1 00:00:15,316 --> 00:00:24,076 Speaker 1: Pushkin. For a while there, the discourse was full of 2 00:00:24,316 --> 00:00:27,836 Speaker 1: self driving cars. They were right around the corner. Then 3 00:00:27,876 --> 00:00:30,476 Speaker 1: they were not right around the corner. Now we have 4 00:00:30,596 --> 00:00:34,076 Speaker 1: robotaxis in a few places, but not in most places, 5 00:00:34,476 --> 00:00:39,436 Speaker 1: And for normal cars, autopilot does not really mean autopilot. 6 00:00:40,196 --> 00:00:43,356 Speaker 1: In all this binary will they or won't they drive 7 00:00:43,436 --> 00:00:46,596 Speaker 1: themselves talk? We've lost sight of something. Lots of people 8 00:00:46,636 --> 00:00:50,716 Speaker 1: are figuring out big, interesting ways to bring new technologies 9 00:00:50,756 --> 00:00:54,556 Speaker 1: to cars, even before we get to self driving cars, 10 00:00:54,916 --> 00:00:57,316 Speaker 1: which looks like at this point it could take a while. 11 00:00:57,596 --> 00:01:01,396 Speaker 1: These new technologies could save thousands and thousands of lives. 12 00:01:05,756 --> 00:01:08,156 Speaker 1: I'm Jacob Goldstein and this is What's Your Problem, the 13 00:01:08,196 --> 00:01:10,476 Speaker 1: show where I talk to people who are trying to 14 00:01:10,516 --> 00:01:14,836 Speaker 1: make technological progress. My guest today is Austin Russell. He's 15 00:01:14,876 --> 00:01:17,556 Speaker 1: the founder of Luminar, a company that is developing a 16 00:01:17,596 --> 00:01:21,676 Speaker 1: technology called lidar. Austin's problem is this, how do you 17 00:01:21,676 --> 00:01:24,956 Speaker 1: make lidar cheap enough and good enough to use in 18 00:01:25,116 --> 00:01:31,156 Speaker 1: millions of cars. LDAR stands for light detection and ranging. 19 00:01:31,356 --> 00:01:35,116 Speaker 1: It's kind of a technological sibling of sonar and radar. 20 00:01:35,676 --> 00:01:38,796 Speaker 1: A lightar system makes a detailed map of its surroundings 21 00:01:38,876 --> 00:01:42,836 Speaker 1: by sending out pulses of light and analyzing the reflections 22 00:01:42,836 --> 00:01:46,636 Speaker 1: that come back. Luminar's LDAR system will come standard on 23 00:01:46,676 --> 00:01:50,076 Speaker 1: a new Volvo SUV that's going into production later this year. 24 00:01:50,476 --> 00:01:53,956 Speaker 1: The company also has deals with Mercedes Benz and with Saic, 25 00:01:54,316 --> 00:01:57,636 Speaker 1: a Chinese car company. Austin says LDAR can do a 26 00:01:57,676 --> 00:02:00,836 Speaker 1: lot for cars even before we get to full autonomy. 27 00:02:01,356 --> 00:02:05,076 Speaker 1: It's not about replacing the driver, it's about enhancing the driver. 28 00:02:05,356 --> 00:02:07,876 Speaker 1: I think this is a really really important part around 29 00:02:07,916 --> 00:02:10,276 Speaker 1: what we're doing is they we're not trying to do 30 00:02:10,356 --> 00:02:14,516 Speaker 1: some moonshot of replacing the driver altogether and having are 31 00:02:14,556 --> 00:02:17,916 Speaker 1: welcoming our robotaxi overlords in a city near you like 32 00:02:17,996 --> 00:02:20,596 Speaker 1: that's actually a very very challenging problem out on its own. 33 00:02:20,836 --> 00:02:24,636 Speaker 1: Our goal is about enhancing the driver, making cars dramatically safer, 34 00:02:24,796 --> 00:02:26,716 Speaker 1: going back to the original vision around what all this 35 00:02:26,716 --> 00:02:28,916 Speaker 1: stuff was supposed to be in the first place, except 36 00:02:28,916 --> 00:02:31,516 Speaker 1: actually making it happen. The reality is is that right now, 37 00:02:31,836 --> 00:02:34,236 Speaker 1: you know, as many as one and a half million 38 00:02:34,276 --> 00:02:37,836 Speaker 1: people have their lives lost on the road every year 39 00:02:38,036 --> 00:02:40,156 Speaker 1: as a result of vehicle accidents. And this is something 40 00:02:40,196 --> 00:02:43,076 Speaker 1: that's totally preventable for that matter. Like you know, it 41 00:02:43,156 --> 00:02:45,436 Speaker 1: sounds like a basic problem, but just don't let your 42 00:02:45,476 --> 00:02:47,156 Speaker 1: car smash into the thing right in front of you. 43 00:02:47,676 --> 00:02:50,436 Speaker 1: That's how you prevent most accidents. Turns out that's actually 44 00:02:50,676 --> 00:02:52,556 Speaker 1: not as straightforward as a problem as you think, and 45 00:02:52,836 --> 00:02:54,836 Speaker 1: to be able to have the confidence to you know, 46 00:02:55,396 --> 00:02:57,636 Speaker 1: fully take over from the driver and those kinds of 47 00:02:57,636 --> 00:03:00,996 Speaker 1: situations is not an easy thing. So that's where the 48 00:03:01,036 --> 00:03:02,756 Speaker 1: lighter is coming into play for this kind of high 49 00:03:02,756 --> 00:03:05,236 Speaker 1: performance lightar that gives you confidence to do that and 50 00:03:05,476 --> 00:03:07,356 Speaker 1: can enable the car to take over the breaking system 51 00:03:07,356 --> 00:03:08,836 Speaker 1: and steering wheel to get you out of those kinds 52 00:03:08,876 --> 00:03:12,396 Speaker 1: of situations. Austin has been working on this problem for 53 00:03:12,436 --> 00:03:15,596 Speaker 1: a long time. He founded Luminar back in twenty twelve. 54 00:03:15,996 --> 00:03:19,876 Speaker 1: At the time, Austin was just seventeen years old. Also, 55 00:03:19,996 --> 00:03:22,596 Speaker 1: at the time, the use of LDAR in cars was, 56 00:03:22,836 --> 00:03:25,756 Speaker 1: like Austin, barely out of its infancy. You take a 57 00:03:25,796 --> 00:03:27,556 Speaker 1: look back in the day, you have these hundred thousand 58 00:03:27,556 --> 00:03:29,676 Speaker 1: dollars fitting systems, you know, out of the roof of 59 00:03:29,716 --> 00:03:32,636 Speaker 1: these you know, test cars that are out there, and 60 00:03:33,596 --> 00:03:35,956 Speaker 1: you know, generally low performance, you know, not something that's 61 00:03:35,956 --> 00:03:37,556 Speaker 1: nearly as high performance is what it can be. So 62 00:03:37,596 --> 00:03:39,436 Speaker 1: the moment you're starting to think about this is that 63 00:03:39,516 --> 00:03:41,916 Speaker 1: moment when like what we kind of think of as 64 00:03:41,996 --> 00:03:44,396 Speaker 1: like the LDAR on the roof of whatever, the early 65 00:03:44,476 --> 00:03:47,076 Speaker 1: like self driving Google vans or something like this great 66 00:03:47,076 --> 00:03:49,556 Speaker 1: big thing and it costs one hundred thousand dollars. I 67 00:03:49,596 --> 00:03:52,596 Speaker 1: mean it feels sort of analogous to the like vacuum 68 00:03:52,636 --> 00:03:55,516 Speaker 1: tube computers of the like nineteen forties fifties. I mean, 69 00:03:55,636 --> 00:03:57,476 Speaker 1: is that the right way to be thinking about it? Like, Oh, 70 00:03:57,476 --> 00:04:00,156 Speaker 1: it's this giant thing. It doesn't work that well, but 71 00:04:00,196 --> 00:04:03,556 Speaker 1: it's clear that somebody should be able to figure out 72 00:04:03,556 --> 00:04:07,236 Speaker 1: how to make it smaller and cheaper and better. Absolutely, yeah, 73 00:04:07,276 --> 00:04:08,796 Speaker 1: I mean the key thing is is that you could 74 00:04:08,796 --> 00:04:11,236 Speaker 1: see some times of things up to one hundred meters 75 00:04:11,276 --> 00:04:13,156 Speaker 1: out with those systems. But the hard part was is 76 00:04:13,196 --> 00:04:16,516 Speaker 1: seeing you know, low reflectivity objects like like dark objects 77 00:04:16,556 --> 00:04:18,716 Speaker 1: like a like a plaque car, a tire on the road, 78 00:04:18,756 --> 00:04:21,436 Speaker 1: at person in dark clothing and anything begin to is 79 00:04:21,516 --> 00:04:23,076 Speaker 1: very very difficult. So you set out to make this 80 00:04:23,156 --> 00:04:29,076 Speaker 1: thing smaller and cheaper, like about two orders of magnitude cheaper, right, 81 00:04:29,116 --> 00:04:31,276 Speaker 1: You got to drive down the price, like it's got 82 00:04:31,276 --> 00:04:34,156 Speaker 1: to customs of what it costs, and it's got to 83 00:04:34,156 --> 00:04:36,916 Speaker 1: be better. Right, that's that's that's what you're setting out 84 00:04:36,916 --> 00:04:38,956 Speaker 1: to do. It's got to be tenets better at one 85 00:04:39,556 --> 00:04:43,556 Speaker 1: of the cost. Yes, very good classics tech problem a day. 86 00:04:43,556 --> 00:04:47,636 Speaker 1: It's just hard because it's hardware. Um. So, so I mean, 87 00:04:47,676 --> 00:04:51,516 Speaker 1: tell me a little bit about about doing that, right, Like, 88 00:04:51,596 --> 00:04:53,676 Speaker 1: surely there were moments when things weren't working, when you 89 00:04:53,756 --> 00:04:56,196 Speaker 1: thought it was harder than it seemed. Tell me tell 90 00:04:56,196 --> 00:05:00,876 Speaker 1: me about one of those moments. Yeah. Absolutely, Um I 91 00:05:00,916 --> 00:05:04,756 Speaker 1: would say, by the way, it's actually absurdly difficult. I 92 00:05:04,796 --> 00:05:08,396 Speaker 1: wouldn't necessarily recommend it for anyone there too, just because 93 00:05:09,236 --> 00:05:11,516 Speaker 1: in this case it's a convergence of multiple things all 94 00:05:11,516 --> 00:05:14,156 Speaker 1: at the same time. As you pointed out, hardware is 95 00:05:14,156 --> 00:05:17,676 Speaker 1: hard already on its own, very very challenging. There's two 96 00:05:17,676 --> 00:05:21,076 Speaker 1: other aspects of it are unique. There's the hardware. There's 97 00:05:21,116 --> 00:05:23,916 Speaker 1: the fact that we're building crazy lasers and you know, 98 00:05:24,036 --> 00:05:27,476 Speaker 1: and receivers and new custom chips that go into the systems. 99 00:05:27,476 --> 00:05:29,756 Speaker 1: This is not something that you're using official parts like, 100 00:05:29,756 --> 00:05:34,196 Speaker 1: you're creating new kinds of you know, three five special materials. 101 00:05:34,316 --> 00:05:37,196 Speaker 1: You know that are not you know, your everyday silicon, 102 00:05:37,276 --> 00:05:38,596 Speaker 1: you know that you can just be able to go 103 00:05:38,676 --> 00:05:41,116 Speaker 1: and use and fab with. So there's a lot of 104 00:05:41,276 --> 00:05:43,276 Speaker 1: very special things that go into this that we had 105 00:05:43,276 --> 00:05:47,196 Speaker 1: to truly innovate from the physics up. So there's that part, 106 00:05:47,236 --> 00:05:49,756 Speaker 1: and then there's also the automotive side of it, which 107 00:05:50,036 --> 00:05:52,516 Speaker 1: by the way, is absurdly difficult out on its own. 108 00:05:52,596 --> 00:05:54,956 Speaker 1: You know, just to be able to create something for 109 00:05:55,556 --> 00:05:58,956 Speaker 1: automotive series production isn't by no means an easy task 110 00:05:59,036 --> 00:06:03,156 Speaker 1: at all, high regulatory barriers, has to be super reliable, 111 00:06:03,756 --> 00:06:06,636 Speaker 1: has to be super scalable. Yeah. Yeah. But the thing 112 00:06:06,796 --> 00:06:09,756 Speaker 1: is that these are long design side goals here too. 113 00:06:09,756 --> 00:06:12,436 Speaker 1: I mean you're talking about you know, five plus years 114 00:06:12,476 --> 00:06:14,276 Speaker 1: of planning, you know, to go into it before where 115 00:06:14,476 --> 00:06:16,156 Speaker 1: we're going to see the light of day in a car, 116 00:06:16,356 --> 00:06:18,556 Speaker 1: and you know, very serious stuff, and you're and you're 117 00:06:18,596 --> 00:06:20,676 Speaker 1: talking also probably the better part of a billion dollars 118 00:06:20,716 --> 00:06:22,516 Speaker 1: worth of investment to be able to get there if 119 00:06:22,516 --> 00:06:24,876 Speaker 1: you want to actually get into a production car. Good. 120 00:06:24,956 --> 00:06:27,676 Speaker 1: So I am persuaded that it's really hard that you're 121 00:06:27,676 --> 00:06:30,756 Speaker 1: setting out to do this very very hard thing. Yeah, 122 00:06:30,796 --> 00:06:34,396 Speaker 1: I mean I can imagine there must have been multiple 123 00:06:34,756 --> 00:06:39,356 Speaker 1: difficult moments, moments when some didn't work. Tell me, tell 124 00:06:39,396 --> 00:06:45,636 Speaker 1: me one of those. Oh, that's probablys of like, you know, 125 00:06:45,956 --> 00:06:49,556 Speaker 1: hundreds of things, but you know you don't in the 126 00:06:49,636 --> 00:06:52,636 Speaker 1: earlier stages and something else, especially you know for some 127 00:06:53,196 --> 00:06:55,516 Speaker 1: crazy sixteen or seventeen year old at the time you know, 128 00:06:55,756 --> 00:06:58,436 Speaker 1: first started out, you know that not everyone was always 129 00:06:58,436 --> 00:07:00,836 Speaker 1: on the same page. You know, they're too or even 130 00:07:01,076 --> 00:07:04,036 Speaker 1: understood half the things I was saying, or ninety percent 131 00:07:04,036 --> 00:07:06,076 Speaker 1: of the things I was saying. I mean, especially in 132 00:07:06,116 --> 00:07:09,636 Speaker 1: the very early days. I mean, architecturally, tried every different 133 00:07:09,636 --> 00:07:13,116 Speaker 1: type of combination of components, of wavelength of lights, of 134 00:07:13,236 --> 00:07:16,516 Speaker 1: you know, semiconductor materials, of everything to be able to 135 00:07:16,556 --> 00:07:19,156 Speaker 1: do this, and I think we actually counted, Yeah, it 136 00:07:19,196 --> 00:07:20,996 Speaker 1: was over two thousand. I think it ended up being 137 00:07:20,996 --> 00:07:25,156 Speaker 1: four thousand different ways of failing before you could actually 138 00:07:25,196 --> 00:07:28,756 Speaker 1: have the right combination of components that can solve all 139 00:07:28,756 --> 00:07:30,676 Speaker 1: of these different problems that you're trying to solve. For 140 00:07:31,036 --> 00:07:32,596 Speaker 1: what does it even mean to say you're trying four 141 00:07:32,596 --> 00:07:35,636 Speaker 1: thousand different things. That's a lot of different things. It's 142 00:07:35,836 --> 00:07:37,596 Speaker 1: it's a lot of different things. I mean, in some 143 00:07:37,636 --> 00:07:39,876 Speaker 1: of those cases we were prototyping it. In most of 144 00:07:39,876 --> 00:07:42,236 Speaker 1: those cases, it was it was more on paper or 145 00:07:42,276 --> 00:07:45,036 Speaker 1: in simulation, and you know, it turns out the really 146 00:07:45,476 --> 00:07:48,916 Speaker 1: presumably to some extent, you're putting things on cars, you're 147 00:07:48,916 --> 00:07:52,236 Speaker 1: trying things. You're putting Jane prototypes on type of cars 148 00:07:52,236 --> 00:07:57,036 Speaker 1: and driving around the parking lot or whatever. Yeah, totally, totally, yeah, exactly. 149 00:07:57,116 --> 00:07:59,116 Speaker 1: I think the first system that you have together, you know, 150 00:07:59,196 --> 00:08:01,316 Speaker 1: took up a whole trunk, you know, of the car, 151 00:08:01,436 --> 00:08:03,476 Speaker 1: just to be able to prove out the concept. By 152 00:08:03,516 --> 00:08:06,156 Speaker 1: the way, when you first put together these prototypes, you know, 153 00:08:06,196 --> 00:08:08,996 Speaker 1: without your own custom components in it, there too still 154 00:08:09,196 --> 00:08:11,836 Speaker 1: would blow up. You'd have you know, receivers that would 155 00:08:11,836 --> 00:08:14,876 Speaker 1: like suddenly fry themselves. When you say blow up, do 156 00:08:14,916 --> 00:08:18,676 Speaker 1: you mean literally or metaphorically? Oh no, literally literally something 157 00:08:18,836 --> 00:08:20,836 Speaker 1: blew up in the trunk of your car or something. 158 00:08:20,956 --> 00:08:23,076 Speaker 1: Or tell me tell me about something blowing up. That's 159 00:08:23,116 --> 00:08:26,476 Speaker 1: what I wanted. Yeah, well, I mean you used to 160 00:08:26,556 --> 00:08:29,236 Speaker 1: have it where you know, the office shelf receivers, you know, 161 00:08:29,316 --> 00:08:32,076 Speaker 1: before we building our own custom receivers, they would end up, 162 00:08:32,796 --> 00:08:36,476 Speaker 1: you know, they couldn't handle the amount of the energy 163 00:08:36,516 --> 00:08:38,876 Speaker 1: from the laser pulse and they would just fry themselves, 164 00:08:38,956 --> 00:08:40,676 Speaker 1: you know, so it would be it would be stuff 165 00:08:40,716 --> 00:08:44,756 Speaker 1: like that. Um, talk about problem and talk about trying 166 00:08:44,796 --> 00:08:47,276 Speaker 1: to do a you know, a demo ten years ago. 167 00:08:47,316 --> 00:08:49,556 Speaker 1: There two of what you're of, what you're putting together, 168 00:08:49,596 --> 00:08:52,676 Speaker 1: and then next thing, you know, your your your receivers pride. 169 00:08:53,276 --> 00:08:57,276 Speaker 1: So that's that's smoking smoke coming out of the trunk. Yeah, yeah, yeah, yeah, 170 00:08:57,276 --> 00:09:00,316 Speaker 1: exactly why why did Chip let the smoke out? You know? Yeah, 171 00:09:01,596 --> 00:09:04,436 Speaker 1: stay inside? It was like a mad scientist lab that 172 00:09:04,556 --> 00:09:05,916 Speaker 1: we had there, you know, I mean it was it 173 00:09:05,996 --> 00:09:08,116 Speaker 1: was the real deal. So so okay, how do you 174 00:09:08,156 --> 00:09:10,356 Speaker 1: go from you know, things blowing up in the lab 175 00:09:10,756 --> 00:09:13,596 Speaker 1: to a system that actually works where it's cheap enough 176 00:09:13,636 --> 00:09:16,236 Speaker 1: to put on regular cars. The key was, you know, 177 00:09:16,276 --> 00:09:18,796 Speaker 1: reducing the number of components you know, in the device. 178 00:09:18,916 --> 00:09:22,596 Speaker 1: Normally you require these you know, hundred laser hundred receiver 179 00:09:22,756 --> 00:09:26,036 Speaker 1: arrays you know to build you know, something that has 180 00:09:26,076 --> 00:09:28,676 Speaker 1: any reasonable level performance. You know. Basically found a way 181 00:09:28,676 --> 00:09:30,356 Speaker 1: to be able to do that using only a single 182 00:09:30,396 --> 00:09:33,596 Speaker 1: laser in single receiver as opposed to requiring one hundred 183 00:09:33,636 --> 00:09:36,436 Speaker 1: or hundreds. And that was part of the magic. But 184 00:09:36,556 --> 00:09:38,676 Speaker 1: we had to end up creating the most you know, 185 00:09:38,716 --> 00:09:42,196 Speaker 1: special and powerful, you know, and most sensitive receiver of 186 00:09:42,196 --> 00:09:44,716 Speaker 1: its kind in the world, and these special custom chips 187 00:09:44,716 --> 00:09:46,996 Speaker 1: and a very special laser and all these things to 188 00:09:46,996 --> 00:09:50,956 Speaker 1: make that all possible. Is that the key innovation, for 189 00:09:51,076 --> 00:09:55,116 Speaker 1: lack of a better word, is figuring out how to 190 00:09:55,436 --> 00:10:02,196 Speaker 1: get more range and sensitivity with fewer lasers and receivers. Yeah, 191 00:10:02,236 --> 00:10:06,276 Speaker 1: that was a key part of the critical innovation and 192 00:10:06,316 --> 00:10:07,956 Speaker 1: secret sauce and other stuff to be able to make 193 00:10:07,996 --> 00:10:10,436 Speaker 1: that happen. Absolutely well, how do you do it? I 194 00:10:10,436 --> 00:10:14,956 Speaker 1: mean presumably everybody else would have used fewer lasers and 195 00:10:15,076 --> 00:10:17,796 Speaker 1: receivers if they could have, Like is it software? Is 196 00:10:17,796 --> 00:10:19,956 Speaker 1: it hardware? Is it? But yeah, I mean we literally 197 00:10:19,956 --> 00:10:21,796 Speaker 1: had to make our own custom chips in our own 198 00:10:21,836 --> 00:10:23,996 Speaker 1: systems there too, to be able to make this possible. Otherwise, 199 00:10:24,036 --> 00:10:25,596 Speaker 1: as you said, everybody else would have done it. It's 200 00:10:25,596 --> 00:10:27,716 Speaker 1: not out So you're not you're not just like monkey 201 00:10:27,756 --> 00:10:30,276 Speaker 1: with the laser. You're you're designing a chip. You're designing 202 00:10:30,316 --> 00:10:32,876 Speaker 1: the semiconductor that's going to go inside of it. Yeah, 203 00:10:32,876 --> 00:10:35,796 Speaker 1: oh yeah, yeah, we're crazy like that, Like that's that's 204 00:10:35,796 --> 00:10:37,636 Speaker 1: what we had to do, and there was no other way. 205 00:10:37,836 --> 00:10:40,596 Speaker 1: Everything else would just cost thousands or tens of thousands 206 00:10:40,596 --> 00:10:41,956 Speaker 1: of dollars, and we had to even work at a 207 00:10:41,956 --> 00:10:44,996 Speaker 1: completely different wavelength of light and completely different special materials. 208 00:10:45,116 --> 00:10:50,316 Speaker 1: Those to the existing stuff just wouldn't get you still 209 00:10:50,356 --> 00:10:53,236 Speaker 1: to come on the show and still to come for Luminar. 210 00:10:53,716 --> 00:10:56,116 Speaker 1: How do you build and sell wide our systems at 211 00:10:56,116 --> 00:11:06,196 Speaker 1: scale and make a profit. That's the end of the ads. 212 00:11:06,636 --> 00:11:08,756 Speaker 1: Now we're going back to the show. Is there a 213 00:11:08,796 --> 00:11:11,396 Speaker 1: moment when things and I know it's not binary, but 214 00:11:11,516 --> 00:11:13,596 Speaker 1: is there some moment when it sort of flips from 215 00:11:13,596 --> 00:11:17,636 Speaker 1: basically not working to basically working? One of them? I 216 00:11:17,636 --> 00:11:19,756 Speaker 1: didn't you still see the image online? It was it 217 00:11:19,796 --> 00:11:25,516 Speaker 1: was down Palm Drive here and Stanford exactly exactly, Yeah, 218 00:11:25,516 --> 00:11:27,756 Speaker 1: it's Stanford. And are you watching it in real time? 219 00:11:27,876 --> 00:11:29,836 Speaker 1: Is the car like drive on a Palm drive and 220 00:11:29,876 --> 00:11:32,836 Speaker 1: you're looking at the car controlling the system there too, 221 00:11:32,876 --> 00:11:35,276 Speaker 1: I'm looking at it? Yeah, yeah, absolutely, So you're looking 222 00:11:35,316 --> 00:11:37,596 Speaker 1: at it on your laptop. You're basically seeing what the 223 00:11:37,636 --> 00:11:40,196 Speaker 1: system is seeing on your laptop in the car. Yeah. 224 00:11:40,236 --> 00:11:42,676 Speaker 1: I had a monitor rigged up in the car there too, 225 00:11:42,716 --> 00:11:45,076 Speaker 1: so I can actually go go watch and had a keyboard, 226 00:11:45,156 --> 00:11:46,916 Speaker 1: had had a whole set up there too. Then, so 227 00:11:46,956 --> 00:11:50,476 Speaker 1: you're sitting there, you got the thing rigged up, somebody's driving, 228 00:11:50,516 --> 00:11:55,236 Speaker 1: you're looking at the laptop and what so they're driving. 229 00:11:55,636 --> 00:11:58,236 Speaker 1: I'm there, I have a system set up, boot it 230 00:11:58,356 --> 00:12:00,716 Speaker 1: up to go, turn it on and see what the 231 00:12:00,756 --> 00:12:03,516 Speaker 1: output is. And then next thing, you know, you just 232 00:12:03,556 --> 00:12:08,676 Speaker 1: have this just this beautiful image of everything, or and 233 00:12:08,836 --> 00:12:12,356 Speaker 1: I say beautiful in an almost artistic way, because like 234 00:12:12,956 --> 00:12:16,476 Speaker 1: I've just been you know, for ten years and even 235 00:12:16,516 --> 00:12:18,796 Speaker 1: at that time for years, just so deeply embedded in 236 00:12:18,796 --> 00:12:22,116 Speaker 1: the data. I almost like see data, you know, like 237 00:12:22,156 --> 00:12:26,116 Speaker 1: the matrix, like like the matrix exactly. No, that's the thing. 238 00:12:26,156 --> 00:12:28,756 Speaker 1: It literally looks like you're in the matrix. Because you 239 00:12:28,796 --> 00:12:33,876 Speaker 1: can fly around this three D world. It's like totally abstract, 240 00:12:34,036 --> 00:12:37,716 Speaker 1: you know, colorations and perspectives and everything that you could 241 00:12:37,716 --> 00:12:39,996 Speaker 1: never do in the real world. So it actually is 242 00:12:40,076 --> 00:12:42,716 Speaker 1: kind of wild. What do you see in in this instance? 243 00:12:42,716 --> 00:12:46,596 Speaker 1: What do you see? So I'm seeing rows of these 244 00:12:46,676 --> 00:12:49,236 Speaker 1: palm trees here, which I think it turns out, are 245 00:12:49,236 --> 00:12:51,916 Speaker 1: just really really distinctive and really interesting looking in the 246 00:12:51,996 --> 00:12:54,196 Speaker 1: live ar at least because you have these different palm 247 00:12:54,276 --> 00:12:58,036 Speaker 1: fronts that then all become incredibly distinctive where you can 248 00:12:58,076 --> 00:13:02,636 Speaker 1: just see even all like the texture and the detail 249 00:13:02,716 --> 00:13:05,356 Speaker 1: of these leaves, where it can zoom and fly through them, 250 00:13:05,756 --> 00:13:08,716 Speaker 1: you know, in three D because you could rerender it 251 00:13:08,796 --> 00:13:10,956 Speaker 1: any angle. That's the part of the software that we had. 252 00:13:11,276 --> 00:13:13,836 Speaker 1: You know that that was just absolutely mind blowing to 253 00:13:13,876 --> 00:13:16,396 Speaker 1: me at the time. And I was like, oh my gosh, 254 00:13:16,436 --> 00:13:18,956 Speaker 1: it's it's it really all came together to work because 255 00:13:18,956 --> 00:13:20,316 Speaker 1: you don't know, right, you know, I mean, you you 256 00:13:20,596 --> 00:13:22,916 Speaker 1: try this stuff over and over and over and over 257 00:13:22,956 --> 00:13:24,796 Speaker 1: and over again until you can get like you can 258 00:13:24,836 --> 00:13:26,756 Speaker 1: get it to be in a really good place. So 259 00:13:26,876 --> 00:13:29,276 Speaker 1: not only are the pump chase beautiful, but your your 260 00:13:29,316 --> 00:13:32,956 Speaker 1: thing is working, your system is working. Yeah, exactly, Like 261 00:13:32,996 --> 00:13:34,676 Speaker 1: I always had these tears of joy, and I was 262 00:13:34,876 --> 00:13:38,476 Speaker 1: like looking out of you know, because it's picking up 263 00:13:38,516 --> 00:13:41,276 Speaker 1: stuff so far off, because it's you can see so 264 00:13:41,636 --> 00:13:44,396 Speaker 1: small away. It's picking up stuff one hundreds of meters away, 265 00:13:44,516 --> 00:13:47,396 Speaker 1: centimeter level precision detail. I mean, not to mention the 266 00:13:47,396 --> 00:13:49,476 Speaker 1: whole thing's actually working in the first place. You know, 267 00:13:50,316 --> 00:13:53,236 Speaker 1: it's it's um because the tech all came together and 268 00:13:53,516 --> 00:13:55,996 Speaker 1: that there's stuff like that that that just makes all 269 00:13:55,996 --> 00:13:58,356 Speaker 1: that different let's talk about kind of where you are 270 00:13:58,436 --> 00:14:00,516 Speaker 1: right now in terms of what vehicles you're in and 271 00:14:00,596 --> 00:14:02,716 Speaker 1: going into, and also where you've gotten the cost to 272 00:14:03,316 --> 00:14:05,356 Speaker 1: and sort of what you still need to do that 273 00:14:05,396 --> 00:14:08,756 Speaker 1: you haven't done yet. The overall goal of what we 274 00:14:08,796 --> 00:14:12,076 Speaker 1: have from a big picture standpoint is to move. It 275 00:14:12,156 --> 00:14:16,236 Speaker 1: is to build and enable the uncrashable car. So to say, 276 00:14:16,276 --> 00:14:19,076 Speaker 1: you know something that where we can eliminate vehicle accidents 277 00:14:19,076 --> 00:14:21,676 Speaker 1: and make any thing of the past, And is there 278 00:14:21,876 --> 00:14:23,716 Speaker 1: I mean, do you have like a well one hundred 279 00:14:23,756 --> 00:14:25,996 Speaker 1: year vision when you say the uncrashable car, do you 280 00:14:26,036 --> 00:14:28,636 Speaker 1: have a year in your mind when it'll be possible 281 00:14:28,836 --> 00:14:32,476 Speaker 1: to build the uncrashable car? Well, that's why I said 282 00:14:32,476 --> 00:14:35,836 Speaker 1: the hundred year vision. But no, no, no, But but 283 00:14:36,516 --> 00:14:40,476 Speaker 1: I think lifting already from a system that's there today 284 00:14:41,076 --> 00:14:43,956 Speaker 1: much less for the future, could already start preventing massive 285 00:14:43,956 --> 00:14:47,476 Speaker 1: amounts of accidents. Tell me about what what sort of 286 00:14:47,476 --> 00:14:50,756 Speaker 1: the frontier for you? Clearly you've made this thing work, 287 00:14:51,356 --> 00:14:54,436 Speaker 1: carmakers are starting to buy it. What haven't you figured out? Yet? 288 00:14:54,436 --> 00:14:56,356 Speaker 1: What are you working on now that you still haven't solved? 289 00:14:57,236 --> 00:15:02,036 Speaker 1: Execution scale? You know, continuing to evolve and mature of 290 00:15:02,076 --> 00:15:04,956 Speaker 1: the business, you know, building software layers on top of 291 00:15:04,996 --> 00:15:07,996 Speaker 1: the hardware. How is the how is the unit economics? 292 00:15:08,036 --> 00:15:10,316 Speaker 1: The unit economics seem like they could be hard. You're 293 00:15:10,356 --> 00:15:14,556 Speaker 1: not the only company selling lighter There are other big companies, 294 00:15:14,596 --> 00:15:17,316 Speaker 1: like you know, there's a hard Wares is a famously 295 00:15:17,396 --> 00:15:20,876 Speaker 1: hard business, partly for that reason, Like is that hard? 296 00:15:21,796 --> 00:15:24,756 Speaker 1: That part's less hard? Actually? Um, I think that's something 297 00:15:24,796 --> 00:15:30,516 Speaker 1: when you have a fundamentally differentiated and value technology there too, 298 00:15:30,556 --> 00:15:33,956 Speaker 1: that just is distinctive. The only thing that actually matters 299 00:15:34,036 --> 00:15:36,076 Speaker 1: is how you can successfully execute and how you can 300 00:15:36,116 --> 00:15:38,956 Speaker 1: be able to build this up. Because right now, what's 301 00:15:38,996 --> 00:15:41,476 Speaker 1: the percentage of cars out there that are that are 302 00:15:41,476 --> 00:15:43,796 Speaker 1: currently lighter equipped or going to be lighter equipped? I 303 00:15:43,796 --> 00:15:46,476 Speaker 1: mean in the immediate it's still like, you know, less 304 00:15:46,476 --> 00:15:48,996 Speaker 1: than a percent, you know of the overall market. Like 305 00:15:49,036 --> 00:15:51,756 Speaker 1: this I was going to get your market opportunity, Yeah, 306 00:15:51,756 --> 00:15:53,836 Speaker 1: it's it's like, yeah, it rounds to zero, you know. 307 00:15:54,116 --> 00:15:56,316 Speaker 1: So that that's the thing is that you know when 308 00:15:56,396 --> 00:15:58,436 Speaker 1: you when it comes in with this whole next wave, 309 00:15:58,556 --> 00:16:01,156 Speaker 1: you know there's that trillion dollars of opportunity, you know 310 00:16:01,196 --> 00:16:03,876 Speaker 1: that you have ahead to do it. It's actually, I mean, 311 00:16:03,996 --> 00:16:06,476 Speaker 1: is it is it hard to be profitable? Like, is 312 00:16:06,476 --> 00:16:09,116 Speaker 1: it hard to get the profitable? Yeah? Yeah, yeah, yeah, 313 00:16:08,996 --> 00:16:11,596 Speaker 1: I mean but here's the thing, here's the thing right now, 314 00:16:11,996 --> 00:16:15,396 Speaker 1: it's a game of survival ever everything. I mean, you're 315 00:16:15,396 --> 00:16:18,396 Speaker 1: you're you're familiar with the macroeconomic environment and everything for 316 00:16:18,436 --> 00:16:21,756 Speaker 1: like growth technology companies there too. You can't raise money, 317 00:16:22,036 --> 00:16:25,076 Speaker 1: you know, you're basically ninety percent of companies out there 318 00:16:25,076 --> 00:16:28,076 Speaker 1: are totally screwed, you know, because there's no opportunity to 319 00:16:28,076 --> 00:16:32,676 Speaker 1: be able to actually deliver on you know, a real 320 00:16:32,796 --> 00:16:35,316 Speaker 1: product towards the real industry with real revenue and real 321 00:16:35,716 --> 00:16:39,356 Speaker 1: capabilities or any real order book. Luminar is losing money, right, 322 00:16:39,436 --> 00:16:42,436 Speaker 1: Luminar is not breaking even, No, we're not. We're not 323 00:16:42,516 --> 00:16:46,156 Speaker 1: profitable overall as a company on a unit economics basis obviously, 324 00:16:46,276 --> 00:16:47,916 Speaker 1: you know, for for the contracts of the stuff that 325 00:16:47,916 --> 00:16:50,636 Speaker 1: we have signed up for that that is you know, uh, 326 00:16:50,956 --> 00:16:54,596 Speaker 1: generating real money there too for us. But is it profitable? 327 00:16:54,916 --> 00:16:57,436 Speaker 1: Are the unit economics profitable? I mean, obviously it's generating 328 00:16:57,476 --> 00:17:00,596 Speaker 1: revenue yeah yeah, yeah for the production absolutely yeah yeah, 329 00:17:00,676 --> 00:17:03,956 Speaker 1: so um yeah no no, And that's important of course, 330 00:17:03,996 --> 00:17:06,556 Speaker 1: because you know it we lose money on every linear 331 00:17:06,596 --> 00:17:08,356 Speaker 1: but we make it up in volume. Yeah, you make 332 00:17:08,356 --> 00:17:11,036 Speaker 1: it up in volume. Yeah, exactly, exactly, Which, just to 333 00:17:11,076 --> 00:17:14,796 Speaker 1: be clear, that is the strategy of like a lot 334 00:17:14,876 --> 00:17:17,436 Speaker 1: of these growth companies and technology of these A lighter 335 00:17:17,476 --> 00:17:20,956 Speaker 1: companies and av companies and everything is just obviously that's 336 00:17:20,996 --> 00:17:23,436 Speaker 1: sustainable only for so long as long as you can 337 00:17:23,516 --> 00:17:27,076 Speaker 1: have venture money funding it. Yeah, when the venture money 338 00:17:27,796 --> 00:17:31,596 Speaker 1: isn't there, So when you're going to be profitable in theory, 339 00:17:31,636 --> 00:17:33,676 Speaker 1: if you know, if you said, hell or high water, 340 00:17:33,676 --> 00:17:35,596 Speaker 1: we're going to become profitable this year, you can you 341 00:17:35,596 --> 00:17:37,396 Speaker 1: could find I con find a way to do that. 342 00:17:37,436 --> 00:17:39,396 Speaker 1: It wouldn't be the right move for the business, but 343 00:17:39,556 --> 00:17:41,436 Speaker 1: you know, there's also ways that you can love her. 344 00:17:41,996 --> 00:17:44,636 Speaker 1: You mean, basically, stop stop spending money on R and 345 00:17:44,716 --> 00:17:46,916 Speaker 1: D essentially and just make and sell the thing that 346 00:17:46,996 --> 00:17:49,756 Speaker 1: you already know how to make and sell. Yeah, yeah, exactly. 347 00:17:49,756 --> 00:17:53,356 Speaker 1: I could stop all investments for our future exactly, you know, 348 00:17:53,396 --> 00:17:55,516 Speaker 1: but but that would be dumb as well. So you know, 349 00:17:55,516 --> 00:17:57,756 Speaker 1: we've been in a good position, but it's it's just 350 00:17:57,796 --> 00:17:59,956 Speaker 1: to be clear, it is not easy, Like this stuff 351 00:18:00,036 --> 00:18:05,316 Speaker 1: is really freaking hard. In a minute, the lightning round, 352 00:18:05,796 --> 00:18:08,836 Speaker 1: including the finer points of dropping out of college and 353 00:18:09,156 --> 00:18:12,716 Speaker 1: how to hack both hardware and, maybe more impressively, the 354 00:18:12,796 --> 00:18:20,916 Speaker 1: Bay Area real estate market. Now let's get back to 355 00:18:20,956 --> 00:18:23,956 Speaker 1: the show. I want to finish. I appreciate your time. 356 00:18:24,156 --> 00:18:28,316 Speaker 1: Almost done. I want to finish with Lightning round A 357 00:18:28,396 --> 00:18:33,396 Speaker 1: bunch of quick questions. Absolutely, what's the most useful thing 358 00:18:33,436 --> 00:18:38,676 Speaker 1: you learned in your twelve weeks of college? That it's 359 00:18:38,756 --> 00:18:42,916 Speaker 1: not for me? Good? Who should drop out of college? 360 00:18:43,476 --> 00:18:47,276 Speaker 1: Anybody that wants to build a great business and not 361 00:18:47,396 --> 00:18:50,956 Speaker 1: being massive amounts of student debt. Okay, it's a ringing 362 00:18:51,036 --> 00:18:54,116 Speaker 1: endorsement of dropping out. I like it's it's it's a 363 00:18:54,196 --> 00:18:57,516 Speaker 1: spicy take, absolutely, and it's not for everyone. It's it's not, 364 00:18:57,596 --> 00:18:59,596 Speaker 1: but but the reality is is that it is for 365 00:18:59,636 --> 00:19:01,636 Speaker 1: a lot of people, and I think that's something that 366 00:19:01,676 --> 00:19:06,916 Speaker 1: needs to be a more celebrated path. Who's your favorite inventor? Oh? 367 00:19:07,036 --> 00:19:11,156 Speaker 1: Probably maybe Isaac Newton. Uh it's interesting to think about 368 00:19:11,236 --> 00:19:14,596 Speaker 1: him as an inventor, Like, well, in your mind, what 369 00:19:14,636 --> 00:19:19,436 Speaker 1: did he invent? Calculus? Yes? Sorry, the field of optics? 370 00:19:19,636 --> 00:19:23,956 Speaker 1: You know from optics? Yea, optics and of course, uh 371 00:19:24,316 --> 00:19:25,956 Speaker 1: I read that when you were a kid and your 372 00:19:25,996 --> 00:19:28,116 Speaker 1: parents wouldn't let you have a have a cell phone. 373 00:19:28,156 --> 00:19:30,556 Speaker 1: You hacked your Nintendo Wei to make it a phone? 374 00:19:30,836 --> 00:19:32,556 Speaker 1: First of all, is that true? And second of all, 375 00:19:32,596 --> 00:19:35,076 Speaker 1: if so, do you still hack stuff around the house now? 376 00:19:35,636 --> 00:19:39,716 Speaker 1: So source it's it's partially true. It was my Nintendo DS, 377 00:19:39,716 --> 00:19:42,156 Speaker 1: so that's that's all it is true. I did also 378 00:19:42,156 --> 00:19:45,556 Speaker 1: hacking Nintendo, but as a difference, yes, DS makes more sense. Yeah, yeah, 379 00:19:45,636 --> 00:19:47,516 Speaker 1: yeah exactly. Then a guy actually care about has a 380 00:19:47,596 --> 00:19:50,076 Speaker 1: has a microphone at a speaker, had you know a 381 00:19:50,196 --> 00:19:52,996 Speaker 1: void capabilities there too, so you know it always do 382 00:19:53,076 --> 00:19:55,756 Speaker 1: fun stuff like that. Any example of a big or 383 00:19:55,876 --> 00:19:59,956 Speaker 1: small hack in your adult life, actually talk about hacks. 384 00:19:59,996 --> 00:20:02,276 Speaker 1: There was literally something like a content called like a 385 00:20:02,316 --> 00:20:04,116 Speaker 1: hacker house, you know that you'd have that you were, 386 00:20:04,156 --> 00:20:05,796 Speaker 1: you'd have a bunch of entrepreneurs. Let me the same 387 00:20:05,796 --> 00:20:09,956 Speaker 1: plant watch Silicon Valley. Yes exactly. So when when I 388 00:20:10,076 --> 00:20:13,556 Speaker 1: first came to Silicon Valley, I actually ran a number 389 00:20:13,556 --> 00:20:16,356 Speaker 1: of hacker houses there too, which was talk about a hack. 390 00:20:16,796 --> 00:20:18,796 Speaker 1: You know, no one would fund my business or do 391 00:20:18,796 --> 00:20:20,396 Speaker 1: I have anything to do with this early on because 392 00:20:20,396 --> 00:20:22,116 Speaker 1: they're like what the heck? You know, like that sounds 393 00:20:22,156 --> 00:20:23,956 Speaker 1: crazy and all this stuff. So I actually ended up 394 00:20:23,956 --> 00:20:26,076 Speaker 1: self funding it by renting out a bunch of these 395 00:20:26,116 --> 00:20:28,996 Speaker 1: crazy mansions, you know, take out debt to go do that, 396 00:20:29,196 --> 00:20:32,236 Speaker 1: and then would sublet each of the individual rooms to 397 00:20:32,276 --> 00:20:35,276 Speaker 1: other entrepreneurs, you know, for thousands of dollars per month um, 398 00:20:35,516 --> 00:20:38,076 Speaker 1: you know, to be able to actually then generate revenue, 399 00:20:38,076 --> 00:20:40,516 Speaker 1: generate income that it would would fund towards the business. 400 00:20:40,516 --> 00:20:43,036 Speaker 1: Is that the way you funded Luminar at the beginning? 401 00:20:43,516 --> 00:20:45,596 Speaker 1: That was a huge part of it. Yeah, yeah, that 402 00:20:45,756 --> 00:20:52,276 Speaker 1: was that was that was That was it. Austin Russell 403 00:20:52,356 --> 00:20:56,276 Speaker 1: is the co founder and CEO of Luminar. Today's episode 404 00:20:56,396 --> 00:20:59,476 Speaker 1: was produced by Edith Russelo, edited by Robert Smith and 405 00:20:59,556 --> 00:21:03,996 Speaker 1: Sarah Nis, and engineered by Amanda k. Walk. I'm Jacob Goldstein. 406 00:21:04,076 --> 00:21:06,396 Speaker 1: I am not on TikTok, but I am on Twitter 407 00:21:06,596 --> 00:21:09,916 Speaker 1: at Jacob Goldstein. You can also email us at problem 408 00:21:09,996 --> 00:21:13,476 Speaker 1: at Pushkin dot fm. We'll be back next week with 409 00:21:13,556 --> 00:21:14,716 Speaker 1: another episode of the show.