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

Episode Date: September 3, 2026

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

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After your purchase, they'll ask how you found them. Please show your support and let them know we sent you. Boots. Welcome to the show. Thanks for having me. It's awesome to be here. 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, 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 as an investor. And so, or led you around. And so, yeah, I 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, saronic 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 an Iranian port. 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 seronics, boats being used in an offensive operation in the Strait of Hormuz. Wow. Yeah. And so you're the ground guy. That's right.

You're bringing autonomous vehicles to land warfare. So really excited to dig into this. Let me kick it off with an introduction here. Byron Boots, you're the co-founder and CEO of Overland AI, a company building autonomous ground vehicles for the U.S. military. Before founding Overland AI, you earned your Ph.D. 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 of $140 million. first autonomous ground vehicle company to win a production contract with a fully integrated hardware and software platform. Congratulations. Your vehicles are already being used by the military

units around the world to move supplies, support operations, and reduce risk for soldiers in the field. Welcome to the show. Well, thank you so much. 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. Oh, wow. Those are, uh, thank you. Autonomous gummy bears. I'm excited. I love gummy bears. Right on. Right on. And, um, and I got a, I got a question for you. I got a Patreon account. It's 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 prolonged conflict? And do you believe AI in autonomous systems could one day become tools that prevent wars through deterrence and de-escalation? Or do they risk making armed conflict more frequent and easier to justify? That's a great, great question. You know, I think autonomous systems, they're like any other tool. The way that I think about them is really a tool, a technology.

and in the context of defense, it is something which allows a warfighter to be safer, right? So it reduces exposure, pulls them away from the point of contact, and then also potentially provides force multiplication on the battlefield. I'm sure we'll get into some of these things. But it is a tool which is used by humans. And so, you know, how you use them is really, I think, a human question. So, you know, in the context of saving lives, I think that they will save lives for our war fighters on the battlefield. It's very clear how they do that.

But, you know, they also can serve as a deterrence like any technology might. you know if we have significantly stronger robotic systems at our disposal then you know we're going to be a little bit you know tougher to defeat in the battlefield and our adversaries will see that and so in that way you know they can certainly serve as a deterrence as well right on i mean yeah watching some of the models you showed me outside and then you know the the videos and in in how they're going to be integrated in with the war fighters in combat. I mean, it's, you know, is a, is 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. But so it's been over 10 years. But, I mean, I already have. tons of questions and I can see so many different applications where this would be useful in just so many different scenarios. Yeah, we should definitely get into it. Before we do that, though, I do want to give you a gift as well. Oh, I love you. So let me come over here and we got you a chair. Now, this isn't just any chair.

Right on. This is, it looks kind of like an office. but this is actually a seat from one of the vehicles. So as I was explaining earlier, we pulled the seats out of the Polaris ranges and we turn them into those autonomous vehicles that we saw outside. Well, what do you do with the seats 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 it out, but this is awesome. Yeah, yeah. Put this in the office. office. Thank you. Yeah, of course. That's awesome. All right, Byron. So before we get into everything Overland AI, how do, let's do a little backstory on you. Where did you grow up? How did

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

Did you watch The Terminator growing up? I sure did, yeah. Did you? So Terminator 2 was a, like, unbelievable unbelievable movie. And, 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, you know, something that I really enjoyed. Were you a gamer?

Yeah, I used to play. So back when I was in high school, I used to play StarCraft, like, quite a bit. StarCraft 1. It was before StarCraft 2 came out. So Real Time Strategy Games, I did a lot of, played a lot of, played a lot of, of games like that we talked about warcraft before you know i used to play that too um so that that was really what what i was most drawn to um but yeah i mean love computer games how i mean well we'll get into it later i was going to ask how similar you know is what what's happening today is controlling

one of those games well we'll get into that in a little bit so yeah where did you where did you go to school what did you go to school for so i went to college uh at a small liberal art school called Bowden College. It's in Maine. And I spent four years there. I was a computer science and philosophy double major as an undergrad. I started out really thinking about, you know, I love computer science, like I said, you know, all through high school. I also really liked history, and read a lot of history. And so when I went to college, I was thinking about maybe double majoring computer science and history. I thought it would be cool to have a more technical degree and something which is, you know, more humanities oriented with history. But I quickly

kind of figured out my first year that, well, I love history. It involved tons of reading and things like this that I really like to do, but also involved foreign languages. And you need to actually read about, you know, about history through contemporary sources and, you know, in the language that folks wrote in. And so, it's something I was not great at. I was not particularly good at languages, didn't really enjoy them. And so, you know, I started to,

I took a couple courses in philosophy. I started out with a course called Logic Informal Systems, which was really delving into formal frameworks for understanding arguments and analyzing arguments. and I really started to fall in love with that. And so, you know, I brought that together with computer science and majored in both of those areas as an undergrad. Interesting.

You were fascinated with the brain too, correct? Yeah, yeah, yeah. So, I mean, in undergrad, I was, you know, I was taking computer science and took courses in artificial intelligence and started to do research in robotics. And this is back, you know, over 20, years ago. They had artificial intelligence courses 20 years ago? Yeah, it was pretty interesting.

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

but it was just starting to kind of come to the forefront and be an area that was really starting to accelerate around, you know, 2000 when I was an undergrad. 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 human mind worked. Well, yeah. I think that's an interesting discussion itself.

Yeah. I mean, how deep did you get into that before you kind of switched yours? Yeah. So, again, as an undergrad, I double majored in computer science and philosophy. And when I graduated, I was thinking about what I wanted to do next. I initially took a job at a robotics company as an engineer, basically working on problems related to perception and mapping and robotic systems. So these were mobile robots much smaller than the ones that we just saw outside.

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

you know, just sort of how the mind works. So with AI, you know, you're trying to program a computer that can almost like think like a human that can perceive the world that can understand it somehow. And then in philosophy, you're really thinking through language and, you know, by writing arguments, thinking about, you know, how does the human mind work? how does it contend with reality, things like this. But the piece that I was missing was actual neurobiology, right? Like the human nervous system, the substrate of the mind.

And so I decided that before I went back and entered into a graduate program, I needed to learn more about neuroscience. And so I managed to get a job at Duke University. in a neurobiology lab studying human perception. So I worked as an engineer for about a year, and then I went to Duke, and I worked there for two years. And this was really, it wasn't a graduate program, it was just working in a neurobiology lab,

and I was auditing courses on neuroscience and neurobiology while it was there, trying to learn, you know, how does the mind work? I mean, how... Wow. How the human mind perceives the world. Yeah. How, I mean, did you, did that, is that helpful in what you do today?

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

So 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 2D projection, on that 2D image, to understanding what's actually out in the world, like the 3D environment, the surface reflectance properties of things like the wall or the carpet or whatever, how do you sort of solve that problem? And it's called the inverse optics problem. So it's the notion that you have a 2D image and you're trying to kind of understand this complex 3D world.

And the challenge is that there's actually not an easy solution to this. Because you're moving from essentially like three dimensions to two dimensions, information is lost. And so another way to think about this is that an infinite number of different worlds could have produced the same visual image on your retina. And this manifests itself through illusions. So there are certain types of illusions. There's something, for example, called an Ames Room,

where when you look at the room, it looks like a rectangular room. but in fact, it has this kind of crazy shape. You know, it's something you can look up maybe later. But the interesting thing about that is just the fact that something that appears to you to be, you know, like a normal rectangular room, is actually something completely different. So that is just an example of one of these optical illusions.

Now, the interesting thing about illusions is that they basically everything you see in some ways is an illusion, right? So they're not outliers. It's not like every once in a while your mind makes a mistake and you kind of see the world incorrectly. You are always inferring some world that is not quite what is actually out there.

It's the rule, not the exception. And so this is, it forms almost a philosophical problem. It's like If you are looking at the world, but you can't actually infer what generated, you know, the images that you see, how do you even, you know, how do you even interact with it? Like, how do you continue to exist if you're not seeing things properly? And so, you know, the conclusion, one of the conclusions that we came to was that really the way that you see the world is, whatever way is necessary to allow you to continue to persist. So we kind of think about this as like,

you see the world in an evolutionarily sort of appropriate way, in a way which informs your actions so that you kind of do the right things, you continue to exist, you can ultimately reproduce and continue on. And so it's just one of the problems that we wrestled with. Now, what does that mean? It means that your perception of the world is really shaped through experience. You're just perceiving the world in the best possible way

for you to take actions. And some of these fundamental ideas actually carried through into the work I did in graduate school, and even some of the things that we do today with the systems that we build, the robotic systems that we built. Very interesting. Yeah. Wow.

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to start your free trial today. That's right. Start your free trial. It's Shopify.com slash SRS. That's Shopify.com slash SRS. So where do we go from here? So, you know, I think like one thing, one of the, you know, when you think about building a robotic system, one of the ways that we kind of think about this is that when you're perceiving the environment, you're not just measuring it. You're taking the context and your prior experience, and you're using that to predict what you think like the world actually is.

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

to understand what you're seeing in a functional way, and then move, use that in order to move a robot more quickly or more aggressively. And those notions, you know, sort of led me from neurobiology at Duke, like really kind of thinking about data and machine learning as fundamentally interesting things towards my graduate education, which I pursued after that at Carnegie Mellon University. Wow.

But I've not heard everybody talk about that, that has gotten into the machine learning AI stuff. That's pretty fascinating. Yeah. And I think, like, another thing, which is pretty interesting here that I'll also highlight. You know, I was originally went to Duke to try to understand the brain and how it works. And my hope was that by understanding that, that would help me to maybe better understand artificial intelligence or how to build machines and things like this. What I pretty quickly realized was that neuroscience is really hard, right?

People have been studying the human brain for almost 300 years. And progress is slow. It's very difficult to understand how the brain works. We don't have a great grasp of it, even now. We can describe a lot about it, but not really understand it functionally. And so 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 is something that would help me to build better machines or understand, you know, build a better AI. I almost think that focusing on artificial intelligence

and mathematics and probability and statistics and information theory and robotics helps to provide a framework that people may ultimately understand like the brain through. So it almost works the other way, that you have to really understand kind of core principles of perception, planning, control, like these areas which are fundamental in AI and robotics

to understand ultimately what the nervous system might be doing and be able to describe it. Wow, wow. So you worked at, went to Nvidia too as well, didn't you? That's right, yeah. Before starting Overland AI, I worked for about five years at NVIDIA. And this is, well, 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, a professor who is running a research lab, is that you can also work in industry. So I had a research lab which was focused on robotics

and machine learning, and I had a number of PhD students who are working in that lab. But you can take 20% of your time and work in industry simultaneously. And as part of that, spent about five years working at NVIDIA on machine learning and robotics. How was it working there? That 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 the context of robotics. So how can you parallelize tasks? how can you use it, not just these sorts of chips and this type of technology, not just for perceiving the environment, but also controlling vehicles. So an example of this actually is carried through

from work that I was doing at originally Georgia Tech and then Nvidia now to overland, where when that vehicle is out in terrain, so when our uncrewed vehicles are out there and looking at 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 drive after doing that.

It's doing that about 10 times a second. And so, how do you get that to work? Well, you can use Nvidia GPUs to parallelize, you know, these tasks and evaluate many trajectories simultaneously and then decide how you're going to move based on that. Very, very interesting. Interesting. Do you still, do you miss being a professor? Well, yeah, I'm currently, I've got a 5% appointment at the University of Washington,

which means that I'm there, you know, every couple weeks working with students. You know, I like teaching. I like interacting with students. I think that's one of the great benefits of being a professor is, you know, just engaging with students and people who want to 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. So, you know, there's only so much you can do in a smaller research lab. And so in 2018, the Army Research Laboratory spotted you at an IEEEE. E-E conference, demoing machine learning.

What is an IEEE? It's an IEEE. So it's an association for electrical engineers, but it's one of the major types of conferences that folks publish in. So when you're a professor, one of your main goals is to publish papers, right? And those scientific papers further human knowledge.

And so in computer science, the way that you do this is you publish papers at a lot of conferences. You work with your graduate student. You develop a new technology. You then tell the world about it, right? You publish it in a paper, and you do this on a pretty, pretty fast, iterative basis. So this is one of the major conferences in robotics.

And we had some work there. And some folks in the Army were seeing what we were doing and had some cool ideas of how we could potentially like take that fundamental research and start to apply it to Army problems. Right on, right on. And then the DARPA Racer. Yeah. The crucible that birthed Overland AI.

What was that? What's the... Yeah, yeah. So, okay, so I was, you know, originally working at Georgia Tech and doing some work on 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. And these vehicles are doing things like drifting around turns and things like this. So they learn to do this, which is one of the things, which is cool. Like the vehicle's out there, it's trying to drive faster and faster, and then it's learning how to do things like drift in order to drive even faster. So that's what the Army was looking at.

Now, is it actually learning, or are you programming that into it? So it's actually learning. So you start by bootstrapping it. like you have a human demonstrate, you know, this is how the vehicle should drive. And then it tries to replicate what the human does. And as it does that, sometimes it makes mistakes, sometimes it does well, but it's kind of grading itself.

And then it will start to experiment. Like if I, 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 it learns on its own. And so you're programming in the ability to learn, but then it's looking at its own behavior,

learning from that, and driving, you know, figuring out how to drive faster. Now, is this like on a track? Yeah, like a same loop over and over? 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 turn around. 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 through forests and deserts and beaches and things like this. So, excuse me, what I was gonna ask is, I mean, if this, 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? 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?

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 work. So generally speaking, you want to collect data in a wide variety of environments that are inclusive of the types of places that you might want to be driving in the future.

So for example, you might have a complex dirt track with left and right turns and some wider turns and some sharper turns and things like this. If you train on a track like that, it's very easy to then race on, like, like an oval track, right? Because you've seen all of the things that you're likely to see. So that becomes easy.

But if you, for example, train 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 your learning algorithm. And then it will start to do better on that new type of track.

And that matters 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.

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 off-road, it could be in the middle of a city, but we're talking left turns, right turns, heavy braking, fast acceleration, drifting, all of that stuff. Will it be able, will it have been able, will it have been? eventually learn what it needs to learn to be able to have a complicated new route run it the first time,

and it will run it perfectly. It will go as fast as the machine is capable of. Yeah, it's certainly possible. And like, that's what we're always striving for. So when we're putting, you know, unrued ground vehicles, their autonomous vehicles in new environments, they're already performing really well because it's seen many aspects of that. before and you're trying to get it to perform optimally, right? Like that is the goal, like potentially faster than a human driver, even on environments that it's never seen before. Every time our autonomous vehicles are out in the world, they are basically, you know, we'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. they're constantly learning. So the more environments that we encounter, the more data we get, like the better and better the system gets. Then the goal is always kind of moving towards that optimal movement. How close are you to that goal? Depends on the environment. I mean, I think we can we can already drive faster than humans in some environments. So yeah, it's pretty cool. Wow. Very interesting and so how long did you spend at DARPA so yeah so I started work with army research lab you know again around like 2018 and in 2019 or so I started to talk

with folks at DARPA they were really interested in developing essentially rebooting ground autonomy for defense so like Here's what the situation was. DARPA back in 2004 and 2005 had something called the Grand Challenge or Grand Challenges. So these were challenges where they were trying to intercept ground autonomy. So this actually goes back to the NDAA in 2001.

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

No one knew how to make these vehicles autonomous. Where did the idea come from? Well, I think, you know, there had been work in robotics where people were moving vehicles in simpler environments autonomously. And so the notion was like, well, what if we could do this on the battlefield, right? if we could do it in these more complex environments. If you could take the warfighter out of the vehicle,

you can imagine that not just provide safety, but potentially tactical things that you can do. We can maybe talk about that in a little bit. But in order to make those vehicles autonomous, you needed to know how to do it, right? And they didn't. And so DARPA, one of the things that was great about DARPA is that it is an organization that is designed

to just like create new things, right? Like you have some crazy challenge. DARPA is out there and can create a program, pull together some of the best minds in the US to focus, like really focus on it for up to about four years and try to solve a problem. And so in 2004 and 2005, they came up with the DARPA Grand Challenge where they were trying to race vehicles

from Barstow, California. California to Prim Nevada, it was about 135 miles on dirt roads. And they just laid down a challenge. It said, like, if you can do this, you get prize money. And all sorts of teams came together to attack this problem. So there were university teams from places like Carnegie Mellon and Stanford and MIT and so on. And there were industry teams like Ashkosh, you know, had a team. And there were just people who were trying to put together.

their autonomous vehicles in their garage, like just build robots. And they went out there and raced. And in 2004, the first challenge, no one made it beyond seven miles. Like that was the, you know, so it was, it was, CMU's vehicle, I think, made it that far. It got stuck, caught fire. It was like a whole thing. The next year, though, in 2005, I think five teams competed this challenge. They made it the entire 134 miles.

Oh, shit. Those teams, like the folks from those teams, after that challenge was over, there were a few other ones. There's something called the Urban Challenge and some other DARPA programs which followed up on this. But many of those people then moved into industry and started the self-driving car projects and companies that then turned into things, you know, companies like Waymo or Aurora Innovation and so on. And so these autonomous driving company, commercial companies. So by 2012, let's say, a lot of work was being done in the commercial sector, bootstrapped off of this DARPA work, right? So the military started this whole thing because they wanted autonomous vehicles.

People started to build autonomous vehicles because of this DARPA program, but then basically just went off into industry and we're working on like robotaxis and autonomous trucks and things like this. So by like 2019, coming back to my story, DARPA was in a position where they were like, well, cool, we have autonomous taxis. There's been a lot of progress in this area, but, you know, where's our autonomous tanks, right? Like, where are our autonomous military vehicles? The whole point of this was initially to support the military. And so DARPA Racer was a program that got stood up. It started in 2021 to reboot autonomy for defense. So specifically to take a lot of the learnings that had been produced over the previous 20 years or so for the on-road autonomous

driving industry and work which had been done in robotic perception and robotic vehicle control, bring that back together and focus on defense problems. And so that meant trying to drive much faster, larger and faster vehicles off-road, what we call complex natural terrain. So, you know, no roads at all, right? Like through deserts, through forests, through snow, things like this. And contested terrain. So thinking about, you know, how do you move when you,

not only do you not have infrastructure, which is there to help you, like roads or road networks or signs or things like this, but infrastructure, which might be in the environment, which is there to defeat you, to stop you. And so that's what the DARPA Racer program was. So I had already been doing work at Georgia Tech and then University of Washington, working with the Army on developing

off-road ground vehicle autonomy. And I then put together a team, that's called the performer team, to attack these problems for the military through DARPA, starting in 2021. So did they recruit you from the I-Triple-E conference? So the way that that worked was, like, through the research I was doing and the publications that was, you know, putting out to the world,

Army saw that the technology that we were, the Army Research Lab saw that the technology we were developing might be really helpful for the types of problems they wanted to solve. I then started working with Army. So the way that that works is when you're running a university research lab, You have a bunch of PhD students. You know, they're doing research. You're publishing it.

You're making it publicly available for other scientists to see. But you need funding to run that lab. And so people pay you to essentially do research. They pay your lab to do research. So U.S. Army was one of the organizations that started to fund my research. And when you fund research, you can say, like, hey, here are the problems we want you to solve. We'll give you, you know, this much money to solve them with your PhD. students and then you provide those solutions back to to the army. So that's what I was doing

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

you know, more than, more than 10 years, more than a decade. And my lab was doing a lot of that work in academia, you know, before we spun out Overland. Wow. Wow. Well, before we get into Overland, let's take a quick break. There's been a lot of attention lately on how important sleep is, not just for recovery, but for your brain, hormones, immune system, and for your performance. And the more research comes out, the clearer it gets. If your sleep is off, everything else gets harder. That's why I use Helix. I've had my Helix mattress for a while. 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

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All right, let's talk about the Textron M5. Sure, yeah. So as part of the DARPA racer program, so this is DARPA program where we're trying to develop ground vehicle autonomy for the military. We started out working with Polaris Razor side by side by side. 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,

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 razors. So trying to record the best possible times through a wide variety of different terrains and, you know, test scenarios. As we progressed through the program, you know, the DARPA program manager didn't want us to just be using those small vehicles. So we moved up to the Textron M5.

The Textron M5 is basically a robotic combat vehicle prototype. It's about 12 tons, so it is much larger. He went from a side-by-side to a 12-ton vehicle? Yeah, it's a 12-ton tracked vehicle. Gosh. So it's about the size of a 11-3. So it looks like a tank, 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. Wow. So really, really awesome vehicle to work with. And we're putting autonomy on that vehicle and then driving that through off-road terrain. Okay. Yeah. How did that, and this was a competition, correct? Yeah. So DARPA racer, it started out with three teams. 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 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 side-by-sides, but also the much larger vehicle.

So we're the only team that actually worked with these. And there aren't that many of them. There are something like four or five Textron M-5s ever produced. But, you know, we had all of them, and we were just, like, smashing through terrain in these huge tracked vehicles. Wow. Pretty awesome. And so what is the, what is the kind of the point of the competition? Do they buy the technology from you or, or do they fund the technology?

That's a great question. So, how does it work? Yeah, like, 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, U.S. military, so for our soldiers and Marines. But often in a DARPA program, even if the program is very successful, like ours was, how do you actually take that technology and then get it into the hands of the warfighter?

Like, how do you actually do that? It's a good question, and very few people succeed. So 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 and we had this new capability and we were just like, this is incredible. We can drive extremely fast off roads. 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 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 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 war fighting units could start to work with this technology and start to integrate it into tactics. So that's why we created Overland Day.

We thought the Army, like this is a lot of this is just going to disappear if we don't do this. So why would they fund the, did they fund the research? Yeah, so DARPA funded the research. So DARPA funded all of this, like they, to develop the capability, but just because DARPA- And they just shelf it? So then what has to happen is like the Army and the Marine Corps or whoever,

like one of the services has to then put up money to be able to then like buy and transition that technology. Because from DARPA or from new? So the way that DARPA works is that DARPA, like these DARPA programs are generally four years long. They have a time limit. And that's part 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 move it into warfighting units, right? So, like, you've created it. Now someone has to continue to, like, transfer it, to, like, get it onto military vehicles to push it into units. And that's where a lot of these programs die, because you, like, create the technology, but you can't, that no one picks it up, like, when it's over.

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

So the way that this works is like when, when, so DARPA pays for the development of, you know, this core technology. In this case, I'm running a. a lab at the University of Washington. So they pay the University of Washington to develop the IP. When they do that, that IP belongs to both DARPA and the University of Washington, right? Because, you know, DARPA pays for a university to develop it. You know, they're both parties essentially own that IP. But once you have that IP, in this case, it is like raw technology, right?

Like, it is research-grade code that 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 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 almost like a rough draft. I think there would be... But then someone has to kind of pay 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 warfighter. Are these classified? I mean, I would just, are these classified? I would think there would be venture capital firms galore just surrounding these projects.

Well, yeah, yeah. So sometimes, yes, right? Only 4% make it to the warfighter. That's 96% of projects. It's crazy, right? Like, oh, yeah. Yeah, so, but in some sense, that's actually what happened here, right? Because we said, okay, like, we're going to take this IP.

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 warfighters. So Overland AI got to start by focus. on the software portion of this, right?

Take these good ideas that were developed during DARPA. You know, we licensed that IP at 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 different types of vehicles. We discussed like the side-by-side or, you know,

the big Textron M5 that we can make autonomous. You know, we really wanted to develop up 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, we're a VC backed company, right? So they're essentially making that bet that you're saying, it's like, okay, we can step in, provide additional funding to turn this into a mature product that can then be transitioned over to the 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 or good core IP to actually getting it procured, right? And that takes, you know, time and effort. And so like our story as a defense tech company is really similar to a lot of other stories, except that we got this.

start by, you know, doing all of this, you know, sort of initial research to accept a new technology that the military really wanted. Very interesting. And so, so the DARPA competition ends, you win and yeah. So all the information with you and develop Overland AI. Right. So we, we started Overland AI. And, you know, we, Overland actually was formed before the DARPA

competition completed. So we became part of the competition as well. We then, as the competition was going on, you know, we were developing new technology from moving like really quickly through a lot of different biomes, so different types of environments. And then we were really trying to think, and this is the Overland Day I started in December, 2022, so three and a half years ago. And we were, initially focused on, okay, we have this software, we're refining it, we're turning it into a commercial piece of software, how do you then get traction with like the Army and Marine Corps? 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, again, with a software stack, but we quickly realized that we had to actually also build the hardware so that we could get this idea of autonomous vehicles into the hands of warfighters faster. So we just started to build the vehicle. vehicles to. And that's what resulted in like this ultra vehicle that you've 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 war fighting units to really integrate autonomous systems into their concepts of operation. So our path really is initially research and development starting with DARPA. That then we worked with the Defense Innovation Unit and got a prototype contract with them. We then started to work with Army Applications Lab and, you know, winning, you know, these Sibbers, these small contracts with the Marine Corps and the Army. We were doing really well with those. And, you know, We've then last year we revealed like the ultra vehicle, fully autonomous, vertically integrated vehicle that you could push into hands of war fighters. We started to push this technology into units who were constantly testing them.

And then this led to a production contract. So we just recently won the first production contract for autonomous ground vehicles in the US military, and that's with the Marine Corps. So it's really this whole process. this whole process of, and it didn't take that long, right? It's about three, three and a half years where we went from like university lab, R&D, through prototype fielding with warfighters production contract. Wow.

So we felt that pretty, three and a half years. Yeah, yeah. Wow. I mean, so who won the contract or who, who, who, who, who were you contracted to? Yeah, yeah, with, with the Marine Corps. So the Marine Corps is buying a whole set of the autonomous vehicles 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 air defense systems.

So trying to make sure that they get enough ammunition as they're shooting down drones. Can I ask how many you're going to manufacture for them? Yeah, so we're manufacturing in the first tranche, about 15 of these, you know, in the next year or so. So that's what we're starting out with. And one of the things which is 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 to.

and forth. But we've been doing a lot of work with other units, like 82nd Airborne, 173rd airborne, other warfighting units to put other types of payloads on board the platforms as well. And so one of the things that we've been talking to the Marines about is putting sensors and, 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 counter UAS, for example, payloads on vehicles and moving them away from the places that you're trying to defend, right? So you're disaggregating, you're dispersing, you're essentially making it much harder for an adversary to be able to take everything

else that like all of the pieces out when you're when you're defending an area interesting so they're going to be using this for logistics you're going to be using this for defense and i would imagine there's going to be an offensive commotent yeah yeah so the way that we're thinking about this is um 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, being able to move them through bad weather, they can persistently stay in a location for a long period of time because they're just on the ground. So they might be more effective than drones

for some of these things. You can use them for defense or strike capability, so you can put kinetic payloads on them, you can put drones on them, launch them off of the vehicle, so you can move them into areas that might be too risky for a human, but they can potentially be hold ground or try to take terrain in those areas.

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

next. That's why I'm on the ladder app. I've got a real coach in my ear on every set. What's next? What weight? Why? It's a new plan every week that builds on the last. And it goes, where I go. The garage with dumbbells or a hotel room on the road. 30 to 45 minutes and I'm done for less than a dollar a day. I've trained my whole life in the teams. Nothing we did was random. Somebody who knew more than you wrote the program and you got results. That's the difference between working out and training. Ladder puts that kind of real programming in your ear. Remove the guesswork with Ladder and get a real coach in your ear telling you exactly what to do for every workout. No thinking. Everything planned for you. If you're a guy who's

been meaning to get back to it, this is the way back. If you have an iPhone, head to ladder.comfit slash SRS and take a quick quiz to get matched with your coach and the right plan for you. Use my link and get a free seven-day trial with no credit card and $10 off your first month if you join. All right, we're out here with Byron Boots, CEO and founder of Overland. Land AI, get ready to take a look at this beast here. What are we looking at? So this is an ultra, one of our autonomous vehicles. So let me just kind of show you what it is.

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. At what degree? So actually you can see 360 degrees around it. So like you have these sensors in the front and there's also sensors in the back. So it can just kind of see this 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. 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 LIDARs. again. What does the LiDAR do? So, LiDAR basically allows you to see depth in the area around the robot. So think about it as like a depth sensor. It tells you the distance to surfaces in the terrain.

Okay. Yeah. What engine are you running in here? I think 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 M-Raser commercial side-by-side vehicle. Oh, okay. Okay.

So the engine, drive-change chassis, that all comes from Polaris. We take out the seats from the vehicle, take off the roll cage and everything, and then transform it into this autonomous platform. Right on. And so what kind of stuff would you be mounting down here? So you can put all sorts of different payloads on here. This particular vehicle takes about a thousand pounds of payload. A thousand pounds. So we've done everything from fairly straightforward things like comms, making a communication

node to electronic warfare payloads to remote weapon stations. So you can mount a machine gun on the vehicle. Nice. So if you mount a weapon system on here, is that all run? through autonomous, the autonomous stack? Is that what you call it? So the 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 But the movement of these types of vehicles is autonomous. And what that allows you to do is instead of having to just remote control the vehicle, essentially, you know, drive it through its sensors, the human can just say I want one vehicle to go to this location, I want two vehicles to go to this location, and so on. So it really allows for force multiplication where a single operator can control many different platforms and then access the payloads on those platforms. And we'll show you how to do that. It's like the old computer games like Warcraft. It's exactly like that.

So that's actually how. Go here. How we think about it. Kill this thing. You know, I think like our vision of this is a single operator could control potentially hundreds of different assets on the battlefield. And you're going to do that through an interface, which is a little bit like a real-time strategy game like Warcraft or Starcraft, where it allows you to, you know,

select the units, tell them where to go, execute the payloads. and so on. Wow. So I just, I mean, with the changing landscape of the battlefield now, there's the first land, like autonomous land vehicle I've seen. Yeah. So you can mount like an eperus direct energy weapon on this thing. Yep.

So let's say, I mean, Andurals got their stuff coming out. Shield AI's got that new expat they came out with. You can mount Thornton, you can mount counter UAS. So what I'm asking is eventually, I don't know if we're there yet, maybe we're already there and this is old news, but. Yeah. With all these companies, you know, like yours, the submarines, the Seronic, you know, overland AI, when we do go to a full-scale war. Yeah. Are you going to be like, is the operator or the battle or the ground force commander or just the commander of the entire

entire operation. Are they going to be controlling shield AI's expats, overland AI's, ground vehicles, seronics, surface warfare vehicles, submarines, drones, all of it, all of it on the same system? So 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 that may play out, I think 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 will all have to be linked together to provide that overall awareness of what you're pushing out there. And so if you have

100, 200 of these things right here, what is the name of this? This is an ultra. The ultra. He Yeah. 100, 200, 300 Ultras out here and they've got, you know, surface to air missiles. Yeah. For air defense, they've got, I don't know, 50 calibers for other ground vehicles and anti-personnel and rocket launchers and grenade launchers and drones. Are they all going to read off each other or somebody going to have to... So what the way that this is is going, we're taking...

So the basic idea is that you start with just being able to move one vehicle at a time, right? So you can say, I want this vehicle to go to this location, you just let it go, 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 over, like as we build out the technology and as we field more and more of it,

you're going to have a situation where a single operator will be able to move multiple vehicles into formations, have them, you know, send them to achieve a particular task, and they're going to go out there and just do it. And that's part of what's called mission autonomy or orchestration. So platform autonomy is basically the autonomy that, lives on the vehicle that allows it to see the terrain, see where vehicles and people are out in that terrain and move through it. And then you tie that together where vehicles are coordinating with each other and that's mission autonomy. They're coordinating autonomously to actually conduct like a whole mission. And then all of that, like all of these different autonomous systems and uncrewed systems

which on the battlefield will be pulled into command and control systems which allow people to see everything which is out there. Wow. I got a million questions for you, but it's hot as shit out here in humans. Yeah, yeah. Let's see. We'll do that inside. That sounds great, but let's see what this thing can do. Do you want to drive it? Yep. Yes, I want to drive it. Okay, so the upper part of this interface here, this is where the vehicle is. on a satellite map. And you can see like the name of the vehicle. And then down here gives you a view of what the vehicle sees.

So over here, this is the front camera on the vehicle. And then this is actually like the AI view of the vehicle. You don't have to necessarily spend too much attention like looking at this. But the magenta areas are lethal. So those are areas that the vehicle, you don't want to move the vehicle into. As you're teleoperating it, you can actually move it anywhere that you want. So this is that big pond that we're digging down there probably? This is going to be about 50 meters around, so it's only seeing up here on 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. Okay. And you hit A, and that moves you into autonomy. me and then you can move this joystick moves the vehicle forward. Holy shit. So that's basically moving it forward and then you can turn using this so we can like turn it to the right. And you know when the vehicle is here in front of you it's tempting to look

at the vehicle but you should actually look at this 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. Wow. So, left down, yep, and then push forward. It is, it's weird not looking at the vehicle. Yeah. I want to look over there so. Holy shit. Can I go, how do you go backwards? Okay, so to go backwards, hold down this, so both that and this and

then that and then move that backwards. There you go. It will automatically stop, it won't run us over? Let's not test it. All right. Yes. Yeah, there it is. Slowing for a person. Yeah, yeah. Well, that's not going to work in war, Byron. I'm just kidding. You can, you can turn that off. Dude, this is crazy. So would somebody be looking at this or like a VR headset or does it, I guess you could probably do no matter. You can do whatever. Like generally you're looking at this. You can look through the other sensors on board the vehicle as well. So you can look at the rear sensors or off to the sides. You can access the payloads on the vehicle through the interface up here. Wow. All right. And then if you want to, whoa.

Oh shit. There's a tree. Yeah, maybe back up. In case you can't tell I'm not a gamer. I'm not great at that either. But we like to control it actually through this interface, and I'll show you that in a moment. I think one of the challenges for teleoperation is that you don't have like a vehicle sense, right? Like you can't feel like how the vehicle is moving like you would when you're driving. And now you're trying to interpret what the vehicle can see like through its own sensors. You should probably stop.

Right, yeah. What, 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. We do that? We do that. Let's try that. Let's try that. That's awesome.

All right, so. Oh shit, I did almost hit that damn tree. Okay. Let's send it to go get a pizza. That sounds great. I'm not sure they'll let me do this on the road. Can we go over there? Can we go over here and watch it?

So before you guys run the route, how is that thing determining what is a human being and what's a tree and what's a vehicle and what's a rock? Yeah, it's looking at the whole environment around it. And then it's determining where it can drive and where it can't drive. So that's the first part, this notion of traversability, like what part of the terrain is traversable. And then on top of that, there's a semantic understanding of the terrain, which means you can understand that, you know, they can see a person or a car, you know, determine what that is,

where they are relative to it. And right now, it's running a safety system, which essentially says, like, don't, you know, stop if you get too close to a person or a vehicle. Right on. I mean, how does it different? Does it pick up, like, body temperature or how does it look and, so it's just using a camera and it's, yeah, and it's saying like, this looks like a person, this looks like a vehicle, and it's able to pick them out. Cool. Yeah. And this is full autonomy, what we're getting ready to do here.

Yep. Holy shit. Oh, we're in the way. Let's move back away from it. So yeah, as it's moving through the terrain, it's also tracking where all the people are, right, where the vehicles are, where they are relative to it, and then deciding how to drive. It picked him up. It picked him up.

Yeah. So we can... It's actually pretty wild that it picked him up in the middle of all those weeds and trees and shit. You can see one of the benefits of 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 do its thing. So I was the end of the run, basically came up the hill, you know, went around the field and then stopped there. So very short run for what it normally does, but you can get a taste of kind of like how it moves. That is sick. All right, well, thanks for showing us what the ultra can do.

And let's go wrap up the interview. Yeah, thanks for letting us come out here and show you. Thanks for bringing that badass thing. There's nothing more frustrating than putting in the work and still feeling like your body is not responding the way it used to. You're training. You're trying to eat right. You're trying to sleep.

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That's men go to mars.com for 50% off and three free gifts when you check out. After you purchase, they'll ask you where you heard about them. Please support our show and tell them our show sent you. Okay, so after taking a look at this thing, I, this, this, this just has so many different capabilities. It could be used for so many different things. I mean, even just keep, I mean, you admit, it sounds like the first. The first thing that the military is interested in is logistics, 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 one of my camera guys who was, I mean, really wasn't even that close to it. It looked like he was maybe 25 yards, 30 yards from it. in the middle of a bunch of trees and it picked them up like that. Could this thing pick up IEDs? Is there a way that you could, that it could detect IEDs? I mean, yeah, so you can put a variety of different types of sensors on the vehicle.

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

So that's certainly something they can do. And to your point, you know, I think you can, of course, move things around, but just the ability to push these forward and sense the environment. is, you know, I think we'll be game-changing, right? You don't have to put a person out there to do it. Do you see these integrating with human forces that are, you know, do you see these is a forward observations platform for maybe, you know, a slew of tanks or MRADs or

whatever we're using and then kind of pushing that information back? to a larger force, or is it going all autonomous? I think the re-et, 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. So I think the way that they'll actually work with the U.S. military is that we'll have formations with humans and 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 diversions, right? Like you can use them, 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 something that will be like a force multiplier. I mean, you're saying that you're Ukrainian military is saying that it will replace up to 80% of infantry, 80% of the infantry.

Yeah. And you're saying the U.S. is saying, well, maybe not the U.S., but you see a subtle integration. Why will we do a subtle integration? I mean, I know on one hand, I'm going to piss the warfighter out because they 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 MRAP into hostile territory? Why, like, that's a, that's a, I don't want to say a way. wasted life, but it would be a wasted life if he got killed when we have the capability of going full autonomous right now. I think we'll eventually get there. So one of the value propositions for like ground autonomy is that you're really focused on the, what we just, one of the hardest domains right in the military, right?

So just kind of stepping back, there are a lot of folks who are focused on air power or naval power or missiles. But at the end of the day, wars are ultimately one on 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, you know, where we have to fight. And, you know, I think when you look at the U.S. and, you know, our casualty rates,

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 the warfighter. and I think they're very optimistic about how they want to use them. And ultimately, we do want to do that, right? We want to pull, we want to essentially reduce risk on the battlefield, right? Pull war fighters away from these points of contact,

have more people who are maybe operating teams of robots from, you know, that are far away from a location where they could get hit. But in the meantime, I think that the way they're going to be integrated is in a way that helps war fighters in the ground to stay safer to potentially give them more, more options, more tools without totally pulling them out. So to your point, like, yes, like we want to, we want to ensure that, you know, we ultimately want to pull, you know, as many people 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 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 do maybe more specialized, specialized roles on the battlefield. So I think it'll be a process. I mean, just thinking about my time, you know, in Iraq, Afghanistan alone, I mean, I have right, IED threat was was huge. And then here is a key statistic here. Forty-four percent of U.S. killed in action from 2006 to 2012-21 were killed by IEDs. Forty-four percent. Now, I mean, if if these things did have IED detection capabilities, it's good. It's, it's, it's, it's, it's, it's, gotta be better than humans than i mean there's 40 it's almost 50% yeah people that were killed would still be here today yeah you know just with just in that application i think there's a lot you can do there i mean there's you know sensors you can put on the vehicles human sensor like i was just saying would caught my guy out in the weeds just trying to yeah in a bunch of trees i mean

rolling down iraq i mean there were sniper problems there was the guy the trigger the trigger men of iEDs and I mean, I would think that it would pick them up immediately. I mean, they can't hide from the machine. Yeah. How is it sensing them? Yeah. So the machine has cameras on it, right? And we, depending on 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. I was able, as you said, just be able to pick someone up, even in the, you know, in the woods and in the weeds. But, you know, you should think about, like, there's a lot of different ways that these can be used out, again, out in front of the warfighter. You know, you can have convoys of autonomous vehicles that don't have people on them, right, that can move very quickly and move supplies back and forth. Even if they're targeted, at least no human is being killed. You can have them autonomous vehicles out in front of human 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 as 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, which is the replacement for that, having autonomous vehicles that can provide sensing and protection for those formations. So we think about them as adding to some of the things that we're developing. How many of these scenarios have you kind of war game to run scenarios on out at wherever?

Quite a few. I mean, I think people are, once they start to see the vehicle and they start to think about it, they are coming up with lots of ways of potentially using them. And as we, you know, one of the things we've been trying to do in the last year is work with a lot of different units. So we've worked with over 20 different 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. Let's use this to support our logistics to allow us to move faster. We were able to do that. And so then they were like, okay, 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, they were using these autonomous vehicles in this force-on-force exercise, not just for resupply, but also for intelligence surveillance and reconnaissance. There were snipers who were using the vehicles as decoys, right? There's just a lot of things you can start to do. And the first step is really just getting it in the hands of the warfighter, right? Like, let them think about the problems they're trying to solve, experience the technology,

and then, you know, start to iterate on, you know, potentially new tactics and things like this that you can do with the vehicles. Now, are these truly autonomous or is there somebody, is there an operator in the rear that's going to have to be behind each one of these things with a remote control? Yeah, this is a great question. And so, you know, and this is sometimes kind of glossed over. A lot of people say that they can do autonomous things. And people think about uncrewed ground vehicles. People talk about UGVs all the time in Ukraine versus autonomous vehicles. So what are the differences between these things, right?

So remote control basically means you have a controller, like in your hand, and you're looking at the thing and you're driving it. So think about like a toy car, right? Like that's a remote control vehicle. Now, that's fine, except you have to actually look at the terrain yourself, look at the vehicle, and determine where it's going to go. That obviously doesn't work beyond line of sight or in 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 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 across the world if you have a really good connection to that vehicle, if you have a good satellite connection or radio connection to the vehicle. And a lot of the work which is being done in Ukraine is 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 like a handheld controller or 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 it occupies at least one person's attention

at all times because the person's making all of those decisions. But it also is something where if your comms gets disrupted, the thing's just dead, right? Like it requires a human to drive it and the human can no longer connect to it. So one of the strategies is you cut the comms to the vehicle, you use EW or whatever to cut the comms and then you strike it because it's just a sitting duck. So autonomy is extremely powerful because it allows the vehicle to sense the environment represented, plan through the environment, all on board, like at the edge, makes its own decisions,

and it doesn't require a human to essentially drive it. So when you have an autonomous vehicle, you can tell it where you want it to go, and it will just go there. And that means that you can focus your attention on something else. That might be another autonomous vehicle, it might be another task. So, for example, you can send a vehicle back to resupply you, but you don't have to be focused anymore.

You can do your job. The second thing about autonomy, which is important to understand, is because the vehicle is making decisions based on its own sensing and own onboard compute, if your comms get cut, the thing's going to keep going and continue the mission. And so one of the things we're starting to see out of the Russian-Ukraine conflict

is that the Russians are starting to add autonomy to ground vehicles to deal with the contested comms situation. Because it's so hard for them to maintain comms, autonomy allows the vehicles to continue to move without human oversight. So that's another piece of this. So we think about this as kind of force multiplication and resilience to a contested comms environment. Well, now, so another thing with autonomy too, correct me from wrong,

is let's see you have 100 these. You say have 500 of these. Yeah. And then you have all these other, you know, we were talked about at the beginning, all these other companies that are doing, you know, with Shield AI with their expat. We got Serronic with the autonomous boats. We got Andurals making stuff. We have mock making stuff.

All these companies are jumping all over, you know, lots of autonomous stuff. But just overland AI alone. I mean, let's say that there's 500 of the. vehicles you know we're talking I don't know the invasion of Fallujah yeah these will all be able to communicate with each other on in 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 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 to complete that mission. So we think about the autonomy, which is onboard the vehicle. This is what we call platform autonomy. It's how each individual vehicle analyzes terrain, makes decisions, decides how to drive. Then there's a notion of mission autonomy where multiple vehicles can coordinate with each other. And the real idea here is to make it really easy for one person to have an immense effect on the battlefield,

to control many, many different vehicles and the payloads on them. And so, you know, going back to an earlier part of the conversation, we view this as something like, you know, a gamer who's playing like a real-time strategy game, something like, like Star Trek. aircraft where you're controlling like 200 different units. Can a person do that in the real world with, you know, real platforms like our ultra platform? And that's what we're building up towards. So that whole idea is, you know, single operator, massive force through potentially hundreds of different vehicles and payloads. Wow.

And so will it move to the point where, you know, I just brought up all these other companies? Will it move to the point where these Overland AI vehicles are communicating with Shield AI's expat with Seronics boats? I mean, will it get to the point where the entire battle space is coordinated under one brain? So, you know, the short answer is that, like, yes, the ground vehicles will be communicating with, like, aerial vehicles. vehicles and the different payloads, and they'll be coordinating in order to complete a task. I think we still, you know, one of the things which is really important to keep in mind is, again, like these are all just tools. So you really want to enable an operator to achieve their objective as effectively as possible.

And that means, you know, not just kind of handing everything over to an AI brain, but having, you know, essentially like AI assistants who are allowing a human to choose courses of action, to coordinate, you know, ground vehicles or payloads or aerial vehicles more effectively and so on. Let's talk about African Lion. What happened there? Okay. So African Lion is an exercise. It's one of the largest exercises in Africa.

So it's a place where US war fighters work with partner nations and things like that to essentially train and demonstrate capabilities. We were part of African Lion working with 173rd Airborne. So the 173rd Airborne had two of our ultra vehicles with different types of payloads on them. One vehicle had a crowed. remote weapon station with an M240 machine gun on it.

Another vehicle had a rocket propelled breaching system on it, and they were using these vehicles for a breaching operation. So essentially what happened was they sent one vehicle forward with machine gun on it, providing security, and then a second vehicle was quickly following it, moved to a breach point, essentially applied the payload so the it's an explosive line charge something like a micklick if people know what that is but essentially an explosive rope that gets

launched out in front of the vehicle and produces a it blows up and produces like a safe corridor that can then be proofed with with another vehicle so they're using this to reduce obstacles out in front of the force and 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, every area of the defensive obstacle belts are being watched by an adversary. And as you move forward to try to reduce those obstacles, the combat engineers, everyone's targeting them. So even in a successful breaching operation, you're expecting something like 50% casualty rate. So if you're able to do that with autonomous vehicles, like the way that 173rd Airborne was

demonstrating, that's taking, you know, in their estimate, up to almost two platoons, about 40 people out of that extremely dangerous situation. You're just sending the machines forward to do that, to work that problem, to create the breach in the obstacle belt, and then they're able to move through. So they're very excited about the technology. But it's just an example of war fighters using our vehicles, putting payloads on them, coordinating as part of their units movement maneuver.

So pretty exciting to see. This is fascinating stuff. Yeah. Wow. The game has changed a lot. 40 years. Yeah. Holy shit.

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join thousands in Toronto for the Princess Margaret Cancer Foundation walk. Challenge yourself, friends and family to walk 21 kilometers in support of life-saving research. Together, we can carry the fire and help create a world free from the fear of cancer. Register today at pmcf walk.ca.ca. 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 have mentioned Russia was doing some of this, correct?

What is, what about China? Are they? Yeah. seem to be pretty on the ball with just about everything. 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. 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 working hard to catch up. So what kind of is there any, what other kind of weapon systems do you think will be putting on these, these ultras? I think for any of, you know, you think about uncrewed ground vehicles and autonomous ground vehicles, you can put, it depends on the size of vehicle, you can put like almost anything on them, right? So any type of vehicle that you have today, which can carry anything from, you know, a very small vehicle might be a couple hundred pounds up to tens of thousands of pounds. you can put on a potentially autonomous vehicle. So that's, you think about smaller things,

might be smaller remote weapon stations, might just be sensors, right, radar, optics, things like this. But much larger vehicles, large missile systems, right? So ship interdiction missiles, anti-air missiles, all sorts of things. You could put pretty much anything on this. Pretty much anything. with Epirus? Yes. So you could put Leonidas mounted on one of these damn things and absolutely. Then you have drone defense for an entire fucking battalion. Yeah, usually when we're

thinking about drone defense, you know, it's going to be a layered defense. So you're going to want maybe something like, you know, 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 vehicles or systems, there are more to take their place. Let's talk about the technical mode, the team, and the peer threat. Sure. So the way that we think about the technology that we're developing, we really are thinking about it as, you know, developing an autonomous version of core battlefield functions, right? So like either that ISR or breaching as examples, where you have multiple vehicles with payloads that are performance. a task. Now, in order to do any of that, in order to have a set of autonomous vehicles with

payloads that's, you know, doing something complicated, think about like first principles. Like, what do you have to do first? Well, you have to be able to carry stuff and move it, you know, like move it from one place to another 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 modes is the fact that we're the best in the world at being able to see and understand terrain and move vehicles to it.

We can put payloads like pretty much anywhere that a vehicle can drive, right? And so once you have that, as you now combine this to say, like instead of just moving one vehicle or one type of payload, now I'm moving multiple vehicles, multiple payloads, that foundation allows you to build up those capabilities. So we think that that's one of our biggest technical modes is we have just this incredible team coming from, you know, deep tech places like Waymo and crews 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 move payloads in the environment. So that's technical mode. I can't quite remember the other questions, but that's really a foundation of everything.

I mean, we had, 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 yeah, you know, what does it look like, you know, when we're talking about controlling all these autonomous vehicles, especially when it comes to hundreds, I mean, what, and it's not remote control. So what is it, I mean, we'd kind of discussed a little bit out there, but what, what does it look like for, for the team or the person or whatever is controlling hundreds of of these at once. Yeah, that's a great question.

So you're not going to be just like looking through the vehicle sensor and remote controlling it because you have hundreds of them. How can you do that? So the way that we have designed the software is that you essentially have overhead map, 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 look at, so you can look at terrain features, you can look at where all the vehicles

are, you can see what payloads are on the different vehicles. And then the question is, how do you now coordinate and control hundreds of those assets? And you need to be able to do that by selecting vehicles, grouping vehicles, telling groups of vehicles to go to like one place or another, telling them to execute a certain payload at a particular location so that you can kind of quickly move between, you know, sets of vehicles and, um, and essentially tell them where to go. Again, a lot like a real time strategy game. Uh, we're also working on tools. So this looks like, this is what we were talking about at the beginning.

This looks like sounds weird, looks like World of Warcraft. Yeah, yeah. You're, you're sending a group of things giving it a task. It's, it's not weird. You're on to the next thing. Yeah. I mean, and I think, you know, one of the reasons why we've looked at, you know, 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 in Warcraft. But, you know, the tools that people use for grouping, for moving, for coordinating assets in those types of games are things we can take those ideas and apply them here as well so that a single operator can control many, many different assets. Another thing that we're developing is things like AI assistance, which can help to suggest, 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 the actual decision making to the AI, but the AI can suggest solutions that can help you to handle more assets at once. Wow. Wow. I got a hot question for you. All right. Ready? In 1941, 353 Japanese planes came off six carriers and hit Pearl Harbor in under two hours. They sank or crippled all eight battleships and killed over 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 failure wasn't intelligence. It was imagination. Nobody could picture it until the harbor was on fire. Today, China's arming robot dogs, they build 90% of the world's drones, and they just flew a mother ship not long ago that launched a hundred kamikaze drones in a single swarm. The warnings are everywhere again. Are we sleepwalking into a robotic Pearl Harbor? I hope not, but I think it's correct. There are warnings everywhere.

We're seeing robots used in the battlefield, again, in Ukraine. There's both by the Ukrainians and Russians. So, you know, we and I think other defense tech companies are certainly paying attention to this. Like, we are trying to understand what capabilities our adversaries have and also ensure that the U.S. military has similar or better capabilities. And so we think about these sorts of things all the time, right? It's the new technology, which is going to be defining, like, these next conflicts. And 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 think, like, we are certainly aware of it. I think we're pushing the military to adopt new technology and defenses against it, like, as quickly as possible. But, you know, there may be work to be done. Sometimes these things aren't really real to people until, unfortunately, they actually experience them. So our best bet is to really be watching and taking seriously what is happening in these conflicts and what our Abdufair series are developing. I mean, do you feel the Department of War has taken this seriously?

Do they understand the capabilities that you guys have? I mean, when you looking at that thing out there, what it's capable of, it's very surprising and almost alarming to me that they, you know, 15, 15. 15, you have a contract for 15. Yeah. Why don't you have a contract for 10,000? Yeah. I mean, it should be. You know, I think the Department of Defense is trying to, or Department of War is in, 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've had had on here will probably agree with, right? And something that we're all fighting hard against. Now, we've seen the Department of War speed things up. And some of the, like I was saying a little bit earlier, we have actually gone from, you know,

University research to production contract, even if it's, you know, a relatively small initial one in like three years, which is about right, maybe a little slow for like, you know, the tech sector, but that's lightning fast for Department of War. And we've made use of Defense Innovation Unit and a lot of the new tools, App Fit contracting process and things like that

that have come online recently. So I do want to credit the Department of War for moving faster, but there's still so much work to be done in order to move at the speed that we need to move that. So, you know, it's something I think like we're all working on and trying to. Are other countries better customers than our own country? Or this problem? For like our stuff or like their own stuff or for stuff that America, not not not

overland AI for things that America, Americans, like yourself are developing here in this country or other countries, maybe Ukraine. Yeah, I mean. I think, you know, I've I've heard of these issues happening. And here we have the best that the fucking world has to offer right here, developing groundbreaking new technology that's going to change how war is fought. I mean, it's, it's, yeah, and it, I mean, it's, it's, it's, it's such an upgrade. It's, especially with a company like Epper, all these. companies, man. Yeah. Like what you, everybody, everybody, you know, everybody that sat across with me in the defense tech sector, what you guys are developing is, it's fucking incredible. Yeah.

You know, but I see it being utilized in Ukraine. You know what I mean? And I see it in a, it bothers me when, when we're sending all this stuff. Maybe we aren't, but I see things similar being utilized in Ukraine in, in, in, and, in, It just seems like our country is not taking advantage of the talent that we have here. It's fucking scares me. Yeah. I mean, there's kind of a...

Like, we have all this, we have all this talent like yourself of what you're building. And I think there's things to be hopeful about. So, like, on the one hand, 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't. There's just a statistic out saying something like there's been two million casualties in that conflict, right? And 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 all of this technological innovation. It's existential for them.

They have to do this or they will lose, right? And so they will do whatever it takes to win here. It's not existential for the U.S. yet. And I think that means that adoption is just slow. Like people aren't feeling the pressure to do it. And I think that it's unfortunate, of course, because we have a little bit of luxury 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 forces. But we don't have the urgency, right? And so I think that's part of the problem. On the positive side, I do think that we have the best minds in the world here. I think we can do it. But it's, again, it's a matter of... We are doing it. We are doing it.

It's not fucking keeping up with you guys. But it could be done faster, right? Like we could accelerate this. And, you know, that's a fight that we just have to continue to make, right? To continue to fight to get this tech into the government faster. And I think going back to, you know, thinking about some of the DARPA stuff earlier, like, we're creating incredible new technology. But then you really got a fight to like actually.

they get the Army and Marine Corps and services to actually, you know, try it, iterate, you know, ultimately, ultimately use it and incorporate it. Yeah, yeah. Yeah. If we were attacked tomorrow, do you think we could survive a sea or ground invasion? I mean, I think I'm, I think we would absolutely survive a sea or ground invasion. I think we would mobilize very quickly. I think those, the urgency would be there and, you know, we would, we would do whatever it took to, to, 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. So, me too. Last thing. Yeah. Manufacturing. Are you guys manufacturing these yourselves?

Yeah. Yeah, so scale up. So we're trying to scale as quickly as possible. We are manufacturing the ultra vehicles. I think I mentioned before that they're based on Polaris, you know, engines and drive trains and things like that, but we're upgrading the suspension. We're, you know, putting in the payload deck, adding the compute, the sensors and everything. We've got factory running in in Seattle that's doing this right now.

And we, I think we've like 5X manufacturing over the last six months or so, and we just we need to do a lot more so we're building up that capability is as fast as possible and again the more support we get from from the government the more we we actually work with war fighters the more demand there is and there's there's a ton of demand right now so we're scaling as quickly as we can right on well byron i really appreciate you coming it was an honor to interview you and i love love everything overland ai's doing and that thing out there There is super impressive. Thank you.

Yeah, thank you so much for having me. I really appreciate you taking the time to learn a little bit more about what we're doing and inviting us out and being able to show you some of the things that we're building. So thank you. Thank you. Sure. No matter where you're watching the Sean Ryan show from, if you get anything out of this at all, anything,

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