AI Meets Agriculture: How Colin Raby Is Transforming the Future of Farming

right, farm more effectively, save time, save on input cost, increase yields. But the second component is also being a force for good for American agriculture as such. A lot of people don't realize how kind of fragile and how many cracks there are in the current agricultural system, right? I mean, you can see just last Wednesday Congress passed the Farmviewed National Security Act. And buried in it is actually a provision that

“pays up to 90 cents of every dollar to farmers to adopt the persistionatex often, right?”

Sounds amazing, right? Like that sounds like, of course, this is great. Why wouldn't there be any problem with that?

And the problem is, once you realize the dominant recipient of that funding will be St.

Clinton. St. Genter is owned by Chemtina, a state enterprise of the CCP. So, let's play that out. What we're about to do. Americans are capable of achieving extraordinary things when they have the freedom and opportunity to do so. This is American Potential. Hey everyone, welcome to the American Potential Podcast. I'm your host, David From. So,

we're going to be talking to our guest shortly, his innovator in the AI space and he and his friends created a company that we're going to be discussing. But first, engineer Matt, how do

“you use AI in your work? Hey, hi. Well, I mean, everything could be AI. I could be AI right now.”

I don't know if I'm even real. Maybe you are. Oh, no, I use AI for a handful of things that are really useful. So, for example, if we've got a guest who joins remotely and they've got really, really bad audio. I mean, they dug out the toaster and they're using that for their audio. I mean, it happens at times. The AI is, uh, I have some AI sound restoration tools that are just mind blowing. I mean, over the years, I've been doing this for a while. I've spent hundreds of

dollars on different types of audio plugins and different things to try to fix really bad audio.

And the AI stuff blows it all out of the water. I mean, just like that is pretty incredible.

What it's able to do. So that's one of the ways I use it. I don't use it a lot for editing and for other things like that. But it is really handy for things like that. Yeah, you know, as you guys, you know, with generation older than you, and probably a little further behind in the AI revolution. But I've been, I have picked it up just because it's like, hey, I can do things with far fewer inputs or just in far less time and have it come out just so,

well, I mean, once you get your confidence in using AI, really unleashes, it opens so many doors. So I'm so excited. I keep asking people, like, what are you doing? You know, that I can learn from. And man, I'm like, wow, you're doing that with it. Okay. I'm continually opening my mind and

expanding my boundaries here because there's, you know, there's just the growth is happening so

quickly. I'm just super excited about it. And, you know, I hopefully people can't tell it on the American potential podcast. But we're using AI all the time to, you know, bring the solicitors.

“Absolutely. And there's so many great ways to use AI to make your processes better. And I think”

it's exciting as we see new stuff come out because every, I mean, every day, there's something new, just like what we're about to hear from our guests, a new way that they're using AI to improve the work flows of people to improve things. We're going to be talking about agriculture and the production behind that. And I don't think of AI when it comes to farming, but apparently there's a way to do it. So some people think AI is something brand new. But truth is, we've been using it

more than we probably realize. When you open your email, it's already filtering out junk. When you start typing a text to try to finish your sentences before you do. But now, it's starting to show up in places you wouldn't expect in industries that have been around, well, since before this country was really country, like the agriculture industry. And this AI didn't come from Silicon Valley. It came from college students. I want to welcome to the podcast Colin Rabie, who's the CEO and

CFO for Farm Mind. Colin, welcome. Thank you. And I love the fact that you mentioned that the student come from Silicon Valley. It came from right here in Louisiana. As we've been joking, we're building the Silicon Bayou. So that's a great one. It's a great one. It's actually a trademark battle. I love it. Hey, so, you know, were you raised in an agriculture family? And like what was that like? I personally was not, but this entire kind of endeavor came from a really good thing

that developed actually at Louisiana State University. So if we kind of zoom back about three years, actually me and my co-founder Brett Muslow, we're talking to some of the leadership at LSU about the fact that these things called large language models, which is what the AI is that most people

for a lot of things. But the writing was on the wall. And so we were talking to some of LSU's leadership. And we're like, we need to make a class on how to utilize this technology to use economically

“beneficial things. And so actually, I think Louisiana State University was the first nation in the”

country to start a class on building with large language model systems. And then, uh, we came from that was we ended up being introduced to someone called Professor Stevenson of our Ag Center who just laid out all of the different problems of all of the different kind of failures of software they couldn't have, all the regulations that they spend a ton of time having to deal with, all of the research that's coming out of Ag Center's across the nation. That's high quality, awesome research,

but they never actually reaches the people it needs to reach out in the field. And I cannot

kept layering on these things. And so I've been involved with our Ag Center a little bit. So I went to some friends mind. I'm like, hey, these really kind of the problems that y'all are seeing every day. And it was worse than even he had presented, um, because I was going to my friends and like, one is a fifth generation farmer. And, you know, the software he was using for mapping hadn't been updated since 1998. The software he was right. It's like he was using one outdated

software for mapping, one outdated software for soil tests, one outdated software for his finances, one for the weather, right? And it just kind of kept going. And the more folks we talked to at the Ag Center, the more we realized there was a huge gap in what was possible versus what was being given to folks kind of out in the field and, and, um, I'd pros no matter whether they're a consultant, a researcher, or a grower. And so we got a set on about a two year endeavor to make an AI specifically

“for agriculture. So the problem is a lot of these. Yeah. Yeah, let me ask you just, um, I think there”

a lot of people might not have the right perception of the agriculture industry that like, you know, technology has come a long way and people are reliant on a lot of W, you're talking about a number of different interfaces and tools that people use to succeed in the field. Can you maybe describe a little bit of, you know, the role technology plays in like a typical agriculture, you know, a typical farm? Absolutely. And I think, um, if you go all the way back to

when kind of the last century started, it's really indicative of how agricultural technology is developed,

you know, the the Model T rolled out in around 1908, but the first tractor didn't come about

until the 1918 or the 20 or 1920s. And I think you can generally see that ever since then there's definitely been a trend of ag technology tends to lag about 10 years behind every other industry. And that's one of the things that we're trying to help change. We're trying to actually, if we succeed here, then your average, um, your average farmer, your average researcher, your average consultant, will actually have tools that surpass what a lot of other industries have access to.

You know, it's, it's the case. So agriculture, like you mentioned, one of the manories all this industries, right? There's a lot that hasn't changed, but also you can go and you can track crop yields, you can go and you can track, like all of those things. And you can see that as our technology is gotten better, the yields have gone up, the efficiencies have gone up, right? And that's better than everybody. That's benefited everybody because that's the food we eat. That is the more

plentiful and abundant it is. The better the pricing is for the consumer, the more options we have, all of that, right? It benefits everyone when American agriculture is successful. It benefits everyone. Um, and so while there are some practices that are absolutely kind of go back to the stone age, there are a lot of practices. Most people don't realize just how complex and how many variables are involved with modern day farming, right? Let's just put it, put aside the back office stuff

for a second. Even when to apply your fertilizers, when to a plant, depending on the upcoming

weather cycle, when to harvest, when to if you're going to sure can, when to apply ripener. There are all these different, and that's just a couple of the many different decisions. I mean, ag professionals have to make tens, like 10 plus decisions per day. And every single one of those decisions will determine whether or not they are able to make a profit, or if they have enough bad years in a row have to sell the farm. Um, and so if we can give tools that help make those decisions

ideal, if we can help optimize those decisions, and we can help save them time so they can spend more time making good decisions, that's going to benefit everyone. Um, so is it fair to say that your interest is really in AI that that really grew in your in college? And then you applied it

“to agriculture? Or like, how did you come to really be, you know, marrying up AI in agriculture?”

Yeah, I mean personally, that's definitely the route that was taken. Um, so last that back in 2023, we started working on this project. And for about two years, we were working on it,

looked at a lot of these general models, they just didn't think through agricultural questions well.

You know, if you're asking a question about applying some pesticide herbicide, well, you got to know how to think through it like an agronomist. You got to know, okay, the label is the law. I got to go look at what that label says. Then you got to know, okay, the extensions might have done some great research. Let's go to see what they did. Okay, then you got to know, okay, let's go look at what the companies themselves are putting out about the products. Because

sometimes they'll put out new information, um, yeah, just months before that actually changes how

“you should apply and use these products. Um, and if you get that order wrong, or if you get the”

quality of the information wrong, you'll make the wrong decision, right? What I mean by that, well, obviously, if an independent ag center puts out one thing, and the company puts out another, and generally with the company, uh, recommendation just so happens to say to use more of the product, right? Kind of weird how that works on the research front. Um, but like, if if you don't have an AI that can discriminate between the quality of the sources, then you're going to end up with an AI

that's heavily biased toward having the toward reading and giving answers, where there's the most information present, right? If, uh, any big ag company just kind of floods the zone, floods the internet with a our product is great. Does this, does this, does this, does this, but if you see all the independent ag research, and it says, well, actually, you know, it's good for this, but it's bad for this. You're actually going to want the AI to consider the independent research way more

than all of the stuff of the company is putting out. And then if that research conflicts with

what the law says or the label says, you want to make sure that it's always saying what the

law says or what the label says and that isn't biased by, um, all of that. And so kind of making an AI, it's able to think through things the way an agronomist did. We spent about two years making that working with ag center professionals, um, and, and folks out in the field on that, and then in early 2025, we released the initial version of this kind of agricultural AI and realized that something that we kind of known, but that was solidified of people started

using it. And they're like, oh, this is really cool, but it'd be way cooler and way more helpful. If I could really easily poured in all the information about my operation, right? What my soil tests are and what I'm applying, what the history is, what the yield is. And currently to do that, you would have to connect the system to like six different softwares. So, and like I said earlier, a lot of those softwares are outdated, our clunk, I don't really work that well. And so we figured,

no, what? Let's build basically two things. We built this awesome agricultural AI. Now let's build a

unified, rock, or system of record that can allow folks to run their agricultural operation all in one place

“that perfectly connects to the intelligence layer. And something that I think we'll get into later”

is completely run built right here in America and subject to the kind of most stringent data privacy things, because we believe that especially going forward, it's going to be really important that growers across America own their data, or it will be used against them. How, as you've kind of rolled this out and farmers have been using it, like how have they responded to it? Absolutely. I mean, at first, it's, you show them being able to do something, then their

old software took an entire day to do, then now takes 30 seconds. After that, it's a no-brainer. People do have this perception of agriculture as slow to adopt, right? And I think a lot of that has definitely come from this kind of conflation between the industry as a whole and the individuals in it, right? So if you think of the industry as a whole, agriculture, if you think of kind of the innovation curve, well, obviously the industry of agriculture would fit into the, the ligards category,

right? Not as a, as a, as a negative thing, just as a, it's literally one of humanity's oldest industries. But what people confuse is they take that industry standard and they apply it to everyone in there. You know, most, most farmers today have iPhones, have computers, have technology, right? It turns out that within the industry, if you just show them a product that's way better, if you show, if you solve a problem, of course, they're going to adopt it, just like any other industry.

“And so that's what we've been seeing is there's definitely a little bit of skepticism at the”

beginning. There's definitely like a lot of these folks have been burned by a lot of the big players. They've been, you know, I won't go through the list of names, but a lot of these kind of big players in the space have abused their data, have come out with things that are okay and then ends up being used against them. And so there's definitely a little bit of skepticism at the beginning, but then when we show them the product, when we show them how much time it saves them,

and then we also walked them through our mentality of ensuring that they can benefit from their own data. That's something that nobody else is out here kind of saying. And I think it's something that is really needed in the space and, you know, it's, uh, and then just like any other product, right, when you see one, when you see an iPhone compared to a blackberry, does it take a little

having, having ever not had it. So as a farm mind grows, and you guys are coming alongside farmers, how do you see, um, you know, what kind of increased productivity or like what kind of results do you think that you might be able to generate for folks in the agriculture industry? Absolutely.

“So there's kind of three, three phases, right? Phase number one, we save them a ton of time, right?”

Like I said, some of these old softwares just literally to do even very simple things on the software, took a whole bunch of clicks and a whole bunch of time, you know, you have the antiquated kind of

Microsoft looking thing with a million different tabs, and you almost have to take a class,

and I have to use the software, right? And so they can do this. And then all of a sudden, whether it's rolling over their fields or making a note or all these other things that they do in their daily practice, they can do it in like 20 seconds. It's like great. I didn't want to spend time doing that anyways, right? When it comes to regulatory compliance and being able to check, okay, how does this thing that I've been doing for my entire practice actually square up against

the current structure? So that if they come knocking on my door, I can show them, hey, I'm all good, right? I can take my pesticide application, upload my Bolton's two live information, and have that.

“So if anyone ever asks, I can attach to the weather whenever I'm taking the note. So if anyone”

ever asks, I can show them, I was all good, right? So first it's save time. Then it's save on input costs. And this is a big one that a lot of people don't realize because there's a lot of ways in modern agriculture. You can save on input costs. You can save on the amount of fertilizer. You're putting down. You can save on the seeds you're putting down. You can save on the pesticides and herbicides you're putting down. You can only do it if you do what's called variable right

applications, right? Basically taking the field and saying, okay, over here, because of these sets of conditions, applying at this rate is going to be better. And over here, at these conditions, applying at this rate is going to be better. Now, especially folks in kind of the the North West have kind of started to do this because input prices have forced them to really cut back, but especially in kind of the Gulf States and the Northeast, it hasn't been as much for practice.

And the reason is because it's really hard. Like you almost need to have a PhD in agriculture to be able to do some of these things. And the existing software doesn't make it any easier. And so if we can take all of these things that they're already doing and say, hey, look, we're also going to make it super easy for you to do precision application. Then they can save

on the input costs, which obviously helped the bottom line. So now you're saving time. Now you're

saving on your input costs. And then lastly, it's the increasing the yields, right? If we can take the best practices, the best research coming out from across the nation and apply it to everyone's individual situation, literally say, hey, did you see that that research from Arkansas saw showed that if your soil pH is this and you're growing this plant, that if you do this practice, you could actually increase your yield. Or hey, if we take the trend of your past five years,

which is super easy to do, you can literally do it in two clicks of a button, see the trend of your yields and everything in the last few years. Well, taking that trend, now if you do this one thing different, we can actually increase the yield. And so that's the hard part is we can't say everyone's situation is different, right? If you're already maxing out everything, then not going to be able to do it too much, but for the majority of farmers across the country,

there's marginal gains that they can see in just using kind of the most effective practices

that maybe just nobody has has told them yet, right, as it had maybe never been presented. So

it's like I said, kind of three phases of save time, save on input costs, and then ultimately increase yields. That's exciting. So your company farm mind, what have you guys been a company for what? Two years? How long have you guys been? Yeah. So we officially incorporated after we won the the LSU pitch competition that gave us kind of our initial funding in 2024. So yeah, about two years. And it's certainly seen a huge uptick ever since the beginning of this year. When in January,

we won the American Farm Bureau's agonization challenge. It's kind of been a very upward slope from there. So what's next for farm mind? Absolutely. Well, well, I kind of said earlier, we have the system of record, right? It's the thing that you can use to record everything. You can be out in a field and literally just say, hey, farmer AI, make a field note that I saw this, this and this, and it'll write it all down and note the your location and note your field and all that information,

and then you can go and say, hey, you know, it's most people, you mentioned earlier with AI being integrated in everything. A lot of people are now doing use to AI helping them write,

“right, producing text. But something that still a lot of people aren't used to and that I think”

you're going to see a lot more in the next two years that we're already doing in farm mind is having AI do work for you on the websites you're already using. All right, what do I mean by that?

Those are arbitrary numbers, basically just different varieties of the same plant. Where beforehand,

you know, manually go, select all the fields, change the tag, right? It's not hard, but, you know, or I could just say, hey, farmer, I'll change all my this variety to that variety and you can watch it

“go do the click, select the things and set it all up for you. So all you have to do is accept, right?”

And so even the hardest processes within the software, you can just ask the AI to help you do it and then watch it work, right? So what's next for us on the development front is we currently have a decently robust system of record for agronomic trapping, right for your mapping, for your weather, for your soil tests, for your yield data, right? All those agronomics, what we also realize is to really make this most useful, it needs two more things.

Asset tracking, right? So you can track the depreciation of your tractor, you can to track every single time one of your workers is working on it, they contract the mileage, all of that information. And then if we're helping folks with the agronomics and with the assets, well, why not help manage the finances, right? And so we're also building a quick book style component, just right into the software itself. So in just a few clicks every single year, you can actually

manage the financial implications of every single part of your operation. And the cool part then is you can run next year, say, hey, let's make these agronomic assumptions and see exactly how it would affect your bottom line. And so the kind of last two tenants of the system of record side

“are those assets and finances that we're doing. And I think also just ensuring that the way”

that we're doing this as people adopt it, I see, you know, kind of our goal is two things, right? One, make an amazing product that helps every single ag pro that adopts it, kind of farm smarter, right? Far more effectively, save time, save on input cost, increase yields. But the second component is also being a force for good for American agriculture as such. You know, a lot of people don't realize how kind of fragile and how many cracks there are in the current agricultural system.

Right? I mean, you can see just last Wednesday, Congress passed the Farm Food National Security Act. And buried in it is actually a provision that pays up to 90 cents of every dollar to farmers to adopt precision addicts software, right? Sounds amazing, right? Like that sounds like, of course,

this is great. Why would there be any problem with that? Well, the problem is once you realize

the dominant recipient of that funding will be Sengenta. Sengenta is owned by Chem China, a state enterprise of the CCP. So let's play that out. What we're about to do is American taxpayers are about to subsidize a Chinese state company to collect the old level data of American food supply and production, yield data, GPS coordinates, all of that at scale, we're about to pay for a company to do that, right? When we have the capacity to not, right? We could have domestic companies

that can do it even better than they are and protect growers' data. Because in the long run, that data is not only going to be useful to them to make great decisions, but you see all of these big ad companies, they're going to want to pay for your good data, but they can only pay for it if you own it. And so we're very adamant on if we, you know, both in our softwares and we encourage everyone else in the space to allow growers to own their data so that down the line, if any

researchers want to use it, if any companies want to use it, they will have the option to opt in

and basically increase their bottom line from being able to have optionally, again, completely

optionally to sell parts of their data to increase their bottom line and, you know, maybe even keep the farm in some situations. You can only do that if you own your data and not if you give it away

“especially to something like Syngenta. Yeah, are there any politicians talking about this issue?”

I mean, that does seem pretty scary to me. Yeah, it is, and that's we've been kind of saying some stuff in the last month about it, but it's been really hard to kind of break through and and I know we, um, we actually had a op-ed in the Washington examiner talking about this exactly, and we've been kind of raising the flag about it and trying to get this message out before it's passed through the Senate to basically say, look, this is really close. Like, like, I get the intention

here. The intention was really good, but if we just added a simple amendment, the exact same way that we did for Huawei, the same way that we did for the Chips Act, right? A simple amendment excluding foreign controlled entities, ending the taxpayer subsidies to the foreign controlled entities, that would go a long way to saying, look, we do want to subsidize precision egg for the robustness of American agriculture. But let's also make sure that that data is protected by our domestic laws

and it's saved kind of on American soil. And I've been really surprised that

Yeah, that's uh, I'm glad you guys are raising the flag on that one, because that is

certainly concerning. So, hey, as we wrap up, I'd love to hear your thoughts as you step back and look at the future of AI. I mean, you and how people are going to use it, how businesses will use it, how will use it individually. You know, you're somebody who's built a company on based on AI and you know, it's day in and day out, really thinking about the future. So, I'd love for you to show your thoughts with us. Oh, that's a big one. There's a lot of directions you could go with that.

“You know, I think in a lot of ways, you're seeing the same trends, the same media trends,”

the same societal trends that are happening with AI, or the exact same ones that happened with the internet, um, not too long ago. You know, not too long ago, there was fears that the internet

was going to take put a lot of people out of job, right? It's going to put blockbuster out of job.

It's going to put all the news media out of a job. It's going to put right all of these different groups out of a job. And yet, if you look at kind of what actually happened, there's actually more people putting out media today. There's more opportunity. There's more jobs who would have ever imagined in the 80s that a social media influencer would be a job, right? That that would even exist, right? Imagine telling someone like trying to describe that job and just like it not making sense, right?

So what actually ended up happening is that, you know, what technology does is it broadens the horizon of human possibility, right? Of your individual agency. And so the bar definitely goes up because there's more that's possible, right? 50 years ago, you couldn't send out a thousand letters in a day, right? You know, most people, right, unless you had a whole team of people, right? Or today, you can absolutely send out a thousand

“emails if you need to do, right? Today, you can absolutely send, right? Because the horizon of your”

individual possibility is going up. That's what technology allows for. And so it definitely rewards the folks that learn it, use it, and then I hopefully use it to very positive effect, right?

There will always be people that use it for negative effect, right? But that's only held check

by all the people that are using it to positive effect. And so I think whether or not, you know, the whole conversation about the data center build out, the whole conversation about job loss, the whole conversation, like all these kind of hot button things that you hear about, I guess it's really kind of important for folks. Put it in perspective, think about, okay, where have these arguments used before? How have they actually paned out across human history?

And, you know, don't just believe everything you see on social media about what it is and

“what it's not, especially if you're not using it. You know, go on, try it. I think a lot of”

people might have used AI last year. I mean, like, oh, this is useless and not realized how far it's come in the past even three months. And so if you're, if you're someone that's tried it last year and you're like, oh, this isn't working, go ahead, try it again. Start using it because like even we can see in, you know, obviously we're an AI company, right? We're producing that, but also internally, like the systems we're able to set up, you know, there are

large companies that probably spend 10 people to do the thing that we're able to do with one because we're able to use this technology. And that's not, and that's actually going to create way more jobs because it lowers the, it raises the actual output of every unit work. So it actually becomes more economically beneficial to hire someone to do the work. So anyways, without going into the whole, kind of, I'm sure we could have a really good, like, kind of economics thing on that alone.

Well, I'd be happy to go into it. But, yeah. Yeah. Well, just, you know, I said, I have one more question. So what's something in your personal, the personal, professional life for personal life, where you've started using AI and it's made a real difference or, you know, it's something to maybe have other people aren't doing yet. Any chance that you can use it for kind of customize generation. So what I mean by that. So like, for example, anyone that is working in Excel sheets,

right? Most people don't know, you can use, anthropic, you can use open AI for to do a lot of in Excel, right? You can use it to, hey, I want an Excel table, but does this assist in this, and they can pretty much do, do fill it all out and give it to you. In fact, there are plugins where you can have the actual model, right? Anthropic in your Excel sheet itself to do things. Kind of like if you want to Google sheets recently, it's not really good, but you can get the old Gemini thing out

and tell it to do stuff. But also, just anytime where you're doing personalized generation, right? So let's just say you have a whole kind of corpus of information, right? About yourself, about your company,

give it to an AI in like a project. Well, now you can kind of just give the bullet points of, hey, this is why I'm communicating, and it can put it into that style that you're trying to communicate.

“And I think that's really good because you're still coming up with the ideas. You're still saying,”

look, here's the things I want to communicate, here's the points, here's the information, but then it's able to get past all the, you know, writing it out grammatically correct in this in this so that you can actually just send it off and make it useful. So I would say on one hand, personalized generation, and on the other, like I said, increasingly messing with a lot of computer

“use things that I know a lot of folks have never had experience with, but I think you're going to see”

in the coming years. A lot of companies are going to make this whole computer using a lot more accessible for folks. That was going to be a lot more websites where you can go on and ask the AI to do things for you and literally see it start clicking around and doing things for you in every

single industry. Yeah. Yeah. Well, I can't wait to see, it has changed so fast. It's amazing.

You're talking about months rather than years. It's pretty, it's super exciting, and I appreciate you're cutting edge of it. I really appreciate you joining us and the work that you're doing in

“the Ag industry in particular. Thank you. Yeah, appreciate it. And I think if we get this right,”

it can be the greatest bull work for a can not only benefit a ton of people in Ag, right, because they're going to have tools that previously only the biggest operations have had. So even your smallest farmer can stay in business and have leverage against being bought out and against all these things. So if we do this right, we're going to keep a ton of farmers owning their land in their jobs, but that also as a bull work for our national security to make sure that through everyone utilizing

these tools, we're able to have sufficient production such that we're not reliant on someone else

for our most critical resource, the food that we eat every day. That's great. And so worthwhile.

Thanks, Colin. I appreciate you joining. Absolutely. Good to talk to you, David. Thanks. Hey folks, if you like this episode and would like to stay connected with the podcast, be sure to like our channel as well as following us on Facebook and Instagram and YouTube.

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