The Hardest Computing Problem Ever, According to Arm's Physical AI Chief

Drew Henry, EVP of Arm's Physical AI Business Unit, argues that robotics, humanoids, and autonomous vehicles represent one of the hardest systems-engineering problems in computing, with the physical economy—roughly $70 trillion of the $115 trillion global GDP—running on only a tenth of the compute per dollar of output compared to the digital economy. Henry, who previously led GeForce at Nvidia for 11 years and launched Arm's Neoverse infrastructure business, emphasizes that physical AI's value is time, not automation, and that every physical industry can be broken into four verbs: grown or mined, manufactured, moved, operated.

Drew Henry ran GeForce at Nvidia for 11 years. He launched Arm’s Neoverse infrastructure business. Now he leads Arm’s Physical AI unit. I sat down with him for the latest Bit by Bit conversation. His main argument: robots, humanoids, and autonomous vehicles add up to one of the hardest systems-engineering problems in computing. And the physical half of the global economy still runs on a fraction of the compute the digital half already gets. Physical AI comes down to two numbers: latency from a sensed photon to torque on an actuator, and system weight in grams. No bolting on a bigger cooling system the way a data center would. And Global GDP is about $115T. Roughly $70T is physical, $40T is digital. Almost all compute spend goes to the smaller, digital half. And yet the physical economy runs on a tenth of the compute per dollar of output. That’s pretty mind-blowing More things stood out: The vaue prop of Physical AI is time, not automation. An autonomous vehicle doesn’t remove the drive to your kid’s game. It gives back your attention during it. Every physical industry breaks into four verbs: grown or mined, manufactured, moved, operated. AI gains there flow into two-thirds of global GDP. A humanoid hand may be harder to build than an AI data center. Every joint shares the same latency and torque constraints, zero tolerance for error. “The thing you never want to do is hallucinate during the middle of surgery.” A robot isn’t one compute problem, it’s four: locomotion real-time , conversation LLM-based , actuation orchestration, and cloud sync for retraining. Memory and model size at the edge have no settled answer yet. Henry expects a decade of trading precision for footprint before it converges. Braking stays local, coordination goes to the cloud. No cell-tower latency on a meters-scale decision, but a warehouse of robots needs central coordination. Fleet orchestration, not the chip, is becoming the real edge. Design the robot and the conveyor together, don’t bolt one onto the other. Arm co-designed Graviton with Amazon , once Amazon knew its workloads well enough to spec exactly what it needed. Same pattern now starting in AVs and robotics: go vertical on custom silicon, built on Arm. No ceiling on compute demand, every robotics company tells Henry the same thing. His comparison: PC modding culture from his GeForce days. Full transcript below lightly edited for clarity . Hello listeners. We have a special guest with us today, Drew Henry, EVP of Arm’s Physical AI Business Unit. Welcome, Drew. DH: Thank you, Austin. I’ve been looking forward to this all day. Awesome, good, me too, man. And of course, nice studio you’re in. That looks awesome. DH: Isn’t it nice? I know. I really like it. Yeah, it’s good. Everyone who’s watching take notes. This is how you do it for these Silicon companies out there. Okay, so Drew, before we get into talking about physical AI, which is the topic here, I wanted to go through your background because you had an amazing journey. And so I’m just going to kind of read your LinkedIn profile for the listeners in case they aren’t familiar with you. And I’d love afterward for you to give me some color on maybe the through line of all your interesting experiences and how it helps you in the role that you’re in now. So what I thought was awesome, you co-founded Animated Technologies in the early days of computer graphics and that got acquired. Then you went to Silicon Graphics, which I think most folks have heard of, that’s Jim Clark’s company during its heyday. And from there, in the dotcom boom, you went to a streaming media startup where you were an exec through the IPO. So all already right there, very interesting. And then you went to this little company called Nvidia, which I think most people are familiar with in 2001, and you were a GM there for 11 years. And then you were at SanDisk, which is also a very hot company right now. A couple more startups, and you’ve been at Arm almost nine years now, where first you were the founding GM of the infrastructure business and launched Neoverse. Again, very awesome. Quite a career there, but now you’re taking Arm into physical AI. And also, I noticed you’re on the board of Teradyne. So quite the expansive background from startups to Fortune 500 companies. So, yes, tell us, what’s the through line and how does it apply to what you’re doing now in physical AI? DH: Yeah, thanks. The through line for me is just I just always enjoyed working on really interesting things. And so from early days in studying engineering, both my undergrad and graduate degrees. I just was you just there’s just a curiosity that you have and that you build from something like that. And so that’s kind of the thing. I just always wanted to work on very interesting things through my career and I had the opportunity to do that being here in Silicon Valley. I’m one of the few people, by the way, that’s actually born in Silicon Valley and works in Silicon Valley. The but some of those things have been really informative to me. So for instance, working at Silicon Graphics helped me deeply appreciate what it means to be disrupted and not being a disruptor. So I was through, I went through that entire cycle with that company. I was there in the very early days and when I kind of finished that cycle, I realized, man, I just I really I want to be on where technology is disrupting, where technology is on it on its growth side, where it’s where the company is really well positioned and stuff like that. And so every decision I made from that is based upon being on the disruptor side of things and not being on the disrupted side of things. And when I joined Nvidia was literally a few hundred people at the time that I joined. Very early days. And so that was the fun time of trying to figure out what you’re going to do to create markets and stuff like that. So that’s that through line. The through line was learning painfully early on in my career, some of the lessons of being disrupted and then just figuring out how to be on the side of being a disruptor. Sure, interesting. Really fascinating. I love that chasing your curiosity and then yes, trying to think how can you be on the front line of the disruption. But I think very cool for you as you’re a GM to think there’ll be a natural evolution to what you’re working on and how can you probably prevent the disruption in the long run. DH: Yes. That I wake up and go to bed at night thinking about. DH: Great, that’s great. Okay, so now you’ve moved into physical AI. So tell us more. I’m curious, like, how does Arm even define physical AI? I think it has a broad definition out there. People think of many different things. How do you define physical AI? DH: Yeah, for us, it is we think of it as how AI, the latest in AI technologies are being embodied into physical devices. Devices that can sense what’s going on in the world, can take can make a decision about what to do based upon what it’s sensing and then can go off and take action in it. But do that safely in a way in which you’re really helping your helping solve problems. And the and so that’s how we think about it. When I turn it into kind of technical terms, I think about it as the key metric being the latency between a sensed photon and torque being applied to an actuator. That metric is key. And then the second metric that we always think about is that in this physical AI space, every gram matters. So you can’t have a massively huge computing system with a massively huge cooling system. Like in my GeForce days, we make huge cooling systems we put on those things. These things are everything is about a total system design. So that’s how we think about it. That’s how we kind of think about what’s most important for it. Hm, I like it. Sense, decide and act. I like that at a high level because it’s not focused on a particular embodiment, humanoids or automotive or something, but it’s a little bit more abstract, higher level. And then, yeah, the engineering definitions there are interesting. Photon and torque applied that does helps get people in the mindset of this is sensing the world and taking action in the world. So yeah, very cool. Now, okay, so why should a normal person care about physical AI? Is this about productivity? What is it? DH: Yeah, I’ll put it in terms I think that a lot of people can appreciate. One of the greatest benefits of what we’re trying to work on in physical AI is actually to give people time back. So, for instance, when my I’ve I have two daughters and the and of course, my wife and I, like anyone, very involved in our daughter’s lives and they were volleyball players. So we carting them around to volleyball tournaments and stuff like that. And I was myself or my wife were driving all the time. And the and there’s this amazing amount of time that you have with your kid when you’re driving them around all over the place, but you’re driving, right? So there’s only so much presence or interaction that you get with your kid at during that time. And being able to build an autonomous vehicle where, yeah, I want to be with my kid as they go to the this volleyball tournament or something like that. But man, there’s an opportunity to be more present with them, right? We can tell stories together, we can watch movie together, we can do whatever during that particular time. And so to me, that’s what this benefit of physical AI is about giving people time back or really at the core, just creating productivity. And there and applying that across this whole world of the of physical oriented industries is going to probably result in one of the biggest growth in global GDP in the history of the world. Sure, totally. Really cool. Yeah, I like the framing around time as the KPI, if you will, or the value prop. Obviously, time is such a precious resource for everyone. And then, of course, when you think about enterprises, time matters and you can usually quantify that. But I think even for consumers, framing things around time is really interesting. Of course, we see robots dancing or robots making a cup of coffee, and it’s not often a problem that I can relate to, but the problem of like, I don’t have enough time. I think that is pretty universal and very sort of user-centric. So that as a recovering product manager, that resonates with me. DH: I agree. It’s funny when you I was listening to something recently and somebody framed something in a really interesting way about how to think about the physical AI. And if you just kind of look around the world, things are either grown or mined. They’re then manufactured. They are then moved and then they’re operated in some fashion, right? Yeah. So grown or mined, manufactured and then moved in some fashion or other operated. And the and when you look across all of those things, you think about, wow, if I can improve the productivity of any of those things or whatever metrics matter, that is what the benefit of physical AI actually is to all of those different industries. So that’s transportation, that’s logistics, that’s manufacturing, that’s mining, that’s agriculture. All those things are things that will benefit substantially, if we appropriately apply Yeah. And that, makes your argument that this could be sort of the biggest impact to GDP and obviously a huge TAM because really when you talk about it’s grown or mined, it’s manufactured, it’s moved. I think supply chains and just anything you can think about from fishing to farming to making silicon wafers and chips you can see to making tractors, you can see how physical AI could be a substrate underneath all of that. DH: And it’s interesting. I and I agree with you and I like the way that you described it, Austin. The interesting thing for us when I really think about this is I if you take a step back and you just kind of look at global GDP, gross domestic product. This is just what the world spends on creating valued goods. It’s about $115 trillion last year, ish, right? $70 trillion, are related to things that are manufacturing, transportation, construction, these things that are very physically oriented. A lot of physical labor, all those things I was just talking about. And then the other $40 trillion, approximately speaking, is digital, kind of the digital side of the economy. It is things like finance and entertainment, communications, things like that are all kind of digitally oriented. And the very interesting thing when you look at both those things, is you actually recognize the vast majority that we spend today as a world on compute is actually applied to that digital side of the economy. Yeah. DH: And matter of fact, if you really dig into it’s about a 10x difference between the right the way that compute is used on the digital side of the economy than it is on the physical side of the economy. And so on the physical side of the economy, if we can bring AI the benefit is really coming from the world of AI. If we can bring AI into the physical economy, into transportation, logistics manufacturing, all the areas I just talked about, and just increase productivity small amounts, that’s a massive benefit to what is the largest portion, two-thirds of global GDP. Fascinating. Yeah, I’ve never thought about that way of just how such a percentage of GDP is really physical. And I can think back to previous jobs working like for a grocery retailer and thinking about what was spent on compute and obviously a lot of it was just on like the let’s make a web app or a mobile app or the IT side of things. But really very little on anything supply chain, physical movement of goods and everything. And so yeah, I can totally see how we’re in 40% of that GDP, we’re investing in compute, but what about that other 60% or whatever the numbers were, what it’s just like untouched. What could happen? And of course, not only if we apply compute, but now, obviously, in this era, there’s very interesting AI that can help and we can get into that. Not make interesting value happen physically, not just how do you apply AI to solving the problem of how should we, restock our groceries, but what about something more physical? Could you have something restocking groceries at night, for example? DH: Exactly. Exactly. And do it in a way where there’s an intelligent system that’s behind it that is constantly working on optimizing it and then transferring the optimizations that just recognized there right in the place where the goods are transacting people are walking into a market to buy a to buy something and then up and then upstreaming all that intelligence going, hey, listen, this is what’s hot right now because of whatever demand is interesting in the marketplace. Let’s make sure that we are keeping this well stocked and let’s make sure the entire supply chain is prepared for that. That’s the interesting thing that we’re on the verge of being able to add to this physical side of the economy. Nice, amazing. So now going back, you mentioned every gram matters and that got me thinking about constraints. So talk to me about constraints in physical AI because it’s obviously it’s different than data centers or a laptop on your desk. We’re talking you’ve got batteries because things are moving, but yet there’s actuators, you’re like applying torque somewhere in the system. So there’s like, there’s definitely power constraints, there’s weights, there’s probably thermals, there’s probably connectivity if it’s mobile. So I’m wondering how physical AI system designers balance all of those tradeoffs. They’re a little bit different. But then at the same time, I’m thinking like, well, this does feel a lot like mobile in some respects, weight, thermals, connectivity. So how are you seeing, the similarities and differences in designing around the constraints of physical AI? DH: It’s such an interesting question, Austin, and I really like the way you framed it because it is a it’s a total systems engineering problem. And I actually believe that it is the most complicated computing problem ever. This issue on physical AI, just particularly if you look at like a humanoid. And the and this issue, if you just spend a little bit of time thinking about it, you can appreciate the complexity of it. So, imagine you’re trying to build a humanoid robot and what you care about is the precise movement of this little tiny joint at the end of a finger, okay? Just trying to move that thing. And you want to move that thing and you and it’s got to grab something safely, it can’t crush it’s but it’s got to grip it appropriately. It has to actually get in touch. It’s got to make touch with it. Yeah. DH: The computing system and then the communication, the computing system through all the actuation that exists through that robotic arm, all the way out to this little tiny actuator that’s moving around the very end of the appendage. All of that has to be coordinated extraordinarily well. And if you’re and if each one of those joints is off by even the smallest amount of tolerances, you’re never going to grab what you want to grab. So you’ve got to think about it from the from that endpoint and bring that design thinking all the way through. This is what I’m trying to accomplish and can I accomplish this in a way in which I actually get this task done. And so these issues of latency I see something, I sense something, I touch something to act to informing actuation, the action that you have to take. And then you have to do that all safely because you don’t want to do anything wrong. I tell people all the time, the thing you never want to do is hallucinate during the middle of surgery. Right. Totally. Yes. DH: So these are the constraints, which is what makes it a systems problem and with all due respect to my friends that are building these incredibly massive AI data centers for AI factory kind of data centers. I think the humanoid robotic platform is more complicated because of all those things. Sure. Yeah. No, that is fascinating of thinking about the systems approach. Lots of actuators, lots of sensors, lots of communication, lots of control, latency budgets, to your point. And then I guess maybe even more broadly, because there is an AI model that’s running on the humanoid in this example, it is even bigger systems problem, which is you got the humanoid, but then you do have the like training of the AI model and the deployment of the AI model. So yeah, maybe say a little bit about that briefly, just it doesn’t feel like just embedded computing anymore. It’s got that AI model piece applied to it. DH: Yeah, embedded computing for those that aren’t familiar with it. You’re literally writing exactly what you want everything to do. Literally coding it. You’re just writing, please do this, then do that. Whereas AI is very different. AI is about, hey, I’m creating a model that kind of understands all the different things that this device could do and how do I get that device to do the things that I really want it to do by exploiting AI. The interesting thing to me about this is as we have just gotten continuously deeper and deeper into us. And by the way, we’ve been working in these industries at from arm for a very long time. I remind people that just in the last 12 months, we’ve shipped two billion devices in this in these areas that relate to these physical AI application areas. So it’s not we’re not new to us. But the computing has changed quite a bit. And to your point, that question that you’re asking about Austin about different AI models, there if you look at a autonomous any type of an autonomous system, there’s actually four different compute that compute layers that you need to think about. There’s the compute layer that is the layer of motion. In robotics, it’s referred to as locomotion; in autonomous vehicles, it’s about the autonomous motion through the world. And that is very real-time based. I’m driving down the road, I’ve got to break in that in a very fixed period of time, so there’s actually latency matters. There’s a very fast clock that’s going, so there’s a real-time nature to the way these things work. It’s actually real-time system. That is different than when you’re interacting. So I’m sitting in an autonomous vehicle and I’m having a conversation with that vehicle, right? That is a that conversation with that vehicle or I might be asking that vehicle, hey, change my destination, I want to go somewhere else. That’s not quite the same type of real-time system. It’s more LLM-based than what the other one is based. And so there’s even different AI models that you run inside these different types of systems. And then all of that has to hit the compute plane that manages all the actuation. And actually orchestrates all the actuation. It’s got to do that safely, it’s got to do that reliably and repeatably. And then eventually, the thing does connect to that to connect to the cloud because you want to update it, train it bring up new models and stuff like that. So there’s all these different layers of compute. So when you when we talk to people about how we the computing platforms that you want to design that are built on top of arm, a lot of questions come up on, well, exactly which plane of this computer are you trying to solve for? Because models are different, training is different. Yes. Oh man, that’s that is complex. I can see your argument for it’s the most complex system because yeah, real-time systems, latency budgets, you have to your point of like, you got to get photon and make a decision, hit the brakes if you need to. You’re going 60 miles an hour, it’s got to be so fast because otherwise it’s literally turning into meters until maybe you strike something. But then on the other hand, yeah, I can see like talking to the vehicle is a totally different ball game. So which gets me thinking like, okay, GPUs, CPUs, NPUs, FPGA, like what real-time operating systems, like what are all like the building blocks here? DH: Yeah, those all of the above. I guess is the best way to describe it. We as a as I tell people, we don’t have specifically dog in the hunt on that. Meaning that you’ve got to design a system, the system has to be able to accomplish something. It’s it and it’s an engineering problem and it’s got to do things in a certain amount of time and there are portions of that workload that you execute on the CPU subsystem. There’s portions of that workload that you actually execute on the real-time safety subsystem that underlies this whole thing. So if the thing goes haywire, it has a safe space to fall back to. There’s workload that you, hey, I’ve got this workload. I’ve got to do it in a fraction of a second and so I need a dedicated acceleration unit to do that could be any one of the things that you talked about. It’s a system design and our experience is and as we all the customers that we talk to in this area, and we talk to the world’s leading, there’s just not a robotics or autonomous vehicle company that of that I haven’t talked to in the last 90 days. All of them have just different views about how they want to go about doing this stuff. And that’s the power of what we offer is that we don’t walk in and say, here you go boom, follow this thing go to our the to this specific conference that we’re going to do and we’re going to tell you exactly what you need to build, you’ve got to use this XX platform to use it. Our approach is to enable people to build what is the right solution for the market, while also bringing the benefit of arm, which is that if you just if you do it on top of arm, you’re just going to get the software compatibility across all the stuff that you’re trying to design. So you get the benefit out of open source contributions and the like because there’s just people are just building on top of the arm ecosystem today. So the we offer that flexibility people because it is just a systems engineering problem as you’re talking about earlier, Austin, and you’ve got to have that design flexibility to design across all of it. And we just like I said, we don’t have a dog in a hunt, you just decide what you want. Yeah. So we talked about compute and how your goal at arm is really to support customers’ needs, however they see fit with whatever compute blocks they need, and of course, you’ve got the software compatibility on top of that. I’m curious, the entire system, also memory is like a big thing that’s different these days than AI of the past, before maybe it was convolutional neural networks, working on some of that perception, not as big of models. And now we’re in a world where some folks at the frontier are using like vision language action models and things like this that are these end-to-end models and it’s like taking perception, planning and control and to just like a big one big model and it has like big memory requirements. So obviously, compute is part of it, but how are you guys seeing memory constraints changing at the edge? I know at the same time there’s innovations to like make KD cache smaller and stuff like this. But yeah, what do you see when you talk to all these companies? Are they concerned about memory? Do they have enough? DH: Well, I think the world wish it had more memory right now. Well, true. The simple thing. You can’t listen to anybody’s earning reports today and not know that there’s questions about memory access and the like. But those are constraints that exist at the moment. The so to get to your question, this is what I think this is what I just like the physical AI space and it’s and what’s so interesting to me about it with the kind of the background that I’ve had and things I’ve worked on in my career, is it is all new, right? This isn’t you’re really it’s not a disruption market. This is we’re actually going in and bringing into really well understood areas of manufacturing, mining, all the things I talked about before. And we’re bringing the power of this into that area. And so it’s all new. And so designs are varying. The specific issue about memory is fascinating because it’s this constant tradeoff that people have about, well, how big do I want my model to be based upon the constraints that you talked about and how do I distill that into as small as possible? So that again, I can trade off that this the robot can’t walk around with something a compute system that weighs a ton, right? It’s got to it’s measured in grams, how much it can weigh. And including the memory system subsystem and the like. So you’re constantly making those tradeoffs. So what we find is that what people care about, they really want, they want absolutely as the maximize the value, the performance that they have. Your system has a certain amount of performance in it. How do I maximize that? How do I make it so super fast, which is just architectures that we’re good at. And if memory is a perceived problem for me, well, how do I then reduce my need for it, right? So you can actually you can change data types. Some people use FP floating point data types with lots of bits and those are relatively big for a model. But if you can distill that into another lower resolution data type, but that gives you still sufficient precision to what you want to do, that can substantially change the size of the models that you have. But there’s tradeoffs if you do that. So what we find is that there is no right answer today. This isn’t like this is a world where 20 years of optimizations have existed. This is a world where new models are being invented, new architectures are being explored, new inventions are being decided upon. And there and we will eventually find what are the most efficient of those over the next decade, but it’s going to be a decade to figure all that out. Mhm. Mhm. Nice. I see. Yeah, so it sounds much more nuanced than just like, you got to have 32 gigs of RAM, otherwise you’re not serious. But it’s much it sounds much more like, this is the system architecture that we think makes sense and so how can we do more if memory is a constraint? How can we distill down into a point where we feel like we have enough headroom, it’s not a constraint or whatever. It does seem like I can see some back and forth between like the ML engineers and maybe like the system designers a little ping-pong of like, hey, I want a little bit higher precision versus like, okay, all the tradeoffs. That’s engineering, right? Like, okay, fine, it’s going to the bomb’s going to go off. DH: That’s my day. I bet. I bet. Every day, that’s awesome. Okay, I have another question for you, Drew. So, when we’re talking about these systems, so take the car, for example, there’s obviously some real-time stuff that has to happen. It has to happen there at the edge. And then there’s some LLM inference happening. Like, how do you see like edge to cloud kind of hybrid playing out across physical AI? Like, will we see some stuff runs in the cloud, some stuff runs locally? Like, how are companies thinking about this? DH: Yeah, I think it is going to eventually it’ll distill into kind of both those things. It’ll be very use case dependent. So, let’s go back to the car example. I don’t see anytime soon or ever that a the braking system of a car is going to be something that’s figured out in the cloud. Mm, right. DH: Right? It’s just it’s too there the needs are too instantaneous. And so why would why in the world would you say, hey, listen, I’m going to add a requirement of latency, ping time to a cell tower and then the actual time back to the computer. I’m not you’re just not going to do that. So there are things that without doubt will remain local. Then you kind of get into, well, but every really the most advanced autonomous cars today that we see on the road, everything from what Tesla’s doing to what Waymo’s doing, to what the Wave guys are doing, the Rivian team these are all just world-class teams. They are looking at, okay, there’s a portion of this work that I’m going to do in the cloud. Like, for instance, I’m training all my models in the cloud. So I’ve got a new model. I’m going to download that to the car off offline. So that there’s a connection that exists. And then when you start thinking about robotic systems, you start thinking about, well a humanoid robot will be doing work, but it’s probably let’s project it into the work in into enterprise and into businesses. It is probably part of a fleet. So that fleet then has fleet management that’s associated. Okay, well, this robot needs to go is going to go over there. This robot’s going to go over there. It’s going to do these different tasks. And so there’s a coordination that ends up happening where you want to actually have the system be able to do all the autonomous work that it needs to be able to do, but then recognize that, hey, I want to there’s coordination that needs to happen. And so there’s intercommunication that needs to happen. That intercommunication is going to be cloud-based. And so that’s where I think it becomes a tradeoff. The interesting is when you actually start thinking about logistics centers and factories and how they evolve because in a if you’ve got if I’m in a logistics center like the center that’s shipping packages, goods in, goods out kind of thing. That’s the four walls that make up that building are under the control of the systems designer that’s designed that whole system. And they’ve got robots that are all over the place inside those things today. But you may make decisions that, hey, listen, because I’ve got a whole bunch of robots that are there and I might have central compute that’s available to you. There are some things that I might actually allow the decisions to be done in a compute system because it’s close enough from a latency standpoint. And that’s where that’s the thing that just boggles my mind about this world of physical AI is it’s not just about robots. It’s not just about autonomous vehicles. It’s about how all of that then interacts in those things I talked about before, growing crops, mining, manufacturing, moving, logistics centers, all that stuff. There’s a level of designing it as a at a systems level that is as complicated as just dividing the designing the system the robotic platform itself. Yeah, fascinating. DH: Right? Really interesting to think through. I know it’s like kind of mind-blowing to think through like the fleet level orchestration. Obviously, LLMs and AI will be very helpful there. And then you yeah, you start to get to this like, how do you define the area of responsibility? Like, what is the robot allowed to do for itself? Even making that work is awesome. It can walk over and pick up stuff. And what decisions can it make versus what decisions does it need to send up to like the central planning brain? And then of course, and then this is interesting because then now some organizations their like differentiation and how they compete with others could be not just the platform that they’re using, but that higher level orchestration. Like, are they really sophisticated about getting goods in and out moving, making interesting decisions? DH: Yeah. Yeah. For instance it’s you can just think about a conveyor system, right? A conveyor system is moving goods down the line. And then maybe there’s a robot there that’s sorting goods and things like that. At some point, someone’s going to go, hey, you know what? Let’s design those two things together as a system. Let’s not just stick a robot in front of a conveyor system that’s there. Let’s actually, let’s take a step back and think about all the coordination of those things that happen. That’s this orchestration that occurs. Interesting. So interesting. Okay, so let me ask you a question then. In where my mind is going is like, okay, let’s say we’re in this world where there’s robots, but there’s also like a data center based model, maybe doing some orchestration. Like, is there a benefit? Does architectural consistency matter? Is there a benefit if I’m running arm in the robot, running arm in the cloud? Or how do you guys think about that? DH: Yeah. So first off, the answer to the question is, do we see that happening? The simple answer is yes, we see it happening all over the place. And it’s following the same pattern that we saw even in the as people build large cloud data centers if you take a step back and just kind of look, just squint and look at that large cloud data center, it’s just a big computing system, right? And so the Amazon guys when they when we worked with them to build Graviton, they just knew the workloads that they were building that they were running. They knew the power constraints that they had. They knew the IO requirements that they had. Everything when you’re at designing at that level, not just trying to piece something out of what’s available through a bunch of supply chain points. When you’re really designing that, you really care then about how your compute plane looks. So you actually start looking at, well, how do I customize that to my particular needs? And so they were they all voted with their feet. Every major cloud company’s already decided to do that. We’re seeing the same thing now with the happening with the autonomous drive space. Tesla early first mover on that, right? Tesla is super famous for the fact that they build everything themselves. Rivian if you’re a AI first company like Rivian is, you go, listen, I care about latency. I care about the software load that I have. I care about the data types that I care about. I really want to have something that’s specific to my designs. And so RJ and that team, they just said, we’re going to go after it. We’re going to build our own platform. And now as we see this in the just as the physical AI space into other types of form factors, not just cars, autonomous cars, which are robots on wheels. Other things are happening that other seeing the same thing in other areas around the world, other companies around the world, just going, I just I need to make a decision about when something is so unique to what I’m doing, I want to make sure it’s part of my vertical supply chain. Now, there’s a lot of people that are building really powerful things using technologies that from folks like Nvidia, which is amazing. Their technologies are amazing. And they’re really satisfied with that because it solves the problem that they need. Nvidia does great work. And then there’s people like the Rivian guys who are going, listen, I just I want to do something that’s a little more specific to what I need and I’m going to design my own thing. So we’re seeing it all over the place. Yeah, interesting. Okay, so maybe last thing, as you’re talking to all these leading robotics players, is there something that comes up in every conversation that maybe you’re surprised by or that we haven’t been thinking about us in the audience? DH: What’s interesting to me is and surprising and I guess somewhat validating is that everyone I’ve talked to has said, Drew, I’m convinced that we will have you will not be able to satisfy the amount of compute that I need at any time. So please just keep building more and more compute systems. Because they just take a step back and they go, okay, well, maybe we solve the problem about how a car drives around the world, but then I got to put people in the car and they and we want them to have this natural experience being able to talk to the car and give the car direction, and that it could take action on and have it actually infer through hand gestures and the like. So there’s these everyone comes back and going, it’s a decade or more of high performance. Matter of fact this is I was joking recently and I think this is serious. I was talking to a friend of mine. I was thinking back to the days when I was doing the GeForce business and we had this world of computer enthusiasts, right? The modders, the ones that are building their own computing platforms and the like and tweaking it and overclocking and all that stuff. Austin, I’m sure you were one of them. That is I think it’s going to happen to robotics. Sure. I think we’re going to have people that are going to get robotic platforms and they’re going to want to overclock it. They’re going to want to tweak it. They’re going to want to do their own optimizations on it. We’re in that type of a world where people are just going to want more computers than they can possibly get it and they’re going to even try to figure out how to hack it for themselves. Yeah, that makes a lot of sense. And speaking of GeForce, it does kind of remind me of like computer graphics where it’s like never enough. It’s like, wow, this looks amazing. Can we make it look even more real? Can you give us even more compute? And the same thing. I don’t think people are going to be like, okay, this robotics platform is useful enough you can stop here. DH: Yeah, right. No, I think the analogy is perfect. And where are we now in that? Everything’s neural graphics based, right? We’re using AI technologies now to in to through AI generate frames that don’t exist. It’s not rendered, it’s actually completely AI generated the frames that insert between other rendered frames. And so the so you’re absolutely right. This is one of those spaces where people will thirst for as much computing as they can possibly get. Awesome. I love it. Well, Drew, thank you for this. This got me fired up about physical AI. I look forward to seeing what you guys at Arm do over the next couple years and I’m sure the listeners walked away learning something. So thank you. DH: Austin, it’s always good to see you, man.