Byron Boots - 5 People Could Control an Army of 500 Autonomous Vehicles | SRS #336

And so they're the reason I get to sit down here with you today. And so they get the opportunity to ask every single guest a question. So this is from Thomas W. You've dedicated your career to advancing robotics and AI, including technologies with defense applications. From your perspective, what responsibility do scientists and engineers have to ensure these innovations ultimately reduce human suffering rather than prolong conflict? And do you believe AI and autonomous systems could one day become tools that prevent wars through deterrence and de-escalation? Or do they risk making armed conflict more frequent and easier to justify?

That's a great question. I think autonomous systems, they're like any other tool. The way that I think about them is really a tool, a technology, and in the context of defense, it is something which allows a warfighter to be safer. It reduces exposure, pulls them away from the point of contact. Contact. And then also potentially provides force multiplication on the battlefield. I'm sure we'll get into some of these things, but it is a tool which is used by humans. And so, you know, how you use them is really, I think, a human question. So, you know, in the context of saving lives, I think that they will save lives for our warfighters on the battlefield. It's very clear how they do that, but, you know, they also can serve as a deterrence like any technology might.

You know, if we have significantly stronger robotic systems at our disposal, then, you know, we're going to be a little bit, you know, tougher to defeat in the battlefield, and our adversaries will see that. And so, in that way, you know, they can certainly serve as a deterrence as well. Right on. I mean, yeah, watching some of the models you showed me outside, and then, you know, the videos and how they're going to be integrated in with the war fighters and in combat i mean it's uh you know is a is a former seal who's like seeing what seeing the you know award what war has developed into is just i mean it's it's fascinating and i mean just it's been over 20 years since i've been on the ground in a war and uh well i I guess not. It's been over 10 years. But I mean, I already have tons of questions and I can see so many different applications where this would be useful in just so many different scenarios.

Yeah, we should definitely get into it. Before we do that though, I do want to give you a gift as well. So if you don't mind. So let me come over here and we got you a chair. Now this isn't just any chair right on um this is uh it looks kind of like an office chair but this is actually a seat from one of the um vehicles so as i was explaining earlier we pull the seats out of out of the polaris ranges and we turn them into those autonomous vehicles um that we that we saw outside well what do you do with the seats once once you've pulled them from the vehicle we make them the chairs so we made one uh for you oh man thank you you can check it out this is awesome yeah yeah not bad not bad there you go put this in the office thank you yeah of course that's awesome all right byron so before before we get into everything overland ai how do let's do a little backstory on you where did you grow How did you get into this stuff?

I mean, what's the backstory here? Yeah, I had a whole career before moving into defense tech. So I grew up outside of New York in Connecticut. I was into computers and was in the Boy Scouts and played sports and had, I think, a pretty typical upbringing. So, you know, that's maybe where things got started. Just love the outdoors and taking apart computers and playing video games and doing all the sorts of things that kids often do. Did you watch The Terminator growing up? I sure did, yeah. Did you? So, Terminator 2 was a, like, unbelievable, unbelievable movie. You know, and, you know, happy to talk about that a little bit more in the context of what we're building.

But obviously, robotics and science fiction were, you know, something that I really enjoyed. Were you a gamer? Yeah, I used to play, so back when I was in high school, I used to play StarCraft quite a bit. StarCraft 1, that was before StarCraft 2 came out. So real-time strategy games, I played a lot of games like that. We talked about WarCraft before, I used to play that too. So that was really what I was most drawn to. But yeah, I mean, love computer games. How, I mean, well, we'll get into it later. I was going to ask how similar is what's happening today is controlling one of those games. but we'll get into that in a little bit. So where did you go to school? What did you go to school for? So I went to college at a small liberal arts school called Bowdoin College. It's in Maine.

And I spent four years there. I was a computer science and philosophy double major as an undergrad. I started out really thinking about, I love computer science, like I said, all through high school. I also really liked history and read a lot of history. And so when I went to college, I was thinking about maybe double majoring in computer science and history. I thought it would be cool to have a more technical degree and something which is, you know, more humanities oriented with history. But I quickly, you know, sort of figured out my first year that, well, I love history, it involved tons of reading and things like this that I really like to do, but also involved foreign languages.

And you need to actually read about history through contemporary sources and in the language that folks wrote in. And so it's something I was not great at. I was not particularly good at languages, didn't really enjoy them. And so, you know, I started to, I took a couple courses in philosophy. I started out with a course called Logic and Formal Systems, which was really delving into formal frameworks for understanding arguments and analyzing arguments. And I really started to fall in love with that. And so, you know, I brought that together with computer science and majored in both of those areas as an undergrad.

Interesting. You were fascinated with the brain, too, correct? Yeah, yeah, yeah. So, I mean, in undergrad, I was, you know, I was taking computer science and, you know, took courses in artificial intelligence and started to do research in robotics. And this is back, you know, over 20 years ago. They had artificial intelligence courses 20 years ago? Yeah, it was pretty interesting. So, my university is a small school. There was only four faculty in the computer science department there. And at the time, computer science was seen as like an offshoot of mathematics. And so, you know, a lot of these smaller schools had combined departments with computer science and mathematics, and you take a lot of courses in both areas. But at Bowdoin, two of the four professors were actually folks who studied artificial intelligence. So it was something, you know, it's been around for a long time. I mean, people were working on aspects of AI, you know, back in the 70s and 80s, but it was just starting to kind of come to the forefront and be an area that was really starting to accelerate around, you know, 2000 when I was an undergrad um so i started to study uh ai there and then in philosophy i was thinking about things

like philosophy of mind and philosophy of science and um you're just kind of trying to understand how the the human mind worked well yeah okay i think that's an interesting discussion itself yeah how i mean how deep did you get into that before you you kind of switched years Yeah, so as a, again, as an undergrad, I had a double major in computer science and philosophy, and when I graduated, I was thinking about what I wanted to do next. I initially took a job at a robotics company as an engineer, basically working on problems related to perception and mapping and robotic systems. So these were mobile robots, much smaller than the ones that we just saw outside.

So robots that are about, you know, about this big that moved around inside of buildings and you have to determine where they are and how to get from one place to another and things like that. So I worked as an engineer working on those sorts of problems. But I was really thinking about, you know, kind of like what do I want to do next? I knew I didn't want to just be, you know, kind of working as a software engineer. I wanted to go back to school. And the question was, like, what area should I study? So I really like computer science. I really liked philosophy. And one of the things that I started to think about was cognitive science, you know, just sort of how the mind works. So with AI, you know, you're trying to program a computer that can almost like think like a human, that can perceive the world, that can understand it somehow.

And then in philosophy, you're really thinking through language and, you know, by writing arguments, thinking about, you know, how does the human mind work? How does it contend with, you know, reality, things like this? But the piece that I was missing was actual neurobiology, right? Like the nerve, the human nervous system, the substrate of the mind. And so I decided that before I went back and entered into a graduate program, I needed to learn more about neuroscience. And so I managed to get a job at Duke University in a neurobiology lab studying human perception. So I worked as an engineer for about a year, and then I went to Duke, and I worked there for two years.

And this was really, it wasn't a graduate program. It was just working in a neurobiology lab, and I was auditing courses on neuroscience and neurobiology while I was there, trying to learn, you know, how does the mind work? I mean, wow. How the human mind perceives the world. Yeah. How, I mean, did that, is that helpful in what you do today? It is. It's pretty interesting. So the lab that I was working in was really focused on trying to understand how humans perceive the world. And so let me just give you an example of why this is difficult and interesting. So when you look out at an environment like this room, there's light which bounces off of surfaces.

It comes back and it, you know, moves through your eye and essentially is projected on your retina. So there's, for each of your eyes, there's a 2D projection of light from the room. And the question is, how do you go from that, like, 2D projection, that 2D image, to understanding what's actually out in the world, like the 3D environment, the surface reflectance, you know, properties of things like the wall or, you know, the carpet or whatever, how do you sort of solve that problem? And it's called the inverse optics problem. So, it's the notion that you have a 2D image and you're trying to kind of understand this complex 3D world. And the challenge is that there's actually not an easy solution to this. Because you're moving from essentially like three dimensions to two dimensions, information is lost. And so, another way to think about this is that an...

An infinite number of different worlds could have produced the same visual image on your retina. And this manifests itself through illusions. So there are certain types of illusions. There's something, for example, called an Ames room, where when you look at the room, it looks like a rectangular room. But in fact, it has this kind of crazy shape. It's something you can look up maybe later. But the interesting thing about that is just the fact that something that appears to you to be, you know, like a normal rectangular room is actually something completely different. So that is just an example of one of these optical illusions.

Now, the interesting thing about illusions is that they basically, everything you see in some ways is an illusion, right? So, they're not outliers. It's not like every once in a while your mind makes a mistake and you kind of see the world incorrectly. You are always inferring some world that is not quite what is actually out there. It's the rule, not the exception. And so this is, it forms almost a philosophical problem. It's like, if you are looking at the world, but you can't actually infer what generated the images that you see, how do you even interact with it? Like, how do you continue to exist if you're not seeing things properly?

And so, you know, the conclusion, one of the conclusions that we came to was that really the way that you see the world is whatever way is necessary to allow you to continue to persist. So, we kind of think about this as like you see the world in an evolutionarily sort of appropriate way, in a way which informs your actions so that you kind of do the right things, you continue to exist, you can ultimately reproduce and continue on. And so, it's just one of the problems that we wrestled with. Now, what does that mean? It means that your perception of the world is really shaped through experience.

You're just perceiving the world in the best possible way for you to take actions. And some of these fundamental ideas actually carried through into the work I did in graduate school and even some of the things that we do today with the systems that we built, the robotic systems that we built. Very interesting. Wow. Wow. When we first started building the Sean Ryan Show storefront, we didn't have everything figured out. We had products, merch, ideas, and a growing audience. But turning that into something people could actually shop from and keep them coming back, that takes the right setup. And that's why we built it on Shopify.

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You don't want somebody ready to support your business and then lose them because you got a clunky checkout. I like Shopify's product so much. It's so simple. I had the CEO and founder, Toby, on my show. So if you've been sitting on an idea Waiting until you have it figured out. Stop waiting. You can start simple. Learn as you go and build it from there. If you're ready to hear the of your first sale today, head over to Shopify.com slash SRS to start your free trial today. That's right. Start your free trial at Shopify.com slash SRS. That's Shopify.com slash SRS. So where do we go from here? So, you know, I think like one thing, one of the, you know, when you think about building a robotic system, one of the ways that we kind of think about this is that when you're perceiving the environment, you're not just measuring it, you're taking the context and your prior experience and you're using that to predict what you think like the world actually is.

So, for example, if you look out at a set of trees, you're not just measuring that there's, you know, some obstacles out there in front of you, you're also predicting that there's free space behind them that you can potentially move through. And you get to that point by essentially seeing lots of trees like in your past, right? Like you use that prior experience and then you can understand when you see a pattern like this that actually means that there's sort of space out there behind those trees that you can then leverage in order to make decisions faster to move more aggressively. That's really a machine learning way of thinking about things.

You're using lots of data, lots of experience to understand what you're seeing in a functional way and then move, you know, use that in order to move a robot more quickly or more aggressively. And those notions, you know, sort of led me from neurobiology at Duke, like really kind of thinking about data and machine learning as fundamentally, you know, interesting things towards my graduate education, which I pursued after that at Carnegie Mellon University. Wow. But I've not heard everybody talk about that, that it's gotten into the machine learning AI stuff. That's pretty fascinating. Yeah. And I think like another thing which is pretty interesting here that I'll also highlight, you know, I was originally went to Duke to try to understand the brain and how it works. And my hope was that by understanding that, that would help me to maybe better understand artificial intelligence or how to build machines and things like this. What I pretty quickly realized was that neuroscience is really hard, right? People have been studying the human brain for almost 300 years, and progress is slow.

It's very difficult to understand how the brain works. We don't have a great grasp of it even now. We can describe a lot about it, but not really understand it functionally. And so So one of the lessons from that was that I almost came away with the opposite conclusion. So instead of thinking about the brain as something that would help me to build better machines or build a better AI, I almost think that focusing on artificial intelligence and mathematics and probability and statistics and information theory and robotics helps to provide a framework that people may ultimately understand like the brain through.

So it almost works the other way, that you have to really understand kind of core principles of perception, planning, control. like these areas which are fundamental in AI and robotics to understand ultimately, you know, what the nervous system might be doing and be able to describe it. Wow. Wow. So you worked at, went to NVIDIA too as well, didn't you? That's right, yeah. So I, before starting Overland AI, I worked for about five years at NVIDIA. And this is while I was a professor, so after Carnegie Mellon, I got my PhD there in machine learning, I worked on robotics problems, and then I was a professor at Georgia Tech for five years, and then University of Washington for seven years after that.

And one of the things which is really cool about being a faculty member or a professor who is running a research lab is that you can also work in industry. So, I had a research lab, which was focused on robotics and machine learning, and I had a number of PhD students who were working in that lab. But you can take 20% of your time and work in industry simultaneously, and as part of that, spent about five years working at NVIDIA on machine learning and robotics. How was it working there? It was awesome. I mean, I think I joined around 2018. So it was before NVIDIA was really kind of like a tier one, let's say, tech company.

I think like Google and Microsoft were really up there sort of defining state-of-the-art. But when I went to NVIDIA, I think there were a lot of good people that they were hiring, and people had recently seen the power of GPUs, like this massive parallel processing. I was thinking about that in the context of robotics. How can you parallelize tasks? How can you use it? Not just these chips and this type of technology, not just for perceiving the environment, but also controlling vehicles. An example of this actually is carried through from work that I was doing originally at Georgia Tech and then NVIDIA, now to Overland, where when that vehicle is out in terrain, so when our uncrewed vehicles are out there and looking at terrain, they're evaluating tens of thousands of possible trajectories that they might take, ranking each one of them, determining is this a good one or a bad one, and then choosing how to drive after doing that.

It's doing that about 10 times a second. And so how do you get that to work? Well, you can use NVIDIA GPUs to parallelize, you know, these tasks and evaluate many trajectories simultaneously and then decide how you're going to move based on that. Very, very interesting. Do you still, do you miss being a professor? Well, yeah, I'm currently, I've got a 5% appointment at the University of Washington, which means that I'm there, you know, every couple weeks working with students. You know, I like teaching. I like interacting with students. I think that's one of the great benefits of being a professor is, you know, just engaging with students and people who want to learn um so that part's fantastic and i miss doing that i haven't been teaching uh recently since i've been spun off the company um but i think working in industry also allows you to really scale your ideas more so um you know there's only so much you can do in a in a smaller research lab yeah and so in 2018 the army research laboratory spotted you at an IEEE conference demoing machine learning what is an IEEE It's IEEE, so it's Association for Electrical Engineers, but it's one of the major types of conferences that folks publish in. So, you know, when you're a professor,

one of your main goals is to publish papers, right? And those scientific papers further human knowledge. And so in computer science, the way that you do this is you publish papers at a lot of conferences. You work with your graduate student, you develop a new technology, you then tell the world about it, right? You publish it in a paper and you do this on a pretty fast, iterative basis. So this is one of the major conferences in robotics and we had some work there and some folks in the army were seeing what we were doing and had some cool ideas of how we could potentially take that fundamental research and start to apply it to army problems. Right on, right on. And then the DARPA RACER. Yeah. The crucible that birthed Overland AI. What was that?

Yeah. Yeah. So, okay. So I was, you know, originally working at Georgia Tech and doing some work on ground vehicle autonomy. So the way that this started out, we took one-fifth scale vehicles. So these kind of smaller remote control vehicles. We put computers and sensors on them and made them autonomous. And we were trying to race them as fast as possible. So we were using data that we were collecting while we were driving these cars to learn what's called a policy. You think about it as like AI, essentially, for the vehicle that could perceive the world and try to drive really quickly. And these vehicles were doing things like drifting around turns and things like this.

So they learned to do this, which is one of the things which is cool. Like, the vehicle's out there, it's trying to drive faster and faster, and then it's learning how to do things like drift in order to drive even faster. Oh, shit. So that's what the army was looking at. Now, is it actually learning, or are you programming that into it? So it's actually learning. So you start by bootstrapping it. Like, you have a human demonstrate, you know, this is how the vehicle should drive. And then it tries to replicate what the human does. And as it does that, sometimes it makes mistakes, sometimes it does well, but it's kind of grading itself.

And then it will start to experiment. Like if I accelerate a little bit here or I brake a little bit there, does this make me faster or slower? And as it does that, it learns how to drive faster and faster and it learns on its own. And so you're programming in the ability to learn, but then it's looking at its own behavior, learning from that, and driving, you know, figuring out how to drive faster. Now, is this, like, on a track? Yeah, so we started out by driving on a sand track. And we were able to achieve these, like, really fast lap times where the vehicle's, you know, basically drifting through turns and things like that.

And so when I started to work with the Army, the question was, can you take these fundamental principles and apply them to larger vehicles and figure out how to drive aggressively through all sorts of different terrain. So not just on tracks, but, you know, through forests and deserts and beaches and things like this. So, excuse me, what I was going to ask is, I mean, if this, if, okay, if we have a race car, it's going around a track over and over again, and it is learning, and then you switch up the track, will, I mean, will it be able to take what it... learned on track a and apply it to track b immediately or will it do you understand yeah basically what i'm saying is how do we go from a track to

How close are you to that goal? Depends on the environment. I mean, I think we can already drive faster than humans in some environments. So, yeah, it's pretty cool. Wow, very interesting. And so how long did you spend at DARPA? So, yeah, so I started work with Army Research Lab, you know, again, around like 2018. And in 2019 or so, I started to talk with folks at DARPA. They were really interested in developing, essentially rebooting ground autonomy for defense. So, like, here's what the situation was. DARPA, back in 2004 and 2005, had something called the Grand Challenges or Grand Challenges.

So these were challenges where they were trying to incept ground autonomy. So this actually goes back to the NDAA in 2001. This is basically where Congress decided in 2001, they were like, by 2015, we want a third of all military ground vehicles to be autonomous. So I think about this, you know, over 25 years ago. The problem was no one knew how to do that. Like, there weren't, it wasn't like there were autonomous vehicles, you know, driving all over the place, and this would be easy. No one knew how to make these vehicles autonomous. Where did the idea come from? Well, I think, you know, there had been, you know, work in robotics where people were moving vehicles in simpler environments autonomously.

And so the notion was like, well, what if we could do this on the battlefield, right? What if we could do it in these more complex environments? If you could take the warfighter out of the vehicle, you can imagine, you know, that not just provides safety, but potentially, you know, tactical things that you can do. We can maybe talk about that in a little bit. But in order to make those vehicles autonomous, you needed to know how to do it, right? And they didn't. And so DARPA, one of the things that's great about DARPA is that it is an organization that is designed to just like create new things, right?

Like you have some crazy challenge, DARPA is out there and can create a program, pull together some of the best minds in the US to focus, like really focus on it for, you know, up to about four years and try to solve a problem. And so in 2004 and 2005, they came up with the DARPA Grand Challenges where they were trying to race vehicles from, you know, Barstow, California, to Prim Nevada. It was about 135 miles on dirt roads. And they just laid down a challenge. They said, like, if you can do this, you get prize money. And all sorts of teams came together to attack this problem. So there were university teams from places like Carnegie Mellon and Stanford and MIT and so on, and there were industry teams, like Oshkosh had a team. And there were just people who were trying to put together autonomous vehicles in their garage, like just build robots. And they went out there and raced. And in 2004, the first challenge, no one made it beyond seven miles.

Like that was the, you know, so it was, CMU's vehicle, I think, made it that far. It got stuck, caught fire. It was like a whole thing. The next year, though, in 2005, I think five teams competed this challenge. They made it the entire 134 miles. Oh, shit. Those teams, like the folks from those teams, after that challenge was over, there were a few other ones. There's something called the Urban Challenge and some other DARPA programs which followed up on this. But many of those people then moved into industry and started The self-driving car projects and companies that then turned into things, you know, companies like Waymo or Aurora Innovation and so on.

So these autonomous driving companies, you know, commercial companies. So by, you know, 2012, let's say, a lot of work was being done in the commercial sector bootstrapped off of this DARPA work, right? So the military, you know, started this whole thing because they wanted autonomous vehicles. People started to build autonomous vehicles because of this DARPA program, but then basically just went off into industry and were working on, like, robo-taxis and autonomous trucks and things like this. So, by, like, 2019, coming back to my story, DARPA was in a position where they were like, well, cool, we have autonomous taxis, you know, there's been a lot of progress in this area, but, you know, where's our autonomous tanks, right?

Like, where are autonomous military vehicles? The whole point of this was initially to support the military. And so DARPA RACER was a program that got stood up. It started in 2021 to reboot autonomy for defense. So specifically to take a lot of the learnings that had been produced over the previous, you know, 20 years or so for the on-road autonomous driving industry and, you know, work which had been done in robotic perception and, you know, robotic vehicle control, bring that back together and focus on defense problems. And so that meant trying to drive much faster, you know, larger and faster vehicles off-road, what we call complex natural terrain.

So, you know, no roads at all, right? Like through deserts, through forests, through snow, things like this, and contested terrain. So thinking about, you know, how do you move when you not only do you not have infrastructure which is there to help you like roads or road networks or signs or things like this, but infrastructure which might be in the environment which is there to defeat you, to stop you. And so that's what the DARPA RACER program was. So I had already been doing work at, you know, at Georgia Tech and then University of Washington, working with the Army on developing off-road ground vehicle autonomy.

And I then put together a team, it's called a performer team, to attack these problems for the military through DARPA starting in 2021. So did they recruit you from the IEEE conference? So the way that that worked was through the research I was doing and the publications I was putting out to the world, Army Research Lab saw that the technology we were developing might be really helpful for the types of problems they wanted to solve. I then started working with Army. So the way that that works is when you're running a university research lab, you have a bunch of PhD students, you know, they're doing research, you're publishing it, you're making it publicly available for other scientists to see. Okay. But you need funding to run that lab. And so people pay you to essentially do research. They pay your lab to do research. So Army, U.S. Army was one of the organizations that fund, like started to fund my research. And when you fund research, you can say like, hey, here are the problems we want you to solve. We'll give you, you know, this much money to solve them with your PhD students. And then you provide those solutions back to the Army. So that's what I was doing when I was working at Georgia Tech and University

of Washington was my lab was partially funded by the U.S. Army. Then I worked on problems that were interesting to them. We provided those solutions back to the Army. And then we started to work with DARPA, which is at the time, Department of Defense, but Department of War-like level organization. They saw the work we were doing with the U.S. Army, and then they decided to fund my lab at, like, a much higher level to, like, attack these problems of how do you drive vehicles off-road and all sorts of different types of terrain at high speeds. Wow. So you've been... You've really been at the cutting edge of this thing the entire time.

Yeah. So we've been working on these problems for, you know, more than 10 years, more than a decade. And my lab was doing a lot of that work in academia, you know, before we spun out Overland. Wow, wow. Well, before we get into Overland, let's take a quick break. There's been a lot of attention lately on how important sleep is, not just for recovery, but for your brain, hormones, immune system, and for your performance. And the more research comes out, the clearer it gets. If your sleep is off, everything else gets harder. That's why I use Helix. I've had my Helix mattress for a while now, and it's been a real upgrade from what I was sleeping on before. Helix has over 20 mattress models, so you can find one that actually fits how you sleep instead of guessing your way through it. And if you sleep hot, they've got cooling upgrades that help you stay comfortable through the night, especially during the summer.

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You'll also unlock exclusive Range Day videos, behind-the-scenes content, and premium ambience videos that you're not going to find anywhere else. join the patreon community today and get access to the full experience all right let's talk about the the textron m5 sure yeah so uh as part of the DARPA RACER program so this is darpa program where we're trying to develop um ground vehicle autonomy for the military we started out working with Polaris Razor side-by-sides so these are very capable vehicles they can go very fast they've got four wheels about 3,000 pounds about the size of a small you know small SUV something like this and you know in the first part of the DARPA RACER program we were just there were more.

multiple teams and we were essentially like racing these Polaris razors so trying to record the best possible times through a wide variety of different terrains and you know test scenarios. As we progressed through the program you know the DARPA program manager didn't want us to just be using those small vehicles so we moved up to the Textron M5. The Textron M5 is basically a robotic combat vehicle prototype. It's about 12 tons, so it is much larger. Holy shit! You went from a side-by-side to a 12-ton vehicle? Yeah, it's a 12-ton tracked vehicle. Oh my gosh. So it's about the size of a 113, so it looks like a tank, sort of like a small tank, I guess, but pretty heavy vehicle, tracked.

um it's electric so really aggressive um it has uh the ability to essentially go from you know zero to 60 like super think about like a tesla but like you know like a a tank version of that wow um so really really awesome vehicle to work with and we're putting autonomy on that vehicle and then driving that through off-road terrain okay yeah how did that and this was a competition correct? Yeah. So DARPA RACER, it started out with three teams. It was our team and there were two other teams that we were competing against. By the first summer, about a year into the program, we had by far the best technology and pretty quickly the other teams essentially dropped out and we were the only ones left. We were focused on not only driving on the side-by-sides, but also the the much larger vehicle so we're the only team that actually worked with with these and there aren't that many of them there's something like you know four or five textron m5s ever produced uh but you know we had all of them and we were just like smashing through terrain and in these these huge tracked vehicles pretty awesome and so what is what is the what is the kind of the point of the competition do they buy the technology from you or or do they

They fund the technology. That's a great question. How does it work?

to actually getting it procured. And that takes time and effort. And so our story as a defense tech company is really similar to a lot of other stories, except that we got this start by doing all of this, you know, sort of initial research to accept a new technology that the military really wanted.

Very interesting. And so the DARPA competition ends, you win, and you take all the information with you and develop Overland AI. Right.

and I would imagine there's going to be an offensive commodant. Yeah, yeah. So the way that we're thinking about this is so logistics. So you can think about resupply and casualty evacuation. A lot of people, when they think about autonomous vehicles, this is like the first thing which comes to mind because they think about a vehicle and they're like, oh, okay, like I can put stuff in it and I can move it, right? Like that's like what a vehicle does. But I like to think about these vehicles more like robotic systems. They can sense, they can track, they can move on their own on the battlefield.

And I think like the real product market fit here is that you want to move these vehicles out in front of the war fighters, right? So you want to be using them for things like intelligence, surveillance and reconnaissance, being able to move them through bad weather. They can persistently stay in a location for a long period of time because they're just on the ground. So they might be more effective than drones for some of these things. You can use them for, you know, defense or strike capabilities. So you can put kinetic payloads on them. You can put drones on them and launch them off of the vehicle. So you can, you know, move them into areas that might be too risky for a human, but they can potentially hold ground or try to take terrain in those areas.

Breaching is a major thing that we're working on. So we're working with a number of different combat engineering units on removing people from breaching operations, which are just exceptionally dangerous. And then air defense, as I was discussing, is essentially putting sensors and shooters in different locations and moving them autonomously, reconfiguring what that defensive position might look like. I mean, I could think of a whole slew of things, but let's run out back and take a look at this thing. Yeah, sounds great. You guys know my schedule. I'm in the studio all day. I'm on the road, and I've got kids.

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Remove the guesswork with Ladder, and get a real coach in your ear telling you exactly what to do for every workout. No thinking. Everything planned for you. If you're a guy who's been meaning to get back to it, this is the way back. If you have an iPhone, head to ladder.fit.srs and take a quick quiz to get matched with your coach and the right plan for you. Use my link and get a free 7-day trial with no credit card and $10 off your first month if you join. all right we're out here with byron boots ceo and founder of overland ai get ready to take a look at this beast here what are we looking at so this is an ultra one of our autonomous vehicles so let me just kind of show you what it is it you can tell it's an off-road vehicle it's got long travel suspension and big wheels but it's it's fully autonomous so it has sensors up in the front of the vehicle there's stereo cameras there's actually three of them like one here one there one on the other side um lidar so this allows it to see you know in this whole area in front of the vehicle um what degree uh so it actually you can see 360 degrees around it so like you have these sensors in the front and there's also sensors in the back so it can just

of see this whole whole area and then if you come along this way this is a payload deck so you can put a wide variety of payloads on it you can see there's all these attachment points so it makes it easy to integrate new payloads onto the vehicle below this deck you have compute so that's where your computer systems are your power batteries on the alternator like all this is is below the deck and then back here you've got comms so satellite comms we can integrate tactical mesh comms as well on the vehicle walking around back here you can see there's more sensors so stereo cameras and and lidar again what's what does the lidar do so lidar basically allows you to see depth in the area around the robot so um think about it as like a depth sensor it tells you the distance to surfaces in the terrain okay yeah what uh what what engine are you running in here i think it's it's um uh you have to ask chad it's about about i think 115 horsepower engine so this is based This whole vehicle is actually based on a Polaris M-Razor commercial side-by-side vehicle.

So the engine, drive-change, chassis, that all comes from Polaris. We take out the seats from the vehicle, take off the roll cage and everything, and then transform it into this autonomous platform. Right on. And so what kind of stuff would you be mounting on here? so you can put yeah you can put all sorts of different payloads on here this particular vehicle takes about a thousand pounds of of payloads and pounds um so we've done everything from you know fairly straightforward things like comms making a communication node to electronic warfare payloads to remote weapon stations so you can mount you know a machine gun uh on on the vehicle nice um so if you mount a weapon system on here is that all run through autonomous the autonomous stack is that what you call it so the the autonomous stack controls the vehicle it allows an operator to tell um essentially tell the vehicle where they want the vehicle to go so uh it will say something like you know go 10 kilometers to this location oriented in this way. And then they will access the payload through the comms network. So the human is still in the loop whenever you're using the payload, but the movement of these types of vehicles is autonomous. And what that allows you to do is instead of having to just like remote

control the vehicle, essentially, you know, drive it through its sensors, the human can just say, I want one vehicle to go to this location. I want two vehicles to go to this location and so on. So it really allows for force multiplication where a single operator can control many different platforms and then access the payloads on those platforms. And we'll show you how to do that. It's like the old computer games, like Warcraft. It's exactly like that. So that's actually how we think about it. Kill this thing. You know, I think like our vision of this is a single operator could control potentially hundreds ...of different assets on the battlefield, and you're going to do that through an interface, which is a little bit like a real-time strategy game, like Warcraft or Starcraft, where it allows you to select the units, tell them where to go, execute the payloads, and so on.

Wow. So, I just, I mean, with the changing landscape of the battlefield now, there's the first land, autonomous land. vehicle i've seen yeah so you can mount like an epirus direct energy weapon on this thing yep well so if let's say i mean andurl's got their stuff coming out shield ai's got that new expat they they came out with um you can mount counter uas so what i'm asking is eventually i don't know if we're there yet maybe we're already there and this is old news but yeah with all these companies you know, like yours, the submarines, the, the, the Saronic, you know, overland AI. When we do go to a full scale war, are you going to be like, is the operator or the battle or the ground force commander, or just the commander of the entire operation, are they going to be controlling shield AIs, expats, overland AIs, ground vehicles, sironics, surface warfare vehicles, submarines, drones, all of it, all of it on the same system? So the military is working on this right now, finding ways to integrate all of these different pieces into the same sort of system so that you have a unified view of the battlefield now the way that that may play out i think there you're likely to see um there are single systems where you can see all of the different pieces

and then there will likely be systems which will allow the war fighters to actually be controlling some subset of them on the battlefield but it'll all have to be linked together to provide that overall awareness of of what you're pushing out there and so if you have a hundred two hundred of these things right here what what is the name of this this is an ultra the ultra yeah 100 200 300 ultras out here and they've got you know surface to air missiles yeah uh for for air defense they've got i don't know 50 calibers for other ground vehicles and anti-personnel and rocket launchers and grenade launchers and drones, are they all going to read off each other or is somebody going to have to?

So the way that this is going, we're taking, so the basic idea is that you start with just being able to move like one vehicle at a time, right? So you can say, I want this vehicle to go to this location. You just let it go, you know, execute a payload there. We already can do that really well. So now we're starting to build up coordination where you can move multiple vehicles at once and they coordinate in order to achieve a task. And then we'll keep building on that. So the basic idea is that, you know, over like as we build out the technology and as we field more and more of it, you're going to have a situation where a single operator will be able to move multiple vehicles into formations, have them, you know, send them to achieve a particular task and they're going to go out there and just do it. And that's part of what's called mission autonomy or orchestration. So platform autonomy is basically the autonomy that lives on the vehicle that allows it to see the terrain, see where vehicles and people are out in that terrain and move through it. And then you tie that together where vehicles are coordinating with each other and that's mission autonomy they're coordinating being autonomously to actually conduct like a full mission and then all of that like all of

these different autonomous systems and uncrewed systems which are on the battlefield will be pulled into command and control systems which allow people to see everything uh which is is out there wow i got a i got a million questions for you but it's it's hot as shit out here in humid yeah yeah let's see uh let's see we'll do that inside that that sounds great but let's see what this thing can do you want to drive it yep yes i want to drive it okay so the upper part of this interface here um this is where the vehicle is on a satellite map and you see okay like the name of the vehicle and then down here gives you a view of what the vehicle sees so over here this is the front camera on the vehicle and then this is actually like the ai view of the vehicle you don't have to necessarily spend too much attention like looking at this but um the magenta areas are lethal so those are areas that you know the vehicle you don't want to move the vehicle into as you're as you're tele operating it you can actually move it um anywhere that you want so this is that big pond that we're digging down there uh this is this is going to be about 50 meters around so it's only seeing up here on this um this little area what we can do is give you

the controller and the way that this works is when you pull like you have to hold this uh down so you're lower left okay and you hit a and that moves you into autonomy and then you can move this joystick moves the vehicle forward holy shit so that's basically moving it forward and then you can turn using this so we can like turn it to the right and you know when the vehicle is here in front of you it's tempting to look at the vehicle but you should actually look at this okay which is what the vehicle can see um and uh and then you can control it that's right yeah and you can control it beyond line of site. So you could do this from like 5,000 miles away. Um, wow. So left down. Yup.

And then forward forward. It is, it's weird. Not looking at the vehicle. Yeah. I want, I want to look over there. So holy shit can i go how do you go backwards okay so to go backwards um hold down this uh control uh so both that and this and then that and then move that backwards there you go oh there we are it will automatically stop it won't run us over let's not test it all right yes yeah there it is slowing for a person yeah yeah well that's not going to work in war byron you can you can turn that off dude this is crazy so would somebody be looking at this or like a vr headset or does i guess you could probably do no matter you could do Whatever, like generally you're looking at this, you can look through the other sensors on board the vehicle as well.

So you can look at the rear sensors or off to the sides. You can access the payloads on the vehicle through the interface up here. Wow. all right and then if you want to whoa oh shit there's a tree yeah maybe back up in case you can't tell i'm not a gamer i'm not great at that either but we like to control it actually through this interface and i'll show you that in in a moment i think one of the challenges for teleoperation is that you don't have like a vehicle sense right like you can't feel like how the vehicle is moving like you would when you're driving and now you're trying to interpret what the vehicle can see like through its own sensors you should probably stop what it's actually much safer to put it into autonomy and just tell it where you want it to go and it we'll find a way to get there without you know hitting anything and we do that while staying safe yeah let's try that i can do that that's awesome all right so oh shit i did almost hit that damn tree okay let's send it to go get a pizza that sounds great i'm not sure i'm not sure they'll uh let me do this uh on on the road can we go over there can we go over here and watch it so before you guys run the route how is that thing determining what is a human being and

what's a tree and what's a vehicle and what's a rock and yeah it's it's it's looking at the whole environment around it and then it's determining where it can drive and where it can't drive so that's the first part this notion of traversability like what part of the terrain is traversable and then on top of that there's a semantic understanding of the terrain which means um you can understand that you know it can see a person or a car you know determine what that is where they are relative to it um and right now it's it's running a safety system which essentially says like don't you know stop if you get too close to a person or or a vehicle right on yeah i mean how does it differ does it pick up like body temperature or how does it it looks and um so it's just using a camera and silhouette yeah and saying like this looks like a person this looks like a vehicle um and it's able to pick them out cool yeah and this is full autonomy what we're getting ready to do here yep holy oh we're we're in the way let's move back away from it so yeah as it's moving through the terrain it's also tracking where all the people are right where the vehicles are where they are relative to to it um and then deciding how to

how to drive it picked him up it picked him up yeah so it's actually pretty wild that it picked him up in the middle of all those weeds and trees and shit you can see one of the benefits of of this is you know you can just tell it i want you to go to again like this location and you can let it go and it will will do its thing um so i was in the run basically came up the hill you know went around the the field and then stopped there so very short run for what it normally does but you can get a taste of And I like how it moves. That is sick. All right. Well, thanks for showing us what the ultra can do.

And let's go wrap up the interview. Yeah. Thanks for letting us come out here and show it. Thanks for bringing that badass thing. There's nothing more frustrating than putting in the work and still feeling like your body is not responding the way it used to. You're training. You're trying to eat right. You're trying to sleep. But energy still drops off. Recovery takes longer. and you don't have that same drive that you used to have. That's why I started taking Marsmen. What I like about Marsmen is that it's not forcing hormones into your body. It's designed to help your body use more of what it's already producing.

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That's MenGoToMars.com for 50% off and three free gifts when you check out. After you purchase, they'll ask you where you heard about them. Please support our show and tell them our show sent you. okay so after taking a look at this thing i i this this this just has so many different capabilities it could be used for so many different things i mean even just keep i mean you admit it sounds like the first thing that the military is interested in is is logistics um which i mean just keeping major supply routes open yeah i mean constant detection can these things detect i mean when we were up there it detected i mean i know you guys have that safety feature on it but it detected uh one of my camera guys who was i mean really wasn't even that close to it looked like he was maybe 25 yards 30 yards from it in in the middle of a bunch of trees and it picked them up like that could this thing pick up ieds is there a way that you could that it could detect ieds i mean yeah so you can put a variety of different types of sensors on on the vehicle so um uh you can certainly detect like people and vehicles and then you can also you know we've been putting um tethered drones on on these so The drone can go up in the air, be able to look down on the ground.

So if you have ground-penetrating radar or other types of sensors, you can certainly detect things like IEDs or obstacles in the environment that then can be identified and reduced, right? So that's certainly something they can do. And to your point, I think you can, of course, move things around, But just the ability to push these forward and sense the environment is, you know, I think will be game changing, right? You don't have to put a person out there to do it.

I mean, just thinking about my time in Iraq, afghanistan alone i mean i have right ied throat was was was huge and then here is a key statistic here 44 of us killed in action from 2006 to 2021 were killed by ieds 44 now i mean if if if if these things did have ied detection capabilities it's it's got to be better than humans then And I mean, there's almost 50% of the people that were killed would still be here today, you know, just with that application.

I think there's a lot you can do there. I mean, there's sensors you can put on the vehicles. The human sensor, like I was just saying, caught my guy out in the weeds just trying to, in a bunch of trees. I mean, rolling down Iraq. I mean, there were sniper problems. There was the guy, the trigger men of IEDs. I mean, I would think that it would pick them up immediately. I mean, they can't hide from the machine. Yeah. How is it sensing them? Yeah, so the machine has cameras on it, right? And depending on the loadout for it, you can have sort of normal RGB cameras. You can have thermal cameras, right?

Like there's a lot that you can do to detect people and vehicles in the environment. So I was just using cameras out there. I was able, as you said, just be able to pick someone up even in the, you know, in the woods and in the weeds. But, you know, you should think about, like, there's a lot of different ways that these can be used out, again, out in front of the warfighter.

is there an operator in the rear that's going to have to be... behind each one of these things with a remote control? Yeah, this is a great question.

So, you know, and this is sometimes kind of glossed over.

let's say you have a hundred of these yes they have 500 of these yeah and then you have all these other you know we were talked about at the beginning all these other companies that are doing you know with shield ai with their with their expat we got sironic with the with the autonomous boats we got enduro making stuff we have uh mock making stuff with all these companies are jumping all over you know lots of autonomous stuff i but just just overland ai alone i mean let's say that there's 500 of these vehicles you know we're talking i don't know the invasion of fallujah yeah these will all be able to communicate with each other and accomplish the mission without each one being micromanaged.

They'll all read off each other, communicate with each other, know what everything is doing, correct? Yeah, so think about that. So this is our vision for the company. So you should think about it as, like, imagine that there's five people that are controlling something like 500 vehicles. And, like, each of those five people, you know, maybe each one's controlling 100 vehicles, and those vehicles are coordinating with each other to complete that mission. So we think about the autonomy which is onboard the vehicle. This is what we call platform autonomy. It's how each individual vehicle analyzes terrain, makes decisions, decides how to drive.

Then there's a notion of mission autonomy where multiple vehicles can coordinate with each other. And the real idea here is to make it really easy for one person to have an immense effect on the back. battlefield to control many, many different vehicles and the payloads on them. And so going back to the earlier part of the conversation, we view this as something like a gamer who's playing a real-time strategy game, something like StarCraft, where you're controlling 200 different units. Can a person do that in the real world with real platforms like our ultra-platform, and that's what we're building up towards.

So that whole idea is, you know, single operator, massive force through potentially hundreds of different vehicles and payloads. Wow. And so will it move to the point where, you know, I just brought up all these other companies, will it move to the point where these Overland AI vehicles are communicating with S.H.I.E.L.D. AI's expat with Saronix boats? I mean, will it get to the point where the entire battle space is coordinated under one brain? So, you know, the short answer is that, like, yes, the ground vehicles will be communicating with, like, aerial vehicles and the different payloads, and they'll be coordinating in order to complete a task.

I think we still, you know, one of the things which is really important to keep in mind is, again, like these are all just tools. So you really want to enable an operator to achieve their objective as effectively as possible. And that means not just kind of handing everything over to an AI brain, but having essentially AI assistants who are allowing a human to choose courses of action to coordinate ground vehicles or payloads or aerial vehicles more effectively and so on. Let's talk about African Lion. What happened there?

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what is what about china are they they seem to be pretty on the ball with just about everything yeah we don't have um or at least i don't have detailed knowledge of everything that they're doing, but we do read academic papers that come out of Chinese institutions. And they're certainly working on ground robots. I think there's several different types. They've been working on robotic dogs and integrating those types of robots into infantry formations. And they're also working on off-road ground vehicle autonomy, similar to some of the stuff that we're doing. We think we're pretty far ahead right now, but, you know, they're working hard to catch up, so...

What kind of... Is there any... What other kind of weapon systems do you think will be putting on these Ultras? I think for any of... You know, you think about uncrewed ground vehicles and autonomous ground vehicles, you can put... It depends on the size of the vehicle. you can put like almost anything on them, right? So any type of vehicle that you have today, which can carry anything from a very small vehicle, might be a couple hundred pounds up to tens of thousands of pounds, you can put on a potentially autonomous vehicle. So that's, you think about smaller things, might be smaller remote weapon stations, might just be sensors, right?

Radar, optics, things like this. but much larger vehicles, large. missile systems right so um ship interdiction missiles anti-air missiles um all all sorts of things you could put pretty much anything on this pretty much are you familiar with epirus yes so you could put uh leonitis mounted on one of these damn things and absolutely then you have yeah drone defense for an entire battalion yeah usually when we're thinking about drone defense um you know it's going to be a layered defense so you you're You're going to want maybe something like, you know, EPRES's system, but you might also want kinetic air defense.

That might be drones, like, you know, drone interceptors. It might be machine guns. But you can imagine a whole set of these, you know, many different vehicles with different defensive systems creating that layered defense. And we think it'll probably be something like this. So that, you know, even if drones are getting through one type of defense, they're getting hit by another. I mean, even if you lose some of your vehicles or systems, there are more to take their place. Let's talk about the technical moat, the team, and the peer threat. Sure. So the way that we think about the technology that we're developing, we really are thinking about it as, you know, developing an autonomous version of core battlefield functions, right?

So, like, either that ISR or breaching as examples, where you have multiple vehicles with payloads that are performing a task. Now, in order to do any of that, in order to have a set of autonomous vehicles with payloads that's, you know, doing something complicated, think about like first principles like what do you have to do first well you have to be able to carry stuff and move it you know like move it from one place to another um in the in the environment and when you look at our history as a company we started with what we think is the hardest problem there first which is being able to understand terrain and move through that terrain um given that basis you can then build off of that so you know i one of our strongest technical moats is the fact that we're the best in the world at being able to see and understand terrain and move vehicles through it we can put payloads like pretty much anywhere that a vehicle can can can drive right and so once you have that as you now combine this to say like instead of just moving one vehicle or one type of payload, now I'm moving multiple vehicles, multiple payloads, that foundation allows you to build up those capabilities.

So we think that that's one of our biggest technical moats is we have just this incredible team coming from deep tech, places like Waymo and Cruise and self-driving car companies, some of the top artificial intelligence labs. They're working for us, focused on these problems, and creating this AI, essentially, that allows you to move payloads in the environment. So that's technical moat. I can't quite remember the other questions, but that's really a foundation of everything. I mean, one thing that I didn't ask, I think we started talking about it out there, and then I said we would come back in and discuss it because it was so damn hot out there.

But, you know, what does it look like, you know, when we're talking about controlling all these autonomous vehicles, especially when it comes to hundreds, I mean, what, and it's not remote control. So what is it, I mean, we kind of discussed a little bit out there, but what does it look like? Yeah, for the team or the person or whatever is controlling hundreds of these at once. Yeah, that's a great question. So you're not going to be just like looking through the vehicle sensor and remote controlling it because you have hundreds of them. How can you do that? So the way that we have designed the software is that you essentially have overhead maps, something like a satellite map, and you're able to see all of the different vehicles on that map, all of the different autonomous assets on the map.

so that gives you you know and you can zoom out and you can you know look at so you can look at terrain features you can look at where all the vehicles are you can see what payloads are on the different vehicles and then the question is how do you now coordinate and control hundreds of those assets and you need to be able to do that by selecting vehicles grouping vehicles telling groups of vehicles to go to like one place or another telling them to execute a certain payload you know at a particular location so that you can kind of quickly move between you know sets of vehicles and um and essentially tell them where to go again a lot like a real-time strategy game uh we're also working on tools this looks like this is what we were talking about at the beginning This looks like, sounds weird, but it looks like World of Warcraft.

Yeah, yeah. You're sending a group of things, giving it a task, and then you're on to the next thing. Yeah, and I think one of the reasons why we've looked to some of these types of games is because it's one of the few places where humans are actually controlling something like hundreds of different assets. Starcraft is a great example of that, and Warcraft. But, you know, the tools that people use for grouping, for moving, for coordinating assets in those types of games are things we can take those ideas and apply them here as well so that a single operator can control many, many different assets.

Another thing that we're developing is things like AI assistance, which can help to suggest particular tasks. So you can just say like, hey, I need to move 10 vehicles into this area, provide a reconnaissance here. And it will tell me how those vehicles are going to move, suggest to me solutions to these problems so that I can make decisions faster. So this is part of like essentially using AI to increase your decision advantage. Now, we're not saying we hand over the actual decision-making to the AI, but the AI can suggest solutions that can help you to handle more assets at once. Wow. Wow. I got a hot question for you.

All right. Ready? in 1941 353 japanese planes came off six carriers and hit pearl harbor in under two hours they sank or crippled all eight battleships and killed over 2400 americans and here's the part people forget we had warnings we'd broken their codes our own ambassador flagged a possible attack a year earlier, and Congress later said the real failure wasn't intelligence. It was imagination. Nobody could picture it until the harbor was on fire. Today, China's arming robot dogs. They build 90% of the world's drones, and they just flew a mothership not long ago that launched 100 kamikaze drones in a single swarm. The warnings are everywhere again. Are we sleepwalking into a robotic Pearl Harbor.

I hope not, but I think it's correct. There are warnings everywhere. We're seeing robots used in the battlefield. Again, in Ukraine, you know, there's both by the Ukrainians and Russians. So, you know, we and I think other defense tech companies are certainly paying attention to this. like we are trying to understand what capabilities our adversaries have and also ensure that the U.S. military has similar better capabilities. And so we think about these sorts of things all the time, right? It's the new technology which is going to be defining like these next conflicts. And then I think it's all of our jobs to ensure that the decision makers understand that this technology exists and what it's capable of. So I think we are certainly aware of it.

I think we're pushing the military to adopt new technology and defenses against it as quickly as possible. But, you know, there may be work to be done. Sometimes these things aren't really real to people until, unfortunately, they actually experience them. So our best bet is to really be watching and taking seriously what is happening in these conflicts and what our adversaries are developing. I mean, do you feel the Department of War is taking this seriously? Do they understand the capabilities that you guys have i mean when you looking at that thing out there what it's capable of it's very surprising and almost alarming to me that they you know 15 15 15 you have a contract for 15 yeah why don't you have a contract for 10 000 yeah it i mean it it should be I think the Department of War and the services are trying to move faster, but the procurement system was really designed for a different era and a different type of war.

And that's something that I think all of the defense tech founders who you've had on here will probably agree with, right? And something that we're all fighting hard against. Now, we've seen the Department of War speed things up and, like I was saying a little bit earlier, we have actually gone from university research to production contract, even if it's a relatively small initial one, in like three years, which is about right, maybe a little slow for the tech sector. But that's lightning fast for Department of War. And we've made use of Defense Innovation Unit and a lot of the new tools, AppFit contracting process and things like that that have come online recently.

So, you know, I do want to credit the Department of War for moving faster, but there's still so much work to be done in order to move at the speed that we need to move at. Um, so, you know, it's something I think, like, we're all working on and trying to... Are other countries better customers than our own country? Have you heard of this problem? For, like, our stuff, or for, like, their own stuff, or...? For stuff that Ameri... not Overland AI, for things that America, Americans, like yourself, are developing here in this country. are other countries maybe ukraine yeah i mean i i think you know i've i've i've heard of these issues happening and we here we have the best that the fucking world has to offer right here, developing groundbreaking new technology that's going to change how war is fought. I mean, it's such an upgrade, especially with a company like all these companies, man. Everybody that sat across with me in the defense tech sector, what you guys are developing is it's incredible yeah you know but i see it being utilized in ukraine you know what i mean and and i see it in a and i i it bothers me when when when we're sending all this stuff maybe we aren't but see things similar being utilized in ukraine and in and it just seems like our country is

not taking advantage of the talent that we have here it's fucking scares me yeah i mean we there's

i mean i think i i'm i think we would absolutely survive uh seer ground invasion i think we would mobilize very quickly i think those the urgency would be there and you know we would we would do whatever it took to uh to to win so i'm i'm you know i think very positive and optimistic on that like i i think when we're pressed we can we can do a lot um but i also think that you know we could be better prepared yeah so me too last thing manufacturing are you guys manufacturing these yourselves How fast can you scale up?

So we're trying to scale as quickly as possible. We are manufacturing the Ultra vehicles. I think I mentioned before that they're based on Polaris, you know, engines and drivetrains and things like that. But we're upgrading the suspension. We're, you know, putting in the payload deck, adding the compute, the sensors and everything. We've got factory running in Seattle that's doing this right now. And I think we've, like, 5X manufacturing over the last six months or so. and we just, we need to do a lot more. So, we're building up that capability as fast as possible. And again, the more support we get from the government, the more we actually work with war fighters, the more demand there is, and there's a ton of demand right now.

So, we're scaling as quickly as we can. Right on. Well, Byron, I really appreciate you coming. It was an honor to interview you, and I love everything Overland AI is doing and that thing out there is super impressive thank you yeah thank you so much for having me I really appreciate you taking the time to learn a little bit more about what we're doing and inviting us out and being able to to show you some of the things that we're building. So thank you. Thank you. Sure. No matter where you're watching the Sean Ryan show from, if you get anything out of this at all, anything, please like, comment, and subscribe.

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