Latest / INSPIRE STORY / #0176_ 5 People Could Control an Army of 500 Autonomous Vehicles — The Future of Warfare & AI

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#0176_ 5 People Could Control an Army of 500 Autonomous Vehicles — The Future of Warfare & AI · INSPIRE STORY

• 0:05
  Speaker 1: Fire in Boots, welcome to the show.
• 0:08
  Speaker 2: Thanks for having me. It's awesome to be here.
• 0:10
  Speaker 1: It's awesome to have you. So I can't even remember,
• 0:12
  Speaker 1: I think I actually found you guys on LinkedIn, which
• 0:15
  Speaker 1: I'm never on. And I think I saw like a
• 0:19
  Speaker 1: video on LinkedIn or something of Overland AI and then started,
• 0:24
  Speaker 1: found you guys on YouTube and started following you and
• 0:27
  Speaker 1: then found out APC is an investor, or ledger round.
• 0:35
  Speaker 1: So yeah, I wanted to get in touch and love
• 0:38
  Speaker 1: what you guys are doing. I've had lots of tech
• 0:41
  Speaker 1: giants on here, a lot of drone stuff, ceramic with
• 0:46
  Speaker 1: the water stuff. I didn't even know. Your guy just
• 0:49
  Speaker 1: told me out back that they just hit an Iranian port.
• 0:53
  Speaker 3: Yeah, pretty, pretty amazing. Um, so, you know, first of all, it's,
• 0:56
  Speaker 3: it's an honor to be on the show and in
• 0:58
  Speaker 3: that company. I mean, that's, that's incredible, but, um, yeah,
• 1:02
  Speaker 3: just in the last day or so there was news of, uh,
• 1:05
  Speaker 3: Saronix boats being used, um, in an offensive operation in
• 1:09
  Speaker 3: the Strait of Hormuz.
• 1:10
  Speaker 2: Wow.
• 1:11
  Speaker 4: Yeah.
• 1:11
  Speaker 1: And so you're the ground guy. That's right. You're bringing
• 1:14
  Speaker 1: autonomous wheels, vehicles to, uh, to land warfare. So, uh,
• 1:21
  Speaker 1: Really excited to dig into this. But let me kick
• 1:24
  Speaker 1: it off with an introduction here. Byron Boots, you're the
• 1:28
  Speaker 1: co-founder and CEO of Overland AI, a company building autonomous
• 1:32
  Speaker 1: ground vehicles for the U.S. military. Before founding Overland AI,
• 1:36
  Speaker 1: you earned your Ph.D. in machine learning from Carnegie Mellon,
• 1:40
  Speaker 1: became a professor at the University of Washington, and led
• 1:44
  Speaker 1: the winning team in DARPA's Racer Off-Road Autonomy Program. Overland
• 1:49
  Speaker 1: AI has raised more than $ 140 million and become the
• 1:53
  Speaker 1: first autonomous ground vehicle company to win a production contract
• 1:58
  Speaker 1: with a fully integrated hardware and software platform. Congratulations. Your
• 2:03
  Speaker 1: vehicles are already being used by military units around the
• 2:07
  Speaker 1: world to move supplies, support operations, and reduce risk for
• 2:11
  Speaker 1: soldiers in the field. Welcome to the show.
• 2:15
  Speaker 2: Well, thank you so much.
• 2:17
  Speaker 1: Got a lot to talk about here. It's been a
• 2:19
  Speaker 1: minute since I've talked to somebody that's doing the kind
• 2:23
  Speaker 1: of stuff that you're doing. But before we get going,
• 2:28
  Speaker 1: got a couple things to crank out here. Everybody gets
• 2:30
  Speaker 1: a gift.
• 2:31
  Speaker 2: Oh, wow.
• 2:33
  Speaker 1: Those are autonomous gummy bears.
• 2:37
  Speaker 2: I'm excited. I love gummy bears.
• 2:40
  Speaker 1: Right on. Right on. And I got a question for you.
• 2:45
  Speaker 1: I got a Patreon account. It's a subscription account. And
• 2:49
  Speaker 1: so they're the reason I get to sit down here
• 2:52
  Speaker 1: with you today. And so they get the opportunity to
• 2:55
  Speaker 1: ask every single guest a question. So this is from
• 2:58
  Speaker 1: Thomas W. You've dedicated your career to advancing robotics and AI,
• 3:03
  Speaker 1: including technologies with defense applications. From your perspective, what responsibility
• 3:09
  Speaker 1: do scientists and engineers have to ensure these innovations ultimately
• 3:14
  Speaker 1: reduce human suffering rather than prolong conflict? And do you
• 3:19
  Speaker 1: believe AI and autonomous systems could one day become tools
• 3:23
  Speaker 1: that prevent wars through deterrence and de-escalation? Or do they
• 3:29
  Speaker 1: risk making armed conflict more frequent and easier to justify?
• 3:34
  Speaker 3: That's a great, great question. You know, I think autonomous systems,
• 3:41
  Speaker 3: they're like any other tool. The way that I think
• 3:43
  Speaker 3: about them is really... a tool, a technology, and in
• 3:48
  Speaker 3: the context of defense, it is, you know, something which
• 3:52
  Speaker 3: allows a warfighter to, you know, be safer, right? So,
• 3:56
  Speaker 3: you know, it reduces exposure, pulls them away from the
• 3:59
  Speaker 3: point of contact, and then also potentially provides force multiplication
• 4:05
  Speaker 3: on the battlefield. And I'm sure we'll get into some
• 4:08
  Speaker 3: of these things, but it is a tool which is
• 4:11
  Speaker 3: used by humans and So, you know, how you use
• 4:15
  Speaker 3: them is really, I think, a human question. So, you know,
• 4:22
  Speaker 3: in the context of saving lives, I think that they
• 4:26
  Speaker 3: will save lives for our war fighters.
• 4:30
  Speaker 2: On the battlefield. It's very clear how they do that.
• 4:34
  Speaker 3: But, you know, they also can serve as a deterrence,
• 4:37
  Speaker 3: like any technology might. You know, if we have significantly
• 4:42
  Speaker 3: stronger robotic systems at our disposal, then, you know, we're
• 4:46
  Speaker 3: going to be a little bit, you know, tougher to
• 4:50
  Speaker 3: defeat in the battlefield, and our adversaries will see that.
• 4:52
  Speaker 3: And so, in that way, you know, they can certainly
• 4:55
  Speaker 3: serve as a deterrence as well.
• 4:58
  Speaker 1: Right on. I mean, yeah, watching some of the models
• 5:00
  Speaker 1: you showed me outside, and then, you know, the videos
• 5:05
  Speaker 1: and how they're going to be integrated in with the
• 5:08
  Speaker 1: warfighters in combat. I mean, it's... you know, is a
• 5:13
  Speaker 1: former SEAL. Seeing what, seeing the, you know, war, what
• 5:21
  Speaker 1: war has developed into is just, I mean, it's fascinating.
• 5:24
  Speaker 1: And I mean, just, it's been over 20 years since
• 5:27
  Speaker 1: I've been on the ground in a war. And, well,
• 5:31
  Speaker 1: I guess not, but it's been over 10 years. But,
• 5:34
  Speaker 1: I mean, I already have tons of questions and I
• 5:39
  Speaker 1: can see so many different applications where this would be,
• 5:43
  Speaker 1: useful in just so many different scenarios.
• 5:45
  Speaker 2: Yeah, we should definitely get into it.
• 5:47
  Speaker 3: Before we do that, though, I do want to give
• 5:49
  Speaker 3: you a gift as well, if you don't mind.
• 5:52
  Speaker 2: So let me come over here, and we got you
• 5:59
  Speaker 2: a chair. Now, this isn't just any chair.
• 6:01
  Speaker 1: Right on.
• 6:03
  Speaker 2: This is, it looks kind.
• 6:06
  Speaker 5: Of like an office chair, but this is actually a
• 6:08
  Speaker 5: seat from one of the vehicles. So as I was
• 6:12
  Speaker 5: explaining earlier, we pull the seats out of the Polaris
• 6:16
  Speaker 5: Rangers and we turn them into those autonomous vehicles that
• 6:19
  Speaker 5: we saw outside. Well, what do you do with the
• 6:22
  Speaker 5: seats once you've pulled them from the vehicle?
• 6:25
  Speaker 2: We make them into chairs. So we made one for you.
• 6:28
  Speaker 4: Oh, man.
• 6:29
  Speaker 1: Thank you.
• 6:29
  Speaker 2: You can check it out.
• 6:30
  Speaker 1: This is awesome. Yeah, yeah. Not bad. Not bad.
• 6:37
  Speaker 2: There you go.
• 6:40
  Speaker 4: Put this in the office.
• 6:42
  Speaker 1: Thank you.
• 6:42
  Speaker 3: That's right.
• 6:42
  Speaker 2: Yeah, of course.
• 6:43
  Speaker 1: That's awesome. All right, Byron. So before we get into
• 6:47
  Speaker 1: everything Overland AI, let's do a little backstory on you.
• 6:51
  Speaker 1: Where did you grow up? How did you get into
• 6:53
  Speaker 1: this stuff? I mean, what's the backstory here?
• 6:56
  Speaker 3: Yeah, I had a whole career before moving into defense tech.
• 7:01
  Speaker 3: So I grew up outside of New York in Connecticut.
• 7:07
  Speaker 3: was into computers and, you know, was in the Boy
• 7:11
  Speaker 3: Scouts and played sports and had, I think.
• 7:14
  Speaker 2: A pretty typical upbringing.
• 7:18
  Speaker 3: So, you know, that's maybe where things got started, just
• 7:21
  Speaker 3: love of the outdoors and taking apart computers and playing
• 7:25
  Speaker 3: video games and doing all the sorts of things that
• 7:28
  Speaker 3: kids often do.
• 7:29
  Speaker 1: Did you watch The Terminator growing up?
• 7:31
  Speaker 2: I sure did, yeah.
• 7:32
  Speaker 1: Did you?
• 7:33
  Speaker 2: So.
• 7:35
  Speaker 3: Terminator 2 was an unbelievable, unbelievable movie. And happy to
• 7:44
  Speaker 3: talk about that a little bit more in the context
• 7:46
  Speaker 3: of what we're building. But obviously, robotics and science fiction
• 7:51
  Speaker 3: were something that I really enjoyed.
• 7:54
  Speaker 1: Were you a gamer?
• 7:56
  Speaker 2: Yeah, I used to play.
• 7:58
  Speaker 3: So back when I was in high school, I used
• 8:00
  Speaker 3: to play StarCraft quite a bit.
• 8:02
  Speaker 2: StarCraft 1.
• 8:02
  Speaker 3: That was before StarCraft 2 came out. So real-time strategy games,
• 8:06
  Speaker 3: I did a lot of, played a lot of games
• 8:08
  Speaker 3: like that. We talked about Warcraft before. You know, I
• 8:11
  Speaker 3: used to play that too. So that was really what
• 8:15
  Speaker 3: I was most drawn to. But yeah, I mean, love
• 8:19
  Speaker 3: computer games.
• 8:20
  Speaker 1: How, I mean, well, we'll get into it later. I
• 8:23
  Speaker 1: was going to ask how similar, you know, is what's
• 8:27
  Speaker 1: happening today is controlling one of those games. We'll get
• 8:29
  Speaker 1: into that in a little bit. So where did you
• 8:32
  Speaker 1: go to school? What did you go to school for?
• 8:35
  Speaker 3: So I went to college at a small liberal arts
• 8:37
  Speaker 3: school called Bowdoin College. It's in Maine. And, you know,
• 8:42
  Speaker 3: I spent four years there. I was a computer science
• 8:46
  Speaker 3: and philosophy double major as an undergrad. I started out
• 8:51
  Speaker 3: really thinking about, you know, I love computer science, like
• 8:55
  Speaker 3: I said, you know, all through high school. I also
• 8:56
  Speaker 3: really liked history and read a lot of history. And
• 8:59
  Speaker 3: so when I went to college, I was thinking about
• 9:02
  Speaker 3: maybe double majoring in computer science and history. I thought
• 9:04
  Speaker 3: it would be cool to have a more technical degree
• 9:07
  Speaker 3: and something which is, you know, more humanities-oriented with history.
• 9:14
  Speaker 3: But I quickly, you know, sort of figured out my
• 9:19
  Speaker 3: first year that, while I love history, it involved tons
• 9:24
  Speaker 3: of reading and things.
• 9:24
  Speaker 2: Like this that I really like to do, but also.
• 9:27
  Speaker 3: Involved foreign languages, and you need to actually, read about
• 9:33
  Speaker 3: history through contemporary sources and in the language that folks
• 9:38
  Speaker 3: wrote in. And so it was something I was not
• 9:42
  Speaker 3: great at.
• 9:42
  Speaker 2: I was not.
• 9:43
  Speaker 3: Particularly good at languages, didn't really enjoy them. And so
• 9:51
  Speaker 3: I took a couple of courses in philosophy. I started
• 9:53
  Speaker 3: out with a course called Logic and Formal Systems, which
• 9:57
  Speaker 3: was really delving into formal frameworks for understanding arguments and
• 10:04
  Speaker 3: analyzing arguments.
• 10:05
  Speaker 2: And I really started to fall in love with that.
• 10:08
  Speaker 2: And so.
• 10:11
  Speaker 3: I brought that together with computer science and majored in
• 10:15
  Speaker 3: both of those areas as an undergrad.
• 10:17
  Speaker 1: Interesting. You were fascinated with the brain too, right?
• 10:20
  Speaker 2: Yeah, yeah, yeah.
• 10:21
  Speaker 3: So, I mean, in undergrad, I was, you know, I
• 10:27
  Speaker 3: was taking computer science and, you know, took courses in
• 10:30
  Speaker 3: artificial intelligence and started to do research in robotics.
• 10:34
  Speaker 2: And this is back, you know, over 20 years ago.
• 10:36
  Speaker 1: They had artificial intelligence courses 20 years ago?
• 10:39
  Speaker 3: Yeah, it was pretty interesting. So, my university is a
• 10:41
  Speaker 3: small school. There was only four faculty in the computer
• 10:45
  Speaker 3: science department there. And at the time, computer science was
• 10:49
  Speaker 3: seen as like an offshoot of mathematics. And so, you know,
• 10:54
  Speaker 3: a lot of these smaller schools had combined departments with
• 10:57
  Speaker 3: computer science and mathematics, and you take a lot of
• 11:00
  Speaker 3: courses in both areas. But at Bowdoin, two of the
• 11:04
  Speaker 3: four professors were actually folks who studied artificial intelligence. So
• 11:10
  Speaker 3: it was something, you know, it's been around for a
• 11:12
  Speaker 3: long time. I mean, people were working on aspects of
• 11:15
  Speaker 3: AI you know, back in the 70s and 80s. But
• 11:20
  Speaker 3: it was just starting to kind of come to the
• 11:22
  Speaker 3: forefront and.
• 11:23
  Speaker 2: Be an area that was really.
• 11:27
  Speaker 3: Starting to accelerate around, you know, 2000 when I was
• 11:31
  Speaker 3: an undergrad. So I started to study AI there. And
• 11:35
  Speaker 3: then in philosophy, I was thinking about things like philosophy
• 11:38
  Speaker 3: of mind and philosophy of science and just kind of
• 11:42
  Speaker 3: trying to understand how the human mind worked.
• 11:47
  Speaker 1: Yeah. I think that's an interesting discussion itself.
• 11:52
  Speaker 2: Yeah.
• 11:52
  Speaker 1: I mean, how deep did you get into that before
• 11:54
  Speaker 1: you kind of switched gears?
• 11:56
  Speaker 3: Yeah, so again, as an undergrad, I double majored in
• 12:01
  Speaker 3: computer science and philosophy. And when I graduated, I was
• 12:06
  Speaker 3: thinking about what I wanted to do next. I initially
• 12:10
  Speaker 3: took a job at a robotics company.
• 12:12
  Speaker 2: I was an engineer.
• 12:15
  Speaker 3: Basically working on problems related to perception and mapping and
• 12:21
  Speaker 3: robotic systems. So these were mobile robots, much smaller than
• 12:25
  Speaker 3: the ones that.
• 12:26
  Speaker 2: We just saw outside. So robots that.
• 12:29
  Speaker 3: Are about this big that moved around inside of buildings,
• 12:33
  Speaker 3: and you have to determine where they are and how
• 12:34
  Speaker 3: they get from one place to another and things like that.
• 12:36
  Speaker 3: So I worked as an engineer working on those sorts
• 12:39
  Speaker 3: of problems. But I was really thinking about... you know,
• 12:44
  Speaker 3: kind of like, what do I want to do next?
• 12:46
  Speaker 3: I knew I didn't want to just be, you know,
• 12:48
  Speaker 3: kind of working as a software engineer. I wanted to
• 12:50
  Speaker 3: go back to school. And the question was like, what
• 12:54
  Speaker 3: area should I study? So I really liked computer science.
• 12:57
  Speaker 3: I really liked philosophy. And one of the things that
• 13:00
  Speaker 3: I started to think about was cognitive science, you know,
• 13:04
  Speaker 3: just sort of how the mind works. So with AI,
• 13:07
  Speaker 3: you know, you're trying to program a computer that can
• 13:11
  Speaker 3: almost like think like a human, that can perceive the world,
• 13:13
  Speaker 3: that can understand it somehow. And then in philosophy, you're
• 13:18
  Speaker 3: really thinking through language and, you know, by writing arguments,
• 13:23
  Speaker 3: thinking about, you know, how does the human mind work?
• 13:26
  Speaker 3: How does it contend with, you know, reality, things like this.
• 13:29
  Speaker 3: But the piece that I was missing was actual neurobiology, right?
• 13:33
  Speaker 3: Like the human nervous system, the substrate of, Um, of,
• 13:39
  Speaker 3: of the mind. And so, um, I decided that before
• 13:43
  Speaker 3: I went back and entered into a graduate program, I
• 13:47
  Speaker 3: needed to.
• 13:47
  Speaker 2: Learn more about neuroscience.
• 13:51
  Speaker 3: Um, and so I, I managed to get a job
• 13:55
  Speaker 3: at Duke university, uh, in a neurobiology lab, um, studying
• 14:00
  Speaker 3: human perception. So I worked as an engineer for about
• 14:03
  Speaker 3: a year, and then I went to Duke and I
• 14:05
  Speaker 3: worked there for two years. And this was really, it
• 14:08
  Speaker 3: wasn't a graduate program. It was just working in a
• 14:11
  Speaker 3: neurobiology lab. And I was auditing courses on neuroscience and
• 14:17
  Speaker 3: neurobiology while I was there, trying to learn, you know,
• 14:21
  Speaker 3: how does the mind work?
• 14:22
  Speaker 1: I mean, wow. How the human mind perceives the world. Yeah. How,
• 14:29
  Speaker 1: I mean, did you, did that, is that helpful in
• 14:33
  Speaker 1: what you do today? It is.
• 14:36
  Speaker 3: It's pretty interesting. So the lab that I was working
• 14:39
  Speaker 3: in was really focused on trying to understand how humans
• 14:44
  Speaker 3: perceive the world. And so let me just give you
• 14:46
  Speaker 3: an example of why this is difficult and interesting. So
• 14:54
  Speaker 3: when you look out at an environment like this room,
• 14:58
  Speaker 3: there's light which bounces off of surfaces, it comes back
• 15:02
  Speaker 3: and it moves through your eye and essentially is projected
• 15:05
  Speaker 3: on your retina. So there's, for each of your eyes,
• 15:08
  Speaker 3: there's a 2D projection of light from the room. And
• 15:13
  Speaker 3: the question is, how do you go from that 2D projection,
• 15:18
  Speaker 3: that 2D image, to understanding what's actually out in the world?
• 15:22
  Speaker 3: Like the 3D environment, the surface reflectance properties things like
• 15:29
  Speaker 3: the wall or the carpet or whatever. How do you
• 15:34
  Speaker 3: sort of solve that problem? And it's called the inverse
• 15:37
  Speaker 3: optics problem. So it's the notion that you have a
• 15:40
  Speaker 3: 2D image and you're trying to kind of understand this
• 15:42
  Speaker 3: complex 3D world. And the challenge is that there's actually
• 15:47
  Speaker 3: not an easy solution to this. Because you're moving from
• 15:51
  Speaker 3: essentially like three dimensions to two dimensions, information is lost.
• 15:56
  Speaker 2: And so.
• 15:58
  Speaker 3: Another way to think about this is that an infinite
• 16:00
  Speaker 3: number of different worlds could have produced the same visual
• 16:05
  Speaker 3: image on your retina. And this manifests itself through illusions.
• 16:11
  Speaker 3: So there are certain types of illusions. There's something, for example,
• 16:14
  Speaker 3: called an Ames room, where when you look at the room,
• 16:18
  Speaker 3: it looks like a rectangular room. But in fact, it
• 16:21
  Speaker 3: has this kind of crazy shape, something you can look
• 16:24
  Speaker 3: up maybe later.
• 16:28
  Speaker 2: The interesting thing.
• 16:29
  Speaker 3: About that is just the fact that something that appears
• 16:35
  Speaker 3: to you to be like a normal rectangular room is
• 16:38
  Speaker 3: actually something completely different.
• 16:40
  Speaker 2: So that is just an example of one of these
• 16:45
  Speaker 2: optical illusions.
• 16:46
  Speaker 3: Now, the interesting thing about illusions is that they basically.
• 16:53
  Speaker 2: everything you see in some ways is an illusion, right?
• 16:56
  Speaker 2: So they're not outliers.
• 16:57
  Speaker 3: It's not like every once in a while, your mind
• 17:00
  Speaker 3: makes a mistake and you kind of see the world incorrectly.
• 17:04
  Speaker 3: You are always inferring some world that is not quite
• 17:08
  Speaker 3: what is actually out there. It's the rule, not the exception.
• 17:13
  Speaker 3: And so this is, this is, it forms almost a
• 17:17
  Speaker 3: philosophical problem. It's like, if you are looking at the world,
• 17:22
  Speaker 3: you can't actually infer what generated, you know, the images
• 17:27
  Speaker 3: that you see. How do you even, you know, how
• 17:30
  Speaker 3: do you even interact with it? Like, how do you
• 17:34
  Speaker 3: continue to exist if you're not seeing things properly? And so,
• 17:39
  Speaker 3: you know, the conclusion, one of the conclusions that we
• 17:41
  Speaker 3: came to was that really the way that you see
• 17:47
  Speaker 3: the world is whatever way is necessary to allow you
• 17:52
  Speaker 3: to continue to persist. So, we kind of think about
• 17:55
  Speaker 3: this as like, you see the world in an evolutionarily
• 17:59
  Speaker 3: sort of appropriate way, in a way which informs your
• 18:02
  Speaker 3: actions so that you kind of do the right things,
• 18:05
  Speaker 3: you continue to exist, you can ultimately reproduce and continue on.
• 18:10
  Speaker 3: And so, it's just one of the problems that we
• 18:15
  Speaker 3: wrestled with.
• 18:16
  Speaker 2: Now, what does that mean?
• 18:18
  Speaker 3: It means that your perception of the world is really
• 18:20
  Speaker 3: shaped through experience. You're just perceiving the world in the
• 18:25
  Speaker 3: best possible way for you to take actions. And some
• 18:30
  Speaker 3: of these fundamental ideas actually carried through into the work
• 18:35
  Speaker 3: I did in graduate school and even some of the
• 18:37
  Speaker 3: things that we do today with the systems that we build,
• 18:42
  Speaker 3: the robotic systems that we build.
• 18:44
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• 20:18
  Speaker 1: slash SRS. That's Shopify.com slash SRS. So where do we
• 20:27
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• 20:28
  Speaker 3: So, you know, I think like one thing, one of the,
• 20:33
  Speaker 3: you know, when you think about building a robotic system,
• 20:36
  Speaker 3: one of the ways that Um, we kind of think
• 20:40
  Speaker 3: about this is that when you're perceiving the environment, you're
• 20:43
  Speaker 3: not just measuring it. You're taking the context and your
• 20:48
  Speaker 3: prior experience and you're using that to, um, predict what
• 20:54
  Speaker 3: you think, like, the world actually is. So, for example,
• 20:57
  Speaker 3: if you look out at a set of trees, you're
• 21:00
  Speaker 3: not just measuring that there's, you know, some obstacles out
• 21:02
  Speaker 3: there in front of you. You're also predicting that there's
• 21:05
  Speaker 3: free space behind them that you can potentially move through.
• 21:09
  Speaker 3: And you get to that point by essentially seeing lots
• 21:15
  Speaker 3: of trees like in your past, right? Like you use
• 21:17
  Speaker 3: that prior experience and then you can understand when you
• 21:20
  Speaker 3: see a pattern like this, that actually means that there's
• 21:23
  Speaker 3: sort of space out there behind those trees that you
• 21:27
  Speaker 3: can then leverage in order to make decisions faster, to
• 21:31
  Speaker 3: move more aggressively. a machine learning way of thinking about things.
• 21:37
  Speaker 3: You're using lots of data, lots of experience to understand
• 21:41
  Speaker 3: what you're seeing in a functional way and then move,
• 21:45
  Speaker 3: you know, use that in order to move a robot
• 21:48
  Speaker 3: more quickly or more aggressively. And those notions, you know,
• 21:53
  Speaker 3: sort of led me from neurobiology at Duke, like really
• 21:59
  Speaker 3: kind of thinking about data machine learning as fundamentally, you know,
• 22:05
  Speaker 3: interesting things towards my graduate education, which I pursued after
• 22:09
  Speaker 3: that at Carnegie Mellon University.
• 22:10
  Speaker 4: Wow.
• 22:12
  Speaker 1: But I've not heard everybody talk about that, that it's
• 22:16
  Speaker 1: gotten into the machine learning AI stuff. That's pretty fascinating. Yeah.
• 22:22
  Speaker 3: And I think, like, another thing which is pretty interesting
• 22:25
  Speaker 3: here that I'll also highlight, you know, I was originally
• 22:28
  Speaker 3: went to Duke to try to understand stuff the brain
• 22:32
  Speaker 3: and how it works. And my hope was that by
• 22:36
  Speaker 3: understanding that, that would help me to maybe better understand,
• 22:40
  Speaker 3: you know, artificial intelligence or how to build machines and
• 22:44
  Speaker 3: things like this. What I pretty quickly realized was that,
• 22:50
  Speaker 3: you know, neuroscience is really hard, right? People have been
• 22:53
  Speaker 3: studying the human brain for almost 300 years, and progress
• 22:58
  Speaker 3: is slow. It's very to understand how the brain works.
• 23:02
  Speaker 3: We don't have a great grasp of it, even now.
• 23:06
  Speaker 3: We can describe a lot about it, but not really
• 23:08
  Speaker 3: understand it functionally.
• 23:10
  Speaker 2: And so....
• 23:13
  Speaker 3: One of the lessons from that was that.
• 23:20
  Speaker 3: You know, I almost came away with the opposite conclusion.
• 23:22
  Speaker 3: So instead of thinking about the brain as something that
• 23:26
  Speaker 3: would help me to build better machines or understand, you know,
• 23:30
  Speaker 3: build a better AI. I almost think that focusing on
• 23:34
  Speaker 3: artificial intelligence and mathematics and probability and statistics and information
• 23:41
  Speaker 3: theory and robotics helps to provide a framework that people
• 23:46
  Speaker 3: may ultimately understand like the brain through. So it almost
• 23:50
  Speaker 3: works the other way that you have to really understand
• 23:55
  Speaker 3: kind of core principles of, perception, planning, control, like these
• 24:00
  Speaker 3: areas which are fundamental in AI and robotics, to understand
• 24:07
  Speaker 3: ultimately what the nervous system might be doing and be
• 24:10
  Speaker 3: able to describe it.
• 24:12
  Speaker 1: Wow.
• 24:12
  Speaker 2: Wow.
• 24:14
  Speaker 1: So you worked at... You went to NVIDIA, too, as well,
• 24:19
  Speaker 1: didn't you?
• 24:20
  Speaker 2: That's right, yeah.
• 24:21
  Speaker 3: So before starting Overland AI, I worked for about five
• 24:27
  Speaker 3: years at NVIDIA. And this is while I was a professor.
• 24:32
  Speaker 3: So after Carnegie Mellon, I got my PhD there in
• 24:36
  Speaker 3: machine learning.
• 24:37
  Speaker 2: I worked on robotics problems. And then I.
• 24:41
  Speaker 3: Was a professor at Georgia Tech for five years, and
• 24:45
  Speaker 3: then University of Washington for seven years after that. And
• 24:49
  Speaker 3: one of the things which is really cool about being
• 24:52
  Speaker 3: a a faculty member or a professor who is running
• 24:56
  Speaker 3: a research lab is that you can also work in industry.
• 24:59
  Speaker 3: So I had a research lab which was focused on
• 25:02
  Speaker 3: robotics and machine learning, and I had a number of
• 25:04
  Speaker 3: PhD students who were working in that lab. But you
• 25:08
  Speaker 3: can take 20% of your time and work in industry simultaneously.
• 25:13
  Speaker 3: And as part of that, I spent about five years
• 25:15
  Speaker 3: working at NVIDIA on machine learning and robotics.
• 25:19
  Speaker 1: How was it working there? That was awesome.
• 25:21
  Speaker 2: I mean, I think, you know, I joined around 2018.
• 25:24
  Speaker 3: So it was before NVIDIA was really kind of like
• 25:29
  Speaker 3: a tier one, let's say, tech company. I think like
• 25:33
  Speaker 3: Google and Microsoft were really up there sort of defining
• 25:37
  Speaker 3: state of the art. But when I went to NVIDIA,
• 25:41
  Speaker 3: I think there were a lot of good people that
• 25:42
  Speaker 3: they were hiring and people had recently seen the power
• 25:46
  Speaker 3: of GPUs, right? Like this massive parallel processing And I
• 25:52
  Speaker 3: was thinking about that in the context of robotics. So
• 25:54
  Speaker 3: how can you parallelize tasks? How can you use it,
• 25:57
  Speaker 3: not just these sorts of chips and this type of technology,
• 26:01
  Speaker 3: not just for perceiving the environment, but also controlling vehicles?
• 26:07
  Speaker 3: So an example of this actually is carried through from
• 26:12
  Speaker 3: work that I was doing at originally Georgia Tech and
• 26:15
  Speaker 3: then NVIDIA now to Overland, where When that vehicle is
• 26:20
  Speaker 3: out in terrain, so when our uncrewed vehicles are out
• 26:22
  Speaker 3: there and looking at terrain, they're evaluating tens of thousands
• 26:27
  Speaker 3: of possible trajectories that they might take, ranking each one
• 26:32
  Speaker 3: of them, determining is this a good one or a
• 26:34
  Speaker 3: bad one, and then choosing how to drive after doing that.
• 26:37
  Speaker 3: It's doing that about 10 times a second. And so
• 26:40
  Speaker 3: how do you get that to work? Well, you can
• 26:43
  Speaker 3: use NVIDIA GPUs to parallelize these tasks and evaluate many
• 26:49
  Speaker 3: trajectories simultaneously, and then decide how you're going to move
• 26:53
  Speaker 3: based on that.
• 26:54
  Speaker 1: Very, very interesting. Do you still, do you miss being
• 26:58
  Speaker 1: a professor?
• 26:59
  Speaker 3: Well, yeah, I'm currently, I've got a 5% appointment at
• 27:05
  Speaker 3: the University of Washington, which means that I'm there, you know,
• 27:09
  Speaker 3: every couple weeks working with students. You know, I I
• 27:14
  Speaker 3: like teaching. I like interacting with students. I think that's
• 27:19
  Speaker 3: one of the great benefits of being a professor is
• 27:23
  Speaker 3: just engaging with students and people who want to learn.
• 27:27
  Speaker 3: So that part's fantastic. And I miss doing that. I
• 27:30
  Speaker 3: haven't been teaching recently since I've been spun off the company.
• 27:35
  Speaker 3: But I think working in industry also allows you to
• 27:38
  Speaker 3: really scale your ideas more. Um, you know, there's only
• 27:42
  Speaker 3: so much you can do in a, in a smaller
• 27:45
  Speaker 3: research lab.
• 27:47
  Speaker 1: Yeah. And so in 2018, the army research laboratory spotted
• 27:54
  Speaker 1: you at an IEEE conference, demoing machine learning. What is
• 27:59
  Speaker 1: an IEEE?
• 28:01
  Speaker 2: It's IEEE.
• 28:02
  Speaker 3: So it's, it's a association for electrical engineers, but it's
• 28:05
  Speaker 3: one of the major types of con, uh, conferences that, um, uh,
• 28:11
  Speaker 3: that folks publish in. So when you're a professor, one
• 28:15
  Speaker 3: of your main goals is to publish papers, right? And
• 28:18
  Speaker 3: those scientific papers further human knowledge. And so in computer science,
• 28:24
  Speaker 3: the way that you do this is you publish papers at.
• 28:27
  Speaker 2: A lot of conferences.
• 28:30
  Speaker 3: You work with your graduate student, you develop a new technology,
• 28:34
  Speaker 3: you then tell the world about it, right? You publish
• 28:37
  Speaker 3: it in a paper and you do this on a
• 28:40
  Speaker 3: pretty Um, pretty, you know, fast iterative basis. So, uh,
• 28:46
  Speaker 3: you know, that's, uh, this is one of the major
• 28:48
  Speaker 3: conferences in, in robotics and we had, you know, had
• 28:51
  Speaker 3: some work there. Um, and you know, some folks in
• 28:55
  Speaker 3: the army were seeing what we were doing and had
• 28:57
  Speaker 3: some cool ideas of how, of how we could potentially
• 29:00
  Speaker 3: like take that fundamental research and start to apply it to, uh,
• 29:04
  Speaker 3: to army problems.
• 29:05
  Speaker 1: Right on, right on. And then the DARPA racer. Yeah.
• 29:11
  Speaker 1: the crucible that birthed Overland AI. What was that? What's
• 29:15
  Speaker 1: the big story? Yeah, yeah.
• 29:19
  Speaker 3: Okay, so I was originally working at Georgia Tech and
• 29:24
  Speaker 3: doing some work on ground vehicle autonomy. So the way
• 29:28
  Speaker 3: that this started out, we took one fifth scale vehicle,
• 29:32
  Speaker 3: so these kind of smaller remote control vehicles. We put
• 29:36
  Speaker 3: computers and sensors on them and made them autonomous. And
• 29:39
  Speaker 3: we were trying to race them as fast as possible.
• 29:41
  Speaker 3: So we were using data that we were collecting while
• 29:47
  Speaker 3: we were driving these cars to learn what's called a policy.
• 29:52
  Speaker 3: You think about it as like AI, essentially, like for
• 29:56
  Speaker 3: the that could perceive the world and try to drive
• 30:00
  Speaker 3: really quickly. And these vehicles were doing things like drifting
• 30:03
  Speaker 3: around turns and things like this. So they learned to
• 30:06
  Speaker 3: do this, which is one of the things which is cool. Like,
• 30:08
  Speaker 3: the vehicle's out there, it's trying to drive faster and faster,
• 30:11
  Speaker 3: and then it's learning how to do things like drift
• 30:13
  Speaker 3: in order to.
• 30:14
  Speaker 2: Drive even faster.
• 30:15
  Speaker 1: Oh, shit.
• 30:16
  Speaker 2: So that's what the army was looking at.
• 30:18
  Speaker 1: Now, is it actually learning, or are you programming that
• 30:21
  Speaker 1: into it?
• 30:21
  Speaker 3: So it's actually learning. So you start by bootstrapping it. Like,
• 30:25
  Speaker 3: you have a human demonstrate You know, this is how
• 30:28
  Speaker 3: the vehicle should drive.
• 30:31
  Speaker 2: And then it.
• 30:32
  Speaker 3: Tries to replicate what the human does. And as it
• 30:35
  Speaker 3: does that, sometimes it makes mistakes, sometimes it does well,
• 30:38
  Speaker 3: but it's kind of grading itself. And then it will
• 30:41
  Speaker 3: start to experiment. Like, if I, you know, accelerate a
• 30:44
  Speaker 3: little bit here or I brake a little bit there,
• 30:47
  Speaker 3: does this make me, you know, faster or slower? And
• 30:51
  Speaker 3: as it does that, it learns how to drive faster
• 30:54
  Speaker 3: and faster and learns on its own. You're programming in
• 30:59
  Speaker 3: the ability to learn, but then it's looking at its
• 31:03
  Speaker 3: own behavior, learning from that and driving, you know, figuring
• 31:08
  Speaker 3: out how to drive faster.
• 31:09
  Speaker 1: Now, is this like on a track?
• 31:11
  Speaker 2: Yeah, exactly.
• 31:13
  Speaker 1: Yeah.
• 31:13
  Speaker 3: So we started out by driving on a sand track
• 31:18
  Speaker 3: and we were able to achieve these like really fast
• 31:21
  Speaker 3: lap times where the vehicle's you know, basically drifting through
• 31:24
  Speaker 3: turns and things like that. And so when I started
• 31:26
  Speaker 3: to work with the Army, the question was, can you
• 31:29
  Speaker 3: take these fundamental principles and apply them to larger vehicles
• 31:33
  Speaker 3: and figure out how to drive aggressively through all sorts
• 31:36
  Speaker 3: of different terrain? So not just on tracks, but, you know,
• 31:40
  Speaker 3: through forests and deserts and beaches and things like this.
• 31:44
  Speaker 1: So, excuse me, what I was going to ask is,
• 31:47
  Speaker 1: I mean, if this, if, okay, if we have a
• 31:50
  Speaker 1: race car, it's going around a track over and over again,
• 31:52
  Speaker 1: and it is learning, and then you switch up the track,
• 31:57
  Speaker 1: will it be able to take what it learned on
• 32:01
  Speaker 1: track A and apply it to track B immediately? Or
• 32:05
  Speaker 1: will it, do you understand?
• 32:07
  Speaker 2: Basically what I'm saying is how do we go from.
• 32:09
  Speaker 1: A track to high-speed chase in the middle of, I
• 32:15
  Speaker 1: don't know, Manhattan?
• 32:17
  Speaker 3: So this is kind of like a fundamental challenge in
• 32:21
  Speaker 3: machine learning. If you learn, for example, how to drive
• 32:25
  Speaker 3: really fast on an oval track where you're always going
• 32:29
  Speaker 3: in one direction, you'll learn how to drive really fast
• 32:33
  Speaker 3: while always turning left, essentially, let's say. But then if
• 32:37
  Speaker 3: you go on another track which has right turns, will
• 32:41
  Speaker 3: it be able to generalize and be able to perform
• 32:45
  Speaker 3: well on a track like that? The short answer is,
• 32:50
  Speaker 3: you know, not without some sort of work. So generally speaking,
• 32:54
  Speaker 3: you want to collect data in a wide variety of
• 32:58
  Speaker 3: environments that are inclusive of the types of places that
• 33:02
  Speaker 3: you might want to be driving in the future. So
• 33:04
  Speaker 3: for example, you might have a complex dirt track with
• 33:08
  Speaker 3: left and right turns and some wider turns and some
• 33:10
  Speaker 3: sharper turns and things like this. If you train on
• 33:14
  Speaker 3: a track like that, it's very easy to then race
• 33:17
  Speaker 3: on like an oval track, right? Because you've seen all
• 33:20
  Speaker 3: of the things that you're likely to see.
• 33:24
  Speaker 2: So that becomes easy.
• 33:25
  Speaker 3: But if you, for example, train on a dirt track
• 33:28
  Speaker 3: and now you have to drive on asphalt, you may
• 33:30
  Speaker 3: not be able to do that super well. And so
• 33:33
  Speaker 3: you need to collect new data as you move to
• 33:35
  Speaker 3: that new type of environment or new type of problem
• 33:39
  Speaker 3: incorporate that into your learning algorithm, and then it will
• 33:45
  Speaker 3: start to do better on that new type of track.
• 33:48
  Speaker 3: And that matters even now when we think about where
• 33:52
  Speaker 3: we want to drive our vehicles, because if you only train,
• 33:55
  Speaker 3: for example, in the desert, you're not going to be
• 33:57
  Speaker 3: able to necessarily drive well through a forest or vice versa.
• 34:01
  Speaker 3: And so you want to really train these systems, have
• 34:06
  Speaker 3: them collect data and learn from as wide a set
• 34:09
  Speaker 3: of environments as possible so that they're able, when they
• 34:13
  Speaker 3: encounter a new environment, to still perform well, that there'll
• 34:16
  Speaker 3: still be aspects of that environment that they've seen before.
• 34:19
  Speaker 1: So, I guess what my question is, will it get
• 34:22
  Speaker 1: to the point where... it runs the route for the
• 34:29
  Speaker 1: very first time, like a brand-new route for a very
• 34:32
  Speaker 1: first time? It could be off-road, it could be in
• 34:35
  Speaker 1: the middle of a city, but we're talking... left turns,
• 34:38
  Speaker 1: right turns, heavy braking, fast acceleration, drifting, all of that stuff.
• 34:47
  Speaker 1: Will it eventually learn what it needs to learn to
• 34:50
  Speaker 1: be able to have a complicated new route run it
• 34:56
  Speaker 1: the first time and it will run it perfectly? It
• 34:59
  Speaker 1: will go as fast as the machine is capable of.
• 35:03
  Speaker 3: Yeah, it's certainly possible. And that's what we're always striving for.
• 35:07
  Speaker 3: So When we're putting, you know, uncrewed ground vehicles or
• 35:11
  Speaker 3: autonomous vehicles in new environments, they're already performing really well
• 35:16
  Speaker 3: because it's seen many aspects of that before. And you're
• 35:19
  Speaker 3: trying to get it to perform, you know, optimally, right?
• 35:23
  Speaker 3: Like that is the goal, like potentially faster than a
• 35:26
  Speaker 3: human driver, even on environments that it's never seen before.
• 35:30
  Speaker 3: Every time our autonomous vehicles.
• 35:33
  Speaker 2: Are out in the world, they are.
• 35:37
  Speaker 3: Basically, we've trained them so that we don't pre-map environments
• 35:43
  Speaker 3: or anything like that. It's as if they're encountering that
• 35:45
  Speaker 3: environment for the first time. And they're constantly learning. So
• 35:49
  Speaker 3: the more environments that we encounter, the more data we get,
• 35:52
  Speaker 3: the better and better the system gets. And the goal
• 35:55
  Speaker 3: is always kind of moving towards that optimal movement.
• 36:00
  Speaker 1: How close are you to that goal?
• 36:02
  Speaker 2: Uh, depends, depends on the environment.
• 36:04
  Speaker 3: I mean, I think we can, we can already drive
• 36:06
  Speaker 3: faster than, than humans in some environments. So, um, yeah, it's,
• 36:12
  Speaker 3: it's pretty cool.
• 36:13
  Speaker 1: Wow. Very interesting. And so how long did you spend
• 36:18
  Speaker 1: at DARPA?
• 36:19
  Speaker 2: Oh, so, yeah.
• 36:20
  Speaker 3: So I started work with, um, army research lab, you know, again,
• 36:24
  Speaker 3: around like 2018. Um, and in 2020, 19 or so,
• 36:32
  Speaker 3: I started to talk with folks at DARPA. They were
• 36:35
  Speaker 3: really interested in developing, essentially rebooting ground autonomy for defense.
• 36:43
  Speaker 3: So here's what the situation was. DARPA, back in 2004
• 36:47
  Speaker 3: and 2005, had something called the Grand Challenge or Grand Challenges.
• 36:52
  Speaker 3: So these were challenges where they were trying to incept
• 36:56
  Speaker 3: ground autonomy. So this actually goes back to the NDAA
• 37:01
  Speaker 3: in 2001. This is basically where Congress decided in 2001,
• 37:06
  Speaker 3: they were like, by 2015, we want a third of
• 37:09
  Speaker 3: all military ground vehicles to be autonomous. So, thinking about this,
• 37:13
  Speaker 3: you know, over 25 years ago, the problem was no
• 37:17
  Speaker 3: one knew how to do that. Like, there weren't, it
• 37:20
  Speaker 3: wasn't like there were autonomous vehicles, you know, driving all
• 37:23
  Speaker 3: over the place and this would be easy. No one
• 37:25
  Speaker 3: knew how to make these vehicles autonomous.
• 37:27
  Speaker 1: Where did the idea come from?
• 37:29
  Speaker 3: Well, I think, you know, there had been, you know,
• 37:32
  Speaker 3: work in robotics where people were moving vehicles in simpler
• 37:37
  Speaker 3: environments autonomously. And so the notion was like, well, what
• 37:42
  Speaker 3: if we could do this on the battlefield, right? What
• 37:44
  Speaker 3: if we could do it in these more complex environments?
• 37:47
  Speaker 3: If you could take the warfighter out of the vehicle,
• 37:50
  Speaker 3: you can imagine, you know, that not just provide safety,
• 37:54
  Speaker 3: but potentially, you know, tactical things that you can do.
• 37:57
  Speaker 3: We can maybe talk about that in a little bit.
• 37:59
  Speaker 3: But in order to make those vehicles autonomous, you needed
• 38:04
  Speaker 3: to know how to do it, right?
• 38:05
  Speaker 2: And they didn't.
• 38:06
  Speaker 3: And so DARPA, one of the things that's great about
• 38:08
  Speaker 3: DARPA is that it is an organization that is designed
• 38:13
  Speaker 3: to just like create new things, right? Like you have
• 38:17
  Speaker 3: some crazy challenge, DARPA is out there and can create
• 38:22
  Speaker 3: a program, pull together some of the best minds in
• 38:25
  Speaker 3: the US to focus like really focus on it for
• 38:28
  Speaker 3: up to about four years and try to solve a problem.
• 38:33
  Speaker 3: And so in 2004 and 2005, they came up with
• 38:37
  Speaker 3: the DARPA Grand Challenge where they were trying to race
• 38:40
  Speaker 3: vehicles from Barstow, California to Prim, Nevada. It was about
• 38:47
  Speaker 3: 135 miles on dirt roads. And they just laid down
• 38:53
  Speaker 3: a challenge. They said, like, if you can do this,
• 38:54
  Speaker 3: you get prize money. And all sorts of teams came
• 38:57
  Speaker 3: together to attack this problem.
• 38:59
  Speaker 2: So there were.
• 39:00
  Speaker 3: University teams from places like Carnegie Mellon and Stanford and
• 39:06
  Speaker 3: MIT and so on. And there were industry teams like
• 39:10
  Speaker 3: Oshkosh had a team. And there were just people who
• 39:12
  Speaker 3: were trying to put together autonomous vehicles in their garage,
• 39:15
  Speaker 3: like just build robots.
• 39:16
  Speaker 2: And they went out there and raced.
• 39:18
  Speaker 3: And in 2004, the first challenge, no one made it
• 39:23
  Speaker 3: beyond seven miles.
• 39:24
  Speaker 2: Like that was the you know, so it was... CMU's vehicle,
• 39:29
  Speaker 2: I think, made it that far. It got stuck, caught fire.
• 39:32
  Speaker 2: It was like a whole thing.
• 39:34
  Speaker 3: The next year, though, in 2005, I think five teams
• 39:38
  Speaker 3: competed this challenge. They made it the entire 134 miles.
• 39:42
  Speaker 3: Those teams, like the folks from those teams... After that
• 39:45
  Speaker 3: challenge was over, there were a few other ones. There
• 39:48
  Speaker 3: was something called the Urban Challenge and some other DARPA
• 39:51
  Speaker 3: programs which followed.
• 39:53
  Speaker 2: Up on this.
• 39:53
  Speaker 3: But many of those people then moved into industry and
• 39:59
  Speaker 3: started the self-driving car projects and companies that then turned
• 40:04
  Speaker 3: into things, you know, companies like Waymo or Aurora Innovation
• 40:10
  Speaker 3: and so on. autonomous driving company and commercial companies. So by,
• 40:16
  Speaker 3: you know, 2012, let's say, a lot of work was
• 40:20
  Speaker 3: being done in the commercial sector, bootstrapped off of this
• 40:23
  Speaker 3: DARPA work, right? So the military, you know, started this
• 40:26
  Speaker 3: whole thing because they wanted autonomous vehicles. People started to
• 40:30
  Speaker 3: build autonomous vehicles because of this DARPA program, but then
• 40:35
  Speaker 3: basically just went off into industry and were working on, like,
• 40:38
  Speaker 3: robotaxis and autonomous trucks and things like this. So by 2019,
• 40:44
  Speaker 3: coming back to my story, DARPA was in a position
• 40:49
  Speaker 3: where they were like, well, cool, we have autonomous taxis,
• 40:53
  Speaker 3: there's been a lot of progress in this area, but
• 40:57
  Speaker 3: where's our autonomous tanks? Where are our autonomous military vehicles?
• 41:00
  Speaker 3: The whole point of this was initially to support the military.
• 41:05
  Speaker 3: And so DARPA Racer was a program that got stood up.
• 41:09
  Speaker 3: It started in 2021. to reboot autonomy for defense. So
• 41:14
  Speaker 3: specifically to take a lot of the learnings that had
• 41:17
  Speaker 3: been produced over the previous 20 years or so for
• 41:21
  Speaker 3: the on-road autonomous driving industry and work which had been
• 41:26
  Speaker 3: done in robotic perception and robotic vehicle control, bring that
• 41:32
  Speaker 3: back together and focus on defense problems. And so that
• 41:36
  Speaker 3: meant trying to drive much faster you know, larger and
• 41:41
  Speaker 3: faster vehicles off-road, what we call complex natural terrain.
• 41:46
  Speaker 2: So, you know, no roads.
• 41:48
  Speaker 3: At all, right? Like through deserts, through forests, through snow,
• 41:53
  Speaker 3: things like this, and contested terrain. So thinking about, you know,
• 41:57
  Speaker 3: how do you move when you, not only do you
• 42:00
  Speaker 3: not have infrastructure, which is there to help you like
• 42:03
  Speaker 3: roads or road networks or signs or things like this,
• 42:06
  Speaker 3: but Infrastructure which might be in the environment which is
• 42:09
  Speaker 3: there to defeat you to stop you and so that's
• 42:14
  Speaker 3: what the DARPA racer program was so I had already
• 42:17
  Speaker 3: been doing work at You know at Georgia Tech and
• 42:21
  Speaker 3: then University of Washington working with the army on developing
• 42:26
  Speaker 3: off-road ground vehicle autonomy and I then put together a
• 42:30
  Speaker 3: team that's called a performer team and to attack these
• 42:35
  Speaker 3: problems for the military through DARPA starting in 2021.
• 42:40
  Speaker 1: So, did they recruit you from the IEEE conference?
• 42:44
  Speaker 3: So, the way that that worked was, like, through the
• 42:47
  Speaker 3: research I was doing and the publications that I was,
• 42:49
  Speaker 3: you know, putting out to the world, Army saw that
• 42:52
  Speaker 3: the technology that we were... Army research labs saw that
• 42:55
  Speaker 3: the technology we were developing might be really helpful for
• 42:58
  Speaker 3: the types of problems they wanted to solve. I then
• 43:01
  Speaker 3: started working with army. So the way that that works
• 43:03
  Speaker 3: is when you're running a university research lab, um, you
• 43:07
  Speaker 3: have a bunch of PhD students, you know, they're doing research,
• 43:09
  Speaker 3: you're publishing it, you're making it publicly available for other
• 43:12
  Speaker 3: scientists to see, but you need funding to, to run
• 43:16
  Speaker 3: that lab. And so people pay you to essentially do research.
• 43:20
  Speaker 3: They pay your lab to do research. So army, um,
• 43:23
  Speaker 3: us army was one of the organizations that fund like
• 43:26
  Speaker 3: started to fund my research. When you fund research, you
• 43:30
  Speaker 3: can say, like, hey, here are the problems we want
• 43:32
  Speaker 3: you to solve. We'll give you, you know, this much
• 43:34
  Speaker 3: money to solve them with your PhD students. And then
• 43:38
  Speaker 3: you provide those solutions back to the Army.
• 43:42
  Speaker 2: So that's what.
• 43:42
  Speaker 3: I was doing when I was working at Georgia Tech
• 43:45
  Speaker 3: and University of Washington was my lab was partially funded
• 43:49
  Speaker 3: by the U.S. Army. Then I worked on problems that
• 43:53
  Speaker 3: were interesting to them. We provided those solutions back to
• 43:56
  Speaker 3: the Army. And then we started to work with DARPA,
• 43:59
  Speaker 3: which is, at the time, Department of Defense, but Department
• 44:02
  Speaker 3: of War-level organization. They saw the work we were doing
• 44:07
  Speaker 3: with the U.S. Army, and then they decided to fund
• 44:10
  Speaker 3: my lab at a much higher level to attack these
• 44:15
  Speaker 3: problems of how do you drive vehicles off-road and all
• 44:20
  Speaker 3: sorts of different types of terrain at high speeds.
• 44:22
  Speaker 4: Wow.
• 44:23
  Speaker 1: So you've been... Yeah. You've really been at the cutting
• 44:25
  Speaker 1: edge of this thing the entire time. Yeah.
• 44:28
  Speaker 3: So we've been working on these problems for more than
• 44:31
  Speaker 3: 10 years, more than a decade. And my lab was
• 44:35
  Speaker 3: doing a lot of that work in academia before we
• 44:38
  Speaker 3: spun out Overland. Wow.
• 44:40
  Speaker 4: Wow.
• 44:41
  Speaker 1: Well, before we get into Overland, let's take a quick break.
• 44:48
  Speaker 1: There's been a lot of attention lately on how important
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  Speaker 1: sleep is, not just for recovery, but for your brain, hormones,
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  Speaker 1: slash SRS. Do you ever wonder what it takes to
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  Speaker 1: and get access to the full experience. All right, let's
• 47:20
  Speaker 1: talk about the the Textron M5.
• 47:24
  Speaker 2: Sure, yeah.
• 47:25
  Speaker 3: So As part of the DARPA Racer program, so this
• 47:29
  Speaker 3: is DARPA program where we're trying to develop ground vehicle
• 47:34
  Speaker 3: autonomy for the military, we started out working with Polaris
• 47:39
  Speaker 3: Racer side-by-sides. These are very capable vehicles. They can go
• 47:43
  Speaker 3: very fast. They've got four wheels, about 3,000 pounds, about
• 47:47
  Speaker 3: the size of a small SUV, something like this. In
• 47:54
  Speaker 3: the first part of the DARPA Racer program, there were
• 48:00
  Speaker 3: multiple teams, and we were essentially racing these Polaris racers,
• 48:04
  Speaker 3: so trying to record the best possible times through a
• 48:08
  Speaker 3: wide variety of different terrains and test scenarios. As we
• 48:14
  Speaker 3: progressed through the program, DARPA program manager didn't want us
• 48:21
  Speaker 3: to just be using those small vehicles, so we moved
• 48:25
  Speaker 3: up to the Textron M5. The Textron M5 is basically
• 48:29
  Speaker 3: a robotic combat vehicle prototype. It's about 12 tons, so
• 48:37
  Speaker 3: it is much larger.
• 48:39
  Speaker 1: You went from a side-by-side to a 12-ton vehicle?
• 48:42
  Speaker 3: Yeah, it's a 12-ton tracked vehicle. So it's about the
• 48:46
  Speaker 3: size of a 113. So it looks like a tank.
• 48:50
  Speaker 3: It's sort of like a small tank, I guess, but
• 48:53
  Speaker 3: pretty heavy vehicle, tracked. It's electric, so really aggressive. The
• 49:00
  Speaker 3: ability to essentially go from, you know, zero to 60,
• 49:04
  Speaker 3: like super, think about like a Tesla, but like, you know,
• 49:07
  Speaker 3: like a tank version of that.
• 49:09
  Speaker 4: Wow.
• 49:10
  Speaker 3: So really, really awesome vehicle to work with. And we're
• 49:13
  Speaker 3: putting autonomy on that vehicle and then driving that through
• 49:16
  Speaker 3: off-road terrain.
• 49:17
  Speaker 1: Okay. Yeah. Out of that. And this was. competition, correct?
• 49:21
  Speaker 3: Yeah, so so DARPA racer It started out with three teams.
• 49:26
  Speaker 3: There was our team and there were two other teams
• 49:28
  Speaker 3: that we were competing against by the first summer about
• 49:32
  Speaker 3: a year into the into the program, you know, we
• 49:36
  Speaker 3: had by far the best technology and And pretty quickly,
• 49:41
  Speaker 3: the other teams essentially dropped out, and we were the
• 49:44
  Speaker 3: only ones left. We were focused on not only driving
• 49:47
  Speaker 3: on the side-by-sides, but also the much larger vehicles. So
• 49:50
  Speaker 3: we're the only team that actually worked with these.
• 49:53
  Speaker 2: And there aren't that many of them.
• 49:54
  Speaker 3: There's something like, you know, four or five Textron M5s
• 49:57
  Speaker 3: ever produced. But, you know, we had all of them,
• 50:00
  Speaker 3: and we were just, like, smashing through terrain in these
• 50:03
  Speaker 3: huge tracked vehicles.
• 50:04
  Speaker 1: Pretty awesome. And so what is the.
• 50:10
  Speaker 1: What is kind of the point of the competition? Do
• 50:12
  Speaker 1: they buy the technology from you, or do they fund
• 50:16
  Speaker 1: the technology?
• 50:16
  Speaker 2: That's a great question.
• 50:18
  Speaker 1: How does it work?
• 50:19
  Speaker 3: Yeah, like, this is a really good question, actually, because
• 50:25
  Speaker 3: the way that DARPA normally works, you know, they have
• 50:27
  Speaker 3: these programs where they're trying to develop new technology. In
• 50:30
  Speaker 3: this case, develop ground vehicle autonomy.
• 50:34
  Speaker 2: For the U.S. military, so for our soldiers and Marines.
• 50:39
  Speaker 3: But often in a DARPA program, even if the program
• 50:44
  Speaker 3: is very successful like ours was, how do you actually
• 50:48
  Speaker 3: take that technology and then get it into the hands.
• 50:50
  Speaker 1: Of the warfighter?
• 50:51
  Speaker 2: Like, how do you actually do that? It's a good.
• 50:55
  Speaker 3: Question and very few people succeed. So this is known
• 51:01
  Speaker 3: as like transition, transitioning like a new capability that's developed
• 51:05
  Speaker 3: from DARPA to the actual warfighter is something like 4%
• 51:08
  Speaker 3: of technologies ever actually get transitioned. So just because you
• 51:14
  Speaker 3: succeed in developing a new capability doesn't mean that the
• 51:17
  Speaker 3: warfighter gets that capability. This is actually why we created
• 51:23
  Speaker 3: Overland AI. We were like in this program and we
• 51:28
  Speaker 3: had this new capability and we were just like, this
• 51:31
  Speaker 3: is incredible. drive extremely fast off-road. We can do it
• 51:35
  Speaker 3: in a variety of different types of vehicles. We have
• 51:39
  Speaker 3: the thing that DARPA wanted us to produce. We actually
• 51:45
  Speaker 3: did it. Now, how can the warfighter benefit? And so
• 51:48
  Speaker 3: we felt that the most promising way of making that
• 51:56
  Speaker 3: transition was to spin off a company that would then
• 52:00
  Speaker 3: take the capability that we developed as part of this
• 52:03
  Speaker 3: DARPA program, commercialize it, so turn it into a robust, reliable,
• 52:09
  Speaker 3: commercial autonomy stack, and then sell that back to the
• 52:14
  Speaker 3: Army and Marine Corps so that they could use it
• 52:18
  Speaker 3: on their programs, so on their vehicles, and so that
• 52:22
  Speaker 3: warfighting units could start to work with this technology and
• 52:27
  Speaker 3: start to integrate it into tactics. So that's why we
• 52:31
  Speaker 3: created Overland AI. We thought the Army, this is just
• 52:35
  Speaker 3: going to disappear if we don't do this.
• 52:37
  Speaker 1: So why would they fund the... Did they fund the research?
• 52:41
  Speaker 3: Yeah, so DARPA funded all of this to develop the capability,
• 52:47
  Speaker 3: but just because DARPA.
• 52:47
  Speaker 1: And they just shelf it?
• 52:49
  Speaker 3: So then what has to happen is the Army and
• 52:52
  Speaker 3: the Marine Corps or whoever, one of the services, has
• 52:56
  Speaker 3: to then put up money to be able to than
• 53:01
  Speaker 3: like buy and transition that technology.
• 53:04
  Speaker 2: Because- From DARPA or from you?
• 53:07
  Speaker 3: Yeah, so the way that DARPA works is that DARPA,
• 53:10
  Speaker 3: like these DARPA programs are generally four years long.
• 53:13
  Speaker 2: They have a time limit.
• 53:15
  Speaker 3: And that's part of the point. Like it gives you
• 53:18
  Speaker 3: a very focused window in time to just do whatever
• 53:22
  Speaker 3: it takes to try to develop this new technology. But
• 53:25
  Speaker 3: at the end of those four years, that program is
• 53:27
  Speaker 3: over and you have to find- someone who is then
• 53:31
  Speaker 3: going to support the continuation of that technology, you know,
• 53:37
  Speaker 3: to move it into warfighting units, right? So, like, you've
• 53:43
  Speaker 3: created it. Now someone has to continue to, like, transfer it, to, like,
• 53:48
  Speaker 3: get it onto military vehicles, to push it into.
• 53:53
  Speaker 4: Units.
• 53:54
  Speaker 3: And that's where a lot of these programs die, because you, like,
• 53:57
  Speaker 3: create the technology, but you can't, that no one picks
• 54:01
  Speaker 3: it up like when it's over.
• 54:03
  Speaker 1: I would think there would be, so this is, I mean,
• 54:06
  Speaker 1: what would you call this? Like an incubator? Like you
• 54:09
  Speaker 1: get picked to get funding to be an incubator and
• 54:13
  Speaker 1: then DARPA doesn't even, they don't own the actual.
• 54:16
  Speaker 2: Well, they do.
• 54:16
  Speaker 3: They own the IP, so- You also own the IP? Yeah,
• 54:20
  Speaker 3: so the way that this works is like when, so
• 54:24
  Speaker 3: DARPA pays for the development of this core technology- In
• 54:29
  Speaker 3: this case, I'm running a lab at the University of Washington.
• 54:35
  Speaker 3: So they pay the University of Washington to develop the IP.
• 54:41
  Speaker 3: When they do that, that IP belongs to both DARPA
• 54:45
  Speaker 3: and the University of Washington, right? Because DARPA pays for
• 54:48
  Speaker 3: a university to develop it. Both parties essentially own that IP.
• 54:55
  Speaker 3: But once you have that IP, in this case, it
• 54:58
  Speaker 3: is like, raw technology. It is research-grade code that can
• 55:07
  Speaker 3: demonstrate this capability, that will allow a vehicle to drive fast,
• 55:11
  Speaker 3: but it's not going to be reliable in the same
• 55:15
  Speaker 3: way that a Waymo is, where you have a whole
• 55:19
  Speaker 3: team of engineers that is making production-quality code.
• 55:25
  Speaker 2: They own that.
• 55:27
  Speaker 3: They own the basic capability and they own that IP.
• 55:33
  Speaker 3: But that then has to be taken and turned into
• 55:37
  Speaker 3: essentially a commercial production-ready piece of software. It's almost like
• 55:45
  Speaker 3: a rough draft.
• 55:45
  Speaker 1: I would think there would be.
• 55:47
  Speaker 3: But then someone has to kind of pay for that transition.
• 55:51
  Speaker 3: And that's often, again, where these programs end up having
• 55:54
  Speaker 3: problems where Like you develop a new technology, but then
• 55:59
  Speaker 3: who adopts it? Who actually transitions it to the warfighter?
• 56:04
  Speaker 1: Are these classified? I mean, are these classified? I would
• 56:07
  Speaker 1: think there would be venture capital firms galore just surrounding
• 56:11
  Speaker 1: these projects.
• 56:12
  Speaker 2: Well, yeah, yeah. So sometimes, yes, right?
• 56:15
  Speaker 1: Only 4% make it to the warfighter? Right. 96% of projects.
• 56:19
  Speaker 1: It's crazy, right? It's just like, oh, yeah.
• 56:21
  Speaker 2: Yeah.
• 56:23
  Speaker 3: But in some sense, that's actually what happened here, right?
• 56:25
  Speaker 3: Because we said, okay, we're going to take this IP.
• 56:29
  Speaker 3: We license it, right? So that IP is licensed to
• 56:34
  Speaker 3: Overland AI. We created this company to essentially take that
• 56:37
  Speaker 3: IP and then turn it into a commercial autonomy stack
• 56:42
  Speaker 3: that is reliable. And we keep building on it. We
• 56:46
  Speaker 3: keep improving it. And then we can transition that back
• 56:51
  Speaker 3: to the warfighters. So Overland AI got its start by
• 56:55
  Speaker 3: focusing on the software portion of this, right? Take these
• 56:58
  Speaker 3: good ideas that were developed during DARPA. We licensed that
• 57:03
  Speaker 3: IP at Overland. We build on top of it. We
• 57:07
  Speaker 3: turned it into a commercial autonomy stack. And now we
• 57:09
  Speaker 3: have this software that you can put onto a vehicle
• 57:13
  Speaker 3: to make that vehicle autonomous. And we had worked with,
• 57:16
  Speaker 3: different types of vehicles. We discussed the side-by-side or the
• 57:20
  Speaker 3: big Textron M5 that we can make autonomous. We really
• 57:25
  Speaker 3: wanted to develop software which would work on any vehicle
• 57:29
  Speaker 3: that the military had. And so Overland was stood up
• 57:35
  Speaker 3: to do that. And to your point about VCs, we're
• 57:38
  Speaker 3: a VC-backed company, right? So they're essentially making that bet
• 57:41
  Speaker 3: that you're saying, it's like, okay, we can step in,
• 57:44
  Speaker 3: provide additional funding to turn this into a mature product
• 57:48
  Speaker 3: that can then be transitioned over to the warfighter. But
• 57:53
  Speaker 3: now you're in like defense tech territory, right? Where you
• 57:56
  Speaker 3: now have to fight all of these battles to go
• 57:59
  Speaker 3: from this good core idea or good core IP to
• 58:05
  Speaker 3: actually getting it procured, right?
• 58:09
  Speaker 2: And that takes.
• 58:12
  Speaker 2: You know, time and, and, and, and effort.
• 58:14
  Speaker 3: And, and so like our story as, as a defense
• 58:17
  Speaker 3: tech company is, is really similar to a lot of
• 58:20
  Speaker 3: other stories, except that we got this start by, you know,
• 58:24
  Speaker 3: doing all of this, you know, sort of initial research
• 58:26
  Speaker 3: to accept a new technology that the military really wanted.
• 58:31
  Speaker 1: Very interesting. And so, so the DARPA competition ends, you
• 58:36
  Speaker 1: win and you, all the information with you and develop
• 58:41
  Speaker 1: Overland AI. Right.
• 58:42
  Speaker 3: So we started Overland AI. And Overland actually was formed
• 58:51
  Speaker 3: before the DARPA competition completed. So we became part of
• 58:56
  Speaker 3: the competition as well.
• 58:59
  Speaker 2: We then.
• 59:01
  Speaker 3: As the competition was going on, we were developing new
• 59:05
  Speaker 3: technology for moving really quickly through a lot of different biomes,
• 59:08
  Speaker 3: so different types of environments. Um, and then we were
• 59:12
  Speaker 3: really trying to think, and this is the Overland AI
• 59:14
  Speaker 3: started in, in 20, like December, 2022. Um, so three
• 59:19
  Speaker 3: and a half years ago, and we were. initially focused on, okay,
• 59:24
  Speaker 3: we have this software, we're refining it, we're turning it
• 59:27
  Speaker 3: into a commercial piece of software. How do you then
• 59:32
  Speaker 3: get traction with the Army and Marine Corps? Not just DARPA,
• 59:36
  Speaker 3: but the people who ultimately need to use this technology.
• 59:41
  Speaker 3: And the problem was that even though we had this
• 59:46
  Speaker 3: great piece of software, it has to go on a vehicle.
• 59:48
  Speaker 2: So it was like, what vehicle are we gonna put
• 59:50
  Speaker 2: it on?
• 59:51
  Speaker 3: And we started to talk to companies and units that
• 59:55
  Speaker 3: had vehicles which could be made autonomous, but there just
• 59:57
  Speaker 3: weren't that many of them out there. We had something
• 1:00:00
  Speaker 3: which could be very effective, but we didn't have vehicles
• 1:00:03
  Speaker 3: to put it on. So we started out, you know,
• 1:00:07
  Speaker 3: again with a software stack, but we quickly realized that
• 1:00:09
  Speaker 3: we had to actually also build the hardware so that
• 1:00:13
  Speaker 3: we could get this idea of autonomous vehicles into the
• 1:00:17
  Speaker 3: hands of warfighters faster.
• 1:00:19
  Speaker 2: So we just started to build.
• 1:00:20
  Speaker 1: The vehicles too.
• 1:00:23
  Speaker 3: And that's what resulted in this ultra vehicle that you've
• 1:00:29
  Speaker 3: seen outside where.
• 1:00:32
  Speaker 2: This is an autonomous.
• 1:00:34
  Speaker 3: Vehicle that we built and we put our software on
• 1:00:39
  Speaker 3: so that we could start to put many different types
• 1:00:42
  Speaker 3: of payloads on the vehicles and start to work with
• 1:00:45
  Speaker 3: warfighting units to really integrate autonomous systems into their concepts
• 1:00:51
  Speaker 3: of operations.
• 1:00:53
  Speaker 2: So our path.
• 1:00:54
  Speaker 3: Really, it was initially research and development starting with DARPA.
• 1:00:59
  Speaker 3: That then, we worked with the Defense Innovation Unit and
• 1:01:02
  Speaker 3: got a prototype contract with them. We then started to
• 1:01:07
  Speaker 3: work with Army Applications Lab and winning these SIBRs, these
• 1:01:13
  Speaker 3: small contracts with the Marine Corps and the Army.
• 1:01:15
  Speaker 2: We were doing really well with those. And we've.
• 1:01:21
  Speaker 3: Then last year, we revealed the ultra vehicle, fully autonomous,
• 1:01:26
  Speaker 3: vertically integrated vehicle that you could push into hands.
• 1:01:28
  Speaker 2: Of war fighters.
• 1:01:30
  Speaker 3: We started to push this technology into units who were
• 1:01:34
  Speaker 3: constantly testing them. And then this led to a production contract.
• 1:01:39
  Speaker 3: So we just recently won the first production contract for
• 1:01:43
  Speaker 3: autonomous ground vehicles in the US military.
• 1:01:46
  Speaker 2: And that's with the Marine Corps.
• 1:01:48
  Speaker 3: So it's really this whole process of, and it didn't
• 1:01:52
  Speaker 3: take that long, right? It's about three, three and a
• 1:01:54
  Speaker 3: half years where we went from like university lab, R
• 1:01:58
  Speaker 3: & D through prototype fielding with war fighters production contract.
• 1:02:03
  Speaker 1: Wow. Three and a half years. Yeah, yeah.
• 1:02:07
  Speaker 3: Wow.
• 1:02:08
  Speaker 1: I mean, so who won the contract or who, who, who?
• 1:02:12
  Speaker 1: Who are you contracted to manufacture?
• 1:02:16
  Speaker 2: With the Marine Corps.
• 1:02:16
  Speaker 3: So the Marine Corps is buying a whole set of
• 1:02:21
  Speaker 3: the autonomous vehicles that we produce. It's part of their
• 1:02:24
  Speaker 3: ground-based air defense program. And so they'll initially start using
• 1:02:30
  Speaker 3: those vehicles for autonomous resupply of basically air defense systems.
• 1:02:38
  Speaker 3: So trying to make sure that they get enough ammunition
• 1:02:42
  Speaker 3: as they're shooting down drones.
• 1:02:43
  Speaker 1: Can I ask how many you're going to manufacture for them?
• 1:02:50
  Speaker 3: We're manufacturing, in the first tranche, about 15 of these
• 1:02:57
  Speaker 3: in the next year or so. That's what we're starting
• 1:03:00
  Speaker 3: out with. One of the things which is pretty interesting
• 1:03:05
  Speaker 3: about these vehicles, they're modular. A lot of units want
• 1:03:08
  Speaker 3: to start using them for resupply, where they're just kind
• 1:03:11
  Speaker 3: of putting supplies on them and moving those supplies back
• 1:03:14
  Speaker 3: and forth. But we've been doing a lot of work
• 1:03:16
  Speaker 3: with other units, like 82nd Airborne, 173rd Airborne, other warfighting
• 1:03:22
  Speaker 3: units to put other types of payloads on board the
• 1:03:26
  Speaker 3: platforms as well. And so one of the things that
• 1:03:28
  Speaker 3: we've been talking to Marines about is putting sensors and effectors,
• 1:03:35
  Speaker 3: so basically kinetic counter UAS payloads on the vehicles and
• 1:03:41
  Speaker 3: thinking about how to disaggregate them. One of the ways
• 1:03:47
  Speaker 3: that you can use autonomous vehicles is by essentially putting
• 1:03:52
  Speaker 3: your sensors and putting your counter UAS, for example, payloads
• 1:03:59
  Speaker 3: on vehicles and moving them away from the places that
• 1:04:02
  Speaker 3: you're trying to defend. You're disaggregating, you're dispersing, you're essentially
• 1:04:08
  Speaker 3: making it much harder for an adversary to be able
• 1:04:11
  Speaker 3: to take everything else, like all of the pieces out
• 1:04:15
  Speaker 3: when you're defending an area.
• 1:04:17
  Speaker 1: Interesting. So they're going to be using this for logistics.
• 1:04:21
  Speaker 1: They're going to be using this for defense. And I
• 1:04:23
  Speaker 1: would imagine there's going to be an offensive commodant.
• 1:04:26
  Speaker 2: Yeah, yeah. So the way that we're thinking about this is.
• 1:04:32
  Speaker 3: Logistics so you can think about resupply and casualty evacuation
• 1:04:36
  Speaker 3: a lot of people when they think about autonomous vehicles
• 1:04:39
  Speaker 3: This is like the first thing which comes to mind
• 1:04:41
  Speaker 3: because they think about a vehicle and they're like, oh,
• 1:04:43
  Speaker 3: okay Like I can put stuff in it and I
• 1:04:45
  Speaker 3: can move it right like that's like what a vehicle
• 1:04:48
  Speaker 3: does but I like to think about these vehicles more
• 1:04:51
  Speaker 3: like robotic systems they can sense a They can track,
• 1:04:56
  Speaker 3: they can move on their own on the battlefield. And
• 1:05:00
  Speaker 3: I think like the real product market fit here is
• 1:05:03
  Speaker 3: that you want to move these vehicles out in front
• 1:05:06
  Speaker 3: of the warfighters, right? So you want to be using
• 1:05:08
  Speaker 3: them for things like intelligence surveillance and reconnaissance, being able
• 1:05:13
  Speaker 3: to move them through, you know, bad weather. They can persistently,
• 1:05:17
  Speaker 3: you know, stay in a location for a long period
• 1:05:19
  Speaker 3: of time because they're just on the ground. So, they
• 1:05:22
  Speaker 3: might be more effective than drones for some of these things.
• 1:05:26
  Speaker 3: You can use them for defense or strike capabilities. So,
• 1:05:31
  Speaker 3: you can put kinetic payloads on them. You can put
• 1:05:33
  Speaker 3: drones on them, launch them off of the vehicle. So,
• 1:05:37
  Speaker 3: you can move them into areas that might be too
• 1:05:40
  Speaker 3: risky for a human, but they can potentially hold ground
• 1:05:43
  Speaker 3: or try to take terrain in those areas. Breaching is
• 1:05:47
  Speaker 3: a major thing.
• 1:05:48
  Speaker 2: That we're working on.
• 1:05:49
  Speaker 3: So we're working with a number of different combat engineering
• 1:05:52
  Speaker 3: units on removing people from breaching operations, which are just
• 1:05:57
  Speaker 3: exceptionally dangerous. And then air defense, as I was discussing,
• 1:06:02
  Speaker 3: is essentially putting sensors and shooters in different locations and
• 1:06:06
  Speaker 3: moving them autonomously, reconfiguring what that defensive position might look like.
• 1:06:13
  Speaker 1: I mean, I could think of a whole slew of things,
• 1:06:16
  Speaker 1: but let's run out back and take a look at
• 1:06:18
  Speaker 1: this thing.
• 1:06:19
  Speaker 2: Yeah, sounds great.
• 1:06:22
  Speaker 1: You guys know my schedule. I'm in the studio all day.
• 1:06:26
  Speaker 1: I'm on the road, and I've got kids. The last
• 1:06:28
  Speaker 1: thing I have time for is standing around a gym
• 1:06:31
  Speaker 1: wondering what to do next. That's why I'm on the
• 1:06:34
  Speaker 1: ladder app. I've got a real coach in my ear
• 1:06:36
  Speaker 1: on every set. What's next? What weight? Why? It's a
• 1:06:40
  Speaker 1: new plan every week that builds on the last. And
• 1:06:43
  Speaker 1: it goes where I go. The garage with dumbbells or
• 1:06:46
  Speaker 1: a hotel room on the road. 30 to 45 minutes
• 1:06:50
  Speaker 1: and I'm done for less than a dollar a day.
• 1:06:52
  Speaker 1: I've trained my whole life. In the teams, nothing we
• 1:06:56
  Speaker 1: did was random. Somebody who knew more than you wrote
• 1:06:59
  Speaker 1: the program and you got results. That's the difference between
• 1:07:03
  Speaker 1: working out and training. Ladder puts that kind of real
• 1:07:07
  Speaker 1: programming in your ear. Remove the guesswork with Ladder. and
• 1:07:11
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• 1:07:14
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• 1:07:21
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• 1:07:23
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• 1:07:27
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• 1:07:30
  Speaker 1: and the right plan for you. Use my link and
• 1:07:33
  Speaker 1: get a free 7-day trial with no credit card and
• 1:07:37
  Speaker 1: $ 10 off your first month if you join. All right,
• 1:07:43
  Speaker 1: we're out here with Byron Boots, CEO and founder of
• 1:07:45
  Speaker 1: Overland AI. Get ready to take a look at this
• 1:07:48
  Speaker 1: beast here.
• 1:07:49
  Speaker 4: What are we looking at?
• 1:07:51
  Speaker 3: So this is an Ultra, one of our autonomous vehicles.
• 1:07:54
  Speaker 3: So let me just kind of show you what it is.
• 1:07:58
  Speaker 2: You can tell it's an off-road vehicle.
• 1:08:00
  Speaker 3: It's got long travel suspension and big wheels, but it's
• 1:08:04
  Speaker 3: fully autonomous. So it has sensors up in the front
• 1:08:07
  Speaker 3: of the vehicle. There's stereo cameras. There's actually three of them,
• 1:08:11
  Speaker 3: like one here, one there. one on the other side, LIDAR.
• 1:08:15
  Speaker 3: So this allows it to see, you know, in this
• 1:08:17
  Speaker 3: whole area in front of the vehicle.
• 1:08:20
  Speaker 2: At what degree?
• 1:08:22
  Speaker 3: So actually you can see 360 degrees around it. So
• 1:08:26
  Speaker 3: like you have these sensors in the front and there's
• 1:08:27
  Speaker 3: also sensors in the back. So it can just kind
• 1:08:29
  Speaker 3: of see this whole area. And then if you come
• 1:08:33
  Speaker 3: along this way, this is a payload deck. So you
• 1:08:37
  Speaker 3: can put wide variety of payloads on it you can
• 1:08:40
  Speaker 3: see there's all these attachment points so it makes it
• 1:08:43
  Speaker 3: easy to integrate new payloads onto the vehicle below this
• 1:08:46
  Speaker 3: deck you have compute so that's where your computer systems
• 1:08:51
  Speaker 3: are your power batteries on the alternator like all this
• 1:08:55
  Speaker 3: is below the deck and then back here you've got comms,
• 1:09:01
  Speaker 3: so satellite comms. We can integrate tactical mesh comms as
• 1:09:07
  Speaker 3: well on the vehicle. Walking around back here, you can
• 1:09:12
  Speaker 3: see there's more sensors, so stereo cameras and LIDAR again.
• 1:09:17
  Speaker 2: What does the LIDAR do?
• 1:09:19
  Speaker 3: So LIDAR basically allows you to see depth in the
• 1:09:25
  Speaker 3: area around the robot. So think about it as like
• 1:09:28
  Speaker 3: a depth sensor. It tells you the distance to surfaces
• 1:09:31
  Speaker 3: in the terrain.
• 1:09:33
  Speaker 1: Okay. Yeah. What engine are you running in here?
• 1:09:38
  Speaker 3: I think it's... You have to ask Chad. It's about,
• 1:09:43
  Speaker 3: I think, 115 horsepower engine. So this is based, this
• 1:09:47
  Speaker 3: whole vehicle is actually based on a Polaris M-Razor commercial
• 1:09:53
  Speaker 3: side-by-side vehicle.
• 1:09:54
  Speaker 1: Oh, okay. Okay.
• 1:09:55
  Speaker 3: So the engine, drive chain, chassis, that all comes from Polaris.
• 1:10:00
  Speaker 3: We take out the seats from the vehicle, take off
• 1:10:03
  Speaker 3: the roll cage and everything, and then transform it into
• 1:10:06
  Speaker 3: this autonomous platform.
• 1:10:09
  Speaker 1: Right on. And so.
• 1:10:13
  Speaker 1: What kind of stuff would you be mounting on here?
• 1:10:15
  Speaker 3: So you can put, yeah, you can put all sorts
• 1:10:19
  Speaker 3: of different payloads on here. This particular vehicle takes about
• 1:10:22
  Speaker 3: a thousand pounds of payload.
• 1:10:24
  Speaker 1: A thousand pounds.
• 1:10:26
  Speaker 3: So we've done everything from, you know, fairly straightforward things
• 1:10:30
  Speaker 3: like comms, making a communication node, to electronic warfare payloads,
• 1:10:35
  Speaker 3: to remote weapon stations. So you can mount, you know,
• 1:10:39
  Speaker 3: a machine gun on the vehicle.
• 1:10:41
  Speaker 1: Nice. So if you mount a weapon system on here,
• 1:10:46
  Speaker 1: is that all run through the autonomous stack? Is that
• 1:10:50
  Speaker 1: what you call it?
• 1:10:50
  Speaker 3: So the autonomous stack controls the vehicle. It allows an
• 1:10:54
  Speaker 3: operator to essentially tell the vehicle where they want the
• 1:10:58
  Speaker 3: vehicle to go. So it will say something like, you know,
• 1:11:02
  Speaker 3: go 10 kilometers to this location, orient in this way.
• 1:11:06
  Speaker 2: And then they will access the.
• 1:11:08
  Speaker 3: Payload through the comms network. So the human is still
• 1:11:12
  Speaker 3: in the loop whenever you're using the payload, but the
• 1:11:17
  Speaker 3: movement of these types of vehicles is autonomous. And what
• 1:11:23
  Speaker 3: that allows you to do is instead of having to
• 1:11:25
  Speaker 3: just remote control the vehicle, essentially drive it through its sensors,
• 1:11:31
  Speaker 3: the human can just say, I want one vehicle to
• 1:11:33
  Speaker 3: go to this location, I want two vehicles to go
• 1:11:35
  Speaker 3: to this location, and so on.
• 1:11:37
  Speaker 2: So it really.
• 1:11:38
  Speaker 3: Allows for force multiplication where a single operator can control
• 1:11:41
  Speaker 3: many different platforms and then access the payloads on those platforms.
• 1:11:45
  Speaker 2: And we'll show you how to do that.
• 1:11:46
  Speaker 1: It's like the old computer games like Warcraft.
• 1:11:49
  Speaker 2: It's exactly like that.
• 1:11:50
  Speaker 3: So that's actually how.
• 1:11:53
  Speaker 2: We think about it. Kill this thing.
• 1:11:55
  Speaker 3: I think our vision of this is a single operator
• 1:11:58
  Speaker 3: could control potentially hundreds of different assets on the battlefield.
• 1:12:02
  Speaker 3: And you're going to do that through an interface, which
• 1:12:04
  Speaker 3: is a little bit like a real-time strategy game, like
• 1:12:07
  Speaker 3: Warcraft or Starcraft, where it allows you to select the units,
• 1:12:12
  Speaker 3: tell them where to go, execute the payloads, and so on. Wow.
• 1:12:17
  Speaker 1: So I just.
• 1:12:20
  Speaker 1: I mean, with the changing landscape of the battlefield now,
• 1:12:26
  Speaker 1: there's a first land... like autonomous land vehicle I've seen.
• 1:12:32
  Speaker 1: So you can mount like an Epirus direct energy weapon
• 1:12:35
  Speaker 1: on this thing. So let's say, I mean, Enduro's got
• 1:12:40
  Speaker 1: their stuff coming out. Shield AI's got that new expat
• 1:12:43
  Speaker 1: they came out with.
• 1:12:46
  Speaker 2: You can mount counter UAS.
• 1:12:49
  Speaker 1: So what I'm asking is, I don't know if we're
• 1:12:51
  Speaker 1: there yet. Maybe we're already there and this is old news,
• 1:12:53
  Speaker 1: but with all these companies, you know, like yours, the submarines,
• 1:12:59
  Speaker 1: the Saronic, you.
• 1:13:01
  Speaker 2: Know, Overland AI.
• 1:13:05
  Speaker 1: When we do go to a full-scale war, are you
• 1:13:08
  Speaker 1: going to be, like, is the operator or the ground
• 1:13:12
  Speaker 1: force commander or just the commander of the entire operation,
• 1:13:16
  Speaker 1: are they going to be controlling Shield AIs, expats, Overland AIs,
• 1:13:23
  Speaker 1: ground vehicles, Saronics, surface warfare vehicles, submarines, drones, all of
• 1:13:31
  Speaker 1: it on the same system?
• 1:13:33
  Speaker 3: So the military is working on this right now, finding
• 1:13:36
  Speaker 3: ways to integrate all of these different pieces into the
• 1:13:41
  Speaker 3: same sort of system so that you have a unified
• 1:13:44
  Speaker 3: view of the battlefield. Now, the way that that may
• 1:13:46
  Speaker 3: play out, I think you're likely to see There are
• 1:13:51
  Speaker 3: single systems where you can see all of the different pieces,
• 1:13:55
  Speaker 3: and then there will likely be systems which will allow
• 1:13:58
  Speaker 3: the warfighters to actually be controlling some subset of them
• 1:14:02
  Speaker 3: on the battlefield. But it will all have to be
• 1:14:04
  Speaker 3: linked together to provide that overall awareness of what you're
• 1:14:09
  Speaker 3: pushing out there.
• 1:14:09
  Speaker 1: And so if you have 100, 200 of these things
• 1:14:12
  Speaker 1: right here, what is the name of this?
• 1:14:14
  Speaker 2: This is an Ultra.
• 1:14:15
  Speaker 1: The Ultra.
• 1:14:16
  Speaker 4: Yeah.
• 1:14:16
  Speaker 1: 100, 200, 300 Ultras out here, and they've got surface-to-air
• 1:14:22
  Speaker 1: missiles for air defense. They've got, I don't know,. 50
• 1:14:27
  Speaker 1: calibers for other ground vehicles and anti-personnel and rocket launchers
• 1:14:32
  Speaker 1: and grenade launchers and drones. Are they all going to
• 1:14:36
  Speaker 1: read off each other, or is somebody going to have to.
• 1:14:39
  Speaker 3: So the way that this is going, we're taking.
• 1:14:45
  Speaker 2: So.
• 1:14:45
  Speaker 3: The basic idea is that you start with just being
• 1:14:49
  Speaker 3: able to move one vehicle at a time, right? So
• 1:14:52
  Speaker 3: you can say, I want this vehicle to go to
• 1:14:54
  Speaker 3: this location. You just let it go, execute a payload there.
• 1:14:58
  Speaker 3: We already can do that really well. So now we're
• 1:15:01
  Speaker 3: starting to build up coordination where you can move multiple
• 1:15:05
  Speaker 3: vehicles at once and they coordinate.
• 1:15:08
  Speaker 2: In order to achieve a task. and then we'll keep
• 1:15:10
  Speaker 2: building on that.
• 1:15:11
  Speaker 3: So the basic idea is that over, like as we
• 1:15:15
  Speaker 3: build out the technology and as we field more and
• 1:15:18
  Speaker 3: more of it, you're going to have a situation where
• 1:15:20
  Speaker 3: a single operator will be able to move multiple vehicles
• 1:15:25
  Speaker 3: into formations, have them, send them to achieve a particular task,
• 1:15:31
  Speaker 3: and they're gonna go out there and just do it.
• 1:15:33
  Speaker 3: And that's part of what's called mission autonomy or orchestration.
• 1:15:39
  Speaker 3: So platform autonomy is basically the autonomy that lives on
• 1:15:42
  Speaker 3: the vehicle that allows it to see the terrain, see
• 1:15:47
  Speaker 3: where vehicles and people are out.
• 1:15:49
  Speaker 2: In that terrain and move through it.
• 1:15:51
  Speaker 3: And then you tie that together where vehicles are coordinating
• 1:15:56
  Speaker 3: with each other and that's mission autonomy. They're coordinating autonomously
• 1:16:02
  Speaker 3: to actually conduct.
• 1:16:04
  Speaker 2: Like a full mission.
• 1:16:05
  Speaker 3: And then all of that, like all of these different
• 1:16:07
  Speaker 3: autonomous systems and uncrewed systems which are on the battlefield
• 1:16:10
  Speaker 3: will be pulled into command and control systems, which allow
• 1:16:14
  Speaker 3: people to see everything which is out there.
• 1:16:18
  Speaker 1: Wow. I got a million questions for you, but it's
• 1:16:23
  Speaker 1: hot as shit out here and humid.
• 1:16:25
  Speaker 2: Yeah, yeah.
• 1:16:27
  Speaker 1: Let's see. We'll do that inside.
• 1:16:28
  Speaker 2: That sounds great.
• 1:16:30
  Speaker 1: Let's see what this thing can do.
• 1:16:31
  Speaker 2: Do you want to drive it?
• 1:16:32
  Speaker 3: Yep.
• 1:16:32
  Speaker 1: Yes, I want to drive it.
• 1:16:34
  Speaker 3: Okay, so the upper part of this interface here, this
• 1:16:38
  Speaker 3: is where the vehicle is on a satellite map.
• 1:16:42
  Speaker 2: And you see like the name of the vehicle.
• 1:16:44
  Speaker 3: And then down here gives you a view of what
• 1:16:47
  Speaker 3: the vehicle sees. So over here, this is the front
• 1:16:50
  Speaker 3: camera on the vehicle. And then this.
• 1:16:54
  Speaker 2: Is actually like the AI view of the vehicle.
• 1:16:56
  Speaker 3: You don't have to necessarily spend too much attention like
• 1:16:59
  Speaker 3: looking at this, but the Magenta.
• 1:17:03
  Speaker 2: Areas are lethal. So those are areas.
• 1:17:06
  Speaker 3: That the vehicle don't want to move the vehicle into
• 1:17:09
  Speaker 3: as you're as you're tele operating it, you can actually
• 1:17:11
  Speaker 3: move it anywhere that you want.
• 1:17:13
  Speaker 1: So this is that big pond that we're digging down there.
• 1:17:16
  Speaker 3: This is this is going to be about 50 meters around.
• 1:17:19
  Speaker 3: So it's only seeing up here on this, this little area,
• 1:17:23
  Speaker 3: what we can do is give you the controller. And
• 1:17:25
  Speaker 3: the way that this works is when you pull like
• 1:17:28
  Speaker 3: you have to hold this down. So your lower left, okay,
• 1:17:32
  Speaker 3: and you hit a and that moves you into autonomy.
• 1:17:37
  Speaker 3: And then you can move this joystick moves the vehicle forward.
• 1:17:43
  Speaker 1: Holy shit.
• 1:17:46
  Speaker 3: So that's basically moving it forward and then you can
• 1:17:49
  Speaker 3: turn using this so we can like turn it to
• 1:17:51
  Speaker 3: the right. And you know, when the vehicle is here
• 1:17:59
  Speaker 3: in front of you, it's tempting to look at the vehicle,
• 1:18:01
  Speaker 3: but you should actually look at this, which is what
• 1:18:03
  Speaker 3: the vehicle can see. And then you can control it.
• 1:18:08
  Speaker 1: That's right.
• 1:18:09
  Speaker 2: Yeah.
• 1:18:09
  Speaker 3: And you can control it beyond line of sight. So
• 1:18:11
  Speaker 3: you could do this from like 5,000 miles away.
• 1:18:14
  Speaker 4: Wow.
• 1:18:15
  Speaker 2: So left down. Yep. And then push forward.
• 1:18:28
  Speaker 1: It's weird not looking at the vehicle.
• 1:18:30
  Speaker 4: Yeah.
• 1:18:31
  Speaker 2: I want to look over there.
• 1:18:32
  Speaker 4: Yeah. Holy shit.
• 1:18:48
  Speaker 1: Can I go, how do you go backwards?
• 1:18:49
  Speaker 3: Okay, so to go backwards, hold down this, so both
• 1:18:55
  Speaker 3: that and this, and then move that backwards.
• 1:18:57
  Speaker 2: There you go.
• 1:19:06
  Speaker 1: Oh, there we are. It will automatically stop. It won't
• 1:19:15
  Speaker 1: run us over.
• 1:19:17
  Speaker 2: Let's not test it.
• 1:19:18
  Speaker 4: All right.
• 1:19:18
  Speaker 1: Yes.
• 1:19:18
  Speaker 4: Yeah, there it is.
• 1:19:23
  Speaker 1: Slowing for a person. Yeah, yeah. Well, that's not going
• 1:19:28
  Speaker 1: to work in war, Byron.
• 1:19:29
  Speaker 4: I'm just kidding.
• 1:19:31
  Speaker 2: You can turn that off.
• 1:19:31
  Speaker 4: Dude, this is crazy.
• 1:19:54
  Speaker 1: So would somebody be looking at this or like a
• 1:19:56
  Speaker 1: VR headset or does it? I guess you could probably
• 1:19:59
  Speaker 1: do no matter.
• 1:19:59
  Speaker 2: You can do whatever. Like generally you're looking at this.
• 1:20:03
  Speaker 3: You can look through the other sensors on board the
• 1:20:05
  Speaker 3: vehicle as well. So you can look at the rear
• 1:20:08
  Speaker 3: sensors or off to the sides. You can access the
• 1:20:13
  Speaker 3: payloads on the vehicle through the interface up here.
• 1:20:17
  Speaker 1: Wow.
• 1:20:28
  Speaker 2: Alright, and then if you want to.
• 1:20:31
  Speaker 1: Oh shit, there's a tree.
• 1:20:33
  Speaker 2: Yeah, maybe back up.
• 1:20:36
  Speaker 1: In case you can't tell, I'm not a gamer.
• 1:20:39
  Speaker 3: I'm not great at that either, but we like to
• 1:20:41
  Speaker 3: control it actually through this interface and I'll show you
• 1:20:43
  Speaker 3: that in a moment. I think one of the challenges
• 1:20:47
  Speaker 3: for teleoperation is that you don't have like a vehicle sense, right?
• 1:20:52
  Speaker 3: Like you can't feel like how the vehicle is moving
• 1:20:54
  Speaker 3: like you would when you're driving. And now you're trying
• 1:20:57
  Speaker 3: to interpret what the vehicle can see like through its
• 1:21:00
  Speaker 3: own sensors.
• 1:21:02
  Speaker 2: You should probably stop.
• 1:21:04
  Speaker 1: Right.
• 1:21:05
  Speaker 2: Yeah.
• 1:21:07
  Speaker 3: What it's, it's actually much safer to put it into
• 1:21:10
  Speaker 3: autonomy and just tell it where you want it to go.
• 1:21:12
  Speaker 3: And it will find a way to get there without,
• 1:21:14
  Speaker 3: you know, hitting anything while staying safe.
• 1:21:17
  Speaker 4: Yeah.
• 1:21:17
  Speaker 2: So you can do that.
• 1:21:19
  Speaker 1: That's awesome.
• 1:21:20
  Speaker 4: All right.
• 1:21:20
  Speaker 1: So shit, I did almost hit that damn tree.
• 1:21:22
  Speaker 4: Okay.
• 1:21:24
  Speaker 1: Okay. Let's send it to go get a pizza.
• 1:21:27
  Speaker 2: That sounds great. I'm not sure they'll let me do
• 1:21:32
  Speaker 2: this on the road. Can we go over there?
• 1:21:35
  Speaker 1: Can we go over here and watch it? So before
• 1:21:40
  Speaker 1: you guys run the route, how is that thing determining
• 1:21:44
  Speaker 1: what is a human being and what's a tree and
• 1:21:46
  Speaker 1: what's a vehicle and... What's a rock?
• 1:21:48
  Speaker 3: Yeah, it's looking at the whole environment around it. And
• 1:21:52
  Speaker 3: then it's determining where it can drive and where it
• 1:21:55
  Speaker 3: can't drive. So that's the first part, this notion of traversability,
• 1:21:58
  Speaker 3: like what part of the terrain is traversable. And then
• 1:22:00
  Speaker 3: on top of that, there's a semantic understanding of the terrain,
• 1:22:04
  Speaker 3: which means you can understand that, you know, it can
• 1:22:07
  Speaker 3: see a person or a car, you know, determine what
• 1:22:11
  Speaker 3: that is, where they are relative to it. And right now,
• 1:22:14
  Speaker 3: it's running a safety system, which essentially says, like, don't
• 1:22:18
  Speaker 3: stop if you get too close to a person or
• 1:22:20
  Speaker 3: a vehicle.
• 1:22:22
  Speaker 1: Right on. I mean, how does it differ? Does it
• 1:22:24
  Speaker 1: pick up, like, body temperature?
• 1:22:27
  Speaker 2: It looks, and so it's just using a camera. Using
• 1:22:31
  Speaker 2: a silhouette?
• 1:22:31
  Speaker 3: Yeah, and it's saying, like, this looks like a person,
• 1:22:34
  Speaker 3: this looks like a vehicle, and it's able to pick
• 1:22:36
  Speaker 3: them out.
• 1:22:37
  Speaker 1: Cool.
• 1:22:37
  Speaker 4: Yeah.
• 1:22:39
  Speaker 1: And this is full autonomy, what we're getting ready to
• 1:22:41
  Speaker 1: do here.
• 1:22:42
  Speaker 4: Yep.
• 1:22:49
  Speaker 1: Holy shit.
• 1:22:52
  Speaker 2: Oh, we're in the way.
• 1:22:53
  Speaker 3: Let's move back away from it. So yeah, as it's
• 1:23:09
  Speaker 3: moving through the terrain, it's also tracking where all the
• 1:23:11
  Speaker 3: people are, right?
• 1:23:12
  Speaker 2: Where the vehicles are, where they are relative to it.
• 1:23:17
  Speaker 2: And then deciding how to how to drive. It picked
• 1:23:21
  Speaker 2: him up. It picked him up, yeah.
• 1:23:22
  Speaker 4: It's actually pretty wild that it picked.
• 1:23:24
  Speaker 1: Him up in the middle of all those weeds and
• 1:23:26
  Speaker 1: trees and shit.
• 1:23:39
  Speaker 3: You can see one of the benefits of this is,
• 1:23:41
  Speaker 3: you know, you can just tell it, I want you
• 1:23:43
  Speaker 3: to go to, again, like this location and you can
• 1:23:46
  Speaker 3: let it go and it will do its thing.
• 1:23:50
  Speaker 2: So that was the end of the run.
• 1:23:51
  Speaker 3: Basically came up the hill, you know, went around the
• 1:23:54
  Speaker 3: field and then stopped there. So very short run for
• 1:23:57
  Speaker 3: what it normally does, but you can get a taste
• 1:24:00
  Speaker 3: of kind of like how it moves.
• 1:24:01
  Speaker 1: That is sick. All right. Well, thanks for showing us
• 1:24:05
  Speaker 1: what the Ultra can do. And let's go wrap up
• 1:24:07
  Speaker 1: the interview. Yeah.
• 1:24:08
  Speaker 2: Thanks for letting us come out here and show it.
• 1:24:10
  Speaker 1: Thanks for bringing that badass thing. There's nothing more frustrating
• 1:24:16
  Speaker 1: than putting in the work and still feeling like your
• 1:24:19
  Speaker 1: body is not responding the way it used to. You're training,
• 1:24:23
  Speaker 1: you're trying to eat right, you're trying to sleep, but
• 1:24:26
  Speaker 1: energy still drops off. Recovery takes longer and you don't
• 1:24:30
  Speaker 1: have that same drive that you used to have. That's
• 1:24:34
  Speaker 1: why I started taking Mars Men. What I like about
• 1:24:37
  Speaker 1: Mars Men is that it's not forcing hormones into your body.
• 1:24:41
  Speaker 1: It's designed to help your body use more of what
• 1:24:44
  Speaker 1: it's already producing. As men get older, SHBG can bind
• 1:24:49
  Speaker 1: up testosterone. So even if your levels look fine on paper,
• 1:24:53
  Speaker 1: a lot of it may not be available for your
• 1:24:55
  Speaker 1: body to actually use. Mars Men uses clinically dosed ingredients
• 1:24:59
  Speaker 1: to help free up bound testosterone, plus vitamin D and
• 1:25:03
  Speaker 1: zinc to support healthy T levels, energy, stamina, and recovery.
• 1:25:07
  Speaker 1: And since taking it, I've noticed steadier energy, and better recovery.
• 1:25:12
  Speaker 1: It's not like a caffeine spike. It's more consistent drive
• 1:25:15
  Speaker 1: throughout the day. Mars Men is made in the USA,
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• 1:25:27
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• 1:25:35
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• 1:25:39
  Speaker 1: After you purchase, They'll ask you where you heard about them.
• 1:25:43
  Speaker 1: Please support our show and tell them our show sent you. Okay,
• 1:25:50
  Speaker 1: so after taking a look at this thing, I... This
• 1:25:56
  Speaker 1: just has so many different capabilities. It could be used
• 1:26:00
  Speaker 1: for so many different things. I mean, even just keep...
• 1:26:03
  Speaker 1: I mean, it sounds like the first thing that the
• 1:26:05
  Speaker 1: military is interested in is logistics, which... I mean, just
• 1:26:12
  Speaker 1: keeping major supply routes open.
• 1:26:17
  Speaker 2: Yeah.
• 1:26:17
  Speaker 1: I mean, constant detection. Could these things detect? I mean,
• 1:26:21
  Speaker 1: when we were up there, it detected, I mean, I
• 1:26:24
  Speaker 1: know you guys have that safety feature on it, but
• 1:26:26
  Speaker 1: it detected one of my camera guys who was, I mean,
• 1:26:30
  Speaker 1: really wasn't even that close to it. It looked like
• 1:26:32
  Speaker 1: he was maybe 25 yards, 30 yards from it in
• 1:26:35
  Speaker 1: the middle of a bunch of trees, and it picked
• 1:26:37
  Speaker 1: him up like that. Could this thing pick up? IEDs.
• 1:26:44
  Speaker 1: Is there a way that it could detect IEDs?
• 1:26:48
  Speaker 3: Yeah, so you can put a variety of different types
• 1:26:50
  Speaker 3: of sensors on the vehicle. So you can certainly detect
• 1:26:56
  Speaker 3: people and vehicles. And then you can also, we've been
• 1:27:00
  Speaker 3: putting tethered drones on these. So the drone can go
• 1:27:05
  Speaker 3: up in the air, be able to look down on
• 1:27:08
  Speaker 3: the ground. So If you have ground-penetrating radar or other
• 1:27:13
  Speaker 3: types of sensors, you can certainly detect things like IEDs
• 1:27:17
  Speaker 3: or obstacles in the environment that then can be identified
• 1:27:21
  Speaker 3: and reduced, right? So that's certainly something they can do.
• 1:27:26
  Speaker 3: And to your point, I think you can, of course,
• 1:27:29
  Speaker 3: move things around. But just the ability to push these
• 1:27:33
  Speaker 3: forward and sense the environment is, I think, will be
• 1:27:39
  Speaker 3: be game-changing, right?
• 1:27:40
  Speaker 2: You don't have to put a person out there to
• 1:27:41
  Speaker 2: do it.
• 1:27:42
  Speaker 1: Do you see these integrating with human forces that are...
• 1:27:51
  Speaker 1: Do you see these as a forward observations platform for
• 1:27:55
  Speaker 1: maybe a slew of tanks or MRADs or whatever we're
• 1:28:01
  Speaker 1: using and then kind of pushing that information back to
• 1:28:06
  Speaker 1: a larger force or is it going all autonomous?
• 1:28:11
  Speaker 3: There's a number of statistics out there which are pretty interesting.
• 1:28:17
  Speaker 3: You've got Ukrainian ground commanders basically saying that they're going
• 1:28:21
  Speaker 3: to replace something like 80% of infantry with uncrewed ground
• 1:28:27
  Speaker 3: vehicles in the near term. Those statistics are interesting. I
• 1:28:33
  Speaker 3: think the reality will be a little bit more subtle,
• 1:28:35
  Speaker 3: so these are machines that can integrate with human formations.
• 1:28:42
  Speaker 3: You should think about them as giving humans more capability
• 1:28:46
  Speaker 3: on the battlefield. So I think the way that they'll
• 1:28:51
  Speaker 3: actually work with the U.S. military is that we'll have
• 1:28:55
  Speaker 3: formations with humans in them. They will be pushing these
• 1:28:58
  Speaker 3: vehicles out in front, as you suggested, in order to
• 1:29:02
  Speaker 3: be able to get a better understanding of the terrain
• 1:29:05
  Speaker 3: that they're going to be moving into. But you can
• 1:29:08
  Speaker 3: use them for all sorts of things, right? So it's
• 1:29:09
  Speaker 3: not just sensing what's out there. You can use them
• 1:29:12
  Speaker 3: to create, like, diversions, right? Like, you can use them,
• 1:29:17
  Speaker 3: you know, to... You can use them, you know, for
• 1:29:21
  Speaker 3: strike capability. You can use them to create that kind
• 1:29:24
  Speaker 3: of counter UAS bubble to protect humans. I think about
• 1:29:28
  Speaker 3: them as, you know, something that will be like a
• 1:29:31
  Speaker 3: force multiplier.
• 1:29:33
  Speaker 1: I mean, you're saying that Ukrainian military is saying that
• 1:29:38
  Speaker 1: it will replace up to 80% of infantry, 80% of
• 1:29:46
  Speaker 1: the infantry. Yeah. And you're saying the U.S. is saying, well,
• 1:29:52
  Speaker 1: maybe not the U.S., but you see a subtle integration.
• 1:29:55
  Speaker 1: Why would we do a subtle integration? I mean, I
• 1:29:58
  Speaker 1: know on one hand I'm going to piss the warfighter
• 1:30:00
  Speaker 1: out because they love fighting wars. On the other hand,
• 1:30:02
  Speaker 1: I'm thinking about my toddlers at home, you know, with
• 1:30:06
  Speaker 1: all the wars that we're involved in and re-involved in,
• 1:30:11
  Speaker 1: and I don't want my kids going over there having
• 1:30:14
  Speaker 1: to do it. I get it. You know, and so
• 1:30:16
  Speaker 1: why do you see a subtle integration of this? Why
• 1:30:19
  Speaker 1: wouldn't we go full scale and say, hey, you know,
• 1:30:22
  Speaker 1: like we don't need– Private Joe driving this MRAP into
• 1:30:30
  Speaker 1: hostile territory. That's a... I don't want to say a
• 1:30:35
  Speaker 1: wasted life, but it would be a wasted life if
• 1:30:38
  Speaker 1: he got killed when we have the capability of going
• 1:30:41
  Speaker 1: full autonomous right now.
• 1:30:43
  Speaker 2: I think we'll eventually get there.
• 1:30:45
  Speaker 3: So one of the value propositions for ground autonomy is
• 1:30:50
  Speaker 3: that you're really focused on the.
• 1:30:55
  Speaker 3: One of the hardest domains, right, in the military, right?
• 1:30:58
  Speaker 3: So just kind of stepping back, there are a lot
• 1:31:01
  Speaker 3: of folks who are focused on air power or naval
• 1:31:04
  Speaker 3: power or missiles. But at the end of the day,
• 1:31:08
  Speaker 3: wars are ultimately won on the ground. You know, that's
• 1:31:11
  Speaker 3: where people live, right? We don't live in the sea.
• 1:31:13
  Speaker 3: We don't live in the air. We live on the ground.
• 1:31:15
  Speaker 3: And that's where we have to fight. And, you know,
• 1:31:19
  Speaker 3: I think When you look at the U.S. and our
• 1:31:25
  Speaker 3: casualty rates, something like more than 80% of combat-related casualties
• 1:31:32
  Speaker 3: since World War II are infantry.
• 1:31:35
  Speaker 2: So, of course, you want to, like.
• 1:31:37
  Speaker 3: I think focusing on the ground makes a lot of
• 1:31:40
  Speaker 3: sense because we want to bring technology to the warfighter
• 1:31:43
  Speaker 3: that will save those lives, right?
• 1:31:46
  Speaker 2: And so.
• 1:31:49
  Speaker 3: Yes, we want to take humans out of harm's way.
• 1:31:52
  Speaker 3: And I think, you know, in Ukraine, we're seeing technology,
• 1:31:58
  Speaker 3: you know, increasingly being pushed out in front of the warfighter.
• 1:32:02
  Speaker 3: And I think they're very optimistic about how they want
• 1:32:05
  Speaker 3: to use them. And ultimately, we do want to do that, right?
• 1:32:08
  Speaker 3: We want to pull, we want to essentially reduce risk on.
• 1:32:13
  Speaker 2: The battlefield, right?
• 1:32:14
  Speaker 3: Pull warfighters away from these points of contact. have more
• 1:32:17
  Speaker 3: people who are maybe operating teams of robots that are
• 1:32:22
  Speaker 3: far away from a location where they could get hit.
• 1:32:26
  Speaker 3: But in the meantime, I think that the way they're
• 1:32:29
  Speaker 3: going to be integrated is in a way that helps
• 1:32:33
  Speaker 3: warfighters on the ground to stay safer, to potentially give
• 1:32:38
  Speaker 3: them more options, more tools, without totally pulling them out.
• 1:32:43
  Speaker 3: So to your point, yes, we want to ensure that
• 1:32:49
  Speaker 3: we ultimately want to pull as many people out of
• 1:32:53
  Speaker 3: harm's way as possible.
• 1:32:55
  Speaker 2: But these are.
• 1:32:57
  Speaker 3: Still machines that aren't quite as creative and adaptable as humans.
• 1:33:03
  Speaker 2: And so I think.
• 1:33:05
  Speaker 3: What we're really trying to do is find the places
• 1:33:07
  Speaker 3: where we can maximize the ability for them to be
• 1:33:13
  Speaker 3: able to take risk while also allowing our humans to
• 1:33:19
  Speaker 3: do what they do best and do maybe more specialized
• 1:33:24
  Speaker 3: roles on the battlefield. So I think it'll be a process.
• 1:33:28
  Speaker 1: I mean, just thinking about my time in Iraq, Afghanistan alone,
• 1:33:33
  Speaker 1: I mean, I have right... IED threat was huge. And
• 1:33:37
  Speaker 1: then here is a key statistic here. 44% of U.S.
• 1:33:41
  Speaker 1: killed in action from 2006 to 2021 were killed by IEDs, 44%. Now,
• 1:33:50
  Speaker 1: I mean, if these things did have IED detection capabilities,
• 1:33:57
  Speaker 1: it's gotta be better than humans then, I mean, there's 40,
• 1:34:01
  Speaker 1: it's almost 50% of the people that were killed would
• 1:34:05
  Speaker 1: still be here today. You know, just with, Just in
• 1:34:10
  Speaker 1: that application.
• 1:34:11
  Speaker 3: I think there's a lot you can do there. I mean,
• 1:34:13
  Speaker 3: there's sensors you can put on the vehicles.
• 1:34:15
  Speaker 1: The human sensor, like I was just saying, caught my
• 1:34:18
  Speaker 1: guy out in the weeds trying to, in a bunch
• 1:34:21
  Speaker 1: of trees. I mean, rolling down Iraq, I mean, there
• 1:34:24
  Speaker 1: were sniper problems. There was the guy, the trigger men
• 1:34:27
  Speaker 1: of IEDs. I mean, I would think that it would
• 1:34:31
  Speaker 1: pick them up immediately. I mean, they can't hide from
• 1:34:34
  Speaker 1: the machine.
• 1:34:35
  Speaker 4: Yeah.
• 1:34:36
  Speaker 1: How is it sensing that?
• 1:34:37
  Speaker 3: Yeah. So the machine has cameras on it, right? And
• 1:34:42
  Speaker 3: depending on the loadout for it, you can have sort
• 1:34:47
  Speaker 3: of normal RGB cameras.
• 1:34:49
  Speaker 2: You can have thermal cameras, right?
• 1:34:53
  Speaker 3: Like there's a lot that you can do to detect
• 1:34:56
  Speaker 3: people and vehicles in the environment. So if I was
• 1:34:59
  Speaker 3: just using cameras out there, I was able, as you said,
• 1:35:01
  Speaker 3: just be able to pick someone up even in the
• 1:35:04
  Speaker 3: woods and in the weeds. But You should think about,
• 1:35:09
  Speaker 3: there's a lot of different ways that these can be used. Again,
• 1:35:13
  Speaker 3: out in front of the warfighter, you can have convoys
• 1:35:16
  Speaker 3: of autonomous vehicles that don't have people on them that
• 1:35:20
  Speaker 3: can move very quickly and move supplies back and forth.
• 1:35:23
  Speaker 3: Even if they're targeted, at least no human is being killed.
• 1:35:26
  Speaker 3: You can have them, autonomous vehicles, out in front of
• 1:35:31
  Speaker 3: human convoys as well. really making sure that there is
• 1:35:36
  Speaker 3: no one out there that, you know, moving first through
• 1:35:39
  Speaker 3: the terrain.
• 1:35:40
  Speaker 2: So, generally speaking, there's a lot of ideas.
• 1:35:43
  Speaker 3: In Army and Marine Corps about how to use these
• 1:35:46
  Speaker 3: vehicles out in front, again, like leading convoys or it's
• 1:35:50
  Speaker 3: like a protective onion almost, right? You can think about
• 1:35:53
  Speaker 3: a whole set of vehicles that are surrounding higher value assets.
• 1:35:59
  Speaker 3: whether they're people or, you know, tanks or Bradley fighting
• 1:36:04
  Speaker 3: vehicles or, you know, XM30, which is the replacement for that,
• 1:36:07
  Speaker 3: having autonomous vehicles that can provide sensing and protection for
• 1:36:14
  Speaker 3: those formations. So we think about them as adding to
• 1:36:17
  Speaker 3: some of the things that we're developing.
• 1:36:20
  Speaker 1: How many of these scenarios have you kind of war-gamed
• 1:36:23
  Speaker 1: or run scenarios on out at wherever?
• 1:36:26
  Speaker 2: Uh, quite, quite a few.
• 1:36:27
  Speaker 3: I mean, I think people are, once they start to
• 1:36:29
  Speaker 3: see the vehicle and they start to think about it,
• 1:36:31
  Speaker 3: they are coming up with lots of ways of, of
• 1:36:35
  Speaker 3: potentially using them. Uh, and as we, you know, one
• 1:36:38
  Speaker 3: of the things that we've been trying to do in
• 1:36:39
  Speaker 3: the last year is work with a lot of different units.
• 1:36:42
  Speaker 3: So we've worked with over 20 different, um, different units
• 1:36:46
  Speaker 3: integrating this technology into the unit. Uh, for example, the
• 1:36:50
  Speaker 3: 82nd airborne, uh, we worked with 382 for almost six months,
• 1:36:56
  Speaker 3: just embedded with them up through one of their training
• 1:36:58
  Speaker 3: rotations down at JRTC in Louisiana. And we started out
• 1:37:04
  Speaker 3: just doing resupply. They were like, this is kind of
• 1:37:06
  Speaker 3: the obvious use case for this. Let's use this to
• 1:37:10
  Speaker 3: support our logistics to allow us to move faster.
• 1:37:13
  Speaker 2: And we were able to do that.
• 1:37:14
  Speaker 3: And so then they were like, okay, like, can we
• 1:37:16
  Speaker 3: get cameras and drones and things like that on it?
• 1:37:19
  Speaker 2: The answer is yes. And so We started to add different.
• 1:37:22
  Speaker 3: Types of payloads to it. They started to use them
• 1:37:25
  Speaker 3: in more creative ways. And by the end of this,
• 1:37:28
  Speaker 3: they were using these autonomous vehicles in this force on
• 1:37:33
  Speaker 3: force exercise, not just for resupply, but also for intelligence
• 1:37:37
  Speaker 3: surveillance and reconnaissance. There were snipers who were using the
• 1:37:40
  Speaker 3: vehicles as decoys. There's just a lot of things you
• 1:37:44
  Speaker 3: can start to do. first step is really just getting
• 1:37:50
  Speaker 3: it in the hands of the warfighter. Let them think
• 1:37:53
  Speaker 3: about the problems they're trying to solve, experience the technology,
• 1:37:57
  Speaker 3: and then start to iterate on potentially new tactics and
• 1:38:05
  Speaker 3: things like this that you can do with the vehicles.
• 1:38:07
  Speaker 1: Are these truly autonomous or is there an operator in
• 1:38:12
  Speaker 1: the rear that's going to have to be behind each
• 1:38:17
  Speaker 1: one of these things with a remote control?
• 1:38:18
  Speaker 3: Yeah, this is a great question. And this is sometimes
• 1:38:25
  Speaker 3: kind of glossed over. A lot of people say that
• 1:38:28
  Speaker 3: they can do autonomous things. and people think about uncrewed
• 1:38:32
  Speaker 3: ground vehicles. People talk about UGVs all the time in
• 1:38:34
  Speaker 3: Ukraine versus autonomous vehicles. So what are the differences between
• 1:38:37
  Speaker 3: these things, right? So remote control basically means you have
• 1:38:41
  Speaker 3: a controller like in your hand and you're looking at
• 1:38:44
  Speaker 3: the thing and you're driving it. So think about like
• 1:38:46
  Speaker 3: a toy car, right? Like that's a remote control device.
• 1:38:50
  Speaker 1: Vehicle.
• 1:38:51
  Speaker 3: Now, that's fine, except you have to actually look at
• 1:38:55
  Speaker 3: the terrain yourself, look at the vehicle and determine where
• 1:38:58
  Speaker 3: it's going to go. That obviously doesn't work beyond line
• 1:39:00
  Speaker 3: of sight or in areas where you can't see the
• 1:39:03
  Speaker 3: terrain very well. So the next sort of evolution of
• 1:39:07
  Speaker 3: this is teleoperation. And people talk about teleoperation a lot
• 1:39:11
  Speaker 3: with ground vehicles. This is basically where you take a
• 1:39:14
  Speaker 3: remote control and you look through the vehicle's own sensors,
• 1:39:19
  Speaker 3: whether it's like camera or thermal sensors, and then you
• 1:39:21
  Speaker 3: drive the vehicle based on that sensory feedback that you're
• 1:39:25
  Speaker 3: getting over a network. So you can go beyond line
• 1:39:27
  Speaker 3: of sight because you just see what the vehicle sees.
• 1:39:30
  Speaker 3: You don't have to see the vehicle itself. So you
• 1:39:32
  Speaker 3: can teleoperate something from like across the world if you
• 1:39:36
  Speaker 3: have a really good, you know, connection to that vehicle,
• 1:39:39
  Speaker 3: if you have a good satellite connection or radio connection
• 1:39:42
  Speaker 3: to the vehicle. And a lot of the work which
• 1:39:44
  Speaker 3: is being done in Ukraine is tele-operated uncrewed ground vehicles
• 1:39:48
  Speaker 3: where you'll actually have a set of people, like up
• 1:39:51
  Speaker 3: to five people who are controlling each vehicle and essentially
• 1:39:56
  Speaker 3: driving it through the terrain using, you know, like a
• 1:39:59
  Speaker 3: handheld controller or, you know, they're looking through potentially either
• 1:40:03
  Speaker 3: the vehicle's cameras or the cameras from a drone overhead
• 1:40:06
  Speaker 3: and they're just trying to drive this thing around. The
• 1:40:09
  Speaker 3: thing which is hard about that is that, you know, it,
• 1:40:12
  Speaker 3: occupies at least one person's attention at all times because
• 1:40:16
  Speaker 3: the person's making.
• 1:40:17
  Speaker 2: All of those decisions. But it also.
• 1:40:22
  Speaker 3: Is something where if your comms gets disrupted, the thing's
• 1:40:25
  Speaker 3: just dead, right? Like it requires a human to drive
• 1:40:28
  Speaker 3: it and the human can no longer connect to it.
• 1:40:30
  Speaker 3: So one of the strategies is you cut the comms
• 1:40:34
  Speaker 3: to the vehicle, you use EW or whatever to cut
• 1:40:37
  Speaker 3: the comms and then you strike it because it's just
• 1:40:39
  Speaker 3: a sitting duck. Autonomy is extremely powerful because it allows
• 1:40:46
  Speaker 3: the vehicle to sense the environment, represent it, plan through
• 1:40:50
  Speaker 3: the environment, all on board, like at the edge, makes
• 1:40:53
  Speaker 3: its own decisions. And it doesn't require a human to
• 1:40:57
  Speaker 3: essentially drive it. So when you have an autonomous vehicle,
• 1:41:02
  Speaker 3: you can tell it where you want it to go
• 1:41:05
  Speaker 3: and it will just go there. And that means that
• 1:41:08
  Speaker 3: you can focus your attention on something else. That might
• 1:41:11
  Speaker 3: be another autonomous vehicle. It might be another task. So
• 1:41:14
  Speaker 3: for example, you can send a vehicle back to resupply you,
• 1:41:18
  Speaker 3: but you don't have to be focused on it anymore.
• 1:41:20
  Speaker 3: You can do your job. The second thing about autonomy,
• 1:41:24
  Speaker 3: which is important to understand, is because the vehicle is
• 1:41:28
  Speaker 3: making decisions based on its own sensing and own onboard compute,
• 1:41:33
  Speaker 3: if your comms get cut, the thing's going to keep
• 1:41:36
  Speaker 3: going and continue the mission. And so one of the
• 1:41:40
  Speaker 3: things we're starting to see out of the Russian-Ukraine conflict
• 1:41:44
  Speaker 3: is that the Russians are starting to add autonomy to
• 1:41:48
  Speaker 3: ground vehicles to deal with the contested comms situation. Because
• 1:41:52
  Speaker 3: it's so hard for them to maintain comms, autonomy allows
• 1:41:55
  Speaker 3: the vehicles to continue to move without human oversight. So
• 1:42:01
  Speaker 3: that's another piece of this. So we think about this
• 1:42:04
  Speaker 3: as kind of force multiplication, and resilience to a contested
• 1:42:09
  Speaker 3: comms environment.
• 1:42:10
  Speaker 1: Well, now, so another thing with autonomy too, correct me
• 1:42:13
  Speaker 1: if I'm wrong, is let's say you have a hundred
• 1:42:16
  Speaker 1: of these.
• 1:42:17
  Speaker 4: Yeah.
• 1:42:17
  Speaker 1: Let's say you have 500 of these. Yeah. And then
• 1:42:19
  Speaker 1: you have all these other, you know, we were talked
• 1:42:21
  Speaker 1: about at the beginning, all these other companies that are doing,
• 1:42:24
  Speaker 1: you know, Shield AI with their expat. We got Saronic
• 1:42:29
  Speaker 1: with the autonomous boats. We got Enduro making stuff. We
• 1:42:33
  Speaker 1: have Mach making stuff. All these companies are jumping all over.
• 1:42:38
  Speaker 1: Lots of autonomous stuff. But just overland AI alone. I mean,
• 1:42:44
  Speaker 1: let's say that there's 500 of these vehicles.
• 1:42:47
  Speaker 3: Yeah.
• 1:42:49
  Speaker 1: We're talking, I don't know, the invasion of Fallujah. Yeah.
• 1:42:53
  Speaker 4: These.
• 1:42:54
  Speaker 1: will all be able to communicate with each other and
• 1:42:59
  Speaker 1: accomplish the mission without each one being micromanaged. They'll all
• 1:43:04
  Speaker 1: read off each other, communicate with each other, know what
• 1:43:06
  Speaker 1: everything is doing, correct?
• 1:43:08
  Speaker 3: Yeah, so think about that. So this is our vision
• 1:43:12
  Speaker 3: for the company. So you should think about it as like,
• 1:43:16
  Speaker 3: imagine that there's five people that are controlling something like
• 1:43:20
  Speaker 3: 500 vehicles. And each of those five people, maybe each
• 1:43:26
  Speaker 3: one's controlling 100 vehicles, and those vehicles are coordinating with
• 1:43:30
  Speaker 3: each other to complete that mission. So we think about
• 1:43:35
  Speaker 3: the autonomy, which is onboard the vehicle. This is what
• 1:43:38
  Speaker 3: we call platform autonomy. It's how each individual vehicle analyzes terrain,
• 1:43:43
  Speaker 3: makes decisions, decides how to drive. Then there's a notion
• 1:43:47
  Speaker 3: of mission autonomy, where multiple vehicles can coordinate with each other.
• 1:43:53
  Speaker 3: And the real idea here is to make it really
• 1:43:56
  Speaker 3: easy for one person to have an immense effect on
• 1:44:00
  Speaker 3: the battlefield, to control many, many different vehicles and the
• 1:44:03
  Speaker 3: payloads on them. And so, you know, going back to
• 1:44:06
  Speaker 3: the earlier part of the conversation, we view this as
• 1:44:10
  Speaker 3: something like, you know, a gamer who's playing like a
• 1:44:13
  Speaker 3: real-time strategy game, something like StarCraft, where... you're controlling like
• 1:44:19
  Speaker 3: 200 different units. Can a person do that in the
• 1:44:21
  Speaker 3: real world with, you know, real platforms like our Ultra platform?
• 1:44:27
  Speaker 3: And that's what we're building up towards. So that whole
• 1:44:31
  Speaker 3: idea is, you know, single operator, massive force through potentially
• 1:44:36
  Speaker 3: hundreds of different vehicles and payloads. Wow.
• 1:44:42
  Speaker 1: And so will it move to the point where You know,
• 1:44:44
  Speaker 1: I just brought up all these other companies. Will it
• 1:44:46
  Speaker 1: move to the point where these Overland AI vehicles are
• 1:44:52
  Speaker 1: communicating with Shield AI's expat with Saronix boats? I mean,
• 1:44:59
  Speaker 1: will it get to the point where the entire battle
• 1:45:01
  Speaker 1: space is coordinated under one brain?
• 1:45:07
  Speaker 2: So, you know, the.
• 1:45:10
  Speaker 3: The short answer is that like, yes, the ground vehicles
• 1:45:14
  Speaker 3: will be communicating with like aerial vehicles and the different
• 1:45:19
  Speaker 3: payloads and they'll be coordinating in order to complete a task.
• 1:45:25
  Speaker 3: I think we still, you know, one of the things
• 1:45:28
  Speaker 3: which is really important to keep in mind is again,
• 1:45:30
  Speaker 3: like these are all just tools.
• 1:45:31
  Speaker 2: So you really.
• 1:45:34
  Speaker 3: Want to enable an operator to achieve their objective as
• 1:45:40
  Speaker 3: effectively as possible. And that means, you know, not just
• 1:45:44
  Speaker 3: kind of handing everything over to an AI brain, but having,
• 1:45:51
  Speaker 3: you know, essentially like AI assistants who are allowing a
• 1:45:55
  Speaker 3: human to choose courses of action, to coordinate, you know,
• 1:46:02
  Speaker 3: ground vehicles or payloads or aerial vehicles more effectively and
• 1:46:07
  Speaker 3: so on.
• 1:46:08
  Speaker 1: Let's talk about Let's talk about African Lion. What happened there? Okay.
• 1:46:14
  Speaker 3: So African Lion is an exercise. It's one of the
• 1:46:18
  Speaker 3: largest exercises in Africa. So it's a place where U.S.
• 1:46:22
  Speaker 3: warfighters work with partner nations and things like that to
• 1:46:28
  Speaker 3: essentially train and demonstrate capabilities. We were part of African
• 1:46:35
  Speaker 3: Lion working with 173rd Airborne. So the 173rd Airborne had
• 1:46:40
  Speaker 3: two of our ultra vehicles with different types of payloads
• 1:46:44
  Speaker 3: on them. One vehicle had a Crows remote weapon station
• 1:46:48
  Speaker 3: with an M240 machine gun on it. Another vehicle had
• 1:46:52
  Speaker 3: a rocket-propelled.
• 1:46:55
  Speaker 2: Breaching system on it. And they were using these.
• 1:46:59
  Speaker 3: Vehicles for a breaching operation. So essentially what happened was
• 1:47:06
  Speaker 3: They sent one vehicle forward with machine gun on it
• 1:47:10
  Speaker 3: providing security. And then a second vehicle was quickly following it,
• 1:47:15
  Speaker 3: moved to a breach point, essentially applied the payload. So
• 1:47:21
  Speaker 3: it's an explosive line charge, something like a Miklik, if
• 1:47:24
  Speaker 3: people know what that is. But essentially an explosive rope
• 1:47:27
  Speaker 3: that gets launched out in front of the vehicle and
• 1:47:30
  Speaker 3: produces a it blows up and produces a safe corridor
• 1:47:35
  Speaker 3: that can then be proofed with another vehicle. So, they're
• 1:47:41
  Speaker 3: using this to reduce obstacles out in front of the
• 1:47:43
  Speaker 3: force as part of an assault. And the thing which
• 1:47:47
  Speaker 3: is really important about this is that it's an extremely
• 1:47:50
  Speaker 3: dangerous operation.
• 1:47:51
  Speaker 2: Like, when you're doing things.
• 1:47:52
  Speaker 3: Like breaching, every area of the defensive obstacle belts are
• 1:47:58
  Speaker 3: being watched by an adversary And as you move forward
• 1:48:02
  Speaker 3: to try to reduce those obstacles, the combat engineers, everyone's
• 1:48:07
  Speaker 3: targeting them. So even in a successful breaching operation, you're
• 1:48:10
  Speaker 3: expecting something like 50% casualty rate. So if you're able
• 1:48:17
  Speaker 3: to do that with autonomous vehicles, like the way that
• 1:48:21
  Speaker 3: 173rd Airborne was demonstrating, that's taking, in their estimate, up
• 1:48:26
  Speaker 3: to almost two platoons, about 40 people out of that
• 1:48:31
  Speaker 3: extremely dangerous situation. You're just sending the machines forward to
• 1:48:36
  Speaker 3: do that, to work that problem, to create the breach
• 1:48:39
  Speaker 3: in the obstacle belt, and then they're able to move through.
• 1:48:43
  Speaker 3: So they're very excited about the technology. But it's just
• 1:48:46
  Speaker 3: an example of, like, warfighters using our vehicles, putting payloads
• 1:48:51
  Speaker 3: on them, coordinating as part of their unit's movement and maneuver.
• 1:48:57
  Speaker 2: So pretty exciting to see.
• 1:48:59
  Speaker 1: This is fascinating stuff. Yeah.
• 1:49:01
  Speaker 3: Wow.
• 1:49:02
  Speaker 1: The game has changed a lot. Yeah.
• 1:49:04
  Speaker 2: 40 years. Yeah.
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  Speaker 1: Holy shit. Here's the pitch. It takes two minutes, and
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• 1:49:54
  Speaker 1: download it on the App Store. And remember, privacy isn't paranoia,
• 1:50:00
  Speaker 1: it's protection. All right, so we're back from the break. And,
• 1:50:10
  Speaker 1: you know, one thing I'm kind of wondering is what other,
• 1:50:15
  Speaker 1: I mean, you had mentioned Russia was doing some of this, correct?
• 1:50:18
  Speaker 1: What is, what about China? Are they, they seem to
• 1:50:22
  Speaker 1: be pretty on the ball with just about everything.
• 1:50:25
  Speaker 3: Yeah, we don't have, or at least I don't have
• 1:50:28
  Speaker 3: detailed knowledge of everything that they're doing, but we do
• 1:50:32
  Speaker 3: read academic papers that come out of Chinese institutions and
• 1:50:37
  Speaker 3: They're certainly working on ground robots. I think there's several
• 1:50:40
  Speaker 3: different types. They've been working on robotic dogs and integrating
• 1:50:44
  Speaker 3: those types of robots into infantry formations. And they're also
• 1:50:52
  Speaker 3: working on off-road ground vehicle autonomy, similar to some of
• 1:50:55
  Speaker 3: the stuff that we're doing. We think we're pretty far
• 1:50:59
  Speaker 3: ahead right now, but they're working hard to catch up.
• 1:51:07
  Speaker 1: What kind of, is there any, what other kind of
• 1:51:11
  Speaker 1: weapon systems do you think we'll be putting on these Ultras?
• 1:51:17
  Speaker 3: I think for any of, you know, you think about
• 1:51:21
  Speaker 3: uncrewed ground vehicles and autonomous ground vehicles, you can put,
• 1:51:27
  Speaker 3: it depends on the size of the vehicle, you can
• 1:51:28
  Speaker 3: put like almost anything on them, right? Any type of
• 1:51:33
  Speaker 3: vehicle that you have today, which can carry anything from,
• 1:51:36
  Speaker 3: you know, a very small vehicle might be a couple
• 1:51:38
  Speaker 3: hundred pounds up to tens of thousands of pounds, you
• 1:51:42
  Speaker 3: can put on a potentially autonomous vehicle. So that's, you
• 1:51:48
  Speaker 3: think about smaller things, might be smaller remote weapon stations,
• 1:51:52
  Speaker 3: might just be sensors, right? Radar, optics, things like this.
• 1:51:57
  Speaker 3: But much larger vehicles, large missile systems, right? So ship
• 1:52:03
  Speaker 3: interdiction missiles, anti-air missiles, all sorts of things.
• 1:52:08
  Speaker 1: You could put pretty much anything on this.
• 1:52:10
  Speaker 2: Pretty much anything.
• 1:52:11
  Speaker 1: Are you familiar with Epirus?
• 1:52:12
  Speaker 4: Yes.
• 1:52:13
  Speaker 1: So you could put Leonidas mounted on one of these
• 1:52:15
  Speaker 1: damn things and then you have drone defense for an
• 1:52:21
  Speaker 1: entire fucking battalion.
• 1:52:23
  Speaker 3: Yeah, usually when we're thinking about drone defense, you know,
• 1:52:26
  Speaker 3: it's going to be a layered defense. So you're going
• 1:52:28
  Speaker 3: to want maybe something like, you know, EPRIS' system. But
• 1:52:32
  Speaker 3: you might also want kinetic air defense that might be drones, like,
• 1:52:40
  Speaker 3: you know, drone interceptors. It might be machine guns. But
• 1:52:44
  Speaker 3: you can imagine a whole set of these, you know,
• 1:52:47
  Speaker 3: many different vehicles with different defensive systems. creating that layered defense.
• 1:52:53
  Speaker 3: And we think it'll probably be something like this so that,
• 1:52:56
  Speaker 3: you know, even if drones are getting through one type
• 1:52:58
  Speaker 3: of defense, they're getting hit by another. I mean, even
• 1:53:01
  Speaker 3: if you lose some of your vehicles or systems, there
• 1:53:04
  Speaker 3: are more to take their place.
• 1:53:06
  Speaker 1: Let's talk about the technical mode, the team, and the
• 1:53:09
  Speaker 1: peer threat. Sure.
• 1:53:13
  Speaker 3: So the way that we think about the technology that
• 1:53:16
  Speaker 3: we're developing, we really are thinking about it as.
• 1:53:21
  Speaker 3: You know, developing an autonomous version of core battlefield functions, right?
• 1:53:25
  Speaker 3: So like either the ISR or breaching as examples where
• 1:53:32
  Speaker 3: you have multiple vehicles with payloads that are.
• 1:53:40
  Speaker 2: Performing a task.
• 1:53:42
  Speaker 3: Now, in order to do any of that, in order
• 1:53:44
  Speaker 3: to have a set of autonomous vehicles with payloads that's,
• 1:53:48
  Speaker 3: you know, doing something complicated, Think about first principles. What
• 1:53:52
  Speaker 3: do you have to do first? Well, you have to
• 1:53:54
  Speaker 3: be able to carry stuff and move it, move it
• 1:53:56
  Speaker 3: from one place to.
• 1:53:57
  Speaker 2: Another in the environment.
• 1:54:00
  Speaker 3: And when you look at our history as a company,
• 1:54:05
  Speaker 3: we started with what we think is the hardest problem
• 1:54:08
  Speaker 3: there first, which is being able to understand terrain and
• 1:54:13
  Speaker 3: move through that terrain. Given that basis, you can then
• 1:54:17
  Speaker 3: build off of that. One of our strongest technical moats
• 1:54:23
  Speaker 3: is the fact that we're the best in the world
• 1:54:26
  Speaker 3: at being able to see and understand terrain and move
• 1:54:30
  Speaker 3: vehicles through it. We can put payloads pretty much anywhere
• 1:54:34
  Speaker 3: that a vehicle can drive, right? And so once you
• 1:54:39
  Speaker 3: have that, as you now combine this to say, instead
• 1:54:44
  Speaker 3: of just moving one vehicle or one type of payload,
• 1:54:47
  Speaker 3: now I'm moving multiple vehicles, multiple payloads, that foundation allows
• 1:54:52
  Speaker 3: you to build up those capabilities. So we think that
• 1:54:55
  Speaker 3: that's our, you know, one of our biggest technical moats
• 1:54:57
  Speaker 3: is we have just this incredible team coming from, you know,
• 1:55:02
  Speaker 3: deep tech places like Waymo and Cruise and, you know,
• 1:55:07
  Speaker 3: self-driving car companies, some of the top artificial intelligence labs,
• 1:55:12
  Speaker 3: you know, they're working for us focused on these problems
• 1:55:15
  Speaker 3: and creating, you know, this AI essentially that allows you to,
• 1:55:19
  Speaker 3: to uh move payloads in the environment so um that's
• 1:55:24
  Speaker 3: technical mode i can't quite remember the the other questions
• 1:55:27
  Speaker 3: but that's um really a foundation of everything.
• 1:55:33
  Speaker 1: I mean we had one thing that i didn't ask
• 1:55:36
  Speaker 1: i think we started talking about it out there and
• 1:55:38
  Speaker 1: then i said we would come back in and discuss
• 1:55:40
  Speaker 1: it because it was so damn hot out there but
• 1:55:43
  Speaker 1: What does it look like when we're talking about controlling
• 1:55:46
  Speaker 1: all these autonomous vehicles, especially when it comes to hundreds?
• 1:55:51
  Speaker 1: I mean, and it's not remote control. So what is it?
• 1:55:56
  Speaker 1: I mean, we had kind of discussed a little bit
• 1:55:58
  Speaker 1: out there, but what does it look like for the
• 1:56:03
  Speaker 1: team or the person or whatever? is controlling hundreds of
• 1:56:07
  Speaker 1: these at once?
• 1:56:09
  Speaker 2: That's a great question.
• 1:56:10
  Speaker 3: So you're not going to be just like looking through
• 1:56:12
  Speaker 3: the vehicle sensor and remote controlling it because you have
• 1:56:14
  Speaker 3: hundreds of them.
• 1:56:15
  Speaker 2: How can you do that?
• 1:56:16
  Speaker 3: So the way that we have designed the software is
• 1:56:21
  Speaker 3: that you essentially have overhead maps, something like a satellite map,
• 1:56:26
  Speaker 3: and you're able to see all of the different vehicles
• 1:56:29
  Speaker 3: on that map, all of the different autonomous assets on
• 1:56:33
  Speaker 3: the map.
• 1:56:34
  Speaker 2: So that gives you.
• 1:56:36
  Speaker 3: and you can zoom out and you can look at
• 1:56:40
  Speaker 3: terrain features, you can look at where all the vehicles are,
• 1:56:43
  Speaker 3: you can see what payloads are on the different vehicles.
• 1:56:46
  Speaker 3: And then the question is, how do you now coordinate
• 1:56:49
  Speaker 3: and control hundreds of those assets? And you need to
• 1:56:53
  Speaker 3: be able to do that by selecting vehicles, grouping vehicles,
• 1:56:58
  Speaker 3: telling groups of vehicles to go to one place or another,
• 1:57:01
  Speaker 3: telling them to execute a.
• 1:57:02
  Speaker 2: Certain payload at a particular location.
• 1:57:06
  Speaker 3: So that you can kind of quickly move between sets
• 1:57:10
  Speaker 3: of vehicles and essentially tell them where to go. Again,
• 1:57:14
  Speaker 3: a lot like a real-time strategy game. We're also working
• 1:57:18
  Speaker 3: on tools.
• 1:57:18
  Speaker 1: So this looks like, this is what we were talking
• 1:57:20
  Speaker 1: about at the beginning. This looks like, sounds weird, but
• 1:57:24
  Speaker 1: looks like World of Warcraft.
• 1:57:26
  Speaker 2: Yeah, yeah.
• 1:57:27
  Speaker 1: You're sending a group of things giving it a task,
• 1:57:30
  Speaker 1: and then you're on to the next thing.
• 1:57:32
  Speaker 3: Yeah, I mean, and I think, you know, one of
• 1:57:35
  Speaker 3: the reasons why we've looked to some of these types
• 1:57:39
  Speaker 3: of games is because it's one of the few places
• 1:57:41
  Speaker 3: where humans are actually controlling something like hundreds of different assets.
• 1:57:48
  Speaker 3: StarCraft is a great example of that, and WarCraft.
• 1:57:51
  Speaker 2: But, you know, the.
• 1:57:53
  Speaker 3: Tools that people use for... for grouping, for moving, for
• 1:57:58
  Speaker 3: coordinating assets in those types of games are things we
• 1:58:01
  Speaker 3: can take those ideas and apply them here as well
• 1:58:06
  Speaker 3: so that a single operator can control many, many different
• 1:58:09
  Speaker 3: assets Another thing that we're developing is things like AI assistance,
• 1:58:16
  Speaker 3: which can help to suggest particular tasks. So you can
• 1:58:22
  Speaker 3: just say like, hey, I need to move 10 vehicles
• 1:58:27
  Speaker 3: into this area, provide reconnaissance here, and it will tell
• 1:58:32
  Speaker 3: me how those vehicles are going to move, suggest to
• 1:58:37
  Speaker 3: me solutions to these problems so that I can make
• 1:58:41
  Speaker 3: decisions faster. So, this is part of essentially using AI
• 1:58:45
  Speaker 3: to increase your decision advantage. Now, we're not saying we
• 1:58:48
  Speaker 3: hand over the actual decision making to the AI, but
• 1:58:52
  Speaker 3: the AI can suggest solutions that can help you to handle,
• 1:58:59
  Speaker 3: you know, more assets at once.
• 1:59:01
  Speaker 2: Wow.
• 1:59:02
  Speaker 4: Wow.
• 1:59:03
  Speaker 1: I got a hot question for.
• 1:59:07
  Speaker 4: You.
• 1:59:08
  Speaker 1: All right. Ready? In 1941, 353 Japanese planes came off
• 1:59:11
  Speaker 1: six carriers and hit Pearl Harbor in under two hours.
• 1:59:15
  Speaker 1: They sank or crippled all eight battleships and killed over 2,400 Americans.
• 1:59:21
  Speaker 1: And here's the part people forget. We had warnings. We'd
• 1:59:25
  Speaker 1: broken their codes and Our own ambassador flagged a possible
• 1:59:29
  Speaker 1: attack a year earlier, and Congress later said the real
• 1:59:34
  Speaker 1: failure wasn't intelligence. It was imagination. Nobody could picture it
• 1:59:39
  Speaker 1: until the harbor was on fire. Today, China's arming robot dogs,
• 1:59:45
  Speaker 1: they build 90% of the world's drones, and they just
• 1:59:48
  Speaker 1: flew a mothership not long ago that launched 100 kamikaze
• 1:59:53
  Speaker 1: drones in a single swarm. The warnings are everywhere again.
• 1:59:57
  Speaker 1: Are we sleepwalking into a robotic Pearl Harbor?
• 2:00:01
  Speaker 3: I hope not, but I think it's correct. There are
• 2:00:05
  Speaker 3: warnings everywhere. We're seeing robots used in the battlefield, again,
• 2:00:11
  Speaker 3: in Ukraine. There's both by the Ukrainians and Russians. So,
• 2:00:20
  Speaker 3: you know, We and I think other defense tech companies
• 2:00:24
  Speaker 3: are certainly paying attention to this. We are trying to
• 2:00:27
  Speaker 3: understand what capabilities our adversaries have and also ensure that
• 2:00:33
  Speaker 3: the US military has similar or better capabilities. We think
• 2:00:40
  Speaker 3: about these sorts of things all the time. It's the
• 2:00:44
  Speaker 3: new technology which is going to be defining these next conflicts.
• 2:00:49
  Speaker 2: And I think it's all of our.
• 2:00:51
  Speaker 3: Jobs to ensure that the decision makers understand that this
• 2:00:56
  Speaker 3: technology exists and what it's capable of. So I think
• 2:01:02
  Speaker 3: we are certainly aware of it. I think we're pushing
• 2:01:06
  Speaker 3: the military to adopt new technology and defenses against it
• 2:01:10
  Speaker 3: as quickly as possible. But you know, there may be
• 2:01:15
  Speaker 3: work to be done. Sometimes these things aren't really real
• 2:01:19
  Speaker 3: to people until, unfortunately, they actually experience them. So our
• 2:01:24
  Speaker 3: best bet is to really be watching and taking seriously
• 2:01:29
  Speaker 3: what is happening in these conflicts and what our adversaries
• 2:01:32
  Speaker 3: are developing.
• 2:01:33
  Speaker 1: I mean, do you feel the Department of War has
• 2:01:36
  Speaker 1: taken this seriously? Do they understand the capabilities that you
• 2:01:39
  Speaker 1: guys have? I mean, when you Looking at that thing
• 2:01:43
  Speaker 1: out there, what it's capable of, it's very surprising and
• 2:01:49
  Speaker 1: almost alarming to me that they, you know, 15. 15, 15,
• 2:01:54
  Speaker 1: you have a contract for 15. Why don't you have
• 2:01:56
  Speaker 1: a contract for 10,000? Yeah.
• 2:02:00
  Speaker 2: I mean, it should be.
• 2:02:04
  Speaker 3: You know, I think the Department of Defense is trying to,
• 2:02:06
  Speaker 3: or Department of War is, and the services are trying
• 2:02:09
  Speaker 3: to move faster, but The procurement system was really designed
• 2:02:15
  Speaker 3: for a different era and a different type of war.
• 2:02:18
  Speaker 3: That's something that I think all of the defense tech
• 2:02:22
  Speaker 3: founders who you've had on here will probably agree with
• 2:02:27
  Speaker 3: and something that we're all fighting hard against. Now, we've
• 2:02:32
  Speaker 3: seen the Department of War speed things up and like
• 2:02:38
  Speaker 3: I was saying a little bit We have actually gone
• 2:02:42
  Speaker 3: from university research to production contract, even if it's a
• 2:02:46
  Speaker 3: relatively small initial one in like three years, which is
• 2:02:51
  Speaker 3: about right, maybe a little slow for like the tech sector,
• 2:02:56
  Speaker 3: but that's lightning fast for Department of War. And we've
• 2:03:02
  Speaker 3: made use of Defense Innovation Unit and a lot of
• 2:03:05
  Speaker 3: the new tools, AppFit contracting process and things like that
• 2:03:10
  Speaker 3: that have Come online recently. So I you know, I
• 2:03:14
  Speaker 3: do want to credit the Department of War for moving
• 2:03:17
  Speaker 3: faster But there's still so much work to be done
• 2:03:20
  Speaker 3: in order to to move at the speed that we
• 2:03:22
  Speaker 3: need to move at.
• 2:03:24
  Speaker 2: So, you know, it's something I think like we're all
• 2:03:27
  Speaker 2: working on and trying to.
• 2:03:29
  Speaker 1: Our other countries better customers than our own country.
• 2:03:35
  Speaker 3: For like our stuff or for like their own stuff.
• 2:03:39
  Speaker 1: For stuff that America, not overland AI, for things that America,
• 2:03:45
  Speaker 1: Americans like yourself are developing here in this country or
• 2:03:50
  Speaker 1: other countries, maybe Ukraine. You know what I mean? I think,
• 2:03:55
  Speaker 1: you know, I've heard of these issues happening and here
• 2:03:58
  Speaker 1: we have the best that the fucking world has to
• 2:04:02
  Speaker 1: offer right here. developing groundbreaking new technology that's going to
• 2:04:08
  Speaker 1: change how war is fought. I mean, it's such an upgrade,
• 2:04:19
  Speaker 1: especially with a company like Epper. All these companies, man.
• 2:04:25
  Speaker 1: Everybody that sat across from me in the defense tech sector,
• 2:04:29
  Speaker 1: what you guys are developing is fucking incredible. You know,
• 2:04:34
  Speaker 1: but I see it being utilized in Ukraine. You know
• 2:04:41
  Speaker 1: what I mean? And I see it, and I, it
• 2:04:45
  Speaker 1: bothers me when we're sending all this stuff. Maybe we aren't,
• 2:04:50
  Speaker 1: but I see things similar being utilized in Ukraine, and
• 2:04:56
  Speaker 1: it just seems like our country is not taking advantage
• 2:04:59
  Speaker 1: of the talent that we have here. It fucking scares me.
• 2:05:03
  Speaker 2: Yeah, I mean, there's kind of a.
• 2:05:06
  Speaker 1: Like, we have all this talent like yourself of what
• 2:05:09
  Speaker 1: you're building.
• 2:05:09
  Speaker 3: There's things to be concerned about, and I think there's
• 2:05:12
  Speaker 3: things to be hopeful about. So, like, on the one hand,
• 2:05:17
  Speaker 3: you know, I think you're right. I think countries like Ukraine,
• 2:05:21
  Speaker 3: I mean, they're adopting tech as fast as they possibly can.
• 2:05:25
  Speaker 3: There's just a statistic I was saying, something like there's
• 2:05:27
  Speaker 3: been 2 million casualties in that conflict, right?
• 2:05:31
  Speaker 4: Wow.
• 2:05:32
  Speaker 3: About a million and a half Russian and about half
• 2:05:35
  Speaker 3: a million Ukrainian. And one of the ways that Ukraine
• 2:05:39
  Speaker 3: has been able to continue to fight and hold off
• 2:05:44
  Speaker 3: the Russians is through, you know, all of this technological innovation.
• 2:05:49
  Speaker 3: It's existential for them. They have to do this or
• 2:05:53
  Speaker 3: they will lose, right? Like, and so they will do
• 2:05:57
  Speaker 3: whatever it takes to win here. It's not existential for
• 2:06:02
  Speaker 3: the U.S.
• 2:06:03
  Speaker 1: Yet.
• 2:06:03
  Speaker 3: And I think that means that adoption is just slow.
• 2:06:10
  Speaker 2: People aren't feeling.
• 2:06:12
  Speaker 3: The pressure to do it. And I think that it's unfortunate,
• 2:06:18
  Speaker 3: of course, because we have a little bit of luxury
• 2:06:23
  Speaker 3: right now. We are not in a conflict like the Ukrainians.
• 2:06:28
  Speaker 3: So we have the space to be able to potentially
• 2:06:33
  Speaker 3: build this technology and transform our forces. But we don't
• 2:06:37
  Speaker 3: have the urgency, right? And so I think that's part
• 2:06:41
  Speaker 3: of the problem. On the positive side, I do think
• 2:06:44
  Speaker 3: that we have the best minds in the world here.
• 2:06:49
  Speaker 3: I think we can do it. But again, it's a
• 2:06:53
  Speaker 3: matter of.
• 2:06:53
  Speaker 1: We are doing.
• 2:06:54
  Speaker 3: It's our government that's not fucking keeping up with you guys.
• 2:06:57
  Speaker 3: But it could be done faster, right? Like, we could
• 2:07:00
  Speaker 3: accelerate this. And, you know, that's a fight that we
• 2:07:06
  Speaker 3: just have to continue to.
• 2:07:09
  Speaker 1: Make, right?
• 2:07:10
  Speaker 3: To continue to fight to get this tech into the
• 2:07:13
  Speaker 3: government faster. And I think going back to, you know,
• 2:07:15
  Speaker 3: thinking about some of the DARPA stuff earlier, like, we're
• 2:07:18
  Speaker 3: creating incredible new technology, but then you really got to
• 2:07:22
  Speaker 3: fight to, like, actually get the Army and Marine Corps
• 2:07:26
  Speaker 3: and services to actually, you know, try it, iterate, you know,
• 2:07:34
  Speaker 3: ultimately use it and incorporate it.
• 2:07:37
  Speaker 1: Yeah. If we were attacked tomorrow, do you think we
• 2:07:40
  Speaker 1: could survive a sea or ground invasion?
• 2:07:45
  Speaker 2: I mean, I.
• 2:07:47
  Speaker 3: Think we would absolutely survive a sea or ground invasion.
• 2:07:51
  Speaker 3: I think we would Mobilize very quickly. I think those
• 2:07:55
  Speaker 3: the urgency would be there and you know, we would
• 2:07:59
  Speaker 3: we would do whatever it takes took to to win
• 2:08:01
  Speaker 3: so I'm I'm you know I think very positive and
• 2:08:06
  Speaker 3: optimistic on that like I I think when we're pressed
• 2:08:09
  Speaker 3: we can we can do a lot but I also
• 2:08:12
  Speaker 3: think that you know we could be better prepared so
• 2:08:16
  Speaker 3: me too.
• 2:08:17
  Speaker 1: Last thing manufacturing. Are you guys manufacturing these yourselves?
• 2:08:23
  Speaker 3: How fast do you scale up? So we're trying to
• 2:08:26
  Speaker 3: scale as quickly as possible. We are manufacturing the ultra vehicles.
• 2:08:32
  Speaker 3: I think I mentioned before that they're based on Polaris
• 2:08:35
  Speaker 3: engines and drivetrains and things like that, but we're upgrading
• 2:08:37
  Speaker 3: the suspension. We're putting in the payload deck, adding the compute,
• 2:08:41
  Speaker 3: the sensors and everything. We've got a factory running in
• 2:08:45
  Speaker 3: Seattle that's doing this right now, but And I think
• 2:08:48
  Speaker 3: we've 5X manufacturing over the last six months or so,
• 2:08:52
  Speaker 3: and we need to do a lot more. So we're
• 2:08:55
  Speaker 3: building up that capability as fast as possible. And again,
• 2:08:59
  Speaker 3: the more support we get from the government, the more
• 2:09:03
  Speaker 3: we actually work with warfighters, the more demand there is,
• 2:09:05
  Speaker 3: and there's a ton of demand right now. So we're
• 2:09:09
  Speaker 3: scaling as quickly as we can.
• 2:09:12
  Speaker 1: Right on. Well, Byron? I really appreciate you coming. It
• 2:09:16
  Speaker 1: was an honor to interview you and I love everything
• 2:09:20
  Speaker 1: Overland AI is doing and that thing out there is
• 2:09:22
  Speaker 1: super impressive. Thank you.
• 2:09:24
  Speaker 3: Yeah, thank you so much for having me. I really
• 2:09:26
  Speaker 3: appreciate you taking the time to learn a little bit
• 2:09:28
  Speaker 3: more about what we're doing and inviting us out and
• 2:09:31
  Speaker 3: being able to show you some of the things that
• 2:09:33
  Speaker 3: we're building.
• 2:09:34
  Speaker 2: So thank you.
• 2:09:35
  Speaker 4: Thank you.
• 2:09:49
  Speaker 1: No matter where you're watching The Sean Ryan Show from,
• 2:09:52
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• 2:09:55
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