This discussion examines defense technology and artificial intelligence-enabled system-of-systems integration for modern battlefields. Boaz Levy of IAI, chairman and chief executive officer, brings decades of aerospace and defense engineering experience to a wide-ranging conversation on next-generation battlefield systems. Levy discusses sensors, AI-enabled data fusion, digital twins and system-of-systems architecture and autonomy with John Furrier of theCUBE Research and Dave Vellante of theCUBE Research in the New York Stock Exchange studio; they focus on scalable multi-domain integration and rigorous operational testing.
Key takeaways include the primacy of sensors and big-data fusion for real-time decision-making and the need to design adaptable architectures that accommodate high volumes of threats, Levy emphasizes. They advocate combining autonomous agents with human oversight and iterative testing to validate performance under contested conditions. The hosts underscore time-to-market, resilience and cross-domain data sharing as critical action points for defense modernization.
Topics covered include sensor fusion, command and control architectures, digital twin implementation, autonomy and cybersecurity for operational environments and force scalability. This discussion provides insights for defense planners, systems architects and industry leaders seeking approaches to integrate AI across multi-domain systems and to accelerate operational testing.
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Boaz Levy, IAI
This discussion examines defense technology and artificial intelligence-enabled system-of-systems integration for modern battlefields. Boaz Levy of IAI, chairman and chief executive officer, brings decades of aerospace and defense engineering experience to a wide-ranging conversation on next-generation battlefield systems. Levy discusses sensors, AI-enabled data fusion, digital twins and system-of-systems architecture and autonomy with John Furrier of theCUBE Research and Dave Vellante of theCUBE Research in the New York Stock Exchange studio; they focus on scalable multi-domain integration and rigorous operational testing.
Key takeaways include the primacy of sensors and big-data fusion for real-time decision-making and the need to design adaptable architectures that accommodate high volumes of threats, Levy emphasizes. They advocate combining autonomous agents with human oversight and iterative testing to validate performance under contested conditions. The hosts underscore time-to-market, resilience and cross-domain data sharing as critical action points for defense modernization.
Topics covered include sensor fusion, command and control architectures, digital twin implementation, autonomy and cybersecurity for operational environments and force scalability. This discussion provides insights for defense planners, systems architects and industry leaders seeking approaches to integrate AI across multi-domain systems and to accelerate operational testing.
>> I'm John Furrier, host of theCUBE here in theCUBE's NYSE studio. Of course, we have our Palo Alto studio connecting Silicon Valley to Wall Street. This is our defense tech series. We talk to leaders who are making it happen, who are bringing in the innovation, who are having the conversations that matter to bring AI and all the new technologies that help make the world safer and secure and better for everyone to live in. Boaz Levy's here as the chairman and CEO of IAI. He is the chairman of the board and CEO of IAI, one of Israel's leading aerospace and defense companies. Began his career in the 90s as an engineer on the Arrow program, heralded success, and later became senior role CEO now as the chairman. Boaz, thanks for coming on theCUBE. Really appreciate your time.
John Furrier
>> Thank you, thank you.
John Furrier
>> It's a great honor to be here. Being an engineer back in the day, missile guidance systems, that was so much fun. You had hardware, you had firmware, you built the systems, you built a bunch of rockets, detection, precision. Now it's changed. A lot more drones, you see swarming. We're seeing this next revolution. What is your vision on this next revolution in warfare? Because the battlefield has changed a bit from a technology standpoint, but it's still the same battlefield, but now you've got cyber. What's changed? What's the big revolution in warfare?
Boaz Levy
>> I think that when I'm trying to analyze weapon technology mainly that drives the solution for the battle space, I believe that the real change is that the leaders dare to threaten the real world. That is the main impact over here, and we see it by quantities. When we, and you mentioned it, designed the Arrow weapon system, we never thought that the enemy against us would launch thousands of missiles against us. We prepared our solution, we prepared our systems. The Arrow is a wonderful solution for that. But we thought about dozens, we never thought about hundreds and thousands, and derived new kind of solutions to address these types of threats. And that's what we are doing. So the basics and the physics of what's going on are the same. The solution is based on an interceptor or other mechanisms in order to intercept the incoming ballistic missile in order to create the right defense. But that's what we are doing. We are connecting the layers and bringing up a solution that will defend the civilians anywhere.
John Furrier
>> Yeah, I like that solution view. And again, we've been covering the AI infrastructure market, the NVIDIAs, AI factories. And when you inject intelligence into a system, things can behave differently. Back in the old days, there's a target, there's the coordinates, send the rocket. Now you have a lot of things going on, not just the target, what is it doing, what is it thinking, the situation's changed. So it's a very dynamic environment. Now, how do you write software for that? So these become kind of geek questions. These are questions that engineers have to solve. How complex is it to kind of get a feel for almost every permutation of a scenario? There's a lot going on. You mentioned multiple targets, multiple weapons fired. When you see that kind of payload coming at you, and sometimes the cost might be a lot lower. How are people thinking about this in the state of the art today?
Boaz Levy
>> So we always used intelligence in order to improve our solution. But I think that the name of the game is the sensors. Currently, we have a lot of sensors, many types of sensors. And the answer to that is to derive all the data, the big data that's coming from them, whether it's from space, ground, sea level, or whatever. And tie it together into one pool of data that you can help yourself by having better solutions. That's what we are doing. This is where AI is coming into the picture. Because we always had those type of systems, and we always did analyze the data and bring the solutions to the software engineers. but today we have to address the market with some solution that needs to be tested over a huge bank of data. And the question here is whether we can test it good enough so when it will come to the real day, it will work or not.
John Furrier
>> Yeah, and you got digital twin technology, all kinds of new things happening. I want to ask you about the system design and system optimization because one of the things that I've been observing since 2016, since I started covering the JEDI contract with AWS, tactical edge, kind of the thinking around the tech, I always see it looks beautiful. There's a lot of engineers working on things, but it gets screwed up by procurement. And procurement has this fairness algorithm. Well, we got to make sure we spread the money around between contractors. I'm kind of overplaying that a little bit, to make a point, is that sometimes it's not about spreading the love on the contracts, it's designing the system. Are people realizing that now? Are they looking at this thinking holistically and saying, hey, you know what, these are our requirements, let's not get political. What's your opinion on this? Because this seems to be a topic that is kind of out in the open,but no one's really talking about it.
Boaz Levy
>> The designing part is always the important thing of every system that I know of. And it leads us to the right way of thinking of how to do procurement, whom to purchase from, do we have different types of sources, what can we do ourselves, what kind of resilience do we have in order to maintain the system capability alive whenever we need it to be operated. And that's what we are doing on the initial phases of the program. The ones that are not doing that will suffer those kinds of problems that you mentioned at a later phase. So it will be wise to address them in the design phase.
John Furrier
>> You mentioned sensors, I want to come back to that because I think the discussion around, okay, getting the data, injecting intelligence, using AI to kind of bring that data together. When you have all the sensors and all this data, you can run things on digital twins, you can do a lot of simulations these days. What are some of the things coming that make that happen what is the architecture for deploying massive sensors having that be, you don't know what's going to come in for data you can train it you infer so you're going to have tokens you're going to have prefill, decode all the stuff that happens in the inference has to be deployed in real time what's the architecture because ai certainly is going to be here and we'll get into some of the decision making loops later but you got to set it up first is there an architecture that's required?
Boaz Levy
>> Of course, the architecture of the system is in the brain of the system engineer. It actually drives the system capability. AI is a tool, it will always be a tool, and we should never neglect the human factor out of the system capability. The human factor drives what will be the architecture, and it needs to be wise enough in order to make the system capable to adopt as many sensors as it needs, looking toward the future. Because when you're speaking about defense systems, maybe you're designing it today, but most certainly you will use it 20, 30 years from now. And the system needs to be able to adapt to other capabilities, and other sensors and other features. That's what we are doing. And AI is helping us in order to tailor those sensors into a solution.
John Furrier
>> On the areas of mechanics, electronics, and precision guided technology, what's changed the most in those areas with the current situation with AI and tech? Have there been advances in those three areas? Could you share your thoughts on the key things?
Boaz Levy
>> Of course.All the technologies are changing. Every day we have an additional change. And today you can tailor 3D printers in your solutions so you'll receive any kind of navigation tools or others in order to meet your requirement. That's what we are doing. Now combine those capabilities together with AI and you get even better capability that we can't even think of today.
John Furrier
>> On the battlefield, there's always images of, wait a minute, if drones and autonomous agents are running everything, you've got autonomous vehicles and autonomous weaponry, who makes the decision? What's the impact of the commander and the leadership, and how does that translate to the war fighters or war machines?
Boaz Levy
>> Think that you always want your system to act as autonomous as it can. But you always need the human factor in order to control it. And when you send those autonomous vehicles into battle, somebody is going to watch them from behind. Someone gives them their orders. Someone is trying to see if they are working properly and should be able to stop their mission or to change the mission if you think that it's required. So I cannot just now point where their system would be autonomous, totally autonomous, or where the human factor comes into the picture. I would say that the best solution is a combination between the two and I think that that's what the world is going to.
John Furrier
>> As you look at your successes in the past and where we are now on the future of the battlefield and war. It's a systems game, we touched on that. Is there a way to make sure that the integration all works? In other words, if the system has to act as one and be ready, you gotta have it know everything about everything, but at the same time, you don't want it to be too much overhead involved. So is there a vision around truly reactive, situational, autonomous responses, is that in market today? How does that play out? Because at some point you have too much you're measuring, or too much deployed, or maybe the wrong tech. How do you ensure that these systems are always resilient, always working?
Boaz Levy
>> So what we are doing is we are usually thinking about system capabilities, but we switch from the system world to system of system words. And now the problem became even more severe than you discussed. So the real question is how we test it and how we maintain the capability of the system for a long-term operation. And that's what we are doing through a series of tests. Now when you have an AI machine like that and you can declare what are the possibilities, what are the scenarios, then the AI can derive your test list and you should follow them and do it again and again and again until your system will be as prepared as possible. That's what we are doing today and looking toward the future. I believe that testing the system will be the real challenge because things are changing every day and you need to meet those changes. And you do it by technology and you do it by other capabilities based on AI but using the main factor because you never will be able to cover everything. So you need to have some kind of analysis.
John Furrier
>> Well as we do interviews with business people, we say you got to develop your organization at the speed of AI. Now, in the war arena, you have to execute at the speed of the machines. You're seeing a lot more offense coming to the table. It used to be a great defensive play. Now it's like, well, if you've got machines moving super fast, that changes the tactics. That changes potentially strategy and tactics for the leadership on the battlefield. Talk about that offensive new capability. How real is it? Does it get better? and how does defense come into play? Because you've got now offense and defense getting augmented with faster decision -making, better data.
Boaz Levy
>> So I think that the real answer for that is time to market. And you always have your goals, and you need to prepare yourself toward the customer requirements. And that drives your design from one end and your set of tests from the other end. So you need to bring initial operational capability to the customer, but later on you need to add more and more features to the system. And usually you're doing it by utilizing building blocks. And the goal for the future is to minimize the time between one building block to the other. So you'll be able to change your system in accordance with your enemy or what's happening in the battle space.
John Furrier
>> So basically, you need to scale instantly to a situation without having to do a lot of work. It becomes adaptive, if you will. Alright, let's take that to the multi -domain space because I was just on with Dave DeWalt from NightDragon, he's an investor. He made a comment that cyber's in every domain now, I would agree with him. Infrastructure, you need compute. So technology is across all the different domains, but it used to be conventional wisdom that, oh, land is land, space is space, everyone had their little stove pipes. Okay, if you think about the integration system conversation we just had, you could be great at space and enable someone else through data, so if you're going to go to this data-centric architecture, you don't have to do everything great in each one, you can just mix and match. How does the cross-domain impact the execution of building the system, operating the system, and investing in the system?
Boaz Levy
>> Oh, this kind of question is very familiar to IAI, because IAI, my company, is dealing with everything related to space, air, sea, and land capabilities. But when we are looking today toward the solution, actually everybody is working together. As you said before, integrated together. And in order to bring one good solution, let's say one good attack toward Iran, then you need them both. You need the capabilities of each one of the layers, but you need to unite them. You need space to bring you the data. And the data could go to the pilot at the cockpit, but also to the missile that is making its way toward the threat. it can actually help you to navigate, but it also can help you to receive the BDA, the Battle Damage Assessment, in order for you to know if you hit or not. So that's what is being done today, and what I am trying to say is that we have to control them all. But nowadays, we have a different challenge, and IAI would say that the wars are no longer between frontiers. As you can see, as we all can see, the wars are within cities. And when you are speaking about cities and civilians, then what about rescue teams? What about commercial aircraft or commercial transportation? What about banks, hospitals? Everybody should be connected. And that makes the problem even bigger.
John Furrier
>> And that makes the argument, or the discussion around data is super important. So having the right data at the right time. So this has to be a fully integrated system, but real time becomes super important because how do you know you hit the target? How do you know this happened? How do you avoid this? How do you make changes? All of this needs to be done in real time. That's hard to do in silos.
Boaz Levy
>> It's hard to do in silos, but we need to do it. And actually that's what we are doing today.
John Furrier
>> I talk about IAI, what you guys are doing, because I think this is a template for the future because if we could have a safe environment with autonomy and intelligence around the data, then it's going to be easier to have resolution safely. Avoid conflict, that's hard to do because every time someone tries to recruit land grabbing, they get into arguments, whether it's space. So talk about that piece of it. What is the future for you guys as you look at commercializing and expanding on this architecture and these capabilities?
Boaz Levy
>> So it's just a summary of what we just discussed because we are thinking about those types of solutions from every domain, connecting them all together, connect them also to civilian application and create the mechanism that can derive the data that comes from everybody and makes the right solution. And the right solution should be coming toward the battle space into several ways of operation. It can be from aircraft, from space, from ground, whatever you can think of. So we are doing now the building blocks of every layer, connecting them together through the special agents of AI. And in order to do so, we are creating a lot of software, a lot of communication.
John Furrier
>> You're taking your domain knowledge and you're codifying it with workflows and knowing kind of what worked, all that testing comes in handy when you just give it to a digital agent.
Boaz Levy
>> Definitely, yes. And you have to deal with a lot of pools of data that you need to derive the right data at the right time.
John Furrier
>> Well, I really appreciate your insights and sharing what you're working on and your opinions. My final question for you is, because you're not the young engineer you used to be, you're the chairman, but there are a lot of young engineers out there right now coming into this environment. They could be anywhere from 15 to 30 years old, the young guns, and then you have that other management wave coming on top of that. What is the right skill set to solve the hard problems? An engineering degree, double E, mechanical, those are the old school methods. You work on a project, you create some algorithms, in missile guidance systems. different environments we laid out. What is the skill set for A players, successful people, without job descriptions, is there a certain culture, mindset, what are the things that you see that you could share would be notable for people to understand around? What's it take to be successful?
Boaz Levy
>> So I'll convert your question to another question.
John Furrier
>> Okay.
Boaz Levy
>> Saying what will be the future of the engineer?
John Furrier
>> Yes.
Boaz Levy
>> In this kind of world. And I believe that the future engineers should have a lot of engineering way of thinking. This is the key for everything. Because when you have this way of thinking, you know how to derive any type of technology toward your solution. And it's not enough just to know how to solve an equation. You need to understand how things are working. and you ask for it, so I'm telling you that the key things for that is curiosity. You need to develop a kind of curiosity and your way of thinking in order to find out the best solution toward the future.
John Furrier
>> And some engineers just like to work on hard problems. And there's a lot of hard problems. What are some of the hard problems that need to be solved? Just kind of generic, high level, generically. what are some of the big problems, engineering problems, that need to get worked on in the next, say, five years?
Boaz Levy
>> I would say dealing with quantities, no matter what. But because a few years ago, when you thought about a war, in terms of defense tech, you thought about limited amount of quantities. Now, we're talking about thousands. Thousands require a new set of skills, new way of thinking, thinking out of the box, and that's what you need in order to bring your best solution to the table.
John Furrier
>> Boaz, thank you for coming on. Again, it's an honor to have you on. The accomplishments you've done and the work you do is the beginning, I think it's going to be a new sea change over to a new architecture. You guys are doing it. Thank you for sharing.
Boaz Levy
>> Thank you very much.
John Furrier
>> Again, I'm John Furrier.This is our Defense Tech series. Again, physical AI and robotics extends out into defense tech, which also has commercial applications as well. We're talking about computer science here, talking about engineering, talking about data. it's AI factories meets cyber security meets application, which is prevent and save and have a safe environment for conflict resolution. We're doing our part here in theCUBE. I'm John Furrier, your host. Thanks for watching.
>> I'm John Furrier, host of theCUBE here in theCUBE's NYSE studio. Of course, we have our Palo Alto studio connecting Silicon Valley to Wall Street. This is our defense tech series. We talk to leaders who are making it happen, who are bringing in the innovation, who are having the conversations that matter to bring AI and all the new technologies that help make the world safer and secure and better for everyone to live in. Boaz Levy's here as the chairman and CEO of IAI. He is the chairman of the board and CEO of IAI, one of Israel's leading aerospace and defense companies. Began his career in the 90s as an engineer on the Arrow program, heralded success, and later became senior role CEO now as the chairman. Boaz, thanks for coming on theCUBE. Really appreciate your time.
John Furrier
>> Thank you, thank you.
John Furrier
>> It's a great honor to be here. Being an engineer back in the day, missile guidance systems, that was so much fun. You had hardware, you had firmware, you built the systems, you built a bunch of rockets, detection, precision. Now it's changed. A lot more drones, you see swarming. We're seeing this next revolution. What is your vision on this next revolution in warfare? Because the battlefield has changed a bit from a technology standpoint, but it's still the same battlefield, but now you've got cyber. What's changed? What's the big revolution in warfare?
Boaz Levy
>> I think that when I'm trying to analyze weapon technology mainly that drives the solution for the battle space, I believe that the real change is that the leaders dare to threaten the real world. That is the main impact over here, and we see it by quantities. When we, and you mentioned it, designed the Arrow weapon system, we never thought that the enemy against us would launch thousands of missiles against us. We prepared our solution, we prepared our systems. The Arrow is a wonderful solution for that. But we thought about dozens, we never thought about hundreds and thousands, and derived new kind of solutions to address these types of threats. And that's what we are doing. So the basics and the physics of what's going on are the same. The solution is based on an interceptor or other mechanisms in order to intercept the incoming ballistic missile in order to create the right defense. But that's what we are doing. We are connecting the layers and bringing up a solution that will defend the civilians anywhere.
John Furrier
>> Yeah, I like that solution view. And again, we've been covering the AI infrastructure market, the NVIDIAs, AI factories. And when you inject intelligence into a system, things can behave differently. Back in the old days, there's a target, there's the coordinates, send the rocket. Now you have a lot of things going on, not just the target, what is it doing, what is it thinking, the situation's changed. So it's a very dynamic environment. Now, how do you write software for that? So these become kind of geek questions. These are questions that engineers have to solve. How complex is it to kind of get a feel for almost every permutation of a scenario? There's a lot going on. You mentioned multiple targets, multiple weapons fired. When you see that kind of payload coming at you, and sometimes the cost might be a lot lower. How are people thinking about this in the state of the art today?
Boaz Levy
>> So we always used intelligence in order to improve our solution. But I think that the name of the game is the sensors. Currently, we have a lot of sensors, many types of sensors. And the answer to that is to derive all the data, the big data that's coming from them, whether it's from space, ground, sea level, or whatever. And tie it together into one pool of data that you can help yourself by having better solutions. That's what we are doing. This is where AI is coming into the picture. Because we always had those type of systems, and we always did analyze the data and bring the solutions to the software engineers. but today we have to address the market with some solution that needs to be tested over a huge bank of data. And the question here is whether we can test it good enough so when it will come to the real day, it will work or not.
John Furrier
>> Yeah, and you got digital twin technology, all kinds of new things happening. I want to ask you about the system design and system optimization because one of the things that I've been observing since 2016, since I started covering the JEDI contract with AWS, tactical edge, kind of the thinking around the tech, I always see it looks beautiful. There's a lot of engineers working on things, but it gets screwed up by procurement. And procurement has this fairness algorithm. Well, we got to make sure we spread the money around between contractors. I'm kind of overplaying that a little bit, to make a point, is that sometimes it's not about spreading the love on the contracts, it's designing the system. Are people realizing that now? Are they looking at this thinking holistically and saying, hey, you know what, these are our requirements, let's not get political. What's your opinion on this? Because this seems to be a topic that is kind of out in the open,but no one's really talking about it.
Boaz Levy
>> The designing part is always the important thing of every system that I know of. And it leads us to the right way of thinking of how to do procurement, whom to purchase from, do we have different types of sources, what can we do ourselves, what kind of resilience do we have in order to maintain the system capability alive whenever we need it to be operated. And that's what we are doing on the initial phases of the program. The ones that are not doing that will suffer those kinds of problems that you mentioned at a later phase. So it will be wise to address them in the design phase.
John Furrier
>> You mentioned sensors, I want to come back to that because I think the discussion around, okay, getting the data, injecting intelligence, using AI to kind of bring that data together. When you have all the sensors and all this data, you can run things on digital twins, you can do a lot of simulations these days. What are some of the things coming that make that happen what is the architecture for deploying massive sensors having that be, you don't know what's going to come in for data you can train it you infer so you're going to have tokens you're going to have prefill, decode all the stuff that happens in the inference has to be deployed in real time what's the architecture because ai certainly is going to be here and we'll get into some of the decision making loops later but you got to set it up first is there an architecture that's required?
Boaz Levy
>> Of course, the architecture of the system is in the brain of the system engineer. It actually drives the system capability. AI is a tool, it will always be a tool, and we should never neglect the human factor out of the system capability. The human factor drives what will be the architecture, and it needs to be wise enough in order to make the system capable to adopt as many sensors as it needs, looking toward the future. Because when you're speaking about defense systems, maybe you're designing it today, but most certainly you will use it 20, 30 years from now. And the system needs to be able to adapt to other capabilities, and other sensors and other features. That's what we are doing. And AI is helping us in order to tailor those sensors into a solution.
John Furrier
>> On the areas of mechanics, electronics, and precision guided technology, what's changed the most in those areas with the current situation with AI and tech? Have there been advances in those three areas? Could you share your thoughts on the key things?
Boaz Levy
>> Of course.All the technologies are changing. Every day we have an additional change. And today you can tailor 3D printers in your solutions so you'll receive any kind of navigation tools or others in order to meet your requirement. That's what we are doing. Now combine those capabilities together with AI and you get even better capability that we can't even think of today.
John Furrier
>> On the battlefield, there's always images of, wait a minute, if drones and autonomous agents are running everything, you've got autonomous vehicles and autonomous weaponry, who makes the decision? What's the impact of the commander and the leadership, and how does that translate to the war fighters or war machines?
Boaz Levy
>> Think that you always want your system to act as autonomous as it can. But you always need the human factor in order to control it. And when you send those autonomous vehicles into battle, somebody is going to watch them from behind. Someone gives them their orders. Someone is trying to see if they are working properly and should be able to stop their mission or to change the mission if you think that it's required. So I cannot just now point where their system would be autonomous, totally autonomous, or where the human factor comes into the picture. I would say that the best solution is a combination between the two and I think that that's what the world is going to.
John Furrier
>> As you look at your successes in the past and where we are now on the future of the battlefield and war. It's a systems game, we touched on that. Is there a way to make sure that the integration all works? In other words, if the system has to act as one and be ready, you gotta have it know everything about everything, but at the same time, you don't want it to be too much overhead involved. So is there a vision around truly reactive, situational, autonomous responses, is that in market today? How does that play out? Because at some point you have too much you're measuring, or too much deployed, or maybe the wrong tech. How do you ensure that these systems are always resilient, always working?
Boaz Levy
>> So what we are doing is we are usually thinking about system capabilities, but we switch from the system world to system of system words. And now the problem became even more severe than you discussed. So the real question is how we test it and how we maintain the capability of the system for a long-term operation. And that's what we are doing through a series of tests. Now when you have an AI machine like that and you can declare what are the possibilities, what are the scenarios, then the AI can derive your test list and you should follow them and do it again and again and again until your system will be as prepared as possible. That's what we are doing today and looking toward the future. I believe that testing the system will be the real challenge because things are changing every day and you need to meet those changes. And you do it by technology and you do it by other capabilities based on AI but using the main factor because you never will be able to cover everything. So you need to have some kind of analysis.
John Furrier
>> Well as we do interviews with business people, we say you got to develop your organization at the speed of AI. Now, in the war arena, you have to execute at the speed of the machines. You're seeing a lot more offense coming to the table. It used to be a great defensive play. Now it's like, well, if you've got machines moving super fast, that changes the tactics. That changes potentially strategy and tactics for the leadership on the battlefield. Talk about that offensive new capability. How real is it? Does it get better? and how does defense come into play? Because you've got now offense and defense getting augmented with faster decision -making, better data.
Boaz Levy
>> So I think that the real answer for that is time to market. And you always have your goals, and you need to prepare yourself toward the customer requirements. And that drives your design from one end and your set of tests from the other end. So you need to bring initial operational capability to the customer, but later on you need to add more and more features to the system. And usually you're doing it by utilizing building blocks. And the goal for the future is to minimize the time between one building block to the other. So you'll be able to change your system in accordance with your enemy or what's happening in the battle space.
John Furrier
>> So basically, you need to scale instantly to a situation without having to do a lot of work. It becomes adaptive, if you will. Alright, let's take that to the multi -domain space because I was just on with Dave DeWalt from NightDragon, he's an investor. He made a comment that cyber's in every domain now, I would agree with him. Infrastructure, you need compute. So technology is across all the different domains, but it used to be conventional wisdom that, oh, land is land, space is space, everyone had their little stove pipes. Okay, if you think about the integration system conversation we just had, you could be great at space and enable someone else through data, so if you're going to go to this data-centric architecture, you don't have to do everything great in each one, you can just mix and match. How does the cross-domain impact the execution of building the system, operating the system, and investing in the system?
Boaz Levy
>> Oh, this kind of question is very familiar to IAI, because IAI, my company, is dealing with everything related to space, air, sea, and land capabilities. But when we are looking today toward the solution, actually everybody is working together. As you said before, integrated together. And in order to bring one good solution, let's say one good attack toward Iran, then you need them both. You need the capabilities of each one of the layers, but you need to unite them. You need space to bring you the data. And the data could go to the pilot at the cockpit, but also to the missile that is making its way toward the threat. it can actually help you to navigate, but it also can help you to receive the BDA, the Battle Damage Assessment, in order for you to know if you hit or not. So that's what is being done today, and what I am trying to say is that we have to control them all. But nowadays, we have a different challenge, and IAI would say that the wars are no longer between frontiers. As you can see, as we all can see, the wars are within cities. And when you are speaking about cities and civilians, then what about rescue teams? What about commercial aircraft or commercial transportation? What about banks, hospitals? Everybody should be connected. And that makes the problem even bigger.
John Furrier
>> And that makes the argument, or the discussion around data is super important. So having the right data at the right time. So this has to be a fully integrated system, but real time becomes super important because how do you know you hit the target? How do you know this happened? How do you avoid this? How do you make changes? All of this needs to be done in real time. That's hard to do in silos.
Boaz Levy
>> It's hard to do in silos, but we need to do it. And actually that's what we are doing today.
John Furrier
>> I talk about IAI, what you guys are doing, because I think this is a template for the future because if we could have a safe environment with autonomy and intelligence around the data, then it's going to be easier to have resolution safely. Avoid conflict, that's hard to do because every time someone tries to recruit land grabbing, they get into arguments, whether it's space. So talk about that piece of it. What is the future for you guys as you look at commercializing and expanding on this architecture and these capabilities?
Boaz Levy
>> So it's just a summary of what we just discussed because we are thinking about those types of solutions from every domain, connecting them all together, connect them also to civilian application and create the mechanism that can derive the data that comes from everybody and makes the right solution. And the right solution should be coming toward the battle space into several ways of operation. It can be from aircraft, from space, from ground, whatever you can think of. So we are doing now the building blocks of every layer, connecting them together through the special agents of AI. And in order to do so, we are creating a lot of software, a lot of communication.
John Furrier
>> You're taking your domain knowledge and you're codifying it with workflows and knowing kind of what worked, all that testing comes in handy when you just give it to a digital agent.
Boaz Levy
>> Definitely, yes. And you have to deal with a lot of pools of data that you need to derive the right data at the right time.
John Furrier
>> Well, I really appreciate your insights and sharing what you're working on and your opinions. My final question for you is, because you're not the young engineer you used to be, you're the chairman, but there are a lot of young engineers out there right now coming into this environment. They could be anywhere from 15 to 30 years old, the young guns, and then you have that other management wave coming on top of that. What is the right skill set to solve the hard problems? An engineering degree, double E, mechanical, those are the old school methods. You work on a project, you create some algorithms, in missile guidance systems. different environments we laid out. What is the skill set for A players, successful people, without job descriptions, is there a certain culture, mindset, what are the things that you see that you could share would be notable for people to understand around? What's it take to be successful?
Boaz Levy
>> So I'll convert your question to another question.
John Furrier
>> Okay.
Boaz Levy
>> Saying what will be the future of the engineer?
John Furrier
>> Yes.
Boaz Levy
>> In this kind of world. And I believe that the future engineers should have a lot of engineering way of thinking. This is the key for everything. Because when you have this way of thinking, you know how to derive any type of technology toward your solution. And it's not enough just to know how to solve an equation. You need to understand how things are working. and you ask for it, so I'm telling you that the key things for that is curiosity. You need to develop a kind of curiosity and your way of thinking in order to find out the best solution toward the future.
John Furrier
>> And some engineers just like to work on hard problems. And there's a lot of hard problems. What are some of the hard problems that need to be solved? Just kind of generic, high level, generically. what are some of the big problems, engineering problems, that need to get worked on in the next, say, five years?
Boaz Levy
>> I would say dealing with quantities, no matter what. But because a few years ago, when you thought about a war, in terms of defense tech, you thought about limited amount of quantities. Now, we're talking about thousands. Thousands require a new set of skills, new way of thinking, thinking out of the box, and that's what you need in order to bring your best solution to the table.
John Furrier
>> Boaz, thank you for coming on. Again, it's an honor to have you on. The accomplishments you've done and the work you do is the beginning, I think it's going to be a new sea change over to a new architecture. You guys are doing it. Thank you for sharing.
Boaz Levy
>> Thank you very much.
John Furrier
>> Again, I'm John Furrier.This is our Defense Tech series. Again, physical AI and robotics extends out into defense tech, which also has commercial applications as well. We're talking about computer science here, talking about engineering, talking about data. it's AI factories meets cyber security meets application, which is prevent and save and have a safe environment for conflict resolution. We're doing our part here in theCUBE. I'm John Furrier, your host. Thanks for watching.
John Furrier
