1 00:00:00,120 --> 00:00:03,600 Speaker 1: Hey, Dana, Hi Mark. So what is a peak? It's 2 00:00:03,680 --> 00:00:07,280 Speaker 1: the top of a mountain, Okay. Makes me think of 3 00:00:07,320 --> 00:00:09,400 Speaker 1: the three Peaks challenge here in the UK, where you 4 00:00:09,800 --> 00:00:12,440 Speaker 1: hiked to the top of three separate peaks all in 5 00:00:12,520 --> 00:00:15,840 Speaker 1: one day. Okay. I've never done it. I've wanted to 6 00:00:15,880 --> 00:00:18,640 Speaker 1: do it, but I don't think I'm I'm not ready 7 00:00:18,680 --> 00:00:21,880 Speaker 1: for that yet. Next summer in the recording studio too much, 8 00:00:21,880 --> 00:00:25,360 Speaker 1: not in the gym enough. There you go. Okay, Um, 9 00:00:25,440 --> 00:00:27,720 Speaker 1: so how about some other peaks? What other kinds of 10 00:00:27,720 --> 00:00:29,560 Speaker 1: peaks are there in terms of maybe like sales or 11 00:00:29,680 --> 00:00:32,800 Speaker 1: usuits of stuff. Well, I think of I think of polaroids, 12 00:00:32,800 --> 00:00:35,000 Speaker 1: how they were really popular there for a bit and 13 00:00:35,040 --> 00:00:38,000 Speaker 1: then maybe have dropped off in everybody else's house except 14 00:00:38,000 --> 00:00:41,239 Speaker 1: for mine. My kids love polaroids. Okay, So what other 15 00:00:41,280 --> 00:00:42,640 Speaker 1: types of things have hit a peak? I know that 16 00:00:42,680 --> 00:00:44,800 Speaker 1: we can talk about like land lines for telephones or 17 00:00:44,800 --> 00:00:47,519 Speaker 1: anything like that, anything that was really popular and then 18 00:00:47,560 --> 00:00:50,639 Speaker 1: maybe sales dropped off or peak. Also makes me think 19 00:00:50,680 --> 00:00:53,080 Speaker 1: of the phrase peak oil, which you may have heard of, 20 00:00:53,120 --> 00:00:55,440 Speaker 1: which has to do with actually the extraction of oil 21 00:00:55,480 --> 00:00:57,280 Speaker 1: from the ground. Yeah, and well, the idea is that 22 00:00:57,320 --> 00:00:59,600 Speaker 1: there might not be as much demand for oil in 23 00:00:59,640 --> 00:01:01,760 Speaker 1: the future, and so maybe we've hit a peak who knows, 24 00:01:01,920 --> 00:01:03,960 Speaker 1: or peak for really a lot of other things, and 25 00:01:04,040 --> 00:01:06,800 Speaker 1: one of those things is peak car. Um So the 26 00:01:06,840 --> 00:01:09,600 Speaker 1: idea there is the concept that you know, we've reached 27 00:01:09,600 --> 00:01:11,759 Speaker 1: as many cars as we're gonna get out out there 28 00:01:11,800 --> 00:01:13,720 Speaker 1: on the road. Do you think that's true. I think 29 00:01:13,760 --> 00:01:17,840 Speaker 1: it could be true because I feel true. I think 30 00:01:17,880 --> 00:01:20,440 Speaker 1: it might feel more true for urban nights than other people. Yeah, 31 00:01:20,480 --> 00:01:22,759 Speaker 1: I think you're right. So today we're gonna be talking 32 00:01:22,760 --> 00:01:26,039 Speaker 1: about a research note called peak car by our head 33 00:01:26,080 --> 00:01:28,959 Speaker 1: of Mobility, Ali Azadi, who's going to stop us get 34 00:01:28,959 --> 00:01:30,920 Speaker 1: into this concept and see if we are actually at 35 00:01:30,920 --> 00:01:33,720 Speaker 1: a peak for automobiles out on the road. Please note 36 00:01:33,760 --> 00:01:37,160 Speaker 1: that the ANF does not provide investment or strategy advice, 37 00:01:37,160 --> 00:01:39,240 Speaker 1: and you can hear a full disclaimer at the end 38 00:01:39,240 --> 00:01:42,440 Speaker 1: of the show. I'm Mark Taylor and I am Dana Perkins, 39 00:01:42,520 --> 00:01:48,040 Speaker 1: and this is Switched on the b n F podcast. Ali, 40 00:01:48,080 --> 00:01:50,080 Speaker 1: thanks for joining us today. Thank you very much for 41 00:01:50,160 --> 00:01:53,920 Speaker 1: having me. This note is titled peak car question Mark. 42 00:01:54,000 --> 00:01:56,800 Speaker 1: So theoretically I should be saying peak car, which is 43 00:01:56,840 --> 00:02:00,080 Speaker 1: a bit punchy to be honest, you you're in. I 44 00:02:00,120 --> 00:02:03,400 Speaker 1: see people with cliffhanger jump in because we've got a 45 00:02:03,400 --> 00:02:06,600 Speaker 1: big question mark. So let me ask you, have we 46 00:02:06,640 --> 00:02:09,240 Speaker 1: reached peak car? And if not, when do we reach 47 00:02:09,320 --> 00:02:13,360 Speaker 1: peak car? So whenever people predict something reaching the peak, 48 00:02:13,680 --> 00:02:17,520 Speaker 1: it's it's always turns out to be generally not true. 49 00:02:17,760 --> 00:02:20,120 Speaker 1: So if you remember back in the day where people 50 00:02:20,160 --> 00:02:23,200 Speaker 1: were talking about peak oil supply, now we're talking about 51 00:02:24,480 --> 00:02:26,720 Speaker 1: When we were writing this note, there was a little 52 00:02:26,720 --> 00:02:30,200 Speaker 1: bit of a sense that auto sales are slowing down globally. 53 00:02:30,560 --> 00:02:35,320 Speaker 1: So in last year China auto sales declined for the 54 00:02:35,400 --> 00:02:38,519 Speaker 1: first time in twenty years. This year so far, we've 55 00:02:38,560 --> 00:02:41,760 Speaker 1: seen auto sales declining in many major markets in the US, 56 00:02:42,080 --> 00:02:44,720 Speaker 1: in Europe, and India, so there has been a little 57 00:02:44,760 --> 00:02:47,639 Speaker 1: of a sense that maybe something fundamental is changing this 58 00:02:47,760 --> 00:02:50,280 Speaker 1: time around. So that's why we put the question mark. 59 00:02:50,720 --> 00:02:53,560 Speaker 1: This is all auto sales you're seeing pretty much across 60 00:02:53,600 --> 00:02:57,000 Speaker 1: the world, and this is including trucks. SUV is like, 61 00:02:57,040 --> 00:02:59,600 Speaker 1: what what how do you define autos? Good questions? So 62 00:02:59,639 --> 00:03:02,560 Speaker 1: here we are only talking about cars, so basically thinks 63 00:03:02,600 --> 00:03:05,840 Speaker 1: that individual consumers buy to move around, but we're not 64 00:03:05,880 --> 00:03:09,760 Speaker 1: talking about trucks. That you are talking about SUVs that 65 00:03:09,880 --> 00:03:13,880 Speaker 1: people by, yes, an individual trucks like a flatbed truck 66 00:03:13,919 --> 00:03:15,400 Speaker 1: that you would throw your stuff in, just not a 67 00:03:16,240 --> 00:03:18,720 Speaker 1: or right. Yeah, that's the reason they ask that, because 68 00:03:18,720 --> 00:03:21,760 Speaker 1: people aren't buying sedans anymore. Yeah, so you're also seeing 69 00:03:21,760 --> 00:03:24,800 Speaker 1: consumers taste shift. So in the US, pickup trucks are 70 00:03:24,840 --> 00:03:28,040 Speaker 1: very popular. Outside of the US, you have some markets 71 00:03:28,040 --> 00:03:31,480 Speaker 1: like Thailand that pickup trucks are popular, but SUVs in 72 00:03:31,520 --> 00:03:34,640 Speaker 1: general everywhere are popular, although their types are quite different. 73 00:03:35,200 --> 00:03:38,280 Speaker 1: So there's a shift a little bit from smaller medium 74 00:03:38,560 --> 00:03:43,120 Speaker 1: basically stands two more and more SUVs. There's a variety 75 00:03:43,120 --> 00:03:45,840 Speaker 1: of reasons for that. Some argue that people feel safer 76 00:03:45,880 --> 00:03:49,920 Speaker 1: in them, but some of these also carmakers like subs 77 00:03:49,960 --> 00:03:52,600 Speaker 1: because they make more margins on them. So as they 78 00:03:52,640 --> 00:03:55,480 Speaker 1: make more SUV models available, consumers buy the more. It 79 00:03:55,560 --> 00:03:58,400 Speaker 1: sort of ends up reinforcing a nice cycle. Okay, so 80 00:03:58,760 --> 00:04:02,040 Speaker 1: sales around the world are I don't know week are 81 00:04:02,040 --> 00:04:04,480 Speaker 1: they week by a lot? Is it just kind of 82 00:04:04,480 --> 00:04:06,080 Speaker 1: a one off for the year or is it kind 83 00:04:06,080 --> 00:04:07,680 Speaker 1: of a would you say if it's more of a trend. 84 00:04:07,800 --> 00:04:10,720 Speaker 1: So this is what we were trying to address. UM 85 00:04:10,760 --> 00:04:15,000 Speaker 1: and in our view, in most markets you're gonna see 86 00:04:15,400 --> 00:04:19,159 Speaker 1: sales recover. There are a few markets, for example of Japan, 87 00:04:19,360 --> 00:04:22,560 Speaker 1: where demographics are essentially the main culprit. You have a 88 00:04:22,600 --> 00:04:28,160 Speaker 1: declining total population coupled with an aging society. Essentially fundamental 89 00:04:28,240 --> 00:04:30,520 Speaker 1: demand is going down. But if you look at out 90 00:04:30,520 --> 00:04:34,200 Speaker 1: our markets, say China or say US, we still expect 91 00:04:34,360 --> 00:04:38,520 Speaker 1: sales to gradually recover. In China in particular, the motorization 92 00:04:38,640 --> 00:04:41,720 Speaker 1: rate is still relatively low. A lot of the decline 93 00:04:41,720 --> 00:04:44,719 Speaker 1: that you're saying this series got to do with macroeconomic factors. 94 00:04:45,240 --> 00:04:47,200 Speaker 1: So is this something that you would see in the 95 00:04:47,279 --> 00:04:50,240 Speaker 1: longer term as a bit cyclical because we saw a 96 00:04:50,279 --> 00:04:54,000 Speaker 1: big drop in what was it two eight, and we're 97 00:04:54,000 --> 00:04:57,520 Speaker 1: seeing another drop now. So is this almost a decadal 98 00:04:57,560 --> 00:04:59,760 Speaker 1: thing where we can expect that or is it something 99 00:04:59,800 --> 00:05:02,800 Speaker 1: else to play. Sure, you're absolutely right that car sales 100 00:05:02,960 --> 00:05:09,000 Speaker 1: historically have been somewhat cyclical. They do respond to economic cycles. Now, 101 00:05:09,080 --> 00:05:12,680 Speaker 1: some sometimes people make the mistakes of assuming that GDP 102 00:05:12,800 --> 00:05:16,880 Speaker 1: growth rates and car sales are want to uncorrelated. The 103 00:05:16,920 --> 00:05:19,680 Speaker 1: correlation between new car sales and GDP. There is a 104 00:05:19,760 --> 00:05:22,400 Speaker 1: bit of a b correlation there, But what really correlate 105 00:05:22,400 --> 00:05:25,479 Speaker 1: is very well, would GDP growth rates, or to be 106 00:05:25,560 --> 00:05:29,160 Speaker 1: more accurate, GDP per capital growth rates, it's actually the 107 00:05:29,200 --> 00:05:33,359 Speaker 1: total distance that the fleet drives every year. The reason 108 00:05:33,440 --> 00:05:35,599 Speaker 1: for that is, let's say the financial crisis is a 109 00:05:35,600 --> 00:05:37,800 Speaker 1: good example. What happened in the aftermath of that is 110 00:05:37,839 --> 00:05:39,880 Speaker 1: you had a lot of people losing their job, and 111 00:05:39,960 --> 00:05:42,400 Speaker 1: in a country like US, a lot of people drive 112 00:05:42,480 --> 00:05:44,360 Speaker 1: to go to their work. So once you've lost your 113 00:05:44,440 --> 00:05:46,520 Speaker 1: job and you don't have an income, first of all, 114 00:05:46,520 --> 00:05:48,800 Speaker 1: you're not going to work, but also you have to 115 00:05:48,880 --> 00:05:52,760 Speaker 1: conserve your money, so you're driving less. So historically there's 116 00:05:52,800 --> 00:05:56,359 Speaker 1: a much stronger correlation between GDP per capital growth rates 117 00:05:56,480 --> 00:06:00,680 Speaker 1: as well as the total distance that the fleet drives. 118 00:06:00,720 --> 00:06:05,400 Speaker 1: From that relationship, then you can start calculating, okay, depending 119 00:06:05,400 --> 00:06:08,720 Speaker 1: on how the GDP does, GDP or unemployment rates, because 120 00:06:08,760 --> 00:06:11,200 Speaker 1: it seems like you're trying it to steady income and 121 00:06:11,480 --> 00:06:14,560 Speaker 1: than having a car, so you could make it much 122 00:06:14,600 --> 00:06:17,200 Speaker 1: more granular oil and you could start breaking it apart 123 00:06:17,320 --> 00:06:22,480 Speaker 1: of those factors um Having said that, from a macroeconomic perspective, 124 00:06:22,560 --> 00:06:25,159 Speaker 1: the diduper capita seems to be a good factor. You 125 00:06:25,200 --> 00:06:28,560 Speaker 1: can obviously go much more sophisticated. There's other issues. It 126 00:06:28,640 --> 00:06:33,360 Speaker 1: depends on your araban versus rural population, what's the composition 127 00:06:33,400 --> 00:06:36,080 Speaker 1: of the economy, So you can add more and more 128 00:06:36,120 --> 00:06:39,320 Speaker 1: elements of making it much more sophisticated. But what we 129 00:06:39,400 --> 00:06:41,880 Speaker 1: found out in this note is actually, for a country 130 00:06:41,920 --> 00:06:45,159 Speaker 1: like US, if you look at historical relationship gjper capita 131 00:06:45,320 --> 00:06:47,479 Speaker 1: and the total distance driven by the fly works out 132 00:06:47,520 --> 00:06:50,320 Speaker 1: really nicely. It's actually quite beautiful. It's a constant number 133 00:06:50,400 --> 00:06:52,599 Speaker 1: which then you can use and then you can rely 134 00:06:52,800 --> 00:06:55,800 Speaker 1: on external forecast like what the World Bank says or 135 00:06:56,680 --> 00:06:59,440 Speaker 1: for the GDP and what the U N Population Division 136 00:06:59,520 --> 00:07:02,400 Speaker 1: says for population to then come out with the view 137 00:07:02,400 --> 00:07:05,279 Speaker 1: of how much travel you expect to fleet to do 138 00:07:05,400 --> 00:07:09,440 Speaker 1: in the future. And you're seeing global commonalities, which I 139 00:07:09,480 --> 00:07:11,440 Speaker 1: think is really interesting because each of these markets on 140 00:07:11,480 --> 00:07:15,560 Speaker 1: their own seems unique. Can you just quickly outline what 141 00:07:15,720 --> 00:07:18,360 Speaker 1: other countries you're looking at here and then maybe pick 142 00:07:18,400 --> 00:07:20,880 Speaker 1: the one that you want to expand on. So we've 143 00:07:20,920 --> 00:07:23,560 Speaker 1: tried to cover all the major markets um. So we 144 00:07:23,640 --> 00:07:31,400 Speaker 1: have individual forecast for US, China, India, Japan, Korea, Germany, UK, 145 00:07:31,720 --> 00:07:34,840 Speaker 1: France and Europe as a whole. Um. We also do 146 00:07:34,920 --> 00:07:37,640 Speaker 1: Australia because our team in Australia is very interested in 147 00:07:37,920 --> 00:07:40,400 Speaker 1: knowing what will happen in our market. And then we 148 00:07:40,440 --> 00:07:43,560 Speaker 1: also look at trying to moll forecast the rest of 149 00:07:43,600 --> 00:07:47,400 Speaker 1: the world, which is a fairly big pocket category in 150 00:07:47,520 --> 00:07:52,000 Speaker 1: terms of the differences. While there is commonality in the 151 00:07:52,080 --> 00:07:54,960 Speaker 1: sense that from a macroeconomic perspective, you can make an 152 00:07:54,960 --> 00:07:59,560 Speaker 1: assumption that in most markets the total distance driven by 153 00:07:59,560 --> 00:08:03,720 Speaker 1: the fleet is correlated with geper capita, it's very different 154 00:08:05,000 --> 00:08:09,000 Speaker 1: starting point for each country. So take the US. US 155 00:08:09,080 --> 00:08:11,720 Speaker 1: has one of the highest mortorization rates in the world, 156 00:08:11,760 --> 00:08:14,920 Speaker 1: meaning that people have a lot of cars. In many cases, 157 00:08:14,960 --> 00:08:18,320 Speaker 1: households have more than one car. In a country like 158 00:08:18,360 --> 00:08:21,320 Speaker 1: the US, If you look at your fleet on the street, 159 00:08:21,400 --> 00:08:23,800 Speaker 1: how much of it is owned by individuals, whereas how 160 00:08:23,840 --> 00:08:26,360 Speaker 1: much of it is in taxis or right hailing companies 161 00:08:26,440 --> 00:08:30,720 Speaker 1: or car sharing companies, Those numbers in the US are 162 00:08:30,840 --> 00:08:34,720 Speaker 1: heavily in favor of the privately owned vehicles. So if 163 00:08:34,720 --> 00:08:38,000 Speaker 1: you look at the demand for transportations, if you look 164 00:08:38,000 --> 00:08:41,439 Speaker 1: at it last year, the total distance traveled by the 165 00:08:41,559 --> 00:08:46,800 Speaker 1: car fleet in the US, something around of that distance 166 00:08:47,200 --> 00:08:50,000 Speaker 1: was traveled by the cars that people owned only About 167 00:08:50,040 --> 00:08:54,559 Speaker 1: two point six three percent was by taxis, car sharing 168 00:08:54,559 --> 00:08:57,160 Speaker 1: and right hailing. Even though right hailing you think of 169 00:08:57,280 --> 00:09:00,480 Speaker 1: Uber and Left and Minia leaders your contribution the total 170 00:09:00,520 --> 00:09:03,320 Speaker 1: distance traveled by the fleet is still relatively low. You 171 00:09:03,360 --> 00:09:06,680 Speaker 1: go to a country like India, where private vehicle ownership 172 00:09:06,720 --> 00:09:10,400 Speaker 1: is very low, overall motorization rate is still very low. 173 00:09:11,559 --> 00:09:16,240 Speaker 1: The relative contribution of taxis as well as hailed cars 174 00:09:16,280 --> 00:09:19,439 Speaker 1: like Ala and again Uger operates in India, their contribution 175 00:09:19,440 --> 00:09:22,720 Speaker 1: is a lot higher. In India last year we estimated 176 00:09:23,000 --> 00:09:27,040 Speaker 1: off the distance traveled by the fleet, roughly one third 177 00:09:27,559 --> 00:09:31,319 Speaker 1: was by the shared modes of mobility, very different right 178 00:09:31,400 --> 00:09:33,880 Speaker 1: quite extreme. And then if you go to a country 179 00:09:33,920 --> 00:09:38,480 Speaker 1: like Japan, where public transit infrastructure is really good and 180 00:09:38,559 --> 00:09:43,720 Speaker 1: taxes are relatively expensive and regulation prevents private right hailing, 181 00:09:44,440 --> 00:09:48,400 Speaker 1: then the number contributed by these shared modes even lower, 182 00:09:48,440 --> 00:09:51,480 Speaker 1: so it's below one percent. So there's a very wide 183 00:09:51,679 --> 00:09:56,160 Speaker 1: geographical difference around your starting point, which really has to 184 00:09:56,160 --> 00:09:58,760 Speaker 1: do with how do you get around today? And is 185 00:09:58,840 --> 00:10:02,320 Speaker 1: this alternate better than how you get around today? So 186 00:10:02,360 --> 00:10:04,760 Speaker 1: in India, I suppose it is better than maybe a 187 00:10:04,800 --> 00:10:07,960 Speaker 1: bus or walking or biking or even I've seen multiple 188 00:10:08,000 --> 00:10:11,520 Speaker 1: people climbed on kind of a three wheeler before, and 189 00:10:11,559 --> 00:10:16,160 Speaker 1: are are these vehicles? Are these shared ride services replacing 190 00:10:16,200 --> 00:10:20,320 Speaker 1: that stuff or is it replacing mass transit the buses 191 00:10:20,320 --> 00:10:25,160 Speaker 1: and a few subways. Very good questions. One clarification around 192 00:10:25,200 --> 00:10:27,800 Speaker 1: our forecast, So this time around, we we're only trying 193 00:10:27,840 --> 00:10:30,080 Speaker 1: to forecast the car sales, so we're very much focused 194 00:10:30,080 --> 00:10:33,080 Speaker 1: on the four wheeler. But you're hitting the nail in 195 00:10:33,080 --> 00:10:36,240 Speaker 1: the sense that when people talk about these new shared 196 00:10:36,240 --> 00:10:41,800 Speaker 1: mobility services, there's always a question around are these complementary 197 00:10:41,920 --> 00:10:45,600 Speaker 1: or already is competitive with existing modes. If you look 198 00:10:45,600 --> 00:10:49,800 Speaker 1: at uber and Lift and who did they disrupts today 199 00:10:50,080 --> 00:10:54,040 Speaker 1: in the US, Uber and Lift primarily have disrupted taxis 200 00:10:54,160 --> 00:10:58,680 Speaker 1: and public transit. Essentially in many municipalities like New York, 201 00:10:58,720 --> 00:11:02,480 Speaker 1: like San Francisco, there was a decline in ridership on 202 00:11:02,600 --> 00:11:07,440 Speaker 1: public transit. So from a public policy perspective, given that 203 00:11:07,480 --> 00:11:10,360 Speaker 1: they also increase congestion in those cities, this becomes a 204 00:11:10,360 --> 00:11:12,880 Speaker 1: bit of an issue. But if you go to India 205 00:11:13,000 --> 00:11:17,160 Speaker 1: or Southeast Asia where Grab and Gorjack operate in those markets, 206 00:11:17,280 --> 00:11:22,160 Speaker 1: you had a large segment of the population being underserved 207 00:11:22,200 --> 00:11:27,600 Speaker 1: by existing available options. So privately weekicle ownership is out 208 00:11:27,600 --> 00:11:30,439 Speaker 1: of the reach of many segments in the population. It's 209 00:11:30,559 --> 00:11:34,880 Speaker 1: simply too expensive. Public transit is not enough, and the 210 00:11:34,920 --> 00:11:38,040 Speaker 1: available taxes or even the tuk tuks and the two 211 00:11:38,080 --> 00:11:40,400 Speaker 1: leaders over there, they were still not enough. There was 212 00:11:40,440 --> 00:11:43,160 Speaker 1: not a good way of balancing supply and demand. What 213 00:11:43,320 --> 00:11:45,760 Speaker 1: all of what ubers India operation and what GRAB and 214 00:11:45,760 --> 00:11:48,440 Speaker 1: Gorja have done is essentially they have brought access to 215 00:11:48,520 --> 00:11:52,240 Speaker 1: mobility to a large segment of the population in those 216 00:11:52,240 --> 00:11:55,600 Speaker 1: markets that was underserved. So they're acting as a very 217 00:11:55,760 --> 00:12:01,400 Speaker 1: nice complementary solution to what existed and even enabling social mobility. 218 00:12:01,480 --> 00:12:04,240 Speaker 1: So you could imagine people who wanted to get a 219 00:12:04,360 --> 00:12:07,400 Speaker 1: job but in their local within their walking distance there 220 00:12:07,480 --> 00:12:10,880 Speaker 1: was no job. Now that person can actually afford to 221 00:12:10,880 --> 00:12:13,959 Speaker 1: travel to the further way and get a joll. So 222 00:12:14,080 --> 00:12:19,200 Speaker 1: in this world of now digital right hailing, we've created 223 00:12:19,240 --> 00:12:22,360 Speaker 1: some jobs. And you actually in this note have a 224 00:12:22,360 --> 00:12:24,600 Speaker 1: little part where you say the holy grail for digital 225 00:12:24,600 --> 00:12:29,640 Speaker 1: hailing services is the realization of robotaxis. UM, I don't 226 00:12:29,679 --> 00:12:33,200 Speaker 1: particularly want the terminator to be driving me around, but 227 00:12:34,600 --> 00:12:37,280 Speaker 1: if the way I see it is you're talking about 228 00:12:37,360 --> 00:12:40,880 Speaker 1: job creation and people themselves buying cars. The fleet is 229 00:12:40,920 --> 00:12:43,560 Speaker 1: not owned by these right hailing services is owned by 230 00:12:43,600 --> 00:12:48,280 Speaker 1: the individual who's going to own the cars if they're robotaxis. 231 00:12:49,360 --> 00:12:54,800 Speaker 1: So again here we shall clarify that. Um, there's the 232 00:12:55,200 --> 00:12:57,880 Speaker 1: differences in each geography. If you look at uber and 233 00:12:57,960 --> 00:13:01,400 Speaker 1: left in North America or in Europe. Um, so we 234 00:13:01,400 --> 00:13:04,559 Speaker 1: were operating Europe as well as you. It's European competitors. 235 00:13:05,240 --> 00:13:08,880 Speaker 1: Right now, they're not profitable and if you look at 236 00:13:09,080 --> 00:13:12,920 Speaker 1: their losses, the number one cost that they have is 237 00:13:12,960 --> 00:13:17,560 Speaker 1: the driver pay. So that's the part that it's still 238 00:13:18,000 --> 00:13:21,320 Speaker 1: proving very challenging for them. Now. They argue that they 239 00:13:21,320 --> 00:13:24,559 Speaker 1: will be able to reduce their auto cost, improve operational efficiency, 240 00:13:24,559 --> 00:13:28,240 Speaker 1: and be able to try to improve their profitability by 241 00:13:28,280 --> 00:13:31,319 Speaker 1: controlling the auto cost. The jury is still out whether 242 00:13:31,480 --> 00:13:34,920 Speaker 1: there's enough of other costs that they can cut while 243 00:13:35,000 --> 00:13:38,880 Speaker 1: keeping the driver In markets like Europe and North America, 244 00:13:38,960 --> 00:13:41,400 Speaker 1: will driver pay even the people argue driver pay is 245 00:13:41,440 --> 00:13:43,760 Speaker 1: really low, it's still it's a little bit of a 246 00:13:43,800 --> 00:13:47,079 Speaker 1: problem in the case of the economics of making those 247 00:13:47,120 --> 00:13:51,520 Speaker 1: companies profitable. So if you replace those with autonous vehicles, 248 00:13:51,880 --> 00:13:54,760 Speaker 1: depending on what the capital cost of the autonous vehicles 249 00:13:54,840 --> 00:13:57,680 Speaker 1: is which you're alluding to. You could argue that you're 250 00:13:57,760 --> 00:14:01,120 Speaker 1: essentially getting rid of this opera cost that you always have. 251 00:14:01,200 --> 00:14:03,200 Speaker 1: So if you think about if you are Uber and 252 00:14:03,200 --> 00:14:05,320 Speaker 1: you want to scale the number of rides you're giving, 253 00:14:06,480 --> 00:14:08,280 Speaker 1: you still have to pay the drivers more and more 254 00:14:08,280 --> 00:14:11,040 Speaker 1: and more, Whereas if you have an autonomous vehicle that 255 00:14:11,080 --> 00:14:14,680 Speaker 1: can go around twenty four hours, then you're essentially reducing 256 00:14:15,160 --> 00:14:17,959 Speaker 1: that operational cost that you're paying the drivers. It's a 257 00:14:18,080 --> 00:14:21,600 Speaker 1: big capital investment, and I assume the insurance is covered 258 00:14:21,600 --> 00:14:25,080 Speaker 1: by the drivers as opposed to these other companies. The 259 00:14:25,120 --> 00:14:28,160 Speaker 1: big difference between being a platform and an owner of assets. Right. 260 00:14:28,560 --> 00:14:31,280 Speaker 1: While you're right that the capital cost would be high, 261 00:14:31,400 --> 00:14:34,800 Speaker 1: the capital cost would be more of a one off payment, 262 00:14:34,920 --> 00:14:38,920 Speaker 1: so as you increase the utilization, that doesn't linearly increase, 263 00:14:39,000 --> 00:14:43,080 Speaker 1: whereas with drivers it's essentially a linear relationship that increases. 264 00:14:43,560 --> 00:14:46,560 Speaker 1: So that's that's for those markets. There is this belief 265 00:14:46,640 --> 00:14:49,280 Speaker 1: that if you have atonus vehicles you'll be able to 266 00:14:49,320 --> 00:14:51,400 Speaker 1: do But the reason we call this, by the way, 267 00:14:51,440 --> 00:14:53,760 Speaker 1: the Holy Grail was the Holy Graian was never found 268 00:14:55,200 --> 00:14:58,960 Speaker 1: even by Indiana Jones he found it. But yes, Okay, fine, 269 00:14:58,960 --> 00:15:03,160 Speaker 1: it's fictional. So this has been a first vehicle sales forecast, 270 00:15:03,200 --> 00:15:05,280 Speaker 1: is that correct? That's right? So in that are you 271 00:15:05,320 --> 00:15:08,720 Speaker 1: giving a hint to what you think the the outcome 272 00:15:08,720 --> 00:15:11,760 Speaker 1: will be for robotaxis. You know you're less optimistic, it 273 00:15:11,760 --> 00:15:15,080 Speaker 1: seems I think we're realistic. So we do give a 274 00:15:15,120 --> 00:15:18,400 Speaker 1: forecast in this view and in our forecast for the 275 00:15:18,440 --> 00:15:21,880 Speaker 1: next ten years. So from now until we expect that 276 00:15:21,920 --> 00:15:26,600 Speaker 1: you will have less than two hunder thou robot taxes 277 00:15:26,680 --> 00:15:28,600 Speaker 1: on the road, not enough to make a meaningful difference 278 00:15:28,640 --> 00:15:32,840 Speaker 1: in sales. From an quantitative perspective, that may seem that 279 00:15:32,960 --> 00:15:36,000 Speaker 1: this is very low, it is still meaningful from the 280 00:15:36,080 --> 00:15:40,440 Speaker 1: perspective of the right hailing companies utilization exactly as well 281 00:15:40,520 --> 00:15:43,560 Speaker 1: as automakers. So if you're an automaker, particularly if you're 282 00:15:43,560 --> 00:15:46,160 Speaker 1: a mass market automaker, if you look at uber and 283 00:15:46,280 --> 00:15:50,120 Speaker 1: Lift in the US, the most popular car that uberan 284 00:15:50,200 --> 00:15:53,600 Speaker 1: Lift drivers drive today in US is a used Prius 285 00:15:54,680 --> 00:15:57,320 Speaker 1: because it's upfront cost is relatively low, it has good 286 00:15:57,360 --> 00:16:00,040 Speaker 1: field economy, so the operation cost is really low. So 287 00:16:00,120 --> 00:16:02,960 Speaker 1: if your Turta, you really care about that, and that's 288 00:16:02,960 --> 00:16:05,360 Speaker 1: why Tota is also invest in uber a TG, so 289 00:16:05,480 --> 00:16:09,640 Speaker 1: they're working together on developing autonomous vehicle technology longer term. 290 00:16:09,680 --> 00:16:13,200 Speaker 1: By twenty forty, we expect that about seven percent of 291 00:16:13,320 --> 00:16:16,160 Speaker 1: the load. So again, looking at the total distance that 292 00:16:16,200 --> 00:16:18,760 Speaker 1: definitely drives in twenty we expect seven percent of that 293 00:16:18,920 --> 00:16:22,520 Speaker 1: to be carried by autonomus vehicles. Seven percent may sound 294 00:16:22,600 --> 00:16:26,240 Speaker 1: relatively low, keep in mind, if you look at the 295 00:16:26,360 --> 00:16:30,560 Speaker 1: total distance the car fullet drove last year, only five 296 00:16:30,680 --> 00:16:34,000 Speaker 1: percent of that total distance was by shared mobility. So 297 00:16:34,160 --> 00:16:36,680 Speaker 1: we expect that five percent in total to grow to 298 00:16:38,360 --> 00:16:40,320 Speaker 1: and off that nine pcent, about one third of it 299 00:16:40,400 --> 00:16:42,760 Speaker 1: will be autonomous. Two thirds of it will still be 300 00:16:42,880 --> 00:16:46,080 Speaker 1: human driven. Now you may argue why would still be 301 00:16:46,160 --> 00:16:48,600 Speaker 1: human driven, and it goes back to what data you 302 00:16:48,680 --> 00:16:52,280 Speaker 1: were alluding to earlier. In markets like India or Southeast 303 00:16:52,320 --> 00:16:57,200 Speaker 1: Asia or Africa, or also even in developed economies, say 304 00:16:57,320 --> 00:17:02,240 Speaker 1: rural United States or rural Europe, from a cost and 305 00:17:02,360 --> 00:17:05,800 Speaker 1: technological perspective, it will still not We don't expect it 306 00:17:05,880 --> 00:17:09,200 Speaker 1: to be feasible to rely on Thomas vehicles, So if 307 00:17:09,240 --> 00:17:12,760 Speaker 1: you're in rural Texas, for example, the utilization rate is 308 00:17:12,800 --> 00:17:15,480 Speaker 1: still relatively low. Also, you have to make sure that 309 00:17:15,800 --> 00:17:18,760 Speaker 1: your technology can adapt to that environment that rural Texas, 310 00:17:18,880 --> 00:17:21,520 Speaker 1: to be fair, the weather is not that bad. But 311 00:17:21,640 --> 00:17:24,639 Speaker 1: you go to rural Wisconsin snows a lot. So you 312 00:17:24,760 --> 00:17:28,080 Speaker 1: run into a lot of practical issues. Um and that's 313 00:17:28,119 --> 00:17:31,160 Speaker 1: where while we do expect the technology to improve a lot, 314 00:17:32,480 --> 00:17:36,000 Speaker 1: we're still from what's known today. There's still a lot 315 00:17:36,040 --> 00:17:39,080 Speaker 1: of challenges that remain to be resolved. About halfway through 316 00:17:39,359 --> 00:17:44,280 Speaker 1: peak car question mark, but you get into this place 317 00:17:44,359 --> 00:17:49,280 Speaker 1: of uncertainties and it seems like there is so much 318 00:17:49,480 --> 00:17:51,680 Speaker 1: to wrap your arms around in terms of what is 319 00:17:51,720 --> 00:17:55,520 Speaker 1: actually influencing car ownership rates as opposed to just do 320 00:17:55,680 --> 00:17:57,760 Speaker 1: I want to be on the tube today or do 321 00:17:57,880 --> 00:17:59,280 Speaker 1: I want to be in my own car? And can 322 00:17:59,320 --> 00:18:02,280 Speaker 1: I find park? So there are three things you outline 323 00:18:02,359 --> 00:18:05,159 Speaker 1: under these fundamental changes to actually how people and more 324 00:18:05,200 --> 00:18:08,520 Speaker 1: importantly goods are moving around at the local level but 325 00:18:08,720 --> 00:18:11,200 Speaker 1: really commonly around the world. Can you outline what those 326 00:18:11,320 --> 00:18:14,280 Speaker 1: three areas are? So one, as you alluded earlier on, 327 00:18:14,760 --> 00:18:18,640 Speaker 1: is around sort of black Swan type so financial crisis 328 00:18:18,720 --> 00:18:20,960 Speaker 1: things like that, or you could put in wars and 329 00:18:21,080 --> 00:18:23,760 Speaker 1: things like that. So those definitely impact. And there's something 330 00:18:23,840 --> 00:18:27,400 Speaker 1: that like unfortunately we are mortal analysts recount forecast those things. 331 00:18:28,320 --> 00:18:30,960 Speaker 1: The second one and the third one, which are areas 332 00:18:31,000 --> 00:18:32,280 Speaker 1: that we're going to do a lot more work on 333 00:18:32,480 --> 00:18:35,720 Speaker 1: in the future. Is one is around the whole impact 334 00:18:35,840 --> 00:18:39,320 Speaker 1: of e commerce. So if you think about e commerce 335 00:18:39,400 --> 00:18:44,400 Speaker 1: and how you are now buying your daily necessities, you're 336 00:18:44,480 --> 00:18:47,800 Speaker 1: essentially more and more eliminating they need to drive to 337 00:18:47,920 --> 00:18:52,000 Speaker 1: the grocery store, or they need to go to cost 338 00:18:52,040 --> 00:18:54,240 Speaker 1: goal on the weekend or something like that. So those 339 00:18:54,280 --> 00:18:57,720 Speaker 1: are areas that could fundamentally impact people's need for cars. 340 00:18:58,160 --> 00:19:00,119 Speaker 1: A lot of times when people talk about one to 341 00:19:00,200 --> 00:19:02,720 Speaker 1: own a car is I want to carry something. So 342 00:19:02,800 --> 00:19:05,840 Speaker 1: if you elimit it that part and you don't need 343 00:19:05,920 --> 00:19:09,080 Speaker 1: to carry heavy loads, then you may argue that maybe 344 00:19:09,160 --> 00:19:12,280 Speaker 1: you can rely on a bicycle. Part of the challenge 345 00:19:12,320 --> 00:19:14,959 Speaker 1: today is like, if you look at the historical correlation 346 00:19:15,160 --> 00:19:18,200 Speaker 1: that I mentioned that we use for forecasting the impact 347 00:19:18,280 --> 00:19:22,280 Speaker 1: of e commerce on that historical correlation has not appeared yet. 348 00:19:23,040 --> 00:19:25,960 Speaker 1: So will this end up in what you guys see 349 00:19:26,000 --> 00:19:28,760 Speaker 1: it as see it appearing? It potentially could, So this 350 00:19:28,880 --> 00:19:31,320 Speaker 1: is an area that you're we're looking at quite extensively. 351 00:19:31,400 --> 00:19:33,280 Speaker 1: This and this ties to the second one, which I 352 00:19:33,520 --> 00:19:36,400 Speaker 1: mentioned briefly the bicycle one. So the question of micro mobility, 353 00:19:37,000 --> 00:19:41,080 Speaker 1: So micro mobility to define it is basically stand up scooters, 354 00:19:41,480 --> 00:19:45,359 Speaker 1: electric assists by bicycles which are provided on a shared 355 00:19:45,400 --> 00:19:47,520 Speaker 1: platform so you don't even have to own it yourself. 356 00:19:48,320 --> 00:19:51,800 Speaker 1: And the reason this is an interesting area is pick 357 00:19:51,840 --> 00:19:53,960 Speaker 1: the United States. If you look at the statistics on 358 00:19:54,080 --> 00:19:57,080 Speaker 1: how cars are using the United States. If you look 359 00:19:57,119 --> 00:20:00,400 Speaker 1: at the numbers from recent um the last couple years, 360 00:20:01,480 --> 00:20:06,040 Speaker 1: roughly sixty percent of the average card trip is less 361 00:20:06,200 --> 00:20:11,000 Speaker 1: than six miles. Within that about half of it is 362 00:20:11,240 --> 00:20:14,880 Speaker 1: less than three miles. Essentially, it's people driving the car, 363 00:20:15,160 --> 00:20:17,800 Speaker 1: the big suv, just going down the street to buy 364 00:20:17,880 --> 00:20:22,240 Speaker 1: some stuff. Now, those are distances that if you're within 365 00:20:22,640 --> 00:20:24,520 Speaker 1: less than two miles, we can easily do with a 366 00:20:24,560 --> 00:20:27,560 Speaker 1: stand up scooter. If it's less than if it's between 367 00:20:27,600 --> 00:20:29,000 Speaker 1: two moles and six months, we can do with and 368 00:20:29,080 --> 00:20:32,320 Speaker 1: that actually this bicycle. And we have seen a lot 369 00:20:32,400 --> 00:20:36,359 Speaker 1: of money go to these shared micro mobility applications. So 370 00:20:36,640 --> 00:20:39,440 Speaker 1: Baird was a bird and line where both some of 371 00:20:39,560 --> 00:20:44,080 Speaker 1: the fastest unicorns ever I think burn for bird. It 372 00:20:44,160 --> 00:20:47,119 Speaker 1: took it less than a year to have evaluation of 373 00:20:47,119 --> 00:20:50,440 Speaker 1: world billion dollars, which is quite remarkable. And there's this 374 00:20:50,560 --> 00:20:55,159 Speaker 1: idea one of the famous um Um sort of gurus 375 00:20:55,200 --> 00:20:57,280 Speaker 1: of micro mobility horse did you like to call it? 376 00:20:57,520 --> 00:21:01,480 Speaker 1: The concept of unbundling the car. So essentially you're looking 377 00:21:01,560 --> 00:21:04,840 Speaker 1: at how people use cars for different applications. There's all 378 00:21:04,920 --> 00:21:07,560 Speaker 1: those applications that you can actually use automodes of transport. 379 00:21:08,560 --> 00:21:11,159 Speaker 1: Are you seeing this outside of major urban center, so 380 00:21:11,359 --> 00:21:14,479 Speaker 1: where a car sometimes comes with more hassle than it's 381 00:21:14,560 --> 00:21:17,680 Speaker 1: worth because I can't see, for example, my brother in 382 00:21:17,800 --> 00:21:21,399 Speaker 1: law in Kentucky hopping on a micro mobility scooter and 383 00:21:21,520 --> 00:21:24,320 Speaker 1: running to the store when he's got a car already 384 00:21:24,359 --> 00:21:28,640 Speaker 1: sitting in the driveway as I see as the summer time. Yeah, 385 00:21:28,920 --> 00:21:30,960 Speaker 1: so there are a lot of open questions. One is, 386 00:21:31,000 --> 00:21:33,560 Speaker 1: as you're alluding to, is the rule versus urban, which 387 00:21:33,640 --> 00:21:36,959 Speaker 1: is a very valid one. So again goes down around 388 00:21:37,080 --> 00:21:39,920 Speaker 1: your what are the distances that we're discussing. The other 389 00:21:40,000 --> 00:21:42,440 Speaker 1: one that I would like to point out is, of course, 390 00:21:42,520 --> 00:21:47,280 Speaker 1: whether so these things initially, particularly stand of scooters, took 391 00:21:47,320 --> 00:21:52,720 Speaker 1: off in California. California sunny is relatively dry, but what 392 00:21:52,880 --> 00:21:55,600 Speaker 1: about say winter in New York. But they also took 393 00:21:55,600 --> 00:21:58,080 Speaker 1: off in Paris right where it reigns a lot. Yeah, 394 00:21:58,160 --> 00:22:00,520 Speaker 1: so now we're starting so this is so thank you 395 00:22:00,600 --> 00:22:05,000 Speaker 1: for making the point. Um. There was initially some skepticism 396 00:22:05,480 --> 00:22:08,280 Speaker 1: which was like, oh, these California vcs are just putting 397 00:22:08,320 --> 00:22:10,520 Speaker 1: money behind something that's not going to go around the world. 398 00:22:10,960 --> 00:22:13,359 Speaker 1: And what we've seen actually is now going around the world. 399 00:22:13,560 --> 00:22:16,399 Speaker 1: And you have also while this some people are I 400 00:22:16,600 --> 00:22:19,880 Speaker 1: think that this is a California thing. Actually shared bicycles 401 00:22:20,480 --> 00:22:22,760 Speaker 1: these platforms took over China a lot earlier, and then 402 00:22:22,800 --> 00:22:25,399 Speaker 1: from China and around the world, so we see it 403 00:22:25,520 --> 00:22:32,440 Speaker 1: has legs. But there the question that's still very hard 404 00:22:32,480 --> 00:22:35,639 Speaker 1: to answer is how much of that demand is going 405 00:22:35,680 --> 00:22:39,200 Speaker 1: to get attributed to these services. What we've already seen 406 00:22:39,359 --> 00:22:41,600 Speaker 1: is that there have been two winters already since some 407 00:22:41,680 --> 00:22:44,120 Speaker 1: of these new companies were launched, and they have survived 408 00:22:44,160 --> 00:22:46,400 Speaker 1: those and even in winters, there was still a little 409 00:22:46,400 --> 00:22:49,040 Speaker 1: bit of demand for these services. Um, so we do 410 00:22:49,160 --> 00:22:50,760 Speaker 1: see that it has legs in it. It's just a 411 00:22:50,840 --> 00:22:53,080 Speaker 1: question of how much of the demand will get shifted 412 00:22:53,119 --> 00:22:56,080 Speaker 1: to them. And carmakers are becoming very interested in them 413 00:22:56,119 --> 00:22:58,960 Speaker 1: as well, So carmakers are looking at it. This could 414 00:22:59,000 --> 00:23:01,920 Speaker 1: potentially be a competitor. Is there something we could do 415 00:23:02,000 --> 00:23:04,760 Speaker 1: about it? Should be invest in it. So there's a 416 00:23:04,800 --> 00:23:07,720 Speaker 1: lot of interest around this last mile and this kind 417 00:23:07,760 --> 00:23:10,359 Speaker 1: of space close to your home. And there's this other 418 00:23:10,440 --> 00:23:14,000 Speaker 1: category that you actually go into, which is which are 419 00:23:14,440 --> 00:23:18,640 Speaker 1: autonomous shuttles that looked like they might fill the same 420 00:23:18,760 --> 00:23:21,440 Speaker 1: and maybe get around some of the weather issues. My 421 00:23:21,640 --> 00:23:24,240 Speaker 1: question is though, where where are they? I've not seen 422 00:23:24,280 --> 00:23:26,320 Speaker 1: any yet and are they taking off? Are they a 423 00:23:26,359 --> 00:23:29,360 Speaker 1: lot of potential right now? Very good questions. So when 424 00:23:29,400 --> 00:23:31,280 Speaker 1: I was talking about the roll a taxi, that was 425 00:23:31,359 --> 00:23:34,000 Speaker 1: the idea of that you have an autonous vehicle which 426 00:23:34,040 --> 00:23:37,040 Speaker 1: is kind of similar to your taxes today. It's on 427 00:23:37,200 --> 00:23:42,080 Speaker 1: public roads, it's mixed with traffic that's still include human drivers, 428 00:23:42,240 --> 00:23:44,320 Speaker 1: so they are more or less very similar to car 429 00:23:44,400 --> 00:23:46,959 Speaker 1: and cars by essentially you replace a drive over there. 430 00:23:48,160 --> 00:23:51,920 Speaker 1: In the autono shuttle case, you're talking about low speed 431 00:23:52,000 --> 00:23:57,200 Speaker 1: applications where you have a vehicle that's designated to specific area. 432 00:23:57,600 --> 00:24:01,600 Speaker 1: The technical term dual fenced to make it fancy, so 433 00:24:01,760 --> 00:24:04,480 Speaker 1: you can think of, for example, in the city area 434 00:24:04,640 --> 00:24:08,920 Speaker 1: and just London like or or if you're from down Under, 435 00:24:08,960 --> 00:24:12,000 Speaker 1: you would say the CBD, the and the business district 436 00:24:12,080 --> 00:24:17,320 Speaker 1: basically central this dis district or a university campus, or 437 00:24:17,960 --> 00:24:24,080 Speaker 1: think about airports if you have low speed geo fenced applications. 438 00:24:24,720 --> 00:24:28,640 Speaker 1: The technology is already available. There are companies like two 439 00:24:28,680 --> 00:24:31,440 Speaker 1: French companies, Navya and Easy Mouths. There's US company like 440 00:24:31,560 --> 00:24:34,280 Speaker 1: NA Mobility, which might have been name Mobility. Toyto's venture 441 00:24:34,640 --> 00:24:37,720 Speaker 1: Arms has invested it. They're making these sort of like shuttles. 442 00:24:38,280 --> 00:24:41,320 Speaker 1: They're essentially things that you could you have already seen 443 00:24:41,400 --> 00:24:43,679 Speaker 1: at airports, like sometimes those shuttles that you take out 444 00:24:43,680 --> 00:24:48,159 Speaker 1: airports Togo, but now they're automated with those. What you 445 00:24:48,240 --> 00:24:52,640 Speaker 1: could argue is that essentially there are form of public transit, 446 00:24:53,000 --> 00:24:56,119 Speaker 1: but they're much more flexible. You don't have to worry 447 00:24:56,200 --> 00:24:58,399 Speaker 1: about Okay, if you want to run these things twenty 448 00:24:58,440 --> 00:25:01,040 Speaker 1: four hours, what are we going to do about the drivers? 449 00:25:02,080 --> 00:25:05,840 Speaker 1: There are also a lot more flexible than say subways 450 00:25:06,240 --> 00:25:09,960 Speaker 1: or autonomous model rails for them. The main questions around 451 00:25:10,040 --> 00:25:14,080 Speaker 1: deployment is actually much more about local policy, or a 452 00:25:14,160 --> 00:25:16,959 Speaker 1: better world would be local politics. Let's say you want 453 00:25:17,000 --> 00:25:21,200 Speaker 1: to deploy it in city like London, how would TfL's 454 00:25:21,560 --> 00:25:25,359 Speaker 1: labor union respond to this because essentially you're talking about 455 00:25:26,000 --> 00:25:29,639 Speaker 1: competitive position that could have any impact on jobs. Is 456 00:25:29,680 --> 00:25:32,040 Speaker 1: there a chance this is an area that sort of business, 457 00:25:32,320 --> 00:25:36,479 Speaker 1: which is private technically could expand into because I can 458 00:25:36,520 --> 00:25:40,440 Speaker 1: see people with their local travel card getting a little 459 00:25:40,440 --> 00:25:42,760 Speaker 1: bit frustrated to have an additional fee tacked on to 460 00:25:42,880 --> 00:25:46,720 Speaker 1: the end if you have So this all comes down 461 00:25:46,760 --> 00:25:49,720 Speaker 1: to which type of road you're considering. So one of 462 00:25:49,840 --> 00:25:53,720 Speaker 1: the really interesting deployments around these technologies is in retirement 463 00:25:53,800 --> 00:25:58,160 Speaker 1: communities in United States. So in Florida, for example, there's 464 00:25:58,200 --> 00:26:00,800 Speaker 1: a retirement community called the Villages. Actually, most of retirement 465 00:26:00,800 --> 00:26:02,560 Speaker 1: community in the US are called the Villages. I don't 466 00:26:02,560 --> 00:26:05,720 Speaker 1: know why, but one of the biggest one has a 467 00:26:05,800 --> 00:26:08,840 Speaker 1: partnership with Voyage, one of these new companies that's debluting 468 00:26:08,840 --> 00:26:11,800 Speaker 1: the technology, and they're there. Most of the roads are private, 469 00:26:12,840 --> 00:26:15,359 Speaker 1: so the retirement community can decide what they want to 470 00:26:15,400 --> 00:26:17,720 Speaker 1: do with it, and they've already had this partnership where 471 00:26:17,720 --> 00:26:20,000 Speaker 1: they're deploying them. So yes, I agree with you that 472 00:26:20,119 --> 00:26:22,639 Speaker 1: you could have if if you have large businesses that 473 00:26:22,800 --> 00:26:27,840 Speaker 1: have rights over the roadways like the university campus. But 474 00:26:28,200 --> 00:26:32,399 Speaker 1: in cities it becomes much more complicated. So We've had 475 00:26:32,440 --> 00:26:34,600 Speaker 1: a chance to jump into a few parts of this note, 476 00:26:34,640 --> 00:26:37,000 Speaker 1: and I know that we've not gotten all of the 477 00:26:37,080 --> 00:26:40,000 Speaker 1: stories out of you, so people can read more if 478 00:26:40,040 --> 00:26:43,280 Speaker 1: they want. My question is this seems like it's the 479 00:26:43,359 --> 00:26:46,679 Speaker 1: beginning of a lot more questions that have been raised. 480 00:26:47,160 --> 00:26:50,280 Speaker 1: What is next in your research pipeline? You're absolutely right, 481 00:26:50,520 --> 00:26:53,440 Speaker 1: and there's a lot of art factors we need to consider. 482 00:26:53,960 --> 00:26:57,200 Speaker 1: So the first thing you're doing is to better understand 483 00:26:57,280 --> 00:27:00,359 Speaker 1: how moral shifts might happen. So that question how much 484 00:27:00,400 --> 00:27:03,040 Speaker 1: of the demand we'll go to Michael movies. We want 485 00:27:03,040 --> 00:27:07,959 Speaker 1: to actually look deeper into what happened historically. So one 486 00:27:08,000 --> 00:27:09,960 Speaker 1: of our team members right now is writing a note 487 00:27:10,040 --> 00:27:14,360 Speaker 1: looking at the UK since the nineteen fifties when initially 488 00:27:14,359 --> 00:27:16,719 Speaker 1: everyone was relying on trains and like probably and then 489 00:27:16,760 --> 00:27:19,720 Speaker 1: when the cars became more and more popular, how model 490 00:27:19,760 --> 00:27:22,480 Speaker 1: shift happened there. So we're gonna do a historical deep 491 00:27:22,480 --> 00:27:24,560 Speaker 1: slive on the UK. Then we're going to expand it 492 00:27:24,600 --> 00:27:28,680 Speaker 1: to more countries and better understand how these shifts happen. 493 00:27:29,760 --> 00:27:31,840 Speaker 1: Obviously technology plays a role, but a lot of it 494 00:27:31,920 --> 00:27:36,639 Speaker 1: also comes down to policy and cultural values even and 495 00:27:36,760 --> 00:27:40,359 Speaker 1: based on those try to then come up with scenarios 496 00:27:40,400 --> 00:27:42,200 Speaker 1: of how it may play out in the future, because 497 00:27:42,200 --> 00:27:43,840 Speaker 1: we as a business to a lot of forecasts. So 498 00:27:43,840 --> 00:27:45,359 Speaker 1: I guess you've got to look back before you can 499 00:27:45,400 --> 00:27:48,520 Speaker 1: look forward, right, You're absolutely right, Ali, thank you very 500 00:27:48,600 --> 00:27:51,080 Speaker 1: much for joining us today. Thank you very much, Dania 501 00:27:51,119 --> 00:27:56,080 Speaker 1: and Mark. 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