1 00:00:15,356 --> 00:00:22,876 Speaker 1: Pushkin. Just a quick note, today's episode is the last 2 00:00:22,956 --> 00:00:26,076 Speaker 1: one of twenty twenty five and for twenty twenty six. 3 00:00:26,396 --> 00:00:28,236 Speaker 1: We want to make the show better. We want to 4 00:00:28,236 --> 00:00:31,356 Speaker 1: make a show that you like more. To that end, 5 00:00:31,516 --> 00:00:34,916 Speaker 1: it would be extremely helpful to us and hopefully ultimately 6 00:00:34,916 --> 00:00:37,916 Speaker 1: also helpful to you if you could take a brief 7 00:00:37,956 --> 00:00:41,556 Speaker 1: survey that we have put together online. There's a link 8 00:00:41,596 --> 00:00:43,596 Speaker 1: to the survey in the show notes, and I'll give 9 00:00:43,636 --> 00:00:49,916 Speaker 1: you the url. It's TinyURL dot com slash wyp listener 10 00:00:49,956 --> 00:00:55,956 Speaker 1: survey again TinyURL dot com slash wyp like what's your 11 00:00:55,996 --> 00:00:59,316 Speaker 1: problem listener survey, or just click on the link in 12 00:00:59,356 --> 00:01:02,036 Speaker 1: the show notes. Thank you very much for listening to 13 00:01:02,116 --> 00:01:04,436 Speaker 1: the show, and thank you in advance for helping us 14 00:01:04,516 --> 00:01:08,876 Speaker 1: make the show better. Tim Booker grew up on his 15 00:01:08,916 --> 00:01:12,756 Speaker 1: fan family's dairy farm in rural northern California. 16 00:01:13,276 --> 00:01:15,556 Speaker 2: You know how when you're a kid, you always wait 17 00:01:15,596 --> 00:01:18,916 Speaker 2: for summer vacation from school. My brother and I we 18 00:01:19,716 --> 00:01:24,156 Speaker 2: actually we hated summer vacation because that meant, you know, 19 00:01:24,236 --> 00:01:26,876 Speaker 2: eighty hours a week of work out in the open field. 20 00:01:26,956 --> 00:01:28,796 Speaker 2: So you know, our friends would always say, oh, I 21 00:01:28,836 --> 00:01:31,436 Speaker 2: can't wait until school's out. And we would go no, 22 00:01:31,676 --> 00:01:33,636 Speaker 2: I want to stay in school. 23 00:01:34,116 --> 00:01:37,556 Speaker 1: Tim went off to college, started out studying agriculture, but 24 00:01:37,676 --> 00:01:41,836 Speaker 1: switched to computer science, and after he graduated he founded 25 00:01:41,876 --> 00:01:45,156 Speaker 1: a string of companies. One of them he sold to Microsoft, 26 00:01:45,436 --> 00:01:48,396 Speaker 1: now that he sold to Apple. But all along he 27 00:01:48,516 --> 00:01:52,916 Speaker 1: kept farming on the side, and eventually his farming life 28 00:01:52,996 --> 00:01:55,556 Speaker 1: and his tech life came together. As just kind of 29 00:01:55,596 --> 00:01:58,636 Speaker 1: a weekend side project on the farm he and a 30 00:01:58,676 --> 00:02:03,476 Speaker 1: few friends built on autonomous tractor. That weekend side project 31 00:02:03,596 --> 00:02:07,556 Speaker 1: has now turned into a company powering autonomous tractors across 32 00:02:07,596 --> 00:02:17,596 Speaker 1: the western US and part of Australia. I'm Jacob Goldstein 33 00:02:17,636 --> 00:02:19,396 Speaker 1: and this is What's Your Problem, the show where I 34 00:02:19,436 --> 00:02:22,436 Speaker 1: talk to people who are trying to make technological progress. 35 00:02:22,916 --> 00:02:26,076 Speaker 1: My guest today is Tim Booker. He's a longtime farmer 36 00:02:26,196 --> 00:02:30,636 Speaker 1: and the founder and CEO of Agtonomy. Tim's problem is this, 37 00:02:31,276 --> 00:02:35,356 Speaker 1: how do you bring autonomous vehicles to specialty crops, crops 38 00:02:35,436 --> 00:02:41,036 Speaker 1: like grapes and olives and almonds and apples. There's already 39 00:02:41,276 --> 00:02:44,996 Speaker 1: lots of autonomous equipment for row crops like corn and 40 00:02:45,036 --> 00:02:49,476 Speaker 1: wheat and soybeans, but as you'll hear, specialty crops present 41 00:02:49,556 --> 00:02:53,036 Speaker 1: a particularly tricky set of challenges. Tim and I talked 42 00:02:53,036 --> 00:02:56,756 Speaker 1: about the big picture about what autonomy will mean for 43 00:02:56,796 --> 00:03:00,556 Speaker 1: farming and for food. But to start, I asked him 44 00:03:00,556 --> 00:03:03,836 Speaker 1: about how he got from farming to computer science in 45 00:03:03,876 --> 00:03:08,756 Speaker 1: the first place. So you go off to college study 46 00:03:08,756 --> 00:03:12,196 Speaker 1: agricate culture Davis as a California farm kid does. 47 00:03:12,276 --> 00:03:12,476 Speaker 2: Right. 48 00:03:13,036 --> 00:03:15,796 Speaker 1: And did I hear you say in another interview that 49 00:03:15,836 --> 00:03:19,636 Speaker 1: you lost a bet and as a result of losing 50 00:03:19,636 --> 00:03:22,076 Speaker 1: the bet, had to take a computer science class. 51 00:03:23,356 --> 00:03:24,316 Speaker 2: Yes, you heard right. 52 00:03:24,836 --> 00:03:27,196 Speaker 1: What was the bet? Let's just say it was. 53 00:03:27,156 --> 00:03:30,876 Speaker 2: At a fraternity party, so it might have involved you know, 54 00:03:30,916 --> 00:03:32,316 Speaker 2: it might have involved beer. I don't know. 55 00:03:32,796 --> 00:03:35,956 Speaker 1: I like that it's a drunken fraternity party that leads 56 00:03:35,996 --> 00:03:39,156 Speaker 1: you into a career as a computer scientist. Like that 57 00:03:39,236 --> 00:03:40,436 Speaker 1: is an interesting combination. 58 00:03:40,916 --> 00:03:43,036 Speaker 2: Yeah, it was the It was the greatest bet I 59 00:03:43,036 --> 00:03:47,436 Speaker 2: ever lost. But what was what was interesting? I all 60 00:03:47,476 --> 00:03:50,156 Speaker 2: my friends growing up, their last names were Sagatio, of 61 00:03:50,236 --> 00:03:53,796 Speaker 2: a Piano, Bach, Galupi, Gallo. These are incredible grape growing 62 00:03:53,796 --> 00:03:56,956 Speaker 2: in why making families back in the seventies, even and 63 00:03:58,476 --> 00:04:01,196 Speaker 2: I would always go over to their places to, you know, 64 00:04:01,436 --> 00:04:03,996 Speaker 2: help them, because that's what playdates were back then. Is 65 00:04:04,036 --> 00:04:06,556 Speaker 2: you basically went over to your friend's farm and worked 66 00:04:06,556 --> 00:04:09,956 Speaker 2: with them, and I would learn how to prune grapes 67 00:04:09,996 --> 00:04:13,436 Speaker 2: and even make wine. And I was fascinated by that industry, 68 00:04:13,596 --> 00:04:17,196 Speaker 2: like incredibly fascinated by the growing of grapes. So, you know, 69 00:04:17,236 --> 00:04:21,316 Speaker 2: I went to UC Davis for agriculture, and yes, I 70 00:04:21,356 --> 00:04:24,476 Speaker 2: lost a bet and took a class in a five 71 00:04:24,556 --> 00:04:28,396 Speaker 2: hundred person auditorium. Now keep in mind, I didn't grow 72 00:04:28,436 --> 00:04:31,236 Speaker 2: up with any technology. I mean like tractors. That was 73 00:04:31,276 --> 00:04:36,316 Speaker 2: about it. But the professor started talking and I'm in 74 00:04:36,356 --> 00:04:40,756 Speaker 2: the last row, and the professor started talking, and everything 75 00:04:40,796 --> 00:04:44,356 Speaker 2: he was saying I understood, and it just kind. 76 00:04:44,196 --> 00:04:44,796 Speaker 1: Of hit me. 77 00:04:45,636 --> 00:04:47,876 Speaker 2: And by the end of that quarter, you see Davis 78 00:04:47,956 --> 00:04:51,196 Speaker 2: on the quarter system. I was upfront teaching the class, 79 00:04:51,996 --> 00:04:55,996 Speaker 2: and so I knew something happened, like I found a passion, 80 00:04:57,116 --> 00:05:01,036 Speaker 2: like the passion I had for agriculture. And so I 81 00:05:01,076 --> 00:05:03,036 Speaker 2: had a decision to make, is do do I go 82 00:05:03,196 --> 00:05:06,236 Speaker 2: the agriculture route or do I go this computer science 83 00:05:06,316 --> 00:05:09,916 Speaker 2: route or this you know, high tech route. And I said, 84 00:05:09,996 --> 00:05:12,476 Speaker 2: you know what, I'm going to do both. I then 85 00:05:12,516 --> 00:05:14,996 Speaker 2: graduated from Davis and I went on to Stanford and 86 00:05:15,276 --> 00:05:20,276 Speaker 2: grad school and then started bouncing between Silicon Valley and 87 00:05:20,796 --> 00:05:24,236 Speaker 2: Sonoma County because when I was sixteen, I actually bought 88 00:05:24,236 --> 00:05:28,156 Speaker 2: my first vineyard, a small, little two acres, and I 89 00:05:28,196 --> 00:05:31,996 Speaker 2: loved farming that and that's kind of what got me to, 90 00:05:32,796 --> 00:05:36,076 Speaker 2: you know, get my own larger place, as I did 91 00:05:36,116 --> 00:05:40,796 Speaker 2: through the decades, which is now called Tratory Farms Tratory 92 00:05:40,876 --> 00:05:44,476 Speaker 2: which means tractor and Italian because I wanted to name 93 00:05:44,476 --> 00:05:49,436 Speaker 2: it after my childhood passion. And that's where that's where 94 00:05:49,996 --> 00:05:53,476 Speaker 2: kind of that whole thing started. The duality or the 95 00:05:54,036 --> 00:05:56,236 Speaker 2: dual path of agriculture and high tech. 96 00:05:57,036 --> 00:06:00,516 Speaker 1: You have these sort of parallel lives going for a while, Right, 97 00:06:00,596 --> 00:06:05,396 Speaker 1: you're working for Steve Jobs, you're starting companies. You've got 98 00:06:05,436 --> 00:06:09,116 Speaker 1: a farm up in Sonoma. A little plane you're flying 99 00:06:09,196 --> 00:06:15,476 Speaker 1: back and forth, right, And so tell me about the 100 00:06:15,516 --> 00:06:17,916 Speaker 1: farm part of your life, Like that's you know, we're 101 00:06:17,916 --> 00:06:19,676 Speaker 1: going to bring back the technology in a minute. But 102 00:06:20,116 --> 00:06:23,956 Speaker 1: you're grown up. You have a real farm, Like what's 103 00:06:23,996 --> 00:06:24,436 Speaker 1: your farm? 104 00:06:25,796 --> 00:06:29,756 Speaker 2: Well, it started as a small little vineyard, only two acres, 105 00:06:30,276 --> 00:06:33,116 Speaker 2: and then through the years I would do what's called 106 00:06:33,116 --> 00:06:35,516 Speaker 2: a ten thirty one tax free exchange and get a 107 00:06:35,036 --> 00:06:39,756 Speaker 2: bigger piece of land and then, you know, I don't know, 108 00:06:39,756 --> 00:06:42,316 Speaker 2: about twenty five years ago, I got a much bigger 109 00:06:42,316 --> 00:06:42,996 Speaker 2: piece of land. 110 00:06:43,196 --> 00:06:45,516 Speaker 1: Yeah two acres, by the way, two acres isn't even 111 00:06:45,556 --> 00:06:47,916 Speaker 1: a farm, right, two acres is like a yard. 112 00:06:47,996 --> 00:06:52,276 Speaker 2: Yeah, pretty much a yard. Good good call. But it 113 00:06:52,356 --> 00:06:57,476 Speaker 2: was French colmbard grapes and zinfandel grapes. So so, you know, 114 00:06:57,556 --> 00:07:00,956 Speaker 2: fast forward two years later, as I would kind of 115 00:07:00,956 --> 00:07:03,796 Speaker 2: get bigger and bigger places, I got this one place 116 00:07:03,836 --> 00:07:06,996 Speaker 2: which was amazing beautiful hillsides. I had a vision for it, 117 00:07:07,036 --> 00:07:09,756 Speaker 2: but it was just a forest, and I start clearing 118 00:07:09,796 --> 00:07:14,036 Speaker 2: it for you know, the intention of growing more grapes, 119 00:07:14,756 --> 00:07:18,996 Speaker 2: and I encountered these stumps, you know, equidistance throughout the land, 120 00:07:19,036 --> 00:07:21,516 Speaker 2: and I was like, what is this? And then finally 121 00:07:21,556 --> 00:07:25,356 Speaker 2: I uncovered some old olive trees from like the eighteen hundreds, 122 00:07:26,036 --> 00:07:28,596 Speaker 2: and so did some research and realized, well, this was 123 00:07:28,636 --> 00:07:31,316 Speaker 2: part of a big you know, Spanish land grant growing 124 00:07:31,476 --> 00:07:34,356 Speaker 2: olives back in the eighteen hundreds. So I decided to 125 00:07:34,356 --> 00:07:38,236 Speaker 2: bring that history back and that's what got me into olives. 126 00:07:38,836 --> 00:07:42,396 Speaker 2: So I grow olives for olive oil and grapes for wine, 127 00:07:42,436 --> 00:07:46,316 Speaker 2: and I have a winery operation public tasting room, so 128 00:07:46,956 --> 00:07:49,316 Speaker 2: kind of bouncing between two worlds for decades. 129 00:07:50,476 --> 00:07:56,636 Speaker 1: So before you started etonomy, tell me about the way 130 00:07:56,676 --> 00:07:59,796 Speaker 1: you brought technology to your farm, Like, what did you automate? 131 00:08:00,996 --> 00:08:04,836 Speaker 2: Yeah? I was unique in that I was a farmer, 132 00:08:05,476 --> 00:08:09,076 Speaker 2: but I was also an engineer. I had both, you know, 133 00:08:09,196 --> 00:08:12,836 Speaker 2: electrical engineering background as well as computer science background, so 134 00:08:13,556 --> 00:08:17,876 Speaker 2: I could do things like create you know, automated water 135 00:08:17,956 --> 00:08:21,636 Speaker 2: recycling plants that you know, had the flow meters everywhere 136 00:08:21,636 --> 00:08:25,796 Speaker 2: it knew, you know, how to inject different microbes to 137 00:08:25,836 --> 00:08:29,556 Speaker 2: process the waste water and then inject it back into 138 00:08:29,596 --> 00:08:34,476 Speaker 2: the irrigation automatically. I had, you know, obviously all kinds 139 00:08:34,476 --> 00:08:38,476 Speaker 2: of basic things like cameras and whatnot. But even in 140 00:08:38,516 --> 00:08:43,716 Speaker 2: the fermentation of grapes, the tanks are all computer controlled. 141 00:08:44,116 --> 00:08:48,596 Speaker 2: The computer controls whether to use you know, active refrigeration, 142 00:08:48,716 --> 00:08:52,436 Speaker 2: which costs energy, or if the outside air is cooler 143 00:08:52,476 --> 00:08:55,596 Speaker 2: than the inside, it automatically switches over and brings that 144 00:08:55,676 --> 00:08:58,636 Speaker 2: kind of air in. So you know, everything from fermentation 145 00:08:58,756 --> 00:09:04,356 Speaker 2: processes to water recycling to irrigation is radically automated. So 146 00:09:04,396 --> 00:09:09,316 Speaker 2: I had automated darn near everything I could, and I 147 00:09:09,356 --> 00:09:12,676 Speaker 2: didn't know I was actually doing ag tech. When I 148 00:09:12,756 --> 00:09:15,996 Speaker 2: was doing this, ag tech wasn't a word that existed 149 00:09:15,996 --> 00:09:16,316 Speaker 2: back then. 150 00:09:16,396 --> 00:09:18,876 Speaker 1: You were just like puttering around on the weekends. 151 00:09:19,476 --> 00:09:22,956 Speaker 2: I was puttering around on the weekends or night times whatever. 152 00:09:23,596 --> 00:09:27,436 Speaker 2: I just knew my farm needed technology to automate things, 153 00:09:27,436 --> 00:09:31,316 Speaker 2: because keep in mind, I was also bouncing between Sonoma 154 00:09:31,356 --> 00:09:35,556 Speaker 2: County and Silicon Valley, and so I needed technology to 155 00:09:35,596 --> 00:09:39,156 Speaker 2: also be able to monitor things. Right, But there was 156 00:09:39,196 --> 00:09:41,636 Speaker 2: one area I hadn't automated, and that was the actual 157 00:09:41,716 --> 00:09:44,916 Speaker 2: work in the orchards and the vineyards. And I was 158 00:09:44,956 --> 00:09:46,996 Speaker 2: sitting there going, hell, you know, I automated this. I 159 00:09:47,036 --> 00:09:49,716 Speaker 2: automated that, Hey, maybe I should try my hand at 160 00:09:50,036 --> 00:09:54,196 Speaker 2: automating the field work. There's so much money being invested 161 00:09:54,196 --> 00:09:57,796 Speaker 2: in autonomous passenger vehicles, and there's so much technology that's 162 00:09:57,836 --> 00:10:00,676 Speaker 2: evolved there. I wonder if I could just apply that 163 00:10:01,156 --> 00:10:03,756 Speaker 2: to this space and see if I can create an 164 00:10:03,796 --> 00:10:07,836 Speaker 2: autonomous vehicle that can do these kinds of repetitive tasks 165 00:10:08,396 --> 00:10:13,796 Speaker 2: like mowing, like spraying, you know, like underfining cultivation, like seating, 166 00:10:13,956 --> 00:10:16,076 Speaker 2: like disking, things of that nature where you're just going 167 00:10:16,116 --> 00:10:18,316 Speaker 2: back and forth up and down the rows. 168 00:10:18,476 --> 00:10:22,236 Speaker 1: Right, Just to be clear, there had been a fair 169 00:10:22,276 --> 00:10:24,876 Speaker 1: bit of automation, right, But on the sort of big 170 00:10:24,876 --> 00:10:28,556 Speaker 1: egg row crop like corn and wheat side, as I 171 00:10:28,636 --> 00:10:31,556 Speaker 1: understand it, but not so much on the permanent crop 172 00:10:31,636 --> 00:10:33,436 Speaker 1: kind of fruit and nut side. Is that right? Was 173 00:10:33,436 --> 00:10:34,436 Speaker 1: that the sort of state of. 174 00:10:34,396 --> 00:10:38,716 Speaker 2: Play exactly, Jacob So, in row crops or in broad 175 00:10:38,716 --> 00:10:41,636 Speaker 2: acre farming, you know, the big million dollar tractors or 176 00:10:41,716 --> 00:10:45,996 Speaker 2: half million dollar tractors, there has been automation there for decades, 177 00:10:46,636 --> 00:10:50,356 Speaker 2: things called auto steer. You know, you're on these flat areas, 178 00:10:50,916 --> 00:10:54,756 Speaker 2: very big fields. You can see the sky. GPS had 179 00:10:54,796 --> 00:10:58,676 Speaker 2: evolved so it would be somewhat accurate, you know, a 180 00:10:58,716 --> 00:11:01,076 Speaker 2: meter or whatever it might be, and so you could 181 00:11:01,076 --> 00:11:04,036 Speaker 2: just control the vehicles based on that. When you think 182 00:11:04,076 --> 00:11:08,276 Speaker 2: about permanent crops, which is a huge percentage value wise 183 00:11:08,396 --> 00:11:13,396 Speaker 2: of our foods supply, the permanent crops, you're talking about 184 00:11:13,556 --> 00:11:16,876 Speaker 2: trees and vines that are very very expensive. They take 185 00:11:17,316 --> 00:11:21,076 Speaker 2: many years to develop, and so if you hit them, 186 00:11:21,356 --> 00:11:23,836 Speaker 2: it's kind of a big deal. It's kind of expensive 187 00:11:24,676 --> 00:11:26,476 Speaker 2: and you have to get really close to them. So 188 00:11:26,516 --> 00:11:28,996 Speaker 2: you're not talking about being an open field where you 189 00:11:29,036 --> 00:11:30,716 Speaker 2: can be you know, plus or minus half a meter. 190 00:11:31,236 --> 00:11:34,396 Speaker 2: You're talking about being like one inch away from you know, 191 00:11:34,516 --> 00:11:37,916 Speaker 2: the trunks of these trees, and if you accidentally move 192 00:11:38,156 --> 00:11:41,916 Speaker 2: you know, abruptly, or you know, don't steer correctly, you're 193 00:11:41,916 --> 00:11:44,636 Speaker 2: going to hit it. So you can't rely on kind 194 00:11:44,636 --> 00:11:47,556 Speaker 2: of the things that you can rely on in broad 195 00:11:47,596 --> 00:11:51,236 Speaker 2: acre farming and the rowe crop farming. So there needed 196 00:11:51,236 --> 00:11:55,076 Speaker 2: to be more advanced technology. And thankfully, due to all 197 00:11:55,116 --> 00:11:58,956 Speaker 2: the attention and investments that were being made in you know, 198 00:11:58,996 --> 00:12:05,356 Speaker 2: the autonomous passenger vehicle world, that technology started to become available. 199 00:12:05,556 --> 00:12:08,116 Speaker 2: You know, call it maybe less than a decade ago, 200 00:12:08,676 --> 00:12:11,996 Speaker 2: but when I said, you know, kind of seven years ago, 201 00:12:12,076 --> 00:12:14,876 Speaker 2: like hey, you know, my expenses are continuing to go 202 00:12:14,956 --> 00:12:18,636 Speaker 2: up at tratory. I've automated DAR near everything, but I 203 00:12:18,676 --> 00:12:21,916 Speaker 2: haven't automated the field work. Could we take some of 204 00:12:21,916 --> 00:12:25,116 Speaker 2: that technology and could we create a solution, an autonomous 205 00:12:25,116 --> 00:12:29,516 Speaker 2: solution that could do these repetitive tasks that you need 206 00:12:29,556 --> 00:12:33,756 Speaker 2: to do in permanent crops. And so with some friends 207 00:12:33,956 --> 00:12:38,876 Speaker 2: built a prototype five years ago and the prototype worked 208 00:12:38,916 --> 00:12:43,716 Speaker 2: really well. We literally built an all electric autonomous tractor. 209 00:12:44,596 --> 00:12:46,316 Speaker 1: What did it look like and what did it do? 210 00:12:46,916 --> 00:12:49,556 Speaker 2: Didn't look like a traditional tractor. We took out the 211 00:12:49,556 --> 00:12:54,876 Speaker 2: diesel engine. We put in electric motors and batteries, and 212 00:12:54,996 --> 00:12:58,116 Speaker 2: we put on cameras and we had you know, an 213 00:12:58,196 --> 00:13:02,436 Speaker 2: Nvidia compute system in it, and we started to create 214 00:13:02,516 --> 00:13:07,796 Speaker 2: this sensor based vehicle. It had eight wheels, it articulated, 215 00:13:07,956 --> 00:13:10,076 Speaker 2: and it could do really steep st and one of 216 00:13:10,116 --> 00:13:12,276 Speaker 2: the reason why we did that is because that tratory 217 00:13:12,396 --> 00:13:15,716 Speaker 2: the slopes are very very steep. By the way, one 218 00:13:15,716 --> 00:13:18,676 Speaker 2: of the main inspirations for doing this autonomous vehicle came 219 00:13:18,716 --> 00:13:22,636 Speaker 2: from a documentary I watched. I think it was a 220 00:13:22,716 --> 00:13:25,756 Speaker 2: National geographic. It was called Mission to Mars, The Story 221 00:13:25,756 --> 00:13:28,756 Speaker 2: of Spirit and Opportunity. And these were Mars rovers in 222 00:13:28,796 --> 00:13:33,236 Speaker 2: the nineties that NASA shot up and we're hoping would 223 00:13:33,316 --> 00:13:37,076 Speaker 2: operate for you know, fifteen days or you know, a 224 00:13:37,076 --> 00:13:39,636 Speaker 2: couple of months, and they ended up lasting almost fifteen years. 225 00:13:40,156 --> 00:13:43,756 Speaker 2: And I was watching this documentary before I actually built 226 00:13:43,756 --> 00:13:47,836 Speaker 2: this prototype, and it reminded me that, hey, there are 227 00:13:47,956 --> 00:13:53,076 Speaker 2: environments where you can take your time in making decisions. 228 00:13:53,476 --> 00:13:56,676 Speaker 2: So they had animations of how the rover would approach 229 00:13:56,676 --> 00:13:58,756 Speaker 2: a rock and it would just stop and then it 230 00:13:58,756 --> 00:14:02,116 Speaker 2: would radio JPL, you know, take some time to radio JPL. 231 00:14:02,196 --> 00:14:04,716 Speaker 2: They would take a week and write some new code. 232 00:14:04,716 --> 00:14:07,116 Speaker 2: They would then download it, and then the vehicle, the 233 00:14:07,196 --> 00:14:10,356 Speaker 2: rover would move to the left that whenever it encountered 234 00:14:10,356 --> 00:14:12,036 Speaker 2: a rock, it would move to the laughter to the right. 235 00:14:12,636 --> 00:14:14,636 Speaker 2: And I said to myself, my god, you know, they 236 00:14:15,076 --> 00:14:18,996 Speaker 2: had autonomous vehicles operating on Mars in the nineties and 237 00:14:19,036 --> 00:14:21,036 Speaker 2: they could do it because there wasn't a lot of 238 00:14:21,036 --> 00:14:25,196 Speaker 2: traffic on Mars, just like agriculture. And that's when my 239 00:14:25,276 --> 00:14:28,236 Speaker 2: head really exploded and I said, wait, there are these 240 00:14:28,276 --> 00:14:33,636 Speaker 2: industrial markets that need automation, and you don't necessarily have 241 00:14:33,756 --> 00:14:39,516 Speaker 2: to immediately, you know, be responsive to the vehicle if 242 00:14:39,516 --> 00:14:42,876 Speaker 2: it encounters something it can't figure out, like oh, there's 243 00:14:42,876 --> 00:14:43,716 Speaker 2: a tree in front of. 244 00:14:43,636 --> 00:14:48,796 Speaker 1: Me, unlike on a road in traffic. Correct. Correct, So 245 00:14:48,876 --> 00:14:52,116 Speaker 1: when you build this prototype that you don't even know 246 00:14:52,196 --> 00:14:54,236 Speaker 1: is a prototype as you're telling it, right, this thing 247 00:14:54,316 --> 00:14:57,876 Speaker 1: that you're doing with your friends, I mean it sounds 248 00:14:57,916 --> 00:15:00,076 Speaker 1: like a lot of the sort of tex stack is 249 00:15:00,116 --> 00:15:02,556 Speaker 1: basically commodified by that point. I mean, is it a 250 00:15:02,636 --> 00:15:05,236 Speaker 1: sort of you're at this moment where you can be like, oh, well, 251 00:15:05,236 --> 00:15:08,636 Speaker 1: buy these sensors and we'll start with this piece of software, 252 00:15:08,676 --> 00:15:12,076 Speaker 1: and like you can almost build an autonomous vehicle from 253 00:15:12,156 --> 00:15:15,036 Speaker 1: off the shelf parts. Is that sort of the starting point. 254 00:15:14,756 --> 00:15:18,556 Speaker 2: From a hardware perspective. Absolutely, from a software perspective, no, 255 00:15:20,276 --> 00:15:22,356 Speaker 2: at that time, so no, we were actually building a 256 00:15:22,356 --> 00:15:26,156 Speaker 2: perception stack, you know that could you know do all 257 00:15:26,196 --> 00:15:27,876 Speaker 2: the Well, first you had to localize, so you have 258 00:15:27,916 --> 00:15:29,316 Speaker 2: to figure out where in the world do you are, 259 00:15:30,396 --> 00:15:32,516 Speaker 2: And so we did need to see GPS at least once, 260 00:15:33,476 --> 00:15:36,996 Speaker 2: but then the computer vision technologies and the perception stack 261 00:15:37,076 --> 00:15:40,836 Speaker 2: could then align to the crop itself. One of the 262 00:15:40,836 --> 00:15:44,796 Speaker 2: big differences that in approach that we took that obviously 263 00:15:44,876 --> 00:15:46,916 Speaker 2: the row crops, the broad acre farms don't need to 264 00:15:46,956 --> 00:15:51,116 Speaker 2: do is we actually said, hey, what if we exploit 265 00:15:51,156 --> 00:15:55,236 Speaker 2: the structure the crop itself and utilize that structure to 266 00:15:55,316 --> 00:15:59,196 Speaker 2: navigate safely through it and farm precisely around it. 267 00:15:59,796 --> 00:16:02,476 Speaker 1: Meaning like know what a tree is and know what 268 00:16:02,516 --> 00:16:04,756 Speaker 1: your relationship should be to a tree. Is that what 269 00:16:04,796 --> 00:16:06,196 Speaker 1: that means? Correct? 270 00:16:06,396 --> 00:16:09,316 Speaker 2: And in the early days we said, hey, there's one 271 00:16:09,436 --> 00:16:13,476 Speaker 2: common characteristic and permanent crops. They all have a trunk, 272 00:16:14,316 --> 00:16:18,116 Speaker 2: So what if we could be the best detectors of 273 00:16:18,156 --> 00:16:22,396 Speaker 2: trunks in the world and utilize that for alignment? And 274 00:16:22,556 --> 00:16:25,396 Speaker 2: as I said, also you know, working around that plant. 275 00:16:25,756 --> 00:16:28,236 Speaker 1: So that's the key sort of training piece, Like you 276 00:16:28,356 --> 00:16:30,636 Speaker 1: train really hard on trunks. 277 00:16:30,556 --> 00:16:33,956 Speaker 2: That's right, that's right. We initially labeled trunks. We would 278 00:16:34,036 --> 00:16:36,636 Speaker 2: drive through, we would get you know, tons of photos 279 00:16:37,076 --> 00:16:41,316 Speaker 2: and we would manually label around the trunk area. And 280 00:16:41,356 --> 00:16:44,036 Speaker 2: then we would take those images and we would tweak 281 00:16:44,076 --> 00:16:47,756 Speaker 2: them and create synthetic data and we would label those 282 00:16:48,396 --> 00:16:51,156 Speaker 2: and once you get enough of a data set, you 283 00:16:51,196 --> 00:16:54,636 Speaker 2: can start to do some additional machine learning with that, right. 284 00:16:55,396 --> 00:16:58,036 Speaker 2: And what was interesting was, you know, we got so 285 00:16:58,156 --> 00:17:00,716 Speaker 2: good at detecting trunks. We even called it trunk vision. 286 00:17:00,836 --> 00:17:04,436 Speaker 2: We trademark trunk vision. Not that you know, we do 287 00:17:04,476 --> 00:17:07,676 Speaker 2: a lot more than trunk vision today, but that's how 288 00:17:07,836 --> 00:17:09,956 Speaker 2: that's how we got it to, you know, run through 289 00:17:10,036 --> 00:17:12,836 Speaker 2: the orchards and run through the vineyards. And what was 290 00:17:12,916 --> 00:17:16,716 Speaker 2: really interesting is we initially trained on grapes, on grapevines 291 00:17:17,276 --> 00:17:22,196 Speaker 2: and the rootstock right the trunk. And one day my CTOs, 292 00:17:22,476 --> 00:17:24,876 Speaker 2: you know, he said, hey, let's run it in the olives. 293 00:17:25,516 --> 00:17:27,316 Speaker 2: And I'm like, oh, but you know, you're sure it's 294 00:17:27,316 --> 00:17:29,036 Speaker 2: going to work. He's like, I don't know if it's 295 00:17:29,036 --> 00:17:31,756 Speaker 2: going to work. I don't know how well the training 296 00:17:31,796 --> 00:17:34,636 Speaker 2: will translate. And so we set it off in the 297 00:17:34,716 --> 00:17:39,596 Speaker 2: Olives and it just worked. Now, it wasn't perfect, but 298 00:17:40,196 --> 00:17:42,796 Speaker 2: it made us realize, Wow, there's a lot of leverage 299 00:17:42,836 --> 00:17:46,836 Speaker 2: between crops here in these permanent crop areas. Generalizes, it 300 00:17:46,916 --> 00:17:50,996 Speaker 2: generalizes really well. And of course he's continued to evolve that, 301 00:17:51,196 --> 00:17:53,436 Speaker 2: and you know, we now do all kinds of additional 302 00:17:53,796 --> 00:17:56,916 Speaker 2: training to make sure in a new crop type we 303 00:17:56,956 --> 00:18:00,516 Speaker 2: are very very accurate in exploiting the structure of the crop. 304 00:18:01,116 --> 00:18:05,316 Speaker 1: Besides driving through trees without hitting them, which I recognize 305 00:18:05,356 --> 00:18:08,316 Speaker 1: as non trivial, what else did the tractor do? Did 306 00:18:08,316 --> 00:18:08,996 Speaker 1: it do work? 307 00:18:09,836 --> 00:18:14,036 Speaker 2: Yes, it did work. It mowed initially. The very very 308 00:18:14,116 --> 00:18:16,356 Speaker 2: very first job we did was mowing because we have 309 00:18:16,436 --> 00:18:18,836 Speaker 2: cover crop. Sometimes the cover crop gets like six feet 310 00:18:18,876 --> 00:18:22,076 Speaker 2: tall and you chop it up with a flail mower 311 00:18:22,996 --> 00:18:26,076 Speaker 2: and then it composts in between the trees or the 312 00:18:26,156 --> 00:18:30,196 Speaker 2: vines and and even that, like you think mowing, Oh yeah, 313 00:18:30,236 --> 00:18:31,836 Speaker 2: you just set it at four miles an hour and 314 00:18:31,876 --> 00:18:34,396 Speaker 2: you let it go. You cannot do that, right, because 315 00:18:34,436 --> 00:18:36,636 Speaker 2: think about what a human does. The human's on a vehicle. 316 00:18:37,596 --> 00:18:40,956 Speaker 2: It's really funny. I asked my engineers you know, well, 317 00:18:40,996 --> 00:18:42,556 Speaker 2: did you ever use a push mower when you were 318 00:18:42,596 --> 00:18:45,836 Speaker 2: a kid, And they're like, what's a push mower? Anyway? 319 00:18:45,876 --> 00:18:49,356 Speaker 1: Separate topic, but pushmore is really hard. It's harder than 320 00:18:49,396 --> 00:18:53,236 Speaker 1: you think that was th pushover means the more without 321 00:18:53,276 --> 00:18:56,036 Speaker 1: an engine. It doesn't just mean a gas mower that 322 00:18:56,076 --> 00:18:56,556 Speaker 1: you put. 323 00:18:56,516 --> 00:18:59,916 Speaker 2: Right right right and then and then you know, we 324 00:18:59,956 --> 00:19:04,356 Speaker 2: got motorized mowers for lawns, right that you that you 325 00:19:04,396 --> 00:19:06,756 Speaker 2: know actually had wheels and had an engine on them. 326 00:19:07,116 --> 00:19:09,036 Speaker 2: But even with those, if you would go through the 327 00:19:10,036 --> 00:19:14,596 Speaker 2: and if it would bog down, as the person operating 328 00:19:14,676 --> 00:19:18,076 Speaker 2: that lawn mower, you would slow it down in order 329 00:19:18,116 --> 00:19:21,556 Speaker 2: for the blades to catch up, right. And so if 330 00:19:21,596 --> 00:19:24,276 Speaker 2: you think about it, you can't just set a tractor 331 00:19:24,316 --> 00:19:28,556 Speaker 2: going to mow. You need to get that feedback to 332 00:19:28,716 --> 00:19:32,396 Speaker 2: understand if it's bogging down, and therefore you need to 333 00:19:32,476 --> 00:19:35,996 Speaker 2: slow down or increase the RPM in order to do 334 00:19:36,076 --> 00:19:37,036 Speaker 2: the job properly. 335 00:19:37,636 --> 00:19:37,836 Speaker 1: Right. 336 00:19:38,436 --> 00:19:42,756 Speaker 2: So that's something we learned immediately, But we already thought 337 00:19:42,756 --> 00:19:45,716 Speaker 2: about that because we were all farmers. We had farmer 338 00:19:45,876 --> 00:19:48,636 Speaker 2: DNA in us, and we knew that's how a human operated. 339 00:19:48,676 --> 00:19:50,996 Speaker 2: So we just we started to think, Okay, how does 340 00:19:51,036 --> 00:19:53,276 Speaker 2: a human do this? And what are the things we're 341 00:19:53,276 --> 00:19:56,476 Speaker 2: gonna need feedback on like a human has in order 342 00:19:56,516 --> 00:19:59,876 Speaker 2: to get the job done correctly. So we started with 343 00:19:59,956 --> 00:20:02,276 Speaker 2: mowing and we became really really good at it. 344 00:20:02,876 --> 00:20:06,276 Speaker 1: So you have your prototype and you decide to start 345 00:20:06,316 --> 00:20:11,276 Speaker 1: a company. What do you have to do to turn 346 00:20:11,316 --> 00:20:15,916 Speaker 1: your prototype into you know, something you can sell? Like, yeah, 347 00:20:15,956 --> 00:20:17,676 Speaker 1: what's the what's the leap? 348 00:20:18,276 --> 00:20:22,596 Speaker 2: Yeah, this was a big leap. You know. My mechanical 349 00:20:22,596 --> 00:20:25,556 Speaker 2: engineering co founders was like, you know, whipping up all 350 00:20:25,636 --> 00:20:29,516 Speaker 2: kinds of cool ideas. And then I thought about it 351 00:20:29,556 --> 00:20:32,036 Speaker 2: as a farmer, and I said, wait a minute, as 352 00:20:32,076 --> 00:20:36,516 Speaker 2: a grower, would I trust a startup company, you know, 353 00:20:36,676 --> 00:20:41,276 Speaker 2: for a new tractor? Think about it, right, I'm a 354 00:20:41,316 --> 00:20:46,596 Speaker 2: farm I rely upon equipment to get my job done, 355 00:20:47,436 --> 00:20:51,916 Speaker 2: and that job is vital for the livelihood of the business, 356 00:20:52,156 --> 00:20:55,316 Speaker 2: for the livelihood of the family if it's a family farm. 357 00:20:55,596 --> 00:21:00,836 Speaker 2: So why would I trust a startup instead? What I 358 00:21:00,916 --> 00:21:04,396 Speaker 2: need as a grower is I need trusted brands. I 359 00:21:04,476 --> 00:21:06,996 Speaker 2: need their dealer networks. I need the parts, I need 360 00:21:06,996 --> 00:21:11,476 Speaker 2: the service. This this equipment needs to be operating all 361 00:21:11,516 --> 00:21:14,916 Speaker 2: the times that I need it for the sustainability of 362 00:21:14,956 --> 00:21:18,316 Speaker 2: my business. And so I said to my colleagues, I said, 363 00:21:18,356 --> 00:21:20,396 Speaker 2: you know what, I don't think we should build a tractor. 364 00:21:20,796 --> 00:21:23,676 Speaker 2: I think that would be rather stupid. And I explained why, 365 00:21:23,716 --> 00:21:26,876 Speaker 2: and they said, oh, that makes sense, and they said, well, 366 00:21:27,116 --> 00:21:32,276 Speaker 2: how can we work with existing original equipment manufacturers? These 367 00:21:32,316 --> 00:21:34,916 Speaker 2: incredible equipment manufacturers who've been at it for over one 368 00:21:34,956 --> 00:21:40,316 Speaker 2: hundred years and they build incredible vehicles that really work 369 00:21:40,396 --> 00:21:44,596 Speaker 2: well in these harsh environments. And so you know, at 370 00:21:44,636 --> 00:21:47,476 Speaker 2: first investors that I was talking about, Hey, we're going 371 00:21:47,516 --> 00:21:50,476 Speaker 2: to do an OEM model and you know, original equipment manufacturer, 372 00:21:50,956 --> 00:21:55,316 Speaker 2: we're going to help accelerate their digital transformation because we 373 00:21:55,356 --> 00:21:57,676 Speaker 2: know where the puck is going, right. We know that 374 00:21:57,996 --> 00:22:01,276 Speaker 2: this industry too, just like other industries, needs to go 375 00:22:01,316 --> 00:22:04,876 Speaker 2: through a digital transformation. Every day there's less and less 376 00:22:04,916 --> 00:22:09,476 Speaker 2: skilled operators who go into agriculture. So the equipment manufacturer know, 377 00:22:09,876 --> 00:22:12,516 Speaker 2: you know, they see the writing on the wall. They're 378 00:22:12,516 --> 00:22:14,756 Speaker 2: not going to sell as much equipment because like there's 379 00:22:14,796 --> 00:22:17,916 Speaker 2: less people to operate them. So they know automation is 380 00:22:17,956 --> 00:22:21,516 Speaker 2: really important, and they know how to build incredible iron, 381 00:22:21,596 --> 00:22:23,996 Speaker 2: but do they know how to build an AI factory? 382 00:22:24,676 --> 00:22:26,676 Speaker 2: And that's what I said, you know we would focus 383 00:22:26,716 --> 00:22:31,996 Speaker 2: on is that we would build software to make their 384 00:22:32,076 --> 00:22:38,116 Speaker 2: incredible equipment incredibly smart and provide the solutions that make 385 00:22:38,196 --> 00:22:43,756 Speaker 2: them autonomous and give growers ultimately new technologies that can 386 00:22:43,956 --> 00:22:47,076 Speaker 2: help them save money and get an incredible ROI. It's 387 00:22:47,116 --> 00:22:49,916 Speaker 2: almost like building equipment that comes with a skilled operator 388 00:22:49,916 --> 00:22:51,596 Speaker 2: built in that you can turn on and off any 389 00:22:51,596 --> 00:22:52,516 Speaker 2: time you want. 390 00:22:53,356 --> 00:22:55,836 Speaker 1: As a bonus, you don't have to have the crazy 391 00:22:55,876 --> 00:23:00,236 Speaker 1: capital outlay of building a tractor factory, right like, I mean, 392 00:23:00,316 --> 00:23:02,356 Speaker 1: that's business. It seems way better. 393 00:23:03,556 --> 00:23:08,396 Speaker 2: It does, but there's one catch, and investors early on 394 00:23:08,436 --> 00:23:10,436 Speaker 2: would say, wow, I don't think you can get an OEM. 395 00:23:11,596 --> 00:23:16,036 Speaker 1: And we did an OEM, an original equipment manufacturer attractor. 396 00:23:16,156 --> 00:23:21,036 Speaker 2: Attractor company, and we did an incredible company called Bobcat. 397 00:23:21,276 --> 00:23:21,636 Speaker 2: Douce on. 398 00:23:21,676 --> 00:23:22,196 Speaker 1: Bobcat. 399 00:23:22,756 --> 00:23:26,956 Speaker 2: They make excavators large and small, They make skid steers, 400 00:23:26,996 --> 00:23:33,756 Speaker 2: they make trackloaders, they make tractors like normal looking tractors, right, 401 00:23:34,396 --> 00:23:38,596 Speaker 2: and they they make forklifts. They make all kinds of things. 402 00:23:38,596 --> 00:23:42,236 Speaker 2: So you know, really, what we're doing is exactly what 403 00:23:42,236 --> 00:23:45,836 Speaker 2: I said earlier, which is to accelerate the digital transformation 404 00:23:46,036 --> 00:23:48,316 Speaker 2: of these companies because they know they have to transform, 405 00:23:48,676 --> 00:23:52,156 Speaker 2: they know they have to bring autonomy into their equipment 406 00:23:53,756 --> 00:23:57,716 Speaker 2: and and and create you know, actonomy enabled vehicles. Right, 407 00:23:57,836 --> 00:24:02,276 Speaker 2: So we're not like creating tractors. We're just helping them 408 00:24:02,356 --> 00:24:05,596 Speaker 2: with the with the brains of the operation. 409 00:24:09,276 --> 00:24:22,436 Speaker 1: We'll be back in just a minute. What's out there now? 410 00:24:22,836 --> 00:24:25,396 Speaker 1: Is your software out in the world now? Dive in 411 00:24:25,476 --> 00:24:28,076 Speaker 1: tractors around orchards and what are they doing and where 412 00:24:28,116 --> 00:24:29,476 Speaker 1: are they and how many of them are there? 413 00:24:29,716 --> 00:24:33,196 Speaker 2: Yeah, we have tractors throughout the Western United States, and 414 00:24:33,556 --> 00:24:37,636 Speaker 2: I'm pleased to say that we also have vehicles in Australia. 415 00:24:37,716 --> 00:24:42,676 Speaker 2: So we're super excited about the capabilities we've enabled. In 416 00:24:42,796 --> 00:24:45,756 Speaker 2: the state of Washington, for example, where apples are number 417 00:24:45,796 --> 00:24:48,836 Speaker 2: one in the world, we're doing a lot of spraying 418 00:24:49,316 --> 00:24:52,756 Speaker 2: and a lot of mowing. And it's really it's very 419 00:24:52,756 --> 00:24:56,956 Speaker 2: impactful because you're not just having one vehicle, but our 420 00:24:56,996 --> 00:24:59,876 Speaker 2: customers have many vehicles. They have a fleet of vehicles, 421 00:25:00,356 --> 00:25:03,436 Speaker 2: and they're able to operate those with one of their 422 00:25:03,556 --> 00:25:07,676 Speaker 2: employees that they've upskilled to be able to supervise a fleet. 423 00:25:09,156 --> 00:25:11,316 Speaker 2: And so it's kind of neat to see, you know, 424 00:25:11,476 --> 00:25:15,596 Speaker 2: a whole fleet of vehicles leaving the main shop, driving 425 00:25:15,636 --> 00:25:18,476 Speaker 2: on the dirt roads out to the different areas where 426 00:25:18,476 --> 00:25:21,076 Speaker 2: they need to do work, do the work, and then 427 00:25:21,316 --> 00:25:26,196 Speaker 2: all come back right, So it's a very seamless operation 428 00:25:26,356 --> 00:25:29,636 Speaker 2: for them. Our software is also on mobile applications and 429 00:25:29,676 --> 00:25:34,276 Speaker 2: that's how the main user experience exists. So think of 430 00:25:34,476 --> 00:25:38,076 Speaker 2: you having the tablet or even just your smartphone, and 431 00:25:38,596 --> 00:25:42,716 Speaker 2: the site is mapped out, usually with aerial imagery, and 432 00:25:42,756 --> 00:25:44,796 Speaker 2: you can just point and say, I want to go 433 00:25:44,836 --> 00:25:48,476 Speaker 2: and mow this, you know, this block of the orchard, 434 00:25:48,996 --> 00:25:53,596 Speaker 2: and we basically can get close to where the crop is. 435 00:25:54,236 --> 00:25:56,996 Speaker 2: And then once we're close to the crop, that perception 436 00:25:57,076 --> 00:26:00,116 Speaker 2: stack I mentioned earlier about detecting you know this, or 437 00:26:00,156 --> 00:26:03,436 Speaker 2: exploiting the structure of the crop itself. Take trunk trunk 438 00:26:03,516 --> 00:26:07,436 Speaker 2: vision and it snaps the vehicle to grid, if you will. Right. 439 00:26:08,116 --> 00:26:11,636 Speaker 2: But now, once you have trunk vision and you see 440 00:26:11,676 --> 00:26:14,396 Speaker 2: in front of you, and you can see the terrain 441 00:26:14,676 --> 00:26:19,276 Speaker 2: to a very high degree of accuracy, you can take 442 00:26:19,716 --> 00:26:25,196 Speaker 2: mechanical weaters, for example, and manipulate underneath these trees, you know, 443 00:26:25,236 --> 00:26:32,596 Speaker 2: these robotic weaters, and navigate while you're doing that very precisely, 444 00:26:32,676 --> 00:26:35,956 Speaker 2: all under computer control. Whereas when a human does it, 445 00:26:35,996 --> 00:26:38,356 Speaker 2: you're you know, driving a vehicle six miles an hour 446 00:26:38,396 --> 00:26:41,876 Speaker 2: looking forward, you have your hand on six actuators on 447 00:26:41,916 --> 00:26:44,116 Speaker 2: the you know, the weater and behind you, and you're 448 00:26:44,156 --> 00:26:46,876 Speaker 2: trying to make it all work, and inevitably what happens 449 00:26:46,996 --> 00:26:49,876 Speaker 2: is you don't get the weeds so the efficacy is 450 00:26:49,916 --> 00:26:52,916 Speaker 2: not high, or you hit the tree and take it out, 451 00:26:53,036 --> 00:26:55,356 Speaker 2: and you know that again costs a lot of money. 452 00:26:55,796 --> 00:26:58,716 Speaker 2: So that's why herbicides have been used so much because 453 00:26:58,716 --> 00:27:00,716 Speaker 2: it's really easy. It's called strip spray and you just 454 00:27:00,796 --> 00:27:04,516 Speaker 2: spray it and you're done. But that's that's kind of 455 00:27:05,076 --> 00:27:07,676 Speaker 2: a thing that we didn't realize. You know, this kind 456 00:27:07,716 --> 00:27:11,796 Speaker 2: of autonomous technology would really open up is much more 457 00:27:12,316 --> 00:27:15,756 Speaker 2: offerings for growers so that they don't have to use 458 00:27:16,316 --> 00:27:20,356 Speaker 2: very expensive ag inputs or herbicides that they've been doing 459 00:27:20,436 --> 00:27:21,076 Speaker 2: in the past. 460 00:27:21,836 --> 00:27:24,316 Speaker 1: All this work that you're describing, is it happening in 461 00:27:24,356 --> 00:27:27,436 Speaker 1: a like commercial way now, Like, are you out in 462 00:27:27,436 --> 00:27:29,676 Speaker 1: the world getting paid for this? Yes? And what's that 463 00:27:29,796 --> 00:27:30,596 Speaker 1: business model? 464 00:27:31,036 --> 00:27:35,396 Speaker 2: On that note, Yeah, so we've been operating in paid 465 00:27:35,476 --> 00:27:38,956 Speaker 2: pilots in twenty twenty three and twenty twenty four. We 466 00:27:39,036 --> 00:27:43,436 Speaker 2: never intended to be paid for pilots. What happened was 467 00:27:44,076 --> 00:27:47,316 Speaker 2: we'd have these demo days. We'd invite growers and they 468 00:27:47,396 --> 00:27:49,436 Speaker 2: would say, I want to buy one. I said, well, 469 00:27:49,436 --> 00:27:51,836 Speaker 2: they're not for sale. Well I want to rent one. 470 00:27:52,116 --> 00:27:54,676 Speaker 2: I'm like, well maybe we can do that. Would you 471 00:27:54,716 --> 00:27:57,276 Speaker 2: be interested in doing a pilot? Yes, and I'll pay you. 472 00:27:58,116 --> 00:28:01,276 Speaker 2: And we're like okay. So in those original prototypes that 473 00:28:01,316 --> 00:28:05,556 Speaker 2: we built, we actually rented them and it was kind 474 00:28:05,596 --> 00:28:07,396 Speaker 2: of cool because it was a very small company. We 475 00:28:07,436 --> 00:28:09,916 Speaker 2: didn't actually think we were going to recognize revenue until 476 00:28:10,076 --> 00:28:13,436 Speaker 2: many years in. And I think our first year we did, 477 00:28:13,916 --> 00:28:16,316 Speaker 2: you know, it was actually close to a million dollars. 478 00:28:16,676 --> 00:28:18,796 Speaker 2: But then the second year we did several million dollars 479 00:28:18,996 --> 00:28:22,516 Speaker 2: and that was in twenty twenty four. And this year 480 00:28:22,556 --> 00:28:27,396 Speaker 2: we went big time and we have units operating from Washington, Oregon, 481 00:28:27,516 --> 00:28:32,396 Speaker 2: California and now Australia. And so our business model right 482 00:28:32,436 --> 00:28:36,796 Speaker 2: now is we work with the manufacturers. They manufacture everything, 483 00:28:36,836 --> 00:28:38,956 Speaker 2: they procure all the parts, they bring it into their 484 00:28:38,956 --> 00:28:43,156 Speaker 2: manufacturing facilities and they create these very high quality machines 485 00:28:43,676 --> 00:28:46,156 Speaker 2: that are factory fit. Right, A lot of people think, oh, 486 00:28:46,196 --> 00:28:49,396 Speaker 2: you do retrofit kits on tractors. We do not do that. 487 00:28:50,036 --> 00:28:52,276 Speaker 2: We believe it's important to work with the engineers of 488 00:28:52,316 --> 00:28:56,516 Speaker 2: these OEMs to make the equipment much more reliable, much safer, 489 00:28:57,796 --> 00:29:01,236 Speaker 2: and lower cost, much lower cost when you integrate it in. 490 00:29:01,436 --> 00:29:04,436 Speaker 1: And then when the farmer buys the machine with your 491 00:29:04,996 --> 00:29:07,236 Speaker 1: software in it, with your autonomy package in it, like 492 00:29:08,676 --> 00:29:11,796 Speaker 1: is it are they paying you a subscription? Is the 493 00:29:11,796 --> 00:29:13,956 Speaker 1: price just embedded in the machine and you get some 494 00:29:13,996 --> 00:29:14,796 Speaker 1: of the money. 495 00:29:14,996 --> 00:29:18,196 Speaker 2: So the answer is yes to all of those because 496 00:29:18,196 --> 00:29:22,076 Speaker 2: in some cases the software fees will be embedded in 497 00:29:22,076 --> 00:29:25,676 Speaker 2: the machine. In some cases you will pay a monthly 498 00:29:25,716 --> 00:29:28,036 Speaker 2: fee or an annual fee or a one time fee. 499 00:29:28,476 --> 00:29:28,636 Speaker 1: Right. 500 00:29:29,156 --> 00:29:35,316 Speaker 2: Think of serious XM. Serious XM creates technology, it gets 501 00:29:35,356 --> 00:29:38,036 Speaker 2: embedded by the OEMs and their manufacturing facilities. 502 00:29:38,236 --> 00:29:43,396 Speaker 1: Satellite radio just to satellite radio, yeah yeah. 503 00:29:42,636 --> 00:29:46,436 Speaker 2: And goes to the dealers, and then the dealers basically 504 00:29:46,516 --> 00:29:49,156 Speaker 2: sell to the end customer, and there's a free trial 505 00:29:49,276 --> 00:29:53,876 Speaker 2: that comes and after the free trial, Serious XM retains 506 00:29:53,916 --> 00:29:57,076 Speaker 2: those customers and charges a monthly fee. 507 00:29:57,596 --> 00:30:02,196 Speaker 1: That's a good metaphor satellite radio, but for autonomous tractors, 508 00:30:02,196 --> 00:30:03,156 Speaker 1: for high value. 509 00:30:02,876 --> 00:30:06,516 Speaker 2: Crops exactly, there's another twist. I'll tell you that. By 510 00:30:06,516 --> 00:30:09,676 Speaker 2: the way, the chairman of our company, the chairman of 511 00:30:09,676 --> 00:30:12,036 Speaker 2: the board is Jim Meyer. He's the former CEO of 512 00:30:12,076 --> 00:30:13,076 Speaker 2: Serious ExM. 513 00:30:12,916 --> 00:30:14,436 Speaker 1: Ah okay, yeah. 514 00:30:14,476 --> 00:30:16,876 Speaker 2: And by the way, he's brilliant at business and he 515 00:30:16,956 --> 00:30:19,236 Speaker 2: really helps shape this kind of business model. 516 00:30:19,716 --> 00:30:22,076 Speaker 1: What's the technical thing you haven't figured out yet. 517 00:30:23,596 --> 00:30:26,316 Speaker 2: I'll give you a small example, but it's kind of important. 518 00:30:26,876 --> 00:30:31,596 Speaker 2: So three point turns. If you think about permanent crops, 519 00:30:31,836 --> 00:30:34,916 Speaker 2: you're going down these rows and there's you know, a 520 00:30:34,956 --> 00:30:37,716 Speaker 2: person has a ranch it's called one thousand acres, and 521 00:30:37,756 --> 00:30:40,116 Speaker 2: there might be a fence around the entire thousand acres 522 00:30:40,196 --> 00:30:41,876 Speaker 2: right to keep deer out or whatever it might be. 523 00:30:42,636 --> 00:30:45,596 Speaker 2: And they try to maximize the land they have, so 524 00:30:45,636 --> 00:30:47,596 Speaker 2: they plant the crop pretty close to the edge of 525 00:30:47,636 --> 00:30:50,676 Speaker 2: the fence, let's say. And so when you turn, you 526 00:30:50,716 --> 00:30:54,276 Speaker 2: know in the headlands right, the headland turn, you might 527 00:30:54,316 --> 00:30:54,996 Speaker 2: not have a lot. 528 00:30:54,876 --> 00:30:57,196 Speaker 1: Of space, so you can't just like make a kind 529 00:30:57,236 --> 00:30:58,796 Speaker 1: of U turn at the end of the row and 530 00:30:58,876 --> 00:30:59,796 Speaker 1: go down the next. 531 00:30:59,596 --> 00:31:03,676 Speaker 2: Correct correct correct. So you know, right now we can 532 00:31:03,876 --> 00:31:07,836 Speaker 2: do about eighty percent of farmlands and permanent crops. But 533 00:31:07,916 --> 00:31:11,396 Speaker 2: imagine if we could do point turns, we can achieve 534 00:31:11,436 --> 00:31:14,596 Speaker 2: one hundred percent. But but there's a twist, right because 535 00:31:14,596 --> 00:31:17,476 Speaker 2: sometimes you not you don't have an implement, that's the 536 00:31:17,556 --> 00:31:20,036 Speaker 2: device you attached to the tractor that does the work. 537 00:31:20,116 --> 00:31:23,356 Speaker 2: So you have implements you can attach for mowing, for spraying, 538 00:31:23,476 --> 00:31:26,876 Speaker 2: for weeding, for disking, for all kinds of things. Some 539 00:31:26,956 --> 00:31:29,436 Speaker 2: of those implements are actually trailers. 540 00:31:30,396 --> 00:31:30,756 Speaker 1: H huh. 541 00:31:30,796 --> 00:31:34,396 Speaker 2: Okay, So imagine you know, being on a site slope 542 00:31:34,436 --> 00:31:37,676 Speaker 2: with rocky soil and everything, and you have a trailer 543 00:31:37,836 --> 00:31:40,596 Speaker 2: and you have to do a three point turn to 544 00:31:40,636 --> 00:31:41,636 Speaker 2: get into the next row. 545 00:31:41,796 --> 00:31:44,356 Speaker 1: That's a high skill that's a high skill moment, right, 546 00:31:44,556 --> 00:31:47,676 Speaker 1: that's hi know what they're doing exactly exactly. 547 00:31:47,916 --> 00:31:50,516 Speaker 2: And you know, ectonomy enabled vehicles have lots of cameras. 548 00:31:50,556 --> 00:31:53,396 Speaker 2: They have you know, minimum of eight cameras including rear 549 00:31:53,396 --> 00:31:56,276 Speaker 2: facing cameras and side facing cameras, and so you have 550 00:31:56,316 --> 00:32:00,036 Speaker 2: to really now start to you know, do very sophisticated 551 00:32:00,196 --> 00:32:04,796 Speaker 2: modeling of the you know, physics of that tractor as 552 00:32:04,796 --> 00:32:08,756 Speaker 2: well as that implement or trailer, which we which we 553 00:32:08,756 --> 00:32:10,916 Speaker 2: do today as we go forward, but now we have 554 00:32:10,956 --> 00:32:13,916 Speaker 2: to do it going backwards. So we haven't mastered that 555 00:32:13,996 --> 00:32:17,316 Speaker 2: yet but we're getting close, and I'm super excited about that, 556 00:32:17,676 --> 00:32:21,316 Speaker 2: both from a technological achievement perspective as well as a 557 00:32:21,356 --> 00:32:22,796 Speaker 2: business need. 558 00:32:25,356 --> 00:32:29,756 Speaker 1: It's a nice when they go together. Yeah, so let's 559 00:32:29,756 --> 00:32:32,676 Speaker 1: so madch for a minute, like I'm you know, there 560 00:32:32,676 --> 00:32:39,636 Speaker 1: are other people working on other agricultural technology projects, other 561 00:32:39,716 --> 00:32:42,476 Speaker 1: ag tech projects, even other autonomous projects. Like when you 562 00:32:42,596 --> 00:32:45,756 Speaker 1: zoom out and think about farming and technology more generally 563 00:32:45,916 --> 00:32:50,076 Speaker 1: in the medium term, you know whatever that means, five years, 564 00:32:50,276 --> 00:32:54,036 Speaker 1: ten years, like, how's the world going to change? 565 00:32:55,596 --> 00:32:57,996 Speaker 2: Well, so, first of all, there are a lot of 566 00:32:58,036 --> 00:33:03,236 Speaker 2: others working on similar problems. We all have a slight twist, 567 00:33:03,436 --> 00:33:07,916 Speaker 2: either in business model or in technology approach. I think 568 00:33:08,036 --> 00:33:12,796 Speaker 2: that in the future is all going to be autonomous capable, 569 00:33:13,836 --> 00:33:16,836 Speaker 2: and we see the writing on the wall right when 570 00:33:16,836 --> 00:33:20,596 Speaker 2: it comes to labor challenges that we're having, we have 571 00:33:20,716 --> 00:33:24,276 Speaker 2: a severe labor gap. If I were to ask you 572 00:33:24,356 --> 00:33:26,316 Speaker 2: how many people do you know who you know whose 573 00:33:26,436 --> 00:33:29,076 Speaker 2: kids go into agriculture, the answer is probably going to 574 00:33:29,116 --> 00:33:33,196 Speaker 2: be not that many. I have three children, Jacob, and 575 00:33:33,276 --> 00:33:36,076 Speaker 2: none of them are taking over the farm. And then 576 00:33:36,116 --> 00:33:37,956 Speaker 2: you know, of course we have things like you know, 577 00:33:38,076 --> 00:33:40,956 Speaker 2: some immigration policy changes that have occurred that are only 578 00:33:40,996 --> 00:33:42,196 Speaker 2: exacerbating the problem. 579 00:33:42,636 --> 00:33:45,556 Speaker 1: Even in the absence of a labor shortage, that some margin, 580 00:33:45,676 --> 00:33:48,596 Speaker 1: automation wins, right, Like that's what happened with the tractor 581 00:33:48,676 --> 00:33:52,636 Speaker 1: and like, whatever's right. So yes, I'm fully prepared to believe. 582 00:33:52,676 --> 00:33:55,396 Speaker 1: And yes, I can see how the current politics might 583 00:33:55,436 --> 00:33:58,036 Speaker 1: be accelerating the shift to automation. But yes, so okay, 584 00:33:58,156 --> 00:34:02,636 Speaker 1: automation is going to win. I stipulated, like what's that 585 00:34:02,676 --> 00:34:04,636 Speaker 1: going to mean? What's it going to look like? You know, 586 00:34:04,716 --> 00:34:07,436 Speaker 1: tell me something about the future based on that fact. 587 00:34:07,836 --> 00:34:11,436 Speaker 2: Think about ro crops, right, I believe row crops are 588 00:34:11,436 --> 00:34:13,876 Speaker 2: going to radically change. So here is one big inflection 589 00:34:13,956 --> 00:34:16,596 Speaker 2: point that I think is going to happen. Right now, 590 00:34:16,636 --> 00:34:20,356 Speaker 2: we have these large, large tractors in row crops. It 591 00:34:20,476 --> 00:34:23,596 Speaker 2: cost a million dollars. Fully autonomous ones cost well over that. 592 00:34:24,596 --> 00:34:29,356 Speaker 2: And why are there big tractors. It's because these these farms, 593 00:34:29,436 --> 00:34:32,316 Speaker 2: you know, want as many acres done per hour by 594 00:34:32,356 --> 00:34:36,716 Speaker 2: one person. But now if automation comes in, you don't 595 00:34:36,716 --> 00:34:40,476 Speaker 2: necessarily need these large vehicles that have huge ground compassion. 596 00:34:40,876 --> 00:34:43,116 Speaker 1: This is your trillion dollar play. Now you're going to 597 00:34:43,196 --> 00:34:45,236 Speaker 1: disrupt John Deere. Is that where this is. 598 00:34:45,156 --> 00:34:49,596 Speaker 2: Going yes, So I mean, but think about it, right, if. 599 00:34:50,116 --> 00:34:52,836 Speaker 1: You're the only reason you need to make the vehicle 600 00:34:52,836 --> 00:34:54,396 Speaker 1: as big as possible is if you have to have 601 00:34:54,436 --> 00:34:56,476 Speaker 1: a dude on every one of them. That's what you're saying. 602 00:34:56,476 --> 00:34:58,556 Speaker 1: It's not necessarily it could be like like the way 603 00:34:58,596 --> 00:35:00,876 Speaker 1: it's like a drone swarm, you could do a drone swarm, 604 00:35:00,916 --> 00:35:02,316 Speaker 1: but for tractors. 605 00:35:02,076 --> 00:35:07,116 Speaker 2: Broad acre swarming exactly right, Yeah, And that's there's some 606 00:35:07,156 --> 00:35:09,956 Speaker 2: additional benefits, right, So instead of spending and a million dollars, 607 00:35:10,636 --> 00:35:12,916 Speaker 2: you can do more work with five tractors that cost 608 00:35:13,036 --> 00:35:16,276 Speaker 2: less than that with their implements as well, and now 609 00:35:16,356 --> 00:35:21,156 Speaker 2: you have some additional benefits, so you actually get more 610 00:35:21,156 --> 00:35:24,116 Speaker 2: work done faster, and if one of the tractors breaks down, 611 00:35:24,676 --> 00:35:28,796 Speaker 2: you have redundancy. Right, If your big million dollar tractor 612 00:35:28,836 --> 00:35:32,396 Speaker 2: breaks down, you got issues. It's like thousands of dollars 613 00:35:32,436 --> 00:35:35,476 Speaker 2: a minute that you know that it's not running. So 614 00:35:35,756 --> 00:35:39,316 Speaker 2: I think there's a lot of opportunity in agriculture that 615 00:35:39,396 --> 00:35:42,876 Speaker 2: we hadn't thought about once automation is achieved. I mean, 616 00:35:42,916 --> 00:35:46,916 Speaker 2: I describe the herbicide challenge in permanent crops. I believe 617 00:35:46,956 --> 00:35:52,676 Speaker 2: that is completely can be completely eliminated with automation, and 618 00:35:52,756 --> 00:35:55,596 Speaker 2: you know computer controls, computer vision, like you. 619 00:35:55,636 --> 00:35:58,276 Speaker 1: Just pick the weeds instead. If you have essentially very 620 00:35:58,356 --> 00:36:01,076 Speaker 1: cheap robots that are very dexterous, you just pick the 621 00:36:01,076 --> 00:36:02,196 Speaker 1: weeds instead of spraying them. 622 00:36:02,556 --> 00:36:05,756 Speaker 2: Absolutely, it's already happening in row crops, you know. You 623 00:36:05,756 --> 00:36:08,636 Speaker 2: look at carbon robotics, they're using lasers to kill weeds, 624 00:36:09,196 --> 00:36:12,276 Speaker 2: you know, in lettuce fields and other kinds of crops 625 00:36:12,316 --> 00:36:16,716 Speaker 2: that are called specialty crops. That's huge, right because before 626 00:36:16,876 --> 00:36:19,116 Speaker 2: humans would would go in there, or you'd spray and 627 00:36:19,276 --> 00:36:23,076 Speaker 2: use bag inputs or herbicides. So I think there's a 628 00:36:23,236 --> 00:36:26,796 Speaker 2: huge there is a huge inflection point. But I just 629 00:36:26,876 --> 00:36:28,916 Speaker 2: I want to make it clear to your listeners that 630 00:36:29,436 --> 00:36:31,996 Speaker 2: just the core base of you know, being able to 631 00:36:31,996 --> 00:36:35,196 Speaker 2: produce enough food for the population, we need automation. Four 632 00:36:35,596 --> 00:36:37,756 Speaker 2: there are going to be these other benefits, some of 633 00:36:37,756 --> 00:36:41,676 Speaker 2: which we don't even know yet. So I'm such a 634 00:36:41,676 --> 00:36:44,236 Speaker 2: big believer in automation. It's why I brought my two 635 00:36:44,236 --> 00:36:46,916 Speaker 2: worlds together, and it's why I've really dedicated my life 636 00:36:46,916 --> 00:36:50,076 Speaker 2: to this mission. And I just I've never been as 637 00:36:50,076 --> 00:36:52,956 Speaker 2: excited as I as I am now in terms of 638 00:36:52,996 --> 00:36:58,156 Speaker 2: the impact that this company can have our share of impact, 639 00:36:58,516 --> 00:37:01,676 Speaker 2: but all of us together, there's so much opportunity here. 640 00:37:01,756 --> 00:37:04,596 Speaker 2: And you know, I think the incumbents are the ones 641 00:37:04,636 --> 00:37:07,716 Speaker 2: that the incumbent equipment manufacturers are the ones that are 642 00:37:07,716 --> 00:37:10,676 Speaker 2: really going to make it happen in terms of distribution 643 00:37:10,836 --> 00:37:13,596 Speaker 2: and support and you know, getting it out there to 644 00:37:13,716 --> 00:37:16,476 Speaker 2: the world. So couldn't be more excited. 645 00:37:20,036 --> 00:37:22,116 Speaker 1: We'll be back in a minute with the lightning round. 646 00:37:32,996 --> 00:37:35,756 Speaker 1: Let's finish with the lightning round. I heard you say 647 00:37:35,956 --> 00:37:38,356 Speaker 1: on another interview that you are not a big fan 648 00:37:38,396 --> 00:37:42,156 Speaker 1: of IPOs and that you generally prefer acquisitions. Why what 649 00:37:42,196 --> 00:37:43,996 Speaker 1: do you got against IPOs. 650 00:37:45,076 --> 00:37:47,156 Speaker 2: Well, that's from the old days. I've went through a 651 00:37:47,156 --> 00:37:51,316 Speaker 2: couple of IPOs and just about killed me. So a 652 00:37:51,356 --> 00:37:54,436 Speaker 2: lot of people think, you know, IPOs are glamorous and everything, 653 00:37:54,476 --> 00:37:56,756 Speaker 2: but you know, the road shows are pretty intense and 654 00:37:57,156 --> 00:38:01,076 Speaker 2: the pressures are really intense, and you know, I'm not 655 00:38:01,156 --> 00:38:03,716 Speaker 2: just a fan of acquisitions. I mean, I think you're 656 00:38:03,716 --> 00:38:06,756 Speaker 2: seeing a lot of differences in the private market than 657 00:38:06,956 --> 00:38:09,956 Speaker 2: you have in the past. Companies like open Ai who 658 00:38:09,996 --> 00:38:13,436 Speaker 2: are raising billions of dollars in the private market and 659 00:38:13,956 --> 00:38:15,236 Speaker 2: not having to go public. 660 00:38:15,476 --> 00:38:17,516 Speaker 1: You could just stay private forever. You could just be 661 00:38:17,596 --> 00:38:19,996 Speaker 1: a company that makes more money than it's spends. You 662 00:38:19,996 --> 00:38:21,396 Speaker 1: could do that for as long as. 663 00:38:21,316 --> 00:38:26,516 Speaker 2: You want, exactly exactly. So I think the IPO, you know, 664 00:38:27,356 --> 00:38:30,036 Speaker 2: I mean, just look at the last few years of IPOs. 665 00:38:30,076 --> 00:38:32,796 Speaker 2: There just haven't been that many. And so there are 666 00:38:32,796 --> 00:38:36,316 Speaker 2: other ways to build incredibly valuable companies. I get asked 667 00:38:36,356 --> 00:38:39,796 Speaker 2: all the time, what's your exit strategy? And I always 668 00:38:39,796 --> 00:38:43,596 Speaker 2: have the same answer. I only focus on building value 669 00:38:43,636 --> 00:38:48,756 Speaker 2: opportunities come, and I think entrepreneurs who build companies for 670 00:38:48,836 --> 00:38:52,356 Speaker 2: an exit aren't building the company for the right reasons 671 00:38:52,396 --> 00:38:55,156 Speaker 2: and aren't building really valuable companies either. 672 00:38:56,516 --> 00:38:58,876 Speaker 1: What's one thing that Steve Jobs told you that stuck 673 00:38:58,916 --> 00:38:59,156 Speaker 1: with you. 674 00:39:02,276 --> 00:39:05,156 Speaker 2: I had the pleasure of working for some incredible entrepreneurs, 675 00:39:05,196 --> 00:39:08,476 Speaker 2: Steve Jobs being one of them. And what I did 676 00:39:08,556 --> 00:39:12,156 Speaker 2: learn is that these incredible entrepreneurs, they each had like 677 00:39:12,196 --> 00:39:16,556 Speaker 2: a singular mantra. And for Steve, it was all about design. 678 00:39:16,836 --> 00:39:19,796 Speaker 2: It's all about design, and there were things he would 679 00:39:19,796 --> 00:39:22,636 Speaker 2: push for that we'd sit there and scratch our heads 680 00:39:22,676 --> 00:39:24,676 Speaker 2: and go, wait, that's going to add like one hundred 681 00:39:24,716 --> 00:39:29,156 Speaker 2: dollars cost to this, and and we would do it 682 00:39:29,636 --> 00:39:32,676 Speaker 2: and the product would be successful, and we're like, Okay, 683 00:39:32,796 --> 00:39:37,316 Speaker 2: he was right. You know. I learned from from Bill 684 00:39:37,356 --> 00:39:40,876 Speaker 2: Gates that it's all about software, and you know what, 685 00:39:41,076 --> 00:39:43,156 Speaker 2: he was right. I learned from Michael Dell that it's 686 00:39:43,156 --> 00:39:45,356 Speaker 2: all about costs, and guess what, he was right. So 687 00:39:46,036 --> 00:39:50,516 Speaker 2: I think, you know, the every entrepreneur that makes serious impact, 688 00:39:51,796 --> 00:39:55,356 Speaker 2: you know, has these kind of core mantras. So what's 689 00:39:55,396 --> 00:39:58,876 Speaker 2: yours I viewed as a combination. But there is one 690 00:39:58,916 --> 00:40:02,556 Speaker 2: thing that I will tell you very very clearly in 691 00:40:02,596 --> 00:40:07,596 Speaker 2: your listeners, and I tell my teammates probably every day, 692 00:40:07,636 --> 00:40:11,756 Speaker 2: if not every week. You know, it's all about show me, 693 00:40:12,236 --> 00:40:19,076 Speaker 2: don't tell me. And that particularly applies to these industrial markets. 694 00:40:20,356 --> 00:40:24,276 Speaker 2: The best way to sell industrial equipment, and this is 695 00:40:24,276 --> 00:40:28,156 Speaker 2: what dealers do is, you know, hear Susie farmer take 696 00:40:28,196 --> 00:40:30,476 Speaker 2: this piece of equipment and use it for a day, 697 00:40:31,316 --> 00:40:33,516 Speaker 2: and she'll take that piece of equipment, use it on 698 00:40:33,556 --> 00:40:37,356 Speaker 2: her ranch, on her farm, whatever, and inevitably she will 699 00:40:37,356 --> 00:40:39,996 Speaker 2: buy it because she gets to try, you know before. 700 00:40:40,116 --> 00:40:43,076 Speaker 2: So I think I think the show me mentality is 701 00:40:43,116 --> 00:40:46,316 Speaker 2: really really important in this era, and in ag tech 702 00:40:46,356 --> 00:40:49,796 Speaker 2: in particular, because there've been some companies that have been 703 00:40:49,996 --> 00:40:54,556 Speaker 2: developed by pure tech people and pure tech people. You know, 704 00:40:54,676 --> 00:40:57,436 Speaker 2: telling farmers that you know, we can farm better than 705 00:40:57,476 --> 00:41:00,996 Speaker 2: you is not a good recipe for success, right, And 706 00:41:01,076 --> 00:41:06,356 Speaker 2: so by focusing on show me, it's really what resonates 707 00:41:06,396 --> 00:41:11,516 Speaker 2: with our customers, it's really what our customers need, and 708 00:41:11,556 --> 00:41:14,916 Speaker 2: it's just how business is done in these industrial worlds. 709 00:41:14,956 --> 00:41:18,796 Speaker 2: So that's my mantra, show me, don't tell me. 710 00:41:21,236 --> 00:41:25,196 Speaker 1: What's one ridiculous word somebody once used to describe the 711 00:41:25,236 --> 00:41:30,876 Speaker 1: wine that you grow? Hm hmm. 712 00:41:33,796 --> 00:41:35,356 Speaker 2: I didn't even know what this. I still don't know 713 00:41:35,356 --> 00:41:41,676 Speaker 2: what this word means. But ethereal, I'm like, like, what 714 00:41:41,756 --> 00:41:42,596 Speaker 2: does what does that mean? 715 00:41:42,836 --> 00:41:44,996 Speaker 1: It doesn't stuck around, It's like I don't even know 716 00:41:45,036 --> 00:41:45,676 Speaker 1: I drank. 717 00:41:45,516 --> 00:41:48,676 Speaker 2: Yet, Like yeah, I don't know, right, So you get 718 00:41:48,676 --> 00:41:49,876 Speaker 2: all kinds of feedback. 719 00:41:50,036 --> 00:41:52,556 Speaker 1: Is that a fancy way of saying easy drinking sounds 720 00:41:52,556 --> 00:41:54,116 Speaker 1: like easy drinking? Probably? 721 00:41:54,956 --> 00:41:58,596 Speaker 2: Yeah? Yeah, yeah, But you get all kinds of great feedback. 722 00:41:58,676 --> 00:42:00,956 Speaker 2: And I mean that's the cool thing about sort of 723 00:42:00,996 --> 00:42:04,076 Speaker 2: the show me mentality is when you develop products that 724 00:42:04,756 --> 00:42:09,396 Speaker 2: end users you know, consume or use, and you see 725 00:42:09,436 --> 00:42:14,156 Speaker 2: their reaction and you know, there you you please them, 726 00:42:14,196 --> 00:42:16,956 Speaker 2: You give them something of value That's that's what gets 727 00:42:16,956 --> 00:42:20,196 Speaker 2: me motivated all the time. And that's why you know, 728 00:42:20,236 --> 00:42:22,716 Speaker 2: this space is so exciting for me, because that that 729 00:42:22,876 --> 00:42:26,276 Speaker 2: impact has felt like really really quickly. When when you 730 00:42:26,316 --> 00:42:28,076 Speaker 2: do have a solutions at work, or when you do 731 00:42:28,116 --> 00:42:30,116 Speaker 2: make great wine, or when you do make great olive oil. 732 00:42:32,316 --> 00:42:36,756 Speaker 1: Yeah, I don't think autonomous tractors as ethereal, I please don't. 733 00:42:44,716 --> 00:42:47,716 Speaker 1: Tim Booker is a farmer and the founder and CEO 734 00:42:47,916 --> 00:42:52,956 Speaker 1: of Actonomy. Please email us at problem at pushkin dot fm. 735 00:42:52,996 --> 00:42:55,556 Speaker 1: We are always looking for new guests for the show. 736 00:42:56,436 --> 00:43:00,196 Speaker 1: Today's show was produced by Trinamanino and Gabriel Hunter Chang, 737 00:43:00,556 --> 00:43:04,716 Speaker 1: who was edited by Alexander Garrettson and engineered by Sarah Brugueret. 738 00:43:05,076 --> 00:43:07,876 Speaker 1: I'm Jacob Goldstein. We'll be off for the next few 739 00:43:07,916 --> 00:43:10,476 Speaker 1: weeks for the holidays. I want to thank you very 740 00:43:10,556 --> 00:43:12,676 Speaker 1: much for listening to the show this year. It really 741 00:43:13,196 --> 00:43:15,596 Speaker 1: means a tremendous amount to all of us. I hope 742 00:43:15,596 --> 00:43:18,116 Speaker 1: you have a great holiday, happy New Year, and we 743 00:43:18,196 --> 00:43:21,276 Speaker 1: will be back with more episodes of What's Your Problem 744 00:43:21,436 --> 00:43:22,716 Speaker 1: in twenty twenty six.