In this episode, I speak with Tyler Niday, CEO at Bonsai Robotics. His case for farm autonomy extends into the machine itself: fuel, maintenance and utilization determine whether a grower can make the economics work.
Tyler takes us from an early Blue River lettuce-thinning machine with eighteen desktop computers to his work at John Deere and the specialty-crop applications Bonsai is pursuing. When we recorded this episode in Aug 2026, he said Bonsai had deployed about 70 large machines with equipment partners since the prior year. He sees room to build around the jobs and crops those partners already understand.
My favorite part of the conversation was Tyler’s sharing on where the physical world interrupts the model: birds circle a working machine, simulated farm footage acquires a T. rex, and modular robots travel to Australia. Tyler also compares 35 gallons of diesel a day with about 3 for the hybrid electric machine. He also shared practical advice to robotics founders: how do you learn from early field use while protecting customer trust?
In this episode we cover:
Blue River’s eighteen-computer lettuce-thinning machine
Camera perception in dusty Australian orchards
John Deere, specialty crops and machines designed around autonomy
World models, synthetic training data and cattle egrets
Air freight, modular hardware and hybrid power
Utilization, fuel consumption and cost per acre
Field learning, product readiness and customer trust
Tyler describes the demo as “just 1% of the business.” His advice puts reliability, product-market fit and early paid field experience in the same discussion.
Tyler Niday: https://www.linkedin.com/in/tyler-niday-16a67650
Bonsai Robotics: https://www.bonsairobotics.ai/
Watch on YouTube:
I sit down with founders, researchers and operators building physical AI worldwide, tracing the stack from components and supply chains to deployment. See the published episodes on The Core Matter Show page.
Recorded at Reservoir Farms, Salinas, August 19, 2026.
Chapters
00:00 The demo, the farm and the fuel bill
00:20 Blue River and 18 desktop computers
01:36 From computer vision to learned models
03:00 Dust and the Australian orchard
04:27 Cameras, lidar and working distance
05:57 Why a software company builds machines
08:06 John Deere and the specialty-crop opening
09:09 A mixed fleet of tractors and Amigas
10:12 World models and simulated farm footage
12:06 The birds that stopped the machine
14:20 Why Australia needs farm autonomy
15:36 Shipping robots to Australia
16:50 Hybrid power and the charging constraint
18:34 Utilization and cost per acre
20:20 Orchard units and the Amiga fleet
21:40 Amiga Flex, Amiga Max and the job
22:50 Field learning and customer trust
24:27 The 2031 vision for individual plants
Transcript
[00:00:00.000] Tyler Niday: The demo is just 1% of the business, right? So we can simulate the whole farm before we ever get there. It had 18 desktop computers on the back of a tractor doing lettuce thinning. Instead of burning 35 gallons of diesel a day, it burns about three.
[00:00:20.367] Michelle Sun: So take me back to Blue River.
[00:00:22.720] Tyler Niday: And then I came across Blue River, and it was the most magical ag machine I’d ever seen. It had 18 desktop computers on the back of a tractor doing lettuce thinning. Lettuce thinning is a process that happens right outside here in Salinas that historically, it was about 20 to 40 people with a hoe going through the field and thinning lettuce. The lettuce is planted at like a two-inch spacing because the germination rate of the crop is poor. And then you thin it out to grow.
[00:00:47.160] Tyler Niday: So this machine had 18 desktop computers. It was pre-AI on the back of a machine doing thinning, running at four miles an hour. It was the most beautiful machine I’d ever seen in the agriculture space. It was very, very special. Fast forward, we had a service business. AI came out. I think we were one of the first companies to use the NVIDIA TX1 chip using YOLO-based deep learning. And we built an actual green-on-green detector so we could differentiate weeds from crops. And then the acquisition happened, which was pretty special and awesome.
[00:01:15.820] Tyler Niday: And got to work with one of the best companies in agriculture, John Deere. And then got to go in and build out the autonomy team with the now CEO. We’re focusing on big, large-scale corn, cotton, and soy tractors. So really cool process. A bit crazy to put 18 desktop computers on the back of a tractor and run a service business. But it was a pretty special machine to watch work.
[00:01:36.780] Michelle Sun: You’ve really seen the pre-AI architect to today. So what did you learn in that period that you’re using building Bonsai Robotics today?
[00:01:46.980] Tyler Niday: It’s been crazy to go from early-day computer vision, right, and OpenCV algorithms to detect green versus brown to where we’re at right now with foundation models and world models. And what I learned is that you really have to look to biology to inspire how to build the system. Historically, robotics have been built in a very deterministic way. You write hundreds of thousands of lines of code. If this happens, then this happens. Now with current day and trends, you can do these much more complex tasks that are trained on data from expert operators to enable much bigger platforms.
[00:02:19.860] Tyler Niday: So the big differentiator that we’re doing now is that we’re using these foundation and world models to have one platform that can scale to all the specialty crops and beyond. So I don’t need to write a different code stack for almonds versus the lettuce outside versus the strawberry crops. We have this big platform with one model with no code changes that can go into all these diverse crops. And it works in dust. It works with occlusion. It’s not a deterministic stack. For example, if you had a time of flight sensor and you’re looking at an obstacle and there’s a limb covering it, you’re not going to be able to see through that.
[00:02:51.620] Tyler Niday: Whereas with a biology-inspired learned model, you can infer that something’s there from a trained model, so it’s a pretty exciting time.
[00:03:00.520] Michelle Sun: So we have machines like these on the field and tell me about conditions where you didn’t expect that the machine break. Is it the dust or is it random environmental factors?
[00:03:14.000] Tyler Niday: So we are a full stack robotics company for the rugged world and we started in probably some of the most adverse conditions to make a big machine learning end-to-end model work. The almond orchards of Australia. It’s removed. There’s limited cell service. You don’t have GPS connection. It’s in sandhills. And the amount of dust that’s generated is crazy. So we had to solve this problem so that we could scale to these other crops. I think some big examples are dust, right? Traditional sensors and approaches with lidar-based or stereo vision-based stacks don’t work that great in dust, right?
[00:03:49.460] Tyler Niday: You’re blinded by it. So we tried to model the system like a brain works, right? Where we can infer things and see through things ultimately. That was one big challenge. Yeah, I think our stack is really unique in that we’re a full stack. So we don’t only build our own platforms that go beyond labor. Like this machine removes labor from the equation of a grower, but additionally reduces the cost of the machine and also the operation expense. This is hybrid electric. Instead of burning 35 gallons of diesel a day, it burns about three.
[00:04:18.280] Tyler Niday: So there’s a lot of intricacies in that platform and making a full stack robotic system work that you have to have a lot of great experience to learn through ultimately.
[00:04:27.240] Michelle Sun: You mentioned about some conditions where, you know, the lidar doesn’t work. And so I noticed that you also picked using cameras. So talk to me more about that design choice.
[00:04:37.020] Tyler Niday: I think modeling robot behavior as a human brain works is really good. So we’ve collected a crazy amount of camera data, right? And we’re able to do a lot of things with that. We see the world in color and can associate a 3D environment around us from a learned model. In a lot of our applications, we don’t have to see things super far out very accurately. We’re worried about the next 30 or 50 feet, right? So lidar is a super great tool at seeing things super far out very accurately and creating a really good map.
[00:05:08.880] Tyler Niday: It’s a bit expensive. Prices up came down. But historic methods are much more deterministic. You now can do full end-to-end lidar models, which is great too. But we really wanted to build a reliable, robust solution modeled after a human that works well in these conditions, right? That can infer through dust, right? And visualize the whole world around you accurately like a human does.
[00:05:30.160] Michelle Sun: So how many cameras are there on a robot?
[00:05:33.220] Tyler Niday: Yeah, it depends on the application. This machine has four cameras on it behind us. Some of our platforms have quite a bit more depending on the size of the array. And with our bird’s-eye view model and end-to-end models, we’re able to input these cameras to create the 3D world for us to navigate through prior to feeding it into the world action model to actually go deeper and figure out how to interact with that environment.
[00:05:57.400] Michelle Sun: You mentioned that the industry is moving from iron to intelligence. And you then also operate machines as well. So how do you think about owning a hardware versus going to the model side?
[00:06:11.000] Tyler Niday: Yeah, we’re a full stack company. We build robots. We build world models and foundation models and the complete autonomy layer. Our company started just as a software platform. So we started just building the autonomy stack. We have a very intuitive app that not only enables you to control the autonomous machines, but manage your farm. You have all your operational data. This data comes not only from the autonomous machines, but we have a compute box that you could outfit the rest of your manually driven fleet with as well.
[00:06:37.580] Tyler Niday: So getting that data is critical and informing the user to be able to use, navigate and make informed decisions is a big starting point. Autonomy has to be very reliable and very robust. And we decided to start the business by scaling with our channel partners. We have Orchard Machinery Corporation and Flory that makes really good hardware so we can stay laser focused with the capital we had on the software, right? So that the app, right, the whole perception stack, the AI models and collect tons of data on these platforms.
[00:07:08.000] Tyler Niday: We’ve now deployed about 70 of those big OEM units since last year with those partners. And then what we realized was there was a huge opportunity to design platforms around autonomy versus around a human. Machines got bigger and bigger and bigger to be optimized around a human’s performance. That was the ROI you would get is a bigger machine. And they got really expensive. And if you design the machine around the autonomy system, you’re not only replacing the labor aspect to the grower or end user, but you’re also reducing the cost of the machine.
[00:07:40.900] Tyler Niday: You’re making it easier to work on. You’re reducing the amount of fuel you have to use. So there’s a much bigger savings if we built the full stack on these focus specific tasks that could get to 4,000 hours of utilization a year than just being a software player. You definitely need big tractors and horsepowers for specific things. But there’s a niche, mostly in specialty, where we believe autonomy first form factors can really scale.
[00:08:06.340] Michelle Sun: You know John Deere from the inside, right? So what does a company your size have advantage over a company like John Deere?
[00:08:13.740] Tyler Niday: You know, John Deere is a spectacular company. And I learned a lot there. And they have a bunch of very, very good people. I think one of our advantages is that we are able to focus on industries that they might not put all the attention into up front, like the specialty aspect of vineyard, strawberry [unclear], right, which you saw earlier. That’s an application that I don’t think John Deere has a ton of interest in, but it’s a big, big market for us right now.
[00:08:38.360] Tyler Niday: And there’s a huge amount of labor and replacements that we can go. Additionally, we can move very fast, right? We’re a four-year-old company. We can make decisions quickly and adapt new technology and put it in the field super, super fast. We have 60 people instead of 70,000 people. So making a decision might be a little faster. We might not always make the right decision, but we can get it out there very quickly. And I think that’s a big advantage as well.
[00:09:03.040] Michelle Sun: So more agile and focusing on specialty problems to really get on the field faster. And so we talked about how the company’s full-stack robotics, and so with both the world model going into other machines, but also you have Amiga here, your own machines on the field. Where do you see the split of the company going in, let’s say, the next three to five years?
[00:09:27.820] Tyler Niday: Yeah, so right now the split of existing OEM machines, manufacturing machines, and Amigas is about 50-50. And I see that continuing for the next three years. After three years, I’m not really sure. I think maybe we’ll go more heavy into Amigas as autonomy and implements get more advanced to be able to do more jobs on the farm. Tractors are the ultimate Swiss Army knife, right? You can do everything with it, and you’re going to need tractors to do specific things. So I think it’ll stay about 50-50.
[00:09:56.060] Tyler Niday: We need both. We need these platforms that could drastically improve the ROI of very repetitive tasks. But you also need tractors to do those specific applications. So I think it’ll be this combination of a rainbow fleet to really make the farm succeed and really reduce the bottom-line cost.
[00:10:12.000] Michelle Sun: Tell me more about the biology-inspired world model. And what kind of capability does it have right now? Like, where does it work on the farm? And where is the limitation?
[00:10:21.840] Tyler Niday: Yeah, so we use it in two aspects, right? Our world model is feeding our generative sim data. So we can actually create a field and run a platform. But we also use it to augment and train data. We have a full automated 3D labeling pipeline for our foundation model, right? And that, like, we can generate copious amounts of data for new environments or new implements very quickly. I think our world models for generation of a scene normally starts with an HD map in self-driving cars.
[00:10:49.860] Tyler Niday: Farming usually starts with a 2D map. So we have this world model platform where you can take a farm anywhere in the field from, like, a Google Maps view, And then you generate the full map of the farm, so a [unclear] boundary. You can then plan a path, and then it’ll translate that to an almost picture-perfect scene, right? So we can, like, actually see what our cameras will see, and people can’t even tell the difference. We made a joke of one of our cameras, and we had an elephant walk in front of the machine, right?
[00:11:17.920] Tyler Niday: And I thought it was obvious to people that it was fake, but so many people are like, where are you guys running, you know? And so then we made a T. rex one to just make sure that people actually got it. So we can use this to simulate edge cases, to train new data, but really generate things we’ve never seen before. So we can have the full stack run in sim, thinking that it’s actually out in the real world, Generating dust data, generating holes like negative obstacles and [unclear], and actually seeing what the machine will do.
[00:11:48.340] Tyler Niday: So we can simulate the whole farm before we ever get there. But additionally, let’s say we were going into a fully new environment, full new crop, we can generate that and actually train our models prior to even getting out there and running. So there’s two big aspects we can use it for there. It’s pretty fun.
[00:12:06.800] Michelle Sun: Tell me a story where a machine surprising you. What happened? Like, what did you change afterwards?
[00:12:13.420] Tyler Niday: Yeah, I think the bird one would be a great one. And so this has actually happened to me twice in my career in autonomous ag so far. The first day we were engaging the implement, the job being done. We were chisel plowing a field and we were digging up the ground. And when you dig up the ground, if the soil is fertile, there’s a lot of worms in the ground, right? And what likes worms? Birds. So we had this beautiful system that worked great.
[00:12:38.040] Tyler Niday: We had this amazing segmentation model. And then all the birds were up and we were moving, right? So the birds would just circle the machine. And I don’t think the machine moved more than 10 feet in a 10-hour period, right? Because we’d have all these obstacle stops. Similarly, we were recently deploying with a large grower in Australia and we’re doing slashing. Slashing is the Australian word for mowing in ag. It’s a better word. I think Australians got some good words. I like it.
[00:13:02.780] Tyler Niday: But when you slash, there’s also worms and bugs that are brought up, right? So we had this bird called the cattle egret. And these birds circled the machine and our system was detecting it. It hadn’t seen this before and was stopping. We didn’t have a lot of data historically to train on these birds, right? To make our system at 99.x9s of reliability to drive through these birds. So we used the world model to create copious amounts of data of these cattle egret birds.
[00:13:29.540] Tyler Niday: And it was all auto labeled through our 3D pipeline and then could be deployed in a very short period. We’re able to use this world model to solve these edge case problems that you wouldn’t get until you put a new implement on the machine, until you’re in a new environment and discover it. And I think that will also carry on where the world model can actually get to these applications. Right now, I think a nice parallel is humans have to have dreams to survive.
[00:13:54.280] Tyler Niday: It’s kind of weird, but it’s been proven that if you don’t dream, it’s like this off-channel learning cycle and you’ll go crazy, right? And your dreams are normally crazy edge cases, right? They’re not normally going to happen, but you wake up some days and it’s crazy. I think right now that’s where world models are at, right? You can fine-tune what you’re dreaming about. So you can get these edge cases and you need it as a function for this biology-inspired model to thrive and get past just discovering it in the field.
[00:14:20.220] Michelle Sun: You mentioned Australia quite a bit, and how did you decide to go all the way there?
[00:14:26.840] Tyler Niday: Yeah, well, A, I just like Australia and I think the people are awesome. But apart from that, I think it’s ripe for autonomy. I’ve been going to Australia for 15 years, and so we’re in Salinas right now. Salinas is pretty close to the San Francisco Bay Area. But when you drive to the Central Valley, it’s a little more remote. But generally, to get to a farm, it’s about two hours, right? When you’re in Australia, the closest farms that we work on are sometimes 10 or 12 hours from a major city, and there’s nothing in between.
[00:14:55.360] Tyler Niday: These farms are generally planted along a river, and they’re huge, right? So labor is even more scarce than in the United States. It’s really hard to find someone to operate these machines, but they’re massive operations. Some almond orchards we’re on are 5,000 or 10,000 acres. Like, we’ll start a machine up at the shop, and it’ll have to do its autonomous route to the field. It’s going to spray, and it’s over two miles of autonomous navigation. So there’s a huge need there for autonomy to keep agriculture thriving.
[00:15:23.020] Tyler Niday: And historically, Australia has been about five years ahead on the ag tech adoption curve of new technology. So people need it even more so than the United States right now. So I think it’s a prime hotspot where you can scale quickly.
[00:15:36.720] Michelle Sun: Getting these machines all the way from Salinas or in the area all the way to Australia. So how do they get there? It’s a pretty sizable parcel.
[00:15:46.720] Tyler Niday: So historically, you pack it up in a shipping container and ship it. And we learned this from our OEM partners. And it can take six weeks, right? And then you have to go through customs. However, our Amigas, we actually have air freighted there too. So the little ones we’ll put on a plane, and it’ll be there in two days, which is a bit wild. These ones are quite a bit bigger, and the cost is substantial to ship it. But we have air freighted these Amiga Maxes to Australia as well.
[00:16:09.900] Tyler Niday: You couldn’t do that with a full-size tractor. I think we’re on the upper cusp. The base platform weighs about 1,300 pounds, but pretty cool you can do this. And I think another aspect is these are modular. Like, these are powered, steerable wheels. So it’s really easy to put together. Tractors normally have hydrostatic hydraulic lines. So like, literally, there’s fluid being pumped across the tractor, right? It’s inefficient, but also a very complex system to manage, and you couldn’t assemble things there. This is a robot where it’s all electrical connections, and you can bolt it together and plug it in.
[00:16:41.620] Tyler Niday: It’s like big Lego. So it makes some of these applications doable, where you could flat pack it and get it somewhere in a quicker time than you would with a normal big machine.
[00:16:50.860] Michelle Sun: And you mentioned about the uptime as well, right? So that’s also intentional design.
[00:16:55.260] Tyler Niday: Yeah, most definitely. So my big thesis is electrification of machines has a big impact on the efficiency. Like, I had a Tesla for 100,000 miles, right? And I only changed the windshield wipers, the tires, and the air filters, right? It was very minimal, the amount of work, versus now I have a big F-250 truck, right? And I have to take it in for an oil change. There’s things that go wrong all the time. So I think electrification is a huge win on the efficiency and reliability of these big machines.
[00:17:25.720] Tyler Niday: Tractors are either massive mechanical assemblies with a ton of parts that can fail, or hydraulically driven machines that also have a ton of parts. So electrification simplifies it. However, I think you need farming never stops. Some of our machines run 4,000, 5,000 hours per year. If you needed to stop and charge, recharge a machine during the critical time, it could kill an operation. For a big John Deere 8R series tractor, it would take 13 Tesla Model 3 batteries to run for a few hours, right?
[00:17:55.180] Tyler Niday: It’s not, horsepower needs power. So our big thing is you need to be able to get a machine back up and running in under 10 minutes. And we believe there’s a threshold for how big a battery can be on the platform. It needs to be swappable. So our little machines have swappable batteries. Or these bigger applications are hybrid electric. So this has a small diesel generator on the front that’s trickle charging the battery. So as the battery declines, we just add a bit of power and keep it charged all the time from a diesel power source.
[00:18:22.700] Tyler Niday: And instead of using 30 gallons of diesel or 35 gallons of diesel a day, you’ll use three. So there’s a big, big win, especially with the rising costs of diesel and different fuel sources.
[00:18:34.220] Michelle Sun: So you mentioned about 4,000 hours a year. How does the math work out for a farmer, a farm owner that is buying a machine?
[00:18:40.820] Tyler Niday: Yeah, it all comes down to utilization, right? And if you’re just replacing an operator on an existing tractor, you have to have high utilization to make it pencil out per whatever your autonomy costs are or your subscription. This is where we think these Amigas are very unique in that we’re redesigning the form factor around AI and autonomy instead of designing around a human and a cab, right? Like the cab of a tractor is a lot of money, right? Sometimes there’s like refrigerators in it.
[00:19:08.820] Tyler Niday: There’s amazing sound systems, right? There’s a full suspension seat. It can be a big cost of the tractor. Removing that’s a big aspect. So these platforms have a much quicker ROI than just a straight replacement of a human operating the machine because we’re not only adding the labor aspect with higher reliability, high performance autonomy, but we’re able to make the machine cheaper on a per acre per pass basis by reducing the cost of the machine with electrification, but also drastically decreasing the OPEX around repairs and fuel consumption.
[00:19:41.440] Tyler Niday: So it’s a win-win where we’re kind of stacking all aspects on the per hour per acre operational cost ultimately of the machine.
[00:19:49.720] Michelle Sun: And is that the number that the growers think about as well?
[00:19:52.740] Tyler Niday: Yeah, they are running like a full profit and loss statement based on metrics to make sure it pencils out per their machine matrix, right? Some big operations have 5,000 tractors, right? And it’s not like one tractor, right? There’s like of that 5,000 tractors, there’s probably like 50 to 60 different form factors, right? And getting that matrix right is a big aspect of it. So they’re definitely running very detailed ROI calculations based on real metrics, based on the machine performance to get to an answer.
[00:20:20.920] Michelle Sun: 400 units right now.
[00:20:22.440] Tyler Niday: Yes.
[00:20:23.060] Michelle Sun: Tell me about the split in terms of different farms, different countries. Where are you seeing the most demand coming from?
[00:20:28.780] Tyler Niday: Yeah, most definitely. So this is a new product. We’re very excited about this. This is an Amiga Max and we’re seeing a large demand from this. But historically, we started deploying Bonsai-enabled orchard units last year. Since then, we’ve sold about 70 of those units. And these are big tractors, right? Like about 4 to 5x the size of the one behind us. We see a lot of those going to Australia and California. So about 70 of those units and the rest has been Amigas.
[00:20:57.780] Tyler Niday: Amigas originally were as much smaller footprint, great for specific applications, did a lot of work. But we’ve now expanded and put the full Bonsai autonomy stack on these Amigas and also increased the size of the Amigas. So this is a full new market we’re breaking into as well, which we’re really excited. We were in nine countries globally right now, but we’re seeing predominant focus for the next few years in Australia and the U.S., mostly California, Washington, and Florida.
[00:21:26.580] Michelle Sun: So no Europe yet?
[00:21:27.860] Tyler Niday: There’s a couple machines in Europe and we’re planning to expand towards the end of next year to Europe. We think it’ll be a big use case, but we want to make sure we nail these markets first as we have the full business side of the equation set up in these industries.
[00:21:40.860] Michelle Sun: So you mentioned that there was a small Amiga and then this one Amiga Max. What are the unlocks in terms of capabilities that the Max version is able to use?
[00:21:50.960] Tyler Niday: The small one. The Amiga Flex is a small unit and it’s really about low cost autonomy. It’s a fixed platform. It’s very simple and it can go into a bunch of different applications. We’re deploying, I think there’s one at the Space Force right now to different ag fields across the road. That machine also can do specific applications. Like we have a spot spraying application. So it has a tank of material and instead of spraying the whole ground, it just sprays the plants.
[00:22:16.160] Tyler Niday: The big unlock and the bigger form factor is we can do more and compete much more in these bigger applications. This one’s actually set up to spray apples, right? And a little platform can’t carry 300 gallons worth of material, right? And also can’t power a big, you know, fan to blow the spray out in these applications. So that’s one of the aspects. But additionally, this machine has full articulation so it can go over much more adverse terrain, bigger tires and four wheel steering.
[00:22:42.780] Tyler Niday: So it can get into different hills, conditions and edge cases much quicker than some of the little platforms or even the competitive tractor.
[00:22:50.820] Michelle Sun: There’s been a lot of development in the ag tech industry and lots of physical AI startups coming in. And for someone like yourself that’s been at it for quite a while, like what are some things that you would advise like new startup founders to think about?
[00:23:04.640] Tyler Niday: Yeah, you know, it’s really exciting that robotics in these ruggedized environments is mainstream because it’s such a huge opportunity to solve for. The biggest thing I would advise is that the demo is just 1% of the business, right? Getting past the demo and actually building repeatable, scalable, reliable products is such a big challenge. And you want to be centric around that and not ship machines early, right? Like make sure your product is steady, great, reliable and has product market fit before you jump the gun and continue out past that.
[00:23:38.100] Tyler Niday: So I think that’s a big aspect. And it’s always a battle, right? Like you’re driving for revenue, but your machine has to work. And finding that sweet spot in between the two is a critical moment that has to happen to be successful.
[00:23:50.980] Michelle Sun: Getting the machine on the field is also important to get it better. So there’s this tradeoff to get on the field and still retain the trust to the customers.
[00:24:01.540] Tyler Niday: I think that’s another huge aspect that I’m always pushing my team very hard on. It’s like we’re more or less like getting to the field early because there’s so much new things you learn in the field versus in the shop. You can do every test in the shop you could imagine, but until you see that machine run and get a little bit of paid customer traction, you don’t actually know if it’s a product that’s going to work in scale or not. So get to the field early is a big one for sure.
[00:24:26.160] Tyler Niday: I’m glad you brought that up.
[00:24:27.680] Michelle Sun: By 2031, let’s say it all works out the way that you envision for Bonsai. How does it look like? Who owns the machine? What’s running on the fields?
[00:24:36.900] Tyler Niday: So by 2031, I’m hoping that not only ag, but beyond is the whole operation system of work is reimagined holistically around AI instead of around humans operating machines. The vision of Bonsai was started in ag, right? And we believe autonomy is the enabler to instead of managing fields and having these machines run through every field, We can manage plants and give every plant the master bonsai treatment. There’s competition corn growers in the Midwest that produce 3x the amount of corn versus, like, the top growers, like 600 bushels of corn instead of 200.
[00:25:10.640] Tyler Niday: They’re using the same energy source, the sun, right? But they’re treating the plants differently. And I think autonomy going through the field, you have eyes on every plant sometimes 50 to 100 times a year. You can start managing these farms on a plant level basis and really improve the efficiency on a per acre basis. I’m very excited for that. Farming is one example, but this same thing can be applied to all these outdoor rugged industries, right? Depending on the job you’re doing, whether you’re in mining or construction, you can really optimize the workflows and make things a lot better.
[00:25:42.840] Tyler Niday: And I think autonomy and these big foundation models are the enabler to do that.
[00:25:48.940] Michelle Sun: Thank you.
[00:25:50.620] Tyler Niday: Thank you so much.
[00:25:51.460] Michelle Sun: That was a great conversation.