The Shawn Ryan Show - #336 Byron Boots - He Turned a Polaris RZR Into a Self-Driving Military Vehicle

0:00Spiron Boots. Welcome to the show. Things are having me. It's awesome to be here. It's awesome to have you. I can't even remember. I think I actually found you guys on LinkedIn, which I've never on. I think I saw a video on LinkedIn or something of Overland AI and then started, found you guys on YouTube and started following you and then found out A.P.C. is an investor and so or led you around and so yeah, I wanted to get in touch and love what you guys are doing. It looks, you know, I've had lots of tech giants on here, a lot of drone stuff, Surrounding with the water stuff. I guess they, I didn't even know your guy just told me I'll back that they just hit the hit up. I ran any import. Yeah, pretty, pretty amazing. So, you know, first of all, it's an honor to be on the show and in that company, I mean, that's that's incredible. But yeah, just in the last day or so, there was news of

1:05Surronics Boots being used in offensive operation in the state of Formos. Wow. And so you're the ground guy. That's right. You're bringing autonomous vehicles to land warfare. So, really excited to dig into this. But let me let me kick it off with the introduction here. Byron Boots, you're the co-founder and CEO of Overland AI, a company building autonomous ground vehicles for the US military. Before founding Overland AI, you earned your PhD in machine learning from Carnegie Mellon, became a professor at the University of Washington and led the winning team in DARPA's racer off-road autonomy program. Overland AI has raised more than $140 million and become the first autonomous ground vehicle company to win a production contract with a fully integrated hardware and software platform. Congratulations. Your vehicles are already being used by the military units around the world to move supplies, support operations,

2:09and reduce risk for soldiers in the field. Welcome to the show. Thank you so much. I've got a lot to talk about here. It's been a minute since I've talked to somebody that's doing the kind of stuff that you're doing. But before we get going, I've got a couple of things to crank out here. Everybody gets a gift. Oh, wow. Those are, uh, thank you. Autonomous dummy bears. I'm excited. I love gummubi guys. So right on. Right on. And, um, and I got a, I got a question for you. I got a Patreon account. The subscription account. And, uh, 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

3:11have to ensure these innovations ultimately reduce human suffering rather than prolong conflict? And do you believe AI in 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? Oh, that's a great, great question. Um, you know, I think, I think autonomous systems, they're like any other tool. The way that I think about them is, is really a tool, a technology, and in the context of defense, um, it is, you know, something which allows a war fighter to, uh, you know, be safer, right? So, you know, it reduces exposure, pulls them away from the point of contact, um, and then also potentially, uh, provides, uh, force multiplication on the battlefield. I'm sure we'll get into, um, some of these things, but, um, it is a tool which is used by humans,

4:12and so, you know, how you use them, uh, is really, I think, a, a human question. Um, so, you know, in, in the context of, uh, saving lives, I think that they will save lives for, um, our war fighters, on the battlefield. Um, it's, it's very clear how they do that. Um, but, you know, they also conserve as a deterrence, like any, like any technology might, um, you know, if, if we have significantly stronger, um, robotic systems at our disposal, then, you know, we're going to be, uh, a little bit, you know, tougher to, to defeat in the battlefield, and our adversaries will see that. And so, um, in, in that way, you know, they can certainly serve as, uh, deterrence as well. Right on. I mean, yeah, watching some of the models you showed me outside, and then, um, you know, the, the, the videos and, and, and how they're going to be integrated in with the war fighters, and, and combat, I mean, it's, uh, you know, is a, is a former seal.

5:15What? Seeing what, seeing the, you know, a war, what war has developed into is just, I mean, it's as 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 guess not, but it's open over 10 years. But, um, I mean, I already have tons of questions and, and I can see so many different applications where this would be, uh, useful, and just so many different scenarios. Yeah, we should, we should definitely get into it. Before we do that, though, um, I do want to give you a gift as well. So, if you don't mind. Um, so let me, uh, come over here and, um, 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 pulled the seats out of, out of the Polaris range,

6:16is when we turn them into those autonomous vehicles, um, that we, that we saw outside. Well, what are you doing with the seats once, once you've, uh, pulled them from the vehicle, we make them into chairs. So we made one for you. Oh, yeah. Thank you. You can check it out. But it's awesome. Yeah. Yeah. Not bad. Not bad. There you go. But this in the office, thank you. Right. Yeah. Of course. That's awesome. All right, Byron. So before, before we get into everything overland AI, how did, let's do a little backstory on you. Where did you grow up? How did you get into this stuff? I mean, what's the backstory here? Yeah. I, I had, I had a whole career, uh, before, um, moving into, into defense tech. So, um, I grew up outside in New York in, in Connecticut. Um, uh, I was into computers and, and, uh, you know, was in the Boy Scouts and played sports and added, I think, a pretty typical, um, upbringing. Um, so, you know, that's maybe where things got started. Just love the

7:21outdoors and, uh, um, taking apart computers and playing video games and, and doing all the sorts of things that, that kids often do. Did you watch the Terminator grown up? I sure did. Yeah. Yeah. So, Terminator 2 was a, like, unbelievable, unbelievable movie. Um, you know, and, and, uh, you know, happy to talk about that a little bit more in the context of, of, of what we're building, but obviously robotics and science fiction were, you know, something that, that I really enjoyed. Were you a gamer? Yeah, I, I used to play, um, so, so back when I was in high school, I used to play Starcraft, like, quite a bit. Starcraft 1, as before, Starcraft 2 came out. Uh, so real-time strategy games, I, I did a lot of, um, played a lot of games like that. We, we talked about Warcraft before, you know, I, I used to play that too. Um, so that, that was really what, what I was most drawn to. Um, but yeah, I mean, love computer games. How, I mean,

8:22well, we'll get into a later. I was gonna ask how similar, you know, is, is what, what's happening today is controlling one of those games. We'll, we'll get into that in a little bit. So, yeah, something. Where did you, where did you go to school? What did you go to school for? So I went to college, uh, at a small liberal arts school called Bowdoin College. Um, it's in main, uh, and, you know, I spent four years there. I was a computer science and philosophy double major, um, as an undergrad. Uh, I started out really thinking about, um, you know, I love computer science, like I said, you know, all through high school. I also really liked history, uh, 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, um, something which is, you know, more humanities oriented, uh, with history. Um, but I quickly, you know, sort of figured out my, for my first year or that, um, well, I love history. I, it involved tons of

9:24reading and things like this that I really like to do, but, um, also involved foreign languages. And you need to actually read about, um, you know, about history through, uh, contemporary sources and, you know, in the language that folks wrote in. And so, um, there's something I was, I was not great at. I was not, not particularly good at languages, um, didn't really enjoy them. And so, um, you know, I started to, I took a couple courses in philosophy. I started out with a course called logic and formal systems, um, which was really delving into, uh, formal frameworks for understanding arguments and, um, analyzing arguments. And, um, I really started to fall in love with that. And so, uh, you know, I, I brought that together with, with computer science and, and major in, in, in both of those, those areas as an undergrad. Ventures like you're, you're fast, you're able to brain to, right? Yeah, yeah, yeah. So, um, I mean, in, in undergrad, uh, I was,

10:26you know, it's taking computer science and, you know, took courses in artificial intelligence and started to do research in robotics. Um, and this is back, you know, over 20 years ago. They had artificial intelligence courses 20 years ago. You know, it's pretty interesting. So my, my university is a small school. There was only four faculty in the computer science, um, department there. Um, and at the time, computer science was seen as like an off-shoot of mathematics. And so, um, you know, a lot of these smaller schools had combined departments with computer science and mathematics. And you, you take a lot of, um, courses in both areas. But, uh, at Bowdoin, two of the four professors were actually folks who studied artificial intelligence. So, um, it was something, you know, it's been around for, for a long time. I mean, people were working on aspects of, of AI, um, you know, back in the 70s and 80s. But, uh, it was just starting to kind of come to the, um, forefront and be an area, uh, that was, um, really starting to accelerate around, you know, 2000 when I was, when I was an undergrad.

11:32Um, so it 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. I think that's an interesting discussion. So, yeah, how, I mean, how deep did you get into that before you, you kind of switched years? Yeah. So, uh, as a, um, again, as an undergrad, a double major in computer science and philosophy. And, um, when I graduated, I was, you know, thinking about what I wanted to do next. Um, I initially took a job at a robotics company as an engineer, um, basically working on problems related to perception and mapping and robotic systems. So, um, these were mobile robots, uh, much smaller than the ones that, that we just saw outside. So, robots that are about, you know, about this big, um, that moved around inside of buildings and you have to determine where they are and how to get from

12:35one place to another and things like that. So, I worked as an engineer, um, working on those sorts of problems. But I was really thinking about, um, you know, kind of like, what do, 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, I wanted to go back to school, uh, and the question was like, what area should I study? So, really like computer science, I really liked philosophy. Um, and one of the things that I started to think about was, um, 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. Um, and then in philosophy, you're really thinking through language, um, 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, the nerve, the human nervous

13:35system, the substrate of, um, of the mind. And so, um, I decided that before I went back and entered into a graduate program, I needed to learn more about neuroscience. Um, and so I, I managed to get a job at Duke University, uh, in a neurobiology lab, um, 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. Uh, and this was really, it wasn't a graduate program. It was just working in a neurobiology lab, and I was auditing courses on, um, uh, neuroscience and neurobiology. Well, it was there, um, trying to learn, you know, how does, how does the mind work? I mean, how, wow, how the human mind perceives the world? Yeah. How, I mean, did you, did that, is that helpful in what you do today? It is. Um, it's, it's pretty interesting. So the lab that I was working in was really focused on

14:41trying to understand how humans perceive the world. And so let me just give you an example of, of why this is, um, difficult and interesting. Um, so the, when you look out, like at an environment, like, you know, this room, um, there's light, which bounces off of surfaces that 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. Um, and the question is, how do you go from that, like, 2D projection, um, that 2D image to understanding what's actually out in the world, like the 3D environment, um, the surface reflectance, you know, properties of, um, things like the wall, or, um, you know, the carpet or whatever. Um, 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

15:48solution 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 infinite number of different worlds could have produced the same visual image on your retina. And, um, this manifests itself through illusions. So there are certain types of illusions, or 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, you know, this kind of crazy shape. You know, it's something you can, you can look up, um, maybe later. But the, the, the interesting thing about that is just the fact that, um, 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, um, they basically, uh, everything you see in some ways is an illusion, right? So

16:56they'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. Um, it's the rule, not the exception. And so this is, this is, it forms a almost a philosophical problem. It's like, if you are looking at the world, but you can't actually infer what generated, you know, the images that you see, how do you even, you know, how do you even interact with it? Like how do you, 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, um, was that really the way that you see the world is whatever way is necessary, um, 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

18:00in 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. Uh, and so, um, there's just one of the, the problems that that we wrestled with. Now, what does that mean? It means that your perception of the world is really shaped through experience. Um, 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, um, with the systems that that we build, the robotic systems that we built. Very interesting. Yeah. Oh, wow. Well, 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

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20:05build it from there. If you're ready to hear the of your first sale today, head over to Shopify.com slash SOS to start your free trial today. That's right. Start your free trial at Shopify.com slash SOS. That's Shopify.com slash SOS. 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 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

21:10to 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 sort of led me from neurobiology at Duke, like really kind of thinking about data and machine learning as fundamentally interesting things towards my graduate education, which I pursued after that at Carnegie Mellon University. Wow. But about her,

22:13everybody talk about that that has gotten into the machine learning AI stuff. That's pretty fast knitting. 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, you know, artificial intelligence or how to build machines and things like this, what I pretty quickly realized was that, you know, 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 one of the lessons from that was that,

23:19you know, I almost came away with the opposite conclusion. So instead of thinking about the brain is something that would help me to build better machines or understand, you know, 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, what's in the Nvidia too as well, didn't you?

24:20That's right. Yeah. So I before starting Overland AI, I worked for about five years at Adin Video. And this is, well, it 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 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 are working in that lab. But you can take 20% of your time and work in industry simultaneously. And as part of that, I spent about five years working at Nvidia on machine learning and robotics. How was the work in there? That was awesome. I

25:21mean, I think I joined around 2018. So as 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. And 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, right? Like this massive parallel processing. And I was thinking about that in a context of robotics. So how can you paralyze tasks? How can you use it? Not just these sorts of chips and this type of technology, not just for perceiving the environment, but also controlling vehicles. So an example of this actually, it's carried through from work that I was doing at originally 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

26:23terrain, they're evaluating tens of thousands of possible trajectories that they might take. Looking, ranking each one of them, determining like, is this a good one or a bad one, and then choosing how to 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 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 every couple of weeks working with students. I like teaching, I like interacting with students. I think that's one of the great benefits of being a professor is just engaging with students and people who want to learn. So that part's fantastic.

27:28And I miss doing that. I haven't been teaching recently since I've been spun off the company. But I think working in industry also allows you to really scale your ideas more. So there's only so much you can do in a smaller research lab. And so in 2018, the Army Research Laboratory spotted you at an IEEE conference, demoing machine learning. What is an IEEE? It's an IEEE, so it's association for electrical engineers. There's one of the major types of conferences that folks publish in. So 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,

28:34you then tell the world about it, right? You publish it in a paper and you do this on a pretty fast iterative basis. So that's one of the major conferences in robotics. And we had some work there. And some folks from 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. All right, all right, all right. And then the DARPA racer, the crucible that bird over land AI, what was that? What's the purpose? Yeah, yeah. So, um, 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 vehicle. So these kind of smaller remote control vehicles. We put computers and sensors on them and made them autonomous. And we were trying to

29:40race them as fast as possible. So we were using data that we were collecting while we were driving these cars to learn a, what's called a policy. You think about it as like, you know, AI essentially, like for the vehicle that could perceive the world and try to drive really quickly. And these vehicles are doing things like drifting around turns and things like this. So they learn 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. So that's what the, the army was looking at. And is it actually learning? 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, 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, you know, accelerate a

30:44little bit here or I break a little bit there, does this make me, you know, faster or slower. And as it does that, it learns how to drive faster and faster and 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 it's just like on a track. Yeah, exactly. 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 vehicles, 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 terrains? So not just on tracks, but, you know, through forests and deserts and beaches and things like this. So what I was going to ask is, I mean, if this,

31:48if, 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. Well, 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, I don't know. I just had to go from a track to high speed chase in the middle of, I don't know, Manhattan. So this is kind of like a fundamental challenge in machine learning. Like if you learn, for example, how to drive really fast on a oval track where you're always going in one direction, right, you'll learn how to drive really fast while always turning left essentially, I would say. But then if you go on another track, which has right turns, will it be able to generalize and, you know, be able to perform well on a track like that? And the short answer is, you know, not without some sort of work.

32:53So generally speaking, you want to collect data in a wide variety of environments that are inclusive of the types of places that you might want to be driving in the future. So for example, you might have a complex dirt track with left and right turns and some wider turns and some sharper turns and things like this. If you train on a track like that, it's very easy to then race on like an oval track, right, because you've seen all of the things that you're likely to see. So that becomes easy. But if you, for example, train on a dirt track and now you have to drive on asphalt, you may not be able to do that super well. And so you need to collect new data as you move to that new type of environment or new type of problem, incorporate that into, you know, your learning algorithm and then it will, you know, start to do better on that new type of track. And that matters, you know, even now when we think about where we want to drive our vehicles, because if you

33:54only train, for example, in the desert, you're not going to be able to necessarily drive well through a forest or vice versa. And so you want to really train these systems, have them collect data and learn from as wide a set of environments as possible so that they're able when they encounter a new environment to still perform well, that there'll still be aspects of that environment that they've seen before. So, so I guess what my question is, will it get to the point where it runs the route for the very first time, like a brand new route for a very first time? It could be, it could be off road, it could be, sure, in the middle of a city, but we're talking left turns, right turns, heavy braking, fast acceleration, drifting, all of that stuff will it eventually learn what it needs to learn to be able to to have a complicated new route run at the first time

34:57and it will run it perfectly. It will go as fast as is the machine is capable of. Yeah, it's certainly possible. And like that's what we're always striving for. So when we're putting, you know, ungrilled ground vehicle, autonomous vehicles in new environments, they're already performing really well because it's seen many aspects of that before and you're trying to get it to perform, optimally, right? Like that is the goal, like potentially faster than a human driver, even on environments that it's never seen before. Every time our autonomous vehicles are out in the world, they are basically, you know, we we've trained them so that they're, we don't like pre-map environments or anything like that. It's as if they're encountering that environment for the first time and they're constantly learning. So the more environments that we encounter, the more data we get, like the better and better the system gets. But the goal is always kind of moving towards that optimal movement. How close are you to that goal? Depends, depends on the environment.

36:04I mean, I think we can we can already drive faster than than humans in some environments. So yeah, it's 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 here's what the situation was. DARPA back in 2004 and 2005 had something called the grand challenge or grand challenges. So these were challenges where they were trying to incep 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

37:09third 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 that's 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 provide 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 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

38:10organization 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 U.S. 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 a DARPA grant challenge where they were trying to race vehicles from, you know, Barstow, California to Primnovata, and it was about 135 miles on dirt roads. And they just laid down a challenge. It 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 Ashkash, you know, had a team. And there were just people who were trying to put together

39:13autonomous vehicles and their garage, like just build robots. And they went out there and raced. And in the first, in 2004, the first challenge, no one made it beyond seven miles. Like that was the, yeah. So it was, you see, I'm used 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. Those teams, like the folks from those teams, after that challenge was over, there were a few other ones. There was 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

40:13commercial 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 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 we're working on like robot 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

41:18the 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 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,

42:20you 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 they recruit you from the IEEE conference. So the way that that worked was like through the research I was doing and the publications that was, you know, putting out to the world, Army saw that the technology that we were, the 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. 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, US Army was one of the organizations that

43:25fund, like started to fund my research. And when you fund research, you can say, okay, 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, to the Army. So that's what I was, was doing when I was working at Georgia Tech in University of Washington was my lab was partially funded by the US 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 US Army. And then they decided to fund my lab at like a much higher level to 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, yeah, you've really been at the cutting edge of this thing though entire time.

44:28Yeah. So we've been working on these problems for, you know, more than, 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. For me, Helix feels high quality, durable and comfortable. It's not just a mattress that shows up

45:32in a box. It feels like something it's built to last. Helix ships straight to your door for free in the US. And you get a hundred and twenty night sleep trial, a limited lifetime warranty, and they're happy with Helix guarantee. So returns and exchanges are simple if it's not the right fit. Helix is also the most awarded mattress brand tested and reviewed by experts like Forbes and Wired. Go to helix sleep dot com slash SRS for up to 30% off. That's helix sleep dot com slash SRS for up to 30% off. Make sure you enter our show name after checkout so they know we sent you helix sleep dot com slash SRS. Do you ever wonder what it takes to make an episode of the Sean Ryan show? In this exclusive studio I'm taking you behind the scenes for an in-depth look at every part of the operation from the editing room in the main studio to the spaces where we film range day content and more. You'll see how the show

46:34comes together. Meet some of the people behind it and get a closer look at the work that happens off camera when you become a paid member of the SRS Patreon community. You get more than just the podcast watch new episodes early alongside other members. Join monthly live shows with guest Q&A and submit questions just like you see on the show for upcoming guests on the protector tier. 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 as part of the DARPA racer program so this is DARPA program where we're trying to develop ground vehicle autonomy for the military. We started out working with Polaris Razor side by sides so these are very

47:41capable vehicles that can go very fast. They've got four wheels, about 3,000 pounds about the size of a small SUV something like this. In the first part of the DARPA racer program we were just there were 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. You went from a side by side to a 12 ton vehicle. Yeah it's a 12 ton tracked vehicle. So it's about the

48:46size of a 113 so it looks like a tank like a sort of like a small tank I guess but pretty heavy vehicle tracked is electric so really aggressive. It has the ability to essentially go from you know 0 to 60 like super think about like a Tesla but like you know like a tank version of that. 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 do that and this was a competition correct. Yeah so so DARPA racer it started out with three teams. So it was our team and there were two other teams that we were competing against by the first summer about a year into the 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 on the side by sides but

49:48also 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 text run M5s ever produced but you know we had all of them and we were just like smashing through terrain and these huge tracked vehicles. Wow. Pretty awesome. And so 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 that's a great question. So how does it work? Yeah like this is this is a really good question actually because the way that DARPA normally works you know they have these programs where they're trying to develop new technology. In this case develop ground vehicle autonomy for the US military. So for our soldiers and Marines but often in a DARPA program even if the program is very successful like ours was how do you actually take that technology and then get it into

50:50the hands of the warfighter like how do you actually do that? It's a good question and very few people succeed. So the this known as like transition transitioning like a new capability that's developed from DARPA to the actual warfighter is something like 4% of technologies ever actually get transitioned. So just because you succeed in developing a new capability doesn't mean that the warfighter gets that capability. This is actually why we created Overland AI. We were like in this program and we had this new capability and we're just like this is incredible we can you know drive extremely fast off road we can do it in a variety of different types of vehicles. We have the thing that DARPA was you know wanted us to produce like we actually did it. Now how can the warfighter benefit and so we felt that the most the most promising way of making that transition was to

51:58spin off a company that would then take the capability that we developed as part of this DARPA program. Commercialize it so turn it into you know robust reliable commercial autonomy stack and then sell that back to the Army and Marine Corps so that they could use it on their programs so on their vehicles and so that warfighting units could start to work with this technology and start to integrate it into into tactics. So that's why we created Overland AI. Like we thought the Army like this is just going to disappear if we don't do this. So why would they fund the did they fund the research? Yeah so so DARPA funded all of this like they did to develop the capability but just because DARPA. And they just shelf it. So then what has to happen is like the Army and the Marine Corps or whoever like one of the services has to then put up money

52:58to be able to then like buy and transition that technology because the DARPA or from so yeah so the way that DARPA works is that DARPA like these DARPA programs are generally four years long. They have a time limit so and that's part of of the point like it gives you a very focused window in time to just do whatever it takes to try to develop this new technology but at the end of those four years that program is over and you have to find someone who is then going to support the continuation of that technology you know to like to move it into warfighting units right. So like you've created it now someone has to continue to like transfer it to like get it onto military vehicles to push it into units and that's where a lot of these programs die because you like create the technology but you can't that no one picks it up like when it's over. I would

54:03think there would be so this is I mean what would you call this like an incubator like you get you get you get picked to get funding to be an incubator and then DARPA doesn't even they don't own the actual well they do they they they own the IP so also only IP. Yeah so the way that this works is like when when so DARPA pays for the development of you know this core technology in this case I'm running a a lab at the University of Washington so they pay the University of Washington to develop the IP when they do that that IP belongs to both DARPA and the University of Washington right because you know DARPA pays for a university to develop it you know they're both both parties essentially own that IP but once you have that IP in this case it is like raw technology right like it is research grade code that that can demonstrate this capability that

55:08will allow a vehicle to drive fast but it's not going to be reliable in the same way that you know like a Waymo is where you have a whole team of engineers that is making production quality code so they own that like they own the like basic capability and and they own that IP but that then has to be taken and turned into essentially a commercial production ready piece of software it's it's almost like a rough draft I think I would think there would be so but then someone has to kind of pay for it for that transition and that's often again where these programs end up having problems where like you develop a new technology but then who adopts it who actually transitions it to the to the warfighter are these class of I mean I was are these class of I would think there would be venture capital firms galore just surrounding these projects well yeah yeah

56:13so so sometimes yes right for only 4% make it to the warfighter this 96% of the projects like oh yeah let me yeah so so but but in some sense that's actually what happened here right because we said okay like we're going to take this IP we're going to we license it right so that IP is licensed to overland AI we created this company to essentially take that IP and then turn it into a commercial autonomy stack that you know is reliable and we keep building on it we keep improving it and then we can transition that back to the to the warfighter so overland AI got it start by focusing on the software portion of this right take these good ideas that were developed during DARPA you know we license that IP at overland we build on top of it we turn it into a commercial autonomy stack and now we have this software that you can put on to a vehicle to make that

57:13vehicle autonomous and we had worked with you know different types of vehicles we discussed like the side by side or like you know the big textron M5 that we can make autonomous you know we really want to develop software which would work on any vehicle that the military had and so overland was stood up to do that and to your point about VCs you know we're a VC backed company right so they're essentially making that bet that you're saying it's like okay we can step in provide additional funding to turn this into a mature product that can then be transitioned over to the the warfighter but now you're in like defense tech territory right where you now have to fight all of these battles to go from you know this good core idea good core IP to actually getting it procured right and that that takes you know time and effort and and so like our

58:16story as as a defense tech company is is really similar to a lot of other stories except that we got this start by you know doing all of this you know sort of initial research to accept a new technology that the military really wanted very interesting very and so you so the DARPA competition ends you win and yeah so the information with you and develop overland AI right so we we started overland AI and you know we overland actually was formed before the DARPA competition completed so we became part of of the competition as well we then as the competition was was going on you know we were developing new technology from moving like really quickly through a lot of different biomes so different types of environments and then we were really trying to think and this is the overland AI started in in 20 like December 2022 so three and a half years ago and we were

59:22initially focused on okay we have this software we're you know we're finding it we're turning into a commercial piece of software how do you then get traction with like the army and marine core I mean not just DARPA but like you know the people who ultimately need to use this technology and the problem was that even though we had this like great piece of software it has to go on a vehicle so it's like what vehicle are we going to put it on we started to talk to companies and units that had vehicles which could be made autonomous but there just weren't that many of them out there we had something which could be very effective but we didn't have vehicles to put it on so we started out you know again with a software stack but we quickly realized that we had to actually also build the hardware so that we could get this idea of autonomous vehicles into the hands of warfighters faster like so we just started to build the vehicles too

1:00:23and that's that's what resulted in like this ultra vehicle that you know you've seen seen outside where you know this is an autonomous vehicle that we built and we put our software on so that we could start to put many different types of payloads you know on the vehicles and start to work with warfighting units to really integrate autonomous systems into their concepts of of operation so our path really is initially research and development starting with DARPA that then we worked with the defense innovation unit and got a prototype contract with them we then started to work with Army Applications Lab and you know winning you know these severs these small contracts with the Marine Corps and the Army we're doing really well with those and you know we then last year we revealed like the ultra vehicle fully autonomous you know

1:01:26vertically integrated vehicle that you could push into hands of warfighters we started to push this technology into units who were constantly testing them and then this led to a production contract so we just recently won the first production contract for autonomous ground vehicles in the US military and that's with the Marine Corps and so it's really this this whole process of and it didn't take that long right it's about three three and a half years where we went from like university lab R&D through prototype fielding with warfighters production contract so we felt things were in half years yeah yeah well I mean so who won the contract or who who who who are you contracted to yeah yeah with with with the with the Marine Corps so the Marine Corps is buying a whole set of the autonomous vehicles that that we produce it's part of their ground-based air defense program and so they'll initially start using those vehicles for autonomous

1:02:32resupply of basically air defense systems so trying to make sure that they get you know enough ammunition as they're shooting down drones can I ask how many you're going to manufacture for them? Yeah so we're we're manufacturing you know the first tranche about 15 of these you know in the next next year or so so that's that's what we're starting out with and one of the things which is you know pretty interesting about these vehicles they're modular so a lot of units want to start using them for resupply where they're just kind of putting supplies on them and moving those supplies back and forth but we've been doing a lot of work with with other units like 82nd airborne, 173rd airborne other warfighting units to put other types of payloads on board the platforms as well and so one of the things that we've been talking to the Marines about is putting sensors and

1:03:34you know affectors so basically kinetic counter UAS payloads on the vehicles and thinking about how to disaggregate them so one of the things one of the ways that you can use autonomous vehicles is by you know essentially putting your sensors and putting your your counter UAS for example payloads on vehicles and moving them away from the places that you're trying to defend right so you're disaggregating you're dispersing you're essentially making it much harder for an adversary to be able to take everything else that like all of the pieces out when you're when you're defending an area. Interesting so they're going to be using this for logistics. You're using this for defense and I would imagine there's going to be an offensive commode in. Yeah yeah so no already the way that we're thinking about this is so logistics so you can think about

1:04:34resupply 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 warfighters right so you want to be using them for things like intelligence surveillance and reconnaissance being able to move them through you know bad weather they can persistently you know 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 capability so you can put kinetic payloads on them you can put drones on them launch them off of of the vehicles so you can you know move

1:05:38them 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 essentially putting sensors and shooters in 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 let's run out back and take a look at this. 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 the last thing I have time for is standing around a gym wondering what to do next that's why I'm on the ladder app I've got a real coach in my ear on every set what's next what wait why it's a new plan

1:06:40every week that builds on the last and it goes where I go the garage with dumbbells or a hotel room on the road 30 to 45 minutes and I'm done for less than a dollar a day I've trained my whole life in the teams nothing we did was random somebody who knew more than you wrote the program and you got results that's the difference between working out and training ladder puts that kind of real programming in your ear 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 slash 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 seven day trial with no credit card and ten dollars off your first month if you join hi we're out here with Byron Boots CEO and founder of Overland AI

1:07:47give 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 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 light are so this allows it to see you know in this whole area in front of the vehicle now what degree so you can see 360 degrees around it so like you have these sensors in the front there's also sensors in the back so it can just kind 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 this 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

1:08:50computer systems are your power batteries 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 light are again what's what is the light are do so light are basically allows you to see depth in the area around the robot so think about it as like a depth sensor it tells you the distance to surfaces in the terrain okay yeah what what what engine are you running in here I think it's it's um 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 emraiser commercial side by side vehicle okay okay

1:09:55so the engine drivetrain 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 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 um 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 um 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 with that if you mount a weapon system on here is it all run through autonomous the autonomous stack as I would 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

1:11:00it will say something like you know go 10 kilometers to this location orient in this way and then they will access um the payload through the comms network so the human is still in the loop whenever you're using um the payload but the movement of these types of vehicles uh 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 just like to 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 go here 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

1:12:04an interface which is a little bit like a real-time strategy game like Warcraft or Starcraft where you know it allows you to you know select the units tell them where to go um execute the payloads and and so on wow so I just um I mean with with uh with the changing landscape of the battlefield now is the first land like autonomous land uh vehicle I've seen yeah so you can mount like an apparatus directly on your weapon on this thing yep well so if let's say I mean and Durauls got their stuff coming out shield AI's got that new X-Bat they they came out with um you can mount the drone you can mount counter UAS so what I'm asking is a venture I don't know for 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 Seronic you know overland AI when we do go to a full-scale war

1:13:08yeah are you going to be like is the operator or the battle or the ground force commander or just the the commander of the entire operation are they going to be controlling shield AI's X-Bats overland AI's ground vehicles Seronics surface warfare vehicles submarines drones all of it all of it on the same system the military is working on this right now uh finding ways to integrate all of these different pieces into um 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 they're you're likely to see there are single systems where you can see all of the different pieces and then there will likely be systems which will allow the warfighters to actually be controlling some subset of them on the battlefield but it all have to be linked together to provide that overall awareness of

1:14:08of what you're pushing out there and so if you have a hundred two hundred of these things right here what is the name of this is an ultra the ultra yeah hundred two hundred three hundred altars 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 somebody going to have to so so what the the way that this is is going we're taking so the the basic idea is that you start with just being able to move like one vehicle at a time right so um you can say I want this vehicle to look go to this location you just let it go you know execute a payload there um we're 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

1:15:12the 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 um and they're going to go out there and just do it and that's part of what's called um 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 you know 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 um and that's mission autonomy they're coordinating uh 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 in the battlefield will be pulled into command and control systems which allow people to see everything

1:16:16uh which is is out there wow I got a I got a million questions for you but it's it's hot shit out here in humans 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 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 can see 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 the magenta areas are lethal so those are areas that you know the vehicle you don't want to move the vehicle into with 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 bro this is this is going to be

1:17:18about 50 meters around so it's only seeing up here on this 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 pull 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 sight so you can do this from like 5,000 miles away um wow so let down yep and then forward forward

1:18:18it is it's we're not looking at the vehicle yeah i want i want to look over there so all right far away off holy shit gonna go how do you go backwards okay so to go backwards um hold down this so both that and this and then that and then move that backwards there you go oh there we are it will automatically stop but won't run us over let's not test it all right yes

1:19:21yeah there it is slowing for a person yeah yeah well that's not gonna work and war buy or just get you can you can turn that off haha dude this is crazy so it's somebody be looking at this or like a VR headset or does i guess you could probably do no matter you could do whatever um like generally you're looking at this uh you can look through the other sensors on board the vehicle as well um so you can you know look at the rear sensors or off to the sides um you can access the the payloads uh on on the vehicle um through the interface up here um wow all right and then if you want uh whoa oh shit there's a tree yeah maybe back up

1:20:36okay she 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 um i think one of the challenges for tele operation is that you don't have uh like a vehicle sense right like you can't feel like how the vehicle's moving like you would when you're driving um and now you're trying to interpret uh what the vehicle can see like through its own sensors should probably stop right yeah um what it's it's actually much safer to put it into autonomy and just tell it where you want it to go and it will find a way to get there without you know hitting anything and we do that while staying safe yeah try that you 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 though uh let me do this uh on on the road can we go over there can we go over here

1:21:37watch 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 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 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 different does it pick up like body temperature or how does it look it looks and so it's just using a camera and it's silhouette yeah and saying like this looks like a person this looks like a vehicle and it's able to pick them out cool yeah and this is full autonomy while we're good really to do here

1:22:42yep holy shit 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 them up yeah so we can actually pretty wild that it picked them up in the middle of all those weeds and trees and yep you can see one of the benefits of of this is you know you can just tell it I want you to go to

1:23:43again like this location and you can let it go and it will we'll do its thing so I was in the run basically came up the hill you know went around the field and then stopped there so very short run for what it normally does but you can get a taste of kind of like how it moves that is sick all right well thanks for showing us uh what the old trick can do and let's go wrap up the interview yeah thanks for letting us come out here and show you thanks for bringing that ass 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 Mars men what I like about Mars men is that it's not forcing hormones into your body it's designed to help your body use more of what it's already

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1:25:49okay so after taking a look at this thing I this this this just has so many different capabilities and could be used for so many different things I mean even just keep I mean you'd it sounds like the first thing the military is interested in as is logistics which I mean just keeping major supply routes open yeah I mean constant detection because 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 one of my camera guys who was I mean really wasn't even that close to it looked like he was made of 25 yards 30 yards from it yeah in in the middle of a bunch of trees and I picked him up like that could this thing pick up IEDs is is there a way that you could that it could detect IEDs I mean yeah so you put a variety of different types of sensors on the

1:26:51vehicle so you can certainly detect like people and vehicles and then you can also you know we've been putting tether drones on these so you know the drone can go up in the air be able to look down on on the ground so if you have you know 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 that's certainly something they they can do into your point you know 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 be game changing right you don't have to put a person out there to do it do you see these integrating with with human forces that are that are

1:27:51do you see these is is a forward observations platform for maybe a you know a slew of tanks or emrades or whatever we're using and then kind of push that information back to the larger force or as it go in all autonomous I think the re so you know there's a number of statistics out there which are pretty interesting so you've got like Ukrainian ground commanders basically saying that they're going to replace something like 80% of infantry with uncrewed ground vehicles in in the near term and you know those statistics are are interesting I think the reality will be a little bit more subtle so these are machines that can integrate with human formations you should think about them as giving humans more capability on the battlefield so I think the way that they'll actually work with

1:28:52the US military is that we'll have formations with humans and they will be pushing these vehicles out in front as you suggested in order to be able to get a better understanding of the terrain that they're going to be moving into but you can use them for all sorts of things right so it's not just sensing what's out there you can use them to create like diversions right like you can use them you know to you can use them as you know for strike capability you can use them to create that kind of counter UAS bubble to protect humans I think about them as you know something that will be like a force multiplier I mean you're saying that Ukrainian you Ukrainian military saying that it's it could replace will replace up to 80% of infantry 80% of the infantry yeah and you're saying the US is saying well maybe not the US but you see a subtle integration why will we do a

1:29:56subtle integration I mean I know on one hand I'm going to piss the war fighter up because they love fighting war so on the other hand I'm thinking about my toddlers at home you know with all the wars that were were were were involved in and re-involved in and I don't want my I don't want my kids going over there having to do it I get you know and so why do you see a subtle integration I wouldn't we go full scale and say hey you know like we we don't need private Joe driving this you know driving this M-Rap into hostile territory why don't like that's a that's a I don't want to say a wasted life but it could be it would be a wasted life if he got killed when we have the capability of going full autonomous right now I think we'll eventually get there so one of the value propositions for like ground autonomy is that you're really focused on the what we just one of the hardest domains right in the military right so just kind of stepping back there are a lot of folks who

1:31:02are focused on on air power naval power missiles but at the end of the day wars are ultimately one in the ground you know that's where people live right we don't live in the sea we don't live in the air we live on the ground and that's that's you know where we have to fight and you know I think when you look at at the U.S. and and you know our casualty rates something like more than 80% of combat related casualties since World War II our infantry so of course you want to like you know I think like focusing on the ground makes a lot of sense because we want to bring technology to the war fighter that will save those lives right and so yes we want to take we want to take humans out of harm's way and I think you know in in Ukraine we're seeing technology you know increasingly being pushed out in front of of the war fighter and I think they're very optimistic about about

1:32:04how they want to use them and ultimately we do want to do that right we want to we want to pull we want to essentially reduce risk on the battlefield right pull war fighters away from these points of contact have more people who are you know maybe operating teams of robots from you know that are far away from location where they where they could get hit but in the meantime I think that the way they're going to be integrated is in a way that helps war fighters in the ground to stay safer to potentially give them more more options more tools without totally pulling pulling them out so you know to your point like yes like we want to we want to ensure that you know we ultimately want to pull you know as many people have harms way as possible but you know these are are still machines that aren't you know quite as creative and adaptable as humans and so I think it's the

1:33:05what we're really trying to do is find the places where we can maximize the ability for them to be able to take risk while also allowing our humans to do what they do best and and do maybe more specialized specialized roles on on the battlefield so I think it'll be a process I mean just thinking about my time you know in Iraq I've gone stand alone I mean I have right IED Threv was was was huge and then here is a key statistic here 44% of US killed an 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 I mean there's 43 so almost 50% yeah people that were killed would still be here today yeah you know

1:34:08just with just that application I think there's a lot you can do there I mean there's you know sensors you can put on the vehicle human sensor like I was just saying with caught my guy out in the weeds trying to yeah in a bunch of trees I mean rolling down Iraq I mean there were sniper problems there was the guy the trigger the trigger men of IEDs I mean I would just I would think that it would pick them up a media I mean you can't they can't hide from the machine yeah how is it sensing them yeah so the the machine has cameras on it right and we depending on the the load out for it you have you can have sort of normal RGB cameras you can have or thermal cameras right like there's a lot that you can do to detect people and vehicles in the environment so I mean I was just using cameras out there I was able as you said just be able to pick someone up even in you know in the woods and in the weeds but you know you should think about like there's a lot

1:35:10of different ways that these can be used out again out in front of the war fighter you know you can have convoys of of autonomous vehicles that don't have people on them right that can move very quickly and move supplies back and forth even if they're targeted at least no human is being killed you can have them autonomous vehicles out in front of human convoy as well right so really making sure that there is no one out there that you know moving first through the terrain so generally speaking there's a lot of ideas in army and Marine Corps about how to use these vehicles out in front again like leading convoys or it's like a protective onion almost right you can think about a whole set of vehicles that are surrounding higher value assets whether they're people or you know tanks or proudly fighting vehicles or you know xm 30 which is the replacement for that having autonomous vehicles that can provide sensing and protection for for those formations

1:36:14so we think about them as adding to some of the things that that were we're developing how many of these scenarios of you kind of war game to run scenarios on out at wherever wherever quite quite a few I mean I think people are once they start to see the vehicle and they start to think about it they are coming up with lots of ways of potentially using them and as we you know one of the things we've been trying to do in the last year is work with a lot of different units so we've worked with over 20 different different units integrating this technology into the unit for example the 82nd Airborne we worked with 382 for almost six months just embedded with them up through one of their training rotations down at GRTC and in Louisiana and we started out just doing resupply they were like this is kind of the obvious use case for this let's use this to support our logistics to allow us to move faster we were able to do that and so then they were like okay like can we get cameras and drones

1:37:17and things like that on it the answer is yes and so we started to add different types of payloads to it they started to use them in more creative ways and by the end of this you know they were using these autonomous vehicles in you know in this four-some-fourse exercise not just for resupply but also for intelligence surveillance and reconnaissance there were snipers who were using the vehicles as decoys right like there's just a lot of things you can start to start to do and the first step is really just like getting it in the hands of the warfighter right like let them think about the problems they're trying to solve experience the technology and then you know start to iterate on you know potentially new tactics and and things like this that you can do with the vehicles now are these are these truly autonomous or is there somebody 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 the great

1:38:21question so you know in this is sometimes kind of glossed over a lot of people say that they can do autonomous things and people think about uncrewed ground vehicles people talk about UGVs all the time in Ukraine versus autonomous vehicles so what are the differences between these things right so remote control basically means you have a controller like in your hand and you're looking at the thing and you're driving it so think about like a toy car right like that's that's a remote control vehicle now that's fine except you have to you have to actually look at the terrain yourself look at the vehicle and determine where it's going to go that obviously doesn't work beyond line of sight or I'm in areas where you can't see the terrain very well so the next sort of evolution of this is tele operation and people talk about tele operation a lot with ground vehicles this is basically where you take a remote control and you look through the vehicles own sensors whether it's like camera or thermal sensors and then you drive the vehicle based on that sensory feedback that you're

1:39:25getting over a network so you can go beyond line of sight because you just see what the vehicle sees you don't have to see the vehicle itself so you can tele operate something from like across the world if you have a really good you know connection to that vehicle if you have a good satellite connection or radio connection to the vehicle and a lot of the work which is being done in Ukraine is tele operated uncrewed ground vehicles where you'll actually have a set of people like up to five people who are controlling each vehicle and essentially driving it through the terrain using you know like a handheld controller or you know they're looking through potentially either the vehicles cameras or the cameras from a drone overhead and they're just trying to drive this thing around the thing which is hard about that is that you know it occupies at least one person's attention at all times because the person's making all those decisions but it also is something where if your comms gets disrupted the thing's just dead right like it requires a human to drive it and the human can no

1:40:29longer connect to it so one of the strategies is you cut the comms to the vehicle you you know use EW or whatever to cut the comms and then you strike it because it's just a sitting duck so autonomy is extremely powerful because it allows the vehicle to sense the environment represent it plan through the environment all on board like at the edge makes its own decisions and it doesn't require a human to to essentially drive it so when you have an autonomous vehicle you can tell it where you want it to go and it will just go there and that means that you can focus your attention on something else that might be another autonomous vehicle it might be another task so for example you can send a vehicle back to resupply you but you don't have to be focused anymore you can do your job the second thing about autonomy which is important to understand is because the vehicles making decisions based on its own sensing and own on board compute if your comms get cut the things

1:41:35going to keep going and you know continue the mission and so one of the things we're starting to see out of the Russian Ukraine conflict is that the Russians are starting to add autonomy to ground vehicles to deal with the contested comm situation because it's so hard for them to maintain comms autonomy allows the vehicles to continue to move without human oversight so that's another piece of this so we think about this as kind of force multiplication and resilience to a contested comms environment well now so another thing with autonomy to correct me from wrong is the let's see have 100 these yeah say 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 shield AI with their with their X-pad we got Seronic with the autonomous boats we got and Durow making stuff we have mock making stuff with all these companies are jumping all up you know

1:42:38lots of autonomous stuff I but just just overland AI alone I mean let's say that there's 500 of these vehicles yeah we're talking I don't know the invasion of Fallujah yeah these will all be able to communicate with each other on in accomplish the mission without being mic 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 this is our vision for 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 a hundred 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 make you know

1:43:42analyzes 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 a immense effect on the battlefield to control many many different vehicles and the payloads on them and so you know going back to earlier part of the conversation we view this as as something like you know a gamer who's playing like a real-time strategy game something like like Starcraft where you're controlling like 200 different units can a person do that in the real world with you know real platforms like 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

1:44:44I just brought up all these other companies will it will it move to the point where these overland AI vehicles are communicating with shield AI's X-bat with Seronix boats I mean we'll we'll it will it get to the point where the entire battle space is coordinated under one brain so I you know the the sure 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 you know not just kind of

1:45:44handing everything over to to an AI brain but having you know essentially like AI assistants who are allowing a human to choose courses of action to coordinate you know ground vehicles or payloads or aerial vehicles more effectively and and so on let's talk about let's talk about African lion what happened there okay so African lion is an exercise it's one of the largest exercises in Africa so it's a place where U.S. warfighters work with with partner nations and things like that to essentially train and and demonstrate capabilities we we're part of African lion working with 173rd airborne so the 173rd airborne had two of our ultra vehicles with different types of payloads on them one vehicle had a cruise remote weapon station with an M240 machine gun

1:46:49on it another vehicle had a rocket propelled a breaching system on it and they were using these vehicles for a breaching operation so essentially what happened was they sent one vehicle forward with machine gun on it providing security and the second vehicle was quickly following it moved to a breach point essentially applied the payload so the it's an explosive line charge something like a micklick if people know what that is but essentially an explosive rope that gets launched out in front of the vehicle and produces a it blows up and produces like a safe corridor that can then be proved with with another vehicle so they're using this to reduce obstacles out in front of of the force and as as part of an of an assault and the thing which is really important about this is that is extremely dangerous operation like when you're doing things like breaching every

1:47:54area of the defensive obstacle belts are being watched by by an adversary and as you move forward to try to reduce those obstacles like the combat engineers everyone's targeting them so even in a successful breaching operation you're expecting something like like 50% casualty ratio so if you're able to do that with autonomous vehicles like the way that like 173rd airborne was was demonstrating that's taking you know in their estimate up to almost two platoons about about 40 people out of that extremely dangerous situation you're just sending the machines forward to do that to work that problem to create the breach in the obstacle belt and then they're able to to move through so they're very excited about about the technology but it's just an example of like warfighters using using our vehicles putting payloads on them coordinating as part of their

1:48:54units movement maneuver so pretty exciting to see just fascinating stuff yeah wow the game has changed a lot yeah holy shit here's the pitch it takes two minutes and you'll never think about it again download it hit one button close the app that's the entire job no manual no settings no tech background nothing for you to get wrong and then it just runs blocking the trackers the fishing sites and the surveillance ads across every app on the device all day quietly without anybody having to do anything it's first week out it hit number six on apples top downloaded productivity chart built by former US intelligence professionals amico founder glacier downloaded on the app store and remember

1:49:58privacy isn't paranoia it's protection all right so we're back from the break and you know one thing I'm kind of wondering as it is what other I mean you mentioned Russia was doing some of this correct what is what about China are they yeah pretty on the ball with just about everything yeah we don't have or at least I don't have detailed knowledge of of everything that they're doing but we do read academic papers that come out of 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-rogram vehicle autonomy you know similar to some of the stuff that that we're doing we think we're pretty far ahead right

1:50:59now but you know they're they're working hard to catch up so what kind of is there any what are the kind of weapons systems do you think we'll be putting on these these ultras I think for for any of you know you think about uncrewed ground vehicles in autonomous ground vehicles you can put it depends on the size of vehicle you can put like almost anything on them right so any type of vehicle that you have today which can carry anything from you know a very small vehicle might be a couple hundred pounds up to tens of thousands of pounds you can put on on a potentially time as 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

1:52:00large missile systems right so ship interdiction missiles anti air missiles all sorts of things you could put pretty much anything on those pretty much are you familiar with apparatus yes so you could put Leonidas mounted on one of these damn things and absolutely then you have yeah drone defense for an entire fucking battalion yeah usually when we're thinking about drone defense you know it's going to be a layered defense so you're going to want maybe something like you know appresses 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 uh vehicle you know many different vehicles with different defensive systems creating that 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

1:53:00I mean even if you lose some of your your vehicles or systems they're more to take their place let's talk about the technical mode the team and the piratheret sure um so the way that we think about the technology that we're developing we really are thinking about it as um you know developing an autonomous version of core battlefield functions right so like either the ISR or breaching for for as as examples where um 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

1:54:06what 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 modes is the fact that we're the best in the world at being able to see and understand terrain and move vehicles to 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 um so we think that that's our you know one of our biggest technical modes is we have just this incredible team um coming from you know deep tech uh places like Waymo and Cruise and you know self-driving car companies um some of the top

1:55:10artificial intelligence labs you know they're working for us focused on these problems and creating you know this AI essentially that allows you to to move payloads in the environment so that's technical mode can't quite remember the other questions but that's um really a foundation of everything I mean we'd one thing that I didn't ask I think we started talking about it out there and then I said we'll come back in and discuss it because it was so damn hot out there but yeah uh you know what does it look like you know when we're talking about controlling all these autonomous uh vehicles especially what it comes to you hundreds I mean what and we're in it's not remote control so what is it I mean we'd kind of discuss a little bit out there but what does it look like yeah for for the team or the person or 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

1:56:13controlling 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 the different you know autonomous assets on the map um 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 uh and then the question is how do you now coordinate and control hundreds of 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 add 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

1:57:16strategy game uh we're also working on tools so this looks like this is what we were talking about to begin this looks like sounds weird looks like world of warcraft yeah yeah you're you're sending a group of things giving it a task it's it's not under the next thing yeah i mean and i and i think you know 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 um like Starcraft is a great example of that and Warcraft um but you know the tools that people use for for grouping for moving for coordinating assets in 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 we're developing is um things like AI assistance which can help to

1:58:19um suggest you know particular tasks so you can just say like hey i need to move 10 vehicles you know into this area provide a reconnaissance here and it will tell me how those vehicles are going to move um suggest to me solutions to these problems so that i can make decisions faster so this is a part of like essentially using AI to increase your decision advantage now we're not saying we hand over you know the actual decision making to the AI but the AI can suggest solutions that can can help you to handle you know 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 24 hundred Americans and here's the part people forget we had warnings we'd broken their

1:59:25codes our own ambassador flagged a possible attack a year earlier and congress later said the real failed 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 built 90% of the world's drones and they just flew a mothership not long ago that launched a hundred 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 i think it's correct there are warnings everywhere um we're seeing robots used in the battlefield again in in in Ukraine that you know there's both by the Ukrainians and and russians um 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

2:00:30adversaries have and also ensure that the u.s military has similar better capabilities and so 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 um and 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 um so you know i i think like we are certainly aware of it um i think we're pushing the military to to adopt new technology and defenses against it like as quickly as possible uh but you know there there may be work to be done i sometimes these things aren't really real to people until unfortunately they actually um experienced them so our our best bet is to really be watching and taking seriously uh what is happening in in these conflicts and in what our

2:01:31adversaries are developing i mean do you feel the 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 yeah it's very surprising and almost alarming to me that they you know 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 it should be um you know i think the department of defense is trying to our department of war is and 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 you know all of the defense tech founders who you you've had had on here um we'll probably agree with right and something that we're all fighting hard against now um the we've seen the department of war speed

2:02:36things up and you know some of you like like i was saying but a little bit earlier we have actually gone from you know university research to production contract even if it's you know a relatively small initial one um in like three years which is about right maybe a little slow for like you know the the tech sector but that's lightning fast for for um department of war and we've made use of defense innovation unit and a lot of the new tools um an apt fit contracting process and things like that that have um come online recently so i you know i do want to credit the department of war for moving faster but there's still so much work to be done uh in order to 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 our other countries better customers than our own country for this problem for for like not not not necessarily like their own stuff for for for stuff that

2:03:40a merit not not not overland a i for things that america americans um like yourself are developing here in this country um our other countries maybe you crane yeah i mean i i think you know 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 it's i mean it's it's uh yeah and i mean it's it's it's it's such an upgrade it's especially with a company like emperor all these companies man yeah like which every everybody you know everybody that's sat across with me in the defense tech sector what you guys are developing is it's fucking incredible yeah you know but i see it being utilized in Ukraine you know what i mean and and i see it in i in i

2:04:45it bothers me when when when when we're sending all the stuff maybe we aren't but i 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 scarce me yeah i mean we there's there's kind of uh like we have all this we have all this talent like there's things to be concerned about and i and i think there's there's things to be hopeful about so like on the one hand um um you know you're i think you're right i think countries like you create i mean they're adopting tech as fast as they possibly can there's just a statistic out saying something like there's been two million casualties in in that conflict right and um about a million and a half Russian and about half a million Ukrainian um and one of the ways that Ukraine has been able to continue to fight and hold off the Russians is through you know all of this technological innovation um it's existential

2:05:50for them they they have to do this or they will lose right like and so um they will do whatever it takes to to win here um it's not existential for the us yet and i think that is means that adoption is just slow like people aren't feeling the um the pressure to to do it and i i think that you know it's unfortunate of course because we have a little bit of luxury um right now like we are not in a conflict like the Ukrainians um so we have the space to be able to potentially build this technology and transform our forces but we don't have the urgency right and so i think i think that's part of the problem on the on the positive side i do think that we have the best minds in in the world here i think we can do it um but it's again it's a matter

2:06:53they are doing it we are doing government that's not fun keeping up with you guys but it could be it could be done faster right like we could accelerate this um and uh you know that's a a fight that we just have to continue to um to make right to to continue to fight to get this tech into the government faster and and i think going back to you know thinking about some of the DARPA stuff earlier like we're creating incredible new technology but then you really got a fight to actually get the um army and marine corps and services um to actually you know try it iterate you know ultimately ultimately use it and incorporate it yeah yeah if we were attacked tomorrow do you think we could survive a sea or ground invasion i mean i think i i think we would absolutely survive a sea or ground invasion i think we would mobilize very quickly i think those

2:07:55the urgency would be there and you know we would we would do whatever it took to to win so i'm um you know i think very um positive and optimistic on that like 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 we too last thing yeah manufacturing are you guys manufacturing these yourself yeah so scale up so we're we're trying to scale as quickly as possible um we are manufacturing 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 um factory running in in Seattle that's doing this right now but um and we i think we've like five X manufacturing over the last last six months or so and we just we need to do a lot more so we're building up that capability as as fast as possible and again the more support we get from

2:09:01um from the government the more we we actually work with warfighters the more demand there is and there's there's a ton of demand right now so um we're scaling as quickly as we can right on well barn i really appreciate you covenant it was an honor to interview you and i love love everything overland a ice doing and that thing out there is super impressive thank you yeah thank you so much for for having me i really appreciate you taking the time to to learn a little bit more about what we're doing and inviting us out and and being able to to show you some of the things that we're building so thank you thank you 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 and most importantly share this everywhere you

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