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

Transcript from https://podmenti.com/t/05d0714c6ec131ce

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Yeah.
Yeah.
If you look at the corn crop in the United States, there's four trillion corn seeds that are planted in the United States, 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.
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.
I like the idea of humanoids in agriculture for a whole host of reasons. There are some jobs that no humans want to do.
How do you see the role of AI in feeding the planet, even beyond what it can do for farming?
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.
That's Jamie Heinmann, CTO of John Deere.
The almost 200 year old agriculture supercompany is fascinating as a technology enterprise.
They're developing AI-power 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 C S 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.
What does a fully autonomous mango farm look like?
It's fascinating stuff. So let's get to it.
I'm Rana El Calyubi and this
Is Pioneers of AI, a podcast taking you behind the scenes of the AI revolution.
Hi, Jamie. Welcome to Pioneers of AI. I'm so excited to have you on the show.
Right, it's great to see you. Thank you for having me on the show.
All right. So you're the CTO, Chief Technology Officer of John Deere. Correct. 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.
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 had a
the that farm got consolidated into other farms.
How has that upbringing shaped what you do today and also
kind of your perspective on the whole business.
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 uh 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? We um if you were to rewind the clock fifty years ago,
uh roughly uh thirty to forty percent 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? 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.
Yeah, amazing.
So you started your career at John Deere in nineteen ninety six. That's like thirty years ago. Makes me sound terribly old, but yes.
As a t 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.
We're a hundred and eighty 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 ploughs of the day were largely wooden ploughs and sometimes
cast iron plows and soil was always sticking to these things and the farmers had to, you know
Stop and clean the plow off every, you know.
10 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 call 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 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?
Talk about the inventor's dilemma.
Yes.
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 know, you can look at the models behind me. It's the ubiquitous uh product form for the for the the the company today. 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. 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. 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?
Yeah, it's uh 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'm I'm a mechanical engineer, bachelor's, master's, and PhD, but
My graduate work was focused in uh an area called artificial neural networks, uh, of all things fifteen years ago, right? That's awesome. 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.
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.
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?
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
Why is that so important?
in agriculture it is um
It's important for a couple of 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 30 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 want to have
uh 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 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.
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 four 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. 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.
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. Do you also have like a picture of like
A plant's h health and wellness, right? Like
Yeah. What does that look like?
It's a fa I I love 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 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 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. Uh there's a company called Inner Plant uh that we partnered with that is genetically modifying plants to allow them to fluores 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 fluorescent in a different wavelength and
you pretty soon uh 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.
That is so cool. It's um
Uh I I spent my entire career like building emotion recognition and emotion sensing technology for humans. Right. But that would be the equivalent of like nonverbal communication for plants. That's so cool. For sure. I love that.
Coming up.
A walk through 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.
Humans will never be more intelligent than AI.
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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 830. I don't know if it's one of the ones behind you, is it? It is very similar to that tractor right there. Yeah. That one? Okay. That's great.
So this model can retail to up to like two million dollars. So
What is this tractor built to do and what kind of sensors sit on it?
Yeah. A long time ago we started putting high performance compute and sensing elements on these products.
uh that enable them to start to collect information that was useful to the the farmer. So in the case of that 9R, 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. 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. 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.
Do you use computer vision? Do you use radar, LIDAR? Like what kind of sensing? 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 one point five percent 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? 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? And the cab of the machine.
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 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.
Um and so we've had full autonomy on uh those tractor products.
and limited applications with customers over the last four years.
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 or in uh 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 uh these compute uh devices would not survive. So they're not sitting in an air conditioned data center. 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.
Uh that machine has a boom that will spread out to a hundred and twenty 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.
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. 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 sprabicide 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 and agriculture.
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 side?
Yeah. Um

goes to a 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. 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 umization 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? Uh did we produce, you know, a hundred and fifty bushels of corn on this particular piece of ground or did we produce three hundred bushels of corn on this pr 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, 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 farm.
Yeah.
How has generative AI changed?
Any of of your products and how
Farmers experience the products.
Yeah. The the predictive um side of things is um w 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 stair 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. 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. 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? And 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.
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 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.
It's very likely that
I need to make decision X or decision A or whatever. That's exactly cool. Yep.
It's very interesting.
It is only a an idea that
You can contemplate
tractably doing today because yeah.
The compute computes 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
Yeah, where 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. 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 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? 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.
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.
You envision like I guess um I'm I'm I'm envisioning farmers using just natural language, right? Like a a a kind of an a a chat interface basically to prompt
for data and insights.
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 chat GPT 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?
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.
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.
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 it would be so cool to be able to interrogate that data and just like ask right. Like just natural questions and exactly right. And and that's the reason that they're they're drawn to it is the interface, the user experiences.
It's in the description. It's very natural, right? 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.
Yeah, sure. Um and maybe I'll I'll start
I'll rewind and kinda start
Yeah, 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. There's no on the air updates, right? There was no over the air updates, right? So
Yeah, we we didn't get here um
Yeah, I would say intentionally we got here is 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.
They had to purchase the tool that was uh very similar to the dealer tool that that was used to to update controllers.
But there was still friction in the system. They had to go to a dealer to get that tool.
They had to pay for it too, right? They had to pay for it. Yeah. Is probably the right way to describe it.
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 control or 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 want to update.
uh as a an owner of a piece of equipment. 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 and service on your equipment as you want.
Uh and we still do that today.
There are tractors that are
seventy or eighty years old that we still provide service parts for 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 have 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.
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. Uh
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. Um and they can they can do the mechanical updates if you want them to do that also.
Okay.
Very interesting. Okay. All right. So let's um look forward a bit. And I um I wanna set the stage. Uh 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 garden. Yeah.
And I I've actually visited a couple of mango farms in Cairo because I'm like uh someday I'll
I'll build a mango farm. I don't know. Awesome. 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 to

in us to set a scene.
You game to play?
Yeah, for sure. Let's do it. Build my mango farm together. Yep, we'll do it. 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?
I I actually think there's a
Yeah. There's a digital assistant that's probably talking to you once you get up, uh, tell 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
W 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 that the the trees might have. 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.
I'm just I have like a the 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 AM they started themselves and just headed out.
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 that
The key thing is you don't have to be in it if you don't want to be in it.
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 shipwelding 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.
I like the idea of humanoids in agriculture for a whole host of reasons. Um 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 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 bespoked 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 in 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. Exactly.
Uh I think there's uh an incredible opportunity in that space for them.
Yeah.
How do you see the role of AI in feeding the planet?
even beyond what it can do for farming.
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 in the the integration of those
uh feed stocks into the food that 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 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 it'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 the 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. And how to reimagine it. Yeah. Jamie, this was such a fascinating conversation. Thank you so much for joining us. Yeah, it was my pleasure.
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.
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 Wait What original production.
Our executive producer is Eve Tro. Our producer is Rachel Ishikawa.
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