Will AI Replace Farmers—or Just Make Them Smarter? | The Granary

00:00:00 Artificial intelligence. You know, I've been accused of being artificially intelligent, but that's not what we're talking about. 00:00:05 We're talking about ai. It's impacting your life, it's impacting your business, everything. How's it impacting agriculture? 00:00:11 And what will we be doing differently in the future because of ai? That's the subject in this episode of The Grainery. 00:00:17 You ready for a conversation with some real farmers about real issues? And the best part, you are invited. 00:00:24 Pour yourself a drink, grab a snack. Most importantly, pull up a chair. Welcome to the greenery. Hey Guys. 00:00:40 Hey there. You heard the topic, artificial intelligence. Um, we're gonna cover it. 00:00:45 I've got Clint Freeze with calibrated agronomy. I know that because it says so on his shirt. Now he's joined by Kelly Garrett with Extreme Ag 00:00:52 and our very special guest, Lisa Homer. Lisa Homer, is with UPL sponsor of this episode. Thank you very much for being here. 00:00:59 And more importantly, thank you for your sponsorship, UPL delivering solutions that make a difference. Artificial intelligence, you love this topic 00:01:05 because you're like out there in corporate and you're doing some stuff and you tell people, I'm using AI all the time. 00:01:12 And they say, Well, I mean, they say ai. What, how do you using artificial insemination? And so you have to take it back a step, 00:01:22 but no, um, I think AI is, is gonna be interesting. I mean, when you talk about it in terms of agriculture, they usually look at it as precision farming, 00:01:30 what you're doing in the field. But there's a lot of other stuff that AI can do. Um, and I think it's gonna be interesting to find out 00:01:38 how it actually impacts ag and the decisions that we make and what we do. But from a a corporate standpoint, 00:01:43 it's a blessing and a curse. I think it's got a lot of promise. It's got a lot of, uh, apprehension. 00:01:50 Uh, there's a lot of people that are concerned right now. We're gonna, I just article on LinkedIn yesterday, 00:01:55 how AI's gonna replace you. That's, that's, uh, that's kind of a, shall I say, intimidating. 00:02:01 And to say the least. What are you doing right now? I'm using it to figure costs. Uh, I've been using it some in the carbon market. 00:02:08 You know, you're talking about, uh, cor credits coming online and things like that for airlines to fly into Europe. 00:02:14 I use it for research to understand that. Uh, the other day I was using, uh, I, I use GR off of X mostly. 00:02:20 Uh, the other day I was asking about some carbon markets going into, you know, cor credits into Europe. 00:02:25 As I said, maybe trying to sell US agricultural credits overseas into Europe and Asia, things like that. 00:02:31 And it even offered up a way to let me, and, and previous to that, I'd had a conversation with Grok about how much is it costing me to run my eight RX 00:02:39 and my John Deere toolbar in the VRT to put the anhydrous on. And it, I was fascinated by this. 00:02:45 Grock came back and said, can I show you how if you variable rate your fertility, could I show you how you could build a carbon market off of 00:02:52 that in a sequestration that could come from that? I had never that, that blew me away. I'd had a John Deere, uh, question 00:02:58 or a, with it about calculating cost. Then I'm, you know, asking about this and it, it combined the two things and put it together. 00:03:05 And, uh, that, that that'll Raise your eyebrows just a little bit. Yes. It blew me away. But, so then I have started 00:03:10 to look at my, uh, conversation history with GR and it remembers all of that, and it will refer back to that and things like that. 00:03:16 And it, it very much is ai, it learns. Mm-hmm. And I was fascinated by that first time it's ever happened. Well, that's what's interesting. 00:03:23 'cause there's gonna be someone, and I, I mean, we love our viewers and our listeners. There's probably somebody that's a skeptic as soon 00:03:28 as we open, like, I don't care about your, it ain't gonna change anything but artificial intelligence. That's fine for somebody, you know, these corporate jobs. 00:03:35 But I'm out here on the farm. You just gave the example. That's the example of a specific model of a specific brand 00:03:42 of a tractor on your farming operation. And then three more things It does. You know, I have 00:03:48 learned, no, I've started to use it more and more just because like, you know, the pace at which I try to go, I want the answer now. 00:03:53 And so I'll ask that que that's the fastest place for me to get the answer. But you have to be smart enough 00:03:58 to know if it's correct or not. Uh, I put in a, you know, what's it cost to run a nine Rx when we're spraying plant food? 00:04:04 And it said 40 gallons an hour is what the fuel's gonna, what the fuel consumption's gonna be. 00:04:08 Uh, it, it's not the, the fuel consumption's about 13 for us. So the, it was off there. 00:04:13 And you need to know those things before. You can't just run with the answer all the time. You need to go through it. 00:04:18 That that's the concern you said. Yeah. But then the, is it going to improve or do we end up like that movie Idiocracy 00:04:27 where we're putting, you know, Gatorade on our crops because it has electrolytes in it? Yeah, I, I think there is that risk. 00:04:32 And I think you got, uh, luckily we got people like Elon Musk, that challenge. Um, 'cause a lot of companies aren't trying to make AI 00:04:40 where it stays to the truth and always focuses on the truth. And I think all consumers of AI are the product of ai. 00:04:48 Well, you are the product. And so it gets better. Just as it knew he was already talking about carbon and it put the two and two together with his tractor 00:04:56 and brought it all into one. There is the threat and the risk that we get. Artificial intelligence makes us realistically stupid. 00:05:06 And then like, should I feed my baby mercury? Of course you should feed your baby mercury. I mean, uh, uh, because you, that's the risk. 00:05:13 That's, that's the, the, the, the peril that everyone can say, oh, you're a hyper, you're a conspiracy wacko. 00:05:19 I don't know. Uh, if you didn't know that thing, you didn't Know. If you didn't know that the 00:05:24 tractor doesn't burn 40 gallons an hour, you would take that and run with it. You, you have to be smart enough 00:05:29 to use the tool, which is ai. And There's not an AI tool out there that doesn't have the, the footnote on it somewhere that says, you know, 00:05:37 results could, you know, be inaccurate, all inaccurate, always verify accuracy, always look, and there's a term for what you're talking 00:05:44 about, it's called work slop. Um, right. It's like people who are being able to produce a massive amount of stuff that looks like a lot 00:05:51 of productivity and a lot of work went into it, but it's just volume. Yeah. And I read, read an article the other day 00:05:58 that said it is costing an average of $186 per person per month for somebody to go through and correct all of that. 00:06:07 So it's gonna cost you to use AI because people are spending so much time making corrections if it's not used correctly. 00:06:16 And if you aren't checking your own work. Yeah, of course then you're using AI to check AI in some cases. 00:06:22 I mean, then it, it's where, where's it, you know, ultimately we're not there yet. Do you use it? Yeah, quite often. I know you're gonna find this hard 00:06:29 to believe because I speak and write so well, but I, I, uh, dump a lot of my emails, or if I got a formal letter, I need to get back 00:06:38 to a company, I'll use it to help proofread and, um, kind of put a little more professional touch to it. And it, it just sped up that workflow. 00:06:46 It's a virtual assistant. Yeah. Yeah. It's a little judgy though. It's always asking me, 00:06:51 would you like me to help you with this email? It's like, no, I meant to be sarcastic. Please, step aside. Does, 00:06:57 Does AI not do sarcasm? Apparently it thinks I need to be more professional. Do you know, that's where I'm starting 00:07:04 To get, nobody asked you copilot. I I'm already worried, Lisa, that we're going to end up having, uh, this thing where it replaces me, 00:07:11 but now it not only replaces me, it like corrects me. I don't know if I, I don't know if my psyche can handle this. No. 00:07:16 So I mean, it, again, it's, it's the balance of things, right? It's making sure that you are, you can tell it. 00:07:24 I want more personality. I want a more humorous approach, right? I want the more technical approach. Right. 00:07:30 Or you can say, I don't like that answer. Tell me, you know, some other stuff. So it's only as, it's as good as the prompts. 00:07:38 Uh, it's, um, I did a training class one time and they said, if you have the right prompts, you should protect those. 00:07:44 Like a programmer protects his code because that's really what it is. Mm. You're coding, um, and putting stuff into this system. 00:07:53 So you used an example that is AG specific. And since the bulk of the people that keep up with our extreme ag grain ratio, 00:07:58 or in the, the, the industry, they say, okay, I don't need it to compile emails. I only send two emails a day. 00:08:05 Uh, I don't need it to, uh, proofread something that I'm writing for a business proposal. Where do you use it? Use it on agronomic? 00:08:16 I mean, where do you use it in agriculture? Well, back to your prompts thing, I think I had a, um, a farmer I've been working with, 00:08:23 and we was going over soil test parameters. So, uh, soil tests below X on Foss and a base sa below this, uh, k below this value. 00:08:32 And he was trying to decide where he could cut. Well, he took his entire, um, sample database, dumped it in there, gave it the parameters, 00:08:40 and it told him exactly pinpoint which fields have that problem where maybe he needs to focus on fertility more. 00:08:47 And so he had three fields and a few little isolated areas, and then we're gonna come in 00:08:51 and actually then spread chicken litter on those really poor performing areas, try to boost production. 00:08:57 So he did that all within, you know, he was in the field, got rained out, and at one, you know, 00:09:02 one o'clock he was having the conversation. At four o'clock, he sent me the full report on 10,000 acres. So to me, that is an efficiency driver. 00:09:10 That a real world example of how out. Yeah. Well, you did what would've Yeah, only previously by a few years ago, taken five times, days, days, days 00:09:20 To analyze all, uh, the make's field by Field. And then it, it, again, it's not right all the time, 00:09:26 but it takes the human error out. I use it to, you know, the plant food that we spray, we're gonna go spray 50,000 acres of plant food. 00:09:33 The loads are of varying lengths and things like that. So I dumped in there what the average loads are, things like that when it spit back out. Average 00:09:39 Loads, quantity average, Uh, average loads, you know, s sometimes we're hauling five miles, sometimes we're hauling 25 miles. 00:09:45 Okay. I put in there what's the average length of the, how many miles did my trucking fleet drive to, uh, to spray the plant food? 00:09:51 Mm-hmm. It came back 154,000. And Vern told me, he said, well, dad, it's not right all the time about everything. 00:09:55 You should double check it once in a while. So I went through, I did the math by hand, I double checked it, and I came up with 77,000 miles. 00:10:00 I'm like, ha, it's wrong. It doubled it up. Then Vern looked at my work and he said, dad, you just figured the loaded 00:10:05 miles, you didn't figure the trip back. Ha. So It was exactly right. I it was exactly right. And I was wrong 00:10:09 because I didn't think about the unloaded mouse. So this days of ana, ana like analyzation that you're talking about, what about 00:10:16 the human error that's in it? It, you know, that, that most people are probably smarter than me. Well look at the simple math error I made 00:10:23 right there, that it did not make Some might type into ai. Is analyzation a word? 00:10:29 And they'd say, no analysis is what you're looking for, but that's all right. Answer me this. We're, you're in corporate 00:10:35 and obviously there's a big push about how you're gonna make it using AI to improve your business or to improve his business so 00:10:42 that he's better of a customer. Where's it going? To the company that's, that sells to him and him. 00:10:49 Ah, um, a lot of it is around, uh, targeting, right? Looking for patterns in behavior. So, um, when we get, uh, data about where crops are grown 00:11:01 or about different, you know, growers information that they have turned in, it can help do predictive modeling. 00:11:07 So if it's like, okay, if you've bought this product and this product, um, you would be really interested in this. 00:11:13 So it helps to target the communications better and make sure that you're only seeing, you know, ads about things that would be of interest. So 00:11:20 That's been going on before, really. In other words, if you and I say the word, I mean, we can get listened to if you 00:11:26 and I, if we, if we said the word, I need to buy a rott tiller 17 times, I'd get a Facebook ad that was trying to sell me a rott tiller. You, 00:11:34 You don't even have to say it 17 times if you think it. So we don't, we were doing that before AI though, Lisa. You were, 00:11:39 But now we were doing it at a much different level. Level, right? There is, it increases the targeting, 00:11:45 Increases it, and there is predictive, and then there is prescriptive, right? So it's not only saying, here's what I think he can do, 00:11:52 but it's, here's what you can do about it. Now here's when you could target him. Here's when you can send the message. 00:11:57 This is when people are in email. The most often there is, um, a, a company that I work with that the email, depending on when it's opened, 00:12:06 it says good morning or good afternoon or good evening, depending on when the email is opened. Mm-hmm. So it's that ability to customize, um, 00:12:14 and make somebody feel like you are talking just to me as a marketer. I love it as a consumer. 00:12:21 It creeps me out sometimes. I'm not gonna lie. Do a little watch you're feeling a little surveilled. Yeah. You know, Clint's example 00:12:26 and Lisa's example, both show how it, it speeds us up. We can do a better job because of the analysis and things like that. 00:12:33 But it does eliminate jobs because you said, you said we were doing this before ai, we were, but a human was doing it. 00:12:39 Now the machine is doing it and the machine is doing it better and faster. You scared? Am I scared of ai? Yeah. 00:12:46 No, I think my parents are, but I'm, I'm not. I mean, I think there's just all this, uh, you know, you, you hear things and then you run with it. 00:12:55 Right? But I, I don't think, I think it's no different than, like, when the computer first came out, they used to have, 00:13:00 you know, um, full, full buildings full of people doing hand calculations. Yeah. Um, and then it eliminated all them jobs. 00:13:07 Well, people went elsewhere. And I think, um, I think it's just gonna be that again. I think it's gonna be a change. 00:13:13 Yeah. So there's two levels of are you scared? First you're talking about replacement? Yeah, yeah. Yeah. The steam shovel replaced John Henry. 00:13:19 That's an old fable, right? Uh, kids your age didn't do fables, but anyway, John Henry got replaced by the steam shovel 00:13:24 and all of a sudden, you know, the ditch digger, ditch digger doesn't have a, you know, where's the guy with a pickax? 00:13:29 Uh, and a shovel gonna go. Well, we still, we've been hearing this for more since the industrial revolution 00:13:36 and there's still plenty of things to do. Yes. Is it different this time? Is it different than the steam shovel? Yeah. 00:13:42 Robotic, you know, AI coupled with robotics will eliminate a lot of, a lot of manual labor. Manual labor. Yeah. There'll still be 00:13:52 specialized labor, I think. Um, but I, I, I think a lot of the repeatability labor will be, we'll go by Repetitive task. Like 00:14:01 you say, again, just like the, the, a backhoe is better, many excavator is better at digging a, a tile line from here to the road than a guy with spade. 00:14:09 Alright. I'll give you the example though. My friend Todd that, uh, I do my business of ag group with, he said, okay, I'm out there. 00:14:16 I am a consultant. I do use this at technology, but he says, you know what the problem is, I'm in the swine industry. 00:14:21 You can give all the ways that AI's gonna gimme a better ration, uh, make it more efficient about trips, calculating the miles 00:14:29 that I go from one hog facility to another. She says, you know what? I need some of that stuff I don't even need. 00:14:35 I need a way to get that 600 pound dead sow out of a crate in this 300 foot long building and get it the hell outta here. 00:14:44 Ai that for me. Yeah. I still gotta bring four laborers in Yeah. And drag this thing around. That's the, the tough part. 00:14:51 That's the limitation. There's a lot of things that we do that are not necessarily repetitive. 00:14:55 Right? Yeah. That, that's gonna be the harder ones to, to replace technologically. Yeah. Oh, I agree. 00:15:01 I think anything with moving material, moving things around is still gonna be very dependent on humans. When you ask Clint if he's afraid, the only thing 00:15:10 that I worry about, and I, I don't know how far we are from it, but do you remember the movie Wally? Do you remember that movie where the, uh, 00:15:16 the humans all floated around on these, uh, on these buggies and the machines were trying to clean up the world 00:15:21 because we had trashed the world and things like that. I think about that movie, and I think about us going down because we, we do less and less manual labor 00:15:28 and, um, the machines take over. I, I'm not trying to be a conspiracy theorist or be, you know, a fearmonger or anything like that, 00:15:35 but I wonder if, like, are we going in that direction? But I still use AI all the time. Mm-hmm. I wonder about in my hills, you know, what the, 00:15:42 what the shape and in my fields, can AI tell me how to plant the field more efficiently to, to save time? 'cause the hours on the tractor, like we've 00:15:49 Talked about, can it, or more importantly, can it just do it? Well then, so like first like next year, right? 00:15:55 Next year, can it tell me how to do it more efficiently with less trips? And in a few years, will it just be autonomous 00:16:00 and we'll do it automatically anyway. I, I believe that we're not far from that. And so all the things we've talked about here though is 00:16:05 how you can use AI with existing content and with information it's analyzing and, and things like that. 00:16:13 It, when you said, does anything scare me, the part that gets to me is how real some of the things that are created are 00:16:20 and what that does to, to truth, um, of what's real and what's not. And you know, and how can you tell, I I, 00:16:29 and I thought I was pretty good at it, but I was like, oh, I saw this video on YouTube the other day, and the guys were like, oh, that was, I saw that too, 00:16:35 that that was AI generated. And you know, and I think that the creativity of it, I mean, and I don't mean to, to, you know, 00:16:43 take away the question asking from you, but does it scare you when you're looking at pictures, images, stuff 00:16:49 That, because things will become deceitful, dishonest, things like that from those videos. Uh, one of my South African guys made a video 00:16:56 of me telling him that he could go into Sabrina and get a big bonus Yeah. As a 00:17:02 joke. Yeah. But, you know, and, and you can tell it's not my voice and things like that. If you knew me, right. 00:17:07 If you don't know me, it appears that I'm standing there saying Go collect this check. I think that's the part of the fear thing. 00:17:11 I, the replacement part, again, the, the John Henry, I mean, I worked at a factory where those poor people were unskilled 00:17:17 and generally uneducated and you know, they're putting in a new robotic thing over here on the ceiling tile line, 00:17:21 and that's gonna take away six jobs. I felt for them. I understand that. But that was in 1989. We've been dealing with this for Yeah. 00:17:28 You know, 40 50 since again, the industrial revolution. I think the bigger concern is the deceitful, um, yeah. Using it to fraud, to defraud people. 00:17:37 I mean, there's some bad people have a really good tool right now. They had a knife, then they had a gun, 00:17:45 then they had telephone scams. Now they got a, they got a much better tool to They they To do wrong. Um, 00:17:52 An AI generated image of the president that goes to a third world country that says something that, that's cast on the airway. 00:17:58 So you, I mean you, uh, anything is possible. Yeah. That's the part to be afraid of. That's the part to be afraid of. 00:18:03 And, and being surveilled. Yeah. I mean, if you were like a wacko, maybe you like just sit by your keg box 00:18:09 and stoke the wood stove and have a lot of guns. If you were a wacky person like that is this part judgment? I wouldn't know anyone like that. 00:18:16 This is part of the new judgment. That's part of The No judgment. That's what we talked about before. Yeah. Wacky. 00:18:20 If you're a wacko, no judgment, you're Back. Maybe it's me that I'm talking about. 00:18:23 Lisa, where do you think this is a, where do you think the threat on that stuff goes? Um, you know, there's gonna be, there's gonna be, 00:18:30 farmers have been saying forever, man, all you're doing, you're collecting all my data so 00:18:33 you know everything about me. So you can sell me stuff. I'm nothing but a tool for you to, I'm nothing 00:18:38 but a tool for you to sell your wares. I've been here in that since the whole, uh, thing on your tractor that tracks all your miles 00:18:44 and then they want your data. Yeah. But I think he comes back to the whole as long as, as long as it's handled properly 00:18:50 and then it searches for truth, it should be able to search for truth across all things. 00:18:55 And so whether that's products or cramming things down, you know, the sales tactic cramming on people's throats, I think 00:19:01 that it should decipher some of those things. And, and That's why Elon created his own. But he, that Is why he wanted to, he Said, 00:19:07 but Sophie six to that. Yeah. Uh, well there's gonna be other creative, there are other creators of it and all 00:19:11 That. So you're talking about being surveilled. This was a year or two ago. Our friends Mike and Jenny 00:19:16 Jenny's in the house with the kids. The kids said, we wanna go to Dairy Suite and have an ice cream. 00:19:19 Okay. Her phone is on the counter. They go get in the car, the phone connects to the car. Apple CarPlay comes up with directions to the dairy suite. 00:19:28 She didn't put it in. Mm-hmm. The phone is listening. Yeah. I'm not sure I'm comfortable with all of that. I'm not sure I can stop it. I'm, 00:19:33 I'm quite certain I cannot stop it, but I'm not sure that I'm comfortable with It. You know, in that instance, it's 00:19:37 just trying to make your life simpler as if, you know, the dairy suite's a mile away for that. Yeah. But in, in that 00:19:42 instance, it's just trying to help you. But you're talking about surveilled and the amber makes sure she logs out of everything. 00:19:47 I don't, Amber logs out of everything all the time. 'cause she doesn't want people listening. I'm like, well, I'm not gonna say anything I'm worried 00:19:51 about, but maybe we should be. Alright. So we're talking about these things that are kind of incremental moves. 00:19:58 You used the word predictive and prescriptive. I'll go evolutionary. Not revolutionary, none of this stuff. Okay. A lot of AI is just enhanced search or a new Right. 00:20:08 Or a new reference book. Okay. You could have found that thing about using my eight XR and Miles to the fields 00:20:14 and that you could have done that without it. Those are incremental moves. They're not big. What's it going to do? What is this actually gonna 00:20:22 do five years from now? You're sitting here and we're drinking a beer and going, God, Clint, 00:20:26 remember when we didn't realize, where's it going? Big picture. Uh, get science fiction on me. Uh, This million dollar question. You 00:20:34 know, I've always questioned that you can't remove an agronomist or a farmer out of the field. 00:20:38 That it takes the boots on the ground. Um, because there's infinite variables in agronomy. Yeah. See it? Yeah. Touch it. You 00:20:44 Can see it, touch it, look at it. Um, but with remote sensing and AI putting all the infinite variables together 00:20:51 and for real time analyzation, I don't know what that looks like for a, a digital agronomist per se. I, I think the its ability to put all 00:21:01 of the big picture things together could be. So We put agronomists outta business. I, I don't think, not anytime soon. That's new 00:21:08 John Henry. But, but I, but it, it, I it could be. Where's it go, Lisa? I think that, um, the analysis of, um, new traits 00:21:17 of new products that are coming into the market. I mean, it takes 12, 15 years to take something, you know, from a molecule to figure out if it's gonna work. 00:21:26 I think if you can find ways for AI to go in and do some prescriptive or predictive modeling with those ais 00:21:33 to weed out the ones out of every a hundred thousand ais you have maybe one active Ingredients you're talking about 00:21:39 Now, active ingredient again, sorry. We're Talking about AI within ai. Yes. Right. Active 00:21:42 ingredients, artificial intelligence. Your point is, if there's a hundred thousand active ingredients or molecules Right. 00:21:47 Or molecules, we go through years. Right. Uh, and companies and you know, people in, in white coats and labs and microscopes 00:21:54 and field trials and all this stuff. And shortening time and distance between an idea and a molecule and an actual usable product. 00:22:04 You think instead of it, it shortens that time and distance. I think it's good. Probably accurate. Yes. 00:22:09 And then the products get better and then presumably get less expensive because the company can't say, 00:22:14 well we got 20 years of RD in this. Yeah. You got 18 days or whatever, you know, a week and a half. 00:22:19 I mean, that's the what you're talking about. Right. Or at least one growing season. Right. If you can take some of that expense 00:22:26 and that timing out of it Yes. Then you can bring products to market. Because think about it, I'm, 00:22:29 we're thinking now about products that are gonna solve a problem that's happening now mm-hmm. In 12 years when that product is there, 00:22:35 there could be a seed trade that takes care of it. There could be, it could be eradicated, it could not be an issue. We 00:22:41 Might be remedying a problem that doesn't even exist by the time the product comes out. Does Correct. So shortening of time 00:22:46 and distance on products. I see. Absolutely. Where do you see it going? So, you know, so like you talked about replacing 00:22:52 agronomist, I'd never really thought of this before. This is interesting. No. Why can't I drop in my last three years soil test? 00:22:59 Why can't I drop in my last three years yield data? Why can't I talk about what the, uh, board of trade is doing for return on investment? 00:23:07 You know, your input costs and not just the soil test and the yield data, but also the cost structure and ask, you know, what fertility do I need to put on? 00:23:16 How do I need to VRT? How do I need to very well rate my seed? What should I plant? And if you had a forward thinking 00:23:22 progressive farmer, why do I need an agronomist? Why do I need to, I I it's, it's not there today. But if you had someone that, if you had a, a young farmer 00:23:30 that was progressive enough, like maybe, maybe at some point it does take the agronomist in the farmer outta the field because it, all of the IP 00:23:37 that we talk about that we have, uh, when does the, when does AI have It all? So, you know what I just heard right 00:23:43 there? Mike Evans, his agronomist is gonna be living in my spare room in another couple of years, 00:23:48 But I think that's closer than you think, right? Yeah. 'cause they have tools out there now that can detect the spores that are floating around 00:23:57 and it, it attracts them. It takes a picture of it. It'll tell you what you've got and you know what diseases are coming 00:24:02 and what the threshold is. It will measure whether or not it's hit the economic threshold. 00:24:06 We have tools that'll do that for insects as well. You place all of those out in the field. And I am not diminishing what an agronomist brings 00:24:13 to the table in any way, shape or form. No, I'm not trying to either just, but it, it knows it. Right. Okay. Like you talked about this, 00:24:18 this blows me with the sensors. Evans believes that the rust problem we had came on the snowstorm that we had in March when all the ground in, 00:24:27 not all the ground, but there was a lot of ground in the Midwest that turned red. 'cause it blew up outta Oklahoma. Mm-hmm. 00:24:32 And he'll tell you then he'll, and you know, I didn't hear many other people say, this shows you the intelligence of evidence. 00:24:37 This rust problem in the Midwest this year that decimated some corn crops. Yes. You know, 40, 50, 60 bushel, 00:24:43 it didn't start from the top down. It started from the bottom up. So it did not come in on the wind soil. 00:24:48 It blew in on, it blew in on that blizzard with that red soil that came. So that was in the soil. And you talk about the sensors. 00:24:55 What if the sensor told us, Hey, the rust inoculums here, you need to be prepared for this. 00:25:00 Mm-hmm. I think 'cause we didn't know. I said Evans, we should have sprayed fungicide on the, on the cornstalks. 00:25:05 But we didn't know. We didn't know what came More predictive in modeling. Yes. More predictive. Yes. Predictive modeling. 00:25:09 And you know, I think it was three or four years ago, they, uh, humans have created now synthetic life, synthetic biology. 00:25:18 And so they've, that can be used for a lot of good things. I think that AI is very much gonna be in power. 00:25:25 If you could isolate a certain grasshopper species under a certain, you know, uh, crop that it will go in attack that, 00:25:32 that synthetic biological life, and that'll all be an AI interaction. But the negatives of that technology can go, 00:25:39 you're almost getting to play with God per se, you know, of making life itself. 00:25:44 And you know, there was talking about the negatives of that in a, um, uh, microbiology journal of being able to actually, that biology could secrete a material, 00:25:53 a compound that actually eats metal. You know? And a government could have that to, you know, weaponize against things. 00:26:01 So the, the power of AI, I think is, we probably don't fathom what it can all do, you know, long term. 00:26:08 We absolutely do not Fathom. So most of the stuff we're talking about is using it, you know, for information 00:26:13 and hastening, you know, shortening distance and time and that that's all good. What about the idea that it doesn't, 00:26:21 the human mind can be, you use the word predictive, it can only go on what it knows, right? Right. So there is that limitation or does it overcome it? 00:26:31 Does it, does that an easily overcome limitation? Well, I Don't think it's easily overcome. 00:26:36 I mean, what's the quote by Henry Ford? If I'd asked people what they wanted, they would've set a faster horse. 00:26:40 A faster horse. Right? So it, nobody thought that they needed DVDs. I was fine with the VHS 00:26:46 until somebody showed me something better. So I think that's, you have to start with the problem uhhuh. Um, and I think if we look at a AI as a tool to go, 00:26:54 what can AI do? I think what we have to do is identify issues and problems and then say, how can AI help solve this problem? 00:27:02 Right. Because looking at it the other way around, you're gonna end up with, you know, fixing something or creating something that doesn't really matter. 00:27:10 So what are the issues that need to be solved? That's where, you know, something like speeding up the time to market with stuff, uh, beneficials, right? 00:27:18 How can we make products that will, as you said, attack the pests? We want it to, but not impact beneficials. Okay. 00:27:25 There's something that you start with the problem and then what can AI do? How can it help you with those experiments, those trials 00:27:31 and those things that you need to do to get it there. So it increases shortens time and pay and it, it makes it easier on us. 00:27:37 And then just like the computer, just like the phone right now, you, you get more done in a day than you did 10 years ago 00:27:45 because of all these tools. Yeah. So it increases our productivity. Yep. Everything gets the cell phone. 00:27:51 Okay. Here's the thing. We really don't need any more corn and soybeans and wheat. The world has a lot of it. If we use this tool to make it 00:27:58 so we're, it improves our productivity instead of going up a few percent a year is what we do right now. Right? The corn crop's gonna be seven 00:28:06 bushels more than it was last year. What if all of a sudden these tools make it So we're, we're increasing our productivity by even more. 00:28:13 Uh, we've already got more manufac. We have storage units full of crap that we, that we made. If it makes us, so we're making more stuff can start 00:28:21 to argue like, this is cool, but do we even need much of this stuff? I think you're looking at 00:28:26 productivity from the wrong standpoint. It isn't necessarily about making more yield, it's about doing it with less and less people. 00:28:32 That that's the productivity that I see, or Inputs or expense to Lisa's Point. Yes. Yes. That's the productivity. 00:28:38 It isn't always about quantity of what we're producing. It's quality of what we're producing and quality of what we're producing relative to 00:28:44 how many people we, we we can do it with. There's, it's a, there's a help wanted sign in every business at home. 00:28:50 There's a help wanted sign everywhere. And, and this will help us shore up those weaknesses in shortfalls. 00:28:55 Can I, can I, a person in the office, uh, building where she lives, uh, farm those acres in Iowa without even going there 10 years 00:29:03 from now, because I've got AI tools that do all the sensing. I've got AI that does all the recommendations. 00:29:09 I've got, uh, AI powered machinery. Can I, can I farm remotely 10 years from now with nothing more than screens in front of me. 00:29:20 I, I still think you're gonna have to have a, a shepherd or, you know, they talked about that, you know, somebody 00:29:24 that manages the equipment. But I do think it eliminates people. And is it probably gonna 00:29:31 fail at first when models are out there to do that? I think absolutely. But it's only gonna work one way. And that's get better. Yeah. 00:29:41 Oh, I mean, when, when, what happened when they didn't need milkmen anymore? Right. I mean, there's a lot of jobs that used to happen 00:29:49 that, you know, that technology came in and took the place. And I think that you evolve and you adapt. 00:29:55 So I mean, you talk about we're always going to need humans to move things from here to here or to do that. 00:30:03 So I think you adapt. Mm-hmm. You find that the places where now we need to be. Right. You didn't need computer programmers. 00:30:10 You didn't need those types of things. I mean, I, I don't think AI is ever gonna fix my plumbing. God. 00:30:15 That's the, that's the tough one is the trades. Uh, and, and, and you say, well, why does that impact agriculture? 00:30:20 Well, well look at the infrastructure you have and say, I, my dairyman has three milking 3,300 cows between, you know, three facilities and 40 buildings. 00:30:30 AI can't go out there and make sure that the manure pump is running. You know, AI can't do a lot of those things. Well, 00:30:35 It can tell you if it's not. It can tell you if it's not. But I Can't fix it. They can't do the repair. 00:30:38 Can't fix it. Yep. How long before he can send a robot to fix it? Correct. Okay. 00:30:44 So we're using this tool and we all think that it's coming. We not, we're not gonna stop it. 00:30:47 We're not necessarily scared of it. We kind of are a little bit. But there's one thing that right now we don't have trust. 00:30:55 When I get in my Ford F-150 and drive across country, I have aaa, uh, membership and insurance. 00:31:03 But 100 years ago I would've had a truck full of tools, assuming that somewhere in Oklahoma I break down. 'cause I don't trust the technology of the day 00:31:11 to get me from here to across country. I do now because it has proven itself. We are not there yet with any of this. 00:31:22 He checked the numbers on the semi on the miles driven. 'cause you didn't quite trust. When do we, when do we bridge that? 00:31:30 So I now think that, you know, the math, you ask it a math problem, a very complex math problem with, you know, multiple moving pieces, things like that. 00:31:39 I, I trust it. I think it's there. But if you ask it, build my fertility plant, it's gonna take a little time. 00:31:45 Uh, it would be interesting to ask it to build a fertility plant for a field and then look at what Clint and Cook 00:31:51 and Evans would come up with and see how it compares. It almost would be a comparative over three seasons. Yes. And, uh, right. Or, or even take its recommendation 00:31:59 and trial it over here. I'm, I'm not ready. I will tell you I'm not ready to do that yet. 00:32:03 So, and, and that's how you would define that as trust. It should be interesting to look at it. But I, I would tell you right now that I wouldn't, 00:32:10 I would not blanket statement say I'd go with it. Mm. I think it's trust, but input is another piece of the problem. 00:32:17 Right. Garbage in, garbage out. It is after it's learning. And so what you tell it 00:32:22 and what information you put into it. So I think as the information that goes in gets better and more solid and it learns 00:32:28 and it, the more data points it has, I think that's gonna help build trust. Um, right. So, and thing the data points 00:32:35 that you're putting in are being combined with everybody else in your area that is inputting that data. 00:32:40 So I think the trust will build as the amount of data that's feeding, the information that it's pulling from is there. 00:32:46 But you've gotta make sure you're asking it the right questions and inputting the right data. I agree with that. To get the answers out that you want. 00:32:52 I agree with that. But here's the deal. After trust then becomes reliant. 00:32:55 And after Reliant becomes dependent. And then, then once you're dependent, you're vulnerable. A hundred percent. I, so I went 00:33:03 to the grocery store the other day and I have a key fob for my car. Yeah. I don't have a key. 00:33:08 And I locked the door, went in and came back out and the battery had died. Yeah. And in that amount of time, I could not get in my car. 00:33:15 Mm-hmm. I couldn't manually open with a key. I couldn't do anything. I had to walk home from the grocery store. 00:33:20 I'm acting like it was a thousand miles. It was like a half a mile, but still. Right. I had to walk home carrying a rotisserie chicken. 00:33:26 I felt like I was gonna get attacked by a pack of wolves. Yeah. No, that's a problem. I was reliant 00:33:31 and I was stuck. I could not open it. And so you have a very valid point, but, uh, you're never gonna get rid of the bag of wrenches. 00:33:40 Yeah. So that's a tough part is that, and also even if you had gotten in there without the thing, it may not have started all that stuff. 00:33:46 So then that's the tough part. And that's where the, there's the surveillance that frightens you a little bit or the all that, but then there's the dependence, 00:33:52 or worse yet when you are completely helpless without it. Right. The, the real question here, 00:33:58 or maybe the difference is like with your bag of wrenches and things like that, you, 00:34:02 you can still go work with your hands. And the way that technology has come, the faster horse and things like that, all of that has replaced, 00:34:09 that technology has replaced what we do. Mm-hmm. From a hand standpoint. Now the AI is potentially replacing what we think. 00:34:16 And the scary part is there's a difference between what we do and what we think. And becoming dependent 00:34:22 and not being able to think for yourself is truly the definition of the scary part. How, how soon do we get there, Clint? 00:34:29 I don't know. I, I think we're like, for me, I, I, I trust but verify, like, I think it goes back to description. 00:34:36 If I am, uh, using it to research something in depth with microbiology or plant physiology, I'll make it reference. 00:34:42 Okay, now reference where you got this and then did it come from an agronomic journal? But in the future, I don't know how we'll verify it 00:34:49 and to make sure that it is, um, truthful. It'll be an interesting, uh, term of events for Sure. Alright, well, when we talk about 00:34:57 replacement of humans, um, there's certain tasks that are pretty obviously gonna get farmed out to AI pretty promptly. 00:35:04 Paralegals the one I keep reading about, like those sort of middle, somewhere between administrative and some skill. 00:35:10 Right. Uh, you know, I probably certain levels of attorneys, uh, you, you know, you can just have an 00:35:16 AI contract, I don't know, something like that. Certain levels of medicine, certain Levels of medicine, which probably should 00:35:20 be, it's very expensive. Healthcare is agriculture. Where is agriculture fit in on the replacement, uh, hierarchy? 00:35:29 I think where you, what AI has not been able to do yet is bring in the emotional quotient of something. Right? Yeah. So, 00:35:37 and if I asked you how much does emotion, you know, impact what you do on the farm, would you say 00:35:43 none or would you I would Say more than it Should. Correct. Mm-hmm. More than it should. Right. 00:35:47 Um, or, or think about, you know, like a jury. The emotion in, in those things. So I think that's the part that AI is never gonna be able 00:35:55 to replicate. I know there are things where it can come up and it can be feeling and it can be your friend 00:36:00 and et cetera. But that human emotion is in decision making, is one of the things that I think is gonna slow it down. 00:36:07 And people are spending, companies are spending billions on this and there has not been a return on it yet 00:36:13 because people aren't willing to invest. So long as it's free, I'll play with it. But when it starts to, you know, cost real money, 00:36:21 nobody's going, yes, I want all these tools. I wanna do all this stuff. So, All right. Last thought. 00:36:26 Are you scared, excited, Cautious? No. No. Yeah. I, I, I love it for the analytics. Nervous about the, the creative part of it. 00:36:37 The, the stuff that you can create and how it's, it's not reliable. The trust part of it. So excited on the analytical part. 00:36:46 100%. I think Damien's nervous about it 'cause he thinks it could be funnier than him. I don't think we're that far away 00:36:51 from that happening. You know What? That's very accurate. That's very accurate. Thank you. 00:36:55 Talking about artificial intelligence and what's gonna do the future of agriculture we're doing with my not artificially intelligent 00:37:00 'cause they're very intelligent humans friends. And that's, uh, my friend Kelly Garrett with Extreme Ag, uh, click freeze here with, uh, calibrated agronomy. 00:37:08 And then our sponsor, Lisa Homer came here. She's with UPL, the delivering solutions that make a difference. 00:37:13 We very much appreciate you being at this table. Was it fun for you? Oh God, yeah. Finally, I 00:37:16 wanna learn anything about your product lineup. Where do I go? They Go to up corp.com/us. 00:37:23 I like it. So thank you very much for being here. If you enjoyed this episode, share it with somebody. I can benefit from it. You know what, we do this often. 00:37:30 We've had more than 40 of these episodes go out there and we want you to enjoy it. Uh, go to Extreme Mag Farm, check out all the cool stuff. 00:37:36 Guys like Kelly are shooting videos at their farm to help you farm better. The extreme Ag uh, cutting the Curve podcast 00:37:41 that I've produced, been doing that for four and a half years. It's a library of free information to help you out. 00:37:45 And then also the Grain show. Go to YouTube and check it out and hit subscribe for our Extreme Ag channel. 00:37:51 We'd love that you're here. And if you have a topic you'd like us to cover at this table, submit it and we'll do so. 00:37:56 I promise. I'm Damian Mason with my friends here at the Greenery. 00:37:59.165 --> 00:37:59.645