#336 Byron Boots - He Turned a Polaris RZR Into a Self-Driving Military Vehicle

Professor Byron Boots is the co-founder and CEO of Overland AI and a leading expert in machine learning, robotics, and autonomous systems. A full professor at the University of Washington with a PhD from Carnegie Mellon, he previously held research roles at NVIDIA and Google and led the University of Washington's DARPA RACER team to victory. Through Overland AI, Byron is developing autonomous ground vehicles for the U. S. military, helping modernize battlefield logistics, improve operational effectiveness, and reduce risk to service members. Privacy Isn't Paranoia. It's Protection. Download Glacier - https://srs.site/glacierapp Website - https://theglacierapp.com Shawn Ryan Show Sponsors: Free trial at https://shopify.com/srs Go to https://helixsleep.com/SRS for up to 30% off. If you have an iPhone, go to https://ladder.fit/SRS to take a quick quiz, get matched with your coach, and get a free 7-day trial (no credit card needed) plus $10 off your first month if you join. For a limited time, our listeners get 50% off FOR LIFE, Free Shipping, AND 3 Free Gifts at Mars Men at https://Mengotomars.com Byron Boots Links: X - https://x.com/Overland_AI_X Youtube - https://www.youtube.com/@OverlandAI Website - https://www.overland.ai Learn more about your ad choices. Visit podcastchoices.com/adchoices

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Speaker 00

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• 1:04Shawn Ryan?

  Byron Boots, welcome to the show.

• 1:07Speaker 02

  Thanks for having me. It's awesome to be here.

• 1:09Shawn Ryan?

  It's awesome to have you. So I can't even remember. I think I actually found you guys on LinkedIn, which I'm never on. And I think I saw like 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 APC is an investor. And so, or led you around. And so, yeah, wanted to get in touch and love what you guys are doing. Looks, you know, I've had lots of tech giants on here, a lot of drone stuff, Sironic with the water stuff. I guess they, I didn't even know. Your guy just told me out back that they just hit the, they hit Iranian port?

• 1:53Speaker 02

  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 incredible. But yeah, just in the last day or so, there was news of Sironic's boats being used in an offensive operation in the Strait of Hormuz.

• 2:10Shawn Ryan?

  Wow. And so you're the ground guy?

• 2:13Speaker 02

  That's right.

• 2:13Shawn Ryan?

  You're bringing autonomous vehicles to land warfare? So really excited to get into, to dig into this. But let me, let me kick it off with a 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

• 2:57Shawn Ryan?

  with a fully integrated hardware and software platform. Congratulations. Your vehicles are already being used by military units around the world to move supplies, support operations, and reduce risk for soldiers in the field. Welcome to the show.

• 3:14Speaker 02

  Well, thank you so much.

• 3:17Shawn Ryan?

  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, got a couple of things to crank out here. Everybody gets a gift.

• 3:30Speaker 02

  Oh, wow.

• 3:32Shawn Ryan?

  Those are autonomous gummy bears.

• 3:37Speaker 02

  I'm excited. I love gummy bears.

• 3:38Shawn Ryan?

  Right on, right on. And I got a, I got a question for you. I got a Patreon account, a subscription account. And so they're the reason I get to sit down here with you today. And so they get the opportunity to ask every single guest a question. So this is from Thomas W. You've dedicated your career to advancing robotics and AI, including technologies with defense applications. From your perspective, what responsibility do scientists and engineers have to ensure these innovations ultimately reduce human suffering rather than prolong conflict?

• 4:18Shawn Ryan?

  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?

• 4:35Speaker 02

  That's a great, great question. You know, I think, I think autonomous systems, they're like any other tool. The way that I think about them is really a tool, a technology, and in the context of defense, it is, you know, something which allows a warfighter to, you know, be safer, right? So, you know, it reduces exposure, pulls them away from the point of contact, and then also potentially provides force multiplication on the battlefield. And I'm sure we'll, we'll get into some of these things. But it is a tool which is used by humans. And so, you know, how you use them is really, I think, a human question.

• 5:20Speaker 02

  So, you know, in, in the context of saving lives, I think that they will save lives for our warfighters on the battlefield. It's very clear how they do that. But, you know, they also can serve as a deterrent, like any, like any technology might. You know, if we have significantly stronger robotic systems at our disposal, then, you know, we're going to be a little bit, you know, tougher to defeat in the battlefield and our adversaries will see that. And so in that way, you know, they can certainly serve as a deterrent as well.

• 5:57Shawn Ryan?

  Right on. I mean, yeah, watching some of the models you showed me outside and then, you know, the, the videos and how they're going to be integrated in with the warfighters in combat. I mean, it's, you know, as a, as a former SEAL, seeing what, seeing the, you know, war, what war has developed into is just, I mean, it's fascinating. And I mean, just, it's been over 20 years since I've been on the ground in a war. And, well, I guess not. It's still been over 10 years. But, I mean, I already have tons of questions and I can see so many different applications

• 6:40Shawn Ryan?

  where this would be useful in just so many different scenarios.

• 6:45Speaker 02

  Yeah, we should, we should definitely get into it. Before we do that, though, I do want to give you a gift as well. So if you don't mind, so let me.

• 6:55Speaker 02

  Come over here and we got you a chair. Now, this isn't just any chair.

• 7:01Shawn Ryan?

  Right on.

• 7:03Speaker 02

  This is, it looks kind of like an office chair, but this is actually a seat from one of the vehicles. So as I was explaining earlier, we pulled the seats out of, out of the Polaris Rangers and we turned them into those autonomous vehicles that we, that we saw outside. Well, what do you do with the seats once, once you've pulled them from the vehicle? We make them into chairs. So we made one for you. Oh, man. Thank you. We can check that. This is awesome. Yeah, yeah.

• 7:35Speaker 02

  Not bad, not bad. There you go.

• 7:40Speaker 02

  Put this in the office. Thank you. Yeah, of course. That's awesome.

• 7:44Shawn Ryan?

  All right, Byron. So before, before we get into everything Overland AI, how, 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?

• 7:55Speaker 02

  Yeah, I had, I had a whole career before moving into, into defense tech. So I grew up outside of New York in Connecticut. I was into computers and, and was in the Boy Scouts and played sports and had, I think, a pretty typical upbringing. So, you know, that's maybe where things got started. Just love the outdoors and taking apart computers and playing video games and doing all the sorts of things that kids often do.

• 8:29Shawn Ryan?

  Did you watch the Terminator growing up?

• 8:30Speaker 02

  I sure did, yeah.

• 8:31Shawn Ryan?

  Did you?

• 8:33Speaker 02

  So Terminator 2 was a, like unbelievable, unbelievable movie. You know, and, you know, happy, happy to talk about that a little bit more in the context of what we're building. But obviously robotics and science fiction were something that I really enjoyed.

• 8:54Shawn Ryan?

  Were you a gamer?

• 8:55Speaker 02

  Yeah, I, I used to play. So, so back when I was in high school, I used to play Starcraft like quite a bit. Starcraft 1, that was before Starcraft 2 came out. So real-time strategy games. I did a lot of, played a lot of games like that. We talked about Warcraft before. You know, I used to play that too. So that, that was really what, what I was most drawn to. But yeah, I mean, love computer games.

• 9:19Shawn Ryan?

  Hell, I mean. Well, we'll get into it later. I was gonna ask how similar, you know, is is what what's happening today is control in one of those games. We'll we'll get into that in a little bit. So where did you where did you go to school? What did you go to school for?

• 9:34Speaker 02

  So I I went to college at a small liberal arts school called Bowden College. It's in Maine. And, you know, I spent four years there. I was a computer science and philosophy double major as an undergrad. I started out really thinking about, you know, I love computer science, like I said, you know, all through high school. I also really liked history, read a lot of history. And so when I went to to college, I was thinking about maybe double majoring in computer science and history. I thought it would be cool to have a more technical degree and something which is, you know, more humanities oriented

• 10:12Speaker 02

  with history. But I quickly, you know, sort of figured out my my first year that, well, I love history, it involved tons of reading and things like this that that I really like to do, but also involved foreign languages. And you need to actually read about, you know, about history through contemporary sources and, you know, in the language that that folks wrote in. And so it's something I was I was not great at. I was not not particularly good at languages, didn't really enjoy them. And so, you know, I started to I took a couple courses in philosophy. I started out with a course called Logic and Formal Systems,

• 10:56Speaker 02

  which is really delving into formal frameworks for understanding arguments and analyzing arguments. And I really started to fall in love with that. And so, you know, I brought that together with with computer science and majored in in both of those those areas as an undergrad.

• 11:17Shawn Ryan?

  Interesting. You're you're fascinated with the brain, too, correct?

• 11:20Speaker 02

  Yeah, yeah, yeah. So, I mean, in in undergrad, I was, you know, I was taking computer science and, you know, took courses in artificial intelligence and started to do research in robotics. And this is back, you know, over 20 years ago.

• 11:35Shawn Ryan?

  They had artificial intelligence courses 20 years ago?

• 11:38Speaker 02

  Yeah, it was pretty interesting. So my my university is a small school. There was only four faculty in the computer science department there. And at the time, computer science was seen as like an offshoot of mathematics. And so, you know, a lot of these smaller schools had combined departments of computer science and mathematics, and you you take a lot of courses in both areas. But at Bowden, two of the four professors were actually folks who studied artificial intelligence. So it was something, you know, it's been around for for a long time. I mean, people were working on aspects of of AI, you know, back in the 70s and 80s. But it was just starting to kind of come to the forefront

• 12:23Speaker 02

  and be an area that was really starting to accelerate around, you know, 2000 when I was when I was an undergrad. So I started to study AI there. And then in philosophy, I was thinking about things like philosophy of mind and philosophy of science. And you're just kind of trying to understand how the the human mind worked.

• 12:45Shawn Ryan?

  Well.

• 12:46Speaker 02

  Yeah.

• 12:47Shawn Ryan?

  I think that's an interesting discussion itself.

• 12:51Speaker 02

  Yeah.

• 12:52Shawn Ryan?

  How I mean, how deep did you get into that before you you kind of switched gears?

• 12:55Speaker 02

  Yeah. So as a, again, as an undergrad, I double majored in computer science and philosophy. And when I graduated, I was, you know, thinking about what I wanted to do next. I initially took a job at a robotics company as an engineer, basically working on problems related to perception and mapping and robotic systems. So these were mobile robots, much smaller than the ones that that we just saw outside. So robots that are about, you know, about this big, that moved around inside of buildings, and you have to determine where they are and how to get from one place to another and things like that. So I worked as an engineer,

• 13:38Speaker 02

  working on those sorts of problems. But I was really thinking about, 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 wanted to go back to school. And the question was, like, what area should I study? So I really like computer science. I really liked philosophy. And one of the things that I started to think about was cognitive science, you know, just sort of how the mind works. So with AI, you know, you're trying to program a computer that can almost like think like a human that can perceive the world, that can understand it somehow. And then in philosophy, you're really thinking through language.

• 14:20Speaker 02

  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 system, the substrate of of of the mind. And so I decided that before I went back and entered into a graduate program, I needed to learn more about neuroscience. And so I I managed to get a job at Duke University in a neurobiology lab studying human perception. So I worked as an engineer for about a year, and then I went to Duke, and I worked there

• 15:05Speaker 02

  for two years. And this was really, it wasn't a graduate program. It was just working in a neurobiology lab. And I was auditing courses on on neuroscience and neurobiology while I was there, trying to learn, you know, how does how does the mind work?

• 15:22Shawn Ryan?

  I mean, how, wow. How the human mind perceives the world?

• 15:27Speaker 02

  Yeah.

• 15:28Shawn Ryan?

  How, I mean, did you did that? Is that helpful in what you do today?

• 15:33Speaker 02

  It is. It's it's pretty interesting. So the lab that I was working in was really focused on trying to understand how humans perceive the world. And so let me just give you an example of of why this is difficult and interesting. So the when you look out like at an environment like, you know, this room, there's light which bounces off of surfaces, it comes back and it, you know, moves through your eye and essentially is projected on your retina. So there's for each of your eyes, there's a 2D projection of light from the room. And the question is, how do you go from that, like, 2D projection,

• 16:18Speaker 02

  on that 2D image, to understanding what's actually out in the world, like the 3D environment, the surface reflectance, you know, properties of things like the wall, or, you know, the carpet, or whatever? How do you sort of solve that problem? And it's called the inverse optics problem. So it's the notion that you have a 2D image, and you're trying to kind of understand this complex 3D world. And the challenge is that there's actually not an easy solution to this, because you're moving from essentially, like, three dimensions to two dimensions, information is lost. And so, another way to think about this is that

• 16:59Speaker 02

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

• 17:40Speaker 02

  So that is just an example of of one of these optical illusions. Now, the interesting thing about illusions is that they basically, everything you see in some ways is an illusion, right? So they're not outliers. It's not like every once in a while, your mind makes a mistake and you kind of see the world incorrectly. You are always inferring some world that is not quite what is actually out there. It's the rule, not the exception. And so this is, this is, it it forms a almost a philosophical problem. It's like, if you

• 18:19Speaker 02

  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 if you're not seeing things properly? And so, you know, the conclusion, one of the conclusions that we came to, was that, really, the way that you see the world is whatever way is necessary to allow you to continue to persist. So we kind of think about this as like, you see the world in an evolutionarily sort of appropriate way, in a way which informs your actions, so that you kind of do the

• 19:04Speaker 02

  right things, you continue to exist, you can ultimately reproduce and and continue on. And so, it'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. You're just perceiving the world in the best possible way for you to take actions. And some of these fundamental ideas actually carried through into the work I did in graduate school, and even some of the things that we do today, with the systems that that we built, the robotic systems that we built.

• 19:43Shawn Ryan?

  Very interesting. Wow.

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• 21:19Shawn Ryan?

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• 21:26Shawn Ryan?

  So where do we go from here?

• 21:28Speaker 02

  So, you know, I think, like, one thing, one of the, you know, when you think about building a robotic system, one of the ways that we kind of think about this is that when you're perceiving the environment, you're not just measuring it, you're taking the context and your prior experience and you're using that to predict what you think like the world actually is. So, for example, if you look out at a set of trees, you're not just measuring that there's, you know, some obstacles out there in front of you, you're also predicting that there's free space behind them that you can potentially move through.

• 22:09Speaker 02

  And you get to that point by essentially seeing lots of trees, like, in your past, right? Like, you use that prior experience, and then you can understand when you see a pattern like this, that actually means that there's, you know, 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.

• 22:51Speaker 02

  And those notions, you know, sort of led me from neurobiology at Duke, like, really kind of thinking about data and machine learning as fundamentally, you know, interesting things, towards my graduate education, which I pursued after that at Carnegie Mellon University.

• 23:10Shawn Ryan?

  Wow. But I've not heard anybody talk about that, that has gotten into the machine learning, AI stuff. That's pretty fascinating.

• 23:21Speaker 02

  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.

• 24:05Speaker 02

  We can describe a lot about it, but not really understand it functionally. And so, one of the, one of the lessons from that was that, you know, I almost came away with the opposite conclusion. So instead of thinking about the brain as something that would help me to build better machines or 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.

• 24:48Speaker 02

  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.

• 25:11Shawn Ryan?

  Wow, wow. So you worked at, went to Nvidia too as well, didn't you?

• 25:19Speaker 02

  That's right, yeah. So I, before starting Overland AI, I worked for about five years at Nvidia. And this is while I was a professor. So after Carnegie Mellon, I got my PhD there in machine learning, I worked on robotics problems. And then I was a professor at Georgia Tech for five years, and then University of Washington for seven years after that. And one of the things which is really cool about being a faculty member or 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

• 26:04Speaker 02

  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, spent about five years working at Nvidia on machine learning and robotics.

• 26:18Shawn Ryan?

  How was it working there?

• 26:20Speaker 02

  It was awesome. I mean, I think, you know, I joined around 2018. So it was before Nvidia was really kind of like a tier one, let's say, tech company. I think like Google and Microsoft were really up there, sort of defining state of the art. 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. How can you parallelize tasks? How can you use it not just, you know, these sorts of chips and this type of technology,

• 27:00Speaker 02

  not just for perceiving the environment, but also controlling vehicles? So, an example of this actually, you know, is carried through from work, you know, that I was doing at, originally at Georgia Tech, and then Nvidia, now to Overland, where when that vehicle is out in terrain, so when our uncrewed vehicles are out there, and looking at terrain, they're evaluating tens of thousands of possible trajectories that they might take, 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?

• 27:41Speaker 02

  Well, you can use Nvidia GPUs to parallelize, you know, these tasks and evaluate many trajectories simultaneously, and then decide how you're going to move based on that.

• 27:54Shawn Ryan?

  Very, very interesting. Do you still, do you miss being a professor?

• 27:59Speaker 02

  Well, yeah, I'm currently, I've got a 5% appointment at the University of Washington, which means that I'm there, you know, every couple of weeks, working with students. You know, I like teaching, I like interacting with students, I think that's, that's one of the great benefits of being a professor is, you know, just engaging with students and people who want to learn. So that part's fantastic, and 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.

• 28:40Speaker 02

  So, you know, there's only so much you can do in a smaller research lab.

• 28:45Shawn Ryan?

  Mm-hmm.

• 28:46Speaker 02

  Yeah.

• 28:47Shawn Ryan?

  And so...

• 28:50Shawn Ryan?

  In 2018, the Army Research Laboratory spotted you at an IEEE conference, demoing machine learning. What is an IEEE?

• 29:00Speaker 02

  It's I triple E, so it's it's Association for Electrical Engineers. It's one of the major types of conferences that that folks publish in. So, you know, when you're a professor, one of your main goals is to publish papers, right? And those scientific papers further human knowledge. And so in computer science, the way that you do this is you publish papers at a lot of conferences. You work with with your graduate student, you develop a new technology, you then tell the world about it, right? You publish it in a paper and you do this on a pretty,

• 29:41Speaker 02

  pretty, you know, fast iterative basis. So, you know, that's this is one of the the major conferences in robotics and we had, you know, had some work there. And, you know, some folks in the Army were seeing what we were doing and had some cool ideas of how we could potentially like take that fundamental research and start to apply it to to Army problems.

• 30:04Shawn Ryan?

  Right on, right on. And then the DARPA racer.

• 30:09Speaker 02

  Yeah.

• 30:10Shawn Ryan?

  The crucible that birthed overland AI. What was that? What's the?

• 30:15Speaker 02

  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 vehicles. So these kind of smaller remote control vehicles. We put computers and sensors on them and made them autonomous. And we were trying to race them as fast as possible. So we were using data that we were collecting while we were driving these cars to learn 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.

• 31:01Speaker 02

  And these vehicles are doing things like drifting around turns and things like this. So they 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.

• 31:14Shawn Ryan?

  Oh shit.

• 31:15Speaker 02

  So that's what the the Army was looking at.

• 31:17Shawn Ryan?

  Now, is it actually learning or are you programming that into it?

• 31:21Speaker 02

  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 little 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,

• 32:01Speaker 02

  but then it's looking at its own behavior, learning from that and driving, you know, figuring out how to drive faster.

• 32:09Shawn Ryan?

  Now, is this like on a track?

• 32:11Speaker 02

  Yeah.

• 32:11Shawn Ryan?

  Same like a same?

• 32:12Speaker 02

  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 terrain? So not just on tracks, but, you know, through forests and deserts and beaches and things like this.

• 32:44Shawn Ryan?

  So, uh, excuse me, what I was gonna ask is, I mean, if this, if, okay, if we have a race car, it's going around a track over and over again, and it is learning. And then you switch up the track. Will, I mean, will it be able to take what it learned on track A and apply it to track B immediately? Or will it, do you understand?

• 33:07Speaker 02

  Yeah.

• 33:07Shawn Ryan?

  Basically, what I'm saying is, how do we go from a track to high speed chase in the middle of, I don't know, Manhattan?

• 33:17Speaker 02

  So this is kind of like a fundamental challenge in machine learning. Like if you learn, for example, how to drive really fast on oval track where you're always going in one direction, right? You'll learn how to drive really fast while always turning left, essentially, let's 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 of work. So generally speaking, you want to collect data in a wide variety of of environments that are inclusive

• 33:59Speaker 02

  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 that you're, you know, likely to see. So that becomes easy. But if you, for example, train in 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.

• 34:43Speaker 02

  And then it will, you know, start to do better on that new type of of track. And that matters, you know, even now, when we think about where we want to drive our vehicles. Because if you only 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.

• 35:18Shawn Ryan?

  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

• 35:34Speaker 02

  Sure.

• 35:35Shawn Ryan?

  in the middle of a city, but we're talking left turns, right turns, heavy braking,

• 35:41Speaker 02

  Yep.

• 35:41Shawn Ryan?

  fast acceleration, drifting, all of that stuff, will it be able, will it eventually learn what it needs to learn to be able to to have a complicated new route? Run it the first time and it will run it perfectly? It will go as fast as is is the machine is capable of?

• 36:03Speaker 02

  Yeah, it's certainly possible. And like, that's what we're always striving for. So when we're putting, you know, uncrewed ground vehicles, 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, you know, optimally, right? Like that is the goal, like potentially faster than a human driver, even on environments that it's never seen before. And 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.

• 36:47Speaker 02

  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. And then the goal is always kind of moving towards that optimal movement.

• 37:00Shawn Ryan?

  How close are you to that goal?

• 37:02Speaker 02

  Uh, depends, depends on the environment. I mean, I think we can, we can already drive faster than than humans in some environments. So, um, yeah, it's it's pretty cool.

• 37:12Shawn Ryan?

  Wow, very interesting. And so how long did you spend at DARPA?

• 37:18Speaker 02

  Oh, 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 incept ground autonomy. So this actually goes back to the NDAA in in 2001.

• 38:02Speaker 02

  This is basically where Congress decided in 2001, they were like, by 2015, we want a third of all military ground vehicles to be autonomous. So I think about this, you know, over 25 years ago. The problem was, no one knew how to do that. Like, there weren't, it wasn't like there were autonomous vehicles, you know, driving all over the place and that's, this would be easy. No one, no one knew how to make these vehicles autonomous.

• 38:27Shawn Ryan?

  Where did the idea come from?

• 38:28Speaker 02

  Uh, well, I, I think, you know, there had been, you know, work in robotics where people were moving, um, vehicles in simpler environments autonomously. And so the, the, the notion was like, well, what if we could do this on the battlefield, right? What if we could do it in these more complex environments? If you could take the warfighter out of the vehicle, you can imagine, you know, that not just provides safety, but potentially, you know, tactical things that you can do. We can, 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 organization

• 39:12Speaker 02

  that is designed to just like create new things, right? Like you have some crazy challenge, DARPA is out there, um, and can create a program, pull together some of the best minds in, in the U.S. to focus, like really focus on it for, you know, up to about four years, um, and try to solve a problem. And so in, in 2004 and 2005, they came up with the DARPA Grand Challenge where they were trying to race vehicles from, uh, you know, Barstow, California to, um, Prim Nevada. It was about 135 miles, um, through, on dirt roads. And they just laid down a challenge. They said, like, if you can do this, you get prize money.

• 39:55Speaker 02

  And all sorts of teams came together to attack this problem. So there were university teams, um, from places like Carnegie Mellon and Stanford and, and, um, MIT and so on. And there were, um, industry teams, like Ashkosh, you know, had a team. And there were just people who were trying to put together autonomous vehicles in their garage, like just build robots. And they went out there and raced and in the first, um, in, in 2004, the first challenge, no one made it beyond seven miles. Like that was the, you know, so it was, it was, CMU's vehicle, I think, made it that far. It got stuck, caught fire. It was, it was like a whole thing. Um, the next year though, in 2005, I think five teams competed this challenge.

• 40:38Speaker 02

  They made it the entire 134 miles.

• 40:41Shawn Ryan?

  Oh, shit.

• 40:41Speaker 02

  Those teams, like the folks from those teams, after that challenge was over, there were a few other ones. There's something called the Urban Challenge and, um, some other DARPA programs which followed, uh, 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, um, or Aurora Innovation and, and, and so on. So these, um, autonomous driving company, you know, commercial companies. So by, you know, 2012, let's say, a lot of work was being done in the commercial sector, bootstrapped off of this DARPA work, right?

• 41:23Speaker 02

  So the military, you know, started this whole thing because they wanted autonomous vehicles. People started to build autonomous vehicles because of this DARPA program, but then, um, basically just went off into industry and were working on like robot taxis and autonomous trucks and things like this. So by like 2019, like coming back to, to my story, um, 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.

• 42:04Speaker 02

  And so DARPA Racer was a program that got stood up. It started in 2021 to reboot autonomy for defense. So specifically to take a lot of the learnings that had been produced over the previous, you know, 20 years or so for the on-road autonomous driving industry and, you know, work which had been done in robotic perception and, um, 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. Um, so, you know, no roads at all, right?

• 42:49Speaker 02

  Like through deserts, through forests, through snow, things like this, um, and contested terrain. So thinking about, you know, how do you move when you, not only do you not have infrastructure which is there to help you, like roads or road networks or signs or things like this, but infrastructure which might be in the environment which is there to defeat you, um, to stop you. And so, um, that's what the DARPA Racer program was. So I had already been doing work at, um, you know, at Georgia Tech and then University of Washington, um, working with the Army on developing off-road ground vehicle autonomy.

• 43:28Speaker 02

  And I then put together a team, it's called a performer team, uh, to attack these problems for the military through DARPA, um, starting in, in, in 2021.

• 43:39Shawn Ryan?

  So did they recruit you from the IEEE conference?

• 43:43Speaker 02

  So, so the, the way that that worked was like through the research I was doing and the publications that I 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, um, 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.

• 44:13Shawn Ryan?

  Okay.

• 44:14Speaker 02

  But you need funding to, to run that lab. And so people pay you to essentially do research. They pay your lab to do research. So Army, um, US Army was one of the organizations that funded, like started to fund my research. And when you fund research, you can say like, hey, here are the problems we want you to solve. We'll give you, you know, this much money to solve them with your PhD students. Um, and then you provide those solutions back to, to the Army. Um, so that's what I was, was doing when I was working at Georgia Tech and, and 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.

• 44:56Speaker 02

  Um, and then we started to work with DARPA, which is, um, at the time Department of Defense, but Department of War, like level organization. Um, 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.

• 45:12Shawn Ryan?

  Wow.

• 45:13Speaker 02

  To like attack these problems of how do you drive vehicles, um, off-road and all sorts of different types of terrain at, at high speeds.

• 45:22Shawn Ryan?

  Wow. So you've been.

• 45:23Speaker 02

  Yeah.

• 45:24Shawn Ryan?

  You've really been at the cutting edge of this thing the entire time.

• 45:27Speaker 02

  Yeah. So we've been working on these problems for, you know, more than, more than 10 years, more than a decade. Um, and my lab was doing a lot of that work in academia, you know, before we spun out overland.

• 45:39Shawn Ryan?

  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

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• 47:07Shawn Ryan?

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• 47:51Shawn Ryan?

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• 48:12Shawn Ryan?

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• 48:19Shawn Ryan?

  All right, let's talk about the the Textron M5.

• 48:23Speaker 02

  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 Razr side by sides. So these are very capable vehicles. They can go very fast. They've got four wheels, about 3,000 pounds, about the size of a small, you know, small SUV, something like this. And, you know, in the first part of the DARPA racer program, we were just, there were multiple teams and we were essentially like racing these Polaris Razrs.

• 49:04Speaker 02

  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

• 49:37Shawn Ryan?

  Holy shit.

• 49:38Speaker 02

  Much larger.

• 49:38Shawn Ryan?

  You went from a side by side to a 12 ton vehicle?

• 49:41Speaker 02

  Yeah, it's a 12 ton tracked vehicle. So it's about the size of a 113. So it's, it looks, looks like a tank, like a sort of like a small tank, I guess, but pretty heavy vehicle tracked. It's electric, so really aggressive. It has the ability to essentially go from, you know, zero to 60, like super, think about like a Tesla, but like, you know, like a tank version of that.

• 50:08Shawn Ryan?

  Wow.

• 50:10Speaker 02

  So really, really awesome vehicle to work with. And we're putting autonomy on that vehicle and then driving that through off-road terrain.

• 50:17Shawn Ryan?

  Okay.

• 50:18Speaker 02

  Yeah.

• 50:18Shawn Ryan?

  How did that, and this was a competition, correct?

• 50:20Speaker 02

  Yeah, 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, you know, 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 also the much larger vehicles. So we're the only team that actually worked with, with these. And there aren't that many of them. There were something like, you know, four or five Textron M5s ever produced. But, you know, we had all of them and we were just like smashing through terrain in these, these huge tracked vehicles. Pretty awesome.

• 51:06Shawn Ryan?

  And so what is, what is the, what is the kind of the point of the competition? Do they buy the technology from you or, or do they, they fund the technology?

• 51:16Speaker 02

  That's a great question. So

• 51:18Shawn Ryan?

  How does it work?

• 51:18Speaker 02

  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,

• 51:47Speaker 02

  how do you actually take that technology and then get it into the hands of the warfighter? Like, how do you actually do that? It's a good question and very few people succeed. So the, this is known as like transition, transitioning like a new capability that's developed from DARPA to the actual warfighter. It's 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

• 52:27Speaker 02

  and we had this new capability and we were 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 spin off a company that would then take the capability that we developed as part of this DARPA program, commercialize it. So turn it into,

• 53:07Speaker 02

  you know, a 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. We, like, we, we thought the Army, like, this is just going to disappear if we don't do this.

• 53:36Shawn Ryan?

  So why, why would they fund the, did they fund the research?

• 53:41Speaker 02

  Yeah, so, so DARPA funded all of this, like, like they, to develop the capability, but just because DARPA

• 53:47Shawn Ryan?

  And they just shelved it?

• 53:48Speaker 02

  So, 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 to be able to then like buy and transition that technology. Because

• 54:05Shawn Ryan?

  From DARPA or from you?

• 54:06Speaker 02

  So, yeah, so, 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

• 54:51Speaker 02

  into, 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.

• 55:02Shawn Ryan?

  I would think there would be, so, so this is, I mean, what would you call this? Like a incubator? Like you get, you get, you get picked to, to get funding to be an incubator? And then DARPA doesn't even, they don't own the actual?

• 55:15Speaker 02

  Well, they do. They, they, they, they own the IP, so

• 55:18Shawn Ryan?

  You also own the IP?

• 55:20Speaker 02

  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

• 56:01Speaker 02

  research grade code that, that can demonstrate this capability, that will 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, the like basic capability 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.

• 56:45Shawn Ryan?

  I would think there would be

• 56:47Speaker 02

  So, but then someone has to kind of pay for, 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?

• 57:04Shawn Ryan?

  Are these classified? I mean, I would just, are these classified? I would think there would be venture capital firms go lord just surrounding these projects.

• 57:12Speaker 02

  Well, yeah, yeah. So, so sometimes, yes, right?

• 57:15Shawn Ryan?

  Only 4% make it to the warfighter. There's 96% of projects.

• 57:19Speaker 02

  It's crazy, right?

• 57:20Shawn Ryan?

  Like, oh, yeah.

• 57:21Speaker 02

  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, 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 its 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

• 58:04Speaker 02

  Overland. We build on top of it. We turned it into a commercial autonomy stack. And now we have this software that you can put onto a vehicle to make that vehicle 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 wanted 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

• 58:43Speaker 02

  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, and, and effort. And, and so like our story 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,

• 59:24Speaker 02

  sort of initial research to accept a new technology that the military really wanted.

• 59:30Shawn Ryan?

  Very interesting. And so, so the DARPA competition ends, you win, and

• 59:37Speaker 02

  Yeah.

• 59:39Speaker 02

  So

• 59:39Shawn Ryan?

  All the information with you and develop Overland AI.

• 59:42Speaker 02

  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 for 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 Overland AI started in in twenty, like December twenty twenty two, so three and a half years ago. And we were initially focused on, okay, we have this software, we're, you know, refining

• 1:00:26Speaker 02

  it, we're turning it into a commercial piece of software. How do you then get traction with like the Army and Marine Corps? Like 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? And 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

• 1:01:09Speaker 02

  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. So we just started to build the vehicles too. And 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.

• 1:01:53Speaker 02

  So our our path really is initially research and development, starting with DARPA. That then we 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 CIBRs, these small contracts with the Marine Corps and the Army. We're doing really well with those. And, you know, we've then last year, we revealed like the ultra vehicle, a fully autonomous, you know, vertically 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.

• 1:02:38Speaker 02

  So we just recently won the first production contract for autonomous ground vehicles in the US military and 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 and D, through prototype, fielding with warfighters, production contract.

• 1:03:02Shawn Ryan?

  Wow.

• 1:03:03Speaker 02

  So we felt

• 1:03:03Shawn Ryan?

  Three and a half years.

• 1:03:04Speaker 02

  Yeah, yeah.

• 1:03:07Shawn Ryan?

  I mean, so who won the contract or who who who who are you contracted to?

• 1:03:13Speaker 02

  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 resupply of basically

• 1:03:37Speaker 02

  air defense systems. So trying to make sure that they get, you know, enough ammunition as they're shooting down drones.

• 1:03:43Shawn Ryan?

  Can I ask how many you're going to manufacture for them?

• 1:03:49Speaker 02

  Yeah, so we're we're manufacturing, you know, the first tranche, about fifteen 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 eighty second airborne, one hundred seventy third airborne, other warfighting units to put other types of payloads on on board the platforms as well. And so one of the things that we've been talking to Marines about is putting sensors

• 1:04:32Speaker 02

  and, you know, effectors, 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, like all of the pieces out when you're when you're defending an area.

• 1:05:17Shawn Ryan?

  Interesting. So they're going to be using this for logistics, they're going to be using this for defense, and I would imagine there's going to be an offensive commodant.

• 1:05:25Speaker 02

  Yeah, yeah, so

• 1:05:26Shawn Ryan?

  There isn't already.

• 1:05:27Speaker 02

  The way that we're thinking about this is, so logistics, so you can think about resupply and casualty evacuation. A lot of people, when they think about autonomous vehicles, this is like the first thing which comes to mind because they think about a vehicle and they're like, oh, okay, like I can put stuff in it and I can move it, right? Like, that's like what a vehicle does. But I like to think about these vehicles more like robotic systems. They can sense, they can track, they can move on their own on the battlefield. And I think like the real product market fit here is that you want to move these vehicles out in front of the warfighters, right? So you want to be using them for things like intelligence, surveillance and reconnaissance,

• 1:06:12Speaker 02

  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 for some of these things. You can use them for, you know, defense or strike capabilities, so you can put kinetic payloads on them, you can put drones on them, launch them off of of the vehicle, so you can, you know, move them into areas that might be too risky for a human, but they can potentially hold ground or try to take terrain in those areas. Breaching is a major thing that we're working on, so we're working with a number of different combat engineering units on removing people from breaching operations, which are just

• 1:06:56Speaker 02

  exceptionally dangerous. And then air defense, as I was I was discussing, is essentially putting sensors and shooters in in different locations and moving them autonomously, reconfiguring what that defensive position might look like.

• 1:07:13Shawn Ryan?

  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 thing.

• 1:07:18Speaker 02

  Yeah, sounds great.

• 1:07:22Shawn Ryan?

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• 1:08:42Shawn Ryan?

  Hi, we're out here with Byron Boots, CEO and founder of Overland AI. Get ready to take a look at this beast here. What are we looking at?

• 1:08:50Speaker 02

  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 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. LiDAR. So this allows it to see, you know, in this whole area in front of the vehicle.

• 1:09:20Shawn Ryan?

  At what degree?

• 1:09:22Speaker 02

  So actually 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 there's all these attachment points. So it makes it easy to integrate new payloads onto the vehicle. Below this deck, you have compute. So that's where your computer systems are, your power, batteries. The alternator, like all this is below the deck. And then back here, you've got comms, so satellite comms.

• 1:10:02Speaker 02

  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 LiDAR again.

• 1:10:17Shawn Ryan?

  What's the, what does the LiDAR do?

• 1:10:19Speaker 02

  So LiDAR basically allows you to see depth in the area around the robot. So I'm thinking about it as like a depth sensor. It tells you the distance to surfaces in the terrain.

• 1:10:32Shawn Ryan?

  Okay. Yeah. What, what, what engine are you running in here?

• 1:10:37Speaker 02

  I think it's, it's, you have to ask Chad, it's about, I think 115 horsepower engine. So this is based, this whole vehicle is actually based on a Polaris MRaser commercial side-by-side vehicle.

• 1:10:53Shawn Ryan?

  Oh, okay.

• 1:10:54Speaker 02

  Okay. So the engine, drive chain, 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.

• 1:11:08Shawn Ryan?

  Right on. And so what, what kind of stuff would you be mounting on here?

• 1:11:17Speaker 02

  So you can put, yeah, you can put all sorts of different payloads on here. This particular vehicle takes about 1,000 pounds of payloads.

• 1:11:24Shawn Ryan?

  1,000 pounds.

• 1:11:25Speaker 02

  So we've done everything from, you know, fairly straightforward things like comms, making a communication node, to electronic warfare payloads, to remote weapon stations. So you can mount, you know, a machine gun on, on the vehicle.

• 1:11:40Shawn Ryan?

  Nice.

• 1:11:43Shawn Ryan?

  So if you mount a weapon system on here, is that all run through autonomous, the autonomous stack? Is that what you call it?

• 1:11:50Speaker 02

  So the, the autonomous stack controls the vehicle. It allows an operator to tell, essentially tell the vehicle where they want the vehicle to go. So it will say something like, you know, go 10 kilometers to this location, orient in this way. And then they will access the payload through the comms network. So the human is still in the loop whenever you're using the payload. But the movement of these types of vehicles is autonomous. And what that allows you to do is instead of having to just like remote control the vehicle, essentially, you know, drive it through its sensors, the human can just say, I want one vehicle to go to this location.

• 1:12:33Speaker 02

  I want two vehicles to go to this location and so on. So it really allows for force multiplication where a single operator can control many different platforms and then access the payloads on those platforms. And we'll show you how to do that.

• 1:12:46Shawn Ryan?

  It's like the old computer games like Warcraft.

• 1:12:49Speaker 02

  It's exactly like that. So like that's actually how.

• 1:12:52Shawn Ryan?

  Go here.

• 1:12:53Speaker 02

  How we think about it.

• 1:12:54Shawn Ryan?

  Kill this thing.

• 1:12:55Speaker 02

  You know, I think like our vision of this is a single operator could control potentially hundreds of different assets on the battlefield. And you're going to do that through an interface, which is a little bit like a real-time strategy game like Warcraft or Starcraft, where, you know, it allows you to, you know, select the units, tell them where to go, execute the payloads and so on.

• 1:13:15Shawn Ryan?

  Wow. So I just, I mean, with, with the, with the changing landscape of the battlefield now, this is the first land, like autonomous land vehicle I've seen.

• 1:13:31Speaker 02

  Yeah.

• 1:13:32Shawn Ryan?

  So you can mount like an Epirus direct energy weapon on this thing.

• 1:13:36Speaker 02

  Yep.

• 1:13:36Shawn Ryan?

  Well, so if, let's say, I mean, Anduril's got their stuff coming out. Shield AI has got that new expat they, they came out with.

• 1:13:46Speaker 02

  You can mount Thornton's. You can mount Counter UAS.

• 1:13:48Shawn Ryan?

  So what I'm asking is eventually, I don't know if we're there yet. Maybe we're already there and this is old news, but.

• 1:13:53Speaker 02

  Yeah.

• 1:13:54Shawn Ryan?

  With all these companies, you know, like yours, the submarines, the, the Sironic, you know, Overland AI.

• 1:14:04Shawn Ryan?

  When we do go to a full-scale war.

• 1:14:07Speaker 02

  Yeah.

• 1:14:08Shawn Ryan?

  Are you going to be like, is the operator or the battle, or the ground force commander or just the commander of the entire operation, are they going to be controlling?

• 1:14:20Shawn Ryan?

  Shield AI's expats, Overland AI's ground vehicles, Sironic's surface warfare vehicles, submarines, drones, all of it, all of it on the same system?

• 1:14:32Speaker 02

  So the military is working on this right now, finding ways to integrate all of these different pieces into the same sort of system so that you have a unified view of the battlefield. Now, the way that that may play out, I think there, you're likely to see, 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'll all have to be linked together to provide that overall awareness of, of what you're pushing out there.

• 1:15:09Shawn Ryan?

  And so if you have 100, 200 of these things right here, what, what is the name of this?

• 1:15:14Speaker 02

  This is an Ultra.

• 1:15:14Shawn Ryan?

  The Ultra.

• 1:15:15Speaker 02

  Yeah.

• 1:15:16Shawn Ryan?

  100, 200, 300 Ultras out here and they've got, you know, surface-to-air missiles.

• 1:15:22Speaker 02

  Yeah.

• 1:15:23Shawn Ryan?

  For, for air defense, they've got, I don't know, 50 calibers for other ground vehicles and anti-personnel and rocket launchers and grenade launchers and drones. Are they all going to read off each other or is somebody going to have to?

• 1:15:38Speaker 02

  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 you can say, I want this vehicle to go to this location. You just let it go, you know, execute a payload there. We already can do that really well. So now we're starting to build up coordination where you can move multiple vehicles at once and they coordinate in order to achieve a task. And then we'll keep building on that. So the basic idea is that, you know, over, like as we build out the technology and as we field more and more of it,

• 1:16:18Speaker 02

  you're going to have a situation where a single operator will be able to move multiple vehicles into formations, have them, you know, send them to achieve a particular task. And they're going to go out there and just do it. And that's part of what's called mission autonomy or orchestration. So platform autonomy is basically the autonomy that lives on the vehicle that allows it to see the terrain, see where, 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.

• 1:16:57Speaker 02

  And that's mission autonomy. They're coordinating autonomously to actually conduct like a full mission. And then all of that, like all of these different autonomous systems and uncrewed systems which are on the battlefield will be pulled into command and control systems, which allow people to see everything which is, is out there.

• 1:17:18Shawn Ryan?

  Wow. I got a, I got a million questions for you, but it's, it's hot as shit out here and humid.

• 1:17:24Speaker 02

  Yeah, yeah.

• 1:17:25Shawn Ryan?

  Let's see, uh, let's see, we'll do that inside.

• 1:17:28Speaker 02

  That, that, that sounds great, but.

• 1:17:29Shawn Ryan?

  Let's see what this thing can do.

• 1:17:30Speaker 02

  Do you want to drive it?

• 1:17:31Shawn Ryan?

  Yep. Yes, I want to drive it.

• 1:17:33Speaker 02

  Okay. So the upper part of this interface here, this is where the vehicle is on a satellite map. And you 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. As you're, as you're teleoperating it, you can actually move it anywhere that you want.

• 1:18:13Shawn Ryan?

  So this is that big pond that we're digging down there, bro?

• 1:18:16Speaker 02

  This is, this is going to be about 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 hold this down. So you're lower left.

• 1:18:31Shawn Ryan?

  Okay.

• 1:18:32Speaker 02

  And you hit A and that moves you into autonomy. And then you can move, this joystick moves the vehicle forward.

• 1:18:43Shawn Ryan?

  Holy shit.

• 1:18:45Speaker 02

  So that's basically moving it forward. And then you can turn using this. So we can like turn it to the right.

• 1:18:56Speaker 02

  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, this, which is what the vehicle can see. And then you can control it. That's right. Yeah. And you can control it beyond line of sight. So you could do this from like 5,000 miles away.

• 1:19:13Shawn Ryan?

  Wow.

• 1:19:14Speaker 02

  So left down.

• 1:19:16Shawn Ryan?

  Yep.

• 1:19:16Speaker 02

  And then push forward.

• 1:19:27Shawn Ryan?

  It is, it's weird not looking at the vehicle.

• 1:19:30Speaker 02

  Yeah.

• 1:19:30Shawn Ryan?

  I want, I want to look over there, so.

• 1:19:45Shawn Ryan?

  Holy shit. Can it go, how do you go backwards?

• 1:19:49Speaker 02

  Okay, so to go backwards, um, hold down this, uh, so both that and this and then that, and then move that backwards. There you go.

• 1:20:06Shawn Ryan?

  Oh, there we are.

• 1:20:13Shawn Ryan?

  It will automatically stop and won't run us over?

• 1:20:16Speaker 02

  Let's not test it.

• 1:20:17Shawn Ryan?

  Alright, alright.

• 1:20:21Shawn Ryan?

  Yeah, there it is, slowing for person.

• 1:20:23Speaker 02

  Yeah, yeah.

• 1:20:27Shawn Ryan?

  Well, that's not gonna work in war, Byron. I'm just kidding.

• 1:20:30Speaker 02

  You can, you can turn that off.

• 1:20:43Shawn Ryan?

  Dude, this is crazy.

• 1:20:54Shawn Ryan?

  So would somebody be looking at this or like a VR headset or does, I guess you could probably do no matter?

• 1:20:59Speaker 02

  You can do whatever. Um, like generally you're looking at this. Uh, you can look through the other sensors onboard 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.

• 1:21:19Shawn Ryan?

  Wow.

• 1:21:28Speaker 02

  Alright, and then if you wanna.

• 1:21:30Shawn Ryan?

  Whoa, oh shit, there's a tree.

• 1:21:32Speaker 02

  Yeah, maybe back up.

• 1:21:36Shawn Ryan?

  In case you can't tell, I'm not a gamer.

• 1:21:39Speaker 02

  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 teleoperation 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.

• 1:22:04Shawn Ryan?

  Right, yeah.

• 1:22:05Speaker 02

  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.

• 1:22:15Shawn Ryan?

  Can we do that?

• 1:22:16Speaker 02

  While staying safe, yeah.

• 1:22:17Shawn Ryan?

  Let's try that.

• 1:22:18Speaker 02

  You can do that.

• 1:22:19Shawn Ryan?

  That's awesome.

• 1:22:19Speaker 02

  Alright, so.

• 1:22:20Shawn Ryan?

  Oh shit, I did almost hit that damn tree.

• 1:22:23Speaker 02

  Okay.

• 1:22:24Shawn Ryan?

  Let's send it to go get a pizza.

• 1:22:27Speaker 02

  That sounds great. I'm not, I'm not sure they'll, uh, let me do this, uh, on, on the road.

• 1:22:34Shawn Ryan?

  Can we go over there? Can we go over here and watch it?

• 1:22:39Shawn Ryan?

  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?

• 1:22:48Speaker 02

  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. Um, and right now it's, it's running a safety system which essentially says like, don't, you know, stop if you get too close to a person or, or, or a vehicle.

• 1:23:21Shawn Ryan?

  Right on. I mean, how does it differ? Does it pick up like body temperature or?

• 1:23:26Speaker 02

  Uh, it, it, it looks and, um, so it's just using a camera and.

• 1:23:30Shawn Ryan?

  Using a silhouette?

• 1:23:31Speaker 02

  Yeah, and it's saying like, this looks like a person, this looks like a vehicle, um, and it's able to pick them out.

• 1:23:36Shawn Ryan?

  Cool.

• 1:23:37Speaker 02

  Yeah.

• 1:23:38Shawn Ryan?

  And this is full autonomy, what we're getting ready to do here?

• 1:23:41Speaker 02

  Yep.

• 1:23:48Shawn Ryan?

  Holy shit.

• 1:23:52Speaker 02

  Oh, we're, we're in the way. Let's move back away from it.

• 1:24:08Speaker 02

  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. And then deciding how to, how to drive.

• 1:24:20Shawn Ryan?

  It picked him up?

• 1:24:21Speaker 02

  It picked him up, yeah.

• 1:24:22Shawn Ryan?

  So it's actually pretty wild that it picked him up in the middle of all those weeds and trees and shit.

• 1:24:38Speaker 02

  You can see one of the benefits of, of this is, you know, you can just tell it, I want you to go to, again, like this location and you can let it go and it will, will do its thing.

• 1:24:50Speaker 02

  Um, so I was in the run, basically came up the hill, you know, went around the, the field and then, uh, stopped there. So very short run for what it normally does, but you can get a taste of kinda like how it moves.

• 1:25:01Shawn Ryan?

  That is sick. Alright, well, thanks for showing us, uh, what the Ultra can do and let's go wrap up the interview.

• 1:25:07Speaker 02

  Yeah, thanks for letting us come out here and show it.

• 1:25:10Shawn Ryan?

  Thanks for bringing that badass thing.

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• 1:26:39Shawn Ryan?

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• 1:26:49Shawn Ryan?

  Okay, so after taking a look at this thing, I, I, this, this, this just has so many different capabilities. It could be used for so many different things. I mean, even just keep, I mean, you, you admit, it sounds like the first thing that the military is interested in is, is, is logistics.

• 1:27:10Shawn Ryan?

  Which, I mean, just keeping major supply routes open. Yeah, I mean, constant detection. Could these things detect, I mean, when we were up there, it detected, I mean, I know you guys have that safety feature on it, but it detected, uh, one of my camera guys who was, I mean, really wasn't even that close to it. Looked like he was maybe 25 yards, 30 yards from it. In, in the middle of a bunch of trees and it picked him up like that. Could this thing pick up?

• 1:27:42Shawn Ryan?

  IEDs? Is, is there a way that you could, that it could detect IEDs? I mean?

• 1:27:47Speaker 02

  Yeah, so you, you can put a variety of different types of sensors on, on the vehicle, so, um, uh, you can certainly detect like people and vehicles and then you can also, you know, we've been putting, um, tethered drones on, on these, so, you know, the drone can go up in the air, um, be able to look down, uh, on, on the ground. So if you have, um, you know, ground penetrating radar, other types of sensors, uh, you can certainly detect things like IEDs or obstacles in the environment that then can be identified, um, and reduced, right? So, um, that's, that's certainly something they, they can do.

• 1:28:26Speaker 02

  Um, and to 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, um, is, you know, I think will be, be game-changing, right? You don't have to put a person out there to do it.

• 1:28:41Shawn Ryan?

  Do, do you see these integrating with, with human forces that are, that are, that are? Do you see these as, as a forward observations platform for maybe a, you know, a slew of tanks or MRADs or, or whatever we're using? And then kind of, uh, you know, pushing that information back to, to a larger force? Or is it going all autonomous?

• 1:29:10Speaker 02

  I think the, 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 the near term. And you know, those statistics 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.

• 1:29:47Speaker 02

  So, so I think the way that they'll actually work with the US military is that we'll have formations with humans in them. 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 for strike capability. You can use them to create that kind of counter UAS bubble to protect humans. I think about them as, as you know, something that will be like a force multiplier.

• 1:30:33Shawn Ryan?

  I mean, you're saying that Ukrainian, Ukrainian military is saying that it could replace, will replace up to 80% of infantry, 80% of the infantry.

• 1:30:47Speaker 02

  Yeah.

• 1:30:47Shawn Ryan?

  And you're saying the US is saying, well, maybe not the US, but you see a subtle integration. Why would we do a subtle integration? I mean, I know on one hand, I'm going to piss the warfighter out because I love fighting wars. On the other hand, I'm thinking about my toddlers at home, you know, with all the wars that we're 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 of this? Why wouldn't we go full scale and say, hey, you know, like we don't need Private Joe driving this, you know, driving this MRAP into hostile territory.

• 1:31:30Shawn Ryan?

  Why, 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.

• 1:31:42Speaker 02

  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, just one of the hardest domains right in the military, right? So just kind of stepping back, there are a lot of folks who are focused on, on air power or naval power or 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 US and, and, you know, our casualty rates,

• 1:32:28Speaker 02

  something like more than 80% of combat-related casualties since World War II are 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 warfighter 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 Ukraine, we're seeing technology, you know, increasingly being pushed out in front of, of the warfighter, and I think they're very optimistic about, about how they want to use them. And ultimately, we do want to do that, right?

• 1:33:08Speaker 02

  We want to, we want to pull, we want to essentially reduce risk on the battlefield, right? Pull warfighters 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 a 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 warfighters on 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,

• 1:33:49Speaker 02

  we ultimately want to pull, you know, as many people out of harm's way as possible. But, you know, these are still machines that aren't, you know, quite as creative and adaptable as, as humans. And so I think it's the, what 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.

• 1:34:28Shawn Ryan?

  I mean, just thinking about my time, you know, in Iraq, Afghanistan alone. I mean, I have right, IED threat was, was, was huge. And then here is this key statistic here. 44% of US killed in action from 2006 to 2021 were killed by IEDs. 44%. Now, I mean, if, if, if, if these things did have IED detection capabilities, it's, it's got to be better than humans than, I mean, there's 40, it's almost 50% of the people that were killed would still be here today.

• 1:35:06Speaker 02

  Yeah.

• 1:35:07Shawn Ryan?

  You know, just with, just in that application.

• 1:35:10Speaker 02

  I, I think there's a lot you can do there. I mean, there's, you know, sensors you can put on the vehicles.

• 1:35:15Shawn Ryan?

  The human sensor, like I was just saying, with caught my guy out in the weeds, trying to, in a bunch of trees. I mean, rolling down Iraq, I mean, there were sniper problems. There was the guy, the trigger, the triggerman of IEDs. I mean, I would, I would think that it would pick them up immediate. I mean, you can't, they can't hide from the machine.

• 1:35:35Speaker 02

  Yeah.

• 1:35:35Shawn Ryan?

  How is it sensing them?

• 1:35:37Speaker 02

  Yeah. So the, the machine has cameras on it, right? And we, depending on the, the loadout for it, you have, you can have sort of normal RGB cameras. You can have thermal cameras, right? Like there's a lot that you can do to detect people and vehicles in the environment. So I mean, I was just using cameras out there, was able, as you said, just be able to pick someone up even in the, you know, in the woods and in the weeds. But, you know, you should think about, like, there's a lot of different ways that these can be used out, again, out in front of the warfighter. 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.

• 1:36:22Speaker 02

  Even if they're targeted, at least no human is, is being killed. You can have them, autonomous vehicles out in front of human convoys 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 Bradley Fighting Vehicles or, you know, XM30,

• 1:37:05Speaker 02

  which is the replacement for that, having autonomous vehicles that can provide sensing and protection for, for those formations. So we think about them as adding to some of the things that we're, we're developing.

• 1:37:19Shawn Ryan?

  How many of these scenarios have you kind of war game to run scenarios on out at wherever?

• 1:37:25Speaker 02

  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, 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 JRTC in Louisiana. And we started out just doing resupply. They were like, this is kind of the obvious use case for this.

• 1:38:08Speaker 02

  Let's use this to, to support our logistics to allow us to move faster. And we were able to do that. And so then they were like, okay, like, can we get cameras and drones and 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 force on force exercise, not just for, 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?

• 1:38:51Speaker 02

  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.

• 1:39:06Shawn Ryan?

  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?

• 1:39:16Shawn Ryan?

  Behind each one of these things with a remote control.

• 1:39:18Speaker 02

  Yeah, this is, this is, this is, yeah, this is, this is a great question. So, you know, and this is sometimes uh 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 um and you're driving it. So think about like a toy car, right? Like that's, that's a remote control um 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

• 1:40:00Speaker 02

  line of sight or I mean areas where you can't see the terrain very well. So the next sort of evolution of this is teleoperation and people talk about teleoperation a lot with ground vehicles. This is basically where you take a remote control and you look through the vehicle's uh own sensors, whether it's like camera or thermal sensors, and then you drive the vehicle based on that sensory feedback that you're getting 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 teleoperate something from like across the world uh if you have a really good uh 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

• 1:40:43Speaker 02

  which is being done in Ukraine is teleoperated 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 um or, you know, they're looking through potentially either the vehicle's 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 um at all times because the person's making all of those decisions. Uh but it also uh 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

• 1:41:28Speaker 02

  no longer 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. Um 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. Um and it doesn't require a human to, to essentially drive it. So when you have an autonomous vehicle, um 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

• 1:42:12Speaker 02

  might be another task. Um so for example, you can send a vehicle back to resupply you, but you don't have to be focused on anymore. You can do your job. Um the second thing about autonomy, which is, is important to understand is because the vehicle is making decisions based on its own sensing and own onboard compute, um if your comms get cut, the thing's going to keep going and, you know, continue the mission. Uh and so one of the things we're starting to see out of the Russian-Ukraine conflict is that the Russians um 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

• 1:42:54Speaker 02

  allows the vehicles to continue to move without human oversight. Um so, so that's, that's uh another piece of this. So we think about this as kind of force multiplication and resilience to a contested comms environment.

• 1:43:10Shawn Ryan?

  Well, now, so another thing with autonomy too, correct me if I'm wrong, is let's say you have a hundred of these. Let's say you have 500 of these.

• 1:43:18Speaker 02

  Yeah.

• 1:43:19Shawn Ryan?

  And then you have all these other, you know, we, we talked about at the beginning, all these other companies that are doing, you know, with, with Shield AI with their, with their expat. We got Sironic with the, with the autonomous boats. We got Anduril making stuff. We have uh Mach making stuff. All these companies are jumping all over, you know, lots of autonomous stuff. I,

• 1:43:41Shawn Ryan?

  but just, just Overland AI alone. I mean, let's say that there's 500 of these vehicles, you know? We're talking, I don't know, the invasion of Fallujah.

• 1:43:51Speaker 02

  Yeah.

• 1:43:52Shawn Ryan?

  These will all be able to communicate with each other and, and 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?

• 1:44:08Speaker 02

  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 100 vehicles and those vehicles are coordinating with each other um to, 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, analyzes terrain, makes decisions, decides how to drive. Then there's a notion of mission autonomy uh where multiple vehicles can coordinate with

• 1:44:52Speaker 02

  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, um 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 uh platforms uh like, like our Ultra platform? And that's what we're um building up towards. So that whole idea is, you know, single operator, massive force

• 1:45:34Speaker 02

  through potentially hundreds of different vehicles and payloads.

• 1:45:40Shawn Ryan?

  Wow. And so will it move to the point where, you know, I just brought up all these other companies. Will, will it move to the point where these Overland AI vehicles are communicating with Shield AI's expat, with Sironic's boats? I mean, will, will, will it get to the point where the entire battle space is coordinated under

• 1:46:05Shawn Ryan?

  one brain?

• 1:46:07Speaker 02

  So I, you know, the, the, the short answer is that like, yes, the ground vehicles will be communicating with uh um like aerial vehicles and the different payloads and they'll be coordinating in order to, to complete a task. Um 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 um you really want to enable an operator to achieve their objective as effectively as possible. And that means um, you know, not just kind of handing everything over to, to an AI brain, uh but um having, you

• 1:46:51Speaker 02

  know, essentially like AI assistants who are allowing a, a human to um choose courses of action, to coordinate uh uh, you know, ground vehicles or payloads or um aerial vehicles more effectively and, and so on.

• 1:47:07Shawn Ryan?

  Let's talk about, let's talk about African Lion. What happened there?

• 1:47:13Speaker 02

  Okay, so um African Lion is, is an exercise. It's um one of the largest exercises uh in Africa. So it's a place where um US warfighters work with, with partner nations and things like that um to uh essentially um train and, and demonstrate capabilities. Uh we were part of African Lion uh working with 173rd Airborne. So 173rd Airborne had two of our Ultra vehicles uh with different types of payloads on them. Um one vehicle had a Crow's Remote Weapon Station with an M240 machine gun on it. Um another vehicle had a rocket propelled um uh breaching system on it.

• 1:47:56Speaker 02

  And they were using these vehicles for a um breaching operation. Um so essentially what happened was uh they sent one vehicle forward with machine gun on it, providing security. And then a second vehicle um was quickly following it, moved to a breach point, uh uh essentially applied the payload. So the um it's a explosive line charge, um something like a Miklik, if people know what that is, but essentially an explosive rope that gets launched out in front of the vehicle and produces a um, it blows up and produces like a safe corridor that can then

• 1:48:35Speaker 02

  be proofed uh with, with another vehicle. So um they're using this to, 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 it's an extremely dangerous operation. Like when you're doing things like breaching, uh every area of the um defensive obstacle belts are being watched by, by an adversary. And as you move forward to try to reduce those obstacles, the combat engineers, um everyone's targeting them. So even in a successful breaching operation, you're expecting something like, like 50% um uh casualty rate.

• 1:49:15Speaker 02

  So if you're able to do that with autonomous vehicles, like the way that like 173rd Airborne was, was demonstrating, um that's taking, you know, in, in their estimate, up to almost two platoons, about, about 40 people uh out of um that extremely dangerous situation. You're just sending the machines forward to, to do that, to work that problem, to create the breach in, in the um obstacle belt and then they're able to, to move through. So um they're very excited about, about the technology. But it's just an example of like warfighters using uh, using our vehicles, putting payloads on them, coordinating uh as part of their um units, movement, maneuver. Um so pretty exciting to see.

• 1:49:59Shawn Ryan?

  This is fascinating stuff.

• 1:50:00Speaker 02

  Yeah.

• 1:50:01Shawn Ryan?

  Wow. The game has changed a lot.

• 1:50:03Speaker 02

  Yeah.

• 1:50:04Shawn Ryan?

  Forty years.

• 1:50:04Speaker 02

  Yeah.

• 1:50:05Shawn Ryan?

  Holy shit.

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• 1:50:54Shawn Ryan?

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• 1:51:52Shawn Ryan?

  All right, so we're back from the break. And, you know, one thing I'm kind of wondering is, what other, I mean, you had mentioned Russia was doing some of this, correct? What is, what about China? Are they? They seem to be pretty on the ball with just about everything.

• 1:52:09Speaker 02

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

• 1:52:52Shawn Ryan?

  What kind of, is there any? What other kind of weapon systems do you think will be putting on these, these ultras?

• 1:53:01Speaker 02

  I think for, for any of, you know, you think about uncrewed ground vehicles and autonomous ground vehicles. You can put, it depends on the size of it. 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 autonomous vehicle. So that's, you think about smaller things, might be smaller remote weapon stations, might just be sensors, right? Radar, optics, things like this. But much larger vehicles, large missile systems, right?

• 1:53:46Speaker 02

  So ship interdiction missiles, anti-air missiles, all sorts of things.

• 1:53:53Shawn Ryan?

  You could put pretty much anything on this.

• 1:53:55Speaker 02

  Pretty much anything.

• 1:53:55Shawn Ryan?

  Are you familiar with Epirus?

• 1:53:57Speaker 02

  Yes.

• 1:53:57Shawn Ryan?

  So you could put Leonidas mounted on one of these damn things and...

• 1:54:02Speaker 02

  Absolutely.

• 1:54:03Shawn Ryan?

  Then you have drone defense for an entire fucking battalion.

• 1:54:07Speaker 02

  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, Epirus's system. But you might also want kinetic air defense. That might be drones, like, you know, drone interceptors. It might be machine guns. But you can imagine a whole set of these. You know, many different vehicles with different defensive systems creating that layered defense. And we think it'll probably be something like this. So that, you know, even if drones are getting through one type of defense, they're getting hit by another. I mean, even if you lose some of your, your vehicles or systems, there are more to take their place.

• 1:54:51Shawn Ryan?

  Let's talk about the technical moat, the team, and the peer threat.

• 1:54:55Speaker 02

  Sure. So the way that we think about the technology that we're developing, we really are thinking about it as, you know, developing an autonomous version of core battlefield functions, right? So like either the ISR or breaching for, as examples, where you have multiple vehicles with payloads that are performing a task. Now, in order to do any of that, in order to have a set of autonomous vehicles with payloads that's, you know, doing something complicated, think about like first principles, like what do you have to do first? Well, you have to be able to carry stuff and move it,

• 1:55:40Speaker 02

  you know, like move it from one place to another in the, in the environment. And when you look at our history as a company, we started with what we think is the hardest problem there first, which is being able to understand terrain and move through that terrain. Given that basis, you can then build off of that. So, you know, one of our strongest technical moats is the fact that we're the best in the world at being able to see and understand terrain and move vehicles through it. We can put payloads like pretty much anywhere that a vehicle can, can, can drive, right?

• 1:56:23Speaker 02

  And so, once you have that, as you now combine this to say, like instead of just moving one vehicle or one type of payload, now I'm moving multiple vehicles, multiple payloads, that foundation allows you to build up those capabilities. So we think that that's our, you know, one of our biggest technical moats is we have just this incredible team coming from, you know, deep tech places like Waymo and Cruise and, you know, self-driving car companies, some of the top artificial 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.

• 1:57:06Speaker 02

  So that's technical moat. Can't quite remember the, the other questions, but that's really the foundation of everything.

• 1:57:18Shawn Ryan?

  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 would come back in and discuss it because it was so damn hot out there. But, you know, what does it look like? You know, when we're talking about controlling all these autonomous vehicles, especially when it comes to hundreds? I mean, what? And we're, and it's not remote control. So what is it? I mean, we had kind of discussed a little bit out there, but what, what does it look like? Yeah. For, for the team or the person or whatever is controlling hundreds of these at once?

• 1:57:53Speaker 02

  That's a great question. So you're not going to be just like looking through the vehicle sensor and remote controlling it because you have hundreds of them. How, how can you do that? So the way that we have designed the software is that you essentially have overhead maps, something like a satellite map. And you're able to see all of the different vehicles on that map, all of the different, you know, autonomous assets on the map. So that gives you, you know, and you can zoom out and you can, you know, look at, so you can look at terrain features. You can look at where all the vehicles are. You can see what payloads are on the different vehicles. And then the question is, how do you now coordinate and control hundreds of those assets?

• 1:58:37Speaker 02

  And you need to be able to do that by selecting vehicles, grouping vehicles, telling groups of vehicles to go to like one place or another, telling them to execute a certain payload, you know, at a particular location so that you can kind of quickly move between, you know, sets of vehicles and, and essentially tell them where to go. Again, a lot like a real-time strategy game. We're also working on tools.

• 1:59:03Shawn Ryan?

  So this looks like, this is what we were talking about at the beginning. This looks like, sounds weird, but looks like World of Warcraft.

• 1:59:10Speaker 02

  Yeah, yeah.

• 1:59:11Shawn Ryan?

  You're, you're sending a group of things, giving it a task.

• 1:59:15Speaker 02

  It's, it's not.

• 1:59:16Shawn Ryan?

  You're on to the next thing.

• 1:59:17Speaker 02

  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. Starcraft is a great example of that and Warcraft. But, you know, the tools that people use for, for grouping, for moving, for coordinating assets in those types of games are things we can take those ideas and apply them here as well so that a single operator can control many, many different assets. Another thing that we're, we're developing is things like AI assistants,

• 2:00:00Speaker 02

  which can help to suggest, you know, particular tasks. So you can just say like, hey, I need to move 10 vehicles, you know, into this area, provide reconnaissance here. And it will tell me how those vehicles are going to move, suggest to me solutions to these problems so that I can make decisions faster. So this is part of like essentially using AI to increase your decision advantage. Now, we're not saying we hand over, you know, the actual decision making to the AI, but the AI can suggest solutions that can, can help you

• 2:00:42Speaker 02

  to handle, you know, more assets at once.

• 2:00:46Shawn Ryan?

  Wow, wow. I got a hot question for you.

• 2:00:49Speaker 02

  All right.

• 2:00:49Shawn Ryan?

  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 2,400 Americans. And here's the part people forget. We had warnings. We'd broken their codes. Our own ambassador flagged a possible attack a year earlier. And Congress later said the real fail, the real failure wasn't intelligence. It was imagination.

• 2:01:23Shawn Ryan?

  Nobody could picture it until the harbor was on fire. Today China's arming robot dogs. They build 90% of the world's drones and they just flew a mothership not long ago that launched a hundred kamikaze drones in a single swarm. The warnings are everywhere again. Are we sleepwalking into a robotic Pearl Harbor?

• 2:01:45Speaker 02

  I, I, I hope not, um, 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,

• 2:02:00Speaker 02

  both by Ukrainians and, and Russians. So, you know, we, and I think other defense tech companies are certainly paying attention to this, like we are trying to understand what capabilities our adversaries have and also ensure that the US military has similar, better capabilities. And so, um, so we, 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.

• 2:02:34Speaker 02

  And then I think it's all of our jobs to ensure that the decision makers understand that this technology exists and what it's capable of. So, you know, I, I, I think like we are certainly aware of it. 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, I, sometimes these things aren't really real to people until unfortunately they actually experience them. So our, our best bet is to really be watching and taking seriously what is happening in, in these conflicts and, and what our adversaries are developing.

• 2:03:18Shawn Ryan?

  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?

• 2:03:29Speaker 02

  Yeah.

• 2:03:30Shawn Ryan?

  It's very surprising and almost alarming to me that they, you know, 15, 15, 15, you have a contract for 15.

• 2:03:40Speaker 02

  Yeah.

• 2:03:40Shawn Ryan?

  Why don't you have a contract for 10,000?

• 2:03:44Speaker 02

  Yeah. It, I mean, it, it, it should be, um, you know, I think the Department of Defense is trying to, or 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, will probably agree with, right? And something that we're all fighting hard against. Now, um, the, we've seen the Department of War speed things up and, you know, some of the, like, like I was saying before, a little bit earlier, we have actually gone from, you

• 2:04:27Speaker 02

  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, AppFit contracting process and, 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.

• 2:05:09Speaker 02

  So, you know, it's something I think like we're all working on and, and trying to.

• 2:05:14Shawn Ryan?

  Are other countries better customers than our own country?

• 2:05:19Shawn Ryan?

  Do you have this problem?

• 2:05:20Speaker 02

  For, for like our stuff or for like their own stuff or?

• 2:05:24Shawn Ryan?

  Or for stuff that Ameri-, not, not, not overland AI, for things that America, Americans, like yourself, are developing here in this country, or other countries, maybe Ukraine?

• 2:05:37Speaker 02

  Yeah.

• 2:05:38Shawn Ryan?

  You know what I mean? I, I think, you know, I've, I've, I've heard of these issues happening and we, here we have the best that the fucking world has to offer right here, developing groundbreaking new technology that's going to change how wars fought. It's, I mean, it's, it's, uh, yeah.

• 2:06:00Shawn Ryan?

  On, I mean, it's, it's, it's just such an upgrade. It's especially with a company like Epera, all these companies, man.

• 2:06:08Speaker 02

  Yeah.

• 2:06:08Shawn Ryan?

  Like what you, every, everybody, you know, everybody that sat across from me in the defense tech sector, what you guys are developing is, is fucking incredible.

• 2:06:18Speaker 02

  Yeah.

• 2:06:18Shawn Ryan?

  You know, but

• 2:06:23Shawn Ryan?

  I see it being utilized in Ukraine. You know what I mean? And, and I see it in a, in a, I, it, it bothers me when, when, when we're sending all this stuff. Maybe we aren't, but

• 2:06:37Shawn Ryan?

  see things similar being utilized in Ukraine and, and, and it just seems like our country is not taking advantage of the talent that we have here. It's fucking scares me.

• 2:06:48Speaker 02

  Yeah. I mean, we, there's, there's kind of a.

• 2:06:51Shawn Ryan?

  Like we have all this, we have all this talent like yourself.

• 2:06:54Speaker 02

  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, you know, you're, I think you're right. I think countries like Ukraine, I mean, they're adopting tech as fast as they possibly can. There's just a statistic, I was saying something like, there's been 2 million casualties in, in that conflict, right? And, um, about a million and a half Russian and about half a million Ukrainian. 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. It's existential for them. They, they have to do this or they will lose, right?

• 2:07:39Speaker 02

  Like, and so, um, they will do whatever it takes to, to win here.

• 2:07:46Speaker 02

  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, 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. So we have the space to be able to potentially build this technology and transform our, our forces, but we don't have the urgency, right? And so I think, I think that's part of the problem.

• 2:08:27Speaker 02

  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. But it's, again, it's a matter.

• 2:08:38Shawn Ryan?

  We are doing it.

• 2:08:38Speaker 02

  We are doing.

• 2:08:39Shawn Ryan?

  Government that's not fucking keeping up with you guys.

• 2:08:42Speaker 02

  But it could be, it could be done faster, right? Like we could accelerate this.

• 2:08:47Speaker 02

  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 to fight to like actually get the, um, Army and Marine Corps and, and services, um, to actually, you know, try it, iterate, you know, ultimately, ultimately use it and incorporate it.

• 2:09:21Shawn Ryan?

  Yeah. Yeah.

• 2:09:22Speaker 02

  Yeah.

• 2:09:23Shawn Ryan?

  If we were attacked tomorrow, do you think we could survive a sea or ground invasion?

• 2:09:29Speaker 02

  I mean, I think I, I'm, I think we would absolutely survive a sea or ground invasion. And I think we would mobilize very quickly. I think those, the urgency would be there and, you know, we would, we would do whatever it took to, to, to win. So I'm, I'm, you know, I think very positive and optimistic on that. Like I, I think when we're pressed, we can, we can do a lot. But I also think that, you know, we could be better prepared.

• 2:10:00Shawn Ryan?

  Yep.

• 2:10:00Speaker 02

  So.

• 2:10:01Shawn Ryan?

  Me too. Last thing.

• 2:10:04Speaker 02

  Yeah.

• 2:10:04Shawn Ryan?

  Manufacturing. Are you guys manufacturing these yourselves?

• 2:10:08Speaker 02

  Yeah.

• 2:10:08Shawn Ryan?

  How fast can you scale up?

• 2:10:09Speaker 02

  So we're, we're trying to scale as quickly as possible. Um, we are, uh, manufacturing the ultra vehicles. Uh, 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 5X manufacturing over the last, last six months or so, and we just, we need to do a lot more. So, uh, we're building up that capability as, as, as fast as possible. And again, the more support we get from, um, from the government, the more we, we actually work with warfighters, the more demand there is and, and there's, there's a ton of demand right now.

• 2:10:52Speaker 02

  So, um, we're scaling as quickly as we can.

• 2:10:57Shawn Ryan?

  Right on. Well Byron, I really appreciate you coming. It was an honor to interview you and I, I love, love everything overland AI is doing and that thing out there is super impressive. Thank you.

• 2:11:09Speaker 02

  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.

• 2:11:19Shawn Ryan?

  Thank you. Cheers.

• 2:11:33Shawn Ryan?

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