In this episode of theCUBE's Physical AI Robotics series at the New York Stock Exchange studios, Sean Hehir of BrainChip, chief executive officer, discusses neuromorphic edge computing and energy-efficient artificial intelligence, AI. Hehir describes BrainChip's neuromorphic approach to edge AI and explains the company's silicon products, IP licensing and developer toolchain for deploying models outside the data center.
Hehir explains that neuromorphic event-driven architectures reduce power consumption and latency by computing only on relevant events and placing memory near compute, enabling longer battery life and on-device inference. They identify defense systems, wearable technologies, drones and space as high-growth markets and outline a dual go-to-market strategy combining IP licensing and silicon products. John Furrier of theCUBE and Dave Vellante of theCUBE frame these trends within the Physical AI Robotics series and emphasize the importance of developer-friendly toolchains to accelerate adoption.
The discussion addresses practical applications of neuromorphic computing in robotics, defense, wearables and drones and covers topics such as semiconductors, IP licensing, on-device inference and developer toolchains for energy-efficient AI at the edge. This episode informs engineers, product managers and defense planners about factors to consider when evaluating edge AI solutions.
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Sean Hehir
In this episode of theCUBE's Physical AI Robotics series at the New York Stock Exchange studios, Sean Hehir of BrainChip, chief executive officer, discusses neuromorphic edge computing and energy-efficient artificial intelligence, AI. Hehir describes BrainChip's neuromorphic approach to edge AI and explains the company's silicon products, IP licensing and developer toolchain for deploying models outside the data center.
Hehir explains that neuromorphic event-driven architectures reduce power consumption and latency by computing only on relevant events and placing memory near compute, enabling longer battery life and on-device inference. They identify defense systems, wearable technologies, drones and space as high-growth markets and outline a dual go-to-market strategy combining IP licensing and silicon products. John Furrier of theCUBE and Dave Vellante of theCUBE frame these trends within the Physical AI Robotics series and emphasize the importance of developer-friendly toolchains to accelerate adoption.
The discussion addresses practical applications of neuromorphic computing in robotics, defense, wearables and drones and covers topics such as semiconductors, IP licensing, on-device inference and developer toolchains for energy-efficient AI at the edge. This episode informs engineers, product managers and defense planners about factors to consider when evaluating edge AI solutions.
>> Palo Alto Studio Connection, Silicon Valley and Wall Street. I'm John Furrier, co-host of The Cube here with Dave Vellante, my co-host.
John Furrier
>> Hello, I'm John Furrier, host of The Cube. We are at The Cube's NYSE studios here in New York City. Of course, we have our Palo Alto studio connecting Silicon Valley to Wall Street. This is our Physical AI Robotics series. We talk to the leaders who are making it happen in physical AI and robotics, which also includes a little bit of defense tech. Which we have a whole nother series on. But this is part of the AI infrastructure that's booming and we expect to see more action and more growth in 2027 as AI factories come on board. That's going to open up the edge, that's going to open up robotics, drones, devices. Sean Hersey, CEO of BrainChip. Sean, great to see you. Thanks for coming back. It's been a while. Been on theCUBE.
Sean Hehir
>> Good to see you, John.
John Furrier
>> You're running BrainChip as CEO. You guys doing great. Tell us a little bit about what you guys are doing and where the action is.
Sean Hehir
>> Sure, I could talk a long time, but I'll be brief so we can ask some follow-on questions. So at its simplest level, most people know AI and think of the data center, John, right? These kind of large centralized thing. I know you and I are going to talk about the edge. What we do is enable the edge and everything always starts with silicon, right? So we provide chips. We also provide IP for those companies that want to build custom ASICs or SoCs. Of course, we have a toolchain to put models on there, companies, models, developers, etc., things like that.
John Furrier
>> So BrainChip, is there, is it a chip for the brain or is that a metaphor? 'Cause you know, Elon loves the brain chip. Explain the name.
Sean Hehir
>> Sure. BrainChip comes from the idea of being neuromorphic. And for those who are not familiar with neuromorphic, it's really event-based or sparsity. If you think about things on the edge, they're typically streaming data, they're typically sparse, not a lot of activity till something happens. So it's ideal for that. What neuromorphic means, it's brain-like, brain-inspired, because your brain is the most efficient computational engine known to mankind, right? If nothing is happening, your brain's not firing. And I like to use a very simple example, right? Most people we meet in our daily lives have two eyes, nose, mouth, you know, hair color and things like that. The only thing we notice when we meet new people is what's different— the skin color, facial hair, and things like that. That's what neuromorphic is. Nothing challenging, we don't compute.
John Furrier
>> So on the opportunities, you mentioned the edge, obviously You're starting to see the formation of what AI factories are doing, the big data centers, gigawatts. There's geothermal going on. So energy is being worked on, but still a constraint. Power and money is the big constraint in the AI infrastructure. But when you have intelligence tokens that they're doing, it has value and everyone's talking about that. That's kind of what's happening now, right? But you connect the dots to the edge. Those AI factories are just a node in the network. They're smaller, maybe they're different footprints.
Sean Hehir
>> Yep.
John Furrier
>> That's going to be the next land grab for chips. The systems that are around those chips. What's your vision on that? Because, you know, every enterprise I talk to, the word basically comes out every time, like, we need a brain for our company.
Sean Hehir
>> Mm-hmm.
John Furrier
>> Graph databases are popular. Systems are being organized around the resources like a brain. Yep. The edge is the perfect scenario for that.
Sean Hehir
>> Well, that's a really comprehensive question. I'll give you a kind of a little bit longer answer, right? So you talk about data centers and power and all that. You know, most compute models start centralized and then they decentralize. And so what the industry is finally realizing is the right tool for the right job. So you're going to have that data center do some things, but you're also going to do some edge stuff. And the edge can take form itself as discrete. Maybe it's a standalone medical device, maybe it's something mobile in defense, or maybe it's a wearable device. And I can give some examples a little bit later. Or it can work in that federated way or orchestrated way with the data center. Now, our technology, we certainly— we announced something not that long ago with IBM, with Symphony, which is a product they have. What goes out is kind of an orchestration layer that looks and uses, collects up what we call Akita, which is our brand name for our chips, and uses all that kind of data and brings it into a centralized. So the right compute happens on the edge, the right compute happens in the data center.
John Furrier
>> Who are your customers? You mentioned IBM. So who are your customers? Who do you— work with? What's their application?
Sean Hehir
>> You know, our technology is horizontal enabling across all industries, but I'll answer your question directly. The hot markets are ones that you could expect, the markets— mobility that matters, change matters. Defense right now, if you think about what's going on with defense, it has changed from these kind of large monolithic fixed mobile position systems to mobile, right? Look what's going on in Ukraine. Look what's going on in the Middle East right now. So you're going to do that with technologies that require mobility, and mobility requires long battery life. You require long battery life. You need good edge technology. The best edge technology is neuromorphic. So defense is really, really strong with us right now. People examining cases they need to do, they get decisions quicker, latency, fear of security going back to the data center, defense is ideal. I mentioned wearables a moment ago, enabling use cases for things that you couldn't possibly do. You can't afford the latency. Maybe you're concerned about security. There's a lot of things. If you're putting a thing on your hand, your wrist, your face, you need long battery life. So Those are the kind of hot industries, of course, IoT and others, but the use cases are limitless. And I'm going to make one more comment. When I became CEO of this company 4 and a half years ago, usually the conversations were like this: really interesting technology, breakthrough performance, help me understand what I do with it. Now all day I hear, Sean, here's my use case, here's my performance KPIs, I need you guys to do this. And it's coming from all industries.
John Furrier
>> Battery life's huge. You mentioned that. So talk more about that because Energy is the bounding function, whatever conversation you want to have, but on the edge and mobility specifically, drones have distance limitations because of battery.
Sean Hehir
>> That's right.
John Furrier
>> Power.
Sean Hehir
>> That's right.
John Furrier
>> As an example, what is the prerequisite for really strong battery life in your system?
Sean Hehir
>> Well, it's really the underlying technology, how we do it because we're neuromorphic or event-based, we ignore everything. So basically more conventional computing, I've been in this industry for a long time. Most conventional computing, it's all around, brute force matrix multiplication, right? The Von Neumann architecture, just multiply, multiply zeros and ones, zeros and ones. If it's a zero, the way to look at it, we do nothing. So just that fact alone, if there's nothing happened, that saves huge amounts of power. Secondly, we have our memory right next to the compute elements, so it's called near term. So you're not moving a lot, so very low energy there. And of course, the way we quantize models to do that.
John Furrier
>> So basically blacklist of the zeros, basically you only compute on relevant event data.
Sean Hehir
>> That's called event-driven or sparsity or neuromorphic. Exactly right.
John Furrier
>> Where's this going? Connect the dots because you know what's happening. We know in AI, state AI, which I do have a couple of questions on that, but like it's booming. Yeah, we kind of know the impact, what it means. Lower battery, more power, better, better efficiency. Where does it go? What's the next— connect the dots?
Sean Hehir
>> Well, it's one of these things that I have the great privilege to lead a company where use cases come up every day. Phone calls come in saying, Sean, can you talk to this customer for me? I'm like, sure. And I'm like, Hmm, I never would have thought of that. Never would have thought of that. And I'll give you a good example. One is a company we're working with out of Oman right now, which has wearable glasses for epileptic seizure prediction, not detection. And it has a little sensor up here on your frame of your glasses and says you're going to have a seizure, 98% accuracy in 1 hour. What that allows you to do is get off the road if you're driving or go seek medical attention, things like that. So the use cases are limited. I mean, limitless. They can go anywhere. What this can do, basically enabling any kind of product, any kind kind of technology to do more with less.
John Furrier
>> These are breakthroughs.
Sean Hehir
>> They're really—
John Furrier
>> I mean, when you think about that, I was talking to an entrepreneur. They have this— it's in R&D, but it's getting commercialized. Basically, the eye is— the eyes are a lens into the brain.
Sean Hehir
>> That's right.
John Furrier
>> So the neurological pathways from the eyes can predict. And without AI, they never would have been able to do anything.
Sean Hehir
>> That's right.
John Furrier
>> They were using old statistical samples. That's right. But they're doing real-time eye tracking to look at certain things that AI can predict. Sensors on the glasses. What other things are you seeing out there that are kind of— I won't say fall out of the chair, but like not obvious?
Sean Hehir
>> Well, we have another licensee, some company that licensed us out of Sweden called Frankreich Geyser. They make silicon for spaceships. Now you think of space, that's the ultimate harsh environment, right? You cannot rely on a network. 'Cause it could go away. And you certainly can't, the latency could kill you. You need the ability to do the computation, do it quickly, and do the inference. And that's what's happening in space. So there are a whole bunch of missions going on in space right now that are enabled by our kind of technologies. So things like that that just were not possible or conceivable now can be done.
John Furrier
>> So basically the environment of what used to be a data center application could be space, edge, human wearable.
Sean Hehir
>> Defense.
John Furrier
>> So you're seeing that kind of capability come in.
Sean Hehir
>> Yeah, it's basically the right tool for the right job, right? Depending on what the requirements are. The other thing that's driving it is people, you know, the whole world is more and more competitive every single day. So every company is out there looking for more feature functions in their end product, right? And if this enables a feature function or something, allow them to sell more things, it's incredible.
John Furrier
>> Talk about the business that you're running because, uh, talk about some of the momentum you have. Where's the hot areas? What's working right now?
Sean Hehir
>> It's literally across the board, the industries I talked about right there. But one of the things that's really important about the edge is it really— anything in AI, the models keep changing. They go quickly. BrainChip is mostly scientists and engineers. We hire from the best schools in the world and we're following very closely model trends and we're building the technology to accelerate that at all times. So we've got some really interesting offers that we have now and we've got some incredible offers that are coming even later this year and early into next year. Things that seemed impossible, LLMs or SLMs on the edge with incredible performance. Couple of billion parameter models doing, you know, very rich functionality are very real in today's time.
John Furrier
>> lot of successful companies that we interview here on The Cube and the ones that we see in the industry all the time that are in AI and winning have a deep bench of labs. That's right. Technical people. Even one company I just interviewed this week got $500 million. A bulk of it's going to go to R&D. So having a technical team matters. Speak to that nuance because back there, oh, 10% R&D, maybe 20% if you were aggressive. Not anymore.
John Furrier
>> No, no, I don't— you know, I don't have the numbers in front of me, but I can simply say the vast majority, 70, 80% of our people, all scientists, engineers, mostly PhDs from the best universities in the world, doing breakthrough research, following the model trends, looking at the best way to optimize the edge technology.
John Furrier
>> It's interesting. You want to hire great people, you got to have hard problems to solve.
John Furrier
>> That's right.
John Furrier
>> What are some of the hard problems that you see out there? I mean, money follows constraints. Entrepreneurs follow constraints. Businesses enter new markets through constraints that they solve. What are some of those areas that you guys are eyeing down in the market?
John Furrier
>> Well, it's just the point I said earlier, the ability to do more complex models on the edge. Most people thought the edge is relatively simple CNNs and things like that at one particular point. Now, very aggressive breakthrough type of models. So if you're familiar with a state of models called state-space models, where we have an offering to support them. You know, a lot of companies project that it will surpass transformers as the LLM choice of the future. Of course, we will support both, but we put a tremendous amount of energy into state-based models, supporting them, developing them, because they are really compelling reasons why it will be better kind of LLM.
John Furrier
>> And the neuromorphic way is the benefit.
John Furrier
>> Absolutely. The neuromorphic way is finally catching on. You know, the interesting thing about neuromorphic And it's the same thing, you know, you're a longtime veteran in Silicon Valley as I am too. Breakthrough technology is wonderful, but if you make it too hard to implement, it doesn't go. So what we strive to do is make this incredible breakthrough performance, make it really easy to adopt, meaning the best toolchain to port models on there, make a model zoo for people to try and buy. We have a whole plethora of kind of form factors for people to try and buy. And of course, I put a real customer focus in. I know when customers engage with us, we're enabling their revenue stream. The entire company knows that we're here to make those companies successful.
John Furrier
>> What's the secret sauce on making things easy to deploy? Because you're— that is a huge insight because a lot of people solve a problem and then pass it off to another team. Oh, well, implement it. Well, it's too hard. What's the secret? Is it more AI, using your own AI for your own stuff? Do you build your own models? What are some of the techniques and playbooks?
John Furrier
>> It's all of that, but it starts with the right leadership. And I believe my view of this when I came in was we have got to make this implementable super easy. And that And that means every part of it, whether that's documentation, customer interactions, the toolchains itself, make it more intuitive, the GUIs and things like that. Also to support industry standard frameworks. So as an example, a lot of people think of neuromorphic as overly complex analog. It's fully digital. You can put this in any fab in the world, right? You know, we have chips that are in GlobalFoundries, TSMC, we're part of the Intel Foundry program, so you can move it. So that makes it easy. Models today, a lot of them developed PyTorch, TensorFlow. You don't have to learn a new framework if you've got a bunch of scientists and engineers. You develop those models, use a toolchain and put them on. So it's those kind of thinking. Let the engineers who are familiar with the framework develop it and make it easy to port their models.
John Furrier
>> Yeah. And get value quicker. What's the growth strategy? As CEO, you got to look at the North Star. You got to look at the market. What's your execution plan? What's the growth strategy for BrainChip?
John Furrier
>> Yeah, it's really on two vectors. We sell both IP and chips. Now, IP, if you're familiar with the IP business which I know you are, is very, very powerful because you go in with a license and a royalty. And so we've seeded the market with key licenses and we've got more coming. And as those chips go out, you know, you get— you virtually get a revenue stream that's 100% margin at some point. At the same time, we have silicon because silicon matters for companies who cannot afford to build a chip. Right. And say, hey, I want to maybe add a coprocessor. So we have coprocessors to add it on and integrate it back to the ease. We integrate the runtime very simply. Any engineer in the world can do it. So you grow on on both of those and make it easy to adopt. And that's when you look then at the industries that are uptaking and you lean harder on those. We mentioned a few earlier. We enable them all, but we see one, we lean really hard, like defense right now.
John Furrier
>> And it's hot. And we think robotics is going to take on many different—
John Furrier
>> and drones.
John Furrier
>> And drones. And again, it's a horizontal play, as you pointed out. It's not like the old classic, well, it's a vertical industry. In a way, robotics is going to be infused everywhere.
John Furrier
>> Of course it is.
John Furrier
>> And AI will be everywhere. And injecting intelligence is the key. At a low cost, good power envelope or battery, and get that intelligence kicking. I have to ask you, because you're a Silicon Valley veteran, we both are. The narrative right now on AI is half the world— I mean, oversimplify— half hate it, think it's dangerous, going to kill us. Other half are like, so bullish. And this is also fringe. You get the purists on the left say it's the best thing ever, going to save the world. And the other half wants— we're going to get killed by it. But a bulk of the 50% that are kind of doomers on AI are really normal people influenced by the media, don't know the Silicon Valley playbook. They don't know what innovation looks like and they don't really have confidence. So they're confused. They need clarity.
John Furrier
>> Right.
John Furrier
>> And I want to ask you specifically what your thoughts are on that, because if you look at all these revolutions, they didn't just start overnight. I mean, you had a background in chips, systems. There are people that have come before that have built a lot of stuff.
John Furrier
>> That's right.
John Furrier
>> Maybe one-offs, maybe for pioneers, first movers. Yeah, but now that we're going to a scale market, what's your view on this whole AI debate? Do you agree that the confusion on the doomer side is lack of clarity or they're being influenced by the politics of it? What's your view on this? Because, you know, normal people are scratching their head asking me all the time privately what's going on with this AI thing. I'm like, guys, this is all overblown. Even Jensen. But Jensen's biased because he sells— yeah, yeah, yeah, GPUs. He sells infrastructure. But of course he's right. At least that's my opinion.
John Furrier
>> Yeah, yeah. I think, I think it's all those things. I think it's influenced by politics, lack of knowledge. In the end, this technology is amazing. If you think about what's happened to our world, you know, the agricultural revolution, industrial, the internet, this is going to make all those look small. I see this as nothing but great for humanity. Clearly, there's always risk with anything. There was risk when you did, you know, the internet. There was risk when you did, you know, agriculture. But I I think it's way overblown. I see nothing but goodness for society.
John Furrier
>> And the bubble question comes down to like, where's the— where's the financing coming from? There's real use case. Unlike the dot-com bubble or other bubbles, there really wasn't visibility into the unit economics. We're starting to see already productivity numbers, starting to see literally transformation projects that have revenue tied to it. So this is like real world first, of course, horizontal scale. Of course.
John Furrier
>> I mean, the customers I talk to on a daily basis are very, you know, ROI-oriented, right? What's the costs, look at it, they do their analytics in here, but usually it's because they already have products in the market and they're looking for that next generation to enable. So it's very, very clear. The only other exception to that is where you've got to have it regardless of the cost, and that is defense, right? When somebody else makes a first move, you've got to make a move to make it better.
John Furrier
>> Great. Well, what's, what's next for you? What's on your agenda? I know you travel a lot seeing customers. What's your plan for the second half of the year going into 2027?
John Furrier
>> Well, we've got a tremendous roadmap that I'm highly focused with our team on executing on and announcing that later this year. You're absolutely right. I've got a lot of customer meetings I've got to do, and I look forward to chatting with everybody around the world here because I'm going to be leaving here. I'll be doing some trips to Australia and Asia in the second half of this year.
John Furrier
>> right, Sean, great to see you. And congratulations on taking the helm at BrainChip. And congratulations.
John Furrier
>> Always a pleasure, John.
John Furrier
>> I'm John Furrier at theCUBE. We are here for the Robotics AI Series. Physical AI is the hottest area. It has not yet gone fully mainstream, but you're starting to see the signals. You got robotics, you got drones. You got all kinds of impact across all industries. This is where intelligence meets the physical world, and that's where you start to see new things emerge, new use cases, breakthroughs on the science side, of course, a lot more that affects humanity. I'm John Furrier. Thanks for watching.
>> Palo Alto Studio Connection, Silicon Valley and Wall Street. I'm John Furrier, co-host of The Cube here with Dave Vellante, my co-host.
John Furrier
>> Hello, I'm John Furrier, host of The Cube. We are at The Cube's NYSE studios here in New York City. Of course, we have our Palo Alto studio connecting Silicon Valley to Wall Street. This is our Physical AI Robotics series. We talk to the leaders who are making it happen in physical AI and robotics, which also includes a little bit of defense tech. Which we have a whole nother series on. But this is part of the AI infrastructure that's booming and we expect to see more action and more growth in 2027 as AI factories come on board. That's going to open up the edge, that's going to open up robotics, drones, devices. Sean Hersey, CEO of BrainChip. Sean, great to see you. Thanks for coming back. It's been a while. Been on theCUBE.
Sean Hehir
>> Good to see you, John.
John Furrier
>> You're running BrainChip as CEO. You guys doing great. Tell us a little bit about what you guys are doing and where the action is.
Sean Hehir
>> Sure, I could talk a long time, but I'll be brief so we can ask some follow-on questions. So at its simplest level, most people know AI and think of the data center, John, right? These kind of large centralized thing. I know you and I are going to talk about the edge. What we do is enable the edge and everything always starts with silicon, right? So we provide chips. We also provide IP for those companies that want to build custom ASICs or SoCs. Of course, we have a toolchain to put models on there, companies, models, developers, etc., things like that.
John Furrier
>> So BrainChip, is there, is it a chip for the brain or is that a metaphor? 'Cause you know, Elon loves the brain chip. Explain the name.
Sean Hehir
>> Sure. BrainChip comes from the idea of being neuromorphic. And for those who are not familiar with neuromorphic, it's really event-based or sparsity. If you think about things on the edge, they're typically streaming data, they're typically sparse, not a lot of activity till something happens. So it's ideal for that. What neuromorphic means, it's brain-like, brain-inspired, because your brain is the most efficient computational engine known to mankind, right? If nothing is happening, your brain's not firing. And I like to use a very simple example, right? Most people we meet in our daily lives have two eyes, nose, mouth, you know, hair color and things like that. The only thing we notice when we meet new people is what's different— the skin color, facial hair, and things like that. That's what neuromorphic is. Nothing challenging, we don't compute.
John Furrier
>> So on the opportunities, you mentioned the edge, obviously You're starting to see the formation of what AI factories are doing, the big data centers, gigawatts. There's geothermal going on. So energy is being worked on, but still a constraint. Power and money is the big constraint in the AI infrastructure. But when you have intelligence tokens that they're doing, it has value and everyone's talking about that. That's kind of what's happening now, right? But you connect the dots to the edge. Those AI factories are just a node in the network. They're smaller, maybe they're different footprints.
Sean Hehir
>> Yep.
John Furrier
>> That's going to be the next land grab for chips. The systems that are around those chips. What's your vision on that? Because, you know, every enterprise I talk to, the word basically comes out every time, like, we need a brain for our company.
Sean Hehir
>> Mm-hmm.
John Furrier
>> Graph databases are popular. Systems are being organized around the resources like a brain. Yep. The edge is the perfect scenario for that.
Sean Hehir
>> Well, that's a really comprehensive question. I'll give you a kind of a little bit longer answer, right? So you talk about data centers and power and all that. You know, most compute models start centralized and then they decentralize. And so what the industry is finally realizing is the right tool for the right job. So you're going to have that data center do some things, but you're also going to do some edge stuff. And the edge can take form itself as discrete. Maybe it's a standalone medical device, maybe it's something mobile in defense, or maybe it's a wearable device. And I can give some examples a little bit later. Or it can work in that federated way or orchestrated way with the data center. Now, our technology, we certainly— we announced something not that long ago with IBM, with Symphony, which is a product they have. What goes out is kind of an orchestration layer that looks and uses, collects up what we call Akita, which is our brand name for our chips, and uses all that kind of data and brings it into a centralized. So the right compute happens on the edge, the right compute happens in the data center.
John Furrier
>> Who are your customers? You mentioned IBM. So who are your customers? Who do you— work with? What's their application?
Sean Hehir
>> You know, our technology is horizontal enabling across all industries, but I'll answer your question directly. The hot markets are ones that you could expect, the markets— mobility that matters, change matters. Defense right now, if you think about what's going on with defense, it has changed from these kind of large monolithic fixed mobile position systems to mobile, right? Look what's going on in Ukraine. Look what's going on in the Middle East right now. So you're going to do that with technologies that require mobility, and mobility requires long battery life. You require long battery life. You need good edge technology. The best edge technology is neuromorphic. So defense is really, really strong with us right now. People examining cases they need to do, they get decisions quicker, latency, fear of security going back to the data center, defense is ideal. I mentioned wearables a moment ago, enabling use cases for things that you couldn't possibly do. You can't afford the latency. Maybe you're concerned about security. There's a lot of things. If you're putting a thing on your hand, your wrist, your face, you need long battery life. So Those are the kind of hot industries, of course, IoT and others, but the use cases are limitless. And I'm going to make one more comment. When I became CEO of this company 4 and a half years ago, usually the conversations were like this: really interesting technology, breakthrough performance, help me understand what I do with it. Now all day I hear, Sean, here's my use case, here's my performance KPIs, I need you guys to do this. And it's coming from all industries.
John Furrier
>> Battery life's huge. You mentioned that. So talk more about that because Energy is the bounding function, whatever conversation you want to have, but on the edge and mobility specifically, drones have distance limitations because of battery.
Sean Hehir
>> That's right.
John Furrier
>> Power.
Sean Hehir
>> That's right.
John Furrier
>> As an example, what is the prerequisite for really strong battery life in your system?
Sean Hehir
>> Well, it's really the underlying technology, how we do it because we're neuromorphic or event-based, we ignore everything. So basically more conventional computing, I've been in this industry for a long time. Most conventional computing, it's all around, brute force matrix multiplication, right? The Von Neumann architecture, just multiply, multiply zeros and ones, zeros and ones. If it's a zero, the way to look at it, we do nothing. So just that fact alone, if there's nothing happened, that saves huge amounts of power. Secondly, we have our memory right next to the compute elements, so it's called near term. So you're not moving a lot, so very low energy there. And of course, the way we quantize models to do that.
John Furrier
>> So basically blacklist of the zeros, basically you only compute on relevant event data.
Sean Hehir
>> That's called event-driven or sparsity or neuromorphic. Exactly right.
John Furrier
>> Where's this going? Connect the dots because you know what's happening. We know in AI, state AI, which I do have a couple of questions on that, but like it's booming. Yeah, we kind of know the impact, what it means. Lower battery, more power, better, better efficiency. Where does it go? What's the next— connect the dots?
Sean Hehir
>> Well, it's one of these things that I have the great privilege to lead a company where use cases come up every day. Phone calls come in saying, Sean, can you talk to this customer for me? I'm like, sure. And I'm like, Hmm, I never would have thought of that. Never would have thought of that. And I'll give you a good example. One is a company we're working with out of Oman right now, which has wearable glasses for epileptic seizure prediction, not detection. And it has a little sensor up here on your frame of your glasses and says you're going to have a seizure, 98% accuracy in 1 hour. What that allows you to do is get off the road if you're driving or go seek medical attention, things like that. So the use cases are limited. I mean, limitless. They can go anywhere. What this can do, basically enabling any kind of product, any kind kind of technology to do more with less.
John Furrier
>> These are breakthroughs.
Sean Hehir
>> They're really—
John Furrier
>> I mean, when you think about that, I was talking to an entrepreneur. They have this— it's in R&D, but it's getting commercialized. Basically, the eye is— the eyes are a lens into the brain.
Sean Hehir
>> That's right.
John Furrier
>> So the neurological pathways from the eyes can predict. And without AI, they never would have been able to do anything.
Sean Hehir
>> That's right.
John Furrier
>> They were using old statistical samples. That's right. But they're doing real-time eye tracking to look at certain things that AI can predict. Sensors on the glasses. What other things are you seeing out there that are kind of— I won't say fall out of the chair, but like not obvious?
Sean Hehir
>> Well, we have another licensee, some company that licensed us out of Sweden called Frankreich Geyser. They make silicon for spaceships. Now you think of space, that's the ultimate harsh environment, right? You cannot rely on a network. 'Cause it could go away. And you certainly can't, the latency could kill you. You need the ability to do the computation, do it quickly, and do the inference. And that's what's happening in space. So there are a whole bunch of missions going on in space right now that are enabled by our kind of technologies. So things like that that just were not possible or conceivable now can be done.
John Furrier
>> So basically the environment of what used to be a data center application could be space, edge, human wearable.
Sean Hehir
>> Defense.
John Furrier
>> So you're seeing that kind of capability come in.
Sean Hehir
>> Yeah, it's basically the right tool for the right job, right? Depending on what the requirements are. The other thing that's driving it is people, you know, the whole world is more and more competitive every single day. So every company is out there looking for more feature functions in their end product, right? And if this enables a feature function or something, allow them to sell more things, it's incredible.
John Furrier
>> Talk about the business that you're running because, uh, talk about some of the momentum you have. Where's the hot areas? What's working right now?
Sean Hehir
>> It's literally across the board, the industries I talked about right there. But one of the things that's really important about the edge is it really— anything in AI, the models keep changing. They go quickly. BrainChip is mostly scientists and engineers. We hire from the best schools in the world and we're following very closely model trends and we're building the technology to accelerate that at all times. So we've got some really interesting offers that we have now and we've got some incredible offers that are coming even later this year and early into next year. Things that seemed impossible, LLMs or SLMs on the edge with incredible performance. Couple of billion parameter models doing, you know, very rich functionality are very real in today's time.
John Furrier
>> lot of successful companies that we interview here on The Cube and the ones that we see in the industry all the time that are in AI and winning have a deep bench of labs. That's right. Technical people. Even one company I just interviewed this week got $500 million. A bulk of it's going to go to R&D. So having a technical team matters. Speak to that nuance because back there, oh, 10% R&D, maybe 20% if you were aggressive. Not anymore.
John Furrier
>> No, no, I don't— you know, I don't have the numbers in front of me, but I can simply say the vast majority, 70, 80% of our people, all scientists, engineers, mostly PhDs from the best universities in the world, doing breakthrough research, following the model trends, looking at the best way to optimize the edge technology.
John Furrier
>> It's interesting. You want to hire great people, you got to have hard problems to solve.
John Furrier
>> That's right.
John Furrier
>> What are some of the hard problems that you see out there? I mean, money follows constraints. Entrepreneurs follow constraints. Businesses enter new markets through constraints that they solve. What are some of those areas that you guys are eyeing down in the market?
John Furrier
>> Well, it's just the point I said earlier, the ability to do more complex models on the edge. Most people thought the edge is relatively simple CNNs and things like that at one particular point. Now, very aggressive breakthrough type of models. So if you're familiar with a state of models called state-space models, where we have an offering to support them. You know, a lot of companies project that it will surpass transformers as the LLM choice of the future. Of course, we will support both, but we put a tremendous amount of energy into state-based models, supporting them, developing them, because they are really compelling reasons why it will be better kind of LLM.
John Furrier
>> And the neuromorphic way is the benefit.
John Furrier
>> Absolutely. The neuromorphic way is finally catching on. You know, the interesting thing about neuromorphic And it's the same thing, you know, you're a longtime veteran in Silicon Valley as I am too. Breakthrough technology is wonderful, but if you make it too hard to implement, it doesn't go. So what we strive to do is make this incredible breakthrough performance, make it really easy to adopt, meaning the best toolchain to port models on there, make a model zoo for people to try and buy. We have a whole plethora of kind of form factors for people to try and buy. And of course, I put a real customer focus in. I know when customers engage with us, we're enabling their revenue stream. The entire company knows that we're here to make those companies successful.
John Furrier
>> What's the secret sauce on making things easy to deploy? Because you're— that is a huge insight because a lot of people solve a problem and then pass it off to another team. Oh, well, implement it. Well, it's too hard. What's the secret? Is it more AI, using your own AI for your own stuff? Do you build your own models? What are some of the techniques and playbooks?
John Furrier
>> It's all of that, but it starts with the right leadership. And I believe my view of this when I came in was we have got to make this implementable super easy. And that And that means every part of it, whether that's documentation, customer interactions, the toolchains itself, make it more intuitive, the GUIs and things like that. Also to support industry standard frameworks. So as an example, a lot of people think of neuromorphic as overly complex analog. It's fully digital. You can put this in any fab in the world, right? You know, we have chips that are in GlobalFoundries, TSMC, we're part of the Intel Foundry program, so you can move it. So that makes it easy. Models today, a lot of them developed PyTorch, TensorFlow. You don't have to learn a new framework if you've got a bunch of scientists and engineers. You develop those models, use a toolchain and put them on. So it's those kind of thinking. Let the engineers who are familiar with the framework develop it and make it easy to port their models.
John Furrier
>> Yeah. And get value quicker. What's the growth strategy? As CEO, you got to look at the North Star. You got to look at the market. What's your execution plan? What's the growth strategy for BrainChip?
John Furrier
>> Yeah, it's really on two vectors. We sell both IP and chips. Now, IP, if you're familiar with the IP business which I know you are, is very, very powerful because you go in with a license and a royalty. And so we've seeded the market with key licenses and we've got more coming. And as those chips go out, you know, you get— you virtually get a revenue stream that's 100% margin at some point. At the same time, we have silicon because silicon matters for companies who cannot afford to build a chip. Right. And say, hey, I want to maybe add a coprocessor. So we have coprocessors to add it on and integrate it back to the ease. We integrate the runtime very simply. Any engineer in the world can do it. So you grow on on both of those and make it easy to adopt. And that's when you look then at the industries that are uptaking and you lean harder on those. We mentioned a few earlier. We enable them all, but we see one, we lean really hard, like defense right now.
John Furrier
>> And it's hot. And we think robotics is going to take on many different—
John Furrier
>> and drones.
John Furrier
>> And drones. And again, it's a horizontal play, as you pointed out. It's not like the old classic, well, it's a vertical industry. In a way, robotics is going to be infused everywhere.
John Furrier
>> Of course it is.
John Furrier
>> And AI will be everywhere. And injecting intelligence is the key. At a low cost, good power envelope or battery, and get that intelligence kicking. I have to ask you, because you're a Silicon Valley veteran, we both are. The narrative right now on AI is half the world— I mean, oversimplify— half hate it, think it's dangerous, going to kill us. Other half are like, so bullish. And this is also fringe. You get the purists on the left say it's the best thing ever, going to save the world. And the other half wants— we're going to get killed by it. But a bulk of the 50% that are kind of doomers on AI are really normal people influenced by the media, don't know the Silicon Valley playbook. They don't know what innovation looks like and they don't really have confidence. So they're confused. They need clarity.
John Furrier
>> Right.
John Furrier
>> And I want to ask you specifically what your thoughts are on that, because if you look at all these revolutions, they didn't just start overnight. I mean, you had a background in chips, systems. There are people that have come before that have built a lot of stuff.
John Furrier
>> That's right.
John Furrier
>> Maybe one-offs, maybe for pioneers, first movers. Yeah, but now that we're going to a scale market, what's your view on this whole AI debate? Do you agree that the confusion on the doomer side is lack of clarity or they're being influenced by the politics of it? What's your view on this? Because, you know, normal people are scratching their head asking me all the time privately what's going on with this AI thing. I'm like, guys, this is all overblown. Even Jensen. But Jensen's biased because he sells— yeah, yeah, yeah, GPUs. He sells infrastructure. But of course he's right. At least that's my opinion.
John Furrier
>> Yeah, yeah. I think, I think it's all those things. I think it's influenced by politics, lack of knowledge. In the end, this technology is amazing. If you think about what's happened to our world, you know, the agricultural revolution, industrial, the internet, this is going to make all those look small. I see this as nothing but great for humanity. Clearly, there's always risk with anything. There was risk when you did, you know, the internet. There was risk when you did, you know, agriculture. But I I think it's way overblown. I see nothing but goodness for society.
John Furrier
>> And the bubble question comes down to like, where's the— where's the financing coming from? There's real use case. Unlike the dot-com bubble or other bubbles, there really wasn't visibility into the unit economics. We're starting to see already productivity numbers, starting to see literally transformation projects that have revenue tied to it. So this is like real world first, of course, horizontal scale. Of course.
John Furrier
>> I mean, the customers I talk to on a daily basis are very, you know, ROI-oriented, right? What's the costs, look at it, they do their analytics in here, but usually it's because they already have products in the market and they're looking for that next generation to enable. So it's very, very clear. The only other exception to that is where you've got to have it regardless of the cost, and that is defense, right? When somebody else makes a first move, you've got to make a move to make it better.
John Furrier
>> Great. Well, what's, what's next for you? What's on your agenda? I know you travel a lot seeing customers. What's your plan for the second half of the year going into 2027?
John Furrier
>> Well, we've got a tremendous roadmap that I'm highly focused with our team on executing on and announcing that later this year. You're absolutely right. I've got a lot of customer meetings I've got to do, and I look forward to chatting with everybody around the world here because I'm going to be leaving here. I'll be doing some trips to Australia and Asia in the second half of this year.
John Furrier
>> right, Sean, great to see you. And congratulations on taking the helm at BrainChip. And congratulations.
John Furrier
>> Always a pleasure, John.
John Furrier
>> I'm John Furrier at theCUBE. We are here for the Robotics AI Series. Physical AI is the hottest area. It has not yet gone fully mainstream, but you're starting to see the signals. You got robotics, you got drones. You got all kinds of impact across all industries. This is where intelligence meets the physical world, and that's where you start to see new things emerge, new use cases, breakthroughs on the science side, of course, a lot more that affects humanity. I'm John Furrier. Thanks for watching.
John Furrier