1 00:00:15,356 --> 00:00:22,756 Speaker 1: Pushkin, what do you understand about maps and the world 2 00:00:22,916 --> 00:00:25,676 Speaker 1: because you have been, you know, studying it and working 3 00:00:25,716 --> 00:00:26,516 Speaker 1: on it for so long. 4 00:00:26,876 --> 00:00:30,556 Speaker 2: I think the reason I personally love maps is just 5 00:00:30,596 --> 00:00:33,996 Speaker 2: because there's a super super complex problem and very very 6 00:00:33,996 --> 00:00:36,636 Speaker 2: hard thing to solve. That's why why I'm so passionate 7 00:00:36,676 --> 00:00:37,076 Speaker 2: about it. 8 00:00:37,316 --> 00:00:40,636 Speaker 1: This is Philip Kundall. He's the chief product officer at 9 00:00:40,636 --> 00:00:44,076 Speaker 1: a company called Grab. Grab is huge in Southeast Asia 10 00:00:44,116 --> 00:00:47,476 Speaker 1: and its main businesses are delivery and mobility, kind of 11 00:00:47,516 --> 00:00:50,876 Speaker 1: like a combination between Instacart and Uber, and as a result, 12 00:00:51,116 --> 00:00:53,156 Speaker 1: maps are at the core of its business. 13 00:00:53,556 --> 00:00:56,956 Speaker 2: But the fascinating thing for me is like, once you 14 00:00:56,996 --> 00:01:00,436 Speaker 2: get a digital representation of the real world, you can 15 00:01:00,516 --> 00:01:03,036 Speaker 2: basically the vision that I always had for long, long years. 16 00:01:03,036 --> 00:01:05,076 Speaker 2: If you haven't solved that yet is imagine when he 17 00:01:05,116 --> 00:01:07,836 Speaker 2: went from Yahoo to Google. Right. Yahoo was like the 18 00:01:07,876 --> 00:01:11,516 Speaker 2: static index of web that you could say, for something 19 00:01:11,516 --> 00:01:12,956 Speaker 2: that just leads you to a website. Right if you 20 00:01:12,996 --> 00:01:14,876 Speaker 2: would search for sports and it gets you to a 21 00:01:14,916 --> 00:01:16,796 Speaker 2: sports side and then you need to navigate your way. 22 00:01:16,996 --> 00:01:18,916 Speaker 2: And then Google you can say you search for a 23 00:01:19,076 --> 00:01:23,596 Speaker 2: very specific thing and it brings you exactly to that page. Right, Yeah, 24 00:01:23,636 --> 00:01:26,476 Speaker 2: and that's what we haven't solved with with maps yet. 25 00:01:26,556 --> 00:01:28,876 Speaker 1: Right if you say, if we're still in the Yahoo 26 00:01:28,876 --> 00:01:30,916 Speaker 1: era of maps, is what you're telling me. 27 00:01:31,276 --> 00:01:34,036 Speaker 2: Maybe slightly beyond the Yahoo era. But let's say like 28 00:01:34,076 --> 00:01:36,756 Speaker 2: when you say, where do I find the in Southeast Asia? 29 00:01:36,876 --> 00:01:39,916 Speaker 2: Durian is like a super popular fruit? Right say where 30 00:01:39,916 --> 00:01:42,676 Speaker 2: I said, where do I find the freshest Durian for 31 00:01:42,796 --> 00:01:46,436 Speaker 2: this price? Right now, right now, the real world, right now, 32 00:01:46,596 --> 00:01:49,476 Speaker 2: in the real world, right like, which store stocks a 33 00:01:49,596 --> 00:01:52,476 Speaker 2: Durian right now? Show me where it is and then 34 00:01:52,596 --> 00:01:55,316 Speaker 2: guide me to that store. There's nobody who has solved 35 00:01:55,316 --> 00:01:58,996 Speaker 2: that problem. And and that's the fascinating part for me 36 00:01:59,356 --> 00:02:01,556 Speaker 2: that I want what Google has done for the web. 37 00:02:01,596 --> 00:02:03,196 Speaker 2: I want that for the real world. 38 00:02:03,436 --> 00:02:06,076 Speaker 1: I mean, that's a wild problem. When you formulate it 39 00:02:06,156 --> 00:02:09,356 Speaker 1: that way, you would have to know everything all the time. 40 00:02:09,996 --> 00:02:13,436 Speaker 1: There's an information problem there that is quite hard. 41 00:02:13,876 --> 00:02:15,356 Speaker 2: I mean, the hot problems are the fun ones and 42 00:02:15,396 --> 00:02:31,596 Speaker 2: a yeah. 43 00:02:23,756 --> 00:02:26,036 Speaker 1: I'm Jacob Goldstein, and this is what's your problem? The 44 00:02:26,076 --> 00:02:28,036 Speaker 1: show where I talk to people who are trying to 45 00:02:28,116 --> 00:02:32,356 Speaker 1: make technological progress. Philip Bilton sold a mapping startup before 46 00:02:32,356 --> 00:02:35,476 Speaker 1: he wound up at grab in twenty nineteen. Today, he's 47 00:02:35,476 --> 00:02:38,196 Speaker 1: based in Singapore, which is one of several countries where 48 00:02:38,196 --> 00:02:44,156 Speaker 1: Grab operates. Others include Indonesia, Malaysia, the Philippines, Thailand, and Vietnam. 49 00:02:44,476 --> 00:02:47,596 Speaker 1: And in addition to doing delivery and mobility, they also 50 00:02:47,676 --> 00:02:51,156 Speaker 1: do payments. It's what they call a super app. And 51 00:02:51,756 --> 00:02:55,076 Speaker 1: I wanted to talk to Philip about maps in particular 52 00:02:55,156 --> 00:02:57,956 Speaker 1: because I mean, I guess I just kind of thought 53 00:02:57,996 --> 00:03:01,596 Speaker 1: maps were solved or assumed that without really thinking about it, 54 00:03:01,916 --> 00:03:04,436 Speaker 1: and then when I started learning about GRAB, I realized 55 00:03:04,476 --> 00:03:08,556 Speaker 1: I was very wrong. So there's that dream of real 56 00:03:08,636 --> 00:03:11,636 Speaker 1: time mapp apps that Philip mentioned a minute ago. But 57 00:03:11,716 --> 00:03:14,756 Speaker 1: there's also something that's really interesting and kind of more 58 00:03:14,876 --> 00:03:18,556 Speaker 1: immediately relevant, and that is this The online maps we 59 00:03:18,676 --> 00:03:22,676 Speaker 1: use in the US and other developed countries just don't work. 60 00:03:22,516 --> 00:03:24,516 Speaker 3: Very well in a lot of other parts. 61 00:03:24,236 --> 00:03:26,996 Speaker 1: Of the world. And when Grab launched several years ago 62 00:03:27,076 --> 00:03:29,916 Speaker 1: in Malaysia, that turned out to be a big problem. 63 00:03:30,156 --> 00:03:31,836 Speaker 2: I mean, from day one, you needed maps, so you 64 00:03:31,836 --> 00:03:35,396 Speaker 2: can't run the company without maps, and grabs started using 65 00:03:35,436 --> 00:03:38,276 Speaker 2: a third party service. But then what we've realized a 66 00:03:38,276 --> 00:03:41,076 Speaker 2: lot of these services are built for like a developed 67 00:03:41,076 --> 00:03:44,236 Speaker 2: market like the US, and very built around the mental 68 00:03:44,276 --> 00:03:48,836 Speaker 2: model of like cars, and Southeast Asia is really different. 69 00:03:49,196 --> 00:03:53,636 Speaker 2: We're operating primarily on motorbikes, even two thirds of our 70 00:03:53,676 --> 00:03:56,076 Speaker 2: transport trips on the back of a motorbike. And then 71 00:03:56,116 --> 00:03:58,596 Speaker 2: if you know Southeast Asian cities, then you have these 72 00:03:58,676 --> 00:04:02,516 Speaker 2: narrow alleys and sideways and so on, and traditional maps 73 00:04:02,556 --> 00:04:07,196 Speaker 2: just don't cover them because interesting, basically how maps are 74 00:04:07,196 --> 00:04:10,676 Speaker 2: made traditionally is with these big mapping vans that I'm 75 00:04:10,676 --> 00:04:13,196 Speaker 2: sure you've seen driving through cities. One hundred and fifty 76 00:04:13,516 --> 00:04:16,476 Speaker 2: two hundred thousand dollars basically look like a way more right, 77 00:04:16,516 --> 00:04:19,316 Speaker 2: That's kind of like how they look like, and they 78 00:04:19,316 --> 00:04:21,756 Speaker 2: don't cover the roads that we need to deliver our 79 00:04:21,836 --> 00:04:25,316 Speaker 2: services and really reach our customer because they expect to 80 00:04:25,316 --> 00:04:27,716 Speaker 2: be picked up in their home. They expect that the 81 00:04:27,716 --> 00:04:30,436 Speaker 2: food get delivered to their front door, and not just 82 00:04:30,476 --> 00:04:32,916 Speaker 2: to the nearest street that might be two hundred meters 83 00:04:32,956 --> 00:04:35,636 Speaker 2: away in terms of like a big car drivable street. 84 00:04:35,916 --> 00:04:38,996 Speaker 1: So lots of life, lots of people live, lots of businesses, 85 00:04:39,036 --> 00:04:41,636 Speaker 1: earned streets that a van couldn't even drive down if 86 00:04:41,676 --> 00:04:42,236 Speaker 1: it wanted to. 87 00:04:42,596 --> 00:04:45,436 Speaker 2: Yeah, no chance. I mean, when I do the immersions 88 00:04:45,476 --> 00:04:48,756 Speaker 2: like on the back of a motorbike. There's absolutely no 89 00:04:48,916 --> 00:04:51,796 Speaker 2: chance that with a man you can get there. It 90 00:04:51,876 --> 00:04:55,516 Speaker 2: was a scooter centric world, and Southeast Asia updated so quickly, 91 00:04:56,356 --> 00:04:59,076 Speaker 2: like I mean, there's like entire new neighborhoods springing up, 92 00:04:59,316 --> 00:05:01,476 Speaker 2: and it was traditional maps are built in a way 93 00:05:01,476 --> 00:05:04,756 Speaker 2: that these big vans collect once every one or two years, 94 00:05:05,116 --> 00:05:07,716 Speaker 2: and we need to refresh the maps in like days. 95 00:05:08,116 --> 00:05:11,596 Speaker 1: So you have this problem, like what's the first step? 96 00:05:11,636 --> 00:05:14,356 Speaker 1: So you realize the maps aren't working. You're a mobility company, 97 00:05:14,396 --> 00:05:17,276 Speaker 1: that's that's not gonna work. How do you It seems 98 00:05:17,276 --> 00:05:19,516 Speaker 1: impossible to think, oh, well just map everything. 99 00:05:19,556 --> 00:05:19,996 Speaker 2: How do you go? 100 00:05:20,116 --> 00:05:21,036 Speaker 1: How do you start doing that? 101 00:05:21,476 --> 00:05:24,556 Speaker 2: Yeah? Exactly right. It's a crazy problem, right yeah. I 102 00:05:24,556 --> 00:05:27,796 Speaker 2: mean the numbers that were out there when Google started 103 00:05:27,836 --> 00:05:31,116 Speaker 2: mapping the US, they apparently spent like anywhere between half 104 00:05:31,116 --> 00:05:34,036 Speaker 2: a billion to one billion dollars and it clearly didn't 105 00:05:34,036 --> 00:05:37,916 Speaker 2: have that amount of money to map it. And it 106 00:05:37,956 --> 00:05:40,316 Speaker 2: seemed crazy, right like people when we started doing this, 107 00:05:40,836 --> 00:05:42,916 Speaker 2: I mean, I got like so much feedback that people 108 00:05:42,916 --> 00:05:46,516 Speaker 2: thought we were completely insane because God cost us and 109 00:05:46,636 --> 00:05:48,596 Speaker 2: Southeast Age are right, I mean, we are home to 110 00:05:48,716 --> 00:05:51,756 Speaker 2: like close to seven hundred million people, which is like 111 00:05:51,836 --> 00:05:53,876 Speaker 2: a lot larger than the US. So you can imagine 112 00:05:53,876 --> 00:05:55,756 Speaker 2: like how much it would normally. 113 00:05:55,396 --> 00:05:59,196 Speaker 1: Cost, yes, two x exactly and crazy dense cities, traffic, 114 00:05:59,396 --> 00:06:03,596 Speaker 1: tiny alleys. So how do you even start to undertake 115 00:06:03,596 --> 00:06:05,076 Speaker 1: this project? What do you do? 116 00:06:06,276 --> 00:06:08,436 Speaker 2: Yeah? No, so this was this was really the fun part. 117 00:06:08,836 --> 00:06:12,236 Speaker 2: That's such a fun challenge yourself. But basically what we 118 00:06:12,756 --> 00:06:15,636 Speaker 2: started is we started taking it from first principles like 119 00:06:15,676 --> 00:06:18,036 Speaker 2: why was it so expensive to map? And then we 120 00:06:18,076 --> 00:06:22,516 Speaker 2: tried to solve those problems and the problems why it's 121 00:06:22,596 --> 00:06:25,596 Speaker 2: so expensive normally to produce a map are a few things, 122 00:06:25,636 --> 00:06:27,876 Speaker 2: the ones that I just said. The mapping vehicles are 123 00:06:27,916 --> 00:06:32,116 Speaker 2: extremely expensive. Then the people sitting in the mapping vehicle 124 00:06:32,156 --> 00:06:36,036 Speaker 2: are extremely expensive because you sent them around driving in 125 00:06:36,076 --> 00:06:39,156 Speaker 2: the country, sleeping in hotels. So the cost like to 126 00:06:39,236 --> 00:06:44,356 Speaker 2: send these vans with people around cost a fortune, and 127 00:06:44,476 --> 00:06:48,796 Speaker 2: like operating that is just super super costly. And that's 128 00:06:48,796 --> 00:06:51,876 Speaker 2: what we had a unique advantage because obviously we had 129 00:06:51,876 --> 00:06:57,156 Speaker 2: our drivers already crisscrossing the city in like insane amounts. 130 00:06:57,396 --> 00:07:00,196 Speaker 2: I mean our drivers. Any city is like crossed by 131 00:07:00,196 --> 00:07:02,876 Speaker 2: our drivers like one hundred times or more in a day, 132 00:07:03,396 --> 00:07:06,036 Speaker 2: so there's like no roads that our drivers don't see. 133 00:07:06,116 --> 00:07:08,196 Speaker 2: So we thought about two things that we needed to 134 00:07:08,596 --> 00:07:11,916 Speaker 2: drive the radically down. One is we build our own 135 00:07:11,956 --> 00:07:15,596 Speaker 2: collection hardware. So instead of building these like massive rigs, 136 00:07:15,836 --> 00:07:18,996 Speaker 2: we build small GoPro like cameras with an echip in 137 00:07:19,036 --> 00:07:21,756 Speaker 2: there that we can give to our drivers. And instead 138 00:07:21,796 --> 00:07:24,436 Speaker 2: of costing one hundred two hundred thousand dollars for full 139 00:07:24,476 --> 00:07:26,876 Speaker 2: mapping van, these cameras are under order of like a 140 00:07:26,956 --> 00:07:30,196 Speaker 2: bunch of one hundred dollars, so orders of magnitudes cheaper. 141 00:07:30,516 --> 00:07:32,436 Speaker 2: And then the second thing is we said, the driver's 142 00:07:32,476 --> 00:07:35,716 Speaker 2: drive around the city anyway, can they just have the camera? 143 00:07:35,956 --> 00:07:37,436 Speaker 2: And then we just need to pay them a little 144 00:07:37,436 --> 00:07:40,076 Speaker 2: for them. It's a great extra income. But they get 145 00:07:40,116 --> 00:07:44,236 Speaker 2: their main income from doing food deliveries or doing mobility trips, 146 00:07:44,516 --> 00:07:47,396 Speaker 2: and they get an extra income from that. And that's 147 00:07:47,476 --> 00:07:51,116 Speaker 2: because we could deploy like we have in Southeast Asia 148 00:07:51,156 --> 00:07:54,676 Speaker 2: probably one hundred times at least more cameras than anybody 149 00:07:54,676 --> 00:07:57,156 Speaker 2: else deployed because they're so cheap. And then we don't 150 00:07:57,156 --> 00:07:58,916 Speaker 2: need to send drivers traveling everywhere. 151 00:07:59,196 --> 00:08:02,196 Speaker 1: So let's break it down a little bit like building 152 00:08:02,236 --> 00:08:05,996 Speaker 1: your own hardware, like the driver part is kind of obvious, right, like, oh, 153 00:08:05,996 --> 00:08:08,076 Speaker 1: we've got these guys already out there, could we get 154 00:08:08,076 --> 00:08:10,636 Speaker 1: them to do the mapping. It's not at all obvious 155 00:08:10,676 --> 00:08:12,796 Speaker 1: to me that you would need to build your own hardware. 156 00:08:12,836 --> 00:08:15,196 Speaker 1: So tell me about that, Like, first of all, why 157 00:08:15,276 --> 00:08:16,756 Speaker 1: do you need to build your own hardware? Why not 158 00:08:16,876 --> 00:08:18,596 Speaker 1: just dumb question, buy a camera? 159 00:08:19,276 --> 00:08:21,716 Speaker 2: No, great, great question. Actually, I mean that's what we tried. Okay, 160 00:08:21,996 --> 00:08:24,276 Speaker 2: So I think when we went and looked into this, 161 00:08:24,396 --> 00:08:26,196 Speaker 2: because we didn't want to build hardware, and so basically 162 00:08:26,236 --> 00:08:28,556 Speaker 2: we tried two things. So they were these go pros 163 00:08:28,596 --> 00:08:31,076 Speaker 2: a few hundred bucks cameras, and then they were the 164 00:08:31,276 --> 00:08:35,316 Speaker 2: more professional mapping Greade cameras twenty thousand bucks. So the 165 00:08:35,676 --> 00:08:38,356 Speaker 2: problem with go Pro was a bunch of things they made, 166 00:08:38,436 --> 00:08:40,436 Speaker 2: usually as action cameras, right, like you can go skiing 167 00:08:40,476 --> 00:08:42,676 Speaker 2: and so on with them, but they don't have great 168 00:08:42,716 --> 00:08:46,676 Speaker 2: GPS in them, and basically because you don't need it, 169 00:08:46,756 --> 00:08:49,396 Speaker 2: right Like, if you go like somewhere mountain biking, it's 170 00:08:49,396 --> 00:08:52,276 Speaker 2: typically open sky. It's not like a dense urban environment 171 00:08:52,316 --> 00:08:55,636 Speaker 2: with big skyscrapers that reflect GPS. So they're decent for 172 00:08:55,716 --> 00:08:58,276 Speaker 2: what people usually use. Goal pros, but they're not the 173 00:08:58,316 --> 00:09:02,996 Speaker 2: media level precision we need for high density urban center mapping. 174 00:09:03,876 --> 00:09:06,156 Speaker 2: So that was one problem. And the second problem with 175 00:09:06,236 --> 00:09:08,796 Speaker 2: Goal pros was they just have no tooling that they 176 00:09:08,876 --> 00:09:12,636 Speaker 2: kept operate twenty four to seven drivers upload automatically data, 177 00:09:12,796 --> 00:09:14,916 Speaker 2: so they're not made They usually made for like a 178 00:09:14,956 --> 00:09:18,316 Speaker 2: one two hour recording and not this heavy duty recording 179 00:09:18,356 --> 00:09:22,316 Speaker 2: for the back of our motorbikes for twelve hours in 180 00:09:22,436 --> 00:09:25,916 Speaker 2: blistering heat in Southeast Asia, tough rail and so on. 181 00:09:26,156 --> 00:09:29,156 Speaker 2: So that's basically why both of these cameras that existed 182 00:09:29,236 --> 00:09:32,196 Speaker 2: didn't fit the needs that are very harsh environment. 183 00:09:32,276 --> 00:09:34,236 Speaker 1: So would the mapping camera have worked, but you didn't 184 00:09:34,236 --> 00:09:36,756 Speaker 1: want to spend twenty grand per camera. That was presumably 185 00:09:36,756 --> 00:09:38,396 Speaker 1: the simple problem with the mapping camera. 186 00:09:38,916 --> 00:09:40,916 Speaker 2: Yeah, twenty grand was the problem. And the other problems 187 00:09:40,916 --> 00:09:43,596 Speaker 2: are they're also quite heavy and bulky, So those cameras 188 00:09:43,636 --> 00:09:46,996 Speaker 2: are close to ten kilograms, which is not super easy 189 00:09:47,116 --> 00:09:51,396 Speaker 2: to operate. So they were too bulky and too costly. 190 00:09:52,636 --> 00:09:54,596 Speaker 1: So what do you just get on a plane dest 191 00:09:54,596 --> 00:09:57,156 Speaker 1: engine and tell them what you need, Like, how do 192 00:09:57,236 --> 00:09:58,276 Speaker 1: you build your own camera? 193 00:09:59,316 --> 00:10:02,036 Speaker 2: So this was really certing that we actually had a 194 00:10:02,076 --> 00:10:06,716 Speaker 2: small team and Scenzan already building building off our battery 195 00:10:06,756 --> 00:10:11,196 Speaker 2: swap blockers for scooters. And back then I talked to 196 00:10:11,236 --> 00:10:13,996 Speaker 2: our Cito at that time and he is like kind 197 00:10:14,076 --> 00:10:15,756 Speaker 2: enough to say, like I feel it, like we really 198 00:10:16,156 --> 00:10:17,756 Speaker 2: need to find something next for this team to do. 199 00:10:17,796 --> 00:10:19,796 Speaker 2: So you can have them all. They can build your cameras, 200 00:10:20,356 --> 00:10:22,916 Speaker 2: so amazing, We're very lucky. 201 00:10:23,116 --> 00:10:24,916 Speaker 1: So tell me about the camera they came up with. 202 00:10:25,196 --> 00:10:27,756 Speaker 2: Yeah, so the first camera that we built, we called 203 00:10:27,796 --> 00:10:31,396 Speaker 2: it a Karda cam, so like after like carda in 204 00:10:31,436 --> 00:10:34,716 Speaker 2: like some language, was like map. So basically those cameras 205 00:10:34,756 --> 00:10:37,716 Speaker 2: where the first version was mounted on the helmet of drivers, 206 00:10:37,756 --> 00:10:41,636 Speaker 2: predominantly it was two hundred and fifty grams mounted on 207 00:10:41,636 --> 00:10:44,316 Speaker 2: the helmet of drivers, and it would basically be a 208 00:10:44,316 --> 00:10:47,876 Speaker 2: camera with an AI chip and really highly accurate GPS, 209 00:10:48,116 --> 00:10:50,876 Speaker 2: like that's all we needed. And so they would just 210 00:10:50,916 --> 00:10:52,956 Speaker 2: mount them on their on their helmets and go about 211 00:10:52,996 --> 00:10:55,036 Speaker 2: their day and just at the end of the day, 212 00:10:55,276 --> 00:10:57,316 Speaker 2: take it home, take it on their Wi Fi, upload 213 00:10:57,356 --> 00:11:00,476 Speaker 2: the data and yeah, we would get data from tons 214 00:11:00,516 --> 00:11:01,116 Speaker 2: of cameras. 215 00:11:01,396 --> 00:11:04,476 Speaker 1: And then you made a cardacam too, right, tell me 216 00:11:04,476 --> 00:11:05,396 Speaker 1: about Carteracam too. 217 00:11:05,716 --> 00:11:07,796 Speaker 2: Yeah, we made a bunch of iterations of our hardware. 218 00:11:08,196 --> 00:11:12,676 Speaker 2: So Karda CAN two is our latest generation, which is 219 00:11:12,676 --> 00:11:15,316 Speaker 2: basically a three sixty camera, so it has basically four 220 00:11:15,356 --> 00:11:19,876 Speaker 2: camera lenses and all directions. Has also like more advanced centers, 221 00:11:19,996 --> 00:11:23,036 Speaker 2: like allied our sensors in there, so we basically made 222 00:11:23,036 --> 00:11:25,756 Speaker 2: the setup a lot more professional that we can capture 223 00:11:25,756 --> 00:11:29,036 Speaker 2: maps an even higher level of accuracy to get things 224 00:11:29,076 --> 00:11:33,436 Speaker 2: like lane level navigation for our drivers or advanced safety features. 225 00:11:33,796 --> 00:11:36,756 Speaker 2: So the latest camera is basically a great iteration that 226 00:11:37,076 --> 00:11:39,636 Speaker 2: has taken just a step further to capture more details 227 00:11:39,636 --> 00:11:40,036 Speaker 2: in the map. 228 00:11:40,156 --> 00:11:42,036 Speaker 1: And where does it go? Where does the camera go 229 00:11:42,196 --> 00:11:43,436 Speaker 1: if somebody's driving a scooter. 230 00:11:43,836 --> 00:11:46,116 Speaker 2: Yeah, so now we've built a mount on the back 231 00:11:46,156 --> 00:11:49,236 Speaker 2: of a motorbike. So we've built actually the cameras that 232 00:11:49,276 --> 00:11:50,716 Speaker 2: you can either mount them at the back of your 233 00:11:50,716 --> 00:11:52,676 Speaker 2: motorbike that it doesn't disturb you and in a pole 234 00:11:52,756 --> 00:11:55,356 Speaker 2: so that's high so that you can see because the 235 00:11:55,436 --> 00:11:57,196 Speaker 2: camera obviously needs to have a good view even if 236 00:11:57,196 --> 00:12:00,076 Speaker 2: there's cars around, so we've built a special mount on 237 00:12:00,116 --> 00:12:02,756 Speaker 2: the back of a motorbike. We have mounts also for cars, 238 00:12:02,756 --> 00:12:05,236 Speaker 2: so some drivers also mount them on cars. And then 239 00:12:05,276 --> 00:12:07,236 Speaker 2: we have a backpack that you can carry if we 240 00:12:07,276 --> 00:12:09,996 Speaker 2: want a map indoor. The other thing that's really important 241 00:12:10,036 --> 00:12:14,236 Speaker 2: in Southeast Asia is malls are gigantic. Do you have 242 00:12:14,276 --> 00:12:17,116 Speaker 2: like these malls which it takes you fifteen minutes to walk. 243 00:12:16,956 --> 00:12:20,636 Speaker 1: Around, and so people will get a delivery to whatever 244 00:12:20,716 --> 00:12:22,636 Speaker 1: the noodle shop in the middle of the mall, and 245 00:12:22,676 --> 00:12:24,036 Speaker 1: you've got to put that on the map. 246 00:12:25,236 --> 00:12:27,556 Speaker 2: Well, the other way around, people get a delivery from 247 00:12:27,556 --> 00:12:31,156 Speaker 2: the noodle shop of course, of course, and our driver 248 00:12:31,276 --> 00:12:34,116 Speaker 2: needs to find their way there, and if they get 249 00:12:34,156 --> 00:12:35,956 Speaker 2: into the wrong entrance of the mall, it might cost 250 00:12:35,956 --> 00:12:38,276 Speaker 2: them ten to fifteen minutes extra to walk back and forth. 251 00:12:38,636 --> 00:12:40,756 Speaker 2: So we need to precisely not only tell them go 252 00:12:40,796 --> 00:12:43,196 Speaker 2: into the mall, but we also precisely need to tell 253 00:12:43,236 --> 00:12:46,876 Speaker 2: them where to park their motorbike. So what we emphasize 254 00:12:46,876 --> 00:12:48,556 Speaker 2: when we build our maps that we build them like 255 00:12:48,676 --> 00:12:51,076 Speaker 2: and to end, and we say, here's where you park 256 00:12:51,116 --> 00:12:53,676 Speaker 2: your motorbike, here's where you walk, here's where you pick 257 00:12:53,756 --> 00:12:56,236 Speaker 2: up the noodles, and then you can do the same 258 00:12:56,276 --> 00:12:56,796 Speaker 2: in reverse. 259 00:12:57,636 --> 00:12:59,316 Speaker 1: How many of these cameras do you have out there 260 00:12:59,356 --> 00:13:01,836 Speaker 1: like today? How many people are driving around mapping with 261 00:13:01,876 --> 00:13:04,836 Speaker 1: these cameras today ish by end of this year. 262 00:13:04,716 --> 00:13:07,356 Speaker 2: We have about twenty thousand cameras in the field. And 263 00:13:07,396 --> 00:13:10,076 Speaker 2: to give you a sense, like professional mapping companies in 264 00:13:10,116 --> 00:13:13,116 Speaker 2: Southeast Asia, to my best of my knowledge, have about 265 00:13:13,156 --> 00:13:16,676 Speaker 2: tens of cars, but not tens of thousand tens of cars. 266 00:13:17,436 --> 00:13:19,956 Speaker 1: So does that mean they have basically seeded it to 267 00:13:19,996 --> 00:13:24,716 Speaker 1: you like that you have won the mapping Southeast Asia fight. 268 00:13:25,556 --> 00:13:27,956 Speaker 2: I think it's still so so early. Fair. I mean, 269 00:13:28,156 --> 00:13:29,836 Speaker 2: there's so many things we want to do. 270 00:13:30,036 --> 00:13:32,196 Speaker 1: Interesting. I love that. So let's talk a little more 271 00:13:32,196 --> 00:13:33,956 Speaker 1: about what you're doing now and then let's talk about 272 00:13:34,476 --> 00:13:37,316 Speaker 1: what you want to do. So you have an incredible 273 00:13:37,356 --> 00:13:39,596 Speaker 1: amount of data coming in now, right, I mean, if 274 00:13:39,596 --> 00:13:45,076 Speaker 1: you have twenty thousand cameras driving around every day like 275 00:13:45,516 --> 00:13:48,556 Speaker 1: that is a wild amount of input. What are you 276 00:13:48,596 --> 00:13:50,596 Speaker 1: doing with all that data? I mean there's basic mapping, 277 00:13:50,636 --> 00:13:52,876 Speaker 1: and I'm sure you've got that, but what's the next level? 278 00:13:52,876 --> 00:13:54,796 Speaker 1: Presumably have AI? You said you have light? Are like, 279 00:13:54,836 --> 00:13:57,116 Speaker 1: tell me all the things you know because you have 280 00:13:57,116 --> 00:13:57,716 Speaker 1: all this data. 281 00:13:57,836 --> 00:14:00,676 Speaker 2: Yeah, you're right. I think the basic vision that you 282 00:14:00,756 --> 00:14:03,876 Speaker 2: have is get an accurate representation of the real world 283 00:14:03,996 --> 00:14:07,796 Speaker 2: in real time. That's that's the that's the goal of 284 00:14:07,956 --> 00:14:08,996 Speaker 2: mapping in real time? 285 00:14:09,236 --> 00:14:12,396 Speaker 1: Is crazy? In real time? It's crazy? Yeah, that's wild. 286 00:14:12,676 --> 00:14:16,956 Speaker 1: But fine, that's just like right now, what do you know? 287 00:14:17,076 --> 00:14:18,836 Speaker 2: Yeah, so a bunch of things is I mean, first 288 00:14:18,836 --> 00:14:23,316 Speaker 2: of all, everything that you need for navigation roads, how 289 00:14:23,316 --> 00:14:26,036 Speaker 2: our road's drivable? Is the road safe to drive? Does 290 00:14:26,076 --> 00:14:28,676 Speaker 2: it have a lot of potholes and things like that, 291 00:14:28,916 --> 00:14:31,036 Speaker 2: and that signage obviously is it a one way road? 292 00:14:31,076 --> 00:14:32,236 Speaker 2: Is it not a one way road? 293 00:14:32,356 --> 00:14:34,756 Speaker 1: Can you worry me about a particular pothole? Could you 294 00:14:34,876 --> 00:14:37,236 Speaker 1: be like, be careful, there's a pothole on the left 295 00:14:37,316 --> 00:14:38,836 Speaker 1: coming up, try that right lane. 296 00:14:39,116 --> 00:14:41,716 Speaker 2: And we're doing actually very cool things we're doing right now. 297 00:14:41,756 --> 00:14:46,516 Speaker 2: We've already launched a safety navigation that tells you, hey, 298 00:14:46,596 --> 00:14:48,956 Speaker 2: is the road safe to drive? Based on potholes? And 299 00:14:48,996 --> 00:14:51,236 Speaker 2: does it have street lighting? Is it safe? Because if 300 00:14:51,236 --> 00:14:53,116 Speaker 2: the road at night is well lit, it's a lot 301 00:14:53,156 --> 00:14:55,076 Speaker 2: safer to drive, but it's not. So that's kind of 302 00:14:55,116 --> 00:14:57,756 Speaker 2: like a practical use case that we that we already 303 00:14:57,756 --> 00:14:58,556 Speaker 2: can do with that. 304 00:14:58,716 --> 00:15:02,756 Speaker 1: Interesting as I'm sure you know, like ways in this 305 00:15:02,796 --> 00:15:06,556 Speaker 1: country tells you where there's like a speed camera or 306 00:15:06,636 --> 00:15:09,436 Speaker 1: like a speed trap. Do you do that? 307 00:15:10,116 --> 00:15:14,516 Speaker 2: Yeah. Absolutely. So we have an AI voice reporting that 308 00:15:15,076 --> 00:15:17,476 Speaker 2: drivers can share anything they're like, oh, the right lane 309 00:15:17,516 --> 00:15:20,316 Speaker 2: of this road is closed, and then it gets processed 310 00:15:20,596 --> 00:15:23,916 Speaker 2: and basically used for all the other other drivers. So 311 00:15:23,956 --> 00:15:26,436 Speaker 2: we've launched that and it's been really successful as well. 312 00:15:26,516 --> 00:15:28,396 Speaker 1: So when you say it's AI, does that mean the 313 00:15:28,476 --> 00:15:31,076 Speaker 1: driver just says it and it gets integrated into the 314 00:15:31,076 --> 00:15:33,316 Speaker 1: maps or what does that mean? 315 00:15:33,996 --> 00:15:36,556 Speaker 2: Yeah, that's that's exactly right. There's the future. We actually 316 00:15:36,556 --> 00:15:39,156 Speaker 2: work closely together with our friends at open Eye. So 317 00:15:39,236 --> 00:15:42,636 Speaker 2: the driver natural language reports an issue and then it 318 00:15:42,676 --> 00:15:44,956 Speaker 2: gets processed and if you're not clear, we ask your 319 00:15:44,956 --> 00:15:47,476 Speaker 2: follow up question. If you say, hey, the right lane 320 00:15:47,556 --> 00:15:48,836 Speaker 2: is closed and he said like, oh, do you mean 321 00:15:48,916 --> 00:15:50,556 Speaker 2: this road? And then he says drive but yeah, that's 322 00:15:50,556 --> 00:15:52,836 Speaker 2: exactly the road I'm talking about, and then we process 323 00:15:52,876 --> 00:15:55,836 Speaker 2: it and basically one all the other drivers accordingly. 324 00:15:56,036 --> 00:15:58,236 Speaker 1: So you mentioned your friends at open ai. It seems 325 00:15:58,276 --> 00:16:00,356 Speaker 1: like good friends to have a good place to have friends. 326 00:16:00,676 --> 00:16:03,116 Speaker 1: I know you have a deal with them, Like what 327 00:16:03,236 --> 00:16:05,236 Speaker 1: is the nature of your deal with open ai and 328 00:16:05,716 --> 00:16:08,156 Speaker 1: more generally, what's the work you're doing with them? 329 00:16:08,476 --> 00:16:10,516 Speaker 2: So MAP I think is one of the key things 330 00:16:10,556 --> 00:16:13,436 Speaker 2: we do together with them and use their models to 331 00:16:13,516 --> 00:16:16,996 Speaker 2: improve our maps. The voice stuff that I shared earlier 332 00:16:16,996 --> 00:16:19,596 Speaker 2: is like one of our one of our highlight features. 333 00:16:20,316 --> 00:16:22,956 Speaker 2: And in general, we're just embedding like AI and everything 334 00:16:22,956 --> 00:16:26,276 Speaker 2: we're doing. We're having we're having like over a thousand 335 00:16:26,276 --> 00:16:29,356 Speaker 2: different AI models that we're that we're working on, so 336 00:16:29,396 --> 00:16:31,796 Speaker 2: it's basically deeply, deeply embedded in many many of the 337 00:16:31,836 --> 00:16:33,196 Speaker 2: things that that we're doing. 338 00:16:33,836 --> 00:16:36,196 Speaker 1: Tell me more, like, what are some more examples of 339 00:16:36,196 --> 00:16:37,876 Speaker 1: how you're using AI. 340 00:16:38,356 --> 00:16:41,636 Speaker 2: So one of the latest things that that I really 341 00:16:41,676 --> 00:16:44,356 Speaker 2: like that's really cool is the other thing that has 342 00:16:44,436 --> 00:16:48,356 Speaker 2: really huge impact on our marketplace is weather. In Southeast Asia, 343 00:16:48,636 --> 00:16:52,476 Speaker 2: the rain is insane. It is like pouring down, the 344 00:16:52,556 --> 00:16:57,036 Speaker 2: roads are flooding, so and for our services for mobility 345 00:16:57,236 --> 00:17:00,596 Speaker 2: for deliveries, that has a tremendous impact on that. So 346 00:17:00,876 --> 00:17:04,436 Speaker 2: knowing when it rains early is super super critical so 347 00:17:04,476 --> 00:17:08,196 Speaker 2: we can adjust the marketplace. We've launched AI based rate 348 00:17:08,236 --> 00:17:13,156 Speaker 2: detection that a bunch of things, So we deploy sensors, 349 00:17:13,196 --> 00:17:15,836 Speaker 2: other sensors in cars. So we have basically a device 350 00:17:15,876 --> 00:17:18,236 Speaker 2: that's called a card to dongle that we deploy and 351 00:17:18,276 --> 00:17:19,956 Speaker 2: the cars that we own and it plugs into a 352 00:17:20,036 --> 00:17:21,996 Speaker 2: port in the car. That's called OBEDI two port, and 353 00:17:21,996 --> 00:17:23,836 Speaker 2: that reads when the windshield viper's going. 354 00:17:24,396 --> 00:17:29,756 Speaker 1: Oh genius, such a simple way, such a simple way 355 00:17:29,796 --> 00:17:31,516 Speaker 1: to know if it's rating. Does the car have the 356 00:17:31,556 --> 00:17:34,516 Speaker 1: windshield wipers on? It's so second order. I love it though. 357 00:17:35,036 --> 00:17:36,156 Speaker 2: Yeah, and how fast it is? 358 00:17:36,756 --> 00:17:38,676 Speaker 1: Oh? How fast? It's how fast the car is going, 359 00:17:38,716 --> 00:17:40,796 Speaker 1: because the slower it's going, the heavier the rain. Is 360 00:17:40,796 --> 00:17:42,356 Speaker 1: that the inference how fast. 361 00:17:42,196 --> 00:17:45,036 Speaker 2: The windshield wiper is going. Because you kind of like this, 362 00:17:45,196 --> 00:17:45,876 Speaker 2: are this right? 363 00:17:47,436 --> 00:17:49,996 Speaker 1: So okay? So so and what do you do? What 364 00:17:50,036 --> 00:17:52,116 Speaker 1: do you do with that data? That's the input, what's 365 00:17:52,156 --> 00:17:52,916 Speaker 1: the output? 366 00:17:53,076 --> 00:17:56,636 Speaker 2: The output is basically we know the moment it rains, 367 00:17:56,836 --> 00:17:59,276 Speaker 2: we know demand will go up and supply of drivers 368 00:17:59,276 --> 00:18:01,436 Speaker 2: will go down. So what we do We try to 369 00:18:01,476 --> 00:18:04,276 Speaker 2: activate more drivers, so we would send out to all 370 00:18:04,316 --> 00:18:07,236 Speaker 2: the drivers, Hey, it's starting to rain. There's a fantastic 371 00:18:07,276 --> 00:18:10,796 Speaker 2: earning opportunity. If you're not worth now's a great chance 372 00:18:10,836 --> 00:18:13,636 Speaker 2: to get on the road and make extra money. And 373 00:18:13,676 --> 00:18:17,156 Speaker 2: then we try to actively get the supply of drivers 374 00:18:17,236 --> 00:18:19,636 Speaker 2: up so that we can keep our reliability at the 375 00:18:19,676 --> 00:18:20,356 Speaker 2: levels we need. 376 00:18:20,876 --> 00:18:23,156 Speaker 1: We know people get all worked up about sarge pricing. 377 00:18:23,276 --> 00:18:25,316 Speaker 1: Do you use searge pricing in that setting? It sounds 378 00:18:25,356 --> 00:18:27,676 Speaker 1: like a classic use of search pricing. Everybody wants a driver, 379 00:18:27,796 --> 00:18:29,836 Speaker 1: nobody wants to drive. What you need is a higher price. 380 00:18:29,956 --> 00:18:30,796 Speaker 1: Do you do that? 381 00:18:30,796 --> 00:18:33,036 Speaker 2: That's exactly right? Or like you need to motivate the 382 00:18:33,116 --> 00:18:34,476 Speaker 2: drivers to come on the road. 383 00:18:34,756 --> 00:18:37,516 Speaker 1: Yeah, no, there is a market. I'm pro searge pricing. 384 00:18:37,916 --> 00:18:40,196 Speaker 1: That's a great example. What's another example of the way 385 00:18:40,236 --> 00:18:40,996 Speaker 1: you're using AI? 386 00:18:41,916 --> 00:18:47,036 Speaker 2: So we used AI to translate in many scenarios. For example, 387 00:18:47,356 --> 00:18:49,796 Speaker 2: when I go like to Jakarta, I obviously don't speak 388 00:18:49,796 --> 00:18:52,836 Speaker 2: any Bahasa, but I can message our drivers in the 389 00:18:52,956 --> 00:18:55,636 Speaker 2: real time, translate any messages, send them a chat to 390 00:18:55,676 --> 00:18:58,556 Speaker 2: the driver and he can reply back to me in basade. 391 00:18:58,636 --> 00:19:00,236 Speaker 2: I see it in English on my side, he sees 392 00:19:00,276 --> 00:19:03,076 Speaker 2: it in basade my site. But the more important one 393 00:19:03,076 --> 00:19:05,556 Speaker 2: for me was like food menu translations. So we invested 394 00:19:05,676 --> 00:19:08,436 Speaker 2: quite a bit because whenever I go to Thailand and 395 00:19:08,476 --> 00:19:11,156 Speaker 2: then like I mean, I can't read any tie script obviously, 396 00:19:11,716 --> 00:19:14,156 Speaker 2: and I in the past I look at like some 397 00:19:14,276 --> 00:19:16,356 Speaker 2: things on the menu and I have no ideas that 398 00:19:16,476 --> 00:19:18,636 Speaker 2: like for me, always are worries that ultra spicy can 399 00:19:18,716 --> 00:19:21,196 Speaker 2: I eat it or not, and I didn't even know 400 00:19:21,196 --> 00:19:22,716 Speaker 2: what the item is, Like can look at the picture 401 00:19:22,756 --> 00:19:25,796 Speaker 2: and kind of guess, but sometimes merchants don't have pictures. 402 00:19:26,156 --> 00:19:29,196 Speaker 2: So we used AI to to translate all these menus 403 00:19:30,036 --> 00:19:32,196 Speaker 2: in all kinds of languages, so that that has been 404 00:19:32,196 --> 00:19:39,476 Speaker 2: a super impactful one as well. We'll be back in 405 00:19:39,596 --> 00:19:40,076 Speaker 2: just a minute. 406 00:19:50,076 --> 00:19:54,436 Speaker 1: So I'm curious about grab maps enterprise, right Like, you're 407 00:19:54,516 --> 00:19:59,516 Speaker 1: selling something related to your maps to big companies right 408 00:19:59,556 --> 00:20:02,116 Speaker 1: to to Microsoft, to Amazon and to government. It's like, 409 00:20:02,156 --> 00:20:03,796 Speaker 1: what tell me about that business? 410 00:20:04,356 --> 00:20:06,516 Speaker 2: That was quite an interesting one, right Like, we would 411 00:20:06,556 --> 00:20:08,796 Speaker 2: have never imagined early on when we built our maps 412 00:20:08,796 --> 00:20:11,916 Speaker 2: that this business will be created. But we've got people 413 00:20:11,996 --> 00:20:14,836 Speaker 2: like once we publicized our maps and people have seen 414 00:20:14,836 --> 00:20:17,476 Speaker 2: that they work generally quite well, we've got people approaching 415 00:20:17,516 --> 00:20:19,836 Speaker 2: us say hey, can we use them as well? And 416 00:20:19,876 --> 00:20:24,916 Speaker 2: that was like the genesis of the enterprise business. And 417 00:20:24,956 --> 00:20:29,196 Speaker 2: then we started working very closely with AWS where any 418 00:20:29,236 --> 00:20:31,836 Speaker 2: developer on their platform can use our maps now, so 419 00:20:31,876 --> 00:20:35,356 Speaker 2: we have over one hundred different developers already using it. 420 00:20:36,196 --> 00:20:38,316 Speaker 2: We partner with Microsoft, as he said, a bunch of 421 00:20:38,316 --> 00:20:42,596 Speaker 2: other large tech companies that in Southeast Asia started using 422 00:20:42,676 --> 00:20:45,076 Speaker 2: our maps because they've seen i mean, all these things 423 00:20:45,076 --> 00:20:47,596 Speaker 2: that I shared earlier that just really serves their needs 424 00:20:47,596 --> 00:20:50,116 Speaker 2: in Southeast Asia a lot better than anything else out there. 425 00:20:50,716 --> 00:20:53,756 Speaker 1: What's an example if something someone has built, you know, 426 00:20:53,836 --> 00:20:54,676 Speaker 1: on top of it. 427 00:20:55,076 --> 00:20:58,516 Speaker 2: A really cool startup that I've seen that used our maps. 428 00:20:58,916 --> 00:21:01,556 Speaker 2: What they do They go from merchant to merchant and 429 00:21:01,636 --> 00:21:04,436 Speaker 2: collect like the old oil that they use for cooking 430 00:21:04,476 --> 00:21:07,796 Speaker 2: and that basically recycle it. So they send somebody around 431 00:21:07,876 --> 00:21:11,036 Speaker 2: going to all these merchants collecting that. And in the past, 432 00:21:11,076 --> 00:21:13,356 Speaker 2: and a lot of these merchants are like small neighborhood 433 00:21:13,396 --> 00:21:15,956 Speaker 2: mom and pop shops in all these like little side 434 00:21:16,036 --> 00:21:18,276 Speaker 2: rolls and alleys, and that had a hard time for 435 00:21:18,356 --> 00:21:21,156 Speaker 2: the for the person going around collecting all of this 436 00:21:21,236 --> 00:21:24,356 Speaker 2: stuff finding that. And that's one of these examples where 437 00:21:24,396 --> 00:21:27,396 Speaker 2: they use grab maps to make the navigation of the 438 00:21:27,436 --> 00:21:29,916 Speaker 2: person going around and collecting all of that a lot 439 00:21:29,996 --> 00:21:32,396 Speaker 2: a lot easier. And there's many of these kind of 440 00:21:32,396 --> 00:21:36,156 Speaker 2: stories where people use it for things that that are 441 00:21:36,276 --> 00:21:38,876 Speaker 2: very similar in those kind of finding the last mile 442 00:21:38,916 --> 00:21:42,796 Speaker 2: has been really really hard before. So I'm curious. 443 00:21:43,876 --> 00:21:46,436 Speaker 1: What you're working on now, Like, Yeah, what what are 444 00:21:46,436 --> 00:21:47,876 Speaker 1: some of the things you're trying to figure out that 445 00:21:47,916 --> 00:21:48,996 Speaker 1: you haven't figured out yet. 446 00:21:49,756 --> 00:21:52,356 Speaker 2: I think the one thing that we're really passionate about 447 00:21:52,396 --> 00:21:55,196 Speaker 2: is solving more of the indoor problem. So that's one 448 00:21:55,196 --> 00:21:57,036 Speaker 2: thing that we're really mapping more so. We have a 449 00:21:57,236 --> 00:21:59,476 Speaker 2: decent amount of malls already map, but there's still so 450 00:21:59,556 --> 00:22:02,916 Speaker 2: much more to do. So hopefully we can find that. 451 00:22:02,996 --> 00:22:05,596 Speaker 2: Butever you go into these malls, we can exactly help 452 00:22:05,636 --> 00:22:08,876 Speaker 2: you to find whatever you're looking for, specific store or 453 00:22:09,196 --> 00:22:12,996 Speaker 2: general a general kind of like shop or so. So 454 00:22:13,236 --> 00:22:15,516 Speaker 2: that's that's one thing that I really want us to crack. 455 00:22:16,036 --> 00:22:18,956 Speaker 1: So it's the general shop idea that like, if I'm 456 00:22:18,996 --> 00:22:22,436 Speaker 1: just not working for grab, I'm just in the general 457 00:22:22,436 --> 00:22:24,436 Speaker 1: public and I'm like, I want to buy a pair 458 00:22:24,436 --> 00:22:27,076 Speaker 1: of shorts, I just type that into grab maps, and 459 00:22:27,076 --> 00:22:28,276 Speaker 1: grab maps tells me where to go. 460 00:22:28,436 --> 00:22:30,516 Speaker 2: Is that what you're thinking of there, Yes, but we 461 00:22:30,556 --> 00:22:32,636 Speaker 2: always think about it with a hyperlocal twist. 462 00:22:32,876 --> 00:22:33,236 Speaker 1: Yeah. 463 00:22:33,356 --> 00:22:35,956 Speaker 2: So, and what that means. As an example, let's say 464 00:22:36,036 --> 00:22:40,316 Speaker 2: in Indonesia, a large part of the population is Muslim, 465 00:22:40,436 --> 00:22:45,116 Speaker 2: which means they generally eat halal, and this is like 466 00:22:45,196 --> 00:22:47,356 Speaker 2: all the mapping platforms they don't support that. You can, 467 00:22:47,436 --> 00:22:50,236 Speaker 2: of course for every search for you can say restaurant halal, 468 00:22:50,316 --> 00:22:52,756 Speaker 2: this halal, this halal, but you cannot make it part 469 00:22:52,756 --> 00:22:55,396 Speaker 2: of your user profile. But it's it's like, it's not 470 00:22:55,516 --> 00:22:57,596 Speaker 2: it's not something that you switch every day. Well, like 471 00:22:57,716 --> 00:23:03,156 Speaker 2: either your preference is halal or is not. And and 472 00:23:03,196 --> 00:23:05,916 Speaker 2: a lot of the mapping platforms support putting in your profile. 473 00:23:05,996 --> 00:23:09,036 Speaker 2: I only want halal restaurants because I don't eat anything else. 474 00:23:09,596 --> 00:23:11,716 Speaker 2: And those are the kind of things that we see 475 00:23:11,716 --> 00:23:15,036 Speaker 2: in Southeast Asia that we need to really solve because again, 476 00:23:15,196 --> 00:23:18,756 Speaker 2: nobody else would solve those kind of problems. So capturing 477 00:23:18,796 --> 00:23:21,916 Speaker 2: this data accurately and knowing all these details, those are 478 00:23:21,956 --> 00:23:24,356 Speaker 2: the kind of things that we really put a lot 479 00:23:24,396 --> 00:23:25,116 Speaker 2: of emphasis on. 480 00:23:26,476 --> 00:23:29,996 Speaker 1: Are there technical problems you're working on, Like are there 481 00:23:30,036 --> 00:23:33,636 Speaker 1: things where you haven't figured out the right tech or 482 00:23:33,636 --> 00:23:36,316 Speaker 1: where you need to build something, or where AI models 483 00:23:36,316 --> 00:23:38,236 Speaker 1: aren't quite where they need to be, but you're trying 484 00:23:38,276 --> 00:23:39,556 Speaker 1: to push them. 485 00:23:39,836 --> 00:23:41,196 Speaker 2: I think what we are trying to do is what 486 00:23:41,236 --> 00:23:42,636 Speaker 2: I said earlier, like you want to have a real 487 00:23:42,676 --> 00:23:44,676 Speaker 2: time accurate model of the world. 488 00:23:44,876 --> 00:23:47,436 Speaker 1: Yeah, so let's talk about that. Like real time is 489 00:23:47,556 --> 00:23:51,396 Speaker 1: a crazy phrase in that context, right, Like when you 490 00:23:51,436 --> 00:23:54,236 Speaker 1: say real time a model of the world. What are 491 00:23:54,276 --> 00:23:54,796 Speaker 1: you thinking of? 492 00:23:55,676 --> 00:23:59,436 Speaker 2: Yeah, I mean a simple use case would be nowhere 493 00:23:59,476 --> 00:24:02,836 Speaker 2: there's parking right now? Yeah, not like where there's parking 494 00:24:02,916 --> 00:24:05,636 Speaker 2: in general, but I'd say a roadside parking. If you 495 00:24:05,836 --> 00:24:09,476 Speaker 2: had another crap card driving past and say, hey, fifteen 496 00:24:09,476 --> 00:24:12,476 Speaker 2: seconds ago there was a freak parking spot on the right, 497 00:24:12,876 --> 00:24:15,076 Speaker 2: that's that's extremely useful. 498 00:24:15,516 --> 00:24:18,116 Speaker 1: That is extremely as well that I know people in 499 00:24:18,156 --> 00:24:22,156 Speaker 1: Brooklyn and San Francisco who would love to have that functionality. 500 00:24:22,516 --> 00:24:25,156 Speaker 2: Yeah. I think for us, really the goal is always 501 00:24:25,356 --> 00:24:29,556 Speaker 2: what we know is like shaving off seconds of every delivery, right, Like, 502 00:24:29,636 --> 00:24:34,076 Speaker 2: that's what really really makes a delta. I think that 503 00:24:34,396 --> 00:24:37,116 Speaker 2: I really loved Steve Job's old mental model. But he 504 00:24:37,156 --> 00:24:39,996 Speaker 2: convinced people to make the macboot like ten seconds faster, 505 00:24:40,436 --> 00:24:42,676 Speaker 2: and he said like, well, if you take this, I 506 00:24:42,716 --> 00:24:44,876 Speaker 2: don't remember the exactly now mark for him, but he said, like, 507 00:24:45,076 --> 00:24:47,236 Speaker 2: if you make the macboot ten seconds fast and there's 508 00:24:47,236 --> 00:24:50,076 Speaker 2: fifty million people using it every year, you save like, 509 00:24:50,196 --> 00:24:52,796 Speaker 2: I don't know, it's like ten lifetimes of people's time 510 00:24:52,836 --> 00:24:55,676 Speaker 2: waiting for the Magic Boot something like that, and fast 511 00:24:55,756 --> 00:24:59,316 Speaker 2: right Like across I mean across I calculated to share 512 00:24:59,356 --> 00:25:02,156 Speaker 2: this always with a team. Across a billion deliveries, two 513 00:25:02,156 --> 00:25:05,036 Speaker 2: point five seconds saved across every delivery is roughly one 514 00:25:05,076 --> 00:25:08,356 Speaker 2: lifetime that you can save. So any second we can 515 00:25:08,396 --> 00:25:12,076 Speaker 2: shave off by getting cash to park faster, by getting 516 00:25:12,116 --> 00:25:15,276 Speaker 2: motorbikes to park faster, park at the right space, at 517 00:25:15,276 --> 00:25:17,436 Speaker 2: the right time. That's kind of the problems that we're 518 00:25:17,476 --> 00:25:18,676 Speaker 2: really passionate on solving. 519 00:25:19,276 --> 00:25:21,356 Speaker 1: How long does it take you to do a billion deliveries? 520 00:25:21,356 --> 00:25:23,676 Speaker 1: How is it a billion per per way? 521 00:25:24,156 --> 00:25:27,596 Speaker 2: We don't publish exact data, but it's the auder of 522 00:25:27,676 --> 00:25:29,436 Speaker 2: magnitude of it, like a year or less. 523 00:25:29,716 --> 00:25:34,316 Speaker 1: Oh wow, okay, that's a big number. So how do 524 00:25:34,356 --> 00:25:36,876 Speaker 1: you get real time parking data? I mean, so, is 525 00:25:36,916 --> 00:25:40,116 Speaker 1: the constraint now just getting more cameras to more drivers, 526 00:25:40,316 --> 00:25:42,396 Speaker 1: like the what's the rate limiting step? 527 00:25:42,956 --> 00:25:46,196 Speaker 2: Yeah? Great, great question. I think more cameras is one constraint. 528 00:25:46,196 --> 00:25:49,396 Speaker 2: The other constraint is doing all the smart processing on 529 00:25:49,436 --> 00:25:52,756 Speaker 2: the act because obviously you cannot upload all these data 530 00:25:53,036 --> 00:25:56,556 Speaker 2: because that would be extremely costly, and mobile networks in 531 00:25:56,556 --> 00:25:58,916 Speaker 2: Southeast Asia aren't quite that powerful that you could upload 532 00:25:58,956 --> 00:26:02,396 Speaker 2: millions of video streams to the cloud. So we're working 533 00:26:02,436 --> 00:26:04,436 Speaker 2: a lot on what is called like ATGYI that we 534 00:26:04,476 --> 00:26:07,676 Speaker 2: can run all these models that are powerful but not 535 00:26:07,716 --> 00:26:09,716 Speaker 2: in the cloud but on the app or at least 536 00:26:09,756 --> 00:26:10,796 Speaker 2: on a mix of boths. 537 00:26:11,276 --> 00:26:13,836 Speaker 1: So in this case, is the edge the actual camera? 538 00:26:13,956 --> 00:26:16,636 Speaker 1: I mean, what what is like is the dream the 539 00:26:16,636 --> 00:26:19,396 Speaker 1: camera itself is doing the work and just uploading something 540 00:26:19,516 --> 00:26:21,796 Speaker 1: very simple to the network. 541 00:26:21,796 --> 00:26:24,636 Speaker 2: That's in many places already happening. We already have quite 542 00:26:24,636 --> 00:26:27,796 Speaker 2: powerful AI chips in the cameras. But I mean, of course, 543 00:26:28,076 --> 00:26:30,596 Speaker 2: like no mobile phone, no edge camera right now is 544 00:26:30,596 --> 00:26:33,396 Speaker 2: as powerful as let's say CHET GPT with GBT four O, 545 00:26:33,756 --> 00:26:34,356 Speaker 2: that's sure. 546 00:26:34,436 --> 00:26:36,516 Speaker 1: I mean, you have this sort of spectrum where you 547 00:26:36,556 --> 00:26:39,556 Speaker 1: have a whatever one hundred million dollar data center on 548 00:26:39,556 --> 00:26:41,996 Speaker 1: one end and one hundred dollars camera on the other 549 00:26:42,116 --> 00:26:44,516 Speaker 1: and some things in between. Right, So what can you 550 00:26:44,596 --> 00:26:45,876 Speaker 1: do on the camera now? 551 00:26:46,716 --> 00:26:50,276 Speaker 2: We do things like privacy, so we blur people's faces, 552 00:26:50,316 --> 00:26:52,716 Speaker 2: we blow license plates because we never want to upload 553 00:26:52,716 --> 00:26:56,036 Speaker 2: this information. We do weather detection what I said earlier, 554 00:26:56,036 --> 00:26:58,916 Speaker 2: with rain, so we detect on the cameras if it's raining, 555 00:27:00,076 --> 00:27:02,716 Speaker 2: things like that. We detect traffic signs and see if 556 00:27:02,756 --> 00:27:06,396 Speaker 2: they've changed. So we actually already run large part of 557 00:27:06,436 --> 00:27:10,196 Speaker 2: processing on the edge and then only if something has changed, 558 00:27:10,476 --> 00:27:13,436 Speaker 2: then we upload some data into a validation on the server. 559 00:27:13,716 --> 00:27:15,756 Speaker 2: But that already allows us to reduce what we upload 560 00:27:15,796 --> 00:27:18,836 Speaker 2: by more than ninety percent to. 561 00:27:18,236 --> 00:27:20,036 Speaker 1: Just tell the server if something is different. 562 00:27:20,196 --> 00:27:22,396 Speaker 2: Basically, yeah, exactly. 563 00:27:22,956 --> 00:27:25,316 Speaker 1: So I know, you know, we've talked mostly about maps, 564 00:27:25,916 --> 00:27:28,516 Speaker 1: but obviously GREB is doing a lot of different things. 565 00:27:28,556 --> 00:27:31,116 Speaker 1: Are there other parts of the business that we should 566 00:27:31,116 --> 00:27:31,556 Speaker 1: talk about? 567 00:27:32,076 --> 00:27:34,676 Speaker 2: The business is so fascinating, so if I have time, 568 00:27:34,676 --> 00:27:36,476 Speaker 2: we should talk about all of our business. 569 00:27:36,516 --> 00:27:38,956 Speaker 1: Yes, just tell me what's one other sort of frontier, 570 00:27:39,036 --> 00:27:40,236 Speaker 1: one other thing you're working on. 571 00:27:41,196 --> 00:27:44,556 Speaker 2: I think the other thing which we've really invested deeply, 572 00:27:44,556 --> 00:27:47,476 Speaker 2: which is also quite closely connected to our maps, is 573 00:27:47,796 --> 00:27:50,276 Speaker 2: when we look at across the delivery journey. The other 574 00:27:50,316 --> 00:27:52,996 Speaker 2: part where a lot of time is spent is in 575 00:27:53,036 --> 00:27:57,716 Speaker 2: the merchant cooking and preparing the food. And that's an 576 00:27:57,756 --> 00:28:01,676 Speaker 2: area where you spend a lot of time optimizing together 577 00:28:01,716 --> 00:28:04,836 Speaker 2: with the merchants to make sure that the food is 578 00:28:04,836 --> 00:28:07,116 Speaker 2: prepared in the right time, that the driver arrives in 579 00:28:07,156 --> 00:28:09,436 Speaker 2: the right time. So you've done lots of lots of 580 00:28:09,476 --> 00:28:12,276 Speaker 2: cool things. So, for example, we've built a data science 581 00:28:12,316 --> 00:28:15,876 Speaker 2: model that accurately predicts at every time of the day 582 00:28:15,956 --> 00:28:18,516 Speaker 2: how long the merchant needs to prepare an order. So 583 00:28:18,556 --> 00:28:21,996 Speaker 2: we can detect the busyness of the merchant and say, 584 00:28:22,116 --> 00:28:23,836 Speaker 2: if we know all the merchant has gotten a lot 585 00:28:23,836 --> 00:28:27,756 Speaker 2: of orders, normally they take to prepare, They prepare to 586 00:28:28,276 --> 00:28:30,436 Speaker 2: bud me in like three minutes, but when they're very busy, 587 00:28:30,436 --> 00:28:33,716 Speaker 2: they take seven minutes. And the merchants don't want that 588 00:28:33,756 --> 00:28:36,796 Speaker 2: our drivers crowd their store and weigh in troves, So 589 00:28:36,796 --> 00:28:40,196 Speaker 2: we typically allocate the driver only when we know, okay, 590 00:28:40,236 --> 00:28:42,556 Speaker 2: the food is ready in seven minutes, and we don't 591 00:28:42,596 --> 00:28:45,236 Speaker 2: allocate the driver immediately, but we know all there's a 592 00:28:45,276 --> 00:28:48,276 Speaker 2: driver two minutes away, we just allocate them like five 593 00:28:48,316 --> 00:28:51,556 Speaker 2: minutes later so that he's in the shop just in time. 594 00:28:52,076 --> 00:28:54,276 Speaker 2: And that also cuts the delivery journey, makes a lot 595 00:28:54,316 --> 00:28:56,116 Speaker 2: more pleasant for the merchant not having like a lot 596 00:28:56,116 --> 00:28:59,196 Speaker 2: of people crowd their store, and allows us to offer 597 00:28:59,276 --> 00:29:01,636 Speaker 2: more affordable price to the consumer because they don't need 598 00:29:01,676 --> 00:29:03,556 Speaker 2: to pay for all these minutes of the driver waiting. 599 00:29:03,836 --> 00:29:07,196 Speaker 1: So just all these optimizations, all these different margins where 600 00:29:07,236 --> 00:29:11,236 Speaker 1: you can optimize. So if we think about whatever five years, 601 00:29:11,276 --> 00:29:13,156 Speaker 1: ten years out, when you think about this sort of 602 00:29:14,556 --> 00:29:18,636 Speaker 1: medium to long term like what's your dream, Like, how's 603 00:29:18,676 --> 00:29:19,596 Speaker 1: it work? What's going on? 604 00:29:20,716 --> 00:29:22,636 Speaker 2: I think like for me, the world is changing so fast. 605 00:29:22,716 --> 00:29:24,996 Speaker 2: Five to ten years prediction is really really hard with 606 00:29:25,036 --> 00:29:26,396 Speaker 2: all the AI advancements. 607 00:29:26,796 --> 00:29:27,676 Speaker 1: How far you want to go? 608 00:29:27,796 --> 00:29:28,876 Speaker 2: Two years? Four years? 609 00:29:28,916 --> 00:29:30,596 Speaker 1: You tell me, like what you just give me a 610 00:29:30,676 --> 00:29:31,556 Speaker 1: dream for the future. 611 00:29:31,876 --> 00:29:34,396 Speaker 2: I mean the obvious one that I'm very passionate about 612 00:29:34,476 --> 00:29:38,036 Speaker 2: is robotics that make so much sense in our marketplace. 613 00:29:39,356 --> 00:29:42,356 Speaker 2: So if you can add robotics to our marketplace, that 614 00:29:42,676 --> 00:29:46,276 Speaker 2: will be a huge, huge change and something we're actively 615 00:29:46,316 --> 00:29:48,916 Speaker 2: working on to make happen. So that's I think probably 616 00:29:48,956 --> 00:29:50,956 Speaker 2: the thing I'm most passionate about to change. 617 00:29:51,396 --> 00:29:53,836 Speaker 1: Tell me what you're actively working on with robotics. 618 00:29:54,316 --> 00:29:57,356 Speaker 2: We haven't shared much which we do on the delivery side, 619 00:29:57,916 --> 00:30:01,196 Speaker 2: but for example, I mean things like autonomous cars. We've 620 00:30:01,236 --> 00:30:03,876 Speaker 2: signed an agreement with a bunch of like autonomous car 621 00:30:04,036 --> 00:30:07,036 Speaker 2: providers to come with us to Singapore and so on, 622 00:30:07,396 --> 00:30:09,116 Speaker 2: so we'll do a bunch of things in that space. 623 00:30:09,596 --> 00:30:12,156 Speaker 2: But basically you can imagine that it will be in many, 624 00:30:12,196 --> 00:30:13,876 Speaker 2: many parts of our delivery chain. 625 00:30:14,196 --> 00:30:16,796 Speaker 1: You need to build an autonomous scooter based on what 626 00:30:16,836 --> 00:30:19,116 Speaker 1: you've told me, right, I feel like the analogy to 627 00:30:19,156 --> 00:30:21,436 Speaker 1: the map story is the autonomous scooter that can go 628 00:30:21,516 --> 00:30:22,476 Speaker 1: down all the alleyways. 629 00:30:22,836 --> 00:30:24,236 Speaker 2: Great, if you ever want to have a job in 630 00:30:24,276 --> 00:30:28,796 Speaker 2: our product team, to join us, build autonomous schooters with us, 631 00:30:28,796 --> 00:30:30,836 Speaker 2: because you're exactly right, but you need to build products 632 00:30:30,836 --> 00:30:33,436 Speaker 2: that work in our region. So that's exactly the right 633 00:30:33,476 --> 00:30:35,956 Speaker 2: mindset that we're trying, like what we say always and 634 00:30:36,036 --> 00:30:38,156 Speaker 2: we want to build hyper local products that work in 635 00:30:38,196 --> 00:30:41,236 Speaker 2: Southeast Asia. 636 00:30:42,716 --> 00:30:55,476 Speaker 1: We'll be back in a minute with the lighting round. Okay, 637 00:30:55,796 --> 00:30:58,636 Speaker 1: let's finish with the lightning round. So I read on 638 00:30:58,716 --> 00:31:03,716 Speaker 1: your bio you write that you mostly travel between Singapore, 639 00:31:03,876 --> 00:31:09,636 Speaker 1: San Francisco, Berlin and Cluge. Tell me about Clue the 640 00:31:09,676 --> 00:31:12,116 Speaker 1: other three I'm familiar with Clues. I don't know anything 641 00:31:12,156 --> 00:31:14,316 Speaker 1: about How does that wind up on that list? 642 00:31:14,516 --> 00:31:18,716 Speaker 2: As a city in Romania, in the heart of Transylvania actually, 643 00:31:19,596 --> 00:31:22,076 Speaker 2: and we have a we have a small engineering center 644 00:31:22,116 --> 00:31:25,756 Speaker 2: there in our maps team, So I've spent a lot 645 00:31:25,796 --> 00:31:28,276 Speaker 2: of time there because for my own startup, that's that's 646 00:31:28,276 --> 00:31:31,516 Speaker 2: how I wound up Inclusure. Originally for my startup, Scobbler, 647 00:31:31,556 --> 00:31:34,396 Speaker 2: which was a maps and navigation startup. I built a 648 00:31:34,716 --> 00:31:38,316 Speaker 2: engineering team and cluge, so I've been going to Clusion 649 00:31:38,876 --> 00:31:42,516 Speaker 2: working with engineers there for the last fifteen years or so. 650 00:31:42,796 --> 00:31:47,716 Speaker 2: What's clue Like, it's fun. It's a student town, so high, 651 00:31:47,796 --> 00:31:52,356 Speaker 2: high energy, young population, very smart, the computer science department 652 00:31:52,436 --> 00:31:54,636 Speaker 2: very good. They used to clone the IBM mainframes for 653 00:31:54,676 --> 00:31:57,916 Speaker 2: the Soviet Union, so a bunch of hardcore engineers. 654 00:31:58,476 --> 00:32:01,836 Speaker 1: You also write in that bio you wrote in Berlin, 655 00:32:01,956 --> 00:32:06,196 Speaker 1: I experienced some of the best parties ever. Tell me 656 00:32:06,236 --> 00:32:07,316 Speaker 1: about a Berlin party. 657 00:32:08,076 --> 00:32:11,116 Speaker 2: That was prior line, That was before I moved to 658 00:32:11,356 --> 00:32:14,876 Speaker 2: before I moved to Southeast Asia. But Berlin was a fun, fun, 659 00:32:14,996 --> 00:32:17,716 Speaker 2: fun journey for I lived there for five six years. 660 00:32:17,756 --> 00:32:21,796 Speaker 1: Maybe he didn't tell me anything about any party. So 661 00:32:22,196 --> 00:32:24,116 Speaker 1: and then you also say you lived and worked in 662 00:32:24,156 --> 00:32:27,756 Speaker 1: Singapore and Berlin and San Francisco, and I'm curious, like, 663 00:32:27,836 --> 00:32:32,276 Speaker 1: how is sort of whatever professional culture, work life different 664 00:32:32,356 --> 00:32:34,956 Speaker 1: in those places. What are the sort of striking differences? 665 00:32:35,396 --> 00:32:38,556 Speaker 2: I mean, Germany is extremely direct, but like that's where 666 00:32:38,556 --> 00:32:42,596 Speaker 2: I'm originally from. I'm originally German, and Germans are known 667 00:32:42,636 --> 00:32:47,116 Speaker 2: to be super super direct, which in Southeast Asia doesn't 668 00:32:47,116 --> 00:32:50,476 Speaker 2: work super well. If they're not accustomed to it. So 669 00:32:50,556 --> 00:32:52,156 Speaker 2: I think for me, the thing that I needed to 670 00:32:52,196 --> 00:32:56,756 Speaker 2: adjust most is to become a lot more indirect, to 671 00:32:56,836 --> 00:33:00,716 Speaker 2: become a lot more like have those conversations and like 672 00:33:00,836 --> 00:33:04,436 Speaker 2: smaller groups, not in front of everybody. So I definitely 673 00:33:04,436 --> 00:33:08,556 Speaker 2: had to just my style quite a lot when moving around. 674 00:33:08,636 --> 00:33:11,036 Speaker 2: But I found that always incredibly fun. I always loved 675 00:33:11,076 --> 00:33:15,516 Speaker 2: learning about new cultures, So after some adjustments, I really 676 00:33:15,596 --> 00:33:16,436 Speaker 2: really like it here. 677 00:33:16,916 --> 00:33:21,236 Speaker 1: That is super interesting. Was there, as you were saying 678 00:33:21,276 --> 00:33:24,036 Speaker 1: that I was wondering, like, was there a particular moment 679 00:33:24,956 --> 00:33:29,476 Speaker 1: when you realized, Oh, I'm not behaving correctly in this 680 00:33:29,516 --> 00:33:30,556 Speaker 1: cultural context? 681 00:33:31,756 --> 00:33:34,236 Speaker 2: Yeah, I mean this predate grap So this was when 682 00:33:34,276 --> 00:33:37,316 Speaker 2: I was first doing business in China. So I sold 683 00:33:37,316 --> 00:33:39,996 Speaker 2: my startup to a Silicon Valley company, but I also 684 00:33:40,036 --> 00:33:42,396 Speaker 2: got a two hundred people team in China report to me, 685 00:33:42,436 --> 00:33:44,556 Speaker 2: which was the first time that I ever managed a 686 00:33:44,596 --> 00:33:50,396 Speaker 2: team in China, and I was extremely surprised why they 687 00:33:50,556 --> 00:33:52,916 Speaker 2: wouldn't tell me about all the mistakes, all the things 688 00:33:52,916 --> 00:33:55,516 Speaker 2: that I did wrong. And I said, like, that's very unusual. 689 00:33:55,596 --> 00:33:59,876 Speaker 2: Normally I get a lot of like pushback from engineers. 690 00:34:00,396 --> 00:34:03,756 Speaker 2: And then I started like asking people is why don't 691 00:34:03,796 --> 00:34:05,516 Speaker 2: they do this? And then people said like, well, it's 692 00:34:05,596 --> 00:34:08,476 Speaker 2: kind of root in a public forum to say the 693 00:34:08,516 --> 00:34:13,156 Speaker 2: boss's wrong. And then I realized, like, oh, that's that's 694 00:34:13,156 --> 00:34:16,356 Speaker 2: why I just need to like change how I'm asking questions, 695 00:34:16,436 --> 00:34:19,716 Speaker 2: how I'm managing teams. And that's when I first managed 696 00:34:19,756 --> 00:34:22,596 Speaker 2: teams in China. Took me quite a while to figure out, honestly. 697 00:34:22,516 --> 00:34:26,516 Speaker 1: And how does San Francisco fit in relation to both 698 00:34:26,796 --> 00:34:31,596 Speaker 1: working in Berlin and working in you know, China and Singapore. 699 00:34:31,756 --> 00:34:33,916 Speaker 1: Where's the US on that continuum? 700 00:34:34,916 --> 00:34:36,876 Speaker 2: Do you ask for me? I think the thing that 701 00:34:36,916 --> 00:34:40,636 Speaker 2: I really loved in San Francisco was just the craziness 702 00:34:40,636 --> 00:34:44,316 Speaker 2: of ambition. When I spent like April back in in 703 00:34:44,516 --> 00:34:47,236 Speaker 2: SF four month to like vibe code and hack on 704 00:34:47,236 --> 00:34:49,636 Speaker 2: a bunch of hobby projects, and like you go in 705 00:34:49,716 --> 00:34:51,756 Speaker 2: every random coffee shop and there's somebody who said they 706 00:34:51,836 --> 00:34:55,276 Speaker 2: work on a billion dollar idea and they they're just starting, 707 00:34:55,516 --> 00:34:58,076 Speaker 2: they have nothing yet they're just like, Okay, I'm going 708 00:34:58,156 --> 00:35:01,836 Speaker 2: to make it big. So I think that that ambition 709 00:35:02,236 --> 00:35:05,436 Speaker 2: and that willingness to openly declare it even if people 710 00:35:05,436 --> 00:35:08,076 Speaker 2: know it's super unlikely. There's not thousands of people who 711 00:35:08,116 --> 00:35:10,796 Speaker 2: start billion dollar company, but in the Bay Area that's 712 00:35:10,836 --> 00:35:13,476 Speaker 2: thousands of people who say they will and this conviction 713 00:35:13,796 --> 00:35:16,716 Speaker 2: and this like optimism. I think that was for me 714 00:35:16,796 --> 00:35:20,036 Speaker 2: one of the most striking things in the US, and 715 00:35:20,076 --> 00:35:22,116 Speaker 2: that I still love the energy whenever I go there. 716 00:35:22,156 --> 00:35:32,556 Speaker 3: Basically, Philip Condall is the chief product officer at GRAB. 717 00:35:33,796 --> 00:35:34,836 Speaker 2: Please email us. 718 00:35:34,716 --> 00:35:38,116 Speaker 1: At problem at Pushkin dot fm. We are always looking 719 00:35:38,156 --> 00:35:42,196 Speaker 1: for new guests for the show. Today's show was produced 720 00:35:42,196 --> 00:35:45,636 Speaker 1: by Trinamanino and Gabriel Hunter Chang. It was edited by 721 00:35:45,676 --> 00:35:50,076 Speaker 1: Alexander Garreton and engineered by Sarah Bruguer. I'm Jacob Goldstein 722 00:35:50,076 --> 00:35:52,156 Speaker 1: and we'll be back next week with another episode of 723 00:35:52,156 --> 00:36:02,756 Speaker 1: What's Your Problem.