# John Deere's AI vision for future farms

- **Show:** Pioneers of AI
- **Published:** 6 May 2026
- **Source:** https://prfx.byspotify.com/e/dts.podtrac.com/redirect.mp3/tracking.swap.fm/track/7G0M4p2bOLP0mP50O3CD/pscrb.fm/rss/p/rss.art19.com/episodes/76cbbb4a-3e2b-4eee-b197-ffb2d1d5f6cf.mp3?rss_browser=BAhJIhlwb2RjYXN0Lm9wZW5uYXNoLmNvbQY6BkVU--4dcea1dd70768d62b2466884c2f8513b5f848fbf
- **Transcript:** https://podcasts.opennash.com/shows/pioneers-of-ai/john-deeres-ai-vision-for-future-farms-1bb299/
- **Provider:** OpenNash Podcasts (machine-generated)

## In brief

John Deere's CTO, Jahmy Hindman, argues that farming is a story of ever-greater efficiency and that AI is its next stage. Cameras, GPS, rugged onboard computers and cloud data let machines treat each plant individually, spraying only weeds and eventually running without a driver. He frames this as a tool farmers choose, not a replacement for them.

The central tension is control. Deere owns the whole stack from hardware to software, which makes its products powerful but raises the right-to-repair complaint that farmers cannot update their own machines' software. Hindman answers with a phone-based update tool. A second tension is between a shrinking farm labor pool and technology that can fill the gaps, from autonomous tractors to possible humanoid harvesters.

## Key insights

1. **Deere wants every single plant managed individually** Hindman says about 4 trillion corn seeds are planted in the US each year, and Deere wants each treated where, how and when it needs. GPS precision, planting records and later yield maps make that kind of per-plant management possible.
2. **See and spray cuts herbicide by targeting weeds** A sprayer with 36 cameras and nine onboard GPUs identifies weed pixels at about 15 miles an hour and sprays only those. Hindman calls it a triple win for the farmer, the planet and Deere.
3. **Farm AI must run on rugged edge computers** Autonomous tractors use sixteen cameras and NVIDIA Orin chips hardened against shock, vibration and heat. Hindman says edge chips trail data center chips by about six years, so today's data center capability will reach the field later.
4. **Labor shortages at planting time drive autonomy** With only about 1.5% of Americans working in agriculture, and corn and soybeans planted in a two-to-three-week spring window, labor is scarce at the key moment. Autonomous tractors let farmers choose whether to sit in the cab.
5. **Right to repair is Deere catching up digitally** Hindman says Deere has long supplied mechanical parts and manuals, even for 70-to-80-year-old tractors, but software updates lagged. Its phone-based Operations Center Pro Service lets owners and independent shops push controller updates.

## Questions this episode answers

### How is John Deere using AI in its machines today?

Its tractors and sprayers use cameras and powerful onboard chips to see their surroundings. A sprayer can spot weeds and spray only those, and some tractors drive themselves in limited customer use.

### What does See and Spray actually do?

It is a sprayer with 36 cameras that scans the ground at about 15 miles an hour for weeds. It sprays herbicide only on weed pixels instead of the whole field, which Jahmy Hindman says saves chemicals and money.

### What is the right to repair issue with John Deere?

Farmers wanted to update the software on their own equipment's controllers, which previously meant buying a dealer-style tool or visiting a dealer. Hindman says Deere's newer phone-based tool lets owners and independent repair shops do it.

### Will humanoid robots replace farmworkers?

Hindman does not expect that. He says humanoids could take jobs nobody wants, like cleaning grain bins, and could harvest delicate fruit like mangoes if robot hands become dexterous enough.

### Why does farm equipment need special computer chips?

Tractor computers run in the field, not in air-conditioned data centers, so they must survive shock, vibration and extreme temperatures. Hindman says Deere hardens NVIDIA Orin chips for this.

## Facts

- **Jahmy Hindman** [0:01:48]: "there's 4 trillion corn seeds that are planted in the United States every year, give or take."
- **Jahmy Hindman** [0:05:34]: "thirty to 40% of the US. population would have been involved in agriculture, uh, directly, right? And today that number is like one and a half percent."
- **Jahmy Hindman** [0:07:05]: "John Deere's claim to fame is he fashioned, uh, the first self-scouring steel plow, and we're we're proud of that product, obviously."
- **Jahmy Hindman** [0:07:34]: "John Deere actually didn't, uh, develop the tractor or start the tractor. That was a a company we bought, uh, the Waterloo Gas Engine Company."
- **Jahmy Hindman** [0:08:58]: "my graduate work was focused in a an area called artificial neural networks, uh, of all things fifteen years ago, right?"
- **Jahmy Hindman** [0:10:32]: "precisely putting that seed in the ground at exactly thirty inch rows, time after time after time after time, uh, is a core component of agricultural efficiency."
- **Jahmy Hindman** [0:13:00]: "there's a company called InnerPlant, uh, that we've partnered with that is genetically modifying plants to allow them to fluoresce at a certain wavelength"
- **Jahmy Hindman** [0:17:43]: "we've had full autonomy on, uh, those tractor products in limited applications with customers over the last four years."
- **Jahmy Hindman** [0:18:10]: "sixteen cameras, overlapping fields of view, uh, we do frame by frame calibration so that we get depth from that that sensor array as well."
- **Jahmy Hindman** [0:18:10]: "we run embedded GPUs, NVIDIA, uh, uh, orin, uh, platform GPUs on that on that product."
- **Jahmy Hindman** [0:19:20]: "that machine has a boom that will spread out to 120 feet. Uh, it will travel at about fifteen miles an hour"
- **Jahmy Hindman** [0:19:20]: "we put thirty six cameras across that boom, uh, we put nine, uh, embedded GPUs on that product"
- **Jahmy Hindman** [0:20:40]: "we partner with Starlink to put low Earth orbit, uh, satellite terminals on product"
- **Jahmy Hindman** [0:21:07]: "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."
- **Jahmy Hindman** [0:21:52]: "John Deere Operations Center. Um, it's it's our digital offering to customers"
- **Jahmy Hindman** [0:24:38]: "It trails roughly six years, the data center compute in terms of, um, in terms of, uh, operations that you can you can run on a given unit of power."
- **Jahmy Hindman** [0:26:35]: "how many of you, uh, 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."
- **Jahmy Hindman** [0:29:46]: "last July, in response to that, we came out with, um, Operations Pro, Operations Center Pro Service is what the trade name is."
- **Jahmy Hindman** [0:30:53]: "Today, there are tractors that are seventy or eighty years old that we still provide service parts for."
- **Jahmy Hindman** [0:31:33]: "operations center pro service is also available to independent repair shops."

## Transcript

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**[0:01:48] Jahmy Hindman:** 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. 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.

**[0:02:24] Rana el Kaliouby:** Are there gonna be like teams of humanoid robots on the field and they'll, like, have straw hats and be wearing overalls or something?

**[0:02:32] Jahmy Hindman:** I like the idea of humanoids in agriculture for a whole host of reasons. There are some jobs that no humans want to do.

**[0:02:40] Rana el Kaliouby:** How do you see the role of AI in feeding the planet, even beyond what it can do for farming?

**[0:02:47] Jahmy Hindman:** 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.

**[0:02:54] Rana el Kaliouby:** That's Jahmy Hindman, CTO of John Deere. The almost 200 year old agriculture super company 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 Jahmy 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.

**[0:03:27] Rana el Kaliouby:** 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. What does a fully autonomous mango farm look like? It's fascinating stuff, so let's get to it. I'm Rana El Kalioubi, and this is Pioneers of AI, a podcast taking you behind the scenes of the AI revolution. Hi, Jahmy, welcome to Pioneers of AI. I'm so excited to have you on the show.

**[0:04:03] Jahmy Hindman:** Ryan, it's great to see you. Thank you for having me on the show.

**[0:04:07] Rana el Kaliouby:** All right, so you're the CTO, Chief Technology Officer of John Deere.

**[0:04:10] Jahmy Hindman:** That's correct.

**[0:04:10] Rana el Kaliouby:** And um, 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 wanna 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.

**[0:04:32] Jahmy Hindman:** Yeah, and a story that's pretty common in the upper Midwest, uh, a lot of people can trace their roots to agriculture in some way, shape, or form. Uh, and so, um, my my grandfather would have farmed a a little farm in southwest Iowa. And then 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, uh, and farms just, uh, that that farm got consolidated into other farms.

**[0:05:01] Rana el Kaliouby:** hm. How has that upbringing shaped what you do today and also kind of your perspective on the whole business?

**[0:05:09] Jahmy Hindman:** Yeah, well, I've been around agriculture for a long time, my whole life. Um, maybe not directly involved in it at some periods of my life, but but always around it. Uh, and I'm a technologist at heart. Like I, I grew up with a a engineering professor as a father, so I was around technology my whole life. And I think those two things go together. Uh, agriculture is a, uh, an application that that begs for efficiency improvements, right?

**[0:05:34] Jahmy Hindman:** We, um, if you were to rewind the clock fifty years ago, uh, roughly uh, thirty to 40% of the US. population would have been involved in agriculture, uh, directly, right? And today that number is like one and a half percent. And if you think about that, it's enabled, uh, people like you and I to do the things that we do. We no longer have to worry about how to get our food. We go to the grocery store and it's there for us, uh, but that's done on the the the parts of one and a half percent of the whole population, right?

**[0:06:04] Jahmy Hindman:** Uh, 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.

**[0:06:13] Rana el Kaliouby:** Yeah, amazing. So you started your career at John Deere in 1996. That's like thirty years ago.

**[0:06:19] Jahmy Hindman:** Yeah, it makes me sound terribly old, but yes,

**[0:06:22] Rana el Kaliouby:** As a 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.

**[0:06:35] Jahmy Hindman:** we're 180 nine-year-old company, uh, which we're 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, uh, John Deere, the man himself, uh, who, uh, lived in Vermont, but, uh, moved to the Midwest, moved to the state of Illinois, uh, as the the the country was being built. And he was a blacksmith, and, uh, the f- the plows of the day were largely wooden plows and sometimes cast iron plows, and soil was always sticking to these things.

**[0:07:05] Jahmy Hindman:** And the farmers had to, you know, stop and clean the plow off every, you know, ten meters or so. Uh, and so John Deere's claim to fame is he fashioned, uh, the first self-scouring steel plow, and we're we're proud of that product, obviously. Uh, but one of our more pivotal moments was in the early nineteen hundreds when this, uh, 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.

**[0:07:34] Jahmy Hindman:** And, um, John Deere actually didn't, uh, develop the tractor or start the tractor. That was a a company we bought, uh, the Waterloo Gas Engine Company. Uh, 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?

**[0:07:54] Rana el Kaliouby:** Talk about the inventor's dilemma.

**[0:07:56] Jahmy Hindman:** 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 gonna disrupt your core business or do you not? And it's sort of the, you know, you can look at the models behind me. It's the ubiquitous, uh, product form for the for the the the company today. Uh, and that really started us on this path, right? This ability to take advantage of the efficiency of mechanization, uh, and have people go do other things with their creative potential that, uh, obviously, I think the the world has benefited from.

**[0:08:27] Jahmy Hindman:** We're in the middle of that, like many industries right now, with with artificial intelligence. You know, it's, uh, it's obviously got huge potential, I think, in the agricultural industry, uh, and we view it as a responsibility for our company to 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.

**[0:08:50] Rana el Kaliouby:** 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?

**[0:08:58] Jahmy Hindman:** Yeah, it's a it's a great question. I think my experience sort of falls into the category of I'd rather be lucky than good. Um, I, I'm a mechanical engineer, bachelor's, master's, and PhD. But my graduate work was focused in a an area called artificial neural networks, uh, of all things fifteen years ago, right?

**[0:09:15] Rana el Kaliouby:** I love that. That's awesome.

**[0:09:16] Jahmy Hindman:** And it was not very, uh, a compelling area of research at the time. It was challenged by, you know, lack of compute, not great data sets, um, in our in our space on the edge, the inability to access equipment through, uh, communication paths and all sorts of things were were hurdles and roadblocks that are largely removed today. Um, and so I've been in this place where I understand the mechanical side of our business, which is still really important.

**[0:09:43] Jahmy Hindman:** But we also need to to weave through those pieces of equipment, the modern technology, artificial intelligence, software, uh, that can improve the productivity of that equipment, um, in a way that the mechanical function of that equipment, uh, no longer can.

**[0:09:58] Rana el Kaliouby:** 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?

**[0:10:14] Jahmy Hindman:** Yep, sure, you know, it's really a handful of things that are critical and that we build, uh, the rest of the technology on. Those things are, uh, the ability to locate the piece of equipment on the surface of the planet. We talked about that, the the, uh, GPS receiver and our own GNSS.

**[0:10:30] Rana el Kaliouby:** Why is that so important?

**[0:10:32] Jahmy Hindman:** In agriculture, it is, um, it's important for a couple reasons. One, uh, plants live their best life, uh, when they get the opportunity to equally compete with one another. And so that makes, uh, it interesting for us to create the same row spacing, right? So precisely putting that seed in the ground at exactly thirty inch rows, time after time after time after time, uh, is a core component of agricultural efficiency. Uh, in addition to that, we don't like to overlap things like, so if you're putting seed in the ground, you don't wanna have, uh, seed on top of seed, right?

**[0:11:05] Jahmy Hindman:** And so it gives you the ability to know where you're at. If you imagine you're in a a thousand-acre field, this, um, this ability to know where your sixteen rows of planters are in a thousand acres is a very challenging thing to do. 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, uh, yourself from duplicating the work in the field or skipping some of the areas of the field and not doing the work at all.

**[0:11:33] Jahmy Hindman:** So those are the some of the ways that it's important. It we think about it as this idea of plant level management. There's, um, 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, uh, the master gardener experience for every one of those seeds.

**[0:11:55] Jahmy Hindman:** We want every one of them to be treated exactly, uh, where it needs to be treated, how it needs to be treated, and when it needs to be treated to live its best life.

**[0:12:02] Rana el Kaliouby:** Um, this just occurred to me. I I don't know if the analogy makes sense, but as humans, you know, I I'm really into, like, my wearables, right? I track my sleep, I track my steps, I track my activity. Um, 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.

**[0:12:27] Rana el Kaliouby:** Do you also have, like, a picture of, like, a plant's health and wellness, right? Like, what does that look like?

**[0:12:35] Jahmy Hindman:** it's a fan I I lo- I love the analog. I mean, we're an organism, the plants are an organism like it, it fits generally. Um, we don't know as much about the plants as you know about yourself, cause you're more instrumented than the plants are, but we're on a path to instrument plants in a similar way. One of the 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.

**[0:13:00] Jahmy Hindman:** Uh, there's a company called InnerPlant, uh, that we've partnered with that is genetically modifying plants to allow them to fluoresce at a certain wavelength, uh, based upon what stress they're seeing in their in their life. So if they're seeing, uh, in the case of soybeans, um, uh, a stress due to a fungus attacking the plant, they fluoresce in one in one wavelength. Uh, if it were, uh, nitrogen deficiency, they would fluoresce in a different wavelength. If it was water deficiency, they would fluoresce in a different wavelength.

**[0:13:28] Jahmy Hindman:** 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,

**[0:13:39] Rana el Kaliouby:** That is so cool. It's, um, uh, 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. for sure, I love that. 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. The companies winning right now aren't just building great AI.

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**[0:15:04] Rana el Kaliouby:** 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 kinda wanna unpack what that trifecta looks like for John Deere vehicles. And maybe we start with a specific product, the John Deere nine RX eight thirty. I don't know if it's one of the ones behind you, is it?

**[0:15:24] Jahmy Hindman:** it is very similar to that tractor right there. Yeah,

**[0:15:28] Rana el Kaliouby:** 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?

**[0:15:39] Jahmy Hindman:** yeah. A long time ago, we started putting high performance compute and sensing elements on these products, uh, that enabled them to start to collect information that was useful to the the farmer. So, in the case of that nine R, those units would be responsible for, um, collecting and communicating the information of how the planting process went. Like, how many seeds did you plant? where did you plant them? uh, what depth did you plant them into? Those sorts of things.

**[0:16:03] Jahmy Hindman:** So that a farmer can take that agronomic information and understand, okay, what did that produce in terms of germination? How many seeds, uh, germinated and and uh, are going to create, uh, you know, a a a new plant? Um, and I can then use that information and use it either in the next season to determine 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.

**[0:16:30] Jahmy Hindman:** I didn't have good germination, I need to replant those seeds. All of that data goes from the 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.

**[0:16:43] Rana el Kaliouby:** Do you use computer vision? Do you use radar, lidar? Like, what kind of sensing?

**[0:16:48] Jahmy Hindman:** so both on that machine and on that machine, uh, two different applications. We do use, uh, computer vision, uh, on the tractor. There's a 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, um, most of the production systems we're involved in, you need all of the labor at a very specific point of the year, right?

**[0:17:18] Jahmy Hindman:** We plant all of the corn and soybeans, for example, in the 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. So labor's a a challenge. Uh, we've already got, I told you, hands-free guidance on these pieces of equipment, but we haven't replicated, uh, the greatest sensor of all, which is the human right in the cab of the machine.

**[0:17:43] Jahmy Hindman:** Uh, and so we started working in the space of full autonomy, uh, we've been working in that space probably for thirty years, but, uh, more recently, publicly, um, 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 themself in the cab of the machine if they choose to do so. Um, and so we've had full autonomy on, uh, those tractor products in limited applications with customers over the last four years.

**[0:18:10] Jahmy Hindman:** Uh, and uh, that, uh, sensor, uh, sensor modality is primarily a camera array, uh, around the the the top of the the the operator station of that tractor. So sixteen cameras, overlapping fields of view, uh, we do frame by frame calibration so that we get depth from that that sensor array as well. Uh, the the compute to do that is, um, not a trivial task. Uh, so we we run embedded GPUs, NVIDIA, uh, uh, orin, uh, platform GPUs on that on that product.

**[0:18:43] Jahmy Hindman:** And importantly, in our application, they need to be hardened, they need to be able to survive shock and vibe and temperatures that traditionally, uh, these compute uh devices would not survive. So

**[0:18:53] Rana el Kaliouby:** Right? They're not sitting in an air-conditioned data center,

**[0:18:56] Jahmy Hindman:** no, they are definitely not. So so we we take, um, a lot of pride in our ability to harden, uh, some of these devices that, uh, traditionally would, to your point, not find their way into our applications. Uh, because we need them, we need the compute capability in order to do that perception problem on the machine. In the, uh, machine right below it, this is called a self-propelled sprayer. Uh, we use computer vision in a very, uh, different way.

**[0:19:20] Jahmy Hindman:** Uh, that machine has a boom that will spread out to 120 feet. Uh, it will travel at about fifteen miles an hour, uh, and traditionally it would apply, uh, herbicide, pesticide, or fungicide across every, uh, square meter of the field. Um, even if that part of the field didn't need the application, uh, because there was no way to sense whether or not the crops needed it in that particular application. So, um, we put thirty six cameras across that boom, uh, we put nine, uh, embedded GPUs on that product, uh, and we do what we call see and spray.

**[0:19:55] Jahmy Hindman:** So we sense the ground at fifteen miles an hour, and we look for pixels that contain weeds, uh, 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, uh, 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 and they're great applications of AI in agriculture.

**[0:20:21] Rana el Kaliouby:** 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?

**[0:20:40] Jahmy Hindman:** Yeah, um, so the data goes to a a cloud instance, um, but in many of these locations, it's remember they're rural locations, and so you may not have terrestrial cell, uh, even here in the US. So, uh, we partner with Starlink to put low Earth orbit, uh, satellite terminals on product to in in areas where we don't have great terrestrial cellular connectivity. But in one way, shape, or form, uh, data gets pushed into a cloud instance, and then we do a lot of different things to it.

**[0:21:07] Jahmy Hindman:** We process the data into, uh, usable pieces for the the farmer so they can look at things like, uh, a yield coverage map would be a good example. So we, because we know where every seed was planted and we know where the machine is when it's being harvested, uh, we can tell you in, uh, a fertilization 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.

**[0:21:31] Jahmy Hindman:** I refer to it as the farmer report card. It's how good did we do? Uh, 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, 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, uh, information that's necessary for them to make better farming decisions, better agronomic decisions.

**[0:21:52] Jahmy Hindman:** 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 they get that exposed to them in two different ways. We expose it in what we call John Deere Operations Center. Um, it's it's our digital offering to customers, uh, 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, uh, post-harvest, uh, or they can we expose it in, uh, a mobile environment, Android or iOS, uh, which is where sort of the the feature set that is more near-term, uh, resides so that they can understand logistics on the

**[0:22:29] Jahmy Hindman:** farm.

**[0:22:30] Rana el Kaliouby:** Yeah, how has generative AI changed any of of your products and how farmers experience the products?

**[0:22:38] Jahmy Hindman:** Yeah, the the predictive, um, side of things is, um, we we've done that for a long time. We do things like, um, 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 that the data set, uh, data sets can be used. The, um, generative AI and and I would argue transformer networks in general are interesting to us for a whole variety of reasons.

**[0:23:07] Jahmy Hindman:** I think the first is, um, agricultural data is often, uh, poorly structured, um, and so it's messy, it's complicated. But, uh, generative models have given us the ability to sort of reject the noise in the data and focus on the signal, um, which is, and and to be able to do that at, um, 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.

**[0:23:36] Jahmy Hindman:** I talked to you about the 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. Uh, what, 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, uh, 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?

**[0:24:01] Rana el Kaliouby:** Okay, so that's like, 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 gonna go back to, like, you know, the farmer's mobile phone, and the farmer's gonna say, you know what, click action A taken. Instead, you can actually have all this run on the edge, where in in the tractor, basically, where the tractor can say, Okay, I have all this data, da-da-da.

**[0:24:30] Rana el Kaliouby:** It's very likely that I need to make decision X or decision A or whatever.

**[0:24:35] Jahmy Hindman:** that's exactly right.

**[0:24:36] Rana el Kaliouby:** That's so cool. It's very interesting.

**[0:24:38] Jahmy Hindman:** 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, um, in terms of, uh, operations that you can you can run on a given unit of power.

**[0:25:08] Jahmy Hindman:** 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 gonna have that capability in your hands at the field edge. What are you gonna do with it then? Like, it's a pretty, it's it's a tantalizing intellectual, uh, thought experiment to go through. So,

**[0:25:25] Rana el Kaliouby:** 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?

**[0:25:34] Jahmy Hindman:** right, you saw Google, they're separating, right? Uh, inference is gonna 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, um, play with the compute topologies on the edge in a way that we traditionally have not been able to.

**[0:25:56] Rana el Kaliouby:** 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. Do you envision, like, I guess, um, I'm I'm envisioning farmers using just natural language, right? Like a a a kind of a a chat interface, basically, to prompt for data and insights.

**[0:26:35] Jahmy Hindman:** It's, uh, I, 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 sixteen or seventeen folks. But I started out the presentation, the conversation with how many of you, uh, 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. And then, uh, the next question was, how many of you use it regularly, like every day?

**[0:27:01] Jahmy Hindman:** 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 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.

**[0:27:24] Jahmy Hindman:** Um, and there is an opportunity for that to start to contribute to, um, making the farming practices more efficient and more effective over time.

**[0:27:33] Rana el Kaliouby:** Wow, yeah, it's so cool because, again, like, you're collecting so much data, and I imagine, you know, there's kinda set dashboards where you can see different views. But it it would be so cool to be able to interrogate that data and just like, ask, right, like, just natural questions. And

**[0:27:50] Jahmy Hindman:** e- exactly right. And 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?

**[0:27:59] Rana el Kaliouby:** yeah, so this is a very strong business model to own the entire tech stack, um, but from a farmer's perspective, it feels like a little bit of a monopoly, right? And I I I wanna talk about the right to repair, uh, 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, um, your perspective on this, and you know, what happens next.

**[0:28:25] Jahmy Hindman:** Yeah, sure, um, and maybe I'll, I'll start, I'll rewind and kind of start, you know, twenty five or thirty years ago when we started to put, um, microcontrollers on equipment. Um, this this idea that a farmer was going to want to reprogram those microcontrollers was never a thought, right? It just it wasn't twenty five or thirty years ago. And so I would say the the current state is sort of an evolution from that point. Um, in fact, we wouldn't have thought about reprogramming those controllers back then either, unless, you know, uh, something had failed and and and we needed to to load new software.

**[0:29:02] Rana el Kaliouby:** There's no on-the-air updates, right?

**[0:29:04] Jahmy Hindman:** There was no over-the-air updates, right? So, you know, we we didn't get here, um, you know, I would say intentionally, we got here as a consequence of technology changing over time and customer sentiment, uh, changing over time. And there are customers today who, uh, want the ability to up update, uh, software on their controllers. Um, that's at the core of the the argument. And, um, up until, um, we produced a a product called, uh, Customer Service Advisor that gave customers the ability to do that.

**[0:29:34] Jahmy Hindman:** They had to purchase the tool that was, uh, very similar to the dealer tool that that was used to to update controllers, uh, but there was still friction in the system. They had to go to a dealer to get that tool.

**[0:29:44] Rana el Kaliouby:** They had to pay for it too, right? Like,

**[0:29:46] Jahmy Hindman:** You had to pay for it. Yeah, th- th- there were. There were technical, uh, hurdles between here and there, and it wasn't, it wasn't very, uh, efficient is probably the right way to describe it. Uh, and so last July, in response to that, we came out with, um, Operations Pro, Operations Center Pro Service is what the trade name is. But it's effectively the ability for you to download, uh, any controller payload, uh, to your phone, uh, to your smart device, and then, uh, walk out to your tractor or your sprayer and push that payload file to the controller that you wanna update, uh, as a an owner of the piece of equipment.

**[0:30:19] Jahmy Hindman:** So we've made that, um, update process, uh, much more efficient, much more effective, and you can you can do it, uh, through your smart device. That, in a nutshell, was our response to, um, this, this idea of right to repair, um, because the the argument is mostly centered around the software space, uh, 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, uh, and service on your equipment as you want, uh, and we still do that.

**[0:30:53] Jahmy Hindman:** Today, there are tractors that are seventy or eighty years old that we still provide service parts for. And and, uh, 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, um, that customers had the ability to repair their equipment. Uh, I'd say the right to repair is is us catching up on the digital side of being able to do that.

**[0:31:19] Rana el Kaliouby:** 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 kinda thing and just fix it for a lot less money, really. Um, yeah, it, it, it's, yeah, h- what's

**[0:31:33] Jahmy Hindman:** 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, um, and they can, they can do the mechanical updates if you want them to do that. Also.

**[0:31:46] Rana el Kaliouby:** interesting? Okay, very interesting. Okay, all right. So let's, um, look forward a bit, and I, um, I wanna set the stage. I there's an alter ego version of me that, um, I I'm originally from Egypt. I love mangoes. My grandma had not a mango farm, but she had mango trees in her, in her yard. Yeah, and I I've actually visited a couple of mango farms in Cairo. Cause I'm like, Oh, someday I'll I'll build a mango farm.

**[0:32:12] Rana el Kaliouby:** I don't know, um, but it's gonna be the farm of the future. So I would love for you to describe what that future look farm looks like. I wanna pull it all together in a s to set a scene. You game to play?

**[0:32:27] Jahmy Hindman:** yeah, for sure. Let's do it.

**[0:32:28] Rana el Kaliouby:** Build build my mango farm together.

**[0:32:29] Jahmy Hindman:** Yep, we'll do it.

**[0:32:31] Rana el Kaliouby:** 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?

**[0:32:40] Jahmy Hindman:** I, I actually think there's a there's a digital assistant that's probably talking to you once you get up, uh, telling you what the weather's like on the mango fields that you have, uh, telling you what, um, you know, the the potential, uh, steps you should take that day are that that maximize your your, uh, farm productivity, uh, whether that's, you know, telling you the health of those trees, uh, the state of the trees relative to harvest, um, uh, what nutrients those trees need, um, what sort of of, uh, illness the the the trees might have.

**[0:33:16] Jahmy Hindman:** Like, all of those things probably are are being communicated to you. I I think you're not reading them, I think you're listening to them.

**[0:33:23] Rana el Kaliouby:** I'm just I have, like, a a voice AI agent that's, uh, 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 four a.m. they started themselves and just headed out.

**[0:33:37] Jahmy Hindman:** I I think the way I think about autonomy on the farm is, uh, 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, uh, you know, unprompted, um, for sure, that can be, uh, 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.

**[0:34:03] Rana el Kaliouby:** Hm, okay, cool. And then I am really fascinated by the field of humanoid robots, and and actually, we're looking at a number of, uh, robotics companies that are specifically building humanoid robots for heavy industry, like ship welding and whatnot. Are there gonna be, like, teams of humanoid robots on the field and they'll, like, have straw hats and be wearing overalls or something?

**[0:34:28] Jahmy Hindman:** 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, uh, 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. Uh, 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?

**[0:34:59] Jahmy Hindman:** Um, and what a perfect application for something like a humanoid. I also think in the mango farm, um, there there's a whole host of crops that are, uh, challenging to harvest mechanically, right? Fruits and nuts, you know, they're fruits especially are more challenging. And um, 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 the the thing that they all struggle with is the manipulation that that humans can provide.

**[0:35:32] Jahmy Hindman:** And in this, right? This is a hard thing to replicate. And I think, um, humanoids have a space in the harvesting, uh, part of agriculture, uh, if we can get the hand right, um, because there's a dexterity associated with that that is just difficult to replicate in other form factors. And especially in things like citrus and and, uh, and mangoes, and, uh, these higher value crops, berries.

**[0:35:58] Rana el Kaliouby:** the blueberries.

**[0:36:00] Jahmy Hindman:** Exactly, uh, I think there's, uh, an incredible opportunity in that space for them. Yeah,

**[0:36:04] Rana el Kaliouby:** How do you see the role of AI in feeding the planet, even beyond what it can do for farming?

**[0:36:10] Jahmy Hindman:** sure, yeah, I think, you know, the the food chain, uh, 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 and the the integration of those, uh, feedstocks into the food that that we consume, and then the distribution of that food.

**[0:36:34] Jahmy Hindman:** 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, uh, the raw materials for food production, and then the actual food production part of things. The products that we would see in the store, uh, and then the consumption and 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 wanna think about it that way.

**[0:37:04] Jahmy Hindman:** 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.

**[0:37:12] Rana el Kaliouby:** And how to reimagine it? Yeah, Jahmy, this was such a fascinating conversation. Thank you so much for joining us.

**[0:37:18] Jahmy Hindman:** Cool, yeah, it was my pleasure.

**[0:37:23] Rana el Kaliouby:** Jahmy 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 Jahmy 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.

**[0:37:53] Rana el Kaliouby:** 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. Find me on Instagram or LinkedIn. Thank you so much for listening. We will be back with a new episode next week. Pioneers of AI is a WaitWhat original production.

**[0:38:24] Rana el Kaliouby:** Our executive producer is Eve Trow. Our producer is Rachel Ishikawa. Our senior talent executive is Stephanie Stern. Mixing and mastering by Brian Pute. Video editing by Eric Purcell. Original music by Ryan Holliday. Our head of podcasts is Lital Malad. You can join the conversation across social media platforms. Just look for us at Pioneers of AI. Thanks so much for listening.