Pioneers of AI: John Deere's AI vision for future farms

Tractors are smarter than you think. John Deere, the nearly 200-year-old company, is combining sensors, data, and machine learning in highly advanced vehicles and software to reinvent how the world grows food. On this Pioneers of AI episode, John Deere CTO Jahmy Hindman breaks down how AI is making farms faster, smarter, and more sustainable. Plus he shares his vision for a fully autonomous farm. Learn more about Pioneers of AI: http://pioneersof.ai/ Follow Pioneers of AI on all channels: https://linktr.ee/pioneersofai See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

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• 2:10Speaker 02

  If you look at the corn crop in the United States, there's 4 trillion corn seeds that are planted in the United States every year, give or take.

• 2:19Speaker 02

  And our mission is to be able to provide in a mechanized form the master gardener experience for every one of those seeds. We want every one of them to be treated exactly where it needs to be treated, how it needs to be treated, and when it needs to be treated to live its best life.

• 2:33Speaker 03

  Are there going to be like teams of humanoid robots on the field? And they'll like have straw hats and be wearing overalls or something?

• 2:41Speaker 02

  I like the idea of humanoids in agriculture for a whole host of reasons. There are some jobs that no humans want to do.

• 2:49Speaker 03

  How do you see the role of AI in feeding the planet, even beyond what it can do for farming?

• 2:56Speaker 02

  I think AI gives you the opportunity to sort of interrogate, where are the inefficiencies in this system? And how can we be more effective moving forward?

• 3:03Speaker 03

  That's Jamie Heineman, CTO of John Deere. The almost 200-year-old agriculture supercompany is fascinating as a technology enterprise. They're developing AI-powered tools that help farmers be more efficient and more productive. And the stakes are high for humanity and the planet. I first met Jamie several years ago at CES, the legendary consumer tech show. And I have to admit, I had never thought about the technology underpinning farm equipment before then, but I've wanted to talk to him ever since. Our conversation covers why John Deere owns the tech stack for farming, the company's vision for the future of agriculture, and the most important question of all.

• 3:46Speaker 03

  What does a fully autonomous mango farm look like? It's fascinating stuff, so let's get to it. I'm Rana Elkhalyubi, and this is Pioneers of AI, a podcast taking you behind the scenes of the AI revolution.

• 4:08Speaker 03

  Hi, Jamie. Welcome to Pioneers of AI. I'm so excited to have you on the show.

• 4:12Speaker 02

  Rana, it's great to see you. Thank you for having me on the show.

• 4:16Speaker 03

  All right, so you're the CTO, Chief Technology Officer of John Deere.

• 4:19Speaker 02

  Correct.

• 4:19Speaker 03

  And it's safe to say that if most non-farming people were to name one brand in farming, it would be John Deere. But I also think most people don't associate John Deere with being a technology company. I do want to start with your background. You grew up around farming. Your grandfather was a farmer in Iowa. And I guess you still have family members who are in the farming business?

• 4:41Speaker 02

  Yeah, in a story that's pretty common in the Upper Midwest, a lot of people can trace their roots to agriculture in some way, shape, or form. And so my grandfather would have farmed a little farm in southwest Iowa. And in a story that repeats itself over and over again, it was not large enough to support the whole family. And so my dad became a college professor. He taught aerospace engineering for his career. And farms just, that farm got consolidated into other farms.

• 5:11Speaker 03

  How has that upbringing shaped what you do today? And also kind of your perspective on the whole business?

• 5:18Speaker 02

  Yeah, well, I've been around agriculture for a long time, my whole life. Maybe not directly involved in it at some periods of my life, but always around it. And I'm a technologist at heart. Like, I grew up with an engineering professor as a father. So I was around technology my whole life. And I think those two things go together. Agriculture is an application that begs for efficiency improvements, right? We, if you were to rewind the clock 50 years ago, roughly 30% to 40% of the US population would have been involved in agriculture directly, right? And today, that number is like 1.5%. And if you think about that, it's enabled people like you and I to do the things that we do.

• 6:03Speaker 02

  We no longer have to worry about how to get our food. We go to the grocery store and it's there for us. But that's done on the parts of 1.5% of the whole population, right? And so agriculture has been a story of efficiency. And technology is sort of the underpinning or the foundation for how that efficiency's happened.

• 6:22Speaker 03

  Yeah, amazing. So you started your career at John Deere in 1996. That's like 30 years ago.

• 6:28Speaker 02

  Yeah, it makes me sound terribly old, but yes.

• 6:32Speaker 03

  As a test engineer, you've seen the evolution of the company. I would love for you to walk us through the company's very first product and then the range of products you guys have today.

• 6:44Speaker 02

  We're a 189-year-old company, which we're proud of. That means we've had to reinvent ourselves multiple times in the history of the company. We trace our roots back to John Deere, the man himself, who lived in Vermont, but moved to the Midwest, moved to the state of Illinois as the country was being built. And he was a blacksmith. And the plows of the day were largely wooden plows and sometimes cast iron plows. And soil was always sticking to these things. And the farmers had to stop and clean the plow off every 10 meters or so. And so John Deere's claim to fame is he fashioned the first self-scouring steel plow. And we're proud of that product, obviously.

• 7:29Speaker 02

  But one of our more pivotal moments was in the early 1900s, when this thing called the internal combustion engine started to happen. And we no longer had to rely on animal power to do farming. And the tractor was born. And John Deere actually didn't develop the tractor or start the tractor. That was a company we bought, the Waterloo Gas Engine Company. They happened to have an engine that they put in the form of a tractor. We purchased that company. We produced implements that were drawn by animals, not by tractors. And so it was the inventor's dilemma, right?

• 8:03Speaker 03

  Talk about the inventor's dilemma.

• 8:05Speaker 02

  At that point in time, it was this pivotal question of do you jump into this business of the tractors and recognize that it's going to disrupt your core business? Or do you not? And it's sort of the, you can look at the models behind me. It's the ubiquitous product form for the company today. And that really started us on this path, right? This ability to take advantage of the efficiency of mechanization and have people go do other things with their creative potential that obviously I think the world has benefited from. We're in the middle of that, like many industries right now, with artificial intelligence. You know, it's obviously got huge potential, I think, in the agricultural industry. And we view it as a responsibility for our company

• 8:49Speaker 02

  to be able to utilize that technology for the benefit of the customers and to walk hand in hand with our customers and make sure that they agree with us, that it's doing the things that are beneficial.

• 8:59Speaker 03

  So how, what have you had to learn to stay kind of at the forefront of all of this? Do you have particular mindsets that you apply?

• 9:07Speaker 02

  Yeah, it's a great question. I think my experience sort of falls into the category of I'd rather be lucky than good. I'm a mechanical engineer, bachelor's, master's, and PhD. But my graduate work was focused in an area called artificial neural networks, of all things, 15 years ago, right?

• 9:24Speaker 03

  That's awesome.

• 9:25Speaker 02

  And it was not very compelling area of research at the time. It was challenged by, you know, lack of compute, not great data sets. And our space on the edge, the inability to access equipment through communication paths and all sorts of things were hurdles and roadblocks that are largely removed today. And so I've been in this place where I understand the mechanical side of our business, which is still really important. But we also need to weave through those pieces of equipment, the modern technology, artificial intelligence, software, that can improve the productivity of that equipment in a way that the mechanical function of that equipment no longer can.

• 10:07Speaker 03

  Yeah, yeah, very cool. So you oversee the entire tech stack for John Deere. And that's the hardware, as you said, the software, all of it. But it would be helpful to unpack, what does a tech stack mean, again, in the agriculture space? Like, what does it look like?

• 10:24Speaker 02

  Sure. You know, it's really a handful of things that are critical and that we build the rest of the technology on. Those things are the ability to locate the piece of equipment on the surface of the planet. We talked about that, the GPS receiver and our own GNSS.

• 10:39Speaker 03

  Why is that so important?

• 10:41Speaker 02

  In agriculture, it is.

• 10:44Speaker 02

  It's important for a couple of reasons. One, plants live their best life when they get the opportunity to equally compete with one another. And so that makes it interesting for us to create the same row spacing, right? So precisely putting that seed in the ground at exactly 30-inch rows, time after time after time after time, is a core component of agricultural efficiency. In addition to that, we don't like to overlap things. Like, so if you're putting seed in the ground, you don't want to have seed on top of seed, right? And so it gives you the ability to know where you're at. If you imagine you're in a 1,000-acre field, this ability to know where your 16 rows of planters are in 1,000 acres is a very challenging thing to do.

• 11:27Speaker 02

  And before GNSS, you were guessing often about where the tractor had already been or where the piece of equipment had already been. So it gives you the ability to keep yourself from duplicating the work in the field or skipping some of the areas of the field and not doing the work at all. So those are the, some of the ways that it's important. We think about it as this idea of plant-level management. There's, you know, if you look at the corn crop in the United States, there's 4 trillion corn seeds that are planted in the, in the United States every year, give or take. And our mission is to be able to provide in a mechanized form the master gardener experience for every one of those seeds. We want every one of them to be treated exactly where it needs to be treated, how it needs to be treated, and when it needs to be treated to live its best life.

• 12:11Speaker 03

  Um, this just occurred to me. I, I don't know if the analogy makes sense, but as humans, you know, I'm really into like my wearables, right? I track my sleep, I track my steps, I track my activity, and if there was an easy way to track my nutrition and hormone, like all of it, right? And, and I, it just occurred to me that, like, in this kind of mission to like help plants live their best lives, do you also have like a picture of like a plant's health and wellness, right? Like, what does that look like?

• 12:44Speaker 02

  It's a, I, I love, I love the analog. I mean, we're an organism, the plants are an organism, like it, it fits generally. We don't know as much about the plants as you know about yourself because you're more instrumented than the plants are, but we're on a path to instrument plants in a similar way. One of the interesting technologies that we're exploring at the moment is to give a plant the ability to communicate what it needs, what its stresses are in its life. There's a company called InnerPlant that we partnered with that is genetically modifying plants to allow them to fluoresce at a certain wavelength based upon what stress they're seeing in their, in their life. So if they're seeing, in the case of soybeans, a stress due to a fungus attacking the plant,

• 13:27Speaker 02

  they fluoresce in one, in one wavelength. If it were nitrogen deficiency, they would fluoresce in a different wavelength. If it was water deficiency, they would fluoresce in a different wavelength. And you pretty soon, you can conjure up this mental image of the ability to sort of listen to the plant and understand exactly what it needs and then treat it. So yeah, absolutely. I think the analog holds true.

• 13:48Speaker 03

  That is so cool. It's, I, I spent my entire career like building emotion recognition and emotion sensing technology for humans, but that would be the equivalent of like nonverbal communication for plants. That's so cool.

• 14:02Speaker 02

  For sure.

• 14:02Speaker 03

  I love that.

• 14:05Speaker 03

  Coming up, a walkthrough of how John Deere brings together sensors, data, and AI in one of its high-performance machines, and what the company does with all that data to help farmers get better results.

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• 16:26Speaker 03

  Now, as you know, I also spent a lot of time in the automotive industry, bringing together kind of, again, this idea of sensors, data, AI. So I kind of want to unpack what that trifecta looks like for John Deere vehicles, and maybe we start with a specific product, the John Deere 9RX 830. I don't know if it's one of the ones behind you, is it?

• 16:46Speaker 02

  It is very similar to that tractor right there, yeah.

• 16:50Speaker 03

  That one? Okay, that's great. So this model can retail to up to like $2 million. So what is this tractor built to do and what kind of sensors sit on it?

• 17:01Speaker 02

  Yeah. A long time ago, we started putting high-performance compute and sensing elements on these products that enabled them to start to collect information that was useful to the farmer. So in the case of that 9R, those units would be responsible for collecting and communicating the information of how the planting process went. Like, how many seeds did you plant? Where did you plant them? What depth did you plant them into? Those sorts of things. So that a farmer can take that agronomic information and understand, okay, what did that produce in terms of germination? How many seeds germinated and, and are going to create, you know, a, a new plant? And I can then use that information and use it either in the next season to determine

• 17:45Speaker 02

  how many seeds to plant and how deep to plant them, or I can use it sort of in-season to understand where I might need to replant my field. I didn't have good germination, I need to replant those seeds. All of that data goes from the, the planter behind that tractor, through the tractor, up to a cloud instance and into a mobile application that the farmer would be able to access.

• 18:04Speaker 03

  Do you use computer vision? Do you use radar, LiDAR? Like, what kind of sensing?

• 18:10Speaker 02

  So both on that machine and on that machine, two different applications. We do use a computer vision on the tractor. There's a fundamental issue in agriculture. We mentioned that 1.5% of the population is involved in agriculture. What that fundamentally means is, in many cases, there's not enough labor to do all the work on the farm, or at least labor's a challenge on the farm. Primarily because in most of the production systems we're involved in, you need all of the labor at a very specific point of the year, right? We plant all of the corn and soybeans, for example, in, in the US are generally planted in a two- to three-week window in the spring. And so every, every farmer is busy planting, right? And so you need these maximum amounts of labor in a very short period of time.

• 18:55Speaker 02

  So labor's a, a challenge. We've already got, I told you, hands-free guidance on these pieces of equipment, but we haven't replicated the greatest sensor of all, which is the human, right? In the cab of the machine. And so we started working in the space of full autonomy. We've been working in that space probably for 30 years, but more recently, publicly, we've started to show the world what we're up to in terms of replacing or giving the, the farmer the choice to replace themselves in the cab of the machine if they choose to do so. And so we've had full autonomy on those tractor products in limited applications with customers over the last four years.

• 19:33Speaker 02

  And that sensor, sensor modality is primarily a camera array around the, the, the top of the, the operator station of that tractor. So 16 cameras overlapping fields of view. We do frame-by-frame calibration so that we get depth from that, that sensor array as well. The, the compute to do that is not a trivial task. So we, we run embedded GPUs, Nvidia, or in platform GPUs on that, on that product. And importantly, in our application, they need to be hardened. They need to be able to survive shock and vibe and temperatures that traditionally these compute devices would not survive, so.

• 20:15Speaker 03

  Right, they're not sitting in an air-conditioned data center.

• 20:17Speaker 02

  No, they are definitely not. So, so we, we take a lot of pride in our ability to harden some of these devices that traditionally would, to your point, not find their way into our applications, because we need them. We need the compute capability in order to do that perception problem on the machine. In the machine right below it, this is called a self-propelled sprayer, we use computer vision in a very different way. That machine has a boom that will spread out to 120 feet. It will travel at about 15 miles an hour, and traditionally, it would apply herbicide, pesticide, or fungicide across every square meter of the field.

• 20:59Speaker 02

  Even if that part of the field didn't need the application, because there was no way to sense whether or not the crops needed it in that particular application. So we put 36 cameras across that boom. We put nine embedded GPUs on that product and we do what we call C and spray. So we sense the ground at 15 miles an hour and we look for pixels that contain weeds and pixels that don't contain weeds. And the idea is you don't need to spray herbicide on ground that doesn't have weeds. And so you only spray the weeds, which is it's good for the farmer. It's good for the planet. Nobody wants to spray more herbicide than they need to, right? It's good business for us. So it's a bit of a triple win and they're great applications of AI in agriculture.

• 21:44Speaker 03

  Yeah, that's awesome. One way to think about these tractors are that they're a massive data collection machine, right? And to your point, you're uploading or you're processing all this data, uploading it to a cloud somewhere. What do you then do to the data? And then how does that then show up on the operator or the farmer's side?

• 22:02Speaker 02

  Yeah. So the data goes to a cloud instance, but in many of these locations, it's remember they're rural locations. And so you may not have terrestrial cell, even here in the US. So we partner with Starlink to put low earth orbit satellite terminals on product to in areas where we don't have great terrestrial cellular connectivity. But in one way, shape or form, data gets pushed into a cloud instance. And then we do a lot of different things to it. We process the data into usable pieces for the farmer. So they can look at things like a yield coverage map would be a good example. So because we know where every seed was planted and we know where the machine is when it's being harvested, we can tell

• 22:43Speaker 02

  you in a critization of about the size of a pizza box, maybe one square meter of resolution, how much corn or soybeans or whatever the crop is came from that particular area. I refer to it as the farmer report card. It's how good did we do? Did we produce, you know, 150 bushels of corn on this particular piece of ground? Or did we produce 300 bushels of corn on this piece of ground? And at the end of the day, that is what the farmer wants to know because it provides them the information that's necessary for them to make better farming decisions, better agronomic decisions, whether that's genetic variety of the crop, whether it's the seed density, whether it's the nutrient schedule, all of these things for the next year. They get that exposed to them in two different ways. We expose

• 23:25Speaker 02

  it in what we call John Deere Operations Center. It's our digital offering to customers. And they can see that either in a desktop version, which is the way that most of them will do their back office data understanding and data analysis post harvest. Or they can, we expose it in a mobile environment, Android or iOS, which is where sort of the feature set that is more near term resides so that they can understand logistics on the farm.

• 23:52Speaker 03

  Yeah. How has generative AI changed any of your products and how farmers experience the products?

• 24:00Speaker 02

  Yeah, the predictive side of things is we've done that for a long time. We do things like try to, based upon aggregated data sets, anonymized aggregated data sets, we look at those and say, when is the best time to plant? So that's, that's sort of the conventional way that the data sets can be used. The generative AI and I would argue transformer networks in general are interesting to us for a whole variety of reasons. I think the first is agricultural data is often poorly structured. And so it's messy, it's complicated. But generative models have given us the ability to sort of reject the noise in the data and

• 24:45Speaker 02

  focus on the signal, which is, and to be able to do that at faster clock speeds than we've traditionally been able to do it. So you can unpack, I think, more insights out of the data. We're also interested though, in them for edge use cases. I talked to you about the autonomous use case as an example. This notion that you can do an end to end, hey, let's just understand what a farmer would do. What, how do they manipulate the controls when their eyes see these things, when their ears hear these things? In that way, you can sort of completely emulate what the farmer is doing within the operation as opposed to just assuming that they're going to make these decisions when the sensory inputs are X, right?

• 25:23Speaker 03

  Okay. So that's really interesting. What you're saying, I think, is that, okay, take one of these tractors, right? The kind of the default model is it's collecting all this data, it's going to the cloud, it's going to go back to like, you know, the farmer's mobile phone and the farmer's going to say, you know what? Click, action A taken. Instead, you can actually have all this run on the edge where in the tractor, basically where the tractor can say, okay, I have all this data, da, da, da. It's very likely that I need to make decision X or decision A or whatever.

• 25:57Speaker 02

  That's exactly right.

• 25:58Speaker 03

  That's so cool. It's very interesting.

• 26:00Speaker 02

  It is only a, an idea that you can contemplate tractably doing today because, you know, the compute, compute's expensive, don't get me wrong, but it's nearly, it's not the limiting factor anymore. Traditionally, it's always been the limiting factor for us on the edge. And, you know, we're the, the embedded GPU compute, it trails roughly six years, the data center compute in terms of, in terms of operations that you can, you can run on a given unit of power. And, and so you can kind of think forward, like you think of what's happening in data center compute today. And in five, six years, you're going to have that capability in your hands at the field edge. What are you going to do with

• 26:42Speaker 02

  it then? Like, it's a pretty, it's a tantalizing intellectual thought experiment to go through.

• 26:47Speaker 03

  Yeah. I mean, the whole semiconductor space is, there's a lot of new players that are essentially just focusing on the inference problem, right?

• 26:56Speaker 02

  Right. You saw Google, they're separating, right? Inference is going to be a separate compute device. And I think that's interesting in its own way. We still need some inference on the edge, but no doubt, but separating that out of the equation gives us the ability to sort of play with the compute topologies on the edge in a way that we traditionally have not been able to.

• 27:18Speaker 03

  We have more to get into. How farmers are using AI on the ground today and what role robots might play in the field. Stay tuned.

• 27:43Speaker 00

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• 29:25Speaker 04

  Humans will never be more intelligent than AI.

• 29:29Speaker 05

  There's going to be two types of companies. Those are great at AI and those that went out of business because they weren't.

• 29:34Speaker 06

  How do we build a future that is human centered?

• 29:39Speaker 03

  I'm Rana Elkhayubi. And on my podcast, Pioneers of AI, we answer that question and so many more. As an AI scientist, entrepreneur and investor, I know what it takes to build AI that works for everyone. Every week I sit down with the pioneers shaping our future and we take you behind the scenes of the AI that's transforming our lives. Find Pioneers of AI wherever you tune in.

• 30:12Speaker 03

  Do you envision, like, I guess I'm envisioning farmers using just natural language, right? Like a kind of a chat interface, basically, to prompt for data and insights?

• 30:24Speaker 02

  It's a, I was meeting with growers in Pasco, Washington, farmers in Pasco, Washington. I don't know, this was a year ago. And there was probably 16 or 17 folks, but I started out the presentation, the conversation with how many of you have heard of ChatGPT? And all their hands went up. And I said, how many of you use it on the farm? And almost all their hands went up.

• 30:44Speaker 03

  Wow.

• 30:45Speaker 02

  And then the next question was, how many of you use it regularly, like every day? And about half of them did, right? And, and, and so that's an interesting observation, like they're using it as a thought partner to assimilate the data that, you know, that's on the farm and to juxtapose that against decisions that they would make and to spar with them, you know, intellectually a little bit about what are the decisions I should be making on the farm? So I do think there is, there is an appetite for it, for sure. And there is an opportunity for that to start to contribute to making the farming practices more efficient, more effective over time.

• 31:22Speaker 03

  Yeah, it's so cool because, again, like, you're collecting so much data. And I imagine, you know, there's kind of set dashboards where you can see different views, but it would be so cool to be able to interrogate that data and just like, ask, right? Like, just natural questions and

• 31:39Speaker 02

  Exactly right. And that's the reason that they're, they're drawn to it is the interface, the user experience is, it's in the description. It's very natural, right?

• 31:47Speaker 03

  Yeah. So this is a very strong business model to own the entire tech stack. But from a farmer's perspective, it feels like a little bit of a monopoly, right? And I, I want to talk about the right to repair for a moment. So John Deere just settled a lawsuit around this. I would love to first have you explain what is the right to repair issue? And then your perspective on this and, you know, what happens next?

• 32:13Speaker 02

  Yeah, sure. And maybe I'll, I'll start, I'll rewind and kind of start, you know, 25 or 30 years ago, when we started to put microcontrollers on equipment. This, this idea that a farmer was going to want to reprogram those microcontrollers was never a thought, right? It just, it wasn't 25 or 30 years ago. And so I would say the, the current state is sort of an evolution from that point. In fact, we wouldn't have thought about reprogramming those controllers back then either, unless, you know, something had failed and, and we needed to load new software.

• 32:50Speaker 03

  There's no on the air updates, right?

• 32:52Speaker 02

  There was no over the air updates, right? So, you know, we didn't get here, you know, I would say intentionally, we got here as a consequence of technology changing over time and customer sentiment changing over time. And there are customers today who want the ability to update software on their controllers. That's at the core of the, the argument. And up until we produced a product called Customer Service Advisor that gave customers the ability to do that, they had to purchase the tool that was very similar to the dealer tool that was used to update controllers. But there was still friction in the system. They had to go to a dealer to get that tool.

• 33:32Speaker 03

  They had to pay for it too, right?

• 33:34Speaker 02

  They had to pay for it. Yeah, there were, there were technical hurdles between here and there. And it wasn't, it wasn't very efficient is probably the right way to describe it. And so last July, in response to that, we came out with Operations, Operation Center Pro Service is what the trade name is, but it's effectively the ability for you to download any controller payload to your phone, to your smart device, and then walk out to your tractor or your sprayer and push that payload file to the controller that you want to update as an owner of the piece of equipment. So we've made that update process much more efficient, much more effective. And you can, you can do it through your smart device.

• 34:16Speaker 02

  That, in a nutshell, was our response to this, this idea of right to repair. Because the, the argument is mostly centered around the software space as opposed to, you know, the hardware side of things. Customers, for, for our complete existence, we've provided service parts and service manuals and those sorts of things to be able to do the maintenance and service on your equipment as you want. And we still do that today. There are tractors that are 70 or 80 years old that we still provide service parts for. And, and you can still, you know, go to a John Deere dealer and buy the service part and update the piece of equipment yourself. So for a long time, we've been proficient at the mechanical side of, of making sure that, that customers had the ability to repair their equipment. I'd

• 35:01Speaker 02

  say the right to repair is, is us catching up on the digital side of being able to do that.

• 35:07Speaker 03

  So this is slightly different than, than this idea of, like, for example, if my car breaks down, I can take it to my dealership, but I can also go to the, like, mechanic down the street kind of thing and just fix it for a lot less money, really.

• 35:20Speaker 02

  Yeah, it's, it's similar. So this, this Operations Center Pro Service is also available to independent repair shops. So you can take your, your tractor to an independent repair shop and they can do the software update as well. And they can, they can do the mechanical updates if you want them to do that also.

• 35:35Speaker 03

  Okay, very interesting. Okay. All right. So let's look forward a bit. And I, I want to set the stage. There's an alter ego version of me that I'm originally from Egypt. I love mangoes. My grandma had not a mango farm, but she had mango trees in her, in her, yeah. And I, I've actually visited a couple of mango farms in Cairo because I'm like, oh, someday I'll, I'll build a mango farm. I don't know.

• 36:01Speaker 02

  Awesome.

• 36:02Speaker 03

  But it's going to be the farm of the future. So I would love for you to describe what that future look farm looks like. I want to pull it all together in a, to set a scene. You game to play?

• 36:15Speaker 02

  Yeah, for sure. Let's do it.

• 36:16Speaker 03

  Build my mango farm together?

• 36:18Speaker 02

  Yeah, we'll do it.

• 36:19Speaker 03

  So we're starting in the morning and farmer gets up and then what's next? You like pull up your laptop and you look at a data dashboard? Like, is that step one?

• 36:28Speaker 02

  I actually think there's a, there's a digital assistant that's probably talking to you once you get up, telling you what the weather's like on the mango fields that you have, telling you what, you know, the, the potential steps you should take that day are that maximize your, your farm productivity, whether that's, you know, telling you the health of those trees, the state of the trees relative to harvest, what nutrients those trees need, what sort of, of illness that the, the trees might have, like, all of those things probably are being communicated to you. I think you're not reading them. I think you're listening to them.

• 37:11Speaker 03

  I'm just, I have like a, a voice AI agent that's talking to me. Okay. All right. And then are the tractors already out on the field? Because I don't know, they're like autonomous and they, 4am, they started themselves and just headed out?

• 37:25Speaker 02

  I think, the way I think about autonomy on the farm is, is it is a tool in the toolbox for the grower and it is their choice. So it could be, you could absolutely have, if you wanted that tractor to go start its work, you know, unprompted, for sure, that can be a thing that happens. If you want to prompt the work, that is also a thing that can happen. I think the, the key thing is you don't have to be in it if you don't want to be in it.

• 37:52Speaker 03

  Okay, cool. And then I am really fascinated by the field of humanoid robots. And actually, we're looking at a number of robotics companies that are specifically building humanoid robots for heavy industry, like ship welding and whatnot. Are there going to be like teams of humanoid robots on the field and they'll like have straw hats and be wearing overalls or something?

• 38:16Speaker 02

  I like the idea of humanoids in agriculture for a whole host of reasons. There are some jobs that no humans want to do. Like in the upper Midwest, going into a grain bin, you know, at the, the, this time of the year and cleaning it out and preparing it for the next season's harvest. It's a dusty, dirty, dark job. You know, you have to wear a respiratory mask. You have to, it is not a place that anybody, no, no farmer, no honest farmer is going to say, I love to do the grain bin job, right? And what a perfect application for something like a humanoid. I also think in the mango farm, there, there's a whole host of crops that are challenging to harvest mechanically, right? Fruits and

• 39:01Speaker 02

  nuts, you know, they're, fruits especially are more challenging. And, and so I do think there is, and lots of companies have created bespoke robotic harvesters for grapes and strawberries and tomatoes and these sorts of things. But I think that the thing that they all struggle with is the manipulation that, that humans can provide in, in this, right? This is a hard thing to replicate. And I think humanoids have a space in the harvesting part of agriculture, if we can get the hand right. Because there's a dexterity associated with that, that is just difficult to replicate in other form factors. And especially

• 39:40Speaker 02

  in things like citrus and, and mangoes and these higher value crops, berries.

• 39:47Speaker 03

  Blueberries.

• 39:48Speaker 02

  Exactly. I think there's an incredible opportunity in that space for them. Yeah.

• 39:53Speaker 03

  How do you see the role of AI in feeding the planet, even beyond what it can do for farming?

• 39:59Speaker 02

  Yeah, I think, you know, the, the food chain, farmers sort of are the, the nucleus for the things that you and I get to consume on a daily basis. But there's a, a whole host of inefficiencies that exist in the food production system, from the point of the farmer, all the way through the vertical, the, the integration of those feed stocks into the food that we consume, and then the distribution of that food. So I think there's a role for AI to play across the whole value chain of agriculture. We, as humans, sort of compartmentalize this into the agricultural space. And then maybe the, the conversion of those crops into the raw materials for food production, and then the actual food production part of things, the products that we would see in the store, and then the consumption

• 40:43Speaker 02

  and the delivery and the logistics of those things. I think there's, nobody's ever been able to look at it and put their hands completely around that whole cycle, if you want to think about it that way. And I think AI gives you the opportunity to do that and sort of interrogate, where are the inefficiencies in this system? And how can we be more effective moving forward?

• 41:01Speaker 03

  And how to reimagine it? Yeah. Jamie, this was such a fascinating conversation. Thank you so much for joining us.

• 41:07Speaker 02

  Yeah, it was my pleasure.

• 41:11Speaker 03

  Jamie shared so many fascinating details on how AI is transforming one of the world's most important industries, like how they're using AI to track crop and soil health. I actually love how Jamie put it. They're helping plants live their best lives. And if you're not in the industry, I think it's easy to have an image of farming that's stuck in the past. But who knew the amount of technology powering equipment like tractors? I didn't. I also really appreciated our discussion on robots. There's a fear that humanoid robots could replace jobs. But in farming, they could take on the work that no one wants to do. And this is an incredible example of AI augmenting, not replacing humans. And if you're building in this space, I would love to hear from you.

• 41:56Speaker 03

  Find me on Instagram or LinkedIn. Thank you so much for listening. We will be back with a new episode next week.

• 42:05Speaker 07

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• 42:44Speaker 03

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• 42:49Speaker 03

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