B (2:09)

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. The 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.

A (2:35)

What are some industries that could benefit from that? And maybe double click on some of

B (2:39)

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, we're in port, you name it.

A (2:53)

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.

B (3:03)

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 for they're renting lots of equipment every day. And per day a piece of equipment may cost a couple hundred dollars or a couple 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. Now that never happens at construction. Why? Cause things are rented on a weekly basis, a monthly basis. A site manager says, I don't use that dump truck three weeks from now. So 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 US they went $300 million over budget. 120 million was this one issue. So what do 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 nonprofitable moves just moved to gather the way getting this 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 save a dump truck. Supposed to make 50 trips in a day some point supposed to make 30. It's only made 20. We're sending alert, sending you 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 gentic 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. We tell you right now you've got two jump tucks on site that aren't being used. You have three people on site that are certified to operate them. One of them is in a non critical path task. Would you like us to proactively reallocate that worker to mitigate this delay for you? Yes.

C (4:27)

Great.

B (4:27)

Reallocating that worker. Now this is not some theory. We're doing this now.

A (4:31)

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 going to ask you to dumb it down to maybe an 8th grader. How exactly are you gathering the physical AI data that you need for somebody like a construction site to make these decisions?

B (4:51)

Let me answer that in a couple ways. But it's even more patents, but that's fine. But there's been over over 95 issued, I think close 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 people 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 applied to everything in with the ethernet cords and things like that. We saw this shift going to wireless. What was driving that? You'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' 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. We're able to identify and locate point of origin to sub 10cm which becomes essentially the central nervous system for this whole ecosystem where you know, where every single thing is. Phone, car, drone, robot, IoT device in real time, incredibly accurate always.

A (6:03)

So when you're dealing with this construction site. You're focusing on the existing technology that are in people's phones. And you have some, some other pretty simple off the shelf technology.

B (6:12)

We're leveraging the networks that are already there and we put a piece of software in the network that allows you to locate an id, any device that is getting Internet or connection from that network automatically in real time. So 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 physically our data sets are.

A (6:40)

I may regret this, but tell me about the basic physics.

B (6:42)

So it's what you learned back in middle school. Distance equals rate times time. You remember the story 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 Zion's technology is our ability to synchronize and distribute time thousand to ten thousand times better than anyone else in the world. Sub nanosecond. What does that mean? Well, if every nanosecond translates to 30 cm 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.

A (7:18)

So you're able to track just in time, literally in real time, where everybody is exactly right.

B (7:23)

Because literally using Disney goes right to time because speed of light is 30 centimeters per nanosecond. So we can measure a time to a nanosecond be location. 30 centimeters. We're better than a nanosecond, so we get incredibly accurate location.

A (7:33)

One of the mind blowing things I'm still trying to wrap my head around is that you guys stayed stealth for nine years. How did you manage to recruit and build a business in stealth mode for nine years?

B (7:42)

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 say, hey, I want to work with this researcher, I want to work with these people. And we got the best of the best talent. We've been able to get pretty much anyone we've ever wanted.

A (8:15)

It seems idiosyncratic, 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 had not yet joined SpaceX. Incessant 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.

B (8:55)

The talent that we have here is second to none. And that's where 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 many 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, data space. But 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 gonna be a trillion dollar company. That's why, you know, you mentioned SpaceX. We, we share board members with SpaceX. Right? We share board members with Steve J. Steve Jurson. Yeah. And that it's really the same set of investors that we have.

A (9:44)

Take me back to your origin story. How did you start and when did you first have an inkling that this could be big?

B (9:49)

I 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 masters. And he was working on some of the beginnings of Zeinar. At the time we did not realize the implications. We knew it was big. We 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 then the ability and ubiquity and falling in price point of software defined radios are embedded in everything. And 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 hundred billion dollar markets is endless that this comes up. And I will 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 we have over 120something patents. 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. We took the bet and it works. And here we are.

A (10:59)

And you were at Stanford Business School and how did that come together?

B (11:03)

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 multidisciplinary of 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 remembered Phil cause 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. It's crazy. And so I was interested. What was he interested about? What's he excited about? And that was the beginning of Ziner. Literally. Fast forward to January of that same year. This was like and this was probably November and in January of you know, during break we started Crash Tracker which then became Zeinar.

D (12:02)

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A (13:49)

course become a unicorn now but tell me about the fundraising path.

D (13:52)

How difficult was it and what were

A (13:54)

some of your strategies for raising capital early on?

B (13:58)

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. And so first was Jerry Yang who is the founder of Yahoo. Totally got it and got us in touch. Ultimately it was Steve who ended up leading our next round. 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 ready for scale.

A (14:41)

It's funny because some companies are built almost on these momentum machines, these hype cycles and they just PR cycles and some companies like Zane are are built on some the scientific method essentially almost like an FDA approved drug. You're taking it down the milestone.

B (14:54)

I mean that's exactly 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 of our 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 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, was a car sensor. We could detect every single car using their tire pressure sensors and ID them. And 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 could 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 use our 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 physical AI was going to be big. And that's what 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 are a thousand and ten thousand times better than X Best and foundational to it.

A (16:03)

Focus is such an important thing for startup seminar gets the number one thing. How do you build a business that has so many use cases and what are the first principles?

B (16:12)

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. I was 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. That was not the end game. We wanted to show we could, but not that we wanted to go and persecute it. That's why we have hundreds of millions of dollars in contracts.

A (16:49)

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 very fully encapsulated demo of it working before they believe they're not going to understand the physics, they're not going to.

C (17:08)

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A (19:26)

understand first principles thinking Even though now it's vogue you need to show them something that works.

B (19:30)

It's good to 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 so we can do that there. And, and guess what we spent the last five years deploying in the most challenging real world environment. Light construction that's 50 meters underground. Every six feet is a layer of solid metal bottom layers, water going up and down. Things 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 that we're ready for scale. But it's been, we've been 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 and make it to the scalable point

A (20:25)

of the day through this, these years of stealth, this years of people questioning whether your sanity I imagine and I'm sure family members as well. Have you built a muscle of resilience or were you always just like bullheaded and, and just didn't care?

B (20:38)

A lot of people may describe me as shameless. I don't give a fuck. Right. It's true.

A (20:42)

Was it always the case?

B (20:43)

It's always been the case. I've always had incredibly thick skin, you know I. Rejection has never been I, I'm very much. You missed 100% of shots you don't take and I think that's, you know, we're taking a big swing here and guess what? We hit it. And I honestly believe we're sitting on you know probably one of the most valuable potential companies out there because the implication and that's what'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.

A (21:19)

Talk to me about your strategic partnerships and how critical were they to the development of the company.

B (21:23)

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. And from that point, it's like outsourcing their R and D to solving massive problems for them and for us. It's this amazing field that we couldn't possibly dream of building ourselves, that we can get to go out and deploy in the real world. People doing random stuff, very few percentage of our cap TABLE strategic take me

A (21:48)

to 20, 36, 10 years from now. How will physical AI affect the world and affect the financial markets?

B (21:54)

It's gonna be everything, right? And so what we're building, think of us as the nervous system we know where everything everywhere, all at once is just like the movie, right? Where things are that, that gives you that. Now there's different components. We're the data layer, right? That sensing layer that builds everything. Your AI companies are then the brain that are taking all that data and inputs and making your algorithms, your outcomes and your robotics companies are like your arm, right? You're literally picking things up and moving them. But we're that foundational layer that literally sensing everything around it. This is going to change every single industry, period. And because what we're doing is not just physical. So physical is a huge application we're doing, but timing in of itself, you can massively increase throughput in data centers. You can load balance energy grid, energy grids to detect traveling, wavefall detection. This is a foundational technology that spans so much more than that. But I do want to spend time talking about the missing data set in physical AI, because that's really where we've come into play now. And you don't need some futuristic story behind it.

A (22:49)

Talk to me about that.

B (22:50)

So right now, AI, all the big AI companies, all of the data that they're training their models on comes from the Internet. It's code, it's images, it's text. But they have no way to break up from the digital domain to the physical world. As you said, Jensen puts out a $50 trillion market. But the problem is, unlike the Internet, which is the corpus of human knowledge, that data set for physical AI doesn't exist. But why doesn't it exist? Well, location today happens like on your phone, happens on your device. There's no central repository I can go pull and train from. Camera networks are just joined. You have to not only connect them, you then also have to annotate them. It's not feasible. So to unlock the data set, you actually need three things. One has to be centralized, but centralized at scale for every device. Phones, cars, drones, robots, IoT devices. Two, it has to be accurate. So it's be better than a meter or way better than like single digit centimeter. I'm not that. And the third thing is actually not obvious. It's timing. And why is that? Well, in order to understand interactions across objects, they have to be in the same time plane. Why? Every action is an equal and opposite reaction. So if you're off by a fraction of a second, you actually train your model on the exact opposite physics of what's happening. And so not to go down a rabbit hole, but it turns out you need nanosecond level thinking. And we're better than is that.

A (24:04)

That's physics. For every reaction there's an equal but opposite reaction.

B (24:06)

That's exactly right. Because again, bump into the.

A (24:08)

I think that's also sixth grade.

B (24:09)

Yeah, exactly. You know, sixth grade changed my life. There we go. Who knew? But no, these are foundational principles. And so what we're doing now is we're the only company that's creating these 2D and 3D vectors of how things move about at an enormous scale ground in real world. Then you have all your world models and simulations that get built off of that real data and that's become defined. And guess what? That's what people are paying for right now. Because they need to build out these systems. Once you know where every phone card driver of IoT in real time, the amount of optimization at a city level, you can start to do think traffic management. First. First response for public safety. Again, it embeds in every single system, but also how we talk about just robotics. As I think as people know, we're working with a lot of robotics companies. That's sort of classic. We haven't talked about that yet, but happy to share more there.

A (24:50)

Yeah, I want to go specifically, last time we chatted you said something that blew my mind, which is the future of IoT and of physical AI will be Swarm.

B (24:58)

Yeah.

A (24:59)

Not linear. Talk to me about that.

B (25:00)

Yeah, it's a great, great, great point. Why are all the robot companies coming to us to work with us? Because they all use cameras. Today or slam to know how they are. The problem with that is you only know where things are. You can visually see. Even knowing where you are is a challenge. Because if you're a robot and you're looking at a white wall and every single wall is white, well, the only way to know where you are is keep track of where you've been. I passed by room 23C and the longer you operate, the more you have to keep track of. It's called inference stacking. Because the compute compounds and the errors drift. It's a huge topic of Nvidia. What we do is we take all that processing, we take it off the robot and put on the edge, freeing up the robot for higher functioning tasks. With about 20 millisecond latency. We're giving a real time feed of where the robot is in the context of its environment. But in that feed we tell the robot not just where it is, we tell where every other robot is, where every human worker is, where every piece of equipment is non line of sight in real time. And what do we just do to your point? We just unlock swarm intelligence because now you can have coordination at the enterprise level, just like we talked about a second ago at the city level for optimization. And, and the big shift we're seeing with all these robotics companies is they're now just getting to the point of maturity where they don't need to just focus on getting the robot to work. They're now trying to figure out how do I optimize my fleet, how do I optimize my system? And this comes more and more important. You see major companies shift from just camera only because people just have eyes. I can get, if I can rationalize and move about, that's good enough. Well, they're realizing that was a huge flawed assumption. Well, because that assumes robots can only ever be as good as humans or marginally better. Robots can be way fucking better because they can operate as a unit and swarm and groups in ways we can't even imagine about collaborating. And so that's what really the future is unlocking. We're enabling that, but it's not to them then we're unlocking the human potential today, but making humans smarter and be able to act more efficiently as a group.

A (26:43)

Right now you have a lot of capital going into robotics, Specifically Optimus with SpaceX figure what's the future of robotics and how quickly do we get there?

B (26:53)

So my view may be a little bit different what you see in the press, because we're working with a lot of these Companies, the vision of the robot doing your dishes in your home. I think we're a long way from that.

A (27:04)

And have you been reading my chat GPT?

B (27:06)

No. But I think the reason is right, those environments, the stakes are so high and they're so complex, right? The robot, oh, falls over and squishes your kid, Oops, I knocked over grandma's ashes. Right, Toot. Impossibly too high. And so I think the great applications of robotics now are for controlled environments. In industrial environments that are clean, not moving around too much, that are repeatable, simple tasks, over time we'll get better. And I get it. Why do all these companies go to the home and talk about it? There's actually a good answer for that. And it's not because they want to serve the home. It's because they're training their models in complexity. If you don't train your model now in all the variables of change and complexity, you train your models that are too simplified and they don't to seem scale beyond industrial. Now industrial is one of the biggest markets, so it's not a bad approach just to go do that. But they're building for the end game. It's all these big companies, they want to be able to scale everywhere. But you can't do that if you have a very simple model on it. The other thing I think that it's going to change is we're very successful with humanoids and it makes sense, right? Because like make things in our own image. It's what sci fi has been growing up on things. And most of these built industrial environments were made for humans. So if you have something that acts like a human, you can get around and do most activities. But that is crazy in my opinion. Why? Because why am I making a robot that has two arms? Why doesn't it have 10? It can be so much more efficient and I think it's a comfort level thing. So over time robots are going to look less and less like humans. They're going to be designed for function that's repeatable and optimized for the set of tasks that they're doing. That's why I love these robots that are like cart robots, they're automating cart pushing.

C (28:38)

Do you much time?

B (28:39)

We spend carpushing a ton and we automate. It's simple tasks, it's repeatable, but now they can store and move things. That's way more economical than something that's as universally ubiquitous, which is almost impossibly large challenge. And so we are going to get there, right? It's just, it's going to go step by step, industry by industry. You got to train the model with physical AI data for the all that's

A (28:56)

the bottleneck because it's fine dandy if your robot has 10 hands or if it flies, but if it doesn't know down to the millisecond exactly what's going on on your shop floor, then it can't really react to real information.

B (29:07)

Knowing where it is in the context of its environment is incredibly important. I think what we are doing is two things. One, how do I even get there? Well, you have to train in simulation, but if your simulation's not grounded in real data, you're training in bogus and things break and get creative. And that's what, that's the problem. That, that that's the missing data set is because you. It doesn't have the Internet, right? There's the corpus of human knowledge to train on images, code and text. You're training on physical movements. There is no data set for that. We are that data set. We are the Internet, just like the Internet was for, you know, actual AI. We are the Internet for physical AI equivalent because we're that missing data set at enormous scale. And that's why it's so valuable side

A (29:46)

of investing in your company, which we can't solicit here. Smart investors start thinking about building portfolios for a physical AI world. Where should they invest their money?

B (29:56)

Dina? It's what are the inputs? Right? There's going to be thousands of robotics companies picking the winner is going to be hard. Now a couple will emerge. And so it's what are the inputs that everyone has to use, the picks and shovels? It's the data layers, it's the processing, it's the edge compute. It's. It's things that are the needed inputs, regardless of who the winner is. Also, if you want to get into robotics, that's okay. It's figuring out someone who's figured out a really good repeatable task that there's a lot of that happens all over, like moving carts or, you know, picking shelves or something that's repeatable and great and they're going to go and nail it. Is it the biggest TAM in the world? No, but you're going to get a good return because it's going to automate things and help us scale into it.

A (30:37)

That's where the value is going to accrue.

B (30:38)

The data layer, I think the data, absolutely. And it's also inputs with the compute layers. It's anyone like Nvidia I think is a great investment still. Right. It's anyone that's going to have a critical input that regardless of which individual company is the winner is going to win.

A (30:51)

Going full circle to how we started, Jensen Wang said that physical AI is going to be a $50 trillion market. Do you take the over or the under on that?

B (31:01)

I think it matters which time horizon. Ultimately it will become everything. Right. Like you go fast forward enough in the future, everything will be automated. And guess what? You're going to need to know where everything everywhere, all ones is. And that's the label we provide.

A (31:13)

And how do you quantify that? Where does this 50 trillion come from?

B (31:16)

It's human labor of how things globally. Right. That's how things move. If you can automate.

A (31:21)

So that's just today's number.

B (31:23)

Yeah. And guess what? It's going to push us in ways we can't even imagine. I'm not going to look in here and tell you I know exactly what the future is going to be. No one does. But what I can say is we're going one direction, which is towards automation. Whether that's in your coding online. Right. Look at what that's done, like Claude code or anything have done which those things. The same thing is going to happen in the physical space. It's going to take longer because the data set's not there. It's harder to do that. Those are the last jobs that get augmented. Which is why what we love about what we're doing this is not some just futuristic things that come in the future. We are meat and potatoes applications right now. They're deployed right now all over the world. Why? Because we're leveling up human workers by giving them better situational awareness to make their job safer and more efficient. And it's saving companies and then both safety and millions of dollars.

A (32:06)

It's very underestimated. But you found a way to bridge the gap to the robotics future while actually making money and building in profitable way.

B (32:13)

And not only that, are we helping enterprises in just a phenomenal way? The data that's going on, helping those enterprises, it's what's then using to then

A (32:23)

you're building your moat.

B (32:24)

Exactly. It's a amazing virtuous cycle. And I told you what we do things is incredibly deliberate. Right. We've kind of plotted out this sort of, you know, whole chess game and step by step for going through it.

A (32:37)

What have you changed your mind on? Very recently that's changed fundamentally how you run the business.

B (32:42)

One of the things that I wonder if it was super recently, but it was certainly a big shift in the company is we're very remote friendly. Initially we were Silicon Valley only. And having everyone in here we thought was really super important. And it's good. But we realize we're limiting ourselves because the talent flu is not just the Silicon Valley, it's global. We have offices now. We have a whole lab in Europe, we just opened up one in Japan. We've got different hubs all across the US and if you look around here, you see we've got robots all over. And what do we see? You actually see some of these move, I'm sure in the background because we have our team all over the world can mode in and remove robots and have them move to different ground truths. They can test algorithms 24, 7. And we do that day and night over the weekend as well and have people run different tests that are repeatable in a scalable way in ways that you can't do if you're just local.

A (33:31)

What's your biggest regret since starting the company a decade ago?

B (33:34)

Even though in the beginning we were sooner to go in these challenging real world environments, a lot of what we did in the first year or two was theoretical. We were in sort of lab environments or very controlled environments, which is important to do from a scientific method. You want to control variable by variable. But just even understanding what we're getting into is, you know, would have been helpful because you can better design endgame by better understanding the end game.

A (33:55)

Well, Danny, I don't know any smart investor that's not focused on physical AI as the next step after the LLMs. And congratulations on what you've built in just nine years of being in stealth and all the success that you've had.

B (34:07)

Thank you. It's just the beginning.