04,040 --> 00:12:05,880 Speaker 2: I don't know. I think I've missed the boat on 270 00:12:05,880 --> 00:12:08,880 Speaker 2: that one. But anyway, okay, but there are all these 271 00:12:08,880 --> 00:12:13,200 Speaker 2: different possibilities that a self driving car could be grappling with. 272 00:12:13,320 --> 00:12:16,480 Speaker 2: So for instance, an animal runs out in the middle 273 00:12:16,520 --> 00:12:19,880 Speaker 2: of the street, and you know, maybe after that happens 274 00:12:19,920 --> 00:12:23,360 Speaker 2: several times, the self driving car starts to realize, well, 275 00:12:23,400 --> 00:12:25,560 Speaker 2: it's this animal, and then it's going to behave in 276 00:12:25,600 --> 00:12:27,880 Speaker 2: this way and keep moving or stop, and I need 277 00:12:27,920 --> 00:12:31,080 Speaker 2: to respond to it in a certain way. But that 278 00:12:31,240 --> 00:12:34,440 Speaker 2: kind of seems to be the issue here as far 279 00:12:34,480 --> 00:12:35,360 Speaker 2: as I understand it. 280 00:12:36,080 --> 00:12:37,760 Speaker 5: Yes, absolutely, And there's a bunch of ways that the 281 00:12:37,760 --> 00:12:39,800 Speaker 5: companies have tried to do this. So, for example, Google 282 00:12:39,840 --> 00:12:43,440 Speaker 5: has long had a big test track facility out in 283 00:12:43,440 --> 00:12:45,439 Speaker 5: in California about an hour I went out there a 284 00:12:45,440 --> 00:12:47,960 Speaker 5: few years ago, where they have some fake roads and 285 00:12:48,000 --> 00:12:50,880 Speaker 5: they'll create kind of fake scenarios. They'll have cars cutting 286 00:12:51,000 --> 00:12:53,360 Speaker 5: other cars off, or have somebody like moving box of 287 00:12:53,400 --> 00:12:55,720 Speaker 5: the Cross, the three People and Halloween costumes something like that, 288 00:12:55,920 --> 00:12:57,440 Speaker 5: and so they try to think of what are all 289 00:12:57,440 --> 00:12:59,680 Speaker 5: the situations of the self driving car could run into 290 00:13:00,040 --> 00:13:03,079 Speaker 5: and kind of anticipate that. And this is also why 291 00:13:03,160 --> 00:13:05,600 Speaker 5: they started in Phoenix, is one of their strategies was, Okay, 292 00:13:05,600 --> 00:13:07,360 Speaker 5: there's so many ED cases, we can't do them allay 293 00:13:08,040 --> 00:13:10,280 Speaker 5: all at once, and so let's start a kind of 294 00:13:10,320 --> 00:13:13,840 Speaker 5: easy mode. And so Phoenix has very nice weather, nice 295 00:13:13,840 --> 00:13:17,679 Speaker 5: wide streets, well marked, you know, not a lot of pedestrians, 296 00:13:17,679 --> 00:13:20,000 Speaker 5: not a lot of bicyclists, and so that was kind 297 00:13:20,000 --> 00:13:22,560 Speaker 5: of way most theory was that we'll do the easiest 298 00:13:22,559 --> 00:13:25,439 Speaker 5: one first. The issue with that is that the economics 299 00:13:25,440 --> 00:13:27,160 Speaker 5: of running a taxi service in theaters are not that 300 00:13:27,200 --> 00:13:30,319 Speaker 5: great because most people already have cars, and so Cruise 301 00:13:30,320 --> 00:13:31,920 Speaker 5: has kind of had the opposite approach. It's is, we 302 00:13:31,960 --> 00:13:34,400 Speaker 5: want to see these educations as fast as possible, So 303 00:13:34,480 --> 00:13:36,800 Speaker 5: let's start in downtown San Francisco, because that's where there's 304 00:13:36,800 --> 00:13:39,360 Speaker 5: a ton of crazy situations, and so we will kind 305 00:13:39,360 --> 00:13:41,000 Speaker 5: of be harder in the first place, but we gathering 306 00:13:41,040 --> 00:13:42,839 Speaker 5: data very quickly and they will master it. 307 00:13:43,000 --> 00:13:44,040 Speaker 4: And is not yet clear yet. 308 00:13:44,040 --> 00:13:46,240 Speaker 5: I mean, both both companies now seem to think they're ready, 309 00:13:46,240 --> 00:13:48,280 Speaker 5: but I don't think we've seen them in a while 310 00:13:48,360 --> 00:13:50,240 Speaker 5: long enough to have a sense for kind of which 311 00:13:50,280 --> 00:13:51,480 Speaker 5: of those strategies are working better. 312 00:13:51,520 --> 00:13:53,080 Speaker 4: But yeah, it's really tough, and so so I. 313 00:13:53,080 --> 00:13:55,120 Speaker 5: Should say, like for the first few years, both of 314 00:13:55,160 --> 00:13:57,800 Speaker 5: these companies had safety divers behind the wheel of every vehicle, 315 00:13:58,000 --> 00:14:00,080 Speaker 5: and so the vehicle was mostly diving itself. But if 316 00:14:00,080 --> 00:14:02,080 Speaker 5: it got stuck, that says or down would have to 317 00:14:02,280 --> 00:14:04,840 Speaker 5: take over. And the kind of big switching. The big 318 00:14:04,960 --> 00:14:07,360 Speaker 5: risky point is when they take the first starts out 319 00:14:07,360 --> 00:14:09,800 Speaker 5: of the car, which WM one did about two years ago, 320 00:14:09,880 --> 00:14:11,080 Speaker 5: and cruise did I think maybe a. 321 00:14:11,120 --> 00:14:13,440 Speaker 4: Year ago, and then you know, and then that is 322 00:14:13,520 --> 00:14:13,960 Speaker 4: it because the. 323 00:14:13,960 --> 00:14:16,720 Speaker 5: Must trickier because if the vehicle screws up, it's a 324 00:14:16,720 --> 00:14:17,120 Speaker 5: big deal. 325 00:14:17,640 --> 00:14:19,680 Speaker 2: I love the idea of having to train the self 326 00:14:19,760 --> 00:14:23,000 Speaker 2: driving cars by like putting people in Halloween costumes in 327 00:14:23,040 --> 00:14:24,680 Speaker 2: front of them, and it reminds me a lot of 328 00:14:24,760 --> 00:14:27,840 Speaker 2: socializing my dog because we used to have to like 329 00:14:28,000 --> 00:14:31,480 Speaker 2: wear weird hats right, or like bring balloons into the 330 00:14:31,520 --> 00:14:33,520 Speaker 2: house so that he would get used to them and 331 00:14:33,560 --> 00:14:37,240 Speaker 2: not freak out. But this goes back to something that 332 00:14:37,320 --> 00:14:40,600 Speaker 2: I mentioned earlier, which is is the issue here the software, 333 00:14:40,720 --> 00:14:44,880 Speaker 2: so like the actual modeling of the reaction to an 334 00:14:44,960 --> 00:14:49,920 Speaker 2: unknown or unfamiliar event or stimulus, or is it more 335 00:14:50,120 --> 00:14:53,920 Speaker 2: on the hardware side where maybe you need better sensors 336 00:14:54,040 --> 00:14:57,040 Speaker 2: that are better able to appreciate the things in front 337 00:14:57,040 --> 00:14:57,240 Speaker 2: of you. 338 00:14:58,560 --> 00:15:01,400 Speaker 5: I would say it's more software, particularly more data. But yeah, 339 00:15:01,480 --> 00:15:04,040 Speaker 5: the hardware has stayed pretty constant. I mean that the trio 340 00:15:04,080 --> 00:15:07,240 Speaker 5: of sensors. Most of these vehicles have our cameras, radar, 341 00:15:07,280 --> 00:15:10,840 Speaker 5: and then lighter, which is a like laser laser range 342 00:15:10,840 --> 00:15:12,640 Speaker 5: finding technology that gives you kind of a three D 343 00:15:12,720 --> 00:15:15,920 Speaker 5: map of your environment. And so ten years ago Google's 344 00:15:15,960 --> 00:15:18,080 Speaker 5: car has had those three sensors, and I think now 345 00:15:18,080 --> 00:15:20,320 Speaker 5: those sensors are better. But I don't think anybody thinks 346 00:15:20,120 --> 00:15:22,320 Speaker 5: the main issues that we need to upgrade in the 347 00:15:22,400 --> 00:15:24,520 Speaker 5: quality to light are really they just need they need 348 00:15:24,560 --> 00:15:27,640 Speaker 5: examples of every possible educase, and they you know, it's 349 00:15:27,680 --> 00:15:30,160 Speaker 5: hard to get enough of that data because some educses 350 00:15:30,200 --> 00:15:32,480 Speaker 5: happen very rarely but can be very serious if you 351 00:15:32,560 --> 00:15:49,640 Speaker 5: encounter them. 352 00:15:49,840 --> 00:15:52,520 Speaker 1: Can you give us a quick industry overview? You know 353 00:15:52,600 --> 00:15:59,480 Speaker 1: you mentioned Weimo, it's Weimo's Google Cruises, GM Cruises GM 354 00:15:59,640 --> 00:16:03,320 Speaker 1: and then obviously Tesla and Elon's all Like if you 355 00:16:03,480 --> 00:16:05,960 Speaker 1: just like raid Elon's Twitter feed, you would think that 356 00:16:06,000 --> 00:16:08,880 Speaker 1: they've already had self driving cars like in the wild, 357 00:16:08,960 --> 00:16:10,920 Speaker 1: And I don't really think that's true, but I don't 358 00:16:10,920 --> 00:16:12,880 Speaker 1: really understand what's going on. Can you give like a 359 00:16:12,920 --> 00:16:15,640 Speaker 1: really just sort of quick like overview who the big 360 00:16:15,680 --> 00:16:18,160 Speaker 1: players are, like who owns them, and just sort of 361 00:16:18,200 --> 00:16:19,120 Speaker 1: like what their status is. 362 00:16:19,520 --> 00:16:20,480 Speaker 4: Yes, absolutely so. 363 00:16:20,720 --> 00:16:23,680 Speaker 5: Wemo is mainly owned by Google, Cruise is mainly owned 364 00:16:23,680 --> 00:16:26,280 Speaker 5: by GM. I consider Tesla to be in a different market, 365 00:16:26,280 --> 00:16:28,000 Speaker 5: and some of the Tesla fans get mad at me 366 00:16:28,040 --> 00:16:29,880 Speaker 5: when I say this, But Tesla is building a. 367 00:16:29,880 --> 00:16:31,040 Speaker 4: Driver systems product. 368 00:16:31,120 --> 00:16:33,760 Speaker 5: So all pretty much any car you drive now they 369 00:16:33,760 --> 00:16:36,040 Speaker 5: have advanced cruise control where it stays in your lane 370 00:16:36,160 --> 00:16:37,960 Speaker 5: and doesn't hit the car in front of you. In 371 00:16:38,000 --> 00:16:39,880 Speaker 5: some ways, I think Tesla has a more advanced version 372 00:16:39,920 --> 00:16:41,560 Speaker 5: of that, although also in some ways I think it's 373 00:16:41,600 --> 00:16:43,640 Speaker 5: Elon must just has a low or risk tolerance and 374 00:16:43,680 --> 00:16:46,400 Speaker 5: so he's kind of pushing a technology that's anyway. But 375 00:16:46,440 --> 00:16:48,840 Speaker 5: so so, the key thing about the Tesla product is 376 00:16:48,880 --> 00:16:50,640 Speaker 5: you were not supposed to, like crawl in the back 377 00:16:50,680 --> 00:16:52,520 Speaker 5: seat and take a nap, right You're supposed to be 378 00:16:52,560 --> 00:16:53,840 Speaker 5: there making sure it doesn't break. 379 00:16:54,240 --> 00:16:56,680 Speaker 1: And have people crawled in the back seat and taken 380 00:16:56,720 --> 00:16:57,080 Speaker 1: a nap. 381 00:16:57,360 --> 00:16:59,720 Speaker 5: I'm sure somebody has that. There are videos of people 382 00:16:59,760 --> 00:17:02,040 Speaker 5: doing it appropriate things well behind the wheel of the Tesla, 383 00:17:02,040 --> 00:17:03,760 Speaker 5: but you're definitely not supposed to. And you know the 384 00:17:04,119 --> 00:17:05,480 Speaker 5: vehicles that they have ways. 385 00:17:05,240 --> 00:17:07,720 Speaker 4: Of monitoring the driver so that that doesn't happen. 386 00:17:07,680 --> 00:17:10,520 Speaker 5: But anyway, So theoretically Elon Musk thinks they're going to 387 00:17:10,560 --> 00:17:11,600 Speaker 5: at some point to get to the point where you 388 00:17:11,600 --> 00:17:12,800 Speaker 5: don't have to be behind the wheel. But I do 389 00:17:12,880 --> 00:17:15,240 Speaker 5: not think they're close to that or really laying groundwork, 390 00:17:15,280 --> 00:17:17,320 Speaker 5: because one of the things for any service like that 391 00:17:17,400 --> 00:17:19,680 Speaker 5: is you need an operation staff because the vehicle is 392 00:17:19,760 --> 00:17:21,919 Speaker 5: occasionally get to get stuck, and when that happens, it 393 00:17:21,960 --> 00:17:23,280 Speaker 5: used to be able to phone home and get kind 394 00:17:23,280 --> 00:17:25,240 Speaker 5: of remote guidance about how to deal with it. And 395 00:17:25,280 --> 00:17:26,879 Speaker 5: as far as they know, Tesla's not doing it anyway. 396 00:17:26,880 --> 00:17:29,800 Speaker 5: So that's Tesla and then the other two companies. There 397 00:17:29,800 --> 00:17:31,640 Speaker 5: are a few other companies I would say a little behind. 398 00:17:31,640 --> 00:17:33,600 Speaker 5: So Amazon has a company called Zookes that used to 399 00:17:33,640 --> 00:17:35,560 Speaker 5: be a startup but acquired by Amazon a couple of 400 00:17:35,600 --> 00:17:36,040 Speaker 5: years ago. 401 00:17:36,520 --> 00:17:38,360 Speaker 4: And there's a company called Motional. 402 00:17:38,359 --> 00:17:41,879 Speaker 5: That is also I think close to being ready for 403 00:17:41,960 --> 00:17:44,560 Speaker 5: driver lists, but not to driver lists. And then there's 404 00:17:44,560 --> 00:17:47,320 Speaker 5: a company called MOBILEI that supplies the hardware for most 405 00:17:47,359 --> 00:17:49,920 Speaker 5: of these driver assistance systems and they have been working 406 00:17:49,960 --> 00:17:52,040 Speaker 5: on this technology. So that's another company. But guess I 407 00:17:52,040 --> 00:17:53,960 Speaker 5: say those four or five companies are the kind of 408 00:17:54,000 --> 00:17:55,200 Speaker 5: the remating players. 409 00:17:55,359 --> 00:17:58,919 Speaker 1: Am I hallucinating this memory? Or was there a situation 410 00:17:59,119 --> 00:18:03,560 Speaker 1: in which Uber hired every single member of the Carnegie 411 00:18:03,640 --> 00:18:08,320 Speaker 1: Mellon University Robotics department to work out self driving cars 412 00:18:08,320 --> 00:18:09,320 Speaker 1: for them? 413 00:18:09,640 --> 00:18:11,320 Speaker 4: Yes? Absolutely, so, yeah that was one. 414 00:18:11,400 --> 00:18:13,000 Speaker 3: That's a real thing that actually happened. 415 00:18:13,640 --> 00:18:14,879 Speaker 5: Yes, that was in I mean, I don't know if 416 00:18:14,920 --> 00:18:16,840 Speaker 5: it was every member, but yes, Uber hired a bunch 417 00:18:16,840 --> 00:18:19,719 Speaker 5: of talent in twenty sixteen, twenty seventeen, and then one 418 00:18:19,760 --> 00:18:23,000 Speaker 5: of their vehicles struck and killed somebody in Tempa, Arizona 419 00:18:23,040 --> 00:18:25,960 Speaker 5: in twenty eighteen, and that basically destroyed their program. And 420 00:18:26,040 --> 00:18:30,040 Speaker 5: so I think the remnants Oh actually I should say 421 00:18:30,040 --> 00:18:32,760 Speaker 5: there's a startup called Aurora that is doing tracking. 422 00:18:32,800 --> 00:18:35,159 Speaker 4: I think Uber. They acquired Uber's thing. 423 00:18:35,200 --> 00:18:37,639 Speaker 5: But anyway, yeah, so Uber is now not a player 424 00:18:37,720 --> 00:18:40,880 Speaker 5: because in large part because they're really the only only 425 00:18:40,920 --> 00:18:43,120 Speaker 5: one of these fully self driving programs that have had 426 00:18:43,119 --> 00:18:45,040 Speaker 5: a fatality with their with their testing. 427 00:18:45,800 --> 00:18:50,680 Speaker 2: So let's assume that self driving cars become a realistic thing. 428 00:18:51,000 --> 00:18:54,760 Speaker 2: How viable is that as a business model, Because on 429 00:18:54,800 --> 00:18:59,600 Speaker 2: a first reading, it seems extremely expensive to develop, possibly 430 00:18:59,680 --> 00:19:03,440 Speaker 2: extra extremely expensive to maintain if you have to provide 431 00:19:03,480 --> 00:19:06,960 Speaker 2: operational support to all these robot cars out in the field. 432 00:19:07,240 --> 00:19:09,800 Speaker 2: And then thirdly, it does seem like there's a big 433 00:19:09,960 --> 00:19:16,080 Speaker 2: regulatory slash, safety slash, maybe legal liability risk if something 434 00:19:16,400 --> 00:19:17,439 Speaker 2: were to happen. 435 00:19:18,480 --> 00:19:20,400 Speaker 5: I mean, I'm pretty optimistic about it because you think 436 00:19:20,400 --> 00:19:22,760 Speaker 5: about if you think about Uber and Lyft, about half 437 00:19:23,000 --> 00:19:25,800 Speaker 5: of the cost of running Uber and Lyft is the 438 00:19:25,920 --> 00:19:29,080 Speaker 5: labor of the human driver, and so if you take 439 00:19:29,280 --> 00:19:33,040 Speaker 5: that out, then Wayman Cruise need to get the new costs. 440 00:19:33,119 --> 00:19:36,560 Speaker 5: The cost of the sensors plus whatever operational stuff in 441 00:19:36,680 --> 00:19:38,399 Speaker 5: r indeed to be less than half the cost of 442 00:19:38,440 --> 00:19:40,920 Speaker 5: the driver, and that's a pretty significant amount of money. 443 00:19:40,960 --> 00:19:42,959 Speaker 5: And so I think it'll take them a while to 444 00:19:42,960 --> 00:19:46,359 Speaker 5: get to the scale where it's profitable, because certainly Wayman 445 00:19:46,440 --> 00:19:49,240 Speaker 5: crews both have I think hundreds or maybe thousands of 446 00:19:49,280 --> 00:19:52,000 Speaker 5: people working on this technology, and the sensors are currently 447 00:19:52,000 --> 00:19:54,879 Speaker 5: pretty expensive. But one of the most predictable things in 448 00:19:55,359 --> 00:19:59,240 Speaker 5: business is that mass manufacturing technologies like light our sensors 449 00:19:59,240 --> 00:20:02,119 Speaker 5: and computer chips get cheaper at scale, and so I 450 00:20:02,200 --> 00:20:03,840 Speaker 5: have no doubt that in the long run this is 451 00:20:03,880 --> 00:20:06,119 Speaker 5: going to be a viable business and is really I 452 00:20:06,160 --> 00:20:09,120 Speaker 5: think a question of how much patience the big companies 453 00:20:09,160 --> 00:20:12,399 Speaker 5: back in Google, GM and Amazon companies like that, how 454 00:20:12,440 --> 00:20:14,160 Speaker 5: many billions of dollars they want to spend to get 455 00:20:14,160 --> 00:20:16,080 Speaker 5: to this. But I think that in the long run, 456 00:20:16,119 --> 00:20:19,639 Speaker 5: I think that the taxi industry will be operated by 457 00:20:19,720 --> 00:20:21,560 Speaker 5: self driving cars. And I think that in the long run, 458 00:20:21,560 --> 00:20:23,720 Speaker 5: I also think it'll be cheaper and probably expand the 459 00:20:23,720 --> 00:20:26,560 Speaker 5: market a lot. So my long run expectation is that 460 00:20:26,560 --> 00:20:29,280 Speaker 5: this is going to be a big and profitable industry. 461 00:20:29,480 --> 00:20:33,680 Speaker 2: Do you envision it just or primarily for taxis or 462 00:20:33,720 --> 00:20:37,480 Speaker 2: could you have a situation where people like me are 463 00:20:37,520 --> 00:20:38,680 Speaker 2: buying self driving cars? 464 00:20:38,760 --> 00:20:42,280 Speaker 1: Well, just to add on to Tracy's question, because sort 465 00:20:42,320 --> 00:20:46,080 Speaker 1: of dovetails, could Tracy drive to work and then make 466 00:20:46,119 --> 00:20:48,760 Speaker 1: some extra money by during the day when it would 467 00:20:48,760 --> 00:20:51,960 Speaker 1: be parking for eight hours, have it be a taxi, 468 00:20:52,200 --> 00:20:55,520 Speaker 1: and then could that impair total volume sales for the 469 00:20:55,560 --> 00:20:59,920 Speaker 1: automakers because basically Tracy takes her self driving car to work, 470 00:21:00,240 --> 00:21:03,720 Speaker 1: but then also is a You know, I could the taxi. 471 00:21:03,480 --> 00:21:06,920 Speaker 2: Industry driving car capitalists. 472 00:21:06,280 --> 00:21:08,320 Speaker 1: Rather than having the car sit for eight hours in 473 00:21:08,320 --> 00:21:09,960 Speaker 1: the parking lot ten hours? 474 00:21:10,000 --> 00:21:10,359 Speaker 2: Smart? 475 00:21:10,440 --> 00:21:10,600 Speaker 4: Right? 476 00:21:10,640 --> 00:21:13,560 Speaker 5: So I think certainly, I think the initial product is 477 00:21:13,600 --> 00:21:15,119 Speaker 5: going to be a taxi service. That's what all the 478 00:21:15,160 --> 00:21:18,600 Speaker 5: people doing passenger nobody's talking about selling them in the 479 00:21:18,680 --> 00:21:22,240 Speaker 5: short run. Obviously, people like owning cars, and so in 480 00:21:22,280 --> 00:21:23,200 Speaker 5: the long run, I think there. 481 00:21:23,080 --> 00:21:24,679 Speaker 4: Will be a business model you'll be able to have 482 00:21:24,720 --> 00:21:25,119 Speaker 4: a car. 483 00:21:25,560 --> 00:21:27,760 Speaker 5: My guess is that it's going to be something more 484 00:21:27,840 --> 00:21:30,600 Speaker 5: like a long term lease than actual outright ownership. 485 00:21:30,119 --> 00:21:31,560 Speaker 4: But partly for liability reasons. 486 00:21:31,600 --> 00:21:33,520 Speaker 5: I mean, if you imagine if you own the car 487 00:21:33,640 --> 00:21:35,480 Speaker 5: and the brak needs a replacement and you don't replace 488 00:21:35,520 --> 00:21:37,920 Speaker 5: it in the car crashes to kill somebody, the people 489 00:21:38,000 --> 00:21:39,560 Speaker 5: who made the software going to get suited for that, 490 00:21:39,600 --> 00:21:42,320 Speaker 5: and so I think they're going to be reluctant to 491 00:21:42,320 --> 00:21:44,800 Speaker 5: sell people sell driving cars out right, but you might 492 00:21:44,800 --> 00:21:46,680 Speaker 5: be able to have something that's a long term release. 493 00:21:46,680 --> 00:21:49,280 Speaker 5: It's effectively the same as ownership. I'm not sure it 494 00:21:49,280 --> 00:21:50,800 Speaker 5: would make that much sense. I mean, if if you're 495 00:21:50,840 --> 00:21:53,520 Speaker 5: the kind of person who wants to share a car 496 00:21:53,600 --> 00:21:55,800 Speaker 5: with other people, then probably you would just take a taxi. 497 00:21:56,040 --> 00:21:56,760 Speaker 4: So I'm not sure. 498 00:21:56,800 --> 00:21:58,360 Speaker 5: I mean, there's a lot of ways the economics could 499 00:21:58,359 --> 00:21:59,679 Speaker 5: work out with My guess is that you'll have some 500 00:21:59,680 --> 00:22:02,640 Speaker 5: people who will lease a South diardan car long term, 501 00:22:02,680 --> 00:22:05,119 Speaker 5: and other people who will just take taxis. And I 502 00:22:05,119 --> 00:22:07,600 Speaker 5: think that hopefully, like in the long run, if economy 503 00:22:07,640 --> 00:22:09,920 Speaker 5: skill bring costs down, it'll be much cheaper than the 504 00:22:09,960 --> 00:22:12,439 Speaker 5: taxi today, like roughly half the price if you figured 505 00:22:12,480 --> 00:22:15,840 Speaker 5: that half is labor and so then that'll allow lots 506 00:22:15,880 --> 00:22:18,639 Speaker 5: of people, especially in cities, to own fewer cars and 507 00:22:19,520 --> 00:22:20,719 Speaker 5: take more taxi rides. 508 00:22:21,000 --> 00:22:22,720 Speaker 1: But I was just going to say, even if you didn't, 509 00:22:22,760 --> 00:22:24,800 Speaker 1: and I know other people have said this before, but 510 00:22:24,800 --> 00:22:26,719 Speaker 1: maybe Tracy doesn't want to share her car with other 511 00:22:26,760 --> 00:22:30,000 Speaker 1: people during the day, but it could mean less need 512 00:22:30,040 --> 00:22:32,320 Speaker 1: for parking, right, the car could drive home and go 513 00:22:32,400 --> 00:22:35,040 Speaker 1: back into your driveway or garage while you're at work, yes, 514 00:22:35,080 --> 00:22:37,399 Speaker 1: and then pick you up with Then then the amount 515 00:22:37,520 --> 00:22:40,440 Speaker 1: of space that a city or a neighborhood needs for 516 00:22:40,840 --> 00:22:43,000 Speaker 1: parking probably could be significantly diminished. 517 00:22:43,760 --> 00:22:46,280 Speaker 5: Yes, absolutely, And I think I think one underestimated benefit 518 00:22:46,320 --> 00:22:48,480 Speaker 5: of this from an urban planning perspective is it'll be 519 00:22:48,560 --> 00:22:51,520 Speaker 5: much easier to do congest and charging because the vehicles 520 00:22:51,520 --> 00:22:54,840 Speaker 5: will all be connected, and so I could imagine more 521 00:22:54,920 --> 00:22:57,920 Speaker 5: kind of complicated pricing where you give people a strong 522 00:22:57,960 --> 00:23:00,320 Speaker 5: incentive not to drive their car into downtown. If you're 523 00:23:00,320 --> 00:23:02,800 Speaker 5: going to go downtown, take a taxi or maybe some 524 00:23:02,880 --> 00:23:04,320 Speaker 5: kind of shared vehicles. 525 00:23:04,320 --> 00:23:06,200 Speaker 4: So there's a lot of I think sell driving cars will. 526 00:23:06,040 --> 00:23:08,480 Speaker 5: Open up a lot of new options for the way 527 00:23:08,520 --> 00:23:11,400 Speaker 5: you kind of organize, especially commutes, because yeah, you can 528 00:23:11,440 --> 00:23:13,760 Speaker 5: have different kinds of vehicles and different kinds of business 529 00:23:13,760 --> 00:23:15,080 Speaker 5: models for how people pay for them. 530 00:23:15,240 --> 00:23:18,760 Speaker 2: Could I use my self driving car to deliver packages 531 00:23:18,880 --> 00:23:21,439 Speaker 2: as a sort of gig FedEx worker or something. I 532 00:23:21,480 --> 00:23:25,640 Speaker 2: hear ups drivers cost a lot nowadays, so you know. 533 00:23:25,720 --> 00:23:28,439 Speaker 5: Right, I mean again, I think that'll be a different market. 534 00:23:28,520 --> 00:23:30,520 Speaker 5: So there's a company called Neuro that is trying to 535 00:23:30,560 --> 00:23:32,520 Speaker 5: do this. Several companies act, but I think they're the 536 00:23:32,560 --> 00:23:35,320 Speaker 5: market leader, so I think it's possible. I mean, one 537 00:23:35,320 --> 00:23:37,159 Speaker 5: of the issues is, you know, with the FedEx drive, 538 00:23:37,240 --> 00:23:39,400 Speaker 5: the FedEx Star physically gets out of the car, out 539 00:23:39,400 --> 00:23:41,480 Speaker 5: of the truck and carries the package to your front door. 540 00:23:41,840 --> 00:23:43,560 Speaker 4: And obviously your self driving cars can to be able 541 00:23:43,600 --> 00:23:43,879 Speaker 4: to do that. 542 00:23:43,920 --> 00:23:45,679 Speaker 5: So I'm not sure exactly what that market will look like, 543 00:23:45,720 --> 00:23:48,560 Speaker 5: but my guess is that there'll be customized delivery vehicles 544 00:23:48,560 --> 00:23:51,320 Speaker 5: that are much smaller and lighter and cheaper than a 545 00:23:51,359 --> 00:23:52,960 Speaker 5: full sized car, because there's no reason you need a 546 00:23:52,960 --> 00:23:54,800 Speaker 5: full car if there's nobody in the vehicle. 547 00:23:55,119 --> 00:23:57,719 Speaker 1: Can I ask a question about safety? You know, you 548 00:23:57,880 --> 00:24:02,600 Speaker 1: mentioned that Uber's self driving car pilot program ended basically 549 00:24:02,640 --> 00:24:06,520 Speaker 1: because a car struck and killed a pedestrian. It is 550 00:24:06,640 --> 00:24:11,440 Speaker 1: also true that human driven cars are killing people every day. 551 00:24:11,440 --> 00:24:13,920 Speaker 1: I believe there's tens of thousands of people every year 552 00:24:14,480 --> 00:24:17,879 Speaker 1: die in auto accident. Do we have meaningful apples to 553 00:24:17,920 --> 00:24:21,439 Speaker 1: Apple statistics or is it that's still so far that 554 00:24:21,520 --> 00:24:25,840 Speaker 1: the test that the self driving car universe is too 555 00:24:26,440 --> 00:24:29,320 Speaker 1: narrow or in two ideal conditions to actually do a 556 00:24:29,640 --> 00:24:30,639 Speaker 1: safety comparison. 557 00:24:31,720 --> 00:24:34,040 Speaker 5: It's actually just the raw number of miles is not 558 00:24:34,080 --> 00:24:37,240 Speaker 5: high enough. So well, it's true that humans kill forty 559 00:24:37,240 --> 00:24:38,240 Speaker 5: thousand people a year. 560 00:24:40,440 --> 00:24:43,359 Speaker 3: That's a staggering number. Yeah, number of people. 561 00:24:43,400 --> 00:24:47,280 Speaker 5: But humans humans drive billions or trillions of miles every year, 562 00:24:47,359 --> 00:24:49,680 Speaker 5: and so it's like one there's a fatality once every 563 00:24:49,760 --> 00:24:54,320 Speaker 5: hundred to hundred million miles roughly on the roads. And 564 00:24:54,440 --> 00:24:56,760 Speaker 5: self driving cars are in the tens of millions of miles. 565 00:24:56,840 --> 00:24:59,200 Speaker 5: So if they were as safe as a human you 566 00:24:59,200 --> 00:25:02,600 Speaker 5: would expect about one less than one death so far, 567 00:25:02,800 --> 00:25:04,480 Speaker 5: and so the fact that there has been only one 568 00:25:04,520 --> 00:25:07,440 Speaker 5: death doesn't really tell you that much about. 569 00:25:06,840 --> 00:25:08,520 Speaker 4: You know, is is it more or less safe? 570 00:25:08,880 --> 00:25:11,160 Speaker 5: I mean, so far the way one Cruise, the leaders 571 00:25:11,200 --> 00:25:13,400 Speaker 5: have had zero deaths, but they've gotten less than hundred 572 00:25:13,400 --> 00:25:13,840 Speaker 5: million miles. 573 00:25:13,840 --> 00:25:15,720 Speaker 4: So you just guessed. I think it's just too soon 574 00:25:15,760 --> 00:25:16,360 Speaker 4: to say for sure. 575 00:25:16,840 --> 00:25:20,520 Speaker 2: How much does the business model or the eventual profitability 576 00:25:20,680 --> 00:25:23,160 Speaker 2: of a lot of these self driving car companies depend 577 00:25:23,400 --> 00:25:27,639 Speaker 2: on the way the insurers react, because I imagine, you know, 578 00:25:28,119 --> 00:25:31,200 Speaker 2: if there is an accident involving a self driving car 579 00:25:31,320 --> 00:25:33,760 Speaker 2: and there's negligence involved, or you know, something's wrong with 580 00:25:33,760 --> 00:25:38,560 Speaker 2: the model, the legal liability is almost infinite at that point, 581 00:25:38,600 --> 00:25:41,520 Speaker 2: potentially millions and millions of dollars of payout if there 582 00:25:41,560 --> 00:25:45,040 Speaker 2: are actual fatalities. And I guess my question is a 583 00:25:45,040 --> 00:25:47,000 Speaker 2: lot of this is going to depend on the insurers 584 00:25:47,040 --> 00:25:50,639 Speaker 2: being willing to take on that risk, right, Yeah, you. 585 00:25:50,640 --> 00:25:53,880 Speaker 5: Know, I'm not actually sure exactly what Google and Cruise's 586 00:25:54,200 --> 00:25:56,679 Speaker 5: insurance situations are. I mean, they're big enough companies that 587 00:25:56,680 --> 00:25:59,280 Speaker 5: I would guess they can self ensure, so that's actually 588 00:25:59,280 --> 00:26:01,840 Speaker 5: not something I have and I assume they've disclosed in 589 00:26:01,880 --> 00:26:04,840 Speaker 5: some regulatory filing how they're insured. But it's a different 590 00:26:04,840 --> 00:26:07,399 Speaker 5: market because it's not especially in the early years, it's 591 00:26:07,400 --> 00:26:10,960 Speaker 5: not going to be individual consumers buying insurance. And so yeah, 592 00:26:11,000 --> 00:26:13,040 Speaker 5: I'm not actually sure what the structure of that market 593 00:26:13,080 --> 00:26:15,479 Speaker 5: is right now, and whether they have third party insurance 594 00:26:15,560 --> 00:26:17,480 Speaker 5: or they're just on the hook for the liability. 595 00:26:17,560 --> 00:26:19,040 Speaker 2: That'd be interesting to look at. 596 00:26:19,080 --> 00:26:22,479 Speaker 1: You can this just a really simple regulatory question right now. 597 00:26:23,000 --> 00:26:26,080 Speaker 1: If one of these companies said, we're good, we got it. 598 00:26:26,280 --> 00:26:28,440 Speaker 1: You want to get a taxi at or you want 599 00:26:28,480 --> 00:26:30,840 Speaker 1: to do canniball run, and you want to go coast 600 00:26:30,880 --> 00:26:33,960 Speaker 1: to coast We'll drive you from New York to California. 601 00:26:34,040 --> 00:26:37,600 Speaker 1: Could they legally do it or is there still some 602 00:26:37,640 --> 00:26:41,119 Speaker 1: sort of like regulatory blessing that would need to happen 603 00:26:41,119 --> 00:26:41,960 Speaker 1: for that to exist. 604 00:26:43,080 --> 00:26:45,320 Speaker 5: There's very little regulation at the federal level. There's some 605 00:26:45,359 --> 00:26:48,040 Speaker 5: regulation of the design of the vehicle. For example, you 606 00:26:48,040 --> 00:26:49,680 Speaker 5: still need to have a steering wheel in the car, 607 00:26:49,880 --> 00:26:51,840 Speaker 5: but at the federal level, I don't think there'd. 608 00:26:51,600 --> 00:26:53,800 Speaker 4: Be any legal barriers to do that at the state. 609 00:26:53,640 --> 00:26:55,960 Speaker 5: Level, at state by state, I think if you weren't challenged, 610 00:26:55,960 --> 00:26:57,520 Speaker 5: if you weren't charging for it, and just doing as 611 00:26:57,520 --> 00:26:58,119 Speaker 5: a demonstration. 612 00:26:58,240 --> 00:27:00,639 Speaker 4: I don't think there'd be any issue in most but. 613 00:27:00,680 --> 00:27:02,399 Speaker 5: As I mentioned, so, California I think is one of 614 00:27:02,440 --> 00:27:05,000 Speaker 5: the states that regulates these things more heavily, and they 615 00:27:05,000 --> 00:27:08,280 Speaker 5: do have a fairly substantial process. They treat Way one 616 00:27:08,440 --> 00:27:11,480 Speaker 5: cruise similarly to the way Uber lift or regulated, and 617 00:27:11,600 --> 00:27:14,800 Speaker 5: they just got the approval to start doing commercial taxi 618 00:27:14,920 --> 00:27:17,440 Speaker 5: rides in San Francisco. So yeah, it's state by state, 619 00:27:17,600 --> 00:27:20,520 Speaker 5: and Phoenix I believe this close to no regulation of 620 00:27:21,040 --> 00:27:23,800 Speaker 5: that kind of thing, so yeah, and I think Texas 621 00:27:23,800 --> 00:27:26,760 Speaker 5: is probably similar, so that the more kind of Republican 622 00:27:26,840 --> 00:27:29,840 Speaker 5: leaning states, there's very regulation. In California has some, but 623 00:27:30,280 --> 00:27:32,880 Speaker 5: not enough that it's really I think of a major bottlement. 624 00:27:49,760 --> 00:27:53,240 Speaker 2: So we discussed that there are some self driving cars 625 00:27:53,320 --> 00:27:56,240 Speaker 2: available out there, but they're kind of a novelty slash 626 00:27:56,480 --> 00:28:00,080 Speaker 2: experiment at the moment. How will we know when self 627 00:28:00,160 --> 00:28:04,919 Speaker 2: driving cars are a sort of viable, realistic thing. What 628 00:28:04,960 --> 00:28:05,960 Speaker 2: are you watching out for? 629 00:28:06,880 --> 00:28:09,760 Speaker 5: I think they're running the experiment right now. So Cruz 630 00:28:09,920 --> 00:28:13,560 Speaker 5: has announced I think eight to ten new cities, mostly 631 00:28:13,600 --> 00:28:18,320 Speaker 5: in the Southwest, places like Houston, Dallas, Miami, Atlanta, Nashville, 632 00:28:18,800 --> 00:28:21,600 Speaker 5: and we'll just kind of have to see how quickly 633 00:28:21,640 --> 00:28:23,600 Speaker 5: that happens, or if it happens. I mean, it certainly 634 00:28:23,640 --> 00:28:25,320 Speaker 5: wouldn't be the first time that a company has made 635 00:28:25,560 --> 00:28:28,200 Speaker 5: an announcement in the self driving space that hasn't panned out. 636 00:28:28,280 --> 00:28:31,600 Speaker 5: But they've gone from just Phoenix to now Phoenix and 637 00:28:31,640 --> 00:28:36,280 Speaker 5: San Francisco, and I think anyway, so we'll just have 638 00:28:36,320 --> 00:28:38,560 Speaker 5: to watch and see if those announcements actually turned into 639 00:28:38,600 --> 00:28:41,160 Speaker 5: operating services. Like I said, right now you can go 640 00:28:41,200 --> 00:28:43,000 Speaker 5: to Phoenix, you can try it. I think in the 641 00:28:43,000 --> 00:28:44,960 Speaker 5: next few weeks or months, you'll be able anybody to 642 00:28:45,120 --> 00:28:47,480 Speaker 5: be able to pail a car in San Francisco. And 643 00:28:47,520 --> 00:28:49,920 Speaker 5: then the other thing is the service territory. So right 644 00:28:49,920 --> 00:28:52,520 Speaker 5: now it's not all of the Phoenix Matoria, it's I 645 00:28:52,520 --> 00:28:55,720 Speaker 5: think a couple hundred square miles, and so yeah, we'll 646 00:28:55,760 --> 00:28:58,280 Speaker 5: want to watch what cities are they're going into and 647 00:28:58,640 --> 00:29:00,600 Speaker 5: how big of a service foot and then that does 648 00:29:00,600 --> 00:29:03,560 Speaker 5: that service footprint grow over time, And then ultimately we'll 649 00:29:03,600 --> 00:29:05,480 Speaker 5: have to see the financial results. I mean, these are 650 00:29:05,600 --> 00:29:08,160 Speaker 5: both publicly traded companies, so eventually I assume they'll tell 651 00:29:08,240 --> 00:29:10,320 Speaker 5: us if it's profitable. I don't think it is yet, 652 00:29:10,360 --> 00:29:12,920 Speaker 5: but yeah. I think if you see them rapidly scaling 653 00:29:13,000 --> 00:29:15,000 Speaker 5: up the number of vehicles in the number of cities, 654 00:29:15,000 --> 00:29:16,880 Speaker 5: then that'll be a sign that it's going well. 655 00:29:16,880 --> 00:29:19,080 Speaker 4: And if it's if it doesn't, then probably isn't going 656 00:29:19,120 --> 00:29:19,480 Speaker 4: as well. 657 00:29:20,080 --> 00:29:22,080 Speaker 1: I'm not kidding, by the way, about going to Arizona 658 00:29:22,160 --> 00:29:23,720 Speaker 1: just to travel, because we already want to do an 659 00:29:23,760 --> 00:29:26,720 Speaker 1: Arizona tour anyway with all of our land and water 660 00:29:26,920 --> 00:29:30,200 Speaker 1: and ELFELFA and Chips episodes we do there, So we 661 00:29:30,280 --> 00:29:32,600 Speaker 1: got to fly there just to take a self driving car. 662 00:29:32,760 --> 00:29:35,600 Speaker 1: I just have like one more question, and it's basically, 663 00:29:35,800 --> 00:29:38,240 Speaker 1: you know here in New York, I don't think there's anything. 664 00:29:38,240 --> 00:29:40,360 Speaker 1: But let's put a real time frame on this, like 665 00:29:40,440 --> 00:29:42,480 Speaker 1: you say, like you know, you say they're coming. We're 666 00:29:42,480 --> 00:29:44,160 Speaker 1: going to start seeing them more and more in some 667 00:29:44,200 --> 00:29:47,080 Speaker 1: of these other cities. When can we say, like, you know, 668 00:29:47,120 --> 00:29:50,520 Speaker 1: when will we have them in New York and give 669 00:29:50,600 --> 00:29:53,040 Speaker 1: us a year by which we could say, Okay, Tim 670 00:29:53,120 --> 00:29:54,240 Speaker 1: was right or Tim was wrong. 671 00:29:56,240 --> 00:29:57,640 Speaker 5: So I don't have to make if I'm making a 672 00:29:57,680 --> 00:30:00,480 Speaker 5: strong prediction that you know that on a specific So 673 00:30:00,480 --> 00:30:02,240 Speaker 5: I will say what Wimo and Cruz have said. 674 00:30:02,360 --> 00:30:04,560 Speaker 4: I believe Weimo has said. 675 00:30:04,640 --> 00:30:08,160 Speaker 5: They're planning to increase their footprint by ten x by 676 00:30:08,160 --> 00:30:10,360 Speaker 5: the end of next year, and Cruise has says they're 677 00:30:10,360 --> 00:30:12,000 Speaker 5: going to reach a billion dollars in revenue, which I 678 00:30:12,040 --> 00:30:14,280 Speaker 5: think will be about a fifty x increase by twenty 679 00:30:14,280 --> 00:30:14,800 Speaker 5: twenty five. 680 00:30:15,440 --> 00:30:16,600 Speaker 4: So I'm a little. 681 00:30:16,360 --> 00:30:18,680 Speaker 5: Skeptical that hit those numbers, but that's that's the scale 682 00:30:18,680 --> 00:30:21,160 Speaker 5: they're talking about now. That would still be a small 683 00:30:21,200 --> 00:30:24,040 Speaker 5: fraction of the overall taxi industry, sure, And I think 684 00:30:24,040 --> 00:30:26,040 Speaker 5: one of the things you'll see is that they haven't 685 00:30:26,400 --> 00:30:29,920 Speaker 5: entirely figured out the weather situation, and also to some extent, 686 00:30:29,960 --> 00:30:32,280 Speaker 5: they're like really dense infrastructure. So if this question of 687 00:30:32,320 --> 00:30:34,440 Speaker 5: when will you be able to hail a vehicle in Manhattan, 688 00:30:34,800 --> 00:30:37,040 Speaker 5: I could still see that being five to ten years away. 689 00:30:37,400 --> 00:30:42,120 Speaker 5: But I would not be surprised if Los Angeles, Houston, Dallas, Miami, 690 00:30:42,160 --> 00:30:45,160 Speaker 5: those kind of cities, you know, Southwestern kind of suburban cities. 691 00:30:45,560 --> 00:30:48,400 Speaker 5: If three to five years from now, it's very common 692 00:30:48,440 --> 00:30:52,080 Speaker 5: to see self diving taxis as just like a on 693 00:30:52,160 --> 00:30:52,520 Speaker 5: power with the. 694 00:30:52,560 --> 00:30:54,680 Speaker 4: Uber and left in terms of popularity. 695 00:30:55,560 --> 00:30:58,920 Speaker 1: Similarly, we'll have you back in five years and we'll 696 00:30:59,280 --> 00:31:01,720 Speaker 1: see if all of this born. I really appreciate you 697 00:31:02,120 --> 00:31:03,120 Speaker 1: coming on the podcast. 698 00:31:04,000 --> 00:31:04,800 Speaker 4: Sound good, thank you. 699 00:31:17,160 --> 00:31:20,120 Speaker 1: I'm telling you, Tracy, it always comes back to Arizona 700 00:31:20,320 --> 00:31:27,719 Speaker 1: for US Chips. Seriously, we're gonna chips, water, ELFLFA. 701 00:31:27,600 --> 00:31:28,800 Speaker 3: And now driving. 702 00:31:30,120 --> 00:31:31,720 Speaker 2: Okay, I'm serious. 703 00:31:35,000 --> 00:31:37,080 Speaker 3: Intersect with I'm telling you they got to take a trip. 704 00:31:37,120 --> 00:31:38,440 Speaker 3: I'm not being facetious. 705 00:31:38,920 --> 00:31:39,240 Speaker 5: Okay. 706 00:31:39,280 --> 00:31:41,480 Speaker 2: Well, I would happily go to Arizona. I think that'd 707 00:31:41,480 --> 00:31:44,480 Speaker 2: be fun, but I don't know. I'm just going to 708 00:31:44,520 --> 00:31:46,040 Speaker 2: go back to what I said earlier, which is like 709 00:31:46,040 --> 00:31:49,240 Speaker 2: self driving cars at once feel far away and very 710 00:31:49,280 --> 00:31:52,640 Speaker 2: close and sort of inevitable and also quite difficult, if 711 00:31:52,640 --> 00:31:53,280 Speaker 2: that makes sense. 712 00:31:53,680 --> 00:31:55,800 Speaker 1: You know what, if it was really fascinating and I 713 00:31:55,840 --> 00:31:59,000 Speaker 1: hadn't really appreciated this, his point about one of the 714 00:31:59,040 --> 00:32:03,040 Speaker 1: companies starting in Phoenix, where they're driving is super easy, 715 00:32:03,240 --> 00:32:05,920 Speaker 1: and then you sort of like progressively get better to 716 00:32:06,160 --> 00:32:09,120 Speaker 1: go to more complicated places, and then the other one 717 00:32:09,960 --> 00:32:13,760 Speaker 1: starting or mainly operating in San Francisco, where the driving 718 00:32:13,840 --> 00:32:16,240 Speaker 1: is really difficult, and it's like, if you can master 719 00:32:16,320 --> 00:32:18,640 Speaker 1: San Francisco, you could probably master anyway. 720 00:32:18,640 --> 00:32:21,200 Speaker 3: I wonder what the better approaches, like getting. 721 00:32:20,840 --> 00:32:24,640 Speaker 1: Progressively you know, progressively better, or just like really taking 722 00:32:24,680 --> 00:32:26,320 Speaker 1: all the hard stuff on day one. 723 00:32:26,680 --> 00:32:29,040 Speaker 2: Well, you know what I don't get just thinking about 724 00:32:29,080 --> 00:32:31,960 Speaker 2: that conversation. You know how all the captures to identify 725 00:32:32,080 --> 00:32:36,120 Speaker 2: robots are like identify the motorcycles in this photo or 726 00:32:36,160 --> 00:32:39,840 Speaker 2: identify the buses. That doesn't bode well for self driving cars. 727 00:32:40,320 --> 00:32:40,840 Speaker 3: Why Why? 728 00:32:41,000 --> 00:32:44,640 Speaker 2: Well, because it seems like robots struggle to identify motorcycles 729 00:32:44,680 --> 00:32:46,360 Speaker 2: on the road and humans doe. 730 00:32:46,920 --> 00:32:49,440 Speaker 1: I see what you're saying, right, Like, our whole approach 731 00:32:49,480 --> 00:32:51,640 Speaker 1: to even identifying whether someone is it's. 732 00:32:51,760 --> 00:32:56,200 Speaker 2: Always traffic lights or cars or motorcycles. So maybe maybe 733 00:32:56,280 --> 00:32:58,880 Speaker 2: actually self driving cars are ultimately a threat to our 734 00:32:58,920 --> 00:33:00,719 Speaker 2: existing captures. 735 00:33:00,760 --> 00:33:04,160 Speaker 1: Wow, yeah, right, Like if we could solve self driving cars, 736 00:33:04,240 --> 00:33:06,440 Speaker 1: that guarantees that we're going to have spam. 737 00:33:06,120 --> 00:33:09,360 Speaker 3: And other internet attacks. Right, I hadn't really thought about that. 738 00:33:09,400 --> 00:33:11,479 Speaker 2: Well, I mean, I think we didn't touch on it 739 00:33:11,800 --> 00:33:16,320 Speaker 2: much there. But there are also obviously societal implications of this. 740 00:33:16,480 --> 00:33:18,280 Speaker 2: We talked a little bit about the notion that well, 741 00:33:18,320 --> 00:33:21,080 Speaker 2: maybe companies could just replace all the taxi or the 742 00:33:21,200 --> 00:33:26,120 Speaker 2: Uber drivers, maybe even some mail delivery drivers get replaced. 743 00:33:26,120 --> 00:33:27,600 Speaker 2: That seems to be an issue as well. And then 744 00:33:27,600 --> 00:33:29,960 Speaker 2: the other thing, Actually, I want to look into this 745 00:33:30,040 --> 00:33:32,920 Speaker 2: after this conversation, but I am really curious what the 746 00:33:32,960 --> 00:33:35,680 Speaker 2: insurance is like on these things and who's provided. 747 00:33:35,360 --> 00:33:37,760 Speaker 1: Yeah, who has to pay and how I do think 748 00:33:37,760 --> 00:33:40,320 Speaker 1: there are a lot of big questions like that, or 749 00:33:40,320 --> 00:33:43,880 Speaker 1: like who's ultimately responsible when when these malfunction? I think 750 00:33:43,920 --> 00:33:45,920 Speaker 1: in San Francisco recently there was something where a bunch 751 00:33:45,920 --> 00:33:47,480 Speaker 1: of them all shut down at the same time and 752 00:33:47,520 --> 00:33:50,840 Speaker 1: they create all these traffic problems, which is also not 753 00:33:51,000 --> 00:33:53,480 Speaker 1: something that comes up with human drivers. I also think 754 00:33:53,520 --> 00:33:56,640 Speaker 1: like the political debates are going to get like super weird, 755 00:33:56,960 --> 00:34:00,840 Speaker 1: like what if they say, well, you know, to mention 756 00:34:01,200 --> 00:34:04,440 Speaker 1: congestion taxes. What if they say, oh, like you can't 757 00:34:04,440 --> 00:34:07,160 Speaker 1: even do that route because the computer is determined that 758 00:34:07,280 --> 00:34:08,680 Speaker 1: would like use too much energy? 759 00:34:09,000 --> 00:34:09,640 Speaker 3: Could it end? 760 00:34:09,760 --> 00:34:13,040 Speaker 1: The interesting could like you know, it's interesting like the 761 00:34:13,080 --> 00:34:14,719 Speaker 1: Red States, as you mentioned, have been a bit more 762 00:34:14,760 --> 00:34:18,040 Speaker 1: liberal about allowing them. But then there's all in twenty years, 763 00:34:18,080 --> 00:34:20,399 Speaker 1: will you be allowed to be a human driver, were 764 00:34:20,480 --> 00:34:22,440 Speaker 1: allowed to be like gohot sight seeing? Like all these 765 00:34:22,480 --> 00:34:25,840 Speaker 1: things like kind of some like big interesting questions that 766 00:34:25,880 --> 00:34:28,920 Speaker 1: could reshape society and then the reshaping like sort of 767 00:34:28,920 --> 00:34:31,480 Speaker 1: of our physical space maybe less need for parking. If 768 00:34:31,480 --> 00:34:34,640 Speaker 1: these actually take off, I think like it will change 769 00:34:34,640 --> 00:34:36,239 Speaker 1: the world in ways we don't really anticipate. 770 00:34:36,400 --> 00:34:38,400 Speaker 2: Yeah, maybe we need to do a self driving cars 771 00:34:38,480 --> 00:34:41,319 Speaker 2: episode from the perspective of a city planner or that's 772 00:34:41,360 --> 00:34:44,000 Speaker 2: a good idea yea interesting? Yeah, all right, well shall 773 00:34:44,040 --> 00:34:44,879 Speaker 2: we leave it there for now? 774 00:34:45,040 --> 00:34:45,520 Speaker 3: Leave it there? 775 00:34:45,600 --> 00:34:48,160 Speaker 2: Okay, And this has been another episode of the All 776 00:34:48,200 --> 00:34:51,120 Speaker 2: Thoughts podcast. I'm Tracy Alloway. You can follow me at 777 00:34:51,160 --> 00:34:53,319 Speaker 2: Tracy Alloway and I'm Jill Wisenthal. 778 00:34:53,400 --> 00:34:55,959 Speaker 1: You can follow me at the Stalwart. Follow our guest 779 00:34:56,040 --> 00:34:59,840 Speaker 1: Tim Lee, He's at Binary Bits, Follow our producers Kerman 780 00:35:00,040 --> 00:35:03,400 Speaker 1: Rodriguez at Carmen Arman and dash Ol Bennett at dashbot. 781 00:35:03,680 --> 00:35:06,120 Speaker 1: And check out all of the Bloomberg podcasts under the 782 00:35:06,160 --> 00:35:09,920 Speaker 1: handle at podcasts. And for more Oddlots content, go to 783 00:35:09,920 --> 00:35:13,920 Speaker 1: Bloomberg dot com slash odd Lots, where we have a transcript, 784 00:35:14,040 --> 00:35:17,760 Speaker 1: we have blog and a newsletter. And check out the Discord. 785 00:35:17,800 --> 00:35:20,640 Speaker 1: We have a transportation and an AI channel and there 786 00:35:20,680 --> 00:35:23,480 Speaker 1: so people will be talking about this episode. Go in there, 787 00:35:23,560 --> 00:35:26,400 Speaker 1: hang out with other listeners twenty four to seven Discord 788 00:35:26,680 --> 00:35:28,279 Speaker 1: dot gg slash. 789 00:35:27,920 --> 00:35:31,799 Speaker 6: Odlines and if you enjoy odd Lots If you like 790 00:35:31,920 --> 00:35:34,919 Speaker 6: caring our thoughts about self driving cars, then please leave 791 00:35:35,000 --> 00:35:38,319 Speaker 6: us a positive review on your favorite podcast platform. 792 00:35:38,360 --> 00:35:39,080 Speaker 2: Thanks for listening 793 00:36:04,800 --> 00:36:05,520 Speaker 6: In the e.