John Deere turned tractors into computers — what’s next?
OneOne of our themes on Decoder is that basically everything is a computer now, and farming equipment like tractors and combines are no different. My guest this week is Jahmy Hindman, chief technology officer at John Deere, the world’s biggest manufacturer of farming machinery. And I think our conversation will surprise you.
Jahmy told me that John Deere employs more software engineers than mechanical engineers now, which completely surprised me. But the entire business of farming is moving toward something called precision agriculture, which means farmers are closely tracking where seeds are planted, how well they’re growing, what those plants need, and how much they yield.
The idea, Jahmy says, is to have each plant on a massive commercial farm tended with individual care — a process which requires collecting and analyzing a massive amount of data. If you get it right, precision agriculture means farmers can be way more efficient — they can get better crop yields with less work and lower costs.
The idea, Jahmy says, is to have each plant on a massive commercial farm tended with individual care — a process which requires collecting and analyzing a massive amount of data. If you get it right, precision agriculture means farmers can be way more efficient — they can get better crop yields with less work and lower costs.
But as Decoder listeners know by now, turning everything into computers means everything has computer problems now. Like all that farming data: who owns it? Where is it processed? How do you get it off the tractors without reliable broadband networks? What format is it in? If you want to use your John Deere tractor with another farming analysis vendor, how easy is that? Is it easy enough?
And then there are the tractors themselves — unlike phones, or laptops, or even cars, tractors get used for decades. How should they get upgraded? How can they be kept secure? And most importantly, who gets to fix them when they break?
John Deere is one of the companies at the center of a nationwide reckoning over the right to repair. Right now, tech companies like Samsung and Apple and John Deere all get to determine who can repair their products and what official parts are available.
And because these things are all computers, these manufacturers can also control the software to lock out parts from other suppliers. But it’s a huge deal in the context of farming equipment, which is still extremely mechanical, often located far away from service providers and not so easy to move, and which farmers have been repairing themselves for decades. In fact, right now the prices of older, pre-computerized tractors are skyrocketing because they’re easier to repair.
Half of the states in the country are now considering right to repair laws that would require manufacturers to disable software locks and provide parts to repair shops, and a lot of it is being driven — in a bipartisan way — by the needs of farmers.
John Deere is famously a tractor company. You make a lot of equipment for farmers, for construction sites, that sort of thing. Give me the short version of what the chief technology officer at John Deere does.
[As] chief technology officer, my role is really to try to set the strategic direction from a technology perspective for the company, across both our agricultural products as well as our construction, forestry, and road-building products. It’s a cool job. I get to look out five, 10, 15, 20 years into the future and try to make sure that we’re putting into place the pieces that we need in order to have the technology solutions that are going to be important for our customers in the future.
One of the reasons I am very excited to have you on Decoder is there are a lot of computer solutions in your products. There’s hardware, software, services that I think of as sort of traditional computer company problems. Do you also oversee the portfolio of technologies that [also] make combines more efficient and tractor wheels move faster?
We’ve got a centrally-organized technology stack organization. We call it the intelligent solutions group, and its job is really to do exactly that. It’s to make sure that we’re developing technologies that can scale across the complete organization, across those combines you referenced, and the tractors and the sprayers, and the construction products, and deploy that technology as quickly as possible.
One of the things The Verge wrestles with almost every day is the question of, “What is a computer?” We wrestle with it in very small and obvious ways — we argue about whether the iPad or an Xbox is a computer. Then you can zoom all the way out: we had Jim Farley, who’s the CEO of Ford, on Decoder a couple of weeks ago, and he and I talked about how Ford’s cars are effectively rolling computers now.
Is that how you see a tractor or a combine or construction equipment — that these are gigantic computers that have big mechanical functions as well?
They absolutely are. That’s what they’ve become over time. I would call them mobile sensor suites that have computational capability, not only on-board, but to your point, off-board as well. They are continuously streaming data from whatever it is — let’s say the tractor and the planter — to the cloud. We’re doing computational work on that data in the cloud, and then serving that information, those insights, up to farmers, either on their desktop computer or on a mobile handheld device or something like that.
As much as they are doing productive work in the field, planting as an example, they are also data acquisition and computational devices.
How much of that is in-house at John Deere? How big is the team that is building your mobile apps? Is that something you outsource? Is that something you develop internally? How have you structured the company to enable this kind of work?
We do a significant amount of that work internally. It might surprise you, we have more software development engineers today within Deere than we have mechanical design engineers. That’s kind of mind-blowing for a company that’s 184 years old and has been steeped in mechanical product development, but that’s the case. We do nearly all of our own internal app development inside the four walls of Deere.
That said, our data application for customers in the ag space, for example, is the Operations Center. We do utilize third parties. There’s roughly 184 companies that have been connected to Operations Center through encrypted APIs, that are writing applications against that data for the benefit of the customers, the farmers that want to use those applications within their business.
One of the reasons we’re always debating what a computer is and isn’t is that once you describe something as a computer, you inherit a bunch of expectations about how computers work. You inherit a bunch of problems about how computers work and don’t work. You inherit a bunch of control; API access is a way of exercising control over an ecosystem or an economy.
Have you shifted the way that John Deere thinks about its products? As new abilities are created because you have computerized so much of a tractor, you also increase your responsibility, because you have a bunch more control.
There’s no doubt. We’re having to think about things like security of data, as an example, that previously, 30 years ago, was not necessarily a topic of conversation. We didn’t have competency in it. We’ve had to become competent in areas like that because of exactly the point you’re making, that the product has become more computer-like than conventional tractor-like over time.
That leads to huge questions. You mentioned security. Looking at some of your recent numbers, you have a very big business in China. Thirty years ago, you would export a tractor to China and that’s the end of that conversation. Now, there’s a huge conversation about cybersecurity, data sharing with companies in China, down the line, a set of very complicated issues for a tractor company that 30 years ago wouldn’t have any of those problems. How do you balance all those out?
It’s a different set of problems for sure, and more complicated for geopolitical reasons in the case of China, as you mentioned. Let’s take security as an example. We have gone through the change that many technology companies have had to go through in the space of security, where it’s no longer bolted on at the end, it’s built in from the ground up. So it’s the security-by-design approach. We’ve got folks embedded in development organizations across the company that do nothing every day, other than get up and think about how to make the product more secure, make the datasets more secure, make sure that the data is being used for its intended purposes and only those.
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That’s a new skill. That’s a skill that we didn’t have in the organization 20 years ago that we’ve had to create and hire the necessary talent in order to develop that skill set within the company at the scale that we need to develop it at.
Go through a very basic farming season with a John Deere combine and tractor. The farmer wakes up, they say, “Okay, I’ve got a field. I’ve got to plant some seeds. We’ve got to tend to them. Eventually, we’ve got to harvest some plants.” What are the points at which data is collected, what are the points at which it’s useful, and where does the feedback loop come in?
I’m going to spin it a little bit and not start with planting.
I’m going to tell you that the next season for a farmer actually starts at harvest of the previous season, and that’s where the data thread for the next season actually starts. It starts when that combine is in the field harvesting whatever it is, corn, soybeans, cotton, whatever. And the farmer is creating, while they’re running the combine through the field, a dataset that we call a yield map. It is geospatially referenced. These combines are running through the field on satellite guidance. We know where they’re at at any point in time, latitude, longitude, and we know how much they’re harvesting at that point in time.
So we create this three-dimensional map that is the yield across whatever field they happen to be in. That data is the inception for a winter’s worth of work, in the Northern hemisphere, that a farmer goes through to assess their yield and understand what changes they should make in the next season that might optimize that yield even further.
They might have areas within the field that they go into and know they need to change seeding density, or they need to change crop type, or they need to change how much nutrients they provide in the next season. And all of those decisions are going through their head because they [have] to seed in December, they have to order their nutrients in late winter. They’re making those plans based upon that initial dataset of harvest information.
And then they get into the field in the spring, to your point, with a tractor and a planter, and that tractor and planter are taking the prescription that the farmer developed with the yield data that they took from the previous harvest. They’re using that prescription to apply changes to that field in real time as they’re going through the field, with the existing data from the yield map and the data in real time that they’re collecting with the tractor to modify things like seeding rate, and fertilizer rate and all of those things in order to make sure that they’re minimizing the inputs to the operation while at the same time working to maximize the output.
That data is then going into the cloud, and they’re referencing it. For example, that track the tractor and the planter took through the field is being used to inform the sprayer. When the sprayer goes into the field after emergence, when the crops come out of the ground, it’s being used to inform that sprayer what the optimal path is to drive through the field in order to spray only what needs to be sprayed and no more, to damage the crop the least amount possible, all in an effort to optimize that productivity at the end of the year, to make that yield map that is [a] report card at the end of the year for the farmer, to make that turn out to have a better grade.
That’s a lot of data. Who collects it? Is John Deere collecting it? Can I hire a third-party SaaS software company to manage that data for me? How does that part work?
A significant amount of that data is collected on the fly while the machines are in the field, and it’s collected, in the case of Deere machines, by Deere equipment running through the field. There are other companies that create the data, and they can be imported into things like the Deere Operations Center so that you have the data from whatever source that you wanted to collect it from. I think the important thing there is historically, it’s been more difficult to get the data off the machine, because of connectivity limitations, into a database that you can actually do something with it.
Today, the disproportionate number of machines in large agriculture are connected. They’re connected through terrestrial cell networks. They’re streaming data bi-directionally to the cloud and back from the cloud. So that data connectivity infrastructure that’s been built out over the last decade has really enabled two-way communication, and it’s taken the friction out of getting the data off of a mobile piece of equipment. So it’s happening seamlessly for that operator. And that’s a benefit, because they can act on it then in more near real time, as opposed to having to wait for somebody to upload data at some point in the future.
Whose data is this? Is it the farmer’s data? Is it John Deere’s data? Is there a terms of service agreement for a combine? How does that work?
Certainly [there is] a terms of service agreement. Our position is pretty simple. It’s the farmer’s data. They control it. So if they want to share it through an API with somebody that is a trusted adviser from their perspective, they have the right to do that. If they don’t want to share it, they don’t have to do that. It is their data to control.
Is it portable? When I say there are “computer problems” here, can my tractor deliver me, for example, an Excel file?
They certainly can export the data in form factors that are convenient for them, and they do. Spreadsheet math is still routinely done on the farm, and then [they can] utilize the spreadsheet to do some basic data analytics if they want. I would tell you, though, that what’s happening is that the amount of data that is being collected and curated and made available to them to draw insights from is so massive that while you can still use spreadsheets to manipulate some of it, it’s just not tractable in all cases. So that’s why we’re building functionality into things like the Operations Center to help do data analytics and serve up insights to growers.
It’s their data. They can choose to look at the insights or not, but we can serve those insights up to them, because the data analysis part of this problem is becoming significantly larger because the datasets are so complex and large, not to mention the fact that you’ve got more data coming in all the time. Different sensors are being applied. We can measure different things. There [are] unique pieces of information that are coming in and routinely building to overall ecosystems of data that they have at their disposal.
We’ve talked a lot about the feedback loop of data with the machinery in particular. There’s one really important component to this, which is the seeds. There are a lot of seed manufacturers out in the world. They want this data. They have GMO seeds, they can adjust the seeds to different locations. Where do they come into the mix?
The data, from our perspective, is the farmer’s data. They’re the ones who are controlling the access to it. So if they want to share their data with someone, they have that ability to do it. And they do today. They’ll share their yield map with whoever their local seed salesman is and try to optimize the seed variety for the next planting season in the spring.
So that data exists. It’s not ours, so we’re not at liberty to share it with seed companies, and we don’t. It has to come through the grower because it’s their productivity data. They’re the ones that have the opportunity to share it. We don’t.
You do have a lot of data. Maybe you can’t share it widely, but you can aggregate it. You must have a very unique view of climate change. You must see where the foodways are moving, where different kinds of crops are succeeding and failing. What is your view of climate change, given the amount of data that you’re taking in?
The reality is for us that we’re hindered in answering that question by the recency of the data. So, broad-scale data acquisition from production agriculture is really only a five- to 10-year-old phenomenon. So the datasets are getting richer. They’re getting better.
We have the opportunity to see trends in that data across the datasets that exist today, but I think it’s too early. I don’t think the data is mature enough yet for us to be able to draw any conclusions from a climate change perspective with respect to the data that we have.
The other thing that I’ll add is that the data intensity is not universal across the globe. So if you think of climate change on a global perspective, we’ve got a lot of data for North America, a fair amount of data that gets taken by growers in Europe, a little bit in South America, but it’s not rich enough across the global agricultural footprint for us to be able to make any sort of statements about how climate change is impacting it right now.
Is that something you’re interested in doing?
Yes. I couldn’t predict when, but I think that the data will eventually be rich enough for insights to be drawn from it. It’s just not there yet.
Do you think about doing a fully electric tractor? Is that in your technology roadmap, that you’ve got to get rid of these diesel engines?
You’ve got to be interested in EVs right now. And the answer is yes. Whether it’s a tractor or whether it’s some other product in our product line, alternative forms of propulsion, alternative forms of power are definitely something that we’re thinking about. We’ve done it in the past with, I would say, hybrid solutions like a diesel engine driving an electric generator, and then the rest of the machine being electrified from a propulsion perspective.
But we’re just getting to the point now where battery technology, lithium-ion technology, is power-dense enough for us to see it starting to creep into our portfolio. Probably from the bottom up. Lower power density applications first, before it gets into some of the very large production ag equipment that we’ve talked about today.
What’s the timeline to a fully EV combine, do you think?
I think it’ll be a long time for a combine.
I picked the biggest thing I could, basically.
It has got to run 14, 15, 16 hours per day. It’s got a very short window to run in. You can’t take all day to charge it. Those sorts of problems, they’re not insurmountable. They’re just not solved by anything that’s on the roadmap today, from a lithium-ion perspective, anyway.
You and I are talking two days after Apple had its developers’ conference. Apple famously sells hardware, software, services, as an integrated solution. Do you think of John Deere’s equipment as integrated suites of hardware, software, and services, or is it a piece of hardware that spits off data, and then maybe you can buy our services, or maybe buy somebody else’s services?
I think it’s most efficient when we think of it collectively as a system. It doesn’t have to be that way, and one of the differences I would say to an Apple comparison would be the life of the product, the iron product in our case, the tractor or the combine, is measured in decades. It may be in service for a very long time, and so we have to take that into account as we think about the technology [and] apps that we put on top of it, which have a much shorter shelf life. They’re two, three, four, five years, and then they’re obsolete, and the next best thing has come along.
We have to think about the discontinuity that occurs between product buy cycles as a consequence of that. I do think it’s most efficient to think of it all together. It isn’t always necessarily that way. There are lots of farmers that run multi-colored fleets. It’s not Deere only. So we have to be able to provide an opportunity for them to get data off of whatever their product is into the environment that best enables them to make good decisions from it.
Is that how you characterize the competition, multi-colored fleets?
Absolutely, for sure. I would love the world to be completely [John Deere] green, but it’s not quite that way.
On my way to school every day in Wisconsin growing up, I drove by a Case plant. They’re red. John Deere is famously green, Case is red, International Harvester is yellow.
Yep. Case is red, Deere is green, and then there’s a rainbow of colors outside of those two for sure.
Who are your biggest competitors? And are they adopting the same business model as you? Is this an iOS versus Android situation, or is it widely different?
Our traditional competitors in the ag space, no surprise, you mentioned one of them. Case New Holland is a great example. AGCO would be another. I think everybody’s headed down the path of precision agriculture. [It’s] the term that is ubiquitous for where the industry’s headed.
I’m going to paint a picture for you: It’s this idea of enabling each individual plant in production agriculture to be tended to by a master gardener. The master gardener is in this case probably some AI that is enabling a farmer to know exactly what that particular plant needs, when it needs it, and then our equipment provides them the capability of executing on that plan that master gardener has created for that plant on an extremely large scale.
You’re talking about, in the case of corn, for example, 50,000 plants per acre, so a master gardener taking care of 50,000 plants for every acre of corn. That’s where this is headed, and you can picture the data intensity of that. Two hundred million acres of corn ground, times 50,000 plants per acre; each one of those plants is creating data, and that’s the enormity of the scale of production agriculture when you start to get to this plant-by-plant management basis.
Let’s talk about the enormity of the data and the amount of computation — that’s in tension with how long the equipment lasts. Are you upgrading the computers and the tractors every year, or are you just trying to pull the data into your cloud where you can do the intense computation you want to do?
It’s a combination of both, I would tell you. There are components within the vehicles that do get upgraded from time to time. The displays and the servers that operate in the vehicles do go through upgrade cycles within the existing fleet.
There’s enough appetite, Nilay, for technology in agriculture that we’re also seeing older equipment get updated with new technology. So it’s not uncommon today for a customer who’s purchased a John Deere planter that might be 10 years old to want the latest technology on that planter. And instead of buying a new planter, they might buy the upgrade kit for that planter that allows them to have the latest technology on the existing planter that they own. That sort of stuff is happening all the time across the industry.
I would tell you, though, that what is maybe different now versus 10 years ago is the amount of computation that happens in the cloud, to serve up this enormity of data in bite-sized forms and in digestible pieces that actually can be acted upon for the grower. Very little of that is done on-board machines today. Most of that is done off-board.
We cover rural broadband very heavily. There’s some real-time data collection happening here, but what you’re really talking about is that at the end of a session you’ve got a big asynchronous dataset. You want to send it off somewhere, have some computation done to it, and brought back to you so you can react to it.
What is your relationship to the connectivity providers, or to the Biden administration, that is trying to roll out a broadband plan? Are you pushing to get better networks for the next generation of your products, or are you kind of happy with where things are now?
We’re pro-rural broadband, and in particular newer technologies, 5G as an example. And it’s not just for agricultural purposes, let’s just be frank. There’s a ton of benefits that accrue to a society that’s connected with a sufficient network to do things like online schooling, in particular, coming through the pandemic that we’re in the midst of, and hopefully on the tail end of here. I think that’s just highlighted the use cases for connectivity in rural locations.
Agriculture is but one of those, but there’s some really cool feature unlocks that better connectivity, both in terms of coverage and in terms of bandwidth and latency, provide in agriculture. I’ll give you an example. You think of 5G and the ability to get to incredibly low latency numbers. It allows us to do some things from a computational perspective on the edge of the network that today we don’t have the capability to do. We either do it on-board the machine, or we don’t do it at all. So things like serving up the real-time location of where a farmer’s combine is, instead of having to route that data all the way to the cloud and then back to a handheld device that the farmer might have, wouldn’t it be great if we could do that math on the edge and just ping tower to tower and serve it back down and do it really, really quickly. Those are the sorts of use cases that open up when you get to talking about not just connectivity rurally, but 5G specifically, that are pretty exciting.
Are the networks in place to do all the things you want to do?
Globally, the answer is no. Within the US and Canadian markets, coverage improves every day. There are towers that are going up every day and we are working with our terrestrial cell coverage partners across the globe to expand coverage, and they’re responding. They see, generally, the need, in particular with respect to agriculture, for rural connectivity. They understand the power that it can provide [and] the efficiency that it can derive into food production globally. So they are incentivized to do that. And they’ve been good partners in this space. That said, they recognize that there are still gaps and there’s still a lot of ground to cover, literally in some cases, with connectivity solutions in rural locations.
You mentioned your partners. The parallels to a smartphone here are strong. Do you have different chipsets for AT&T and Verizon? Can you activate your AT&T plan right from the screen in the tractor? How does that work?
AT&T is our dominant partner in North America. That is our go-to, primarily from a coverage perspective. They’re the partner that we’ve chosen that I think serves our customers the best in the most locations.
Do you get free HBO Max if you sign up?
[laughs] Unfortunately, no.
They’re putting it everywhere. You have no idea.
I look at the broadband gap everywhere. You mentioned schooling. We cover these very deep consumer needs. On the flip side, you need to run a lot of fiber to make 5G work, especially with the low latency that you’re talking about. You can’t have too many nodes in the way. Do you support millimeter wave 5G on a farm?
Yeah, it is something we’ve looked at. It’s intriguing. How you scale it is the question. I think if we could crack that nut, it would be really interesting.
Just for listeners, an example of millimeter wave if you’re unfamiliar — you’re standing on just the right street corner in New York City, you could get gigabit speeds to a phone. You cross the street, and it goes away. That does not seem tenable on a farm.
That’s right. Not all data needs to be transmitted at the same rate. Not to cover the broad acreage, but you can envision a case where potentially, when you come into range of millimeter wave, you dump a bunch of data all at once. And then when you’re out of range, you’re still collecting data and transmitting it slower perhaps. But having the ability to have millimeter wave type of bandwidth is pretty intriguing for being able to take opportunistic advantage of it when it’s available.
What’s something you want to do that the network isn’t there for you to do yet?
I think that the biggest piece is just a coverage answer from my perspective. We intentionally buffer data on the vehicle in places where we don’t have great coverage in order to wait until that machine has coverage, in order to send the data. But the reality is that means that a grower is waiting in some cases 30 minutes or an hour until the data is synced up in the cloud and something actionable has been done with it and it’s back down to them. And by that point in time, the decision has already been made. It’s not useful because it’s time sensitive. I think that’s probably the biggest gap that we have today. It’s not universal. It happens in pockets and in geographies, but where it happens, the need is real. And those growers don’t benefit as much as growers that do have areas of good coverage.
Is that improvement going as fast as you’d like? Is that a place where you’re saying to the Biden administration, whoever it might be, “Hey, we’re missing out on opportunities because there aren’t the networks we need to go faster.”
It is not going as fast as we would like, full stop. We should be moving faster in that space. Just to tease the thought out a little bit, maybe it’s not just terrestrial cell. Maybe it’s Starlink, maybe it’s a satellite-based type of infrastructure that provides that coverage for us in the future. But it’s certainly not moving at a pace that’s rapid enough for us, given the appetite for data that growers have and what they’ve seen as an ability for that data to significantly optimize their operations.
Have you talked to the Starlink folks?
We have.It’s super interesting. It’s an intriguing idea. The question for us is a mobile one. All of our devices are mobile. Tractors are driving around a field, combines are driving around a field. You get into questions around, what does the receiver need to look like in order to make that work? It’s an interesting idea at this point. I’m ever the optimist, glass-half-full sort of person. I think it’s conceivable that in the not too distant future, that could be a very viable option for some of these locations that are underserved with terrestrial connectivity today.
Walk me through the pricing model of a tractor. These things are very expensive. They’re hundreds of thousands of dollars. What is the recurring cost for an AT&T plan necessary to run that tractor? What is the recurring cost for your data services that you provide? How does that all break down?
Our data services are free today, interestingly enough. Free in the sense [of] the hosting of the data in the cloud and the serving up of that data through Operations Center. If you buy a piece of connected Deere equipment, that service is part of your purchase. I’ll just put it that way.
The recurring expense on the consumer side of things for the connectivity is not unlike what you would experience for a cell phone plan. It’s pretty similar. The difference is for large growers, it’s not just a single cell phone.
They might have 10, 15, 20 devices that are all connected. So we do what we can to make sure that the overhead associated with all of those different connected devices is minimized, but it’s not unlike what you’d experience with an iPhone or an Android device.
Do you have large growers in pockets where the connectivity is just so bad, they’ve had to resort to other means?
We have a multitude of ways of getting data off of mobile equipment. Cell is but one. We’re also able to take it off with Wi-Fi, if you can find a hotspot that you can connect to. Growers also routinely use a USB stick, when all else fails, that works regardless. So we make it possible no matter what their connectivity situation is to get the data off.
But to the point we already talked about, the less friction you’ve got in that system to get the data off, the more data you end up pushing. The more data you push, the more insights you can generate. The more insights you generate, the more optimal your operation is. So to the extent that you don’t have cell connectivity, we do see the intensity of the data usage, it tracks with connectivity.
So if your cloud services are free with the purchase of a connected tractor, is that built into the price or the lease agreement of the tractor for you on your P&L? You’re just saying, “We’re giving this away for free, but baking it into the price.”
Can you buy a tractor without that stuff for cheaper?
You can buy products that aren’t connected that do not have a telematics gateway or the cell connection, absolutely. It is uncommon, especially in large ag. I would hesitate to throw a number at you at what the take rate is, but it’s standard equipment in all of our large agricultural products. That said, you can still get it without that if you need to.
How long until these products just don’t have steering wheels and seats and Sirius radios in them? How long until you have a fully autonomous farm?
I love that question. [With] a fully autonomous farm, you’ve got to draw some boundaries around it in order to make it digestible. I think we could have fully autonomous tractors in low single digit years. I’ll leave it a little bit gray just to let the mind wander a little bit.
Taking the cab completely off the tractor, I think, is a ways away, only because the tractor gets used for lots of things that it may not be programmed for, from an autonomous perspective, to do. It’s sort of a Swiss Army knife in a farm environment. But that operatorless operation in, say, fall tillage or spring planting, we’re right on the doorstep of that. We’re knocking on the door of being able to do it.
It’s due to some really interesting technology that’s come together all in one place at one time. It’s the confluence of high capability-compute onboard machines. So we’re putting GPUs on machines today to do vision processing that would blow your mind. Nvidia GPUs are not just for the gaming community or the autonomous car community. They’re happening on tractors and sprayers and things too. So that’s one stream of technology that’s coming together with advanced algorithms. Machine learning, reinforcement learning, convolutional neural networks, all of that going into being able to mimic the human sight capability from a mechanical and computational perspective. That’s come together to give us the ability to start seriously considering taking an operator out of the cab of the tractor.
One of the things that is different, though, for agriculture versus maybe the on-highway autonomous cars, is that tractors don’t just go from point A to point B. Their mission in life is not just to transport. It’s to do productive work. They’re pulling a tillage tool behind them or pulling a planter behind them planting seed. So we not only have to be able to automate the driving of the tractor, but we have to automate the function that it’s doing as well, and make sure that it’s doing a great job of doing the tillage operation that normally the farmer would be observing in the cab of the tractor. Now we have to do that and be able to ascertain whether or not that job quality that’s happening as a consequence of the tractor going through the field is meeting the requirements or not.
What’s the challenge there?
I think it’s the variety of jobs. In this case, let’s take the tractor example again — it’s not only is it doing the tillage right with this particular tillage tool, but a farmer might use three or four different tillage tools in their operation. They all have different use cases. They all require different artificial intelligence models to be trained and to be validated. So scaling out across all of those different conceivable operations, I think is the biggest challenge.
You mentioned GPUs. GPUs are hard to get right now.
It’s impacting us. Weekly, I’m in conversations with semiconductor manufacturers trying to get the parts that we need. It is an ongoing battle. We had thought probably six or seven months ago, like everybody else, that it would be relatively short-term. But I think we’re into this for the next 12 to 18 months. I think we’ll come out of it as capacity comes online, but it’s going to take a little while before that happens.
I’ve talked to a few people about the chip shortage now. The best consensus I’ve gotten is that the problem isn’t at the state of the art. The problem is with older process nodes — five or 10-year-old technology. Is that where the problem is for you as well or are you thinking about moving beyond that?
It’s most acute with older tech. So we’ve got 16-bit chipsets that we’re still working with on legacy controllers that are a pain point. But that said, we’ve also got some really recent, modern stuff that is also a pain point. I was where your head is at three months ago. And then in the three months since, we’ve felt the pain everywhere.
When you say 18 months from now, is that you think there’s going to be more supply or you think the demand is going to tail off?
Supply is certainly coming online. [The] semiconductor industry is doing the right thing. They’re trying to bring capacity online to meet the demand. I would argue it’s just a classic bullwhip effect that’s happened in the marketplace. So I think that will happen. I think there’s certainly some behavior in the industry at the moment around what the demand side is. That’s made it hard for semiconductor manufacturers to understand what real demand is because there’s a panic situation in some respects in the marketplace at the moment.
That said, I think it’s clear there’s only one direction that semiconductor volume is going, and it’s going up. Everything is going to demand it moving forward and demand more of it. So I think once we work through the next 12 to 18 months and work through this sort of immediate and near-term issue, the semiconductor industry is going to have a better handle on things, but capacity has to go up in order to meet the demand. There’s no doubt about it. A lot of that demand is real.
Are you thinking, “Man, I have these 16-bit systems. We should rearchitect things to be more modular, to be more modern, and faster,” or are you saying, “Supply will catch up”?
No, very much the former. I would say two things. One, more prevalent in supply for sure. And then the second one is, easier to change when we need to change. There’s some tech debt that we’re continuing to battle against and pay off over time. And it’s times like these when it rises to the surface and you wish you’d made decisions a little bit differently 10 years ago or five years ago.
My father-in-law, my wife’s cousins, are all farmers up and down. A lot of John Deere hats in my family. I texted them all and asked what they wanted to know. All of them came back and said “right to repair” down the line. Every single one of them. That’s what they asked me to ask you about.
I set up this whole conversation to talk about these things as computers. We understand the problems of computers. It is notable to me that John Deere and Apple had the same effective position on right to repair, which is, we would prefer if you didn’t do it and you let us do it. But there’s a lot of pushback. There are right-to-repair bills in an ever-growing number of states. How do you see that playing out right now? People want to repair their tractors. It is getting harder and harder to do it because they’re computers and you control the parts.
It’s a complex topic, first and foremost. I think the first thing I would tell you is that we have and remain committed to enabling customers to repair the products that they buy. The reality is that 98 percent of the repairs that customers want to do on John Deere products today, they can do. There’s nothing that prohibits them from doing them. Their wrenches are the same size as our wrenches. That all works. If somebody wants to go repair a diesel engine in a tractor, they can tear it down and fix it. We make the service manuals available. We make the parts available, we make the how-to available for them to tear it down to the ground and build it back up again.
That is not really what I’ve heard. I hear that a sensor goes off, the tractor goes into what people call “limp mode.” They have to bring it into a service center. They need a John Deere-certified laptop to pull the codes and actually do that work.
The diagnostic trouble codes are pushed out onto the display. The customer can see what those diagnostic trouble codes are. They may not understand or be able to connect what that sensor issue is with a root cause. There may be an underlying root cause that’s not immediately obvious to the customer based upon the fault code, but the fault code information is there. There is expertise that exists within the John Deere dealer environment, because they’ve seen those issues over time that allows them to understand what the probable cause is for that particular issue. That said, anybody can go buy the sensor. Anybody can go replace it. That’s just a reality.
There is, though, this 2 percent-ish of the repairs that occur on equipment today [that] involve software. And to your point, they’re computer environments that are driving around on wheels. So there is a software component to them. Where we differ with the right-to-repair folks is that software, in many cases, it’s regulated. So let’s take the diesel engine example. We are required, because it’s a regulated emissions environment, to make sure that diesel engine performs at a certain emission output, nitrous oxide, particulate matter, etc., and so on. Modifying software changes that. It changes the output characteristics of the emissions of the engine and that’s a regulated device. So we’re pretty sensitive to changes that would impact that. And disproportionately, those are software changes. Like going in and changing governor gain scheduling, for example, on a diesel engine would have a negative consequence on the emissions that [an] engine produces.
The same argument would apply in brake-by-wire and steer-by-wire. Do you really want a tractor going down the road with software on it that has been modified for steering or modified for braking in some way that might have a consequence that nobody thought of? We know the rigorous nature of testing that we go through in order to push software out into a production landscape. We want to make sure that that product is as safe and reliable and performs to the intended expectations of the regulatory environment that we operate in.
But people are doing it anyway. That’s the real issue here. Again, these are computer problems. This is what I hear from Apple about repairing your own iPhone. Here’s the device with all your data on it that’s on the network. Do you really want to run unsupported software on it? The valence of the debate feels the same to me.
At the same time though, is it their tractor or is it your tractor? Shouldn’t I be allowed to run whatever software I want on my computer?
I think the difference with the Apple argument is that the iPhone isn’t driving down the road at 20 miles an hour with oncoming traffic coming at it. There’s a seriousness of the change that you could make to a product. These things are large. They cost a lot of money. It’s a 40,000-pound tractor going down the road at 20 miles an hour. Do you really want to expose untested, unplanned, unknown introductions of software into a product like that that’s out in the public landscape?
But they were doing it mechanically before. Making it computerized allows you to control that behavior in a way that you cannot on a purely mechanical tractor. I know there are a lot of farmers who did dumb stuff with their mechanical tractors and that was just part of the ecosystem.
Sure. I grew up on one of those. I think the difference there is that the system is so much more complicated today, in part because of software, that it’s not always evident immediately if I make a change here, what it’s going to produce over there. When it was all mechanical, I knew, if I changed the size of the tires or the steering linkage geometry, what was going to happen. I could physically see it and the system was self-contained because it was a mechanical-only system.
I think when we’re talking about a modern piece of equipment and the complexity of the system, it’s a ripple effect. You don’t know what a change that you make over here is going to impact over there any longer. It’s not intuitively obvious to somebody who would make a change in isolation to software, for example, over here. It is a tremendously complex problem. It’s one that we’ve got a tremendously large organization that’s responsible for understanding that complete system and making sure that when the product is produced, that it is reliable and it is safe and it does meet emissions and all of those things.
I look at some of the coverage and there are farmers who are downloading software of unknown provenance that can hack around some of the restrictions. Some of that software appears to be coming from groups in the Ukraine. They’re now using other software to get around the restrictions that, in some cases, could make it even worse, and lead to other unintended consequences, whereas providing the opportunities or making that more official might actually solve some of those problems in a more straightforward way.
I think we’ve taken steps to try to help. One of those is customer service. Service Advisor is the John Deere software that a dealership would use in order to diagnose and troubleshoot equipment. We’ve made available the customer version of Service Advisor as well in order to provide some of the ability for them to have insights — to your point about fault codes before — insights into what are those issues, and what can I learn about them as a customer? How might I go about fixing them? There have been efforts underway in order to try to bridge some of that gap to the extent possible.
We are, though, not in a position where we would ever condone or support a third-party software being put on products of ours, because we just don’t know what the consequences of that are going to be. It’s not something that we’ve tested. We don’t know what it might make the equipment do or not do. And we don’t know what the long-term impacts of that are.
I feel like a lot of people listening to the show own a car. I’ve got a pickup truck. I can go buy a device that will upload a new tune for my Ford pickup truck’s engine. Is that something you can do to a John Deere tractor?
There are third-party outfits that will do exactly that to a John Deere engine. Yep.
But can you do that yourself?
I suspect if you had the right technical knowledge, you could probably figure out a way to do it yourself. If a third-party company figured it out, there is a way for a consumer to do it too.
Where’s the line? Where do you think your control of the system ends and the consumer’s begins? I ask that because I think that might be the most important question in computing right now, just broadly across every kind of computer in our lives. At some point, the manufacturer is like, “I’m still right here with you and I’m putting a line in front of you.” Where’s your line?
We talked about the corner cases, the use cases I think that for us are the lines. They’re around the regulated environment from an emissions perspective. We’ve got a responsibility when we sell a piece of equipment to make sure that it’s meeting the regulatory environment that we sold it into. And then I think the other one is in and around safety, critical systems, things that they can impact others in the environment that, again, in a regulated fashion, we have a responsibility to produce a product that meets the requirements that the regulatory environment requires.
Not only that, but I think there’s a societal responsibility, frankly, that we make sure that the product is as safe as it can be for as long as it can be in operation. And those are where I think we spend a lot of time talking about what amounts to a very small part of the repair of a product. The statistics are real: 98 percent of the repairs that happen on a product can be done by a customer today. So we’re talking about a very small number of them, but they tend to be around those sort of sensitive use cases, regulatory and safety.
Right to Repair legislation is very bipartisan. You’re talking about big commercial operations in a lot of states. It’s America. It’s apple pie and corn farmers. They have a lot of political weight and they’re able to make a very bipartisan push, which is pretty rare in this country right now. Is that a signal you see as, “Oh man, if we don’t get this right, the government is coming for our products?”
I think the government’s certainly one voice in this, and it’s stemming from feedback from some customers. Obviously you’ve done your own bit of work across the farmers in your family. So it is a topic that is being discussed for sure. And we’re all in favor of that discussion, by the way. I think that what we want to make sure of is that it’s an objective discussion. There are ramifications across all dimensions of this. We want to make sure that those are well understood, because it’s such an important topic and has significant enough consequences, so we want to make sure we get it right. The unintended consequences of this are not small. They will impact the industry, some of them in a negative way. And so we just want to make sure that the discussion is objective.
The other signal I’d ask you about is that prices of pre-computer tractors are skyrocketing. Maybe you see that a different way, but I’m looking at some coverage that says old tractors, pre-1990 tractors, are selling for double what they were a year or two ago. There are incredible price hikes on these old tractors. And that the demand is there because people don’t want computers in their tractors. Is that a market signal to you, that you should change the way your products work? Or are you saying, “Well, eventually those tractors will die and you won’t have a choice except to buy one of the new products”?
I think the benefits that accrue from technology are significant enough for consumers. We see this happening with the consumer vote by dollar, by what they purchase. Consumers are continuing to purchase higher levels of technology as we go on. So while yes, the demand for older tractors has gone up, in part it’s because the demand for tractors has gone up completely. Our own technology solutions, we’ve seen upticks in take rates year over year over year over year. So if people were averse to technology, I don’t think you’d see that. At some point we have to recognize that the benefits that technology brings outweigh the downsides of the technology. I think that’s just this part of the technology adoption curve that we’re all on.
That’s the same conversation around smartphones. I get it with smartphones. Everyone has them in their pocket. They collect all this personal data. You may want a gatekeeper there because you don’t have a sophisticated user base.
Your customers are very self-interested, commercial customers.
Do you think you have a different kind of responsibility than, I don’t know, the Xbox Live team has to the Xbox Live community? In terms of data, in terms of control, in terms of relinquishing control of the product once it’s sold.
It certainly is a different market. It’s a different customer base. It’s a different clientele. To your point, they are dependent upon the product for their livelihood. So we do everything we can to make sure that product is reliable. It produces when it needs to produce in order to make sure that their businesses are productive and sustainable. I do think the biggest difference from the consumer market that you referenced to our market is the technology life cycle that we’re on.
You brought up tractors that are 20 years old that don’t have a ton of computers on-board versus what we have today. But what we have today is significantly more efficient than what we had 20 years ago. The tractors that you referenced are still in the market. People are still using them. They’re still putting them to work, productive work. In fact, on my family farm, they’re still being used for productive work. And I think that’s what’s different between the consumer market and the ag market. We don’t have a disposable product. You don’t just pick it up and throw it away. We have to be able to plan for that technology use across decades as opposed to maybe single-digit years.
In terms of the benefits of technology and selling that through, one of the other questions I got from the folks in my family was about the next thing that technology can enable. It seems like the equipment can’t physically get much bigger. The next thing to tackle is speed — making things faster for increased productivity.
Is that how you think about selling the benefits of technology — now the combine is as big as it can be, and it’s efficient at this massive scale. Is the next step to make it more efficient in terms of speed?
You’ve seen the industry trend that way. You look at planting as a great example. Ten years ago, we planted at three miles an hour. Today, we plant at 10 miles an hour. And what enabled that was technology. It was electric motors on row units that can react really, really quickly, that are highly controllable and can place seed really, really accurately, right? I think that’s the trend. Wisconsin’s a great place to talk about it. Whether it’s a row crop farm, there’s a small window in the spring, a couple of weeks, where it’s optimal to get those crops in the ground. And so it’s an insurance policy to be able to go faster because the weather may not be great for both of those weeks that you’ve got that are optimal planning weeks. And so you may only have three days or four days in that 10-day window in order to plant all your crops.
And speed is one way to make sure that that happens. Size and the width of the machine is the other. I would agree that we’ve gotten to the point where there’s very little opportunity left in going bigger, and so going faster and, I would argue, going more intelligently, is the way that you improve productivity in the future.
So we’ve talked about a huge set of responsibilities, everything from the physical mechanical design of the machinery to building cloud services, to geopolitics. What is your decision-making process? What’s your framework for how you make decisions?
I think at the root of it, we try to drive everything back to a customer and what we can do to make that customer more productive and more sustainable. And that helps us triage. Of all the great ideas that are out there, all the things that we could work on, what are the things that can move the needle for a customer in their operation as much as possible? And I think that grounding in the customer and the customer’s business is important because, fundamentally, our business is dependent upon the farmer’s business. If the farmer does well, we do well. If the farmer doesn’t do well, we don’t do well. We’re intertwined. There’s a connection there that you can’t and shouldn’t separate.
So driving our decision-making process towards having an intimate knowledge of the customer’s business and what we can do to make their business better frames everything we do.
What’s next for John Deere? What is the short term future for precision farming? Give me a five-year prediction.
I’m super excited about what we’re calling “sense and act.” “See and spray” is the first down payment on that. It’s the ability to create, in software and through electronic and mechanical devices, the human sense of sight, and then act on it. So we’re separating, in this case, weeds from useful crop, and we’re only spraying the weeds. That reduces herbicide use within a field. It reduces the cost for the farmer, input cost into their operation. It’s a win-win-win. And it is step one in the sense-and-act trajectory or sense-and-act runway that we’re on.
There’s a lot more opportunity for us in agriculture to do more sensing and acting, and doing that in an optimal way so that we’re not painting the same picture across a complete field, but doing it more prescriptively and acting more prescriptively in areas of a field that demand different things. I think that sense-and-act type of vision is the roadmap that we’re on. There’s a ton of opportunity in there. It is technology-intensive because you’re talking sensors, you’re talking computers, and you’re talking acting with precision. All of those things require fundamental shifts in technology from where we’re at today.