#226 Nathan Michael: The Future of AI-Powered Drones and Autonomous Warfare (with Shield AI)

Launch, run, and protect your business to make it official TODAY at https://www.legalzoom.com/ and use promo code EYEONAI to get 10% off any LegalZoom business formation product excluding subscriptions and renewals. In this episode of the Eye on AI podcast, we explore the cutting-edge world of AI-powered autonomy with Nathan Michael, CTO of Shield AI. Nathan shares his journey from academia to leading one of the most innovative companies in defense technology, revealing how Shield AI is transforming autonomous systems to operate in the most challenging environments. Throughout the episode, Nathan dives into Shield AI's groundbreaking technologies, including the revolutionary Hivemind platform, which enables drones and uncrewed jets to think, adapt, and act independently—even in GPS-denied and communication-jammed conditions. He explains how these systems are deployed across defense and commercial applications, reshaping intelligence, surveillance, and reconnaissance missions with unprecedented precision and resilience. We also discuss the future of warfare and technology, from the commoditization of AI-driven autonomy to the ethical and strategic considerations of deploying these systems at scale. Nathan offers insights into how rapid iteration cycles and resilient intelligence will define the next era of defense innovation, ensuring mission success in increasingly complex scenarios. Don't forget to like, subscribe, and hit the notification bell to stay updated on the latest advancements in AI, robotics, and the future of autonomous systems! Stay Updated: Craig Smith Twitter: https://twitter.com/craigss Eye on A. I. Twitter: https://twitter.com/EyeOn_AI (00:00) Introduction to Shield AI and Hivemind (03:02) Nathan Michael's journey to Shield AI (06:07) Shield AI's mission and technology overview (10:18) VBAT: A versatile tail-sitter aircraft (12:44) Hivemind's swarm capabilities and applications (14:32) Ethics and societal guardrails for autonomous systems (17:24) Combat applications of quadcopters (19:12) Navigating and mapping unknown environments (20:38) Use of VBAT in Ukraine operations (22:10) Intelligence gathering and mission versatility (24:42) Transforming warfare with AI-driven autonomy (28:47) Teaming and communication in autonomous swarms (32:29) Communication networks in jammed conditions (35:34) Coordinated exploration with autonomous systems (38:13) Exploring buildings with quadcopter teams (40:57) Crewed-uncrewed teaming in modern combat (43:28) Global competition in AI and autonomy (46:03) Use of autonomous drones in Ukraine (50:28) Shield AI's vision: Proliferation of resilient intelligence (55:26) Expanding AI autonomy to commercial applications

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

• 0:00Speaker 00

  What we've really focused on is creating the tools and the means to specialize autonomy capabilities and resilient intelligence capabilities to different platforms, different mission sets, different needs. So whether it's one platform or multiple platforms working together. Taking all of the different parts of robotics, autonomy, that you need to create intelligent systems that can really think for themselves, using the best methodologies possible in terms of the state of the art for all of those different things, how you enable those systems to perceive the world, think about that world, take action within that world, do so as individuals and as a team. And we've created basically a framework for the autonomy development lifecycle and the specialization of that kind of intelligence to different platforms.

• 0:43Craig S. Smith?

  When I started working on my own and I didn't have the structure of an organization behind me, I knew that I needed to incorporate and file taxes and all of that stuff. I looked into getting a lawyer, but it was prohibitively expensive at that point. I really wasn't making much money. So I turned to LegalZoom to get the corporation officially registered and legally compliant. This episode's sponsor is LegalZoom, a trusted leader when it comes to setting up a business right. I'm a true believer because I still use LegalZoom to keep myself.

• 1:26Craig S. Smith?

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• 1:57Craig S. Smith?

  Until the end of the year, get everything you need from setup to success at LegalZoom.com, that's L E G A L Z O O M dot com and use the promo code IONAI. That's E Y E O N A I. LegalZoom.com with promo code IONAI. LegalZoom provides access to independent attorneys and self-service tools, but it's not a law firm and does not provide legal advice, except we're authorized through its subsidiary law firm, LZ Legal Services LLC.

• 2:39Craig S. Smith?

  Can we start by having you introduce yourself, how you got to Shield AI, what Shield AI's mission is, and then I'll start asking questions?

• 2:51Speaker 00

  Great, yes, it's great to talk to you today. So my name is Nathan Michael. I am the CTO of Shield AI. I've worked in the area of AI and robotics, primarily robotics, for about 20 years.

• 3:03Craig S. Smith?

  Yeah.

• 3:04Speaker 00

  I was a faculty member in the robotics institute at Carnegie Mellon University up until about a year ago. And in the robotics institute, I built out a reasonably sized lab that focused on this topic of resilient intelligence, where we were really trying to create systems that could think for themselves and operate in extremely challenging environments, both single platforms going into unknown environments, as well as teams of intelligent agents operating and exploring unknown, challenging conditions which, within today's language, are always degraded and denied, limited to no communications, underground, in tunnel networks, caves,

• 3:47Speaker 00

  those kinds of things, or other types of really challenging conditions where access to external information did not exist, no prior information or limited prior information existed. And so now you needed to create these systems that could really think for themselves, operate with very limited prior information, and then go in, explore, understand, model, interact, and engage.

• 4:09Speaker 00

  In that work, I largely looked at air platforms, but also worked with some surface vehicles and underwater vehicles as well as land vehicles. And homogeneous teams, teams of similar characteristics, as well as heterogeneous teams, and teams with different characteristics working with each other. Around 2016, 2015 to 2016, I started to see what we were creating within a laboratory environment, really kind of stacking up on the shelf in terms of getting stuck in this technological valley of death, where you have these great innovations, but these innovations are not being fielded, they're not being operationalized, they're not being used and leveraged by end users.

• 4:51Speaker 00

  And for a lot of what we were creating, this would have added a lot of value, really delivered tremendous outcomes. From a laboratory context, then, within an academic context, I really focused then on working with different parts of, well, different funding agencies and laboratories, like the Army Research Lab, Air Force Research Lab, of course, the National Science Foundation, and a number of other parts of the US funding, the US funding sources for academia. Ultimately, I came to the conclusion that in order to really have impact and reach scale, I would need to be able to bridge this gap, and started looking at either starting

• 5:33Speaker 00

  my own company or working with an existing company. I started to engage and advise technically, SHIELD AI, which was very small, just a few people at the time, back in 2016, and then ended up agreeing to join the team, the leadership team, back in 2017. At this point, you know, fast forwarding over time, what you can see is a lot of the vision that was laid out back in, say, 2017 toward the future, of building out this high degree of intelligence running onboard these systems, building out this capacity to not just develop, integrate, test, evaluate, deploy, operationalize this high degree of

• 6:15Speaker 00

  intelligence, but doing it across a number of different classes of platforms, and then fully seeing those capabilities fielded, has been something that we've been working on for quite a few years, and we've seen through successfully several times over, or many times over. And so.

• 6:33Speaker 00

  And that really brings us to the present. Today, SHIELD AI's mission is to protect service members and civilians with artificial intelligence systems. We deploy our capabilities on everything, primarily in the air, but everything from really small systems, the quadcopters, all the way to more medium-sized systems. We have a platform called the BBAT, Group Three system, and then all the way up to uncrewed jets. So these would be like Group Four, Group Five platforms. All of these platforms are leveraging what we call our HiveMind capabilities, but really HiveMind is, let's say, jargon or marketing term for really our AI and robotics capabilities,

• 7:17Speaker 00

  which are, consist of sort of an edge-level intelligence that runs on each platform. So you could think of this as an autonomy stack that uses everything from more traditional autonomy capabilities to state-of-the-art and cutting-edge AI capabilities. So everything that makes the most sense to really deliver that high, high degree of intelligence. It includes capabilities related to building. So HiveMind includes capabilities of, you could call like an autonomy factory, in order to really create high-performance capabilities, depending on the scenarios, so the mission, the environment, the context, the needs. And then to apply and specialize those capabilities to these variety of different classes of platforms. So that's, that's sort of what we do and what we're doing.

• 8:00Speaker 00

  Probably just to wrap up that, that intro, we've, we've had a, a degree of success in terms of creating systems that are able to operate in these degraded and denied conditions, where all the intelligence is running onboard the platform. We've seen some recent recognitions for the, the fact that we're deploying these systems in a variety of different conditions, particularly where it's not possible to get external information, not, to be able to consistently communicate with the platform, or to have the platform consistently access external information like GNS, S data, or something.

• 8:34Craig S. Smith?

  Yeah. Well, and you're, you're a software company. You're not building hardware, are you?

• 8:40Speaker 00

  Yes, yes and no. So in fact, SHIELD AI, SHIELD AI has two businesses.

• 8:48Speaker 00

  Really, one business is focused on hardware, and that's building out our aircraft. And that's the Vbat. That's that medium-sized Group Three platform. And then the other business is software, and that's HiveMind, and that's our AI and robotics ecosystem.

• 9:04Craig S. Smith?

  Okay.

• 9:04Speaker 00

  And so I primarily focus, at this point, within the company, on the HiveMind side of the business. But one thing that we really focus on and specialize toward, or one area of strength, is we've really organized our HiveMind business to not just create a product, which I described a little bit about earlier, but also to very efficiently and effectively apply that product, specialize it to meet needs. And so we do that with our Vbat platform. And so that allows us to deploy our Vbat platform into a variety of conditions. So you could almost look at our our Vbat platform as sort of a tip-of-the-spear, leading-edge capability, where we can very efficiently and effectively, through a vertical integration, deploy capabilities onto that platform.

• 9:49Speaker 00

  But then we also are organized to specialize and deploy those same types of AI pilot capabilities to other classes of platforms. And we do so with a variety of other companies that want to be able to have this type of resilient intelligence and autonomy capabilities on their systems.

• 10:07Craig S. Smith?

  Okay, and you're saying Vbat, like virtual bat, is that right?

• 10:12Speaker 00

  No, it's, um, Vbat is Vertical B A T. Actually, there's, there's no explanation for the acronym, if you look at it. So we tried to retroactively come up with something that makes sense for that acronym, but actually, there's, there's not really a explanation for the acronym. But what it is, is it's a tail sitter. And so it's, and you can look it up online. It's very readily accessible. But it's a tail sitter that will, will take off, take off, sort of in a hover state, and then, once it gets to a certain height, it'll transition to on wing. And then that allows it to fly longer distances, operate different kinds of

• 10:53Speaker 00

  surveillance, intelligence surveillance reconnaissance-type missions, and then come back and then land in a tail sitter mode, which allows it to take off and land from fairly confined environments, under a variety of different conditions.

• 11:06Craig S. Smith?

  Yeah, when you talk about a couple of things, you guys have been fairly vocal about opposing lethal autonomous weapons laws. Am I, am I correct on that?

• 11:22Speaker 00

  I'm not sure that, I'm not sure that we've been either one way or another vocal about it. So I'm not sure what you're referencing.

• 11:30Craig S. Smith?

  Yeah.

• 11:31Speaker 00

  So perhaps you're thinking of another, another entity?

• 11:34Craig S. Smith?

  Well, I was, I was, in any case, that's been a debate when I was at the National Security Commission on AI, that was a big debate and, and the US military has been very adamant that it, it's, it's not, it doesn't

• 11:54Craig S. Smith?

  deploy laws or lethal autonomous weapons. But then you have like Palmer Lucky, who's saying, yeah, we're ready. You know, if the Chinese have them, we'll go for it. So

• 12:11Craig S. Smith?

  Hive Mind is, and correct me if I'm wrong, is, is AI that allows multiple V-bots, for example, to operate in a, in a swarm environment or a

• 12:32Speaker 00

  Yeah, let me talk, let me talk about it a little bit because it's interesting when you're building out autonomy capabilities, you're building out and bringing together many different types of technologies. And there's a lot of flexibility and a lot of ways that you can use that technology. And there is no one size fits all solution. And there's no one size fits all methodology, if you will, for, for the type of work that we do. So what we've really focused on is creating the tools and the means to specialize autonomy capabilities and resilient intelligence capabilities to different platforms, different mission sets, different needs. So whether it's

• 13:13Speaker 00

  one platform or multiple platforms working together. So teaming or what you're, you're referencing as swarm. Whether it's VBAS or other classes of systems. And so what we've really focused on is taking all of the different parts of robotics, autonomy, that you need to create intelligent systems that can really think for themselves, using the best methodologies possible in terms of the state of the art for all of those different things, how you enable those systems to perceive the world, think about that world, take action within that world, do so as individuals and as a team. And we've created basically a framework for the autonomy development lifecycle and the specialization of that kind of intelligence to different platforms.

• 13:57Speaker 00

  And what we've really indexed on to your point of sort of this more social question or the question of lethality, if you will, is we focused on enabling an immense amount of flexibility to design exactly the systems you want, to be able to deploy to meet your needs, and to do so as efficiently and effectively as possible. And then on the topic of lethality, our view and the general approach is we really are living within the guardrails of society. And so our society says what they do or do not accept. And that's true for every country.

• 14:37Speaker 00

  And so what we've designed is really a means to develop very quickly and efficiently intelligent autonomous systems with the flexibility and the controls, if you will, from an engineering perspective in place to make sure that whatever you're building meets those expectations and guardrails set forth by society, policies, standards, and so forth. And that will differ and vary between different countries. So in terms of taking one stance or another, I don't think that we're really taking a stance per se, but more deferring to what are the policies, the guardrails, the guidance that our politicians are, and whatever that is set out in whatever country.

• 15:21Speaker 00

  And then what we're focused on is creating the tools, the technologies, the frameworks that make it possible to design exactly the technology that meets those expectations and constraints, as well as the needs of the different operators and users that would be working with the system.

• 15:36Craig S. Smith?

  Right. And the software, you...

• 15:45Craig S. Smith?

  Deployed in a Vbat, for example, but it could be, I mean, you you talked about fighter jets. It could be

• 15:55Craig S. Smith?

  deployed in a in a fighter jet manufactured by one of the big.

• 16:01Speaker 00

  Oh, it has already.

• 16:03Craig S. Smith?

  Okay.

• 16:03Speaker 00

  Yeah, and so, you know, this is public knowledge. We deployed what we call our High Mind Pilot, or sometimes I'll just say AI Pilot, but our High Mind Pilot onto what's called a, well, onto what, one in in F-16 Vista, which is a modified F-16 that's designed to support autonomy. And so that allows then that AI Pilot to live on top of the platform autonomy. And so then, even though it wasn't uncrewed, there was a pilot in inside the platform flying it. They flipped a switch and then the AI Pilot took over and engaged in various maneuvers and so forth. And then, of course, at any time, they could flip the switch back and take back control

• 16:48Speaker 00

  of the platform. So that's one example of where we've done that. Again, this is public knowledge. We've deployed on more uncrewed, pardon me, uncrewed jets. And so these are, and and there's, for example, something called the Fire Jet from a company called Kratos. We integrated and deployed on that. Integrating the capabilities not just onto or one platform, but also multiple platforms working together. And so there there are a lot of examples of where we've deployed our High Mind Pilot onto other classes of systems. On the other side of the the spectrum, we deployed the High Mind Pilot onto small quadcopters. A few years ago, we used to make our own quadcopters. They were called the Nova, and we made a few generations of that.

• 17:32Speaker 00

  But we had a High Mind Pilot instance that was deployed to those. Those platforms actually were recently used in some operational context as well. So these are systems that have been operationalized and and actually used in combat for many high-profile missions. In the quadcopter context, it really focuses on how do you build a system that can really operate in a completely unknown environment? So imagine an underground tunnel network that is far too dangerous to enter for a person, but sending that platform in and being able to navigate within that environment with or without communication, with or without external information, to be able to build up a model and understand the. Both the layout, but also what's in that environment. And so we we built those capabilities

• 18:17Speaker 00

  out as well.

• 18:18Craig S. Smith?

  Yeah. In in the quadcopter case, how is that information then transmitted back if you're in a jammed environment?

• 18:30Speaker 00

  Sure. Yeah. So the the way to think about it is it's it's as if the the robot starts with no prior information. There's no presumption of access of information. So it's sort of, if you will, opening its eyes, turning on its sensors and starting to build that model of the world. All that while, as it's building out that model, it is sharing information back to an operator. But as it starts to enter that environment, it's able to tell that it's no longer able to talk to the operator. It's continuing on its mission. The way to think about the way the operator interacts with the system is it's it's more or less the operator is giving recommendations to the platform. And so if the operator is not

• 19:12Speaker 00

  heard by the platform, the platform will continue to execute its mission without receiving those recommendations. Now, of course, if the operator wants to just take control, they can if they're communicating with the platform. But once it gets into that environment where there's no communication, there's no external information, it's really just executing against its mission at that time, which may be to explore the environment and just build up a map of whatever it can during that time, to go through the environment and find specific or certain things, and so forth. And so it's just executing against that. There is the ability to kind of tell the platform that if you stop communicating and you see something important, or some amount of time has passed, go back to where you could communicate before

• 19:56Speaker 00

  and try to find a means to share the information. But that's sort of how it handles it. And then from a technical perspective, we have a whole set of technologies that allow for really high performance compression of information, kind of storage and compression and and analysis of saliency of information, so that when communication does become available, the most important information is shared to that operator while that that opportunity for communication exists.

• 20:27Craig S. Smith?

  And you were saying that this has been deployed in a combat situation. Can you talk about one of those situations?

• 20:38Speaker 00

  And with with the quadcopter, it was used in in a few high profile mission sets over the last few years. I can't really say more than that. Probably the most recent example that I can comment on was a recent Wall Street Journal article. Just came out, and so this is very much public knowledge of where the the Vbat was being used in a demonstration, if you will, in Ukraine. And it happened to be the case that while it was operating, it saw some items or or things of relevance. And then that information was used as well as the location of those items to by the Ukrainians to to be able to proceed for with a certain mission set that was sort of unplanned.

• 21:22Speaker 00

  In the context of the Vbat operating in Ukraine, certainly those are degraded and denied conditions because it's being jammed, spooked and similar. And so there you have something very similar to what I described with the quadcopter, where you have onboard the intelligence required to be able to understand the world without the presumption of external information. And to be able to then make decisions based on what's seen. In that context, information was shared back to humans. And so communications were regained and then humans made decisions based on what was seen, where it was located, on how to proceed from there.

• 21:59Craig S. Smith?

  Yep, the Vbat flies at what altitude?

• 22:04Speaker 00

  It can vary, anything from a lower altitude, a few hundred feet, all the way up to

• 22:12Speaker 00

  a much higher, you know, 10, 15,000 feet, that that kind of altitude. Or even higher, depending on on the needs of the of the mission set.

• 22:20Craig S. Smith?

  And i- is it primarily an intelligence gathering vehicle or or can it carry armaments?

• 22:30Speaker 00

  It's primarily an intelligence gathering vehicle at this time. It is the case that in a kind of prototypical sense, we've integrated some munitions on the platform, but that was in an R and D style approach. But so what is produced and operationalized and currently out there being used at this time is more ISR oriented.

• 22:54Craig S. Smith?

  Yeah, because so I was at a conference in DC. Actually, you might have been there. The Special Competitive Studies Project, kind of the follow on organization that Eric Schmidt's funding from the National Security Commission on AI. And Mark Milley made a comment that struck me, and I don't remember the numbers, but he said something like, you know, the aircraft carriers that are being built today will be obsolete as soon as they're they're put in the water and

• 23:35Craig S. Smith?

  that in some short number of years, the US military will be again, some large percentage entirely robotic. And then Schmidt was saying, you know, that the war in Ukraine, we're watching this technology and doctrine develop in real time. Because at the beginning of the war, when you had these long convoys of armor headed toward Kiev, and there weren't, you know,

• 24:14Craig S. Smith?

  sufficient drones available, that could happen. But today, that that wouldn't happen because there are enough drones that would would take that out. And so how do you see this kind of technology changing the nature of warfare? And how long do you think before tanks and destroyers really are obsolete?

• 24:50Speaker 00

  It's an interesting question. It certainly is transforming the way that we approach warfare. And for the reasons that you called out. It is certainly the case that these types of intelligence systems can navigate and enter into areas where previously they could not. And not only that, but we're seeing these technologies, at least subsets of them, particularly relevant for Ukraine, move from being something that is less common to commoditized. And you're really starting to see that happen as well. And so with that commoditization of the capability, it's going to be proliferated and everywhere.

• 25:31Speaker 00

  With that said, a lot of the technologies that are coming together in Ukraine are at various levels of sophistication. Yeah. Some work, some do not. And so you're always going to start to see this sort of cat and mouse game emerge, which is exactly what one would expect. Where now you have to think about what protections can you place on the tanks in order to protect against these types of systems? And then you have to build the systems that can navigate and mitigate those types of protections. And so I think what you're going to start to see is that emerge, or not start, we're already seeing it. And you're going to see the same thing with your aircraft carriers as well. And so I think that is going to emerge pretty rapidly. I definitely agree with the statement that autonomy is becoming a commodity.

• 26:16Speaker 00

  The level of sophistication of the autonomy is

• 26:21Speaker 00

  is going to be buried. A lot of the most, a lot of what we're really seeing scaled out there that is being commoditized is fairly simplistic autonomy, but you will see that ramp incrementally as more and more capabilities are drawn in. And so then, as I noted, you're going to start to see more and more techniques used to mitigate and remove those autonomous systems. And so that's what I really expect. I think the key challenge will not be the role of autonomy. The key challenge will be how quickly can we update, modify, adapt, evolve, and improve upon our autonomy within this cat and mouse game? Where we used to be able to move at eighteen months and a typical

• 27:07Speaker 00

  iteration cycle would be one of we have this problem. We need to solve it. Here's the statement of the problem. Engage with industry or academia to come up with a solution, go through a process, potentially implement that problem and end to end from ideation to real world realization. Eighteen months. Now it's going to be far, far faster. You know, at a pace of on a daily basis. Here's what happened yesterday. We iterate. Now we we move forward today. We iterate. We move forward today. And once we hit that daily basis, it's going to become even more compressed such that you are updating it on an almost near real time basis as you're adapting and mitigating these techniques.

• 27:51Speaker 00

  So you're going to see more and more for one software defined vehicles. More and more emphasis on rapid iteration on the autonomy and the AI capabilities. More and more emphasis on guardrails and trustworthiness of what's being produced. Because now you're pushing that to the forward deployed. You're pushing that to the non experts that are in the field. And so those types of capabilities become a real relevant consideration in order to ensure that your iteration in the field based on current information that is being automated and sort of auto generated, if you will, is going to perform as you expect it to on that next day. So that's where we're going to start to see it really start to transform. I would I would claim.

• 28:35Craig S. Smith?

  Yeah, and wherever the hive mind technology is deployed, whether it's in V bat or quadcopters or you know, fighter jets.

• 28:53Craig S. Smith?

  Part of the the the technology allows these this teaming right across or what I like to refer to as swarms.

• 29:04Craig S. Smith?

  How large can those teams be? And how do they communicate with each other? If they're in an environment where there's jamming coming from the other side?

• 29:20Speaker 00

  Yeah, so first off on size, it can be somewhat arbitrary. It depends on the nature and the class of the platforms. When you have more costly platforms, you're going to have smaller numbers for obvious reasons. And then when you have lower cost, highly attributable platforms, it'll be larger numbers. The, it, it, it was historically the case, but it's definitely changing with compute. The level of sophistication and intelligence, it used to be the case that the more expensive the platform, the more you would end up seeing highly sophisticated, exquisite performance. But now we've gotten to the point where you can kind of see that Rolls Royce performance, not just on the exquisite bespoke, or like this, this highly capable performance on, on

• 30:05Speaker 00

  this, uh, on these systems, but you can see this also start to emerge when even this, uh, the level of attributable platforms that are much lower cost. And you can see that both in the defense context, but also in the commercial context. So that, that's, that's pretty cool. In terms of numbers, therefore, it, it really, it, it's kind of arbitrary. The types of behavior and performance changes as you change the numbers. Typically, what you would have is more tightly coordinated teams that are able to behave in a, in a very kind of agile, well synchronized, coordinated manner. But then as you start to go to larger teams, then the techniques start to build on top of that. So that's, that's very similar to humans when we work together and we aggregate, we work well in small groups.

• 30:49Speaker 00

  But as we become larger, we coordinate with each other, but, uh, at, at higher levels in, in terms of coordination across teams. So that, that's what you could think about, and then that kind of scales arbitrarily. Now, the other part of your question is, uh, how do you think about communications? Well, we have communications between the systems, but you said, what happens if they're jammed? Once they're jammed, then you're constantly assessing the communication network. And there's an underlying structure to that network. And if it changes, then the system itself and the team can kind of change their behaviors. Ultimately, we have to design for teams of size equal one or size equal N, and we have To be able to at any moment in time, change our performance based on what the communication network is.

• 31:34Speaker 00

  If we're losing communications, if the teams break apart, if platforms are tritted or something to that effect. Ultimately, once you do get to a point of extremely high jammed condition, then you're going to go to a team of N equals one. No agents can truly talk to each other. And then within that context, the behaviors of the team itself changes, the mission profile changes on what they seek to do. And they'll start to engage in ways that then try to mitigate that loss of communication by repositioning themselves to share information, to create communication relays, these kinds of considerations.

• 32:09Craig S. Smith?

  Yeah, so but but but the base communication is is just radio frequencies. It's not.

• 32:20Craig S. Smith?

  Uh, I don't know that much about, but you're not using Bluetooth between quadcopters that are in proximity to each other, close proximity.

• 32:32Speaker 00

  Well, there's. That there are analogues to Bluetooth, but when they're in close proximity, talking to each other, so mesh networks, radio communications of different forms, Satcom. All of these are options and we'll switch between the different types of communication mechanisms based on what makes the most sense. Or based on how the conditions are changing as as the mission progresses.

• 32:56Craig S. Smith?

  Yeah. Uh, can you, could you, for example, position a net of or a network of quadcopters over a battlefield uh, in in a stationary

• 33:15Craig S. Smith?

  configuration or formation and just uh, watch movement on the battlefield, you know, that we used to do with blimps or balloons in World War One and Two, or or is, uh, yeah, is the sort of thing that you can do with with teaming.

• 33:36Speaker 00

  So certainly, certainly, we could do what you described, but probably we wouldn't use quadcopters because they're not typically long endurance platforms. And so you'd be having to change them out a lot. It probably wouldn't be the right choice of platform for that kind of need. So typically, within the context of teaming, there are coordination roles that the teammates play. Some may be carrying some sensor payloads, others may be carrying other payloads. They're working with each other in order to best leverage those capabilities. Some may have, like, all of their endurance levels will vary based

• 34:15Speaker 00

  on how much capacity they have onboard from a fuel perspective. And they'll coordinate with each other to cover areas or regions, depending on the conditions from a degradation and denial perspective. They'll reposition and change their roles in order to share information or communicate effectively with external people. So those are some of the examples. But certainly, one could use these platforms for situational awareness and monitoring particular regions. And that's certainly the way that they're sometimes used. Again, I don't think that you would use a quadcopter to do that because they just don't have the endurance, often, as contrasted with something that could give you just as much information, but for many more hours.

• 35:00Craig S. Smith?

  Yeah. And another thing that's fascinated me is years ago, I saw a Chinese paper about a swarm flying through a forest, a swarm of quadcopters that were able to navigate through the forest, avoid the trees. Is that the sort of thing that HiveMind can do?

• 35:29Speaker 00

  Sure, I mean, we we deployed our quadcopters in in much harder conditions, I would claim, than that. So think of doing something like that, but in dust clouds, under extremely confined environments, with very small openings and holes. So, yeah, what you're describing is basically the ability for a system to, so in a single context, single intelligent agent context, build up an accurate 3D model of the environment with the onboard sensors, navigate through that environment, and then across multiple agents, build up a common shared picture of that environment and where they are relative to each other, and then control relative to that.

• 36:11Speaker 00

  And so we we thought about that not so much in a forest context, although we were building up 3D models and similar, so it would have been as applicable, but more in the context of, say, complex urban environments where you have teams of quadcopters that need to work together to explore one or more buildings in order to be able to execute particular mission sets.

• 36:34Craig S. Smith?

  Yeah, that's interesting, because

• 36:39Craig S. Smith?

  if if if you have a team working, being coordinated autonomously, they could explore a building rather than have a bunch of human operators, you know, trying to coordinate who goes where, and is that the sort of thing that you're talking about?

• 36:58Speaker 00

  Yeah, so we would think of that as a coordinated exploration problem, and yes. And so what ends up happening there is, it may very well be the case that you want to explore larger buildings, or you just want to quickly explore a building, and so you may send multiple agents in in order to achieve that objective. And that's exactly what would happen. The teams, of course, start with no prior information about the building, so as they build up that model, they're working together to then decompose the environment, figure out who should go where, execute against that, and then, depending on different conditions and considerations, they may change their behaviors. Particularly if they start to have lower battery levels or similar, they may change their behaviors in order to continue to progress forward.

• 37:43Craig S. Smith?

  Yeah, and presumably you've done this with buildings. What percentage of a building could a team of quadcopters?

• 37:55Craig S. Smith?

  Effectively explore because presumably there are parts of the building that are inaccessible.

• 38:02Speaker 00

  Sure. So, uh, under the assumption that the building is accessible, meaning doors are open and things like that, or windows are open, um, it's just purely a function of the size of the building. Um, and so, and it's purely a function of the size of the team. Could, could you send a team of four quadcopters in to explore a multi-story building of three, four, or five stories? Relatively quickly if the doors and the windows were open, yeah, absolutely. Could you have them then go into the next building and the next building and do the same? Sure, absolutely. Um, it's a function of the, um, size of the buildings, the scale of the environment, the accessibility of it, as well as the, let's just say, levels of battery and endurance that the, the platforms have.

• 38:45Craig S. Smith?

  Yeah. Uh, and, and on the, um,

• 38:49Craig S. Smith?

  on the jet platforms, on the large, uh, aircraft, fixed-wing aircraft, um, uh, I've, you know, I've seen on YouTube videos of dogfights and that sort of thing, um, between, um, an autonomous jet and a human pilot. It is, is, am I looking at a hive mind, SHIELD AI's hive mind when I see that? Or, or are there a lot of people in this space?

• 39:24Speaker 00

  It's entirely possible that you're looking at SHIELD AI's capabilities. I mean, pro- probably, uh, probably, uh, we

• 39:35Speaker 00

  have, uh, certainly deployed our capabilities onto uncrewed jets or on the jets like what you just described. Um, it has been publicized and is, has been, has been noted in a number of media, uh, videos and articles and so forth. So quite, quite probably it is, but I'm not a hundred percent sure.

• 39:55Craig S. Smith?

  Sure. And, and the, um, the idea I'm that I'm familiar with is that you would have a human pilot with a fleet of, uh, of fighter jets on either side of him that, that would be coordinating with him, that he's, uh,

• 40:17Craig S. Smith?

  You know, the main pilot and they're following him and then he has some control over their behavior if they're into a combat situation. I'm can you just talk about a a use case where they're real or or imagined, how that would work?

• 40:40Speaker 00

  Sure. So what you're describing is sort of the crude, uncrewed teaming or manned unmanned teaming or MMT scenario. And so this, this is where you have a crude platform. So a pilot is flying that platform. And then you have multiple uncrewed platforms that are being tasked based on that pilot. You're you're correct that that would be a platform that would be there available. There are often considered multiple different types of uncrewed platforms that would serve different purposes. So you could imagine that the pilot may turn to those systems to go check out an area behind beyond visual line of sight to see if something's going on and task them to do something depending on what they see.

• 41:22Speaker 00

  It may task other platforms to achieve certain other objectives to survey a particular area, to clear a particular area in advance, to engage depending on the nature or the context of of what we're talking about here. And so this is where then the systems are operating in support of a pilot in order to help them achieve their objectives. So then that means that the pilot is less, let's say tactically engaged and more strategically engaged in terms of how to use these assets to further the mission while also keeping them at a standoff.

• 41:56Craig S. Smith?

  Is that happening today?

• 41:59Speaker 00

  That kind of teaming in in a in a testing and evaluation, so research, development, testing and evaluation manner? Absolutely. Absolutely, it's the case that we are and and others are as well, beyond Shield AI, but elsewhere as well, of course, executing these types of crude, uncrewed teaming efforts. They happen within the context of jets, as we were just discussing, but it's also happening in the context of helicopters and rotorcraft that are using other and leveraging other platforms around those systems to be able to do a variety of of mission sets. One trivial example would be to go out and

• 42:37Speaker 00

  check if an area is is an appropriate landing zone in certain conditions, but there are lots and lots of examples of how those can be used.

• 42:45Craig S. Smith?

  Yeah, but but not yet in in combat settings.

• 42:50Speaker 00

  Not to my knowledge, yeah.

• 42:51Craig S. Smith?

  Yeah, and you said other people are doing this too, and I mentioned the Chinese. I mean, is the US, whether private sector or or or the government or the military, is the US ahead of Russia and China or other potential adversaries in this kind of technology? Or is everyone kind of at the same level?

• 43:17Speaker 00

  Yeah, that's a great question. I would say it's yes and no. In some areas, we are ahead. In other areas, we're far behind. And so I think on the AI, on the AI side, again, in some areas, we're ahead, but China is extremely far ahead in other areas of AI, in particular, because they have access to such a wealth of data at a large scale. And they're able to use it in a more unfettered manner. And so they've really progressed in those areas, particularly related to intelligence surveillance and reconnaissance, like ISR capabilities, that they're really, really quite sophisticated. Additionally, they have incredible autonomy and robotics capabilities.

• 44:01Speaker 00

  But you can see right now swarms of many hundreds, if not thousands, of platforms working together. They have an excellent industrial base, not just in terms of creating platforms, but also in terms of just the autonomy industrial base and their ability to roll out those capabilities and deploy and scale them. I think that there are areas within the US where we remain ahead, in particular, in terms of the integration of intelligence on the platforms. But I think at this point, I would put us as head to head, or China is probably outpacing us substantially in select areas.

• 44:38Craig S. Smith?

  What about what about Russia?

• 44:42Speaker 00

  Yeah, I was just about to comment. I don't have as much visibility into Russia at this time, so it's harder for me to comment here. I feel like they or it is my sense that they focused on other areas. And so I I just don't have a good sense on that. I have not seen them. So I have not seen any. So unlike with China, where it's just so abundantly clear, with Russia, I've not seen anything to suggest that they are out ahead in any areas related to AI and robotics.

• 45:13Speaker 00

  But again, I just do not have as as strong a visibility there.

• 45:17Craig S. Smith?

  Yeah, and and autonomous, I mean, I should really let you just talk about Shield AI, but this is such an interesting topic in terms of Ukraine with with all the drones that are being fielded on both sides. Are there autonomous drones being fielded? You mentioned Vbat over there. I don't know if that was a test or whether you're whether they're being fielded and if they're being fielded for what purpose?

• 45:52Speaker 00

  Yeah, so I think I interpret that question as both how are autonomous drones being used in Ukraine and how's the Vbat being used in Ukraine?

• 46:01Craig S. Smith?

  Yeah.

• 46:02Speaker 00

  In Ukraine. So I'll answer the the latter first. The Vbat itself, in the context of the example that I gave a little bit earlier, that that was a demonstration. And it is the case that so that demonstration happened to create conditions where then the Ukrainians chose to leverage the Vbat to achieve certain outcomes. It is the case that there is an increased interest to have the Vbat following that demonstration be brought in and used within Ukraine, primarily for the purposes of ISR. But ISR within the context of degraded and denied conditions.

• 46:38Craig S. Smith?

  Right.

• 46:39Speaker 00

  In terms of more more broadly autonomous drones themselves, I think probably the area that I've seen the most growth and impact is on that highly attributable, lower-cost platform area where you're seeing autonomy be truly commoditized. And this is often in the form of one-way attack drones, where they're they're basically sending out either FPVs that are largely driven by pilots or lower-cost platforms that are only designed to really go one way and hit particular targets. And some of those platforms have integrated on them sufficient sensing, compute, and algorithms to figure out where they

• 47:22Speaker 00

  are, to not necessarily need GPS or GNSS information, and then to be able to identify particular targets of interest that are pre-programmed often, and then to to target and and have terminal guidance to those locations.

• 47:38Craig S. Smith?

  Hmm. In other words, um, they, they fly autonomously and, and then hit the, the target.

• 47:47Speaker 00

  That, that's my understanding. Now, we're, we're not doing that. So that's just my understanding.

• 47:51Craig S. Smith?

  Yeah.

• 47:51Speaker 00

  Um, but, but it's being deployed all within combat settings. And so that, that's sort of within that perimeter of those conditions. That's my understanding. Um, that, that is, that is maybe what's, what's going on there.

• 48:03Craig S. Smith?

  Yeah. And, and that kind of, uh, technology, where would they get that autonomous piloting, uh, technology and targeting?

• 48:13Speaker 00

  Well, they're building out a lot of these capabilities on top of open source capabilities. Today, you can go out and you can, you can buy a lot of open source or readily accessible platforms, electronics, software stacks that deploy onto that. Um, you can use that to make a hobbyist quadcopter, uh, to, to use for your own purposes, or you could repurpose that within the context of this type of mission set. So that provides the baseline capabilities from say a platform autonomy perspective. And then they're adding on particular capabilities which are provided by a number of different third parties in terms of state estimation capabilities to be able to navigate or

• 48:54Speaker 00

  estimate your location within a degraded and denied condition as well as, um, detection, classification and recognition capabilities leveraging imagery and data sets.

• 49:06Craig S. Smith?

  Yeah.

• 49:07Speaker 00

  And then they, they integrate that together and there is a, a significant industrial base that has grown out of the last three to four years, um, well, the last three years really that have, um, really ramped. So you went from a very limited industrial base there to many hundreds of companies now focused on these types of topics.

• 49:30Craig S. Smith?

  Yeah. Um, what, what's your, your optimal vision for this kind of technology? Um, let's say for the US, um, military, how would it be deployed? How many? Um, UAVs, um, do you imagine the US deploying? Um, you know, is, is vis a vis, uh, uh, you know, non-autonomous vehicles. It, it just seems to me the future, this is the future that you're going to have.

• 50:08Craig S. Smith?

  Uh, you know, hundreds of thousands of, of, you know.

• 50:13Speaker 00

  Millions.

• 50:14Craig S. Smith?

  Yeah, millions. Okay, millions, yeah.

• 50:17Speaker 00

  So you, you asked sort of for the vision. What, what is the vision and what are we trying to do? Um, at the heart of it, uh, and this goes back to what I was saying even before I started working with Shield AI, I was focused on this question of resilient intelligence. How do, how do you build systems that can think for themselves? And really my focus is on, at this point, proliferation of resilient intelligence. Making as many highly intelligent systems as possible, if, if you will. So proliferation, if you were to put it in one word. So what we focused on is not just creating the autonomy, but creating all the tools and ecosystems to be able to develop, integrate, evaluate, deploy, operationalize that autonomy as quickly as possible.

• 50:58Speaker 00

  And we are focused not just on air, but the autonomy development lifecycle itself, which in many ways is the same whether you're focused on air, ground, water, and so forth. Uh, it's, it's the same kind of set of steps, the same kinds of tools that you need, but there is specialization. So we're focused on not just creating this autonomy and this resilient intelligence, but creating the tools, the, the factory that you need to be able to produce that and do it very, very quickly. Um, so at this point, early on, when we, we started integrating these capabilities on, it took, uh, years. Now we're at the point where we can approach a new platform, and we did this just in the last couple of months, where we can have really sophisticated capabilities on an entirely

• 51:43Speaker 00

  new platform in a matter of six weeks. And so we're getting to this point where we're really oriented around proliferation. Deploying it with and working with as many, um, uh, potential platform providers or customers as possible to integrate and deploy Highmind on there. And not just have Shield AI do that, but enable them to use our tools, our capabilities, everything we've created, so that they can do it themselves. So that they can integrate and deploy these capabilities themselves, update, upgrade, maintain those capabilities themselves. Um, and so. We're looking to work really with four different types of customers. Um. The first are those that need resilient intelligence and they need it today. That's sort of like those in Ukraine or similar.

• 52:26Speaker 00

  Um. Those OEMs that want to be able to deploy and integrate resilient intelligence onto their platforms. Um. And see that as they sell those platforms to have them be hive mind enabled, if you will. We're working with customers that want to build out their own kind of sovereign capacity. So at an international level. Even within the US, but in particular at an international level, there's a pretty big um. Gap between subject matter expertise and personnel that are available. Lots of countries want autonomy. They want to control their own future, control their destiny. But they don't have everyone, uh, they don't have the same level of subject matter expertise. And so what we've really done is we focused on that autonomy development lifecycle.

• 53:09Speaker 00

  Created the good, those guardrails, those workflows that mean now that instead of having to have a PhD or a master's degree. You can enter with a masters or even just an undergrad. You don't have to be an expert in autonomy or AI engineering. You can just be a proficient software engineer. And then you can use our tools, our technologies, our capabilities. To be able to really progress rapidly to deploy resilient intelligence and our autonomy onto platforms. Um. And then the last group of customers we're we're currently engaged with are those that really think about things like. How do they integrate engineering capability or how do they from an engineering services perspective? Integrate and and help their customers meet that need. How do they help test and train and operationalize these capabilities because there's a big?

• 53:52Speaker 00

  Need set around that of not just having the capability, but having the testing and training that's also required to allow others to use that capability. So that's what we're focused on right now. Uh, but that that can all be summarized in one word, and that's proliferation. Um. And that and that's what we're thinking about. And then to your point. It will be hundreds of thousands. And it will be millions and it will be not just air, but water and land. And space and so forth. And what what's emerging is a a large.

• 54:24Speaker 00

  Fragmented autonomy industrial base that's all over the place. It's the Wild West. And our objective is to kind of work with as many as possible to enable them to be able to achieve this outcome and not have to reinvent the wheel or go through the same learning process and just kind of fast forward to a successful outcome as quickly as possible.

• 54:47Craig S. Smith?

  Could this also, you know, I've been, I just took my first ride in a robot taxi out in Phoenix and I've been watching Joby, you know, the EV toll. Could this be, this kind of technology be integrated into a consumer, you know, aircraft?

• 55:12Speaker 00

  Yeah. Absolutely, absolutely it could be. And I'm certainly looking at, so that the product that we're creating is not defense specific. In fact, it's a commercial product that happens to be used in what we've discussed for defense applications, but there are also lots of commercial applications. And so I'm currently working on expanding our engagement there as well. The big call out there is as you go into any new domain and as you engage with any new customer base, understanding their needs, understanding what's required, meeting regulations and compliance and things like airworthiness and some of the examples that you called out, these are all extremely challenging things to do.

• 55:55Speaker 00

  So we're being very cautious in terms of how we progress in our engagement because it's extremely hard to just move from being an expert in the air to being an expert in the air and at sea, right? So we have to be cautious there, but there's nothing about what we're doing that is defense specific. And in fact, the product that we're making is very deliberately commercial so that, so that we have that flexibility.

• 56:21Craig S. Smith?

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• 56:33Craig S. Smith?

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