WEBVTT - Mapping the Unmappable

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<v Speaker 1>Pushkin, what do you understand about maps and the world

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<v Speaker 1>because you have been, you know, studying it and working

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<v Speaker 1>on it for so long.

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<v Speaker 2>I think the reason I personally love maps is just

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<v Speaker 2>because there's a super super complex problem and very very

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<v Speaker 2>hard thing to solve. That's why why I'm so passionate

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<v Speaker 2>about it.

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<v Speaker 1>This is Philip Kundall. He's the chief product officer at

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<v Speaker 1>a company called Grab. Grab is huge in Southeast Asia

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<v Speaker 1>and its main businesses are delivery and mobility, kind of

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<v Speaker 1>like a combination between Instacart and Uber, and as a result,

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<v Speaker 1>maps are at the core of its business.

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<v Speaker 2>But the fascinating thing for me is like, once you

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<v Speaker 2>get a digital representation of the real world, you can

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<v Speaker 2>basically the vision that I always had for long, long years.

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<v Speaker 2>If you haven't solved that yet is imagine when he

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<v Speaker 2>went from Yahoo to Google. Right. Yahoo was like the

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<v Speaker 2>static index of web that you could say, for something

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<v Speaker 2>that just leads you to a website. Right if you

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<v Speaker 2>would search for sports and it gets you to a

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<v Speaker 2>sports side and then you need to navigate your way.

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<v Speaker 2>And then Google you can say you search for a

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<v Speaker 2>very specific thing and it brings you exactly to that page. Right, Yeah,

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<v Speaker 2>and that's what we haven't solved with with maps yet.

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<v Speaker 1>Right if you say, if we're still in the Yahoo

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<v Speaker 1>era of maps, is what you're telling me.

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<v Speaker 2>Maybe slightly beyond the Yahoo era. But let's say like

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<v Speaker 2>when you say, where do I find the in Southeast Asia?

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<v Speaker 2>Durian is like a super popular fruit? Right say where

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<v Speaker 2>I said, where do I find the freshest Durian for

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<v Speaker 2>this price? Right now, right now, the real world, right now,

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<v Speaker 2>in the real world, right like, which store stocks a

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<v Speaker 2>Durian right now? Show me where it is and then

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<v Speaker 2>guide me to that store. There's nobody who has solved

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<v Speaker 2>that problem. And and that's the fascinating part for me

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<v Speaker 2>that I want what Google has done for the web.

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<v Speaker 2>I want that for the real world.

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<v Speaker 1>I mean, that's a wild problem. When you formulate it

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<v Speaker 1>that way, you would have to know everything all the time.

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<v Speaker 1>There's an information problem there that is quite hard.

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<v Speaker 2>I mean, the hot problems are the fun ones and

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<v Speaker 2>a yeah.

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<v Speaker 1>I'm Jacob Goldstein, and this is what's your problem? The

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<v Speaker 1>show where I talk to people who are trying to

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<v Speaker 1>make technological progress. Philip Bilton sold a mapping startup before

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<v Speaker 1>he wound up at grab in twenty nineteen. Today, he's

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<v Speaker 1>based in Singapore, which is one of several countries where

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<v Speaker 1>Grab operates. Others include Indonesia, Malaysia, the Philippines, Thailand, and Vietnam.

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<v Speaker 1>And in addition to doing delivery and mobility, they also

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<v Speaker 1>do payments. It's what they call a super app. And

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<v Speaker 1>I wanted to talk to Philip about maps in particular

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<v Speaker 1>because I mean, I guess I just kind of thought

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<v Speaker 1>maps were solved or assumed that without really thinking about it,

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<v Speaker 1>and then when I started learning about GRAB, I realized

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<v Speaker 1>I was very wrong. So there's that dream of real

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<v Speaker 1>time mapp apps that Philip mentioned a minute ago. But

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<v Speaker 1>there's also something that's really interesting and kind of more

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<v Speaker 1>immediately relevant, and that is this The online maps we

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<v Speaker 1>use in the US and other developed countries just don't work.

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<v Speaker 3>Very well in a lot of other parts.

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<v Speaker 1>Of the world. And when Grab launched several years ago

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<v Speaker 1>in Malaysia, that turned out to be a big problem.

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<v Speaker 2>I mean, from day one, you needed maps, so you

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<v Speaker 2>can't run the company without maps, and grabs started using

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<v Speaker 2>a third party service. But then what we've realized a

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<v Speaker 2>lot of these services are built for like a developed

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<v Speaker 2>market like the US, and very built around the mental

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<v Speaker 2>model of like cars, and Southeast Asia is really different.

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<v Speaker 2>We're operating primarily on motorbikes, even two thirds of our

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<v Speaker 2>transport trips on the back of a motorbike. And then

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<v Speaker 2>if you know Southeast Asian cities, then you have these

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<v Speaker 2>narrow alleys and sideways and so on, and traditional maps

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<v Speaker 2>just don't cover them because interesting, basically how maps are

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<v Speaker 2>made traditionally is with these big mapping vans that I'm

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<v Speaker 2>sure you've seen driving through cities. One hundred and fifty

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<v Speaker 2>two hundred thousand dollars basically look like a way more right,

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<v Speaker 2>That's kind of like how they look like, and they

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<v Speaker 2>don't cover the roads that we need to deliver our

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<v Speaker 2>services and really reach our customer because they expect to

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<v Speaker 2>be picked up in their home. They expect that the

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<v Speaker 2>food get delivered to their front door, and not just

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<v Speaker 2>to the nearest street that might be two hundred meters

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<v Speaker 2>away in terms of like a big car drivable street.

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<v Speaker 1>So lots of life, lots of people live, lots of businesses,

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<v Speaker 1>earned streets that a van couldn't even drive down if

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<v Speaker 1>it wanted to.

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<v Speaker 2>Yeah, no chance. I mean, when I do the immersions

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<v Speaker 2>like on the back of a motorbike. There's absolutely no

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<v Speaker 2>chance that with a man you can get there. It

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<v Speaker 2>was a scooter centric world, and Southeast Asia updated so quickly,

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<v Speaker 2>like I mean, there's like entire new neighborhoods springing up,

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<v Speaker 2>and it was traditional maps are built in a way

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<v Speaker 2>that these big vans collect once every one or two years,

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<v Speaker 2>and we need to refresh the maps in like days.

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<v Speaker 1>So you have this problem, like what's the first step?

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<v Speaker 1>So you realize the maps aren't working. You're a mobility company,

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<v Speaker 1>that's that's not gonna work. How do you It seems

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<v Speaker 1>impossible to think, oh, well just map everything.

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<v Speaker 2>How do you go?

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<v Speaker 1>How do you start doing that?

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<v Speaker 2>Yeah? Exactly right. It's a crazy problem, right yeah. I

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<v Speaker 2>mean the numbers that were out there when Google started

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<v Speaker 2>mapping the US, they apparently spent like anywhere between half

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<v Speaker 2>a billion to one billion dollars and it clearly didn't

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<v Speaker 2>have that amount of money to map it. And it

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<v Speaker 2>seemed crazy, right like people when we started doing this,

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<v Speaker 2>I mean, I got like so much feedback that people

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<v Speaker 2>thought we were completely insane because God cost us and

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<v Speaker 2>Southeast Age are right, I mean, we are home to

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<v Speaker 2>like close to seven hundred million people, which is like

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<v Speaker 2>a lot larger than the US. So you can imagine

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<v Speaker 2>like how much it would normally.

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<v Speaker 1>Cost, yes, two x exactly and crazy dense cities, traffic,

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<v Speaker 1>tiny alleys. So how do you even start to undertake

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<v Speaker 1>this project? What do you do?

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<v Speaker 2>Yeah? No, so this was this was really the fun part.

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<v Speaker 2>That's such a fun challenge yourself. But basically what we

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<v Speaker 2>started is we started taking it from first principles like

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<v Speaker 2>why was it so expensive to map? And then we

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<v Speaker 2>tried to solve those problems and the problems why it's

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<v Speaker 2>so expensive normally to produce a map are a few things,

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<v Speaker 2>the ones that I just said. The mapping vehicles are

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<v Speaker 2>extremely expensive. Then the people sitting in the mapping vehicle

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<v Speaker 2>are extremely expensive because you sent them around driving in

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<v Speaker 2>the country, sleeping in hotels. So the cost like to

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<v Speaker 2>send these vans with people around cost a fortune, and

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<v Speaker 2>like operating that is just super super costly. And that's

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<v Speaker 2>what we had a unique advantage because obviously we had

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<v Speaker 2>our drivers already crisscrossing the city in like insane amounts.

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<v Speaker 2>I mean our drivers. Any city is like crossed by

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<v Speaker 2>our drivers like one hundred times or more in a day,

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<v Speaker 2>so there's like no roads that our drivers don't see.

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<v Speaker 2>So we thought about two things that we needed to

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<v Speaker 2>drive the radically down. One is we build our own

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<v Speaker 2>collection hardware. So instead of building these like massive rigs,

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<v Speaker 2>we build small GoPro like cameras with an echip in

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<v Speaker 2>there that we can give to our drivers. And instead

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<v Speaker 2>of costing one hundred two hundred thousand dollars for full

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<v Speaker 2>mapping van, these cameras are under order of like a

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<v Speaker 2>bunch of one hundred dollars, so orders of magnitudes cheaper.

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<v Speaker 2>And then the second thing is we said, the driver's

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<v Speaker 2>drive around the city anyway, can they just have the camera?

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<v Speaker 2>And then we just need to pay them a little

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<v Speaker 2>for them. It's a great extra income. But they get

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<v Speaker 2>their main income from doing food deliveries or doing mobility trips,

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<v Speaker 2>and they get an extra income from that. And that's

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<v Speaker 2>because we could deploy like we have in Southeast Asia

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<v Speaker 2>probably one hundred times at least more cameras than anybody

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<v Speaker 2>else deployed because they're so cheap. And then we don't

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<v Speaker 2>need to send drivers traveling everywhere.

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<v Speaker 1>So let's break it down a little bit like building

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<v Speaker 1>your own hardware, like the driver part is kind of obvious, right, like, oh,

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<v Speaker 1>we've got these guys already out there, could we get

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<v Speaker 1>them to do the mapping. It's not at all obvious

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<v Speaker 1>to me that you would need to build your own hardware.

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<v Speaker 1>So tell me about that, Like, first of all, why

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<v Speaker 1>do you need to build your own hardware? Why not

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<v Speaker 1>just dumb question, buy a camera?

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<v Speaker 2>No, great, great question. Actually, I mean that's what we tried. Okay,

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<v Speaker 2>So I think when we went and looked into this,

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<v Speaker 2>because we didn't want to build hardware, and so basically

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<v Speaker 2>we tried two things. So they were these go pros

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<v Speaker 2>a few hundred bucks cameras, and then they were the

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<v Speaker 2>more professional mapping Greade cameras twenty thousand bucks. So the

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<v Speaker 2>problem with go Pro was a bunch of things they made,

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<v Speaker 2>usually as action cameras, right, like you can go skiing

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<v Speaker 2>and so on with them, but they don't have great

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<v Speaker 2>GPS in them, and basically because you don't need it,

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<v Speaker 2>right Like, if you go like somewhere mountain biking, it's

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<v Speaker 2>typically open sky. It's not like a dense urban environment

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<v Speaker 2>with big skyscrapers that reflect GPS. So they're decent for

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<v Speaker 2>what people usually use. Goal pros, but they're not the

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<v Speaker 2>media level precision we need for high density urban center mapping.

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<v Speaker 2>So that was one problem. And the second problem with

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<v Speaker 2>Goal pros was they just have no tooling that they

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<v Speaker 2>kept operate twenty four to seven drivers upload automatically data,

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<v Speaker 2>so they're not made They usually made for like a

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<v Speaker 2>one two hour recording and not this heavy duty recording

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<v Speaker 2>for the back of our motorbikes for twelve hours in

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<v Speaker 2>blistering heat in Southeast Asia, tough rail and so on.

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<v Speaker 2>So that's basically why both of these cameras that existed

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<v Speaker 2>didn't fit the needs that are very harsh environment.

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<v Speaker 1>So would the mapping camera have worked, but you didn't

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<v Speaker 1>want to spend twenty grand per camera. That was presumably

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<v Speaker 1>the simple problem with the mapping camera.

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<v Speaker 2>Yeah, twenty grand was the problem. And the other problems

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<v Speaker 2>are they're also quite heavy and bulky, So those cameras

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<v Speaker 2>are close to ten kilograms, which is not super easy

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<v Speaker 2>to operate. So they were too bulky and too costly.

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<v Speaker 1>So what do you just get on a plane dest

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<v Speaker 1>engine and tell them what you need, Like, how do

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<v Speaker 1>you build your own camera?

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<v Speaker 2>So this was really certing that we actually had a

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<v Speaker 2>small team and Scenzan already building building off our battery

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<v Speaker 2>swap blockers for scooters. And back then I talked to

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<v Speaker 2>our Cito at that time and he is like kind

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<v Speaker 2>enough to say, like I feel it, like we really

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<v Speaker 2>need to find something next for this team to do.

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<v Speaker 2>So you can have them all. They can build your cameras,

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<v Speaker 2>so amazing, We're very lucky.

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<v Speaker 1>So tell me about the camera they came up with.

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<v Speaker 2>Yeah, so the first camera that we built, we called

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<v Speaker 2>it a Karda cam, so like after like carda in

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<v Speaker 2>like some language, was like map. So basically those cameras

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<v Speaker 2>where the first version was mounted on the helmet of drivers,

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<v Speaker 2>predominantly it was two hundred and fifty grams mounted on

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<v Speaker 2>the helmet of drivers, and it would basically be a

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<v Speaker 2>camera with an AI chip and really highly accurate GPS,

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<v Speaker 2>like that's all we needed. And so they would just

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<v Speaker 2>mount them on their on their helmets and go about

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<v Speaker 2>their day and just at the end of the day,

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<v Speaker 2>take it home, take it on their Wi Fi, upload

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<v Speaker 2>the data and yeah, we would get data from tons

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<v Speaker 2>of cameras.

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<v Speaker 1>And then you made a cardacam too, right, tell me

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<v Speaker 1>about Carteracam too.

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<v Speaker 2>Yeah, we made a bunch of iterations of our hardware.

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<v Speaker 2>So Karda CAN two is our latest generation, which is

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<v Speaker 2>basically a three sixty camera, so it has basically four

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<v Speaker 2>camera lenses and all directions. Has also like more advanced centers,

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<v Speaker 2>like allied our sensors in there, so we basically made

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<v Speaker 2>the setup a lot more professional that we can capture

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<v Speaker 2>maps an even higher level of accuracy to get things

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<v Speaker 2>like lane level navigation for our drivers or advanced safety features.

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<v Speaker 2>So the latest camera is basically a great iteration that

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<v Speaker 2>has taken just a step further to capture more details

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<v Speaker 2>in the map.

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<v Speaker 1>And where does it go? Where does the camera go

0:11:42.196 --> 0:11:43.436
<v Speaker 1>if somebody's driving a scooter.

0:11:43.836 --> 0:11:46.116
<v Speaker 2>Yeah, so now we've built a mount on the back

0:11:46.156 --> 0:11:49.236
<v Speaker 2>of a motorbike. So we've built actually the cameras that

0:11:49.276 --> 0:11:50.716
<v Speaker 2>you can either mount them at the back of your

0:11:50.716 --> 0:11:52.676
<v Speaker 2>motorbike that it doesn't disturb you and in a pole

0:11:52.756 --> 0:11:55.356
<v Speaker 2>so that's high so that you can see because the

0:11:55.436 --> 0:11:57.196
<v Speaker 2>camera obviously needs to have a good view even if

0:11:57.196 --> 0:12:00.076
<v Speaker 2>there's cars around, so we've built a special mount on

0:12:00.116 --> 0:12:02.756
<v Speaker 2>the back of a motorbike. We have mounts also for cars,

0:12:02.756 --> 0:12:05.236
<v Speaker 2>so some drivers also mount them on cars. And then

0:12:05.276 --> 0:12:07.236
<v Speaker 2>we have a backpack that you can carry if we

0:12:07.276 --> 0:12:09.996
<v Speaker 2>want a map indoor. The other thing that's really important

0:12:10.036 --> 0:12:14.236
<v Speaker 2>in Southeast Asia is malls are gigantic. Do you have

0:12:14.276 --> 0:12:17.116
<v Speaker 2>like these malls which it takes you fifteen minutes to walk.

0:12:16.956 --> 0:12:20.636
<v Speaker 1>Around, and so people will get a delivery to whatever

0:12:20.716 --> 0:12:22.636
<v Speaker 1>the noodle shop in the middle of the mall, and

0:12:22.676 --> 0:12:24.036
<v Speaker 1>you've got to put that on the map.

0:12:25.236 --> 0:12:27.556
<v Speaker 2>Well, the other way around, people get a delivery from

0:12:27.556 --> 0:12:31.156
<v Speaker 2>the noodle shop of course, of course, and our driver

0:12:31.276 --> 0:12:34.116
<v Speaker 2>needs to find their way there, and if they get

0:12:34.156 --> 0:12:35.956
<v Speaker 2>into the wrong entrance of the mall, it might cost

0:12:35.956 --> 0:12:38.276
<v Speaker 2>them ten to fifteen minutes extra to walk back and forth.

0:12:38.636 --> 0:12:40.756
<v Speaker 2>So we need to precisely not only tell them go

0:12:40.796 --> 0:12:43.196
<v Speaker 2>into the mall, but we also precisely need to tell

0:12:43.236 --> 0:12:46.876
<v Speaker 2>them where to park their motorbike. So what we emphasize

0:12:46.876 --> 0:12:48.556
<v Speaker 2>when we build our maps that we build them like

0:12:48.676 --> 0:12:51.076
<v Speaker 2>and to end, and we say, here's where you park

0:12:51.116 --> 0:12:53.676
<v Speaker 2>your motorbike, here's where you walk, here's where you pick

0:12:53.756 --> 0:12:56.236
<v Speaker 2>up the noodles, and then you can do the same

0:12:56.276 --> 0:12:56.796
<v Speaker 2>in reverse.

0:12:57.636 --> 0:12:59.316
<v Speaker 1>How many of these cameras do you have out there

0:12:59.356 --> 0:13:01.836
<v Speaker 1>like today? How many people are driving around mapping with

0:13:01.876 --> 0:13:04.836
<v Speaker 1>these cameras today ish by end of this year.

0:13:04.716 --> 0:13:07.356
<v Speaker 2>We have about twenty thousand cameras in the field. And

0:13:07.396 --> 0:13:10.076
<v Speaker 2>to give you a sense, like professional mapping companies in

0:13:10.116 --> 0:13:13.116
<v Speaker 2>Southeast Asia, to my best of my knowledge, have about

0:13:13.156 --> 0:13:16.676
<v Speaker 2>tens of cars, but not tens of thousand tens of cars.

0:13:17.436 --> 0:13:19.956
<v Speaker 1>So does that mean they have basically seeded it to

0:13:19.996 --> 0:13:24.716
<v Speaker 1>you like that you have won the mapping Southeast Asia fight.

0:13:25.556 --> 0:13:27.956
<v Speaker 2>I think it's still so so early. Fair. I mean,

0:13:28.156 --> 0:13:29.836
<v Speaker 2>there's so many things we want to do.

0:13:30.036 --> 0:13:32.196
<v Speaker 1>Interesting. I love that. So let's talk a little more

0:13:32.196 --> 0:13:33.956
<v Speaker 1>about what you're doing now and then let's talk about

0:13:34.476 --> 0:13:37.316
<v Speaker 1>what you want to do. So you have an incredible

0:13:37.356 --> 0:13:39.596
<v Speaker 1>amount of data coming in now, right, I mean, if

0:13:39.596 --> 0:13:45.076
<v Speaker 1>you have twenty thousand cameras driving around every day like

0:13:45.516 --> 0:13:48.556
<v Speaker 1>that is a wild amount of input. What are you

0:13:48.596 --> 0:13:50.596
<v Speaker 1>doing with all that data? I mean there's basic mapping,

0:13:50.636 --> 0:13:52.876
<v Speaker 1>and I'm sure you've got that, but what's the next level?

0:13:52.876 --> 0:13:54.796
<v Speaker 1>Presumably have AI? You said you have light? Are like,

0:13:54.836 --> 0:13:57.116
<v Speaker 1>tell me all the things you know because you have

0:13:57.116 --> 0:13:57.716
<v Speaker 1>all this data.

0:13:57.836 --> 0:14:00.676
<v Speaker 2>Yeah, you're right. I think the basic vision that you

0:14:00.756 --> 0:14:03.876
<v Speaker 2>have is get an accurate representation of the real world

0:14:03.996 --> 0:14:07.796
<v Speaker 2>in real time. That's that's the that's the goal of

0:14:07.956 --> 0:14:08.996
<v Speaker 2>mapping in real time?

0:14:09.236 --> 0:14:12.396
<v Speaker 1>Is crazy? In real time? It's crazy? Yeah, that's wild.

0:14:12.676 --> 0:14:16.956
<v Speaker 1>But fine, that's just like right now, what do you know?

0:14:17.076 --> 0:14:18.836
<v Speaker 2>Yeah, so a bunch of things is I mean, first

0:14:18.836 --> 0:14:23.316
<v Speaker 2>of all, everything that you need for navigation roads, how

0:14:23.316 --> 0:14:26.036
<v Speaker 2>our road's drivable? Is the road safe to drive? Does

0:14:26.076 --> 0:14:28.676
<v Speaker 2>it have a lot of potholes and things like that,

0:14:28.916 --> 0:14:31.036
<v Speaker 2>and that signage obviously is it a one way road?

0:14:31.076 --> 0:14:32.236
<v Speaker 2>Is it not a one way road?

0:14:32.356 --> 0:14:34.756
<v Speaker 1>Can you worry me about a particular pothole? Could you

0:14:34.876 --> 0:14:37.236
<v Speaker 1>be like, be careful, there's a pothole on the left

0:14:37.316 --> 0:14:38.836
<v Speaker 1>coming up, try that right lane.

0:14:39.116 --> 0:14:41.716
<v Speaker 2>And we're doing actually very cool things we're doing right now.

0:14:41.756 --> 0:14:46.516
<v Speaker 2>We've already launched a safety navigation that tells you, hey,

0:14:46.596 --> 0:14:48.956
<v Speaker 2>is the road safe to drive? Based on potholes? And

0:14:48.996 --> 0:14:51.236
<v Speaker 2>does it have street lighting? Is it safe? Because if

0:14:51.236 --> 0:14:53.116
<v Speaker 2>the road at night is well lit, it's a lot

0:14:53.156 --> 0:14:55.076
<v Speaker 2>safer to drive, but it's not. So that's kind of

0:14:55.116 --> 0:14:57.756
<v Speaker 2>like a practical use case that we that we already

0:14:57.756 --> 0:14:58.556
<v Speaker 2>can do with that.

0:14:58.716 --> 0:15:02.756
<v Speaker 1>Interesting as I'm sure you know, like ways in this

0:15:02.796 --> 0:15:06.556
<v Speaker 1>country tells you where there's like a speed camera or

0:15:06.636 --> 0:15:09.436
<v Speaker 1>like a speed trap. Do you do that?

0:15:10.116 --> 0:15:14.516
<v Speaker 2>Yeah. Absolutely. So we have an AI voice reporting that

0:15:15.076 --> 0:15:17.476
<v Speaker 2>drivers can share anything they're like, oh, the right lane

0:15:17.516 --> 0:15:20.316
<v Speaker 2>of this road is closed, and then it gets processed

0:15:20.596 --> 0:15:23.916
<v Speaker 2>and basically used for all the other other drivers. So

0:15:23.956 --> 0:15:26.436
<v Speaker 2>we've launched that and it's been really successful as well.

0:15:26.516 --> 0:15:28.396
<v Speaker 1>So when you say it's AI, does that mean the

0:15:28.476 --> 0:15:31.076
<v Speaker 1>driver just says it and it gets integrated into the

0:15:31.076 --> 0:15:33.316
<v Speaker 1>maps or what does that mean?

0:15:33.996 --> 0:15:36.556
<v Speaker 2>Yeah, that's that's exactly right. There's the future. We actually

0:15:36.556 --> 0:15:39.156
<v Speaker 2>work closely together with our friends at open Eye. So

0:15:39.236 --> 0:15:42.636
<v Speaker 2>the driver natural language reports an issue and then it

0:15:42.676 --> 0:15:44.956
<v Speaker 2>gets processed and if you're not clear, we ask your

0:15:44.956 --> 0:15:47.476
<v Speaker 2>follow up question. If you say, hey, the right lane

0:15:47.556 --> 0:15:48.836
<v Speaker 2>is closed and he said like, oh, do you mean

0:15:48.916 --> 0:15:50.556
<v Speaker 2>this road? And then he says drive but yeah, that's

0:15:50.556 --> 0:15:52.836
<v Speaker 2>exactly the road I'm talking about, and then we process

0:15:52.876 --> 0:15:55.836
<v Speaker 2>it and basically one all the other drivers accordingly.

0:15:56.036 --> 0:15:58.236
<v Speaker 1>So you mentioned your friends at open ai. It seems

0:15:58.276 --> 0:16:00.356
<v Speaker 1>like good friends to have a good place to have friends.

0:16:00.676 --> 0:16:03.116
<v Speaker 1>I know you have a deal with them, Like what

0:16:03.236 --> 0:16:05.236
<v Speaker 1>is the nature of your deal with open ai and

0:16:05.716 --> 0:16:08.156
<v Speaker 1>more generally, what's the work you're doing with them?

0:16:08.476 --> 0:16:10.516
<v Speaker 2>So MAP I think is one of the key things

0:16:10.556 --> 0:16:13.436
<v Speaker 2>we do together with them and use their models to

0:16:13.516 --> 0:16:16.996
<v Speaker 2>improve our maps. The voice stuff that I shared earlier

0:16:16.996 --> 0:16:19.596
<v Speaker 2>is like one of our one of our highlight features.

0:16:20.316 --> 0:16:22.956
<v Speaker 2>And in general, we're just embedding like AI and everything

0:16:22.956 --> 0:16:26.276
<v Speaker 2>we're doing. We're having we're having like over a thousand

0:16:26.276 --> 0:16:29.356
<v Speaker 2>different AI models that we're that we're working on, so

0:16:29.396 --> 0:16:31.796
<v Speaker 2>it's basically deeply, deeply embedded in many many of the

0:16:31.836 --> 0:16:33.196
<v Speaker 2>things that that we're doing.

0:16:33.836 --> 0:16:36.196
<v Speaker 1>Tell me more, like, what are some more examples of

0:16:36.196 --> 0:16:37.876
<v Speaker 1>how you're using AI.

0:16:38.356 --> 0:16:41.636
<v Speaker 2>So one of the latest things that that I really

0:16:41.676 --> 0:16:44.356
<v Speaker 2>like that's really cool is the other thing that has

0:16:44.436 --> 0:16:48.356
<v Speaker 2>really huge impact on our marketplace is weather. In Southeast Asia,

0:16:48.636 --> 0:16:52.476
<v Speaker 2>the rain is insane. It is like pouring down, the

0:16:52.556 --> 0:16:57.036
<v Speaker 2>roads are flooding, so and for our services for mobility

0:16:57.236 --> 0:17:00.596
<v Speaker 2>for deliveries, that has a tremendous impact on that. So

0:17:00.876 --> 0:17:04.436
<v Speaker 2>knowing when it rains early is super super critical so

0:17:04.476 --> 0:17:08.196
<v Speaker 2>we can adjust the marketplace. We've launched AI based rate

0:17:08.236 --> 0:17:13.156
<v Speaker 2>detection that a bunch of things, So we deploy sensors,

0:17:13.196 --> 0:17:15.836
<v Speaker 2>other sensors in cars. So we have basically a device

0:17:15.876 --> 0:17:18.236
<v Speaker 2>that's called a card to dongle that we deploy and

0:17:18.276 --> 0:17:19.956
<v Speaker 2>the cars that we own and it plugs into a

0:17:20.036 --> 0:17:21.996
<v Speaker 2>port in the car. That's called OBEDI two port, and

0:17:21.996 --> 0:17:23.836
<v Speaker 2>that reads when the windshield viper's going.

0:17:24.396 --> 0:17:29.756
<v Speaker 1>Oh genius, such a simple way, such a simple way

0:17:29.796 --> 0:17:31.516
<v Speaker 1>to know if it's rating. Does the car have the

0:17:31.556 --> 0:17:34.516
<v Speaker 1>windshield wipers on? It's so second order. I love it though.

0:17:35.036 --> 0:17:36.156
<v Speaker 2>Yeah, and how fast it is?

0:17:36.756 --> 0:17:38.676
<v Speaker 1>Oh? How fast? It's how fast the car is going,

0:17:38.716 --> 0:17:40.796
<v Speaker 1>because the slower it's going, the heavier the rain. Is

0:17:40.796 --> 0:17:42.356
<v Speaker 1>that the inference how fast.

0:17:42.196 --> 0:17:45.036
<v Speaker 2>The windshield wiper is going. Because you kind of like this,

0:17:45.196 --> 0:17:45.876
<v Speaker 2>are this right?

0:17:47.436 --> 0:17:49.996
<v Speaker 1>So okay? So so and what do you do? What

0:17:50.036 --> 0:17:52.116
<v Speaker 1>do you do with that data? That's the input, what's

0:17:52.156 --> 0:17:52.916
<v Speaker 1>the output?

0:17:53.076 --> 0:17:56.636
<v Speaker 2>The output is basically we know the moment it rains,

0:17:56.836 --> 0:17:59.276
<v Speaker 2>we know demand will go up and supply of drivers

0:17:59.276 --> 0:18:01.436
<v Speaker 2>will go down. So what we do We try to

0:18:01.476 --> 0:18:04.276
<v Speaker 2>activate more drivers, so we would send out to all

0:18:04.316 --> 0:18:07.236
<v Speaker 2>the drivers, Hey, it's starting to rain. There's a fantastic

0:18:07.276 --> 0:18:10.796
<v Speaker 2>earning opportunity. If you're not worth now's a great chance

0:18:10.836 --> 0:18:13.636
<v Speaker 2>to get on the road and make extra money. And

0:18:13.676 --> 0:18:17.156
<v Speaker 2>then we try to actively get the supply of drivers

0:18:17.236 --> 0:18:19.636
<v Speaker 2>up so that we can keep our reliability at the

0:18:19.676 --> 0:18:20.356
<v Speaker 2>levels we need.

0:18:20.876 --> 0:18:23.156
<v Speaker 1>We know people get all worked up about sarge pricing.

0:18:23.276 --> 0:18:25.316
<v Speaker 1>Do you use searge pricing in that setting? It sounds

0:18:25.356 --> 0:18:27.676
<v Speaker 1>like a classic use of search pricing. Everybody wants a driver,

0:18:27.796 --> 0:18:29.836
<v Speaker 1>nobody wants to drive. What you need is a higher price.

0:18:29.956 --> 0:18:30.796
<v Speaker 1>Do you do that?

0:18:30.796 --> 0:18:33.036
<v Speaker 2>That's exactly right? Or like you need to motivate the

0:18:33.116 --> 0:18:34.476
<v Speaker 2>drivers to come on the road.

0:18:34.756 --> 0:18:37.516
<v Speaker 1>Yeah, no, there is a market. I'm pro searge pricing.

0:18:37.916 --> 0:18:40.196
<v Speaker 1>That's a great example. What's another example of the way

0:18:40.236 --> 0:18:40.996
<v Speaker 1>you're using AI?

0:18:41.916 --> 0:18:47.036
<v Speaker 2>So we used AI to translate in many scenarios. For example,

0:18:47.356 --> 0:18:49.796
<v Speaker 2>when I go like to Jakarta, I obviously don't speak

0:18:49.796 --> 0:18:52.836
<v Speaker 2>any Bahasa, but I can message our drivers in the

0:18:52.956 --> 0:18:55.636
<v Speaker 2>real time, translate any messages, send them a chat to

0:18:55.676 --> 0:18:58.556
<v Speaker 2>the driver and he can reply back to me in basade.

0:18:58.636 --> 0:19:00.236
<v Speaker 2>I see it in English on my side, he sees

0:19:00.276 --> 0:19:03.076
<v Speaker 2>it in basade my site. But the more important one

0:19:03.076 --> 0:19:05.556
<v Speaker 2>for me was like food menu translations. So we invested

0:19:05.676 --> 0:19:08.436
<v Speaker 2>quite a bit because whenever I go to Thailand and

0:19:08.476 --> 0:19:11.156
<v Speaker 2>then like I mean, I can't read any tie script obviously,

0:19:11.716 --> 0:19:14.156
<v Speaker 2>and I in the past I look at like some

0:19:14.276 --> 0:19:16.356
<v Speaker 2>things on the menu and I have no ideas that

0:19:16.476 --> 0:19:18.636
<v Speaker 2>like for me, always are worries that ultra spicy can

0:19:18.716 --> 0:19:21.196
<v Speaker 2>I eat it or not, and I didn't even know

0:19:21.196 --> 0:19:22.716
<v Speaker 2>what the item is, Like can look at the picture

0:19:22.756 --> 0:19:25.796
<v Speaker 2>and kind of guess, but sometimes merchants don't have pictures.

0:19:26.156 --> 0:19:29.196
<v Speaker 2>So we used AI to to translate all these menus

0:19:30.036 --> 0:19:32.196
<v Speaker 2>in all kinds of languages, so that that has been

0:19:32.196 --> 0:19:39.476
<v Speaker 2>a super impactful one as well. We'll be back in

0:19:39.596 --> 0:19:40.076
<v Speaker 2>just a minute.

0:19:50.076 --> 0:19:54.436
<v Speaker 1>So I'm curious about grab maps enterprise, right Like, you're

0:19:54.516 --> 0:19:59.516
<v Speaker 1>selling something related to your maps to big companies right

0:19:59.556 --> 0:20:02.116
<v Speaker 1>to to Microsoft, to Amazon and to government. It's like,

0:20:02.156 --> 0:20:03.796
<v Speaker 1>what tell me about that business?

0:20:04.356 --> 0:20:06.516
<v Speaker 2>That was quite an interesting one, right Like, we would

0:20:06.556 --> 0:20:08.796
<v Speaker 2>have never imagined early on when we built our maps

0:20:08.796 --> 0:20:11.916
<v Speaker 2>that this business will be created. But we've got people

0:20:11.996 --> 0:20:14.836
<v Speaker 2>like once we publicized our maps and people have seen

0:20:14.836 --> 0:20:17.476
<v Speaker 2>that they work generally quite well, we've got people approaching

0:20:17.516 --> 0:20:19.836
<v Speaker 2>us say hey, can we use them as well? And

0:20:19.876 --> 0:20:24.916
<v Speaker 2>that was like the genesis of the enterprise business. And

0:20:24.956 --> 0:20:29.196
<v Speaker 2>then we started working very closely with AWS where any

0:20:29.236 --> 0:20:31.836
<v Speaker 2>developer on their platform can use our maps now, so

0:20:31.876 --> 0:20:35.356
<v Speaker 2>we have over one hundred different developers already using it.

0:20:36.196 --> 0:20:38.316
<v Speaker 2>We partner with Microsoft, as he said, a bunch of

0:20:38.316 --> 0:20:42.596
<v Speaker 2>other large tech companies that in Southeast Asia started using

0:20:42.676 --> 0:20:45.076
<v Speaker 2>our maps because they've seen i mean, all these things

0:20:45.076 --> 0:20:47.596
<v Speaker 2>that I shared earlier that just really serves their needs

0:20:47.596 --> 0:20:50.116
<v Speaker 2>in Southeast Asia a lot better than anything else out there.

0:20:50.716 --> 0:20:53.756
<v Speaker 1>What's an example if something someone has built, you know,

0:20:53.836 --> 0:20:54.676
<v Speaker 1>on top of it.

0:20:55.076 --> 0:20:58.516
<v Speaker 2>A really cool startup that I've seen that used our maps.

0:20:58.916 --> 0:21:01.556
<v Speaker 2>What they do They go from merchant to merchant and

0:21:01.636 --> 0:21:04.436
<v Speaker 2>collect like the old oil that they use for cooking

0:21:04.476 --> 0:21:07.796
<v Speaker 2>and that basically recycle it. So they send somebody around

0:21:07.876 --> 0:21:11.036
<v Speaker 2>going to all these merchants collecting that. And in the past,

0:21:11.076 --> 0:21:13.356
<v Speaker 2>and a lot of these merchants are like small neighborhood

0:21:13.396 --> 0:21:15.956
<v Speaker 2>mom and pop shops in all these like little side

0:21:16.036 --> 0:21:18.276
<v Speaker 2>rolls and alleys, and that had a hard time for

0:21:18.356 --> 0:21:21.156
<v Speaker 2>the for the person going around collecting all of this

0:21:21.236 --> 0:21:24.356
<v Speaker 2>stuff finding that. And that's one of these examples where

0:21:24.396 --> 0:21:27.396
<v Speaker 2>they use grab maps to make the navigation of the

0:21:27.436 --> 0:21:29.916
<v Speaker 2>person going around and collecting all of that a lot

0:21:29.996 --> 0:21:32.396
<v Speaker 2>a lot easier. And there's many of these kind of

0:21:32.396 --> 0:21:36.156
<v Speaker 2>stories where people use it for things that that are

0:21:36.276 --> 0:21:38.876
<v Speaker 2>very similar in those kind of finding the last mile

0:21:38.916 --> 0:21:42.796
<v Speaker 2>has been really really hard before. So I'm curious.

0:21:43.876 --> 0:21:46.436
<v Speaker 1>What you're working on now, Like, Yeah, what what are

0:21:46.436 --> 0:21:47.876
<v Speaker 1>some of the things you're trying to figure out that

0:21:47.916 --> 0:21:48.996
<v Speaker 1>you haven't figured out yet.

0:21:49.756 --> 0:21:52.356
<v Speaker 2>I think the one thing that we're really passionate about

0:21:52.396 --> 0:21:55.196
<v Speaker 2>is solving more of the indoor problem. So that's one

0:21:55.196 --> 0:21:57.036
<v Speaker 2>thing that we're really mapping more so. We have a

0:21:57.236 --> 0:21:59.476
<v Speaker 2>decent amount of malls already map, but there's still so

0:21:59.556 --> 0:22:02.916
<v Speaker 2>much more to do. So hopefully we can find that.

0:22:02.996 --> 0:22:05.596
<v Speaker 2>Butever you go into these malls, we can exactly help

0:22:05.636 --> 0:22:08.876
<v Speaker 2>you to find whatever you're looking for, specific store or

0:22:09.196 --> 0:22:12.996
<v Speaker 2>general a general kind of like shop or so. So

0:22:13.236 --> 0:22:15.516
<v Speaker 2>that's that's one thing that I really want us to crack.

0:22:16.036 --> 0:22:18.956
<v Speaker 1>So it's the general shop idea that like, if I'm

0:22:18.996 --> 0:22:22.436
<v Speaker 1>just not working for grab, I'm just in the general

0:22:22.436 --> 0:22:24.436
<v Speaker 1>public and I'm like, I want to buy a pair

0:22:24.436 --> 0:22:27.076
<v Speaker 1>of shorts, I just type that into grab maps, and

0:22:27.076 --> 0:22:28.276
<v Speaker 1>grab maps tells me where to go.

0:22:28.436 --> 0:22:30.516
<v Speaker 2>Is that what you're thinking of there, Yes, but we

0:22:30.556 --> 0:22:32.636
<v Speaker 2>always think about it with a hyperlocal twist.

0:22:32.876 --> 0:22:33.236
<v Speaker 1>Yeah.

0:22:33.356 --> 0:22:35.956
<v Speaker 2>So, and what that means. As an example, let's say

0:22:36.036 --> 0:22:40.316
<v Speaker 2>in Indonesia, a large part of the population is Muslim,

0:22:40.436 --> 0:22:45.116
<v Speaker 2>which means they generally eat halal, and this is like

0:22:45.196 --> 0:22:47.356
<v Speaker 2>all the mapping platforms they don't support that. You can,

0:22:47.436 --> 0:22:50.236
<v Speaker 2>of course for every search for you can say restaurant halal,

0:22:50.316 --> 0:22:52.756
<v Speaker 2>this halal, this halal, but you cannot make it part

0:22:52.756 --> 0:22:55.396
<v Speaker 2>of your user profile. But it's it's like, it's not

0:22:55.516 --> 0:22:57.596
<v Speaker 2>it's not something that you switch every day. Well, like

0:22:57.716 --> 0:23:03.156
<v Speaker 2>either your preference is halal or is not. And and

0:23:03.196 --> 0:23:05.916
<v Speaker 2>a lot of the mapping platforms support putting in your profile.

0:23:05.996 --> 0:23:09.036
<v Speaker 2>I only want halal restaurants because I don't eat anything else.

0:23:09.596 --> 0:23:11.716
<v Speaker 2>And those are the kind of things that we see

0:23:11.716 --> 0:23:15.036
<v Speaker 2>in Southeast Asia that we need to really solve because again,

0:23:15.196 --> 0:23:18.756
<v Speaker 2>nobody else would solve those kind of problems. So capturing

0:23:18.796 --> 0:23:21.916
<v Speaker 2>this data accurately and knowing all these details, those are

0:23:21.956 --> 0:23:24.356
<v Speaker 2>the kind of things that we really put a lot

0:23:24.396 --> 0:23:25.116
<v Speaker 2>of emphasis on.

0:23:26.476 --> 0:23:29.996
<v Speaker 1>Are there technical problems you're working on, Like are there

0:23:30.036 --> 0:23:33.636
<v Speaker 1>things where you haven't figured out the right tech or

0:23:33.636 --> 0:23:36.316
<v Speaker 1>where you need to build something, or where AI models

0:23:36.316 --> 0:23:38.236
<v Speaker 1>aren't quite where they need to be, but you're trying

0:23:38.276 --> 0:23:39.556
<v Speaker 1>to push them.

0:23:39.836 --> 0:23:41.196
<v Speaker 2>I think what we are trying to do is what

0:23:41.236 --> 0:23:42.636
<v Speaker 2>I said earlier, like you want to have a real

0:23:42.676 --> 0:23:44.676
<v Speaker 2>time accurate model of the world.

0:23:44.876 --> 0:23:47.436
<v Speaker 1>Yeah, so let's talk about that. Like real time is

0:23:47.556 --> 0:23:51.396
<v Speaker 1>a crazy phrase in that context, right, Like when you

0:23:51.436 --> 0:23:54.236
<v Speaker 1>say real time a model of the world. What are

0:23:54.276 --> 0:23:54.796
<v Speaker 1>you thinking of?

0:23:55.676 --> 0:23:59.436
<v Speaker 2>Yeah, I mean a simple use case would be nowhere

0:23:59.476 --> 0:24:02.836
<v Speaker 2>there's parking right now? Yeah, not like where there's parking

0:24:02.916 --> 0:24:05.636
<v Speaker 2>in general, but I'd say a roadside parking. If you

0:24:05.836 --> 0:24:09.476
<v Speaker 2>had another crap card driving past and say, hey, fifteen

0:24:09.476 --> 0:24:12.476
<v Speaker 2>seconds ago there was a freak parking spot on the right,

0:24:12.876 --> 0:24:15.076
<v Speaker 2>that's that's extremely useful.

0:24:15.516 --> 0:24:18.116
<v Speaker 1>That is extremely as well that I know people in

0:24:18.156 --> 0:24:22.156
<v Speaker 1>Brooklyn and San Francisco who would love to have that functionality.

0:24:22.516 --> 0:24:25.156
<v Speaker 2>Yeah. I think for us, really the goal is always

0:24:25.356 --> 0:24:29.556
<v Speaker 2>what we know is like shaving off seconds of every delivery, right, Like,

0:24:29.636 --> 0:24:34.076
<v Speaker 2>that's what really really makes a delta. I think that

0:24:34.396 --> 0:24:37.116
<v Speaker 2>I really loved Steve Job's old mental model. But he

0:24:37.156 --> 0:24:39.996
<v Speaker 2>convinced people to make the macboot like ten seconds faster,

0:24:40.436 --> 0:24:42.676
<v Speaker 2>and he said like, well, if you take this, I

0:24:42.716 --> 0:24:44.876
<v Speaker 2>don't remember the exactly now mark for him, but he said, like,

0:24:45.076 --> 0:24:47.236
<v Speaker 2>if you make the macboot ten seconds fast and there's

0:24:47.236 --> 0:24:50.076
<v Speaker 2>fifty million people using it every year, you save like,

0:24:50.196 --> 0:24:52.796
<v Speaker 2>I don't know, it's like ten lifetimes of people's time

0:24:52.836 --> 0:24:55.676
<v Speaker 2>waiting for the Magic Boot something like that, and fast

0:24:55.756 --> 0:24:59.316
<v Speaker 2>right Like across I mean across I calculated to share

0:24:59.356 --> 0:25:02.156
<v Speaker 2>this always with a team. Across a billion deliveries, two

0:25:02.156 --> 0:25:05.036
<v Speaker 2>point five seconds saved across every delivery is roughly one

0:25:05.076 --> 0:25:08.356
<v Speaker 2>lifetime that you can save. So any second we can

0:25:08.396 --> 0:25:12.076
<v Speaker 2>shave off by getting cash to park faster, by getting

0:25:12.116 --> 0:25:15.276
<v Speaker 2>motorbikes to park faster, park at the right space, at

0:25:15.276 --> 0:25:17.436
<v Speaker 2>the right time. That's kind of the problems that we're

0:25:17.476 --> 0:25:18.676
<v Speaker 2>really passionate on solving.

0:25:19.276 --> 0:25:21.356
<v Speaker 1>How long does it take you to do a billion deliveries?

0:25:21.356 --> 0:25:23.676
<v Speaker 1>How is it a billion per per way?

0:25:24.156 --> 0:25:27.596
<v Speaker 2>We don't publish exact data, but it's the auder of

0:25:27.676 --> 0:25:29.436
<v Speaker 2>magnitude of it, like a year or less.

0:25:29.716 --> 0:25:34.316
<v Speaker 1>Oh wow, okay, that's a big number. So how do

0:25:34.356 --> 0:25:36.876
<v Speaker 1>you get real time parking data? I mean, so, is

0:25:36.916 --> 0:25:40.116
<v Speaker 1>the constraint now just getting more cameras to more drivers,

0:25:40.316 --> 0:25:42.396
<v Speaker 1>like the what's the rate limiting step?

0:25:42.956 --> 0:25:46.196
<v Speaker 2>Yeah? Great, great question. I think more cameras is one constraint.

0:25:46.196 --> 0:25:49.396
<v Speaker 2>The other constraint is doing all the smart processing on

0:25:49.436 --> 0:25:52.756
<v Speaker 2>the act because obviously you cannot upload all these data

0:25:53.036 --> 0:25:56.556
<v Speaker 2>because that would be extremely costly, and mobile networks in

0:25:56.556 --> 0:25:58.916
<v Speaker 2>Southeast Asia aren't quite that powerful that you could upload

0:25:58.956 --> 0:26:02.396
<v Speaker 2>millions of video streams to the cloud. So we're working

0:26:02.436 --> 0:26:04.436
<v Speaker 2>a lot on what is called like ATGYI that we

0:26:04.476 --> 0:26:07.676
<v Speaker 2>can run all these models that are powerful but not

0:26:07.716 --> 0:26:09.716
<v Speaker 2>in the cloud but on the app or at least

0:26:09.756 --> 0:26:10.796
<v Speaker 2>on a mix of boths.

0:26:11.276 --> 0:26:13.836
<v Speaker 1>So in this case, is the edge the actual camera?

0:26:13.956 --> 0:26:16.636
<v Speaker 1>I mean, what what is like is the dream the

0:26:16.636 --> 0:26:19.396
<v Speaker 1>camera itself is doing the work and just uploading something

0:26:19.516 --> 0:26:21.796
<v Speaker 1>very simple to the network.

0:26:21.796 --> 0:26:24.636
<v Speaker 2>That's in many places already happening. We already have quite

0:26:24.636 --> 0:26:27.796
<v Speaker 2>powerful AI chips in the cameras. But I mean, of course,

0:26:28.076 --> 0:26:30.596
<v Speaker 2>like no mobile phone, no edge camera right now is

0:26:30.596 --> 0:26:33.396
<v Speaker 2>as powerful as let's say CHET GPT with GBT four O,

0:26:33.756 --> 0:26:34.356
<v Speaker 2>that's sure.

0:26:34.436 --> 0:26:36.516
<v Speaker 1>I mean, you have this sort of spectrum where you

0:26:36.556 --> 0:26:39.556
<v Speaker 1>have a whatever one hundred million dollar data center on

0:26:39.556 --> 0:26:41.996
<v Speaker 1>one end and one hundred dollars camera on the other

0:26:42.116 --> 0:26:44.516
<v Speaker 1>and some things in between. Right, So what can you

0:26:44.596 --> 0:26:45.876
<v Speaker 1>do on the camera now?

0:26:46.716 --> 0:26:50.276
<v Speaker 2>We do things like privacy, so we blur people's faces,

0:26:50.316 --> 0:26:52.716
<v Speaker 2>we blow license plates because we never want to upload

0:26:52.716 --> 0:26:56.036
<v Speaker 2>this information. We do weather detection what I said earlier,

0:26:56.036 --> 0:26:58.916
<v Speaker 2>with rain, so we detect on the cameras if it's raining,

0:27:00.076 --> 0:27:02.716
<v Speaker 2>things like that. We detect traffic signs and see if

0:27:02.756 --> 0:27:06.396
<v Speaker 2>they've changed. So we actually already run large part of

0:27:06.436 --> 0:27:10.196
<v Speaker 2>processing on the edge and then only if something has changed,

0:27:10.476 --> 0:27:13.436
<v Speaker 2>then we upload some data into a validation on the server.

0:27:13.716 --> 0:27:15.756
<v Speaker 2>But that already allows us to reduce what we upload

0:27:15.796 --> 0:27:18.836
<v Speaker 2>by more than ninety percent to.

0:27:18.236 --> 0:27:20.036
<v Speaker 1>Just tell the server if something is different.

0:27:20.196 --> 0:27:22.396
<v Speaker 2>Basically, yeah, exactly.

0:27:22.956 --> 0:27:25.316
<v Speaker 1>So I know, you know, we've talked mostly about maps,

0:27:25.916 --> 0:27:28.516
<v Speaker 1>but obviously GREB is doing a lot of different things.

0:27:28.556 --> 0:27:31.116
<v Speaker 1>Are there other parts of the business that we should

0:27:31.116 --> 0:27:31.556
<v Speaker 1>talk about?

0:27:32.076 --> 0:27:34.676
<v Speaker 2>The business is so fascinating, so if I have time,

0:27:34.676 --> 0:27:36.476
<v Speaker 2>we should talk about all of our business.

0:27:36.516 --> 0:27:38.956
<v Speaker 1>Yes, just tell me what's one other sort of frontier,

0:27:39.036 --> 0:27:40.236
<v Speaker 1>one other thing you're working on.

0:27:41.196 --> 0:27:44.556
<v Speaker 2>I think the other thing which we've really invested deeply,

0:27:44.556 --> 0:27:47.476
<v Speaker 2>which is also quite closely connected to our maps, is

0:27:47.796 --> 0:27:50.276
<v Speaker 2>when we look at across the delivery journey. The other

0:27:50.316 --> 0:27:52.996
<v Speaker 2>part where a lot of time is spent is in

0:27:53.036 --> 0:27:57.716
<v Speaker 2>the merchant cooking and preparing the food. And that's an

0:27:57.756 --> 0:28:01.676
<v Speaker 2>area where you spend a lot of time optimizing together

0:28:01.716 --> 0:28:04.836
<v Speaker 2>with the merchants to make sure that the food is

0:28:04.836 --> 0:28:07.116
<v Speaker 2>prepared in the right time, that the driver arrives in

0:28:07.156 --> 0:28:09.436
<v Speaker 2>the right time. So you've done lots of lots of

0:28:09.476 --> 0:28:12.276
<v Speaker 2>cool things. So, for example, we've built a data science

0:28:12.316 --> 0:28:15.876
<v Speaker 2>model that accurately predicts at every time of the day

0:28:15.956 --> 0:28:18.516
<v Speaker 2>how long the merchant needs to prepare an order. So

0:28:18.556 --> 0:28:21.996
<v Speaker 2>we can detect the busyness of the merchant and say,

0:28:22.116 --> 0:28:23.836
<v Speaker 2>if we know all the merchant has gotten a lot

0:28:23.836 --> 0:28:27.756
<v Speaker 2>of orders, normally they take to prepare, They prepare to

0:28:28.276 --> 0:28:30.436
<v Speaker 2>bud me in like three minutes, but when they're very busy,

0:28:30.436 --> 0:28:33.716
<v Speaker 2>they take seven minutes. And the merchants don't want that

0:28:33.756 --> 0:28:36.796
<v Speaker 2>our drivers crowd their store and weigh in troves, So

0:28:36.796 --> 0:28:40.196
<v Speaker 2>we typically allocate the driver only when we know, okay,

0:28:40.236 --> 0:28:42.556
<v Speaker 2>the food is ready in seven minutes, and we don't

0:28:42.596 --> 0:28:45.236
<v Speaker 2>allocate the driver immediately, but we know all there's a

0:28:45.276 --> 0:28:48.276
<v Speaker 2>driver two minutes away, we just allocate them like five

0:28:48.316 --> 0:28:51.556
<v Speaker 2>minutes later so that he's in the shop just in time.

0:28:52.076 --> 0:28:54.276
<v Speaker 2>And that also cuts the delivery journey, makes a lot

0:28:54.316 --> 0:28:56.116
<v Speaker 2>more pleasant for the merchant not having like a lot

0:28:56.116 --> 0:28:59.196
<v Speaker 2>of people crowd their store, and allows us to offer

0:28:59.276 --> 0:29:01.636
<v Speaker 2>more affordable price to the consumer because they don't need

0:29:01.676 --> 0:29:03.556
<v Speaker 2>to pay for all these minutes of the driver waiting.

0:29:03.836 --> 0:29:07.196
<v Speaker 1>So just all these optimizations, all these different margins where

0:29:07.236 --> 0:29:11.236
<v Speaker 1>you can optimize. So if we think about whatever five years,

0:29:11.276 --> 0:29:13.156
<v Speaker 1>ten years out, when you think about this sort of

0:29:14.556 --> 0:29:18.636
<v Speaker 1>medium to long term like what's your dream, Like, how's

0:29:18.676 --> 0:29:19.596
<v Speaker 1>it work? What's going on?

0:29:20.716 --> 0:29:22.636
<v Speaker 2>I think like for me, the world is changing so fast.

0:29:22.716 --> 0:29:24.996
<v Speaker 2>Five to ten years prediction is really really hard with

0:29:25.036 --> 0:29:26.396
<v Speaker 2>all the AI advancements.

0:29:26.796 --> 0:29:27.676
<v Speaker 1>How far you want to go?

0:29:27.796 --> 0:29:28.876
<v Speaker 2>Two years? Four years?

0:29:28.916 --> 0:29:30.596
<v Speaker 1>You tell me, like what you just give me a

0:29:30.676 --> 0:29:31.556
<v Speaker 1>dream for the future.

0:29:31.876 --> 0:29:34.396
<v Speaker 2>I mean the obvious one that I'm very passionate about

0:29:34.476 --> 0:29:38.036
<v Speaker 2>is robotics that make so much sense in our marketplace.

0:29:39.356 --> 0:29:42.356
<v Speaker 2>So if you can add robotics to our marketplace, that

0:29:42.676 --> 0:29:46.276
<v Speaker 2>will be a huge, huge change and something we're actively

0:29:46.316 --> 0:29:48.916
<v Speaker 2>working on to make happen. So that's I think probably

0:29:48.956 --> 0:29:50.956
<v Speaker 2>the thing I'm most passionate about to change.

0:29:51.396 --> 0:29:53.836
<v Speaker 1>Tell me what you're actively working on with robotics.

0:29:54.316 --> 0:29:57.356
<v Speaker 2>We haven't shared much which we do on the delivery side,

0:29:57.916 --> 0:30:01.196
<v Speaker 2>but for example, I mean things like autonomous cars. We've

0:30:01.236 --> 0:30:03.876
<v Speaker 2>signed an agreement with a bunch of like autonomous car

0:30:04.036 --> 0:30:07.036
<v Speaker 2>providers to come with us to Singapore and so on,

0:30:07.396 --> 0:30:09.116
<v Speaker 2>so we'll do a bunch of things in that space.

0:30:09.596 --> 0:30:12.156
<v Speaker 2>But basically you can imagine that it will be in many,

0:30:12.196 --> 0:30:13.876
<v Speaker 2>many parts of our delivery chain.

0:30:14.196 --> 0:30:16.796
<v Speaker 1>You need to build an autonomous scooter based on what

0:30:16.836 --> 0:30:19.116
<v Speaker 1>you've told me, right, I feel like the analogy to

0:30:19.156 --> 0:30:21.436
<v Speaker 1>the map story is the autonomous scooter that can go

0:30:21.516 --> 0:30:22.476
<v Speaker 1>down all the alleyways.

0:30:22.836 --> 0:30:24.236
<v Speaker 2>Great, if you ever want to have a job in

0:30:24.276 --> 0:30:28.796
<v Speaker 2>our product team, to join us, build autonomous schooters with us,

0:30:28.796 --> 0:30:30.836
<v Speaker 2>because you're exactly right, but you need to build products

0:30:30.836 --> 0:30:33.436
<v Speaker 2>that work in our region. So that's exactly the right

0:30:33.476 --> 0:30:35.956
<v Speaker 2>mindset that we're trying, like what we say always and

0:30:36.036 --> 0:30:38.156
<v Speaker 2>we want to build hyper local products that work in

0:30:38.196 --> 0:30:41.236
<v Speaker 2>Southeast Asia.

0:30:42.716 --> 0:30:55.476
<v Speaker 1>We'll be back in a minute with the lighting round. Okay,

0:30:55.796 --> 0:30:58.636
<v Speaker 1>let's finish with the lightning round. So I read on

0:30:58.716 --> 0:31:03.716
<v Speaker 1>your bio you write that you mostly travel between Singapore,

0:31:03.876 --> 0:31:09.636
<v Speaker 1>San Francisco, Berlin and Cluge. Tell me about Clue the

0:31:09.676 --> 0:31:12.116
<v Speaker 1>other three I'm familiar with Clues. I don't know anything

0:31:12.156 --> 0:31:14.316
<v Speaker 1>about How does that wind up on that list?

0:31:14.516 --> 0:31:18.716
<v Speaker 2>As a city in Romania, in the heart of Transylvania actually,

0:31:19.596 --> 0:31:22.076
<v Speaker 2>and we have a we have a small engineering center

0:31:22.116 --> 0:31:25.756
<v Speaker 2>there in our maps team, So I've spent a lot

0:31:25.796 --> 0:31:28.276
<v Speaker 2>of time there because for my own startup, that's that's

0:31:28.276 --> 0:31:31.516
<v Speaker 2>how I wound up Inclusure. Originally for my startup, Scobbler,

0:31:31.556 --> 0:31:34.396
<v Speaker 2>which was a maps and navigation startup. I built a

0:31:34.716 --> 0:31:38.316
<v Speaker 2>engineering team and cluge, so I've been going to Clusion

0:31:38.876 --> 0:31:42.516
<v Speaker 2>working with engineers there for the last fifteen years or so.

0:31:42.796 --> 0:31:47.716
<v Speaker 2>What's clue Like, it's fun. It's a student town, so high,

0:31:47.796 --> 0:31:52.356
<v Speaker 2>high energy, young population, very smart, the computer science department

0:31:52.436 --> 0:31:54.636
<v Speaker 2>very good. They used to clone the IBM mainframes for

0:31:54.676 --> 0:31:57.916
<v Speaker 2>the Soviet Union, so a bunch of hardcore engineers.

0:31:58.476 --> 0:32:01.836
<v Speaker 1>You also write in that bio you wrote in Berlin,

0:32:01.956 --> 0:32:06.196
<v Speaker 1>I experienced some of the best parties ever. Tell me

0:32:06.236 --> 0:32:07.316
<v Speaker 1>about a Berlin party.

0:32:08.076 --> 0:32:11.116
<v Speaker 2>That was prior line, That was before I moved to

0:32:11.356 --> 0:32:14.876
<v Speaker 2>before I moved to Southeast Asia. But Berlin was a fun, fun,

0:32:14.996 --> 0:32:17.716
<v Speaker 2>fun journey for I lived there for five six years.

0:32:17.756 --> 0:32:21.796
<v Speaker 1>Maybe he didn't tell me anything about any party. So

0:32:22.196 --> 0:32:24.116
<v Speaker 1>and then you also say you lived and worked in

0:32:24.156 --> 0:32:27.756
<v Speaker 1>Singapore and Berlin and San Francisco, and I'm curious, like,

0:32:27.836 --> 0:32:32.276
<v Speaker 1>how is sort of whatever professional culture, work life different

0:32:32.356 --> 0:32:34.956
<v Speaker 1>in those places. What are the sort of striking differences?

0:32:35.396 --> 0:32:38.556
<v Speaker 2>I mean, Germany is extremely direct, but like that's where

0:32:38.556 --> 0:32:42.596
<v Speaker 2>I'm originally from. I'm originally German, and Germans are known

0:32:42.636 --> 0:32:47.116
<v Speaker 2>to be super super direct, which in Southeast Asia doesn't

0:32:47.116 --> 0:32:50.476
<v Speaker 2>work super well. If they're not accustomed to it. So

0:32:50.556 --> 0:32:52.156
<v Speaker 2>I think for me, the thing that I needed to

0:32:52.196 --> 0:32:56.756
<v Speaker 2>adjust most is to become a lot more indirect, to

0:32:56.836 --> 0:33:00.716
<v Speaker 2>become a lot more like have those conversations and like

0:33:00.836 --> 0:33:04.436
<v Speaker 2>smaller groups, not in front of everybody. So I definitely

0:33:04.436 --> 0:33:08.556
<v Speaker 2>had to just my style quite a lot when moving around.

0:33:08.636 --> 0:33:11.036
<v Speaker 2>But I found that always incredibly fun. I always loved

0:33:11.076 --> 0:33:15.516
<v Speaker 2>learning about new cultures, So after some adjustments, I really

0:33:15.596 --> 0:33:16.436
<v Speaker 2>really like it here.

0:33:16.916 --> 0:33:21.236
<v Speaker 1>That is super interesting. Was there, as you were saying

0:33:21.276 --> 0:33:24.036
<v Speaker 1>that I was wondering, like, was there a particular moment

0:33:24.956 --> 0:33:29.476
<v Speaker 1>when you realized, Oh, I'm not behaving correctly in this

0:33:29.516 --> 0:33:30.556
<v Speaker 1>cultural context?

0:33:31.756 --> 0:33:34.236
<v Speaker 2>Yeah, I mean this predate grap So this was when

0:33:34.276 --> 0:33:37.316
<v Speaker 2>I was first doing business in China. So I sold

0:33:37.316 --> 0:33:39.996
<v Speaker 2>my startup to a Silicon Valley company, but I also

0:33:40.036 --> 0:33:42.396
<v Speaker 2>got a two hundred people team in China report to me,

0:33:42.436 --> 0:33:44.556
<v Speaker 2>which was the first time that I ever managed a

0:33:44.596 --> 0:33:50.396
<v Speaker 2>team in China, and I was extremely surprised why they

0:33:50.556 --> 0:33:52.916
<v Speaker 2>wouldn't tell me about all the mistakes, all the things

0:33:52.916 --> 0:33:55.516
<v Speaker 2>that I did wrong. And I said, like, that's very unusual.

0:33:55.596 --> 0:33:59.876
<v Speaker 2>Normally I get a lot of like pushback from engineers.

0:34:00.396 --> 0:34:03.756
<v Speaker 2>And then I started like asking people is why don't

0:34:03.796 --> 0:34:05.516
<v Speaker 2>they do this? And then people said like, well, it's

0:34:05.596 --> 0:34:08.476
<v Speaker 2>kind of root in a public forum to say the

0:34:08.516 --> 0:34:13.156
<v Speaker 2>boss's wrong. And then I realized, like, oh, that's that's

0:34:13.156 --> 0:34:16.356
<v Speaker 2>why I just need to like change how I'm asking questions,

0:34:16.436 --> 0:34:19.716
<v Speaker 2>how I'm managing teams. And that's when I first managed

0:34:19.756 --> 0:34:22.596
<v Speaker 2>teams in China. Took me quite a while to figure out, honestly.

0:34:22.516 --> 0:34:26.516
<v Speaker 1>And how does San Francisco fit in relation to both

0:34:26.796 --> 0:34:31.596
<v Speaker 1>working in Berlin and working in you know, China and Singapore.

0:34:31.756 --> 0:34:33.916
<v Speaker 1>Where's the US on that continuum?

0:34:34.916 --> 0:34:36.876
<v Speaker 2>Do you ask for me? I think the thing that

0:34:36.916 --> 0:34:40.636
<v Speaker 2>I really loved in San Francisco was just the craziness

0:34:40.636 --> 0:34:44.316
<v Speaker 2>of ambition. When I spent like April back in in

0:34:44.516 --> 0:34:47.236
<v Speaker 2>SF four month to like vibe code and hack on

0:34:47.236 --> 0:34:49.636
<v Speaker 2>a bunch of hobby projects, and like you go in

0:34:49.716 --> 0:34:51.756
<v Speaker 2>every random coffee shop and there's somebody who said they

0:34:51.836 --> 0:34:55.276
<v Speaker 2>work on a billion dollar idea and they they're just starting,

0:34:55.516 --> 0:34:58.076
<v Speaker 2>they have nothing yet they're just like, Okay, I'm going

0:34:58.156 --> 0:35:01.836
<v Speaker 2>to make it big. So I think that that ambition

0:35:02.236 --> 0:35:05.436
<v Speaker 2>and that willingness to openly declare it even if people

0:35:05.436 --> 0:35:08.076
<v Speaker 2>know it's super unlikely. There's not thousands of people who

0:35:08.116 --> 0:35:10.796
<v Speaker 2>start billion dollar company, but in the Bay Area that's

0:35:10.836 --> 0:35:13.476
<v Speaker 2>thousands of people who say they will and this conviction

0:35:13.796 --> 0:35:16.716
<v Speaker 2>and this like optimism. I think that was for me

0:35:16.796 --> 0:35:20.036
<v Speaker 2>one of the most striking things in the US, and

0:35:20.076 --> 0:35:22.116
<v Speaker 2>that I still love the energy whenever I go there.

0:35:22.156 --> 0:35:32.556
<v Speaker 3>Basically, Philip Condall is the chief product officer at GRAB.

0:35:33.796 --> 0:35:34.836
<v Speaker 2>Please email us.

0:35:34.716 --> 0:35:38.116
<v Speaker 1>At problem at Pushkin dot fm. We are always looking

0:35:38.156 --> 0:35:42.196
<v Speaker 1>for new guests for the show. Today's show was produced

0:35:42.196 --> 0:35:45.636
<v Speaker 1>by Trinamanino and Gabriel Hunter Chang. It was edited by

0:35:45.676 --> 0:35:50.076
<v Speaker 1>Alexander Garreton and engineered by Sarah Bruguer. I'm Jacob Goldstein

0:35:50.076 --> 0:35:52.156
<v Speaker 1>and we'll be back next week with another episode of

0:35:52.156 --> 0:36:02.756
<v Speaker 1>What's Your Problem.