1 00:00:03,640 --> 00:00:07,280 Speaker 1: The real insight here, and really why I think autonomy 2 00:00:07,440 --> 00:00:11,119 Speaker 1: is the biggest thing in HAGG is not actually for 3 00:00:11,200 --> 00:00:14,880 Speaker 1: the automation itself. It's because we're using all these sensors, 4 00:00:15,080 --> 00:00:18,280 Speaker 1: radars and cameras and light ars for safety making sure 5 00:00:18,320 --> 00:00:21,720 Speaker 1: the tractor doesn't do something dangerous. The insights that we're 6 00:00:21,800 --> 00:00:24,360 Speaker 1: gleaning from these sensors we're using for safety to do 7 00:00:24,400 --> 00:00:27,840 Speaker 1: the automation can then be used to feedback value to 8 00:00:27,960 --> 00:00:30,560 Speaker 1: farmers as we get this larger data set on a 9 00:00:30,640 --> 00:00:33,560 Speaker 1: performed basis that will allow them to be many orders 10 00:00:33,680 --> 00:00:38,760 Speaker 1: of magnitude more productive than previous generations. Welcome to the 11 00:00:38,800 --> 00:00:42,640 Speaker 1: restless Ones. I'm Jonathan Strickland. As you may know, I've 12 00:00:42,680 --> 00:00:46,159 Speaker 1: spent the last fifteen years covering technology and learning how 13 00:00:46,159 --> 00:00:51,120 Speaker 1: it works, demystifying everything from massive parallel processing to advanced 14 00:00:51,240 --> 00:00:56,840 Speaker 1: robotics and everything in between. Yet it's the conversations with 15 00:00:56,880 --> 00:00:59,600 Speaker 1: some of the most forward thinking leaders, those at the 16 00:00:59,600 --> 00:01:03,040 Speaker 1: inner side action of technology and business that fascinated me 17 00:01:03,120 --> 00:01:09,319 Speaker 1: the most. Gino Cafilero really fits the description of a 18 00:01:09,360 --> 00:01:13,319 Speaker 1: restless one. He graduated from Carnegie Mellon University with a 19 00:01:13,360 --> 00:01:17,240 Speaker 1: bachelor's degree in Electrical and computer engineering. He then earned 20 00:01:17,319 --> 00:01:22,240 Speaker 1: his first master's degree at Stanford University, again in electrical engineering. 21 00:01:22,880 --> 00:01:26,119 Speaker 1: He became a software engineer and later attended the Wharton 22 00:01:26,240 --> 00:01:30,000 Speaker 1: School where he earned his second master's degree, this time 23 00:01:30,040 --> 00:01:34,200 Speaker 1: in business administration. And in between working and learning and 24 00:01:34,280 --> 00:01:38,000 Speaker 1: asking questions, he came up with the idea for Bare 25 00:01:38,120 --> 00:01:41,959 Speaker 1: Flag Robotics. The company is in the business of providing 26 00:01:42,000 --> 00:01:46,759 Speaker 1: automation solutions for the agricultural industry. We'll hear more about 27 00:01:46,760 --> 00:01:49,520 Speaker 1: a Geno's path to bear Flag Robotics in a moment. 28 00:01:49,960 --> 00:01:51,920 Speaker 1: I just want to take a second to acknowledge that 29 00:01:52,360 --> 00:01:56,640 Speaker 1: a Geno's enthusiasm and attitude towards taking on challenging problems 30 00:01:56,680 --> 00:02:01,280 Speaker 1: and finding innovative solutions is absolutely content pages. I guess 31 00:02:01,320 --> 00:02:04,680 Speaker 1: what I'm saying is that during this conversation I geeked 32 00:02:04,680 --> 00:02:08,040 Speaker 1: out more than just a little bit. As you'll hear, 33 00:02:08,200 --> 00:02:11,200 Speaker 1: the Geno's work focuses on a sector that's a blind 34 00:02:11,240 --> 00:02:14,440 Speaker 1: spot for a lot of people. There's a misguided concept 35 00:02:14,680 --> 00:02:18,680 Speaker 1: that farming hasn't significantly changed for decades, when in fact, 36 00:02:18,960 --> 00:02:23,000 Speaker 1: the agricultural industry represents a field where bleeding edge technology 37 00:02:23,120 --> 00:02:26,480 Speaker 1: and the benefits of high speed, low latency, connectivity are 38 00:02:26,560 --> 00:02:30,880 Speaker 1: fueling monumental changes. But before we get too deep into that, 39 00:02:31,240 --> 00:02:38,480 Speaker 1: I first wanted to learn more about a Gino himself. Well, Gino, 40 00:02:38,639 --> 00:02:41,840 Speaker 1: First of all, let me welcome you to the Restless Ones. 41 00:02:41,880 --> 00:02:44,960 Speaker 1: It is a pleasure to have you on the show, Jonathan, 42 00:02:44,960 --> 00:02:46,679 Speaker 1: Thank you so much. It's a thrill to be here. 43 00:02:47,120 --> 00:02:49,400 Speaker 1: I first like to get to know my guests before 44 00:02:49,400 --> 00:02:52,720 Speaker 1: we start diving into all the nitty gritty. So the 45 00:02:52,800 --> 00:02:55,080 Speaker 1: question I have for you then, is how did you 46 00:02:55,160 --> 00:02:59,240 Speaker 1: first get interested in technology? What was it that sparked 47 00:02:59,600 --> 00:03:03,600 Speaker 1: your desire to pursue that, Johnathan, that's such a broad 48 00:03:03,720 --> 00:03:08,080 Speaker 1: prompt um. I mean, the five year old boy and 49 00:03:08,120 --> 00:03:12,359 Speaker 1: me always love taking things apart and understanding how they worked. Right, 50 00:03:12,440 --> 00:03:15,520 Speaker 1: All my toys were always in pieces. I was always 51 00:03:15,520 --> 00:03:17,520 Speaker 1: asking my dad to help me put it back together, 52 00:03:18,040 --> 00:03:19,639 Speaker 1: just because I wanted to understand how it worked. And 53 00:03:19,680 --> 00:03:22,960 Speaker 1: then the adult went to engineering school, became a software 54 00:03:23,000 --> 00:03:27,160 Speaker 1: developer by trade, formerly an electrical engineer. But in the background, 55 00:03:27,200 --> 00:03:30,800 Speaker 1: I've always worked on projects in the garage, working on 56 00:03:30,840 --> 00:03:34,000 Speaker 1: old Beater trucks and trying to build anything that I 57 00:03:34,000 --> 00:03:36,880 Speaker 1: thought was cool, which you know, fast forwarding to bear 58 00:03:36,880 --> 00:03:38,920 Speaker 1: flag was actually, you know, the skill needed. How can 59 00:03:38,960 --> 00:03:43,200 Speaker 1: we marry cutting a bleeding edge robotics and software with 60 00:03:43,280 --> 00:03:45,680 Speaker 1: the physical world. And that's the quick version of how 61 00:03:45,720 --> 00:03:48,120 Speaker 1: we got to where we are. I love that because 62 00:03:48,120 --> 00:03:51,600 Speaker 1: it starts off with taking things apart and then learning 63 00:03:51,880 --> 00:03:54,360 Speaker 1: how to put them back together again in order to 64 00:03:54,480 --> 00:03:58,000 Speaker 1: have them work again. It's it's like the perfect genesis 65 00:03:58,040 --> 00:04:02,000 Speaker 1: story of an engineer. Well, I think the old adages, 66 00:04:02,200 --> 00:04:03,840 Speaker 1: you know, if it's not broken, it doesn't have enough 67 00:04:03,880 --> 00:04:07,640 Speaker 1: features yet. And certainly, certainly something we've fallen in the 68 00:04:07,680 --> 00:04:10,960 Speaker 1: trap of. Before you talked about becoming a software engineer, 69 00:04:11,280 --> 00:04:14,320 Speaker 1: you were working a lot with companies that were really 70 00:04:14,360 --> 00:04:19,440 Speaker 1: focused on developing and supporting infrastructure. What sort of lessons 71 00:04:19,520 --> 00:04:22,400 Speaker 1: did you learn along the way as you were working 72 00:04:22,400 --> 00:04:26,039 Speaker 1: through these jobs. I'd love to say that, you know, 73 00:04:26,120 --> 00:04:29,360 Speaker 1: my career was just deliberate sort of, Hey, I'm gonna 74 00:04:29,360 --> 00:04:31,240 Speaker 1: go from step A to step B because I know 75 00:04:31,520 --> 00:04:33,640 Speaker 1: I want to get to step X, you know, someday. 76 00:04:33,680 --> 00:04:36,520 Speaker 1: But maybe some folks are like that. My foresight was 77 00:04:36,560 --> 00:04:40,360 Speaker 1: not that strong, you know, very very tactically. I did 78 00:04:40,400 --> 00:04:43,720 Speaker 1: try to set myself up on teams to understand how 79 00:04:43,800 --> 00:04:47,440 Speaker 1: problems are solved at scale. I'm always fascinated with this, 80 00:04:47,800 --> 00:04:51,400 Speaker 1: how do we take something from a whiteboard to having 81 00:04:51,480 --> 00:04:55,120 Speaker 1: world impact? And I'm very lucky that in previous jobs 82 00:04:55,160 --> 00:04:58,120 Speaker 1: is able to do that. I was super fortunate to 83 00:04:58,160 --> 00:05:02,240 Speaker 1: be in the room when my previous company they were 84 00:05:02,240 --> 00:05:04,679 Speaker 1: just white boarding out how they wanted to do this solution, 85 00:05:04,720 --> 00:05:07,360 Speaker 1: and you know, listen, every single thing on that whiteboard 86 00:05:07,440 --> 00:05:09,320 Speaker 1: changed over the course the next five years. Don't get 87 00:05:09,360 --> 00:05:11,440 Speaker 1: me wrong, but it was cool to be in that 88 00:05:11,560 --> 00:05:15,200 Speaker 1: room and have mentors and people that I highly respected, 89 00:05:15,680 --> 00:05:19,200 Speaker 1: having these lively debates about how we want architect solutions 90 00:05:19,560 --> 00:05:22,159 Speaker 1: and thinking not only about how we're going to do 91 00:05:22,279 --> 00:05:25,240 Speaker 1: step one through two, but also step like through a 92 00:05:25,320 --> 00:05:28,680 Speaker 1: hundred as well. And that paradox was thrilling and then 93 00:05:28,720 --> 00:05:31,520 Speaker 1: something we lived through completely at Bear Flag as well. 94 00:05:31,920 --> 00:05:35,400 Speaker 1: The ideas that we had when he founded the company 95 00:05:35,600 --> 00:05:40,320 Speaker 1: were based in sound reason about what we knew to 96 00:05:40,360 --> 00:05:41,920 Speaker 1: be true in the world, but they have all very 97 00:05:42,000 --> 00:05:45,479 Speaker 1: quickly as we learned new things and had more experiences. 98 00:05:45,880 --> 00:05:49,359 Speaker 1: I would say that while I was extremely fortunate to 99 00:05:49,440 --> 00:05:53,000 Speaker 1: work on these incredible teams with folks who have shaped 100 00:05:53,080 --> 00:05:56,159 Speaker 1: industry in the first half of my career, I found 101 00:05:56,160 --> 00:05:59,000 Speaker 1: a true passion here in agriculture. I remember having this 102 00:05:59,120 --> 00:06:02,039 Speaker 1: distinct thought early in my career that hey, listen, I'm 103 00:06:02,080 --> 00:06:05,080 Speaker 1: okay at this and I can do this, but it's 104 00:06:05,080 --> 00:06:07,320 Speaker 1: not what lights me up. I don't leave work and 105 00:06:07,360 --> 00:06:10,080 Speaker 1: think about this. I'm not thinking about this that stop 106 00:06:10,120 --> 00:06:12,800 Speaker 1: signs and in the shower and on the weekends and 107 00:06:12,839 --> 00:06:15,280 Speaker 1: when I'm doing other things. For some reason, the dots 108 00:06:15,320 --> 00:06:18,800 Speaker 1: connected so clearly that I won't have the impact that 109 00:06:18,880 --> 00:06:20,440 Speaker 1: I really want to have in my life if I'm 110 00:06:20,480 --> 00:06:22,400 Speaker 1: not passionate about what I'm doing. And that's why Baar 111 00:06:22,480 --> 00:06:25,920 Speaker 1: Flag has been such an incredible journey. It's been thrilling 112 00:06:25,960 --> 00:06:29,440 Speaker 1: and awful and exciting and terrifying. But the glue there 113 00:06:29,520 --> 00:06:31,159 Speaker 1: is the passion for what we're doing, and I think 114 00:06:31,160 --> 00:06:32,960 Speaker 1: that's why a lot of folks on the team are 115 00:06:33,000 --> 00:06:35,760 Speaker 1: working hard. Here. Can you talk a bit about the 116 00:06:35,800 --> 00:06:40,600 Speaker 1: genesis for bear Flag Robotics. I was at a really 117 00:06:40,640 --> 00:06:43,960 Speaker 1: fortunate juncture in my career. I just finished business school 118 00:06:44,240 --> 00:06:47,200 Speaker 1: and I was spending time with my in laws in 119 00:06:47,440 --> 00:06:50,479 Speaker 1: rural Oregon. By father in law is a commercial fisherman. 120 00:06:50,520 --> 00:06:53,640 Speaker 1: The other one's a professional water well driller, and as 121 00:06:53,720 --> 00:06:56,440 Speaker 1: you know his company, but my wife's uncle actually owns 122 00:06:56,480 --> 00:07:00,719 Speaker 1: a construction aggregate rock quarry in Williamtte Valley, and I 123 00:07:00,800 --> 00:07:02,640 Speaker 1: was exceedingly interested in that. So I went out with 124 00:07:02,680 --> 00:07:05,120 Speaker 1: Tom and his son Eric and spent some days with 125 00:07:05,160 --> 00:07:07,919 Speaker 1: them and learned about that operation. Honestly, just because I 126 00:07:07,960 --> 00:07:10,040 Speaker 1: was curious. I want to know more. This is really cool. 127 00:07:10,040 --> 00:07:12,320 Speaker 1: This is an industry I knew nothing about. When I 128 00:07:12,360 --> 00:07:14,400 Speaker 1: talked to Tom and Eric quite a bit and started 129 00:07:14,440 --> 00:07:17,080 Speaker 1: to understand about how they think about their operation. And 130 00:07:17,280 --> 00:07:20,040 Speaker 1: they own their land, they have their equipment paid off 131 00:07:20,080 --> 00:07:22,160 Speaker 1: there on top of their finances. They just can't find 132 00:07:22,240 --> 00:07:25,560 Speaker 1: labor at wages that makes sense for their operation. This 133 00:07:25,640 --> 00:07:28,800 Speaker 1: is right. You know, when cruise was making big news 134 00:07:28,840 --> 00:07:32,760 Speaker 1: and Weymo was becoming sort of an idea in engineering 135 00:07:32,800 --> 00:07:35,440 Speaker 1: circles right, and folks were starting to pay more attention 136 00:07:35,480 --> 00:07:38,000 Speaker 1: to a V and the dots started to connect for 137 00:07:38,080 --> 00:07:40,560 Speaker 1: me that hey, we can really help quite a bit here. 138 00:07:41,000 --> 00:07:42,960 Speaker 1: And so I started noodling on these ideas and started 139 00:07:43,000 --> 00:07:46,960 Speaker 1: looking at other industries to you know, obviously mining within construction, 140 00:07:47,000 --> 00:07:50,160 Speaker 1: marine trucking applications are coming online. So there was a 141 00:07:50,160 --> 00:07:52,320 Speaker 1: lot of movement and I called up one of my 142 00:07:52,480 --> 00:07:56,760 Speaker 1: very close friends from undergrad, Aubrey, and she's an engineer too, 143 00:07:56,760 --> 00:07:58,920 Speaker 1: but a gun into consulting and I was like, hey, Obbs, 144 00:07:58,960 --> 00:08:01,520 Speaker 1: listen like this idea, like can you read team this 145 00:08:01,640 --> 00:08:03,560 Speaker 1: with me? Can you shoot some holes in it? And 146 00:08:03,600 --> 00:08:05,440 Speaker 1: we started talking and you know, you know, half an 147 00:08:05,440 --> 00:08:07,640 Speaker 1: hour talk started turning into three or four hour talks 148 00:08:07,680 --> 00:08:10,760 Speaker 1: just about the space, and sort of our excitement kept 149 00:08:10,760 --> 00:08:13,680 Speaker 1: feeding off one another, and albody's like, hey, do you know, 150 00:08:13,680 --> 00:08:17,960 Speaker 1: I think this is actually pretty pretty solid, Like there's 151 00:08:17,960 --> 00:08:20,120 Speaker 1: something here, there's a they're there. And so we spent 152 00:08:20,200 --> 00:08:22,120 Speaker 1: you know, four or six weeks really looking at mining 153 00:08:22,520 --> 00:08:24,240 Speaker 1: and then the story fast forward and I was trying 154 00:08:24,240 --> 00:08:26,760 Speaker 1: to meet with mining customers just to understand more, and 155 00:08:26,800 --> 00:08:30,560 Speaker 1: I had the privilege of visiting a farm in Wheatland, California, 156 00:08:30,720 --> 00:08:32,960 Speaker 1: And like I said, it sounds corny, Jonathan, but like 157 00:08:33,000 --> 00:08:35,680 Speaker 1: that day really did change our lives. They said, listen, 158 00:08:35,720 --> 00:08:38,360 Speaker 1: like the rock quarries here, this this isn't really our problem. 159 00:08:38,679 --> 00:08:40,360 Speaker 1: Our pain point is in the orchard. We just can't 160 00:08:40,360 --> 00:08:43,040 Speaker 1: find labor to run these tractors and do these operations 161 00:08:43,080 --> 00:08:45,440 Speaker 1: we need for our business. Can you help here? And 162 00:08:45,559 --> 00:08:49,600 Speaker 1: very shortly after we became an agriculture company. Clearly, the 163 00:08:49,720 --> 00:08:53,440 Speaker 1: labor issue is a huge part of why automation is 164 00:08:54,280 --> 00:08:57,720 Speaker 1: so important already and going to become even more important 165 00:08:57,720 --> 00:09:01,200 Speaker 1: in agriculture over the years. Are there other elements of 166 00:09:01,240 --> 00:09:06,960 Speaker 1: agriculture where automation is just going to be transformative in 167 00:09:07,000 --> 00:09:11,760 Speaker 1: that industry? Here's the big idea. So the labor problem 168 00:09:11,800 --> 00:09:15,000 Speaker 1: in AGG is the existential threat. You cannot talk to 169 00:09:15,080 --> 00:09:19,080 Speaker 1: a farmer without them talking about some sort of labor issue. 170 00:09:19,080 --> 00:09:21,600 Speaker 1: The folks that they have on their farm, the high performers, 171 00:09:21,640 --> 00:09:24,040 Speaker 1: the folks who come in on time, understand the operation, 172 00:09:24,160 --> 00:09:26,720 Speaker 1: or easy on the equipment, are dependable. Those are the 173 00:09:26,800 --> 00:09:28,320 Speaker 1: m v p s of the operation. They're the most 174 00:09:28,440 --> 00:09:31,800 Speaker 1: valuable folks, and farmers will do anything to protect those folks. 175 00:09:31,800 --> 00:09:36,280 Speaker 1: But it's just increasingly difficult to find more really good folks, 176 00:09:36,280 --> 00:09:39,559 Speaker 1: and so they're looking for automation anyway they can, and 177 00:09:39,760 --> 00:09:42,560 Speaker 1: we can add a tremendous amount of value doing that. 178 00:09:43,000 --> 00:09:46,720 Speaker 1: The real insight here, and really why I think autonomy 179 00:09:46,840 --> 00:09:51,000 Speaker 1: is the biggest thing in AGG is not actually for 180 00:09:51,080 --> 00:09:55,040 Speaker 1: the automation itself. It's because we're using all these sensors 181 00:09:55,080 --> 00:09:58,400 Speaker 1: bare flag use radars and cameras and light rs and 182 00:09:58,760 --> 00:10:01,719 Speaker 1: there's other sensors to the we've experimented with. But we're 183 00:10:01,800 --> 00:10:04,920 Speaker 1: using these sensors first for safety, right making sure the 184 00:10:04,960 --> 00:10:08,560 Speaker 1: tractor doesn't do something dangerous harm equipment, you know, God forbid, 185 00:10:08,600 --> 00:10:11,480 Speaker 1: harm a person. But those same sensors that we're using 186 00:10:11,520 --> 00:10:14,840 Speaker 1: for safety are being used constantly as we go through 187 00:10:14,880 --> 00:10:18,280 Speaker 1: these fields. The insights that we're gleaning from these same 188 00:10:18,360 --> 00:10:21,319 Speaker 1: sensors we're using for safety to do the automation can 189 00:10:21,360 --> 00:10:25,040 Speaker 1: then be used to feedback value to farmers. And as 190 00:10:25,080 --> 00:10:27,080 Speaker 1: we learn more and as we get this larger data 191 00:10:27,120 --> 00:10:30,200 Speaker 1: set on a performed basis, we can provide these insights 192 00:10:30,200 --> 00:10:32,200 Speaker 1: to farmers that will allow them to be many orders 193 00:10:32,520 --> 00:10:37,280 Speaker 1: of magnitude more productive than previous generations. That's that's the 194 00:10:37,320 --> 00:10:41,000 Speaker 1: really big idea, that's the exciting part. Yes, we're gonna 195 00:10:41,000 --> 00:10:44,800 Speaker 1: help farmers today, Yes we're gonna allow them to farm 196 00:10:44,880 --> 00:10:47,640 Speaker 1: more with less, but this is really just the tip 197 00:10:47,679 --> 00:10:49,400 Speaker 1: of the iceberg. And if you look forward a decade, 198 00:10:49,400 --> 00:10:52,640 Speaker 1: two kid decades, god forbid, over the next century, the 199 00:10:52,720 --> 00:10:55,280 Speaker 1: kind of positive impact we will be able to have 200 00:10:55,320 --> 00:10:58,000 Speaker 1: on the global food supply is really exciting. That's why 201 00:10:58,080 --> 00:11:00,720 Speaker 1: we're here. That's the big idea. To me. It's so 202 00:11:00,760 --> 00:11:04,319 Speaker 1: exciting because we're looking at sort of the genesis of 203 00:11:04,360 --> 00:11:08,800 Speaker 1: the transformation of agriculture, right We're seeing that moment where 204 00:11:08,920 --> 00:11:12,959 Speaker 1: it's going to explode, and we aren't entirely sure what 205 00:11:13,080 --> 00:11:16,160 Speaker 1: the landscape is going to look like afterward. But the 206 00:11:16,160 --> 00:11:21,800 Speaker 1: potential benefit of having more efficient farms that are growing 207 00:11:21,920 --> 00:11:24,079 Speaker 1: what needs to be grown, when it needs to be grown, 208 00:11:24,120 --> 00:11:27,440 Speaker 1: getting to where it needs to go, addressing shortages where 209 00:11:27,440 --> 00:11:31,080 Speaker 1: they are. It's the sort of text story that I 210 00:11:31,160 --> 00:11:36,400 Speaker 1: personally find thrilling and inspiring. I couldn't agree more. One 211 00:11:36,400 --> 00:11:38,320 Speaker 1: of the things maybe for the listeners to know, you 212 00:11:38,440 --> 00:11:41,079 Speaker 1: Bare Flags a start up. In the fall of twenty one, 213 00:11:41,120 --> 00:11:42,959 Speaker 1: we are acquired by John Deere, and so now we're 214 00:11:43,040 --> 00:11:45,160 Speaker 1: a start up operating inside of John Deere. And the 215 00:11:45,200 --> 00:11:48,400 Speaker 1: CEO of John Deer recently was giving a talk and 216 00:11:48,800 --> 00:11:50,480 Speaker 1: I was happy to be in the audience and the 217 00:11:50,520 --> 00:11:53,080 Speaker 1: insight he had his listen Like John Deere historically has 218 00:11:53,120 --> 00:11:55,600 Speaker 1: been very good at doing more with more. We could 219 00:11:55,640 --> 00:11:59,679 Speaker 1: always produce bigger equipment, higher uptime, bigger engines. And now 220 00:11:59,760 --> 00:12:02,280 Speaker 1: the challenge of our generation today is to do more 221 00:12:02,280 --> 00:12:04,360 Speaker 1: with less. And when we look at the kind of 222 00:12:04,400 --> 00:12:07,480 Speaker 1: technology that Johndear is investing in like see and spray, 223 00:12:07,559 --> 00:12:11,160 Speaker 1: this is technology that allows you to spray exclusively weeds 224 00:12:11,280 --> 00:12:14,760 Speaker 1: rather than drenching your whole field in applicants and you 225 00:12:14,760 --> 00:12:18,640 Speaker 1: can just select specifically through computer vision the weeds to spray. 226 00:12:19,040 --> 00:12:22,439 Speaker 1: And then obviously autonomy and push through electrification now too, 227 00:12:22,880 --> 00:12:25,080 Speaker 1: how can we grow more with less? And that's an 228 00:12:25,120 --> 00:12:28,360 Speaker 1: idea that is extremely attractive to us as we think 229 00:12:28,360 --> 00:12:32,000 Speaker 1: about a sustainable future. As I understand it, your company, 230 00:12:32,080 --> 00:12:35,200 Speaker 1: what you do is you take these pieces of equipment 231 00:12:35,240 --> 00:12:39,040 Speaker 1: that have been manufactured and then you retrofit them with 232 00:12:39,120 --> 00:12:43,560 Speaker 1: the various sensors and systems. Is that correct, That's exactly right. 233 00:12:43,679 --> 00:12:46,160 Speaker 1: So the you know, the one liner for Bare Flag, 234 00:12:46,600 --> 00:12:49,440 Speaker 1: you know, as a startup, was we build autonomous technology 235 00:12:49,520 --> 00:12:52,600 Speaker 1: for farm tractors. We had no interest in building the tractors. 236 00:12:53,000 --> 00:12:55,000 Speaker 1: We didn't anticipate we could create a lot of new 237 00:12:55,080 --> 00:12:58,080 Speaker 1: value there as far as the farmer is concerned. But 238 00:12:58,200 --> 00:13:02,880 Speaker 1: we would procure the machines attractors themselves from rental fleets, dealerships, 239 00:13:02,920 --> 00:13:06,000 Speaker 1: customers themselves, put the sensors, compute necessary to make them 240 00:13:06,000 --> 00:13:08,160 Speaker 1: autonomous on them and the deploy them as a service, 241 00:13:08,640 --> 00:13:11,120 Speaker 1: and it was working really well. We were really making 242 00:13:11,120 --> 00:13:14,040 Speaker 1: waves and picking up the clip on operations. That being said, 243 00:13:14,559 --> 00:13:16,880 Speaker 1: we have had a long standing relationship with John Deer, 244 00:13:16,920 --> 00:13:20,320 Speaker 1: and when they propose that we partnered together, we sort 245 00:13:20,320 --> 00:13:21,720 Speaker 1: of looked at each other and like looked at our 246 00:13:21,720 --> 00:13:24,440 Speaker 1: mission realized that hey, listen, we're on a march here. 247 00:13:24,720 --> 00:13:26,319 Speaker 1: We can do this the hard way, or we can 248 00:13:26,360 --> 00:13:28,760 Speaker 1: do this the fast way, which is to work with 249 00:13:28,840 --> 00:13:31,360 Speaker 1: John Deer, which has the largest install base in the world. 250 00:13:31,760 --> 00:13:36,480 Speaker 1: Talking to their leadership, we were very quickly understood their 251 00:13:36,520 --> 00:13:38,920 Speaker 1: priority to get autonomous to the world, which aligned with 252 00:13:38,960 --> 00:13:42,680 Speaker 1: our priority to and it's been a really good fit. Well. 253 00:13:43,480 --> 00:13:45,800 Speaker 1: One thing that we on the Restless Ones love to 254 00:13:45,840 --> 00:13:49,760 Speaker 1: talk about is connectivity. And when you're talking about automation 255 00:13:49,800 --> 00:13:52,760 Speaker 1: and you're talking about sensors and you're talking about gathering 256 00:13:52,920 --> 00:13:56,000 Speaker 1: data and you're talking about these massive pieces of equipment, 257 00:13:56,600 --> 00:14:00,840 Speaker 1: my first ideas that connectivity apps slutely has to be 258 00:14:00,960 --> 00:14:04,240 Speaker 1: kind of the underlying foundation that makes all that possible. 259 00:14:04,280 --> 00:14:07,240 Speaker 1: Can you speak to that a little yeah, I mean 260 00:14:07,280 --> 00:14:10,520 Speaker 1: we we recognize that too. You know, there's really two 261 00:14:10,520 --> 00:14:13,080 Speaker 1: types of data transfer. One is you know, the back 262 00:14:13,160 --> 00:14:15,440 Speaker 1: haull of all the data you get, and these can 263 00:14:15,520 --> 00:14:19,160 Speaker 1: you know, inform our machine learning models and help train 264 00:14:19,200 --> 00:14:21,960 Speaker 1: our tractors to operate better. But then also just the 265 00:14:22,000 --> 00:14:24,040 Speaker 1: pure insights that are getting from those fields that we 266 00:14:24,040 --> 00:14:26,520 Speaker 1: can then use and turn around and deliver value back 267 00:14:26,520 --> 00:14:28,760 Speaker 1: to that farmer on top of the operation itself. But 268 00:14:28,840 --> 00:14:31,560 Speaker 1: then there's also the real time command and control. The 269 00:14:31,600 --> 00:14:35,240 Speaker 1: bare flag machines had these real time videos web based interfaces. 270 00:14:35,280 --> 00:14:39,440 Speaker 1: You could basically have the incab experience from your tablet 271 00:14:39,480 --> 00:14:41,520 Speaker 1: as you monitor your fleet while you're driving your truck 272 00:14:41,560 --> 00:14:45,360 Speaker 1: around the ranch. And so connectivity was especially cord of that. 273 00:14:45,720 --> 00:14:49,200 Speaker 1: You know, we borrowed a lot of technology ideas from 274 00:14:49,360 --> 00:14:52,360 Speaker 1: other folks in the industry who working in parallel industries 275 00:14:52,400 --> 00:14:55,880 Speaker 1: around how can we degrade the resolution of the video 276 00:14:55,960 --> 00:14:59,920 Speaker 1: when we have lower connectivity and things like that. But truly, 277 00:15:00,520 --> 00:15:03,680 Speaker 1: one of the biggest enabling technologies of autonomy and all 278 00:15:03,680 --> 00:15:07,160 Speaker 1: this goodness we're talking about really is that connectivity. Well, 279 00:15:07,200 --> 00:15:10,040 Speaker 1: and and you were mentioning that sort of in cab 280 00:15:10,720 --> 00:15:14,880 Speaker 1: experience of being able to view the situation as if 281 00:15:14,920 --> 00:15:18,080 Speaker 1: you were actually sitting in the tractor as it's going 282 00:15:18,720 --> 00:15:22,360 Speaker 1: I imagine for that to be useful, to be really useful, 283 00:15:22,680 --> 00:15:25,280 Speaker 1: you need to have connectivity that has very low latency 284 00:15:25,400 --> 00:15:27,440 Speaker 1: so that you see what's going on as it's going on, 285 00:15:27,520 --> 00:15:29,560 Speaker 1: as opposed to, yeah, this is what your tractor was 286 00:15:29,640 --> 00:15:35,160 Speaker 1: doing five ten thirty seconds ago. That's exactly right, Hey listen, Like, 287 00:15:35,200 --> 00:15:38,160 Speaker 1: there's also other really cool ideas. You know, in the future, 288 00:15:38,200 --> 00:15:40,800 Speaker 1: you know, autonomous tractors will be a RoboCop. They'll do everything, 289 00:15:40,840 --> 00:15:42,800 Speaker 1: they'll do it better than the human all the things. 290 00:15:42,800 --> 00:15:44,960 Speaker 1: We're not there yet, and we're frankly like not that 291 00:15:45,080 --> 00:15:49,040 Speaker 1: close to that, so there's still has to be human interactions. 292 00:15:49,080 --> 00:15:53,920 Speaker 1: So while that machine can autonomously till or cultivate or 293 00:15:53,920 --> 00:15:56,760 Speaker 1: harvester plant that field, you still need to move it 294 00:15:56,800 --> 00:15:59,440 Speaker 1: to the next field. And so things like remote piloting 295 00:15:59,440 --> 00:16:01,200 Speaker 1: come into play where hey, listen, I don't want to 296 00:16:01,280 --> 00:16:04,920 Speaker 1: drive out twin dang minutes to drive this tractor thirty 297 00:16:05,200 --> 00:16:08,560 Speaker 1: across a private dirt road. Can I just remote control 298 00:16:08,560 --> 00:16:10,920 Speaker 1: it from my iPad? And the answer is absolutely yes, 299 00:16:11,280 --> 00:16:14,680 Speaker 1: you can, but you need connectivity to do that. Oh yeah, yeah. 300 00:16:14,720 --> 00:16:18,040 Speaker 1: Anytime you're talking about actually controlling the vehicle, particularly one 301 00:16:18,560 --> 00:16:22,520 Speaker 1: as enormous as the ones we see in the agricultural industry. 302 00:16:22,800 --> 00:16:26,680 Speaker 1: Then obviously having that ability to have a low latency 303 00:16:26,880 --> 00:16:30,200 Speaker 1: connection is absolutely critical. We would want to make sure 304 00:16:30,720 --> 00:16:35,120 Speaker 1: that whatever is enabling the decision making has the lowest 305 00:16:35,200 --> 00:16:39,640 Speaker 1: latency possible, because you can't react to a hazard that 306 00:16:39,800 --> 00:16:48,000 Speaker 1: actually happened five seconds ago, then it's too late. Conventional 307 00:16:48,080 --> 00:16:50,320 Speaker 1: thinking says you have to pay more to get more. 308 00:16:50,520 --> 00:16:53,280 Speaker 1: I want the world, But Team Obile for Business uses 309 00:16:53,360 --> 00:16:56,680 Speaker 1: unconventional thinking to deliver premium benefits for better r o 310 00:16:56,840 --> 00:17:00,480 Speaker 1: I from customized five G solutions to three sixty we 311 00:17:00,560 --> 00:17:03,800 Speaker 1: help you reach your business goals right now. I wanted 312 00:17:03,960 --> 00:17:08,840 Speaker 1: now innovating to improve business today and tomorrow. That's unconventional 313 00:17:08,880 --> 00:17:12,000 Speaker 1: thinking from t Mobile for Business. Capable device required covers 314 00:17:12,000 --> 00:17:14,520 Speaker 1: not available in some areas. Some require certain planter features 315 00:17:14,760 --> 00:17:25,040 Speaker 1: mobile dot com do your farmer's interface with this technology 316 00:17:25,440 --> 00:17:28,520 Speaker 1: in a direct way or is this truly attractress as 317 00:17:28,560 --> 00:17:33,400 Speaker 1: a service where you have your own team working on things. Yeah, 318 00:17:33,480 --> 00:17:36,280 Speaker 1: So talking specifically to bear Flag, one of the things 319 00:17:36,359 --> 00:17:38,640 Speaker 1: that we really emphasize is getting the market as soon 320 00:17:38,680 --> 00:17:42,399 Speaker 1: as possible. Startups in general usually wait too long to 321 00:17:42,440 --> 00:17:44,480 Speaker 1: go to market, and we knew that was a trap, 322 00:17:44,560 --> 00:17:46,159 Speaker 1: and so we tried to get in the field as 323 00:17:46,240 --> 00:17:48,560 Speaker 1: quickly sposible. What that meant is that in the very 324 00:17:48,560 --> 00:17:51,320 Speaker 1: early days, listen, the solution was not fully baked. It 325 00:17:51,359 --> 00:17:54,159 Speaker 1: would break in new and novel ways we've never seen before, 326 00:17:54,240 --> 00:17:56,520 Speaker 1: you know, quite frequently, and we needed folks in the 327 00:17:56,600 --> 00:17:59,480 Speaker 1: cab to finish that job up by hand the farm. 328 00:17:59,720 --> 00:18:02,159 Speaker 1: The farm is not paying for an experiment of science 329 00:18:02,160 --> 00:18:04,720 Speaker 1: project in this field. He's paying to have that field tilled. 330 00:18:04,760 --> 00:18:06,959 Speaker 1: And while early doctors did have a ton of patients 331 00:18:06,960 --> 00:18:09,000 Speaker 1: and curiosity, at the end of the day, they had 332 00:18:09,000 --> 00:18:10,399 Speaker 1: a job to do and we need to do that 333 00:18:10,440 --> 00:18:12,680 Speaker 1: for them. And so a service model really makes sense. 334 00:18:12,680 --> 00:18:14,680 Speaker 1: And you see this a lot in robotics in general. 335 00:18:14,960 --> 00:18:18,880 Speaker 1: Robotics companies that are early often do adopt service models 336 00:18:18,920 --> 00:18:22,480 Speaker 1: because they can develop their technology and parallel to developing 337 00:18:22,480 --> 00:18:25,119 Speaker 1: their business. But as we matured more, there was more 338 00:18:25,119 --> 00:18:27,800 Speaker 1: and more that we're handing over to the grower. Fundamentally, 339 00:18:27,840 --> 00:18:30,320 Speaker 1: it'll be the farmer operating their equipment. That's the direction 340 00:18:30,359 --> 00:18:34,359 Speaker 1: we're going. I remember reading a research study in robotics 341 00:18:34,440 --> 00:18:37,080 Speaker 1: where the goal was to teach a robot how to 342 00:18:37,320 --> 00:18:40,320 Speaker 1: open a door, and the robot stared at a door 343 00:18:40,400 --> 00:18:44,240 Speaker 1: for two full days before even attempting, because these are 344 00:18:44,440 --> 00:18:49,560 Speaker 1: non trivial engineering and robotics problems. So having that approach 345 00:18:50,000 --> 00:18:53,920 Speaker 1: where you are cognizant that ultimately the farmer has a 346 00:18:54,000 --> 00:18:57,080 Speaker 1: job that needs doing, so you cannot just leave it 347 00:18:57,160 --> 00:19:01,359 Speaker 1: to chance that everything works perfectly. This whole system is learning. 348 00:19:01,359 --> 00:19:05,280 Speaker 1: Every single time you have a different field, your system 349 00:19:05,359 --> 00:19:08,680 Speaker 1: is learning. It is adding to the base of knowledge 350 00:19:09,080 --> 00:19:12,840 Speaker 1: that all the different systems draw from, and that it 351 00:19:12,920 --> 00:19:16,000 Speaker 1: improves over time. I could not agree more. Would you 352 00:19:16,040 --> 00:19:19,800 Speaker 1: say it's fair to say that now big agricultural machines 353 00:19:19,840 --> 00:19:23,000 Speaker 1: like tractors have joined the Internet of Things? Is that 354 00:19:23,119 --> 00:19:28,320 Speaker 1: an accurate description? Oh? Yes, without hesitancy, and quite frankly too, 355 00:19:28,400 --> 00:19:32,600 Speaker 1: even before fair flag and autonomy came along. These tractors 356 00:19:32,600 --> 00:19:37,080 Speaker 1: are extremely sophisticated devices. The number of computers on this 357 00:19:37,240 --> 00:19:42,399 Speaker 1: machine parallels a car. Right. These farming operations are massively impressive, 358 00:19:42,920 --> 00:19:47,080 Speaker 1: huge oftentimes public companies farming at scale. You know, they 359 00:19:47,160 --> 00:19:51,679 Speaker 1: run their operations like any sort of manufacturing company would 360 00:19:51,760 --> 00:19:54,360 Speaker 1: they have. They understand everything that's going on all the time. 361 00:19:54,359 --> 00:19:57,600 Speaker 1: Their devices are connected they have predictive maintenance on their machines, 362 00:19:57,640 --> 00:20:00,439 Speaker 1: they have GPS technology for guidance to make sure they 363 00:20:00,480 --> 00:20:03,679 Speaker 1: don't have overlap, and you know, allows their drivers to 364 00:20:03,720 --> 00:20:08,280 Speaker 1: go longer without fatigue. All sorts of technology enables these customers. 365 00:20:08,920 --> 00:20:12,000 Speaker 1: Can you talk a little bit about the conclusions that 366 00:20:12,040 --> 00:20:15,240 Speaker 1: farmers will be able to make based upon the data 367 00:20:15,320 --> 00:20:17,920 Speaker 1: that's being gathered by these devices just as they're going 368 00:20:17,960 --> 00:20:22,640 Speaker 1: about their job of tilling or harvesting or what have you. Yeah, 369 00:20:22,680 --> 00:20:25,159 Speaker 1: and this is one of the coolest parts. Right. The 370 00:20:25,200 --> 00:20:27,800 Speaker 1: future is an exciting place, and I'll touch like very 371 00:20:27,840 --> 00:20:30,960 Speaker 1: quickly on what's possible. Farming is a problem with some 372 00:20:31,000 --> 00:20:33,080 Speaker 1: of the most variables that I know of in the world. 373 00:20:33,240 --> 00:20:36,400 Speaker 1: Right there's weather concerns and crop concerns, and disease concerns 374 00:20:36,400 --> 00:20:40,440 Speaker 1: and soil concerns, and market prices, labor markets and biology 375 00:20:40,440 --> 00:20:42,680 Speaker 1: and all sorts of things. And so it's actually really 376 00:20:42,720 --> 00:20:45,679 Speaker 1: hard just to run simple experiments about hey, you know, 377 00:20:45,680 --> 00:20:48,000 Speaker 1: a B test this field in this field because it's 378 00:20:48,040 --> 00:20:51,240 Speaker 1: so hard to control all your variables. And so as 379 00:20:51,320 --> 00:20:54,720 Speaker 1: we are able to collect all kinds of information about 380 00:20:55,240 --> 00:20:57,919 Speaker 1: farmers field over then nextent amount of time, we can 381 00:20:57,920 --> 00:21:00,280 Speaker 1: actually start to pull out those experiments and hide site 382 00:21:00,280 --> 00:21:03,680 Speaker 1: post hoc and that's the real value. That's where we'll 383 00:21:03,720 --> 00:21:05,440 Speaker 1: be able to glean the insights, and that's what we're 384 00:21:05,440 --> 00:21:08,040 Speaker 1: working towards. That being said, it's not all slidewear, it's 385 00:21:08,080 --> 00:21:11,600 Speaker 1: not all tomorrow. Very specifically, what we were working on, 386 00:21:12,080 --> 00:21:13,960 Speaker 1: we you know, bare flag at the privilege of working 387 00:21:14,000 --> 00:21:16,119 Speaker 1: with one of the largest cotton growers in the United States, 388 00:21:16,560 --> 00:21:19,520 Speaker 1: and they would rotate with another crop called staff flower 389 00:21:19,760 --> 00:21:21,399 Speaker 1: to make sure the nutrients are put back in the 390 00:21:21,440 --> 00:21:24,320 Speaker 1: soil properly. And so what we would do is we 391 00:21:24,480 --> 00:21:27,960 Speaker 1: do the spring tillage pass which is on a fallow field, 392 00:21:27,960 --> 00:21:29,720 Speaker 1: a field that doesn't have crops, and we'd be able 393 00:21:29,720 --> 00:21:32,960 Speaker 1: to understand how compact that soil was, and you could 394 00:21:33,000 --> 00:21:36,240 Speaker 1: actually see in the data where someone had driven a 395 00:21:36,320 --> 00:21:38,560 Speaker 1: tractor across that field. Dagon really to cut across the 396 00:21:38,560 --> 00:21:40,639 Speaker 1: field because it so will be more compacted there, and 397 00:21:40,680 --> 00:21:42,480 Speaker 1: that would show up in our data. And then we 398 00:21:42,520 --> 00:21:45,320 Speaker 1: could link that to the spring passes we made while 399 00:21:45,320 --> 00:21:48,359 Speaker 1: the sap flowers growing and use our lighters two sense 400 00:21:48,359 --> 00:21:51,159 Speaker 1: the canopy volume and then start to correlate. Hey, listen, 401 00:21:51,200 --> 00:21:54,720 Speaker 1: like it wasn't obvious to the human eye, and it 402 00:21:54,840 --> 00:21:57,639 Speaker 1: wasn't obvious because we didn't have these insights. But we 403 00:21:57,640 --> 00:22:00,720 Speaker 1: can actually see now where that saff flyer or has 404 00:22:00,800 --> 00:22:03,400 Speaker 1: more canopy volume, and we can go back and fix 405 00:22:03,440 --> 00:22:07,000 Speaker 1: those spots and anticipate where that is to increase yields. 406 00:22:07,040 --> 00:22:10,200 Speaker 1: These are really exciting things once again that weren't obvious 407 00:22:10,240 --> 00:22:12,320 Speaker 1: to the naked eye before, but we can start to 408 00:22:12,320 --> 00:22:15,080 Speaker 1: do through robotics to actually increased heield once again, you know, 409 00:22:15,160 --> 00:22:18,920 Speaker 1: doing more with less. This is beautiful, this idea of 410 00:22:19,960 --> 00:22:23,880 Speaker 1: patterns and and meaning emerging from the collection of data. 411 00:22:23,960 --> 00:22:26,320 Speaker 1: This is obviously the sort of the promise of big 412 00:22:26,400 --> 00:22:29,320 Speaker 1: data at large, right, you know that you've got all 413 00:22:29,359 --> 00:22:33,400 Speaker 1: this information and now comes the task of finding how 414 00:22:33,480 --> 00:22:36,320 Speaker 1: to stiff through that, find the signal versus the noise, 415 00:22:36,400 --> 00:22:39,400 Speaker 1: find the meaning. There the idea that this is something 416 00:22:39,400 --> 00:22:43,040 Speaker 1: that is applicable across the entire industry, but it's also 417 00:22:43,119 --> 00:22:47,919 Speaker 1: going to be customized, tailor made for each individual region, 418 00:22:47,960 --> 00:22:51,480 Speaker 1: each individual field, because as you say, the real world 419 00:22:51,720 --> 00:22:54,879 Speaker 1: is a terrible test environment. There's just you have no 420 00:22:54,960 --> 00:22:59,920 Speaker 1: control over so much that can impact the whole process. 421 00:23:00,240 --> 00:23:04,080 Speaker 1: That's exactly right, Well, I have to also ask you 422 00:23:04,119 --> 00:23:07,880 Speaker 1: what are some of the constraints on modern farms from 423 00:23:07,880 --> 00:23:10,200 Speaker 1: a technological standpoint. You know, we were just talking about 424 00:23:10,240 --> 00:23:13,439 Speaker 1: all the variables that you can't control for technologically. What 425 00:23:13,520 --> 00:23:15,440 Speaker 1: are some of the constraints you have to work within. 426 00:23:16,400 --> 00:23:18,800 Speaker 1: One of the broader challenges, Jonathan, is, like you said, 427 00:23:18,840 --> 00:23:22,040 Speaker 1: every farm is different, and so a challenge for a 428 00:23:22,119 --> 00:23:25,760 Speaker 1: company like bear Flag is picking what are our beachheads 429 00:23:25,800 --> 00:23:29,440 Speaker 1: where we gonna get a foothold, what sort of collection 430 00:23:29,520 --> 00:23:32,119 Speaker 1: of farms are similar enough that we can start to 431 00:23:32,119 --> 00:23:36,000 Speaker 1: build a business around as we extend into other types 432 00:23:36,000 --> 00:23:38,240 Speaker 1: of crops, And then even then it's like, okay, great, 433 00:23:38,560 --> 00:23:42,080 Speaker 1: these farms that have some similarities, well they do sometimes 434 00:23:42,119 --> 00:23:44,680 Speaker 1: you know, on the low end, four different types of operations, 435 00:23:44,720 --> 00:23:46,000 Speaker 1: all the way up to you know, ten or twelve 436 00:23:46,119 --> 00:23:48,920 Speaker 1: different types of operations with their attractor in that same field. 437 00:23:48,960 --> 00:23:50,879 Speaker 1: And so as the startup, once again, how are we 438 00:23:50,920 --> 00:23:53,400 Speaker 1: picking the operations we're gonna do If we can only 439 00:23:53,400 --> 00:23:55,159 Speaker 1: do two of those twelve, how much of how you 440 00:23:55,160 --> 00:23:57,280 Speaker 1: are we actually giving to that farmer? And so when 441 00:23:57,320 --> 00:23:59,840 Speaker 1: I think about the barriers to adoption, you know, why 442 00:23:59,880 --> 00:24:02,040 Speaker 1: do in every farm in the world have autonomy. Today 443 00:24:02,280 --> 00:24:06,359 Speaker 1: I reflect on just how broad agriculture is, not only 444 00:24:06,440 --> 00:24:09,000 Speaker 1: crop to crop, but season to season and region to region. 445 00:24:09,040 --> 00:24:13,280 Speaker 1: There is just so much value to unlock and then 446 00:24:13,320 --> 00:24:15,600 Speaker 1: to deliver. This will be the work of a lifetime. 447 00:24:16,440 --> 00:24:18,720 Speaker 1: I also have to ask you you were talking earlier 448 00:24:19,119 --> 00:24:22,640 Speaker 1: about being acquired by John Deere, that partnership that's been formed, 449 00:24:23,119 --> 00:24:25,880 Speaker 1: that you were operating as a kind of an independent 450 00:24:25,880 --> 00:24:29,760 Speaker 1: startup underneath the auspices of John Deere. For those looking 451 00:24:29,760 --> 00:24:34,360 Speaker 1: to understand the integration of a startup into an established enterprise, 452 00:24:34,960 --> 00:24:37,560 Speaker 1: what was that process like, what sort of challenges did 453 00:24:37,640 --> 00:24:40,360 Speaker 1: it present and what sort of opportunities has it opened up? 454 00:24:41,080 --> 00:24:43,040 Speaker 1: To start with? The reason it was obvious was a 455 00:24:43,040 --> 00:24:45,760 Speaker 1: culture fit. We'd had the privilege of knowing the folks 456 00:24:45,760 --> 00:24:48,919 Speaker 1: that John Deere we're in their startup collaborator in nineteen 457 00:24:49,480 --> 00:24:52,200 Speaker 1: got to know the business development folks, but then also 458 00:24:52,280 --> 00:24:54,960 Speaker 1: the engineers in the leadership, and started to have time 459 00:24:55,000 --> 00:24:57,840 Speaker 1: to really understand who these folks were and what made 460 00:24:57,880 --> 00:24:59,800 Speaker 1: them tick and what inspired and motivated them. And it 461 00:24:59,840 --> 00:25:01,879 Speaker 1: was very clear was a lot of the same things 462 00:25:02,320 --> 00:25:06,240 Speaker 1: folks at John Deer really care about the higher purpose. 463 00:25:06,359 --> 00:25:09,080 Speaker 1: They really care about the mission. They care about taking 464 00:25:09,080 --> 00:25:11,720 Speaker 1: care of farmers, they care about the quality of the 465 00:25:11,760 --> 00:25:14,000 Speaker 1: products that they deliver, and they have immense pride in 466 00:25:14,000 --> 00:25:16,600 Speaker 1: the brand. And that's something that we felt the same too. 467 00:25:16,720 --> 00:25:20,480 Speaker 1: Write like, nothing about agriculture is easy, nothing about startup 468 00:25:20,520 --> 00:25:24,240 Speaker 1: is easy. But there's this intense desire and passion and 469 00:25:24,359 --> 00:25:26,960 Speaker 1: drive to have that impact, to deliver that value. And 470 00:25:27,000 --> 00:25:29,480 Speaker 1: we made the same personal connections with our customers and 471 00:25:29,800 --> 00:25:32,560 Speaker 1: our farmers as Deer has with theirs, and so finding 472 00:25:32,560 --> 00:25:36,560 Speaker 1: this commonality between this higher purpose is what makes it work. Now. Listen, Yeah, 473 00:25:36,600 --> 00:25:40,000 Speaker 1: like Bare Flag is a small, scrappy startup, so there's 474 00:25:40,000 --> 00:25:42,280 Speaker 1: gonna be differences. They're the main thing to call out. 475 00:25:42,359 --> 00:25:45,200 Speaker 1: This isn't like old school thinking, our new school thinking 476 00:25:45,320 --> 00:25:46,600 Speaker 1: that you know, the folks a John dere are some 477 00:25:46,680 --> 00:25:49,720 Speaker 1: of the sharpest that I know in agriculture. It's just 478 00:25:49,760 --> 00:25:54,239 Speaker 1: how you steer a massive cargo tanker compared to how 479 00:25:54,320 --> 00:25:56,960 Speaker 1: you know, steer a ski boat. Like, they just take 480 00:25:56,960 --> 00:26:00,359 Speaker 1: different skills and they have different strengths, and so figure 481 00:26:00,359 --> 00:26:03,160 Speaker 1: out how we complement each other has been really exciting. 482 00:26:03,480 --> 00:26:06,520 Speaker 1: There's a strong model here to Johndie required Blue River 483 00:26:06,600 --> 00:26:09,919 Speaker 1: Technologies about five years ago now, and that was a 484 00:26:10,000 --> 00:26:12,399 Speaker 1: really good model for success. It was, hey, listen, we're 485 00:26:12,400 --> 00:26:14,960 Speaker 1: going to support you and every way you want, but fundamentally, 486 00:26:14,960 --> 00:26:17,119 Speaker 1: we're gonna leave you alone here and allow you to 487 00:26:17,200 --> 00:26:19,520 Speaker 1: keep doing what you do, keep that lightning in a bottle, 488 00:26:19,600 --> 00:26:22,360 Speaker 1: keep you being creative and motivated, an ability to move 489 00:26:22,440 --> 00:26:24,919 Speaker 1: quickly without getting in your way, and Johnie has done 490 00:26:24,960 --> 00:26:29,280 Speaker 1: a remarkable job at that. Before I could let a 491 00:26:29,359 --> 00:26:36,120 Speaker 1: gino go, I had to ask him one more thing. Okay, 492 00:26:36,400 --> 00:26:38,720 Speaker 1: here's the one that I gotta know. What does the 493 00:26:38,800 --> 00:26:44,679 Speaker 1: farm of the future look like? Yeah, oh man, I 494 00:26:44,680 --> 00:26:48,200 Speaker 1: think it's an evolution. Right listen, I'm gonna give you 495 00:26:48,240 --> 00:26:50,000 Speaker 1: an answer, and you'renna think it's a cop out, But 496 00:26:50,040 --> 00:26:52,480 Speaker 1: then we're gonna get to the goodness. The cool thing 497 00:26:52,480 --> 00:26:56,280 Speaker 1: about farmers is two things I've learned. They'll always shoot 498 00:26:56,280 --> 00:26:58,639 Speaker 1: you straight, which might not always be comfortable, but it's 499 00:26:58,640 --> 00:27:02,199 Speaker 1: always useful. And what you have delivers value. They will 500 00:27:02,240 --> 00:27:05,840 Speaker 1: be quick adopters. These guys don't chase trends. They are 501 00:27:06,040 --> 00:27:10,280 Speaker 1: especially as stute. It's sniffing out bologny and cutting to 502 00:27:10,359 --> 00:27:13,720 Speaker 1: like what can actually deliver value to their farm. And 503 00:27:13,800 --> 00:27:16,600 Speaker 1: so as we iterate. The most important thing as we 504 00:27:16,680 --> 00:27:19,439 Speaker 1: go will be to demonstrate that we are delivering value 505 00:27:19,560 --> 00:27:22,200 Speaker 1: every increment. This is sort of like you know evolution, right. 506 00:27:22,560 --> 00:27:26,200 Speaker 1: Evolution is such that the mutations always need to benefit 507 00:27:26,560 --> 00:27:28,479 Speaker 1: the species in order for them to be adopted as 508 00:27:28,520 --> 00:27:30,080 Speaker 1: you go right, and we end up with these incredibly 509 00:27:30,119 --> 00:27:33,160 Speaker 1: complex sort of like eyeballs and hearts, you know, and organs, 510 00:27:33,400 --> 00:27:35,240 Speaker 1: but they're always iterative steps. And I see the same 511 00:27:35,280 --> 00:27:37,639 Speaker 1: thing happening on the farm, you know, the visionless and 512 00:27:37,680 --> 00:27:40,080 Speaker 1: like you sit back and like, how can a corn 513 00:27:40,080 --> 00:27:43,320 Speaker 1: and soy grower really just become like a futures trader 514 00:27:43,359 --> 00:27:45,160 Speaker 1: on their commodity that's in their field and they don't 515 00:27:45,160 --> 00:27:47,639 Speaker 1: need to get into attractive That's that's sort of the 516 00:27:47,720 --> 00:27:49,360 Speaker 1: vision that you have in the back of your mind. 517 00:27:49,359 --> 00:27:51,480 Speaker 1: I think we're candidly a long way away from that. 518 00:27:51,800 --> 00:27:54,840 Speaker 1: Where I really see automation and autonomy helping the short 519 00:27:54,920 --> 00:27:58,359 Speaker 1: run is to make folks more effective, more productive, allow 520 00:27:58,400 --> 00:28:01,080 Speaker 1: them to grow more on fewer acres. When we look 521 00:28:01,119 --> 00:28:05,480 Speaker 1: at things like the economic headroom that autonomy itself unlocks, 522 00:28:05,920 --> 00:28:08,600 Speaker 1: there's a ceiling there. It's really limited by the size 523 00:28:08,640 --> 00:28:10,280 Speaker 1: of the tractor in the number of hours in the day, 524 00:28:10,480 --> 00:28:12,160 Speaker 1: but when we look at the date and the insights 525 00:28:12,200 --> 00:28:15,240 Speaker 1: that's gleaning to help it grower, have more yield, creaker 526 00:28:15,320 --> 00:28:18,440 Speaker 1: to lower cost. From our seat in the ballpark right now, 527 00:28:18,800 --> 00:28:23,320 Speaker 1: that headroom is limitless. There's no known barrier, no practical 528 00:28:23,359 --> 00:28:26,560 Speaker 1: limitations on growing more with less, and I think that's 529 00:28:26,560 --> 00:28:29,800 Speaker 1: the direction we go. The idea of a farmer sort 530 00:28:29,840 --> 00:28:31,800 Speaker 1: of like resting in better this iPad while the whole 531 00:28:31,800 --> 00:28:34,560 Speaker 1: farms running, I think is comical, but also to just 532 00:28:34,680 --> 00:28:37,400 Speaker 1: knowing enough farmers to know they would never be satisfied 533 00:28:37,440 --> 00:28:39,000 Speaker 1: doing that. They want to get out there and work 534 00:28:39,040 --> 00:28:41,240 Speaker 1: their land and run their equipment. There's an element of 535 00:28:41,280 --> 00:28:44,040 Speaker 1: that too well. Gino, I have to say, this has 536 00:28:44,080 --> 00:28:48,760 Speaker 1: been an inspiring, informative, educational conversation. I've really enjoyed it. 537 00:28:48,960 --> 00:28:51,680 Speaker 1: Thank you so much for coming on our show, Jonathan, 538 00:28:51,720 --> 00:28:53,400 Speaker 1: Thank you so much. It's been a thrill, really a 539 00:28:53,400 --> 00:29:01,600 Speaker 1: pleasure to chat. It was impossible for me to suppress 540 00:29:01,680 --> 00:29:04,480 Speaker 1: my enthusiasm for a Gino's work. I think back to 541 00:29:04,600 --> 00:29:07,600 Speaker 1: the challenges my grandparents faced on the farm and how 542 00:29:07,640 --> 00:29:11,120 Speaker 1: they could have benefited from the incredible technology now being deployed, 543 00:29:11,880 --> 00:29:14,720 Speaker 1: and it's really exciting to think about how data will 544 00:29:14,840 --> 00:29:18,800 Speaker 1: ultimately fuel a revolution in food production. It's the sort 545 00:29:18,800 --> 00:29:23,320 Speaker 1: of impact that everyone will experience down the chain. Increased yields, 546 00:29:23,360 --> 00:29:28,320 Speaker 1: healthier crops, lower costs of production. Those will benefit everyone 547 00:29:28,600 --> 00:29:32,000 Speaker 1: from the farmers down to the consumers. Will learn how 548 00:29:32,040 --> 00:29:35,360 Speaker 1: to be better stewards of the land, decreasing the environmental 549 00:29:35,400 --> 00:29:38,479 Speaker 1: impact of agriculture in the process. It's the sort of 550 00:29:38,840 --> 00:29:42,160 Speaker 1: big picture problem that can be tackled with the right approach, 551 00:29:42,600 --> 00:29:46,080 Speaker 1: and it's not about throwing technology at an issue, but 552 00:29:46,240 --> 00:29:50,240 Speaker 1: rather the careful application of a thoughtful approach followed by 553 00:29:50,400 --> 00:29:54,480 Speaker 1: meticulous analysis of the data we gather. That's what's going 554 00:29:54,520 --> 00:29:57,800 Speaker 1: to change the world. None of that would be possible 555 00:29:57,840 --> 00:30:01,640 Speaker 1: without the connectivity part of the picture. To realize this future, 556 00:30:01,760 --> 00:30:05,200 Speaker 1: we need those wireless connections with high throughput and low 557 00:30:05,280 --> 00:30:09,560 Speaker 1: latency in place. Five G technology is literally enabling the 558 00:30:09,560 --> 00:30:13,600 Speaker 1: technologies that will transform how we do business. More than that, 559 00:30:14,160 --> 00:30:17,480 Speaker 1: it's transforming the world and it's phenomenal that we live 560 00:30:17,520 --> 00:30:21,200 Speaker 1: in a time where this kind of connectivity isn't just possible, 561 00:30:21,800 --> 00:30:30,400 Speaker 1: it's here and it's growing. Please be sure to join 562 00:30:30,480 --> 00:30:32,600 Speaker 1: us for future episodes of The Restless ones. As I 563 00:30:32,640 --> 00:30:35,760 Speaker 1: speak with more leaders and disruptors who are building our 564 00:30:35,840 --> 00:30:45,600 Speaker 1: path to the future, I'll see you then. T Mobile 565 00:30:45,600 --> 00:30:48,040 Speaker 1: for Business knows companies want more than a one size 566 00:30:48,040 --> 00:30:51,520 Speaker 1: fits all approach to support. I want the world, so 567 00:30:51,560 --> 00:30:55,200 Speaker 1: we provide three sixty support customized to your business. From 568 00:30:55,240 --> 00:30:58,560 Speaker 1: discovery through post deployment. You'll get a dedicated account team 569 00:30:58,640 --> 00:31:03,000 Speaker 1: and expertise from so Lucian's engineers and industry advisors already 570 00:31:03,280 --> 00:31:08,520 Speaker 1: right now, I want it now. Three six support that's 571 00:31:08,600 --> 00:31:12,960 Speaker 1: customized for your success. That's unconventional thinking from T Mobile 572 00:31:13,040 --> 00:31:13,600 Speaker 1: for Business