E362: Why Jensen Huang Believes Physical AI will be a $50 Trillion Market

0:00Danny, you're the founder and CEO of Xanar, which was a stealth for nine years, which is impressive in itself. But then you became a unicorn. Jensen Wang recently publicly said that physical AI is going to be a$50 trillion market. What is physical AI? Physical AI is the application of AI to the physical world. Meaning anytime you're not sitting behind a screen doing code, images, or text, and you're actually physically moving things. Whether that be a robot or a human, it's how do you optimize that using AI? So essentially everything in the world that's not on your computer. Pretty much. I know you have over 100 patents and you've been working on this for a decade, but how would you describe what you do in a simple way?

0:36How would you describe it to an eighth grader? So we take every single communication network, whether it's Wi-Fi, 5G, or satellite, and we turn that connectivity network into a massive sensor that can locate any device that is on that network. That's phone, car, drone, robot, IoT device. better than a meter, typically sub 10 centimeters, using just the fact that it's on the network. There's no software on the user device. There's no battery drain on the user device. They're just, it's going to be a signal. And we do that without adding anything to the network. You're not adding physical antennas or beacons.

1:10We're just using software that's standards compliant where the processing happens on the communication network, making it incredibly scalable. So how does Xynar fit in to the broader ecosystem of physical AI? So we're actually just with the leads of all the different physical AI companies last night discussing this. and we've come to a shared understanding. Xynar is the central nervous system. We know we're everything, everywhere, all at once. It's that data point. We're using the sensors that are already there. We turn the network into a sensor, just like your nervous system is. Your AI companies, right, your LLM companies are taking that data and putting the algorithms around it to make change or recommendations behind it.

1:47And then your robotics companies are your physical, your arms, your legs, that are literally picking things up and moving them. So we're the nervous system. You've got the sort of brain that's sort of analyzing what to do with it. And then you've got your robots that move things around. I get why physical AI is going to be important when we have a bunch of robots, a bunch of optimists running around. But why is physical AI important today? Physical AI is important today for what we do is to get ready for tomorrow with that from a training perspective. But it's more than just robots. So I think that's a big misconception of physical AI that it's just robots moving around and moving things.

2:22the same information that you would power a robot to have swarm intelligence or make better coordination of what's happening around the environment, you can give to human workers today to level them up, to make them safer, more efficient, more effective. What are some industries that could benefit from that and maybe double click on some of the use cases today? We're working almost every single industry. So it's not where can you apply it? I bet there's very few applications where you cannot apply it. We're in construction. We're in manufacturing. We're in warehousing. We're in healthcare. We're in mining.

2:52we're in port, you name it. So essentially anywhere where there's humans and human labor could be applied to physical AI. So you mentioned construction. Give me an example of how physical AI could benefit construction. Well, we're already doing this, right? And so we're doing this all over the world on construction sites. Think billion dollar projects, whether it's a stadium or a mall or some infrastructure project, they're renting lots of equipment every day. And per day, a piece of equipment may cost a couple hundred dollars or a couple of thousand dollars to rent. And if it's idle, more than two days, it's actually cheaper to send it away and bring it back.

3:23Now, that never happens in construction. Why? Because things are run on a weekly basis, a monthly basis. A site manager says, I don't use that dump truck three weeks from now, so I'll let it sit. Overages are insane. Just to give you some real numbers, one of our clients just bought one of the biggest airports in the U.S. They went$300 million over budget. $120 million was this one issue. So what did we do? We tell you, at the end of the day, you had 15 pieces of equipment that didn't move. You had another 20 that moved, but there were non-profitable moves that just moved to get out of the way.

3:47Gangs were grand enough to tell the difference. And you know there are 30 that you duplicate stuff that were never used at the same time. But go more than that. We do progress tracking. Let's say we have a dump truck. It's supposed to make 50 trips in a day. Some point it's supposed to make 30. It's only made 20. We're sending you an alert. It's sitting right behind schedule. But what's crazy about all these things, and I can go on and on in applications, we go one step further. We've now layered on a Gendic AI on top of that to really change your behavior as an individual level. And so in that exact example that we're running behind schedule, we don't just tell you you're running behind schedule.

4:15We tell you right now, you've got two jump trucks on site that aren't being used. You have three people on site that are certified to operate them. One is a non-critical path task. Would you like us to proactively reallocate that worker to make this delay for you? Yes, great. Reallocate that worker now. This is not some theory. We're doing this now. I mentioned in the open, you have 90 patents. Granted, you have 120 patents in total. These are very technical challenges that you've developed over a decade. So I'm gonna ask you to dumb it down to maybe an eighth grader. how exactly are you gathering the physical AI data that you need for somebody like a construction site to make these decisions?

4:51Let me answer that in a couple of ways, but it's even more patents, but that's fine. How many patents? There's been over 95 issued, I think closer to 130 something filed, but we actually never had a rejection in any of our claims because that's just how different it is what we're doing. What most of us don't realize is that we realized very long time ago, the beginnings of the company is that there's been this massive shift towards wireless. Go back 10 years ago, right? Everything still played everything in with the ethernet cords and things like that. We saw the shift going to wireless. What was driving that?

5:20You've now got software-defined radios that are embedded in every single piece of communication network, whether it's Wi-Fi, 5G, satellite, and software-defined radios enable you to define radios via software. You can write lines of code for things that used to be fixed. And that's really the big unlock is now we can manipulate and understand things in ways we just couldn't. And so what we're really doing as a company is turning these signals that are whipping around through all of us. You've got your phone, your pocket, you've got a smartwatch, you've got your laptop, whipping through the air.

5:49We're able to identify and locate point of origin to sub 10 centimeters, which has become essentially the central nervous system for this whole ecosystem where you know where every single thing is, phone, car, drone, robot, IT device, in real time, incredibly accurate, always. So when you're dealing with this construction site, you're focusing on existing technology that are in people's phones and you have some other pretty simple off-the-shelf technology. We're leveraging the networks that are already there and we put a piece of software in the network that allows you to locate and ID any device that is getting internet or connection from that network automatically in real time.

6:26So it gives you a perfect visibility of every single person, every single thing, every single robot in real time. And I'm happy to talk sort of the sort of basic physics and how we do these things and or the gaps on where physical data sets are. I may regret this, but tell me about the basic physics. So it's what you learned back in middle school. Distance equals rate times time. You remember the story of problems where a train leaves the station going 60 miles an hour. Where is it an hour later? Radio waves travel at a constant rate, speed of light, which is 30 centimeters per nanosecond. The core of Zyner's technology is our ability to synchronize and distribute time 1 ,000 to 10 ,000 times better than anyone else in the world, sub-nanosecond.

7:08What does that mean? Well, if every nanosecond translates to 30 centimeters of accuracy, which is that's how fast the speed of light goes, we're better than a nanosecond, so we get really accurate location. So you're able to track just in time, literally in real time, where everybody is. Exactly right, because literally using distance equals right time just to time, because speed of light is 30 centimeters per nanosecond, so we can measure time to a nanosecond. location 30 centimeters. We're better than a nanosecond. So we get incredibly accurate location. One of the mind blowing things I'm still trying to wrap my head around is that you guys stayed stealth for nine years.

7:39How did you manage to recruit and build a business in stealth mode for nine years? So one, it wasn't always easy from that, just kind of seeing peers and things go out, but our close rate has been almost a hundred percent. And the reason is the challenge that we're going after that we've solved, tactical people get that. And we were very fortunate coming out of Stanford and having very early hires that are incredibly senior in their field in the first 10 people in the company. That had people come to us and say, hey, I want to work with this researcher. I want to work with these people. And we've got the best of the best talent.

8:13We've been able to get pretty much anyone we've ever wanted. It seems it is syncratic, but this is something I see in every single deep, great deep tech startup is their talent magnets. There's a story of Elon Musk when he was first starting out SpaceX in the first three years. And a Stanford professor in the newspaper wrote this piece saying, I don't know what this company is, SpaceX, but five of the top 10 students I've ever had have joined this company called SpaceX. And Elon reached out and asked to get lunch with him. And the professor very quickly after 10 minutes realized that Elon wanted to know the other five people that have not yet joined SpaceX.

8:47Incessant drive to hire the very best people that becomes, I would argue a moat in and of itself. If you bring talent, talent will go out and actually build a company for you. The talent that we have here is second to none. And that's what it gets. It just, it's addicting, right? It's people come and they stay. And the other thing is about it, not only is people incredibly accomplished, the amount of people that left seven figure jobs to join us is half our team. It's our entire executive. Literally every single person on our company that's a VP level of above has personally founded or been an executive at a company that took from zero to over a billion dollars in location and data space.

9:24But you know what? If you didn't know it, you'd have no idea. These people have no egos. We're solving an amazing problem they're trying to solve their whole career and they're here because we've solved it. There are very few opportunities. You can join a company. It's going to be a trillion dollar company. That's why, you know, you mentioned SpaceX. We shared board members with SpaceX, right? We shared board members with Steve Jervison. Steve Jervison, yeah. In fact, it's really the same set of investors that we have. Take me back to your origin story. How did you start? and when did you first have an inkling that this could be big?

9:49I met Phil, our co-founder at Stanford. And guess what? He was the smartest person I've ever met in my life, still to this day. Applied physics, PhD, evolutionary masters. And he was working on some of the beginnings of Zionark. At the time, we did not realize the implications. We knew it was big, but didn't just realize how fundamental and big it was. And again, it goes back to what I was saying earlier is that we saw these megatrends, right? Shift towards wireless, shift towards more connectivity, more devices. The ability and ubiquity and falling in price point of software-defined radios are embedded in everything.

10:25And that's what realized that, oh my goodness, if you know and can read where every signal is and point of origin, the amount of$100 billion markets is endless if this comes up. And I'll tell you though, a lot of our professors beginning at Stanford thought what we were doing was interesting, but also crazy. because the way we're doing it is totally different than anyone else. That's why we have over 120-something patents, and we've never had a single rejection on any of our claims. But they all say the same thing. But if it works, you're going to be orders of magnitude better and it's an industry game changer.

10:56We took the bet, and it works, and here we are. And you were at Stanford Business School? And how did that come together? So actually, Phil and I were both admits to David Kelly's design school class, design garage. It's actually four professors. It's the top head for professors. It's multidisciplinary. It's multidisciplinary of the design school, which is like David Kelly, Bill Burnett, and about 17 students. It's about 30 hours a week commitment. It's incredibly intense on design thinking. And literally after the admit day, I ran to Phil the next day on campus, and we started talking. And then after, and I remember Phil because he struck me as someone who's just one of these Stanford, you know, You go to Stanford hoping to find someone like this who's just next level smart.

11:42It's crazy. And so I was interested. What's he interested about? What's he excited about? And that was the beginning of Zyner. Literally fast forward to January of that same year. This was probably November. And in January during break, we started Crash Tracker, which then became Zyner. Expert calls have always been one of the most powerful ways to build conviction. But today, investors are asked to cover more companies, move faster, and do it with leaner teams. With AlphaSense AI-led expert calls, their TGIS call service team sources experts based on your research criteria and lets the AI interviewer get to work.

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13:38Take advantage of AlphaSense AI-led expert calls now. The first to see wins. The rest follow. Learn more at alpha-sense.com slash how I invest. You've, of course, become a unicorn now, but tell me about the fundraising path. How difficult was it? And what were some of your strategies for raising capital early on? We've actually been very fortunate. With Deep Tech and the talent that we have, we had very patient capital very early who understood the implications. And they understood that Deep Tech is very hard and takes 10 years to monetize. And in fact, we're about two years ahead of schedule, believe it or not.

14:15And so first was Jerry Yang, who is a founder of Yahoo, totally got it and got us in touch. Ultimately, it was Steve who ended up leading around that crowd. And each step of the way, surprisingly, was actually a very calculated, diligent step on this massive roadmap that are designed to systematically de-risk the technology. And that's what we've done. And it took nine years to do it. But we've done it and we're ready for scale. It's funny because some companies are built almost on these momentum machines, these hype cycles and major PR cycles. and some companies like Zainar are built on the scientific method, essentially.

14:52Almost like an FDA-approved drug, you're taking it down the milestone. I mean, that's exactly right. Now, I will say we weren't immune to that. We did have one pivot our first year and we've never looked back since. If you go back to any or even pitch decks from eight years ago, they're identical to now. But I even use some of the same slides. That's how on path we have. But I will say we actually did have a pivot because in 2017, that's when all the self-driving car companies and things were coming out. And the first product we made was a car sensor. We could detect every single car using their tire pressure sensors and ID them.

15:20And it was our first demo. You pull off Stanford campus drive. You go over a speed bump and through a one-foot concrete wall 50 meters away. You can ID and locate every car. We built it. And then we realized, what the fuck are we doing? This could be so much bigger, so much more foundational. Why are we messing around in this? And that's where we did Reset. These are design thinking skills. We interviewed hundreds of businesses to understand what they could do with it. And the crazy thing is everyone had unlimited use cases. So we knew it was going to be big. And then just to kind of fast forward, five years ago is when we realized physically I was going to be big.

15:51And that's where we deliberately did not go out of stealth. We wanted to capture the market, make sure that we were ready for scale, not to tip our hand. So now we're 1 ,000 to 10 ,000 times better than the next best and foundational to it. Focus is such an important thing for a startup. Some of it already gets the number one thing. How do you build a business that has so many use cases and what are the first principles? From a first principles perspective, it's always actually designed with the end in mind and work backwards. right and ultimately we are designing for scale which ultimately actually is 5g that's always been our plan and so we migrated from zigbee to wi-fi to 5g but we couldn't start with 5g from a roadmap perspective why because no third party is just going to integrate with you you have to prove out the technology we could prove it out with wi-fi which is what you see as a legacy business but we deliberately did not go in and try to monetize wi-fi to the fullest extent possible because we knew it was a distraction.

16:43That was not the end game. We want to show we could, but now that we want to go and persecute it, that's why we have hundreds of millions of dollars in contracts. It's one of the most underrated aspects of how Elon builds his business. It's in many ways an ultimate paradox. One is he's able to think 10, 20, 30 years ahead, but he understands the value of a demo. You must show mere humans and mere mortals a fully encapsulated demo of it working before they believe. They're not going to understand the physics. They're not going to... When you find something that just fits right, you end up wearing it more than anything else.

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20:35Get started today. Understand first principles thinking, even though now it's about you need to show them something that works. It's couldn't be more. And literally every step of the way, we had demonstrable milestones to help unlock next set of funding or help unlock what are the next set of experiments we're doing. Again, two-point scientific method. We went from simulation, which got us the first money. Doing this on software-defined radios, being able to synchronize and distribute time across the system, sub-nanosecond, wirelessly across a soccer field. Then from there, we switched protocols to Wi-Fi.

21:01So we can do that there. And guess what? We spent the last five years deploying in the most challenging real-world environments, light construction that's 50 meters underground. Every six feet is a layer of solid metal. Bottom layer is water going up and down. Things that are constantly changing where it's very clear nothing else works. And by only proving it out to ourselves that we work where nothing else possibly can and we work flawlessly, we're ready for scale. But we're very fortunate to have these massive corporations as co-development partners that allow us to actually test and deploy on their sites all over the world to really perfect our technology and make it to the scalable point of today.

21:36Through these years of stealth, these years of people questioning whether your sanity, I imagine, and I'm sure family members as well, have you built this muscle of resilience or were you always just like bullheaded and just didn't care? A lot of people may describe me as shameless. I don't give a fuck, right? It's true. Was it always the case? It's always been the case. I've always had such incredibly thick skin. Rejection has never been – I'm very much – you missed 100 % of the shots you don't take. And I think that's – we're taking a big swing here. And guess what? We hit it. And I honestly believe we're sitting on probably one of the most valuable potential companies out there.

22:12And that's what attracts everybody here is there are very few opportunities in life that you can work on something that's so foundational, that touches so many industries. There's the before and after GPS, before and after AI, before and after internet. I think we're of that magnitude. And guess what? Most of our investors, in fact, all of our investors typically do. Talk to me about your strategic partnerships and how critical were they to the development of the company? We're mostly financial led. We do have select strategics that have been helpful in terms of opening doors, allowing us to test in their facilities and prove out use cases.