1 00:00:00,120 --> 00:00:02,800 Speaker 1: Self driving tech world has a strong New Zealand presence 2 00:00:02,840 --> 00:00:04,880 Speaker 1: with Wave or Wavy. I'll get to that bit in 3 00:00:04,920 --> 00:00:07,360 Speaker 1: the moment. They've secured a one point five billion dollar 4 00:00:07,600 --> 00:00:10,440 Speaker 1: capital raise to support commercial rollout of their robotaxis. 5 00:00:10,480 --> 00:00:10,600 Speaker 2: Now. 6 00:00:10,600 --> 00:00:14,200 Speaker 1: The funding includes people like Mercedes, Nis and Stilantis, Microsoft 7 00:00:14,280 --> 00:00:18,320 Speaker 1: and Video waybe CEO Alex Kendle's with us. Alex morning, 8 00:00:20,160 --> 00:00:22,959 Speaker 1: Good morning Mike. Now is it Wave or Wavy? 9 00:00:24,760 --> 00:00:28,400 Speaker 2: That's Wave. I call the company Wave because I wanted 10 00:00:28,720 --> 00:00:30,600 Speaker 2: to feel like when you're in a self driving car, 11 00:00:30,680 --> 00:00:33,600 Speaker 2: like you're catching a wave. So it's all about riding 12 00:00:33,640 --> 00:00:34,160 Speaker 2: the wave home. 13 00:00:34,320 --> 00:00:36,440 Speaker 1: Okay, what does the one point five billion get you 14 00:00:36,560 --> 00:00:37,239 Speaker 1: or do for you? 15 00:00:39,920 --> 00:00:43,000 Speaker 2: Hey, this isn't an enormous capital raiser. It's a real 16 00:00:43,000 --> 00:00:45,920 Speaker 2: privilege for us because we've been building this technology for 17 00:00:45,960 --> 00:00:49,000 Speaker 2: a decade and it gives us the opportunity now to 18 00:00:49,040 --> 00:00:52,640 Speaker 2: go commercialize that. We've taken a different approach, a contrarian 19 00:00:52,680 --> 00:00:55,520 Speaker 2: approach to the self driving problem. It's different from what 20 00:00:55,600 --> 00:00:58,520 Speaker 2: you see from the robotaxis on the road in China 21 00:00:58,640 --> 00:01:02,280 Speaker 2: or the US today, and this was one that was 22 00:01:02,320 --> 00:01:06,119 Speaker 2: once contrarion. But this capital raise really illustrates the breadth 23 00:01:06,200 --> 00:01:08,800 Speaker 2: of support we are and our seeing for this approach 24 00:01:08,840 --> 00:01:10,720 Speaker 2: with some of the biggest car companies in the world 25 00:01:10,760 --> 00:01:13,679 Speaker 2: backing it, as well as technology giants like the ones you. 26 00:01:13,640 --> 00:01:16,360 Speaker 1: Listed as best as you can, because it's sort of 27 00:01:16,959 --> 00:01:20,240 Speaker 1: that's my main interest in this. How many pathways are 28 00:01:20,280 --> 00:01:24,440 Speaker 1: there to a car that can drive itself technically speaking? 29 00:01:26,800 --> 00:01:31,479 Speaker 2: Well, So the previous approach has really revolved around telling 30 00:01:31,480 --> 00:01:34,120 Speaker 2: the car how to drive, mapping an environment, telling it 31 00:01:34,160 --> 00:01:37,640 Speaker 2: where and how to go, and the problem with that 32 00:01:37,920 --> 00:01:40,080 Speaker 2: is that the world we live in is so complex 33 00:01:40,160 --> 00:01:42,520 Speaker 2: and it's unbelievably hard to be able to do that, 34 00:01:43,120 --> 00:01:45,920 Speaker 2: very expensive, maybe not even possible to do its scale. 35 00:01:46,360 --> 00:01:48,960 Speaker 2: So what we did is took an approach of training 36 00:01:49,000 --> 00:01:52,280 Speaker 2: and artificial intelligence and AI that could learn the complexities 37 00:01:52,280 --> 00:01:54,360 Speaker 2: of the world we live in. And we've shown now 38 00:01:54,400 --> 00:01:56,240 Speaker 2: that this is a system that can drive all around 39 00:01:56,240 --> 00:01:58,720 Speaker 2: the world and multiple continents and places it's never been 40 00:01:58,760 --> 00:02:01,480 Speaker 2: in before and many different vas vehicles. So now the 41 00:02:01,520 --> 00:02:04,480 Speaker 2: more interesting question is how do you commercialize this technology? 42 00:02:04,880 --> 00:02:06,760 Speaker 2: And there are three different ways to do that. You know. 43 00:02:06,800 --> 00:02:09,000 Speaker 2: One is you could sell your own cars, that's what 44 00:02:09,080 --> 00:02:11,640 Speaker 2: Tesla's doing. The problem with that is you're limited to 45 00:02:11,680 --> 00:02:14,760 Speaker 2: just the cars you sell. The second option is you 46 00:02:14,800 --> 00:02:17,200 Speaker 2: can run your own fleets and you see that the 47 00:02:17,320 --> 00:02:20,240 Speaker 2: Google Weaimo robotaxis. But the problem with this is you 48 00:02:20,240 --> 00:02:22,240 Speaker 2: have to go sety by seti and your rollout. And 49 00:02:22,320 --> 00:02:25,080 Speaker 2: so we've taken a third option, which is to license 50 00:02:25,120 --> 00:02:27,560 Speaker 2: this technology to car companies and fleets around the world. 51 00:02:27,840 --> 00:02:29,720 Speaker 2: And that brings us to the larger scale. And it's 52 00:02:29,760 --> 00:02:33,040 Speaker 2: only possible because the AO we've built is so flexible 53 00:02:33,080 --> 00:02:35,920 Speaker 2: and its ability to drive in different vehicles in different places, 54 00:02:36,240 --> 00:02:37,959 Speaker 2: and we think this is what's going to bring self 55 00:02:38,000 --> 00:02:39,480 Speaker 2: driving technology worldwide. 56 00:02:39,560 --> 00:02:42,520 Speaker 1: So when you say license, that's an ongoing payment from 57 00:02:42,560 --> 00:02:45,480 Speaker 1: Mercedes on this and or Stilantis to you and with 58 00:02:45,680 --> 00:02:48,640 Speaker 1: updates coming as tech advances and all that sort of stuff. 59 00:02:50,280 --> 00:02:54,799 Speaker 2: That's right. Every bit of experience AI driver has from 60 00:02:54,919 --> 00:02:58,080 Speaker 2: vehicles around the world it will learn from and continually improve. 61 00:02:58,240 --> 00:03:02,720 Speaker 2: So this year whiching supervised robotaxi trials starting in London 62 00:03:02,720 --> 00:03:05,120 Speaker 2: and growing to ten cities around the world with Uber, 63 00:03:06,200 --> 00:03:08,480 Speaker 2: and then next year we're launching this technology into cars 64 00:03:08,520 --> 00:03:12,240 Speaker 2: you can buy with companies like Nissan, and this will 65 00:03:12,240 --> 00:03:14,280 Speaker 2: start as a hands off driving experience with a car 66 00:03:14,320 --> 00:03:17,440 Speaker 2: will drive autonomously, and you need to monitor the system 67 00:03:18,280 --> 00:03:20,679 Speaker 2: and then improve until it becomes eyes off where you 68 00:03:20,720 --> 00:03:23,839 Speaker 2: can actually take your eyes off the road and let 69 00:03:23,840 --> 00:03:26,160 Speaker 2: the car tak liability for driving, allow you to read 70 00:03:26,200 --> 00:03:29,720 Speaker 2: a book or what you're engage with media, do whatever 71 00:03:29,760 --> 00:03:32,160 Speaker 2: you want with the time, and of course make the 72 00:03:32,240 --> 00:03:34,120 Speaker 2: driving experience that much safer. 73 00:03:34,200 --> 00:03:36,480 Speaker 1: See I'm reading in the BBC this morning, you're arguing 74 00:03:36,480 --> 00:03:38,680 Speaker 1: that you'll be able to deal with potholes in England. 75 00:03:38,720 --> 00:03:40,960 Speaker 1: What about New Zealand, as you would well remember, and 76 00:03:41,400 --> 00:03:43,640 Speaker 1: the roads that don't have any lines on them, and 77 00:03:43,680 --> 00:03:45,720 Speaker 1: then they're wider, and then they're narrower, and then there's 78 00:03:45,760 --> 00:03:47,640 Speaker 1: a truck pucked down on the corner and again of 79 00:03:47,680 --> 00:03:50,240 Speaker 1: the whole New Zealand experience, you say your tech can 80 00:03:50,280 --> 00:03:50,520 Speaker 1: do that. 81 00:03:53,400 --> 00:03:56,520 Speaker 2: Absolutely that all of those scenarios you describe will be 82 00:03:56,520 --> 00:03:59,920 Speaker 2: something that technology naturally learns to drive. I mean, we've tested. 83 00:04:00,640 --> 00:04:02,720 Speaker 2: We haven't come to New Zealand yet, but I can't wait. 84 00:04:02,880 --> 00:04:05,920 Speaker 2: It's on our roadmap. But we've been north of the 85 00:04:06,000 --> 00:04:10,600 Speaker 2: Arctic Circle in Sweden and Finland. We've been to the 86 00:04:10,640 --> 00:04:13,560 Speaker 2: busy market streets of Tokyo and Japan, and of course 87 00:04:13,920 --> 00:04:16,279 Speaker 2: downtown London. So we've been all around the world in 88 00:04:16,279 --> 00:04:18,560 Speaker 2: these kind of places. We've driven around the Arc to 89 00:04:18,640 --> 00:04:21,679 Speaker 2: Triumph in France, and you see whether it's medieval roads 90 00:04:21,680 --> 00:04:25,040 Speaker 2: with cobblestones or whether it's country roads. I'm sure we'll 91 00:04:25,040 --> 00:04:28,119 Speaker 2: be able to go through everywhere through New Zealand as well. 92 00:04:28,560 --> 00:04:30,760 Speaker 2: It's got the intelligence to drive in all of these 93 00:04:30,800 --> 00:04:32,000 Speaker 2: places reliably. 94 00:04:32,600 --> 00:04:35,159 Speaker 1: What about the demand do people actually want it? 95 00:04:37,880 --> 00:04:39,800 Speaker 2: That's the other thing that I think we're seeing is 96 00:04:39,839 --> 00:04:44,480 Speaker 2: that we've now got people are really demonstrating that autonomy 97 00:04:44,480 --> 00:04:48,800 Speaker 2: is something that not only will bring safety and accessibility ements, 98 00:04:48,800 --> 00:04:50,920 Speaker 2: but it's something that people actually want and want to 99 00:04:50,960 --> 00:04:55,000 Speaker 2: pay for. So, for example, in San Francisco, through some 100 00:04:55,040 --> 00:04:59,000 Speaker 2: early trials of robotaxis, you're seeing robotaxis there that they 101 00:04:59,040 --> 00:05:03,000 Speaker 2: have twenty percent more expensive on average, maybe they're thirty 102 00:05:03,000 --> 00:05:09,240 Speaker 2: percent longer wait times than say a human driven taxi service. 103 00:05:09,320 --> 00:05:13,120 Speaker 2: Yet people prefer and want to ride in these experiences 104 00:05:13,440 --> 00:05:18,120 Speaker 2: because they provide a more safe, consistent, hygienic, clean driving experience. 105 00:05:18,760 --> 00:05:22,880 Speaker 2: And then you find, you know, for new vehicles like testas, 106 00:05:22,920 --> 00:05:26,720 Speaker 2: that people they're now seeing billions of revenue, like serious 107 00:05:26,720 --> 00:05:29,760 Speaker 2: money that people are paying for this technology. Now it'll 108 00:05:29,800 --> 00:05:31,919 Speaker 2: be a choice if you want to drive yourself and 109 00:05:31,960 --> 00:05:33,839 Speaker 2: have an AI watch you're driving, or if you want 110 00:05:33,839 --> 00:05:35,479 Speaker 2: to ride in a robotaxi, or if you want to 111 00:05:35,480 --> 00:05:38,960 Speaker 2: take other forms of transportation, that'll be your choice. But 112 00:05:39,000 --> 00:05:42,239 Speaker 2: we're seeing the benefits that really are now being seen 113 00:05:43,320 --> 00:05:45,240 Speaker 2: by consumers. And the question is what is going to 114 00:05:45,240 --> 00:05:47,919 Speaker 2: bring that to scale to everywhere around the world. 115 00:05:48,240 --> 00:05:52,000 Speaker 1: What year do I ring you where this is not 116 00:05:52,120 --> 00:05:55,200 Speaker 1: just a thing, it's not coming, It's like it's every 117 00:05:55,279 --> 00:05:56,920 Speaker 1: day and it's no longer a big deal. 118 00:06:00,800 --> 00:06:03,760 Speaker 2: I think. So what we're going to see this year 119 00:06:04,000 --> 00:06:06,200 Speaker 2: is our trials we brought up in major cities around 120 00:06:06,200 --> 00:06:11,479 Speaker 2: the world, and they'll start with modest vehicle numbers but grow. 121 00:06:11,640 --> 00:06:14,120 Speaker 2: And the thing that we've done is we've partnered with 122 00:06:14,200 --> 00:06:17,680 Speaker 2: automakers so that these vehicles can be mass manufactured. So 123 00:06:17,720 --> 00:06:20,640 Speaker 2: we'll see this technology start entering mass manufacturing from next 124 00:06:20,720 --> 00:06:23,120 Speaker 2: year where it'd be in cars you can own, and 125 00:06:23,160 --> 00:06:25,080 Speaker 2: then very quickly after that we'll get to the point 126 00:06:25,080 --> 00:06:28,840 Speaker 2: where the technology can drive in a safe and scalable way, 127 00:06:29,120 --> 00:06:31,760 Speaker 2: and then it's about operational rollout. And so I think 128 00:06:31,760 --> 00:06:36,320 Speaker 2: by the twenty thirties we're going to see this technology 129 00:06:36,320 --> 00:06:41,120 Speaker 2: fairly universally accessible and with operational rollouts in place, and 130 00:06:41,160 --> 00:06:42,880 Speaker 2: so I mean, you'd have to get to the nuance 131 00:06:42,880 --> 00:06:45,560 Speaker 2: of what you're describing, but it's going to come quickly, 132 00:06:46,080 --> 00:06:49,039 Speaker 2: and I think I think the benefits are going to 133 00:06:49,040 --> 00:06:50,400 Speaker 2: be quite profound. It's going to be one of the 134 00:06:50,400 --> 00:06:53,680 Speaker 2: biggest changes we go through in our generation, this advent 135 00:06:53,680 --> 00:06:57,520 Speaker 2: of autonomy and robotics, and how it's going to improve 136 00:06:59,160 --> 00:07:02,480 Speaker 2: some of the complex asks that maybe are unsafe or 137 00:07:02,600 --> 00:07:04,480 Speaker 2: dangerous or ones that we want to free up our 138 00:07:04,560 --> 00:07:06,920 Speaker 2: time from. And I think it's going to really benefit 139 00:07:06,960 --> 00:07:08,400 Speaker 2: the cities that we live in around the world. 140 00:07:08,480 --> 00:07:11,080 Speaker 1: Brilliant. Alex's well done. Congratulations, nice to talk to you. 141 00:07:11,120 --> 00:07:13,280 Speaker 1: Appreciate it very much. Alex Kendall, who was the Wave 142 00:07:13,400 --> 00:07:16,880 Speaker 1: CEO and obviously from the accent a New Zealander, so 143 00:07:17,400 --> 00:07:20,440 Speaker 1: good on him. I bought the silliest thing over It 144 00:07:20,720 --> 00:07:22,480 Speaker 1: wasn't the silliest, It was one of the silliest things 145 00:07:22,520 --> 00:07:24,280 Speaker 1: I over did is I bought a long wheel based 146 00:07:24,360 --> 00:07:27,200 Speaker 1: rain drover. And I bought a long wheelbased rain drover 147 00:07:27,880 --> 00:07:30,920 Speaker 1: because the back seat was one of those executive back 148 00:07:30,920 --> 00:07:32,800 Speaker 1: seats that it had television in the back. It had 149 00:07:32,840 --> 00:07:36,200 Speaker 1: a champagne cooler and the back seat extended out so 150 00:07:36,240 --> 00:07:39,120 Speaker 1: you could sort of lie down like a business class flight. 151 00:07:39,760 --> 00:07:42,800 Speaker 1: And I thought, what a cool thing. Of course, what 152 00:07:42,880 --> 00:07:45,760 Speaker 1: I didn't realize was I was the driver and I 153 00:07:45,840 --> 00:07:47,760 Speaker 1: was never actually in the back seat, and I was 154 00:07:47,800 --> 00:07:50,320 Speaker 1: never there. And when I said to someone like I 155 00:07:50,320 --> 00:07:51,960 Speaker 1: don't know, Katie, hey, do you want to drive me 156 00:07:52,000 --> 00:07:53,680 Speaker 1: around the block while I sit in the back seat 157 00:07:54,200 --> 00:07:57,400 Speaker 1: so drink and drink champagne and drink champagne while watching television, 158 00:07:57,480 --> 00:08:00,360 Speaker 1: she you know what the answer was, obviously, So it's 159 00:08:00,400 --> 00:08:02,440 Speaker 1: sort of never gotten in use. But with with with 160 00:08:02,720 --> 00:08:08,160 Speaker 1: driverless cars, I could have that car back. It's kind 161 00:08:08,200 --> 00:08:11,080 Speaker 1: of like having Parker, isn't it very similar? 162 00:08:11,160 --> 00:08:11,400 Speaker 2: Yeah? 163 00:08:11,560 --> 00:08:15,000 Speaker 1: Very similar. For more from the Mic Asking Breakfast, listen 164 00:08:15,120 --> 00:08:18,040 Speaker 1: live to news talks. It'd be from six am weekdays, 165 00:08:18,280 --> 00:08:20,320 Speaker 1: or follow the podcast on iHeartRadio.