Robotics CEO: The Humanoid Robot Revolution Is Real & It Starts Now w/ Bernt Bornich & David Blundin | EP #188

You think about robots in the world probably more than anybody else. What's your vision 10 years from now? First of all, what will happen is. Everybody who are here at 1x Technologies in Palo Alto, earned Bornic, the CEO of Founder, Neogamma 1 and Neogamma 2 over here. I imagine we're gonna have the same level of AI eventually in the robot, where I feel like I'm talking to a fully intelligent being. I'm all that is grounded, right? that actually understands what this existence is. I'm what? Nah, that's not about that too. How do we solve their remaining really hard problems in science? This is not going to happen without humanoids. It's almost existential to us for human happiness. So Selim is constantly saying, "Have it look like an octopus and let it operate in all the elegance that an octopus can, rather than trying to constrain it into five fingers on this hand that do certain things and manipulate objects the way we're supposed to manipulate them." So, what's the definitive answer to him? So, let's just say humanosis of face. Now, that's the moonshot, ladies and gentlemen. Alright. Dave Blunden, my moonshot maid and neo-gama. Neogama won and neo-gama two over here. And we just did a tour of the facility and it's pretty extraordinary. We saw, you know, probably dozens of neo-gamas and different stage of development. They literally manufacture everything from head to toe. And how many components inside neo-gama? Roughly? Oh, top secret. Top secret. It's what it's into hundreds, not thousands. Okay. But I should print it out of. I just secured my first neo-gama at my home by the end of the year. Is that right? Just, oh yeah. Okay, fantastic. Great. So, we're about to do a podcast either with burnt or with neo-gama depending upon what you want. And let's go ahead. We'll go over to the podcast area. Would you lead the way and maybe clear the way for me? Awesome. By the way, those bags over there, neo-gama can carry those. Over, over half. Yeah. Okay. Okay, I'm not the neo-gama. Can I give this to you to carry? You can try. It might hate some safety limits, but it usually works. All right. Arms up. Figure it out. Properly. There you go. You can let it go and I can take a few steps. There you go. It might after a few steps decide that like this is a bit unsafe for me. It's thank you, Neo. Incredibly strong. All right. And it's nice to know that neo-gama will clean up the house around you. Yeah. Well, listen, I'm not sure what number you are, but I want to say thank you so much. Thanks for cleaning up. Of course. A pleasure. A pleasure. And thank you very much as well. I want to be polite. You never know when the robot overlords are going to like come after us. I want you to remember I was really polite. I was really polite. Okay, I'm safe. Great. And you've ever been in love. I mean, you meet all these other robots. I mean, some of them got to be turning you on. No? You should take a look at 41. 41. Okay, Gamma 41 is your gig. Okay, got it. Thank you. Okay. Listen, burns here. Let's stop this conversation. Do behave. Yeah. Everybody, welcome to moonshots. I'm here with my moonshot mate Dave Blunden. Selimus male is offline with his kids this weekend or his son this weekend. But I'm here in particular with the CEO and founder of 1x Technologies. Berg Bornek. A pleasure. Berg. Awesome. I'm going forward to this one. Thank you. Yeah. I mean, we just finished this tour and it's pretty extraordinary. When did you move into these facilities here recently? It's like one and a half months ago. Nice. Well, I mean, just many levels of people building robots. No robots building robots yet. We're getting there, but we're getting there. Yeah. So, I mean, when I, you know, I'm very familiar with the robotics, the humanoid robot space. And while companies like figure and Tesla are focused initially going into factories, automotive factories in particular, you made a commitment to the home. Yes. And personally, I'm excited about that. But I'd like to start with why the home. There's like, to me, there's two, like, there's a lot of reasons, but there's like two main reasons. Now, the first one is kind of is, which is just like, I mean, consumer hardware just scales at a different pace than everything else, right? Yeah. We got to more than a billion devices of the iPhone and like a bit more than a decade. And to me, humanoid robots does not make sense unless it's at scale. But there's always a better automation system that you can use for one specific problem. You need scale so that you really get this incredible reliability, incredibly low cost, incredible ecosystem and intelligence. Now the slightly deeper one is also that intelligence comes from diversity. And this has been very clear actually from all the way in the beginning, really, in all kinds of AI research and also now more practical applications of AI across all different domains. Where it is like a language model or an image model or a video model. In this case, a robotics model. You don't really need data of the same thing over and over. Like if you think, think about it, it's very logic, right? So if you're in a automotive factory, you're basically doing the same thing over and over again, you're not learning new stuff. Yeah. And we actually have some data on this. We have some real data because we are previous generation humanoid. We deployed that into both guarding and logistics back in 2022, 2023. And about 20 to 40 hours are always kind of plateau and stop learning for that specific task. Depends on how complex it is. Like if you're guarding a facility and you're driving around because that at wheels, but also humanoid wheels opening the doors and like there's some diversity to that. So then you're more on like the 40 plus hours. And if you're just like moving this cup from here to over here all day, right? Then like you're in the lower end of 20. And there's just no path from there to like general intelligence. And we are maybe kind of a bit different than the rest of the humanoid space in this that I see as more as a company really running towards the AGI and how can we come there as fast as possible versus how can we apply labor in industrial or similar settings as well as robotics and service of building true AGI models and getting enough new rich data to train up these models. Yeah. You said 20 hours, 40 hours for security guard robot. What's the equivalent for all the variety of things you can do in the home? How many hours of we don't know yet? So 10 to 10. Yeah. So like our current scale, we don't really see any kind of cap on diversity. You'll get there and we'll need to diversify. But I think like you ask a very important question, right? Because we want to talk about like what is the goal and like to me, it's not just AGI or robotics. It's a combination because if you think about what this is like, what is abundance, right? It's an abundance of knowledge or intelligence, kind of multiplied by an abundance of labor or goods and services. You kind of need both. And they follow hand in hand. And we can talk more about that. But like the constraints we have in society aren't always only on the intelligence or data layer. They also are on the substrate that we're building on, right? Every week my team and I study the top 10 technology meta trends that will transform industries over the decade ahead. I cover trends ranging from human-owned robotics, AGI and quantum computing to transport energy, longevity and more. There's no fluff. Currently the most important stuff that matters, that impacts our lives, our companies and our careers. If you want me to share these meta trends with you, I writing newsletter twice a week, sending it out is a short two minute read via email. And if you want to discover the most important meta trends 10 years before anyone else, this reports for you. Readers include founders and CEOs from the world's most disruptive companies and entrepreneurs building the world's most disruptive tech. It's not for you if you don't want to be informed about what's coming, why it matters and how you can benefit from it. To subscribe for free, go to demandus.com/metatrends. To gain access to the trends 10 years before anyone else. All right, now back to this episode. So when I think about it, I imagine this is like why a toddler crawling around, playing, investigating the physics universe, it's interacting with different people and different things is learning and building a model in its neocortex. And so is that basically the same? Your neogama is a infant learning in a diverse environment. It is. Yeah. And I think just like to some extent for humans through it, right? Also, but it's more pronounced in older animals, like how much of this kind of intelligence is innate and part of your instincts. You don't want your robot to just go around randomly doing anything. You wanted to try to do things that might succeed. So there is room here for like the more kind of like called like classical AI models where we're training based on internet data, simulation data, synthetic data, everything that everyone else is doing that's useful to get you off ground, but it doesn't fully get you there. It gets you to something that does something seemingly kind of maybe useful. And then you can experiment and you can have the robot really have this interactive learning loop where it's learning in the real world and that can get you. We don't know how far it can get you right? We don't know. And this whole topic of data gathering, you know, it's It's amazing watching them walk around the building here and walk around the kitchen. They're so unantimidated, you walk right up to it intuitively. You don't feel like it's ever going to do anything awkward, hate you or anything like that. So that's going to be incredibly important. You say the data comes out. It's cozy. It's cozy. And it doesn't seem to break the glasses or anything. So I mean, that's got to be really core to the data gathering mission, right? Because you have to, like you said, let it experiment. Otherwise, how's it going to learn? So, going at lines, what design elements did you build into NeoGamma to make it for the home? Sure. This actually goes all the way back to the founding of the company a decade ago now. Yeah. Really, I've been in the feed for a long time. So I kind of like something like that. How long? Since I was a kid. I don't really robots at age what? I was 11 when I decided that I was going to like to humanoid. What was your humanoid robot that you modeled? Was it Star Wars? Was it Star Trek? What was it? Lost in space. Honda Asimov. We said Honda. Honda, Honda's Asimov. Asimov. Asimov. Yeah. It's a beautiful robot, right? They started very early. And you can check out like the Honda Asimov. Yeah, that's right. Yeah. Like there's more modern ones. But like the Honda Asimov P6 was like, end of the 90s. Yeah. And that was walking upstairs. Yeah. Running around stage, giving someone a ball. Like it greeted President Obama. Yeah. That was a bit later, but yes. Okay. And like it was so ahead of its time, right? Yeah. But there's a lot of stuff up through the years. But I think importantly when I started the company and I sat down and thought really deep about this like, okay, there's all these amazing robots that we worked on. And it didn't really work. Why didn't it work? Right? And then comes down to these like fundamental principles. So first of all, if you actually want to make something that's scalable, you know, all of which respect to intelligence, it needs to be able to live in Loura, Manga's. And there's just so many nuances to this through everyday life, right? Everything we do is social. Like work is social. Every task is social. And we navigate these social situations all the time while we do the things we do. And then most of the world's labor also happens in a social context in that there are other people around you when you do it. Objects have social context, right? The coffee cup is empty. You need a new, do you need a new, is it dirty? Or do you want to refill or do you keep your cup out through the day? And like there's this like, right, role of diversity that you want access. So kind of if you're a big believer in that, then it boils down to, okay, the road is to be safe from our first principle point of view. And not able to harm people. Still needs to be very capable and needs to be as strong as a human. Then it just needs to be incredibly affordable. Like you need to find this beautiful combination where you can simplify, simplify, simplify and still get a very capable system so that you can manufacture this at scale and really drive quality up and cost down, right? So that was the founding principle of the company, a decade ago actually said like we're going to make robots that are safe, capable and affordable. And by affordable, I mean, it's going to be like first principles, manufacturable and affordable, very lightweight, very energy fusion. So you can have a small battery, very few parts, the sign in the matter that doesn't require tight tolerances, no special alloys or materials, and just like incredibly simple but performant. That's pretty much what we set out to do. And that's also why it took a decade, right? Because like there's so much novel research that's been done in the company to get to where we have these tender and driven robots. So what's the vision they're relative to the car say? Like one in every household too. Like you mentioned the iPhone, you know, go direct to consumer iPhone sales get to, you know, a billion, but it's exactly one per person is pretty obvious, right? But robots could be two, could be four, could be, I've done that pull. And everybody routinely says I would have at least two depending on price point, right? So price point wise, you know, when I think about this, what I've heard is, you know, 30K, 20K, we've seen Chinese robots in much cheaper price points, but not as capable as is is Neogamma. Do you have a price point that you're thinking about? You're not far off. It's cheaper than what people think. Okay. I mean, it's quite interesting because like, I think this is very important. I want to make sure that we are not only making the best product, we want to be price competitive. I think that's going to be incredibly important. And we are actually still price competitive with the Chinese ones. But you have to count, like you said, it's not the same, right? So if you think about the number of degrees of freedom that the robot has, like how much capability basically how many joins, then we actually have a significantly lower cost. So I think we've done a really good job on reducing complexity to get that number. The numbers that I keep in my mind is like 30K purchase or 300 bucks a month to lease 10 bucks a day, 40 cents an hour. Am I in the right range there? I think we could do better, but yes. Okay. That's fantastic. But I mean, do you need it to do better? No. I mean, it's not going to be that. I mean, it's going to be, it's going to be that. It's going to be that. In a heartbeat, that's good enough. Yeah. But in that case, I think people could imagine having owning a couple of those robots. So I think it really depends on the lens you see this through. So I think clearly everyone's going to want the robot. And I think this is just a beautiful thing about the companion aspect of this, which is so underrated, right? Because the humanoid is just such a beautiful interface for AI. And when you talk to it and you see the body language, it can look at you, it sees who's talking to it, direction, all these things. Like all my 11 year old daughter can do, which has the robots, actually just wants to sit next to it and couch and talk about things. And that is clearly going to be such a big aspect of it. And I see that it's not, I like to say, it's not another pet, but it's not an ordinary human rider. It's something kind of in between. And like I said, it's kind of like the, my hops, like if you're red Calvin and hops, it's the hops. And I think it's going to be incredibly exciting to see how these relationships develop. Because it's the thing that will be like around you all, they're like, right? People remember everything about you. And it's like things that are like really, if you compare C3PO and that vision of a, of a assistant robot and you compare it to what you've actually built, the two things to jump out of me right away. One, it's soft. It's not like a metal outside. And two, the voice is perfect. I'll thank you. Like when you're speaking to it, you immediately are disarmed and you just talk to it because it doesn't have a C3PO robotic voice. It has just a perfectly soothing, normal voice and it's very responsive to anything you say and adjust or anything. So I imagine that these robots will all have advanced AIs at the level of, you know, a GPT-5 or a, you know, a Gemini-3. And in so being those robots will be hyper intelligent and able to understand fully and answer what you need. And once they've learned the physics models fully do whatever you need, you've made a decision to build your AI systems in-house. And I find that fascinating. And in fact, a number of the other robot companies, human-radio-accompanies, not going to put you into a comparison mode here, but I have made that same decision versus partnering with the large hyper scalers. Can you speak to that? Well, we're not doing the same thing. I mean, to me, intelligence does not begin with language. Like, language is this generative artificial construct that we have come up with. And it's incredible. I mean, it's such a efficient, compressed way of conveying meaning and instruction. So language is very useful, but it's not the core of your intelligence. The core of your intelligence is spaged on temporal. And it has to do with how you perceive the world around you both with respect to how you see the world, but also how you feel the world, right? And we're getting to where we're seeing that like models that are native to that modality. And then you add text. We'll be more intelligent and more powerful than the language first. I mean, I've read about intelligence and the belief is that you needed embodiment for intelligence to exist and language for intelligence to scale. I don't feel that I can prove. I don't have rigorous proof that embodiment is needed. I do have very, very strong proof that from an engineering perspective, it's just a way easier path. Right. So if you think about like the information in the world and can you access this, you could train a world model that can predict video and tell you like, Hey, here's a new video frame, right? Render this for you. You could in theory, you could probably train that only on text. Like if you have enough text or descriptions of things, maybe at some point, you could like get in high enough single noise that you actually can get something useful out. At least if you kind of have some feedback loop with like some RLHF or something where you're like, I might happy with this frame. But I mean, why would you do that? That's just like such an inefficient way of doing that. You of course, you train on video because you're going to like, I'll put video, right? So from that perspective, I think it's just obvious that like, you need all the modalities that we experience if you want to get to first and foremost, like human-level intelligence and hopefully pass that again. So then I think there's one other thing about robot, there's two other things actually. They're quite important when it comes to learning. And the first one is quite obvious. And I think we all kind of identify this, which is like robots can do interactive learning, right? So you interact with the world and therefore you can learn. But if you think about it more from a academic point of view of like how those intelligence kind of evolve, how do you get reasoning all these things, then. What we generally do is that we have some observation of the world, like we kind of know how the world works. So I know that if I do this, I know what is going to happen, right? I've seen this before. Yeah. So I actually start with that. And I have a goal. I want to pick up the cup. So now I have a model of the world. I have a goal of picking up the cup. I take an action. I know which action I took. I know the action I took was to like reach for and grasp the cup. And then I observe the result. If you look at the internet or in general, you can look at YouTube, right? All you have is just the observations. You're right. You don't have any of the mental model of like the person in that video. You don't know which actions they took. You don't know what they tried to achieve. You only have the observation. This is not how we learn. You can actually bring it all the way back to the scientific method. It's like you should have a theory, come up with a hypothesis. You test your hypothesis, you observe the result, and then you do it again, and you learn. And that is just not possible with internet data. So there's definitely impossible with the next token, raw internet scrape and with all the video scrape. So then in these limited domains like coding and physics experiments, you can actually have that same experience. But it's only within that domain. Like coding is a good example. Oh, let me try writing it this way. It didn't work. Let me try writing it that way. It didn't work. So you get very, very good at that narrow domain. So I have no intuition about how the world works. No, you can just simulation. No. So again, it's hard to prove that this won't work. So sure, if you have a really good simulator and you just really scale simulation and learning and simulation with agents, maybe you can get something similar. But I mean, the fidelity of your simulator is nowhere near the real world. And it's just like so incredibly hard to get there and close that gap. And it's also so compute inefficient compared to just being in the real world. But I think for me in boils down to not this academic exercise of like proving who's right and wrong, it's more what's the engineering approach that makes sense here? And it's just a way shorter path. You mentioned before in our conversation the amount of data that's being collected relative to Google or YouTube or Tesla. I mean, your mission is get as much possible data during the day of an interaction of these robots in the home. Yeah, I mean, you can do some napkin math, right? And of course, we don't know exactly like what is the most useful data from which we will not just et cetera yet. But if you think about it, if you have 10,000 robots out there, and they gather data most of the day, then that is more data than like non-duplicated useful data that gets uploaded to YouTube each day. So already at that scale, you actually have like your fleet of robots generating more useful data than YouTube. So that's just a 10,000. And then if you think about like how we scale manufacturing here as this starts deploying into society, you actually very quickly come to the conclusion and like, you know what, the internet isn't actually that big. Like you're gonna have way more data from robots than you're gonna have from the internet. So I wanna hit some numbers here just to set them as foundations. You built hundreds of the NeoGamma at roughly, but you're about to, you got a new manufacturing plant that you're about to open. Can you give us a sense of, and then another one that's in plans, right? Without disclosing anything you're not willing to, but can you give me a sense of by the end of 26, how many you're manufacturing on an annual run rate and then in 27, 28, what's the growth path you imagine? - Yeah, first of all just small correction. We haven't built more than 100 of the cameras. - Oh, we've built more than 100 of the robots. - Right. - There's been multiple versions. But the factory run rate end of 2026 is north of 20K. 20,000 annuals. - I know, yep. Of course there's a ramp to get there. So you don't reach quite that number into this. - So a couple thousand a month. - Now the factory after that is kind of like, we're trying to follow an order of magnitude, right? We're not gonna quite be able to do that. I think the iPhone ramp is a very good comparison here where you see like they almost double, but like you have a few plateaus as you reach certain scales where you run into problems. And there are some quite interesting problems or if you're gonna scale the manufacturing of humanoids to the iPhone level, right? Because you run out of some basic stuff like aluminum. For example, you don't use old aluminum on the planet. That's not what I mean. But like there's a certain amount of percentage of current refinement of aluminum you can use before you start to really struggle sourcing aluminum. That might be a challenge. I think. - The iPhone ramp was about doubling. This is interesting. So that hadn't even thought of. - I mean, you get to a billion. There's more like 1.7, but 1.7 annualized over. Wow, that's not as much as I thought. - So you can imagine 100,000. - Well, like, exponentials are quite far. - Yeah, no, I even know. - We heard, we heard, we heard, we heard, we heard, we heard the Rolex. - So, but you can imagine a run rate before the end of this decade of hundreds of thousands per year. - And the list decade way more. - Way more than that. - Yeah, yeah. Now, at that point, you need to really think about like what are the things that will slowly down, right? And it comes down to refining, like mining and refinement, of course, but increasingly, it actually comes down to labor. Like, you're not gonna get there without really using robots for labor. If you think about the iPhone ramp, then Apple kind of displaced large part of like the Chinese population across the country for labor. And they still, they still ran out of labor and had to expand into neighboring countries. - As well. - Now, I think we've done an incredible job in the design. So it's very few parts. It's very simple to assemble. - Yeah, I was just wondering what's on it. - But it's still more complicated to assemble than an iPhone. So, if you can say something, it looks more complicated. - Yeah, it is more complicated than an iPhone, right? So, let's say it takes five times as long. So we need five times as much labor as the iPhone. - Okay. - Then you're in trouble. - So it's gonna be so. - So like you have to automate, right? - Yeah. - And of course, that's the goal anyway. Like we wanna get as quickly as possible to what I call like this hard take of moment, where you have robots building robots, robots building out the data centers, chip fab, the energy infrastructure. And. - What can we learn from the car actually? So you've got the iPhone, fewer parts, one fifth of the labor per unit. Then over here you have a car. It was a part count compared to a car. - So we have a few hundred parts. - A car has 50,000 roughly? - 50,000. - So it's much simpler. - And I mean, a car weighs 4,000 pounds. - Yeah a lot of material. - We are a robot weighs 66. So I think like it's not really comparable to a car. I've seen a lot of like the space compare human rights to cars. But I think that you should go back to the joy board to be honest, like it's not a car. If you do a really good job, very it's closer to a refrigerator. - It's a very complicated refrigerator. But it's closer to a refrigerator in the car. - Let's dive into a little bit of the, let's shape the understanding of the robot for our viewers and listeners. - 66 pounds. Let's talk about battery life, its abilities, describe it from a specific stats point of view if you would. - Oh yeah, sure. So I think first of all, I think the most important stat is this hugable. - It's hugable, yes. - Yes, it is hugable. I have a hug to robot, yes. - And like this is a safety and how it is to feel safe in its space, soft. But from a pure stats point of view, its 66 pounds, it can lift about 150 pounds. - Which is amazing, I mean, in terms of the weight strength ratio. - It is the weight strength ratio of a not-lethic human. - Yeah. - And then it can carry like about 50 pounds around. So hopefully it's all earlier here. And battery life is about four hours. Rechargeable in half an hour or two hours. - Half of it, so like it is. - Of course, if you use a full battery. Now actually interesting enough, I have one in my house, right? So I'm starting to get some date on this now. - And it's five foot four, five foot five, what is it? - Five foot four. - Five foot four, okay. - I think so. - It's a perfect height, by the way, just in case you were wondering. - Yeah, it's also the height of my wife. It's also the height of my wife. So it's like, I agree with you. - It's mine, so that's good. - So it's, what I mean, it's, what's very interesting, I guess we start actually using the product, right? You notice a lot of things that you don't usually show up on a spec sheet. Like the robot is completely quiet. And that's not a coincidence. That's something we worked so hard on. And the first time you put this in your home, and you think like the robot is very quiet, it's fine, you put it in your home, and you're like, first date's fine, second date's a bit annoying, like third day, you're like, oh man, is it gonna leave my living room soon? Because like this sound, right? It's such a requirement for like just dead quiet, right? We're gonna have this in your space. Charging wise? Don't really ever run into the problem. Because the robot just takes like these micro breaks every now and then, when you're not doing something. And like I actually don't care that much about how many hours it can run, I care about that it charges fast enough that it can just always do whatever I want to do. - Yeah, nice. - Well, and I want to talk about quickly, since you said that, specifications, like the number of degrees of freedom, right? Which basically is how many joints does the robot have, right? So like humans have like six drawings, in their each leg, that's 12, you have seven in each arms, that's 14 more. So now you're like 12 plus 14, that's 26. You see all our robots today that have 26, that's quite common. Usually they don't have the wrists, they actually have the neck instead. So two here and then you're like at 26. We have three here. So you have proper expression with your head. That's quite important. We have all the seven from here, we have three in the spine, and then of course we have 22 in each hand. I mean, what I saw in the arm design was incredible. So how many do humans have in the hand? 22. So you match it. Well, depending on how you count your capitol bones. So like the small bones that you have here that allow you to count your hand, you could to some extent see that that's more like four or five degrees of freedom, not really two. So then the humans have a bit more. But functionally it's quite similar. And this again, just is incredibly important to be able to do all those tasks in a home, but also from an AI perspective. There are like we talk about diversity initially, right? It is the one metric for intelligence and the diversity of your data of environment and data. Well, diversity of your data and your diversity comes from two things, or the limit to the diversity you can achieve. It comes from the environment you're deploying in. So right, if you're in a factory doing the same thing every day, it doesn't matter how good your robot is, it's not going to be diverse. And then how capable is your robot? How many things can it do? Right? Because if you kind of do any kind of like in hand manipulation or handling like soft deformables, all these kind of things or delicate objects or whatever, then you get no data of that. So like you really have to kind of go max max on both, right? If you want to maximize your diversity. It was about 18 months ago that I partnered with one of my closest and most brilliant friends, Dave Blunden, to start link exponential ventures. At link we manage about a billion dollars of seed stage money based at a Kendall Square in Cambridge, right between MIT and Harvard. When Dave and I both graduated from MIT, each of us immediately started companies. But at that age, everything is working against you. You have an idea, your challenge to raise money and you can't afford rent. And even with all the accelerators out there, you're competing against thousands of other startups for the same pool of investors. Both Dave and I have spent a big chunk of our lives focusing on how do we inspire and support founders to knock down those barriers, to go big, to create wealth, to impact the world, to build and scale as fast as possible, especially in today's AI everything world. We're seeing so many companies reaching multi-billion dollar valuations in just two to three years faster than ever before. Some companies are adding millions or tens of millions of dollars of value in just weeks. So we started asking ourselves, how do we help these founders go faster and not skip a beat? For example, a couple of months ago we bought an apartment building adjacent MIT where a graduating entrepreneur can move in immediately without slowing down their tech build while they search for a place to live. And so we're doing everything we can to accelerate builders and their super smart teams. Of course, funding is part of it, mentoring is part of it, connecting them with my personal network of abundance, my decios and investors is part of it. We house 66,000 square feet of purpose built incubator space and 26 AI startups call link XPV their home. And the returns have been amazing. I have nothing to ask, but if you are building a company in the AI era, check us out at linkventures.com now back to the episode. Yeah, geeky question for you, but really, really curious to know because when you build something physical and then you attach a neural net to it, it's actually very hard to tell whether the constraint in what it can be. The constraint in what it can and can't do is in the neural net or in the physical construction of the hardware. Is there any way to decouple that and debug the two different sizes are just incredibly impossible. I mean, once it's meshed together, you just can't we have a pretty good neural net here. Yeah. So usually the way I approach this is can we do it in tell you. And if we can, the right neural net can do it with enough data. And that's generally been proven to like be true. Like if we imagine to do something tell you it's just like, okay, now we need a lot of diverse data of similar tasks. So we get some transfer learning and we need a lot better that specific task and almost irrespective of like how complicated that task is, you can get it to work. Now, of course, that doesn't mean you can get everything to work, which an allisation across we're not there yet. But you can see that like, okay, you can get the neural network to do this. Now we need to scale it. So we kind of get this beautiful transfer knowledge between tasks and like our distribution generalization and all these things that we currently see in all of them. So we don't see that much in a robot except we have some pretty cool stuff in turn, they were ever see some science of one picture, those you ask it to make crepes is that or you ask it to do microsurgery. And it can't quite do it. And then you say, well, look, the hardware guys claiming the hardware is good enough. It must be the software guy. And then software guys saying, no, no, the software, the neural net is fine. The hardware just can't do it. And then they fight it out. And then we just say like, well, bring it. We bring in our best teleoperator and we say like, he can do it. Then the hardware can do it clearly. It's literally proof of success. Yeah, okay. So that's where I was going. So you have a remote operator option. You can control the hardware. That's really interesting. So then you get like, well, but we're getting to where this gets hard where we can kind of no longer do this because the hands are just so good. And they have very high fidelity tactile feedback. The human hands are so good. No, the robot hands. Okay. So they have the humans hands are still even better. But like the problem is the robot hands are really, really good. And they have really fast, highly detailed tactile. And we can't really transfer this efficiently enough from the human. Yeah, because the teleoperator is so, so yeah, I mean back to the express right. Yeah, that was our challenge. So it's a really hard problem to transfer that fast enough. And now we start to see that the robot actually learns how to do manipulation way better from reinforcement learning in real. So you like actually have the robot like interactively learning real how to have objects. And you can do things that the operator could just dream of. So that's so now you're going to know we can't do that anymore. I want to talk about three things in sequence tele operations versus full automation. Safety in the home. And privacy in the home. Yes, because those have got to be critically important as you're entering the home. So the robots. So we saw the new gamma out here operating until in teleoperator mode, but also in AI full AI mode. Right. And it was able to do both. And it's AI systems are going to increasingly get better and better and more capable. Again, as we're talking as I'm talking to Gemini three or rock four or you know, GPT five soon. I'm talking to a highly intelligent human and getting a feeling that it understands what I want. And it's able to, you know, sort of like take action on my request. I imagine we're going to have the same level of AI eventually in the robot where I feel like I'm talking to a fully intelligent being in one sense. Clearly, and more or less already. It is. That actually understands to some extent what this existence is, which today's LLM are kind of like they have this kind of abstract notion of it. But it kind of like it's a facade that kind of quickly falls away if you start to pull about it. But that will get there. I think in the teleoperations mode, you've got humans wearing VR headsets and using haptic controls. What would the humans do? They're giving slightly more like high level commands. So just guiding like, hey, put your hands over here like grass this thing. You don't want like all reconstruction system. You want to give it some opportunity to kind of like solve for how to do the task. So we kind of have like the learning coming up from the bottom and kind of like enabling a more and more abstract interface for the operator. And then we have the learning of like the all the large amounts of data we have coming kind of from top and getting more and more like the general behavior that you want the robot to do. And they kind of like meet in the middle right where you don't break the ghost away. You're using for you're using automation and the operations always together in every guard. So everything that enables the robot to do anything that the operator does. Teleoperator is fully learned and like the network outputs talks to the motorists. That's that's very similar to Tesla and Elon Musk. We're saying where the self driving car was originally all C++ code with a little bit of neural net. Maybe 80% C++ 20% neural net. Then every year they went by became more neural net. And now there's 300,000 lines of C++ were eliminated. Yeah, just a few guard rails left and then versus just one neural net. So same thing here. Yeah, it's it's all weights. Right. Yeah. Like the code is just a few hundred lines. It is a really. Yeah. That's all. That's the parameter counters that all super secret. It's kind of secret. But it's it will be small if you compare it like to days neural networks because it's running on the robot very fast. Yeah, it's kind of like your muscle neural system. But it does take envision. So it's not very small. Well, that begs a question of dying to ask, which is you've seen X Marker. Right. When I saw that movie, I'm like, why don't go to stop you on this here? Okay. Okay. Why is the brain the blue blob in the head? Yeah. Why isn't it in the server room? Yeah. So learning is shared between all robots. Yeah. And you can be much bigger. And you know, like if half the power of the robot is going into the thinking, you could say you could run twice as long as battery charge. If you move it over to the server room, I'm just communicating. So right. Why did you choose to put it in? that. Aside from being anthropomorphic and cool. No, no, it has nothing to do with that. Okay. So there's some simple answers to that, which is, I mean, the head is where nothing else is unless you put the brain there. Like the resistive. Yeah, like everything else is pretty freaking full. Like it's building a humanoid with this kind of like power level in such a miniaturized form and still having like enough space to make it like completely soft, you know, it's really hard engineering problem. So it's like, where are we going to put this if we don't put it in the head, if you haven't put it on the physical robot. Now there's smaller arguments. The very high bandwidth thing that happens in your brain is vision and to some extent audio, smell, right tactile, but vision just dominates. Yeah. And you just want to minimize the distance between your eyes and the computer and the computer. For real? So the bandwidth between the sensors, the very high wouldn't make it over the home Wi-Fi. Well, it wouldn't even make it down to the stomach for the robot. Really? Without getting overly complicated on like which physical interface is you would choose for this transfer. It's very high bandwidth. I'm shocked by that. I mean, we're running like no light or no structured light, no wrist cameras, no nothing. We're running pure like a relation of human vision, right? Yeah. So we're relying so heavy on that. So it's a very, very high resolution, very high bandwidth, very high frequency. That's right. Because that's exactly where the human brain is very after the last two. It is now that doesn't mean that you can't do things in the cloud and we do things in the cloud. But it kind of becomes hierarchical from an intelligence point of view. Just like if you think about like your your kind of like your neuromuscular system, this runs quite fast, right? It usually runs with like 25 hertz and it doesn't go up to your brain. There are neurons distributed out through your system that makes decisions. Right. We have this in the robot. We have some of our stuff pushed to the power electronics that controls your latency. And then you have the brain itself, which actually runs pretty fast, right? It's usually like between five and ten hertz and very even though it's five to ten hertz, very low latency. And this runs on the robot. Now if you're running like more like one hertz streaming thing that typically in LLM first time to token land, right? That runs off board. But that that can't solve the like high frequency tactile feedback manipulation tasks. That's too slow. Okay. The first time my Neogamma learns to crack open an egg to make an omelet. The question is do all Neogamas then learn that? Are you shared learning? They do. Now there's a shared learning in the sense that you can say like this data goes to the cloud model that is doing this for all Neogamas. But there's also the distributed models. So of course there would be like a nightly checkpoint where like hey this model is better. We have more data. We validate this. We give it to safety, which I'll talk about later. It comes out you validate models. And then we deploy that to all the robots. So even though it's distributed on the robots, they can still learn from each other. Of course, it's just you need to do like one hop through the server layer and like do the training and propagate this out. There is a future not so far away where I'm pretty bullish on there being a lot of federated learning happening on the voice. And this has to do with how do we have your companion really throughout life learn from all of the experiences that are there to you but private. Yes. So all robots will not be the same, but they will share an intelligence backbone. Let's go into the conversation of privacy and safety. So you're inviting these robots into your home. And where there will be activities that you may not want shared with the world. And then of course you're asleep and the robot is running tasks at night. You don't want to wake up in the morning and find your safe has been opened and the robot's gone. You know to talk about or you don't want the robot to be you know taking care of your aging mother and find out that it's uh you're given her given her shots of scotch at night when she has for them. I mean so how do you deal with safety and privacy? The last one is the hardest one by the way. We can get back to that. Okay. Multi-gave grandma. It's called shots of scotch. Because generally models are they're always kind of tuned to be kind of sick of fans and they end up doing whatever you ask them to do. But so if we started the privacy side. I think first of all is just it's a lot of transparency. Like if you're one of the first people like you Peter that will have a neo-gamma in your house. Yeah wait we are kind of trading a bit on privacy versus being an ordered opt-or because without the data we come to make the product better. Of course we're going to do everything we can to make sure this privacy on your terms and that you are in control. But we do need your data for going to make the product. Sure. I mean I listen I give my data to Google to Amazon to X all the time and I mean people don't realize that you're sitting in the home having a discussion with your spouse and Amazon you know Alex is listening right? It's serious listening. But they're doing something very important which we also do which is no human in our company can hear or see that data. Yes. That is going into the training mode. Yes. But it doesn't go by as human. Right. Now if we want to look at that data and sometimes you might need to right might be like let's figure out what happens here because something clearly is happening across multiple robots that we want to figure out what is. Then we'll send your notification on your phone or say like hey this specific window we want to review the data and you'll get a video of like what that data is. Yeah. And then if you say yes then we get the decryption key and we can look at the data. If we don't if you say no then we can't. So that you're in the control of that and actually even with respect to not going into the training data we always run like a 24 hour delay on training. So if there is something that you really don't want even in the training data. Like this never happened. I'll erase it from existence. You can go in and delete it before it gets into the training. I just want everybody here there is a there are policies and plans that make this acceptable and are used by technology companies and you're going to be implementing the best of those. It's great. There is like so the mode I talked about now is when the robot is what we call best effort autonomy right which is most of the time. So just earlier today we talked to it you asked to do something hopefully it does the right thing. If it doesn't do the right thing then you can say bad robot and hopefully it's better next time but it's very it's actually this is learning in real life. This is really like interactive learning and the robot interesting enough actually progresses faster on tasks when it fails then when it succeeds. Sure it learns more from failures just as we do but in this mode that's the privacy. Now it comes to tell you up then of course there's no way you can do this task without seeing the class. So we do some abstractions so that you actually don't see people people you kind of like just see blobs and like you just see the object or interacting with them. We can do a lot on interaction on like the filtering side to ensure privacy but the most important thing we do here is that no one goes into tell you up in your robot unless you approve it right and it's very visible on the robot like the lighting chain this and like this is like someone is in your robot and it's one of the pre-selected operators that you have approved from like a large set of operators like here are the four that services you so that's kind of like inviting your cleaner or whatever into your house. Another human in an all-rehewine into your house and you just need to make sure that they're actually invited. So to actually take a second and spell us out more detail in the early days when I have near again in my home it'll be baseline autonomous but there will be times where it needs to bring into tell you operator and so you'll have tell you operators and headquarters that if it needs help or doing something complicated or it gets something wrong that tell you operator can step in and actually make the task happen. Yeah it's in the beginning it's actually there's two different modes which so you have the mode which I call like the best effort autonomy that we just talked about. And then you have task scheduling which is like my role that at home now is doing that. So I take my phone and I schedule and say like hey between these hours here are the tasks I want you to do for me. Today it's like do my white laundry and then there's a package coming from Instagram to receive it at a door and unpack it in French and it's just like generally tidy. And I've given it when I'm not home like these hours I'm at work just get it done right now I don't care if that happens autonomously or to a tell operator right so a lot of that happens to a tell operator because some of these tasks are quite complicated and we don't know how to automate them well enough yet. Now of course that tell operator uses autonomy to help improve their efficiency so it's not all tell operation but I don't really care about the mix the task gets done. Yes. So we kind of split it like that and then there's a gray, so on kind of in the middle if it's like you want to like I don't know having your friends over a party and you want they're able to be the bartender and we don't have like a bartender mode yet. Then you can improve a tell operator to do that. So you can use all of the most of all the videos we see of Optimus at you know Tesla's diner or at their events or tell you operation modes. They are but I think tell operation has got this like kind of like underserved bad reputation or name. Why? I think this because people don't have enough like clarity and like hey is this a lot of the easy to autonomous but it is just labeled data. It's expert demonstrations right. If you look at any of the big AMOles that were trained there was an enormous amount of people that sat down and had labeled data and like looked at examples wrote out like question answers some like. BOO! you'd strap kind of like this highly, very high quality data set for this to work, right? So you pre-chain on like general information, we also do that just everything that's happened with your robot. And you have a fine tune data set that is very high quality. And in robotics studies, teleoperation, because it's the expert demonstration, it's the hand labeled data. It's not different. It's just I think there's some lack of transparency and what's going on. - Well, I think the objection is if you have a demo, like a video that makes it look like it can do something and it actually can't because you hand coded it. - Well, it's clear to can, but it can't do it, it'll pull them. - Yeah, I can't do it. But I think you're dead on that the, if it can physically do that, if the mechanism can do that, the neural net will fill in that blind spot instantly anyway. - You know, once you've trained it, so I think it's perfectly legit. - And now it's time for probably the most important segment, the health tech segment of moonshots. It was about a decade ago where a dear friend of mine who was incredible health goes to the hospital with a pain in his side. Only to find out he's got stage four cancer. A few years later, for Tonya brother of mine, dies in his sleep. He was young. He dies in the sleep from a heart attack. And that's when I realized people truly have no idea what's going on inside their bodies, unless they look. We're all optimists about our health, but did you know that 70% of heart attacks happen without any preceding? No, shortness of breath, no pain. Most cancers are detected way too late at stage three or stage four. And the sad fact is that we have all the technology we need to detect and prevent these diseases at scale. And that's when I knew I had to do something. I figured everyone should have access to this tech to find and prevent disease before it's too late. So I partnered with a group of incredible entrepreneurs and friends, Tony Robbins, Bob Hurrey, Bill Cap, to pull together all the key tech and the best positions in scientists to start something called fountain life. Annually I go to fountain life to get a digital upload. 200 gigabytes of data about my body hit the toe collected in four hours to understand what's going on. All that data is fed to our AIs or a medical team. Every year, it's a non-negotiable for me. I have nothing to ask of you other than please become the CEO of your own health. Understand how good your body is at hiding disease and have an understanding of what's going on. You can go to fountainlife.com to talk on my team members there, that's fountainlife.com. - I want to jump into another fun subject, which is the uncanny valley and the face. So, I mean, you've probably had endless conversations internally about how do you make a face look? How human do you make it? How skin-like do you make it? How do you represent it? Can you tell us sort of philosophically, what you and Dar, who on your design team, how do you think about that? Where do you make it human enough? - Is this very delicate line where you want to make sure like body language comes across crystal clear because that's the magic of the device, right? Of the companion. - Yes. - But at the same time, you don't want it to get like, to where your kind of instincts tell you, "Hey, something's wrong, like this is a human, but there's something wrong with it." You don't want it to be a human. And it's actually pretty surprising that there is this gap where people clearly identify this as like, "Hey, this is a being I identify way I understand is body language and everything, but it's also clearly not a human." - Yeah. - And you want to be in that space. And then where you are in that space, kind of like depends a bit on who you ask. Because people have like a different threshold here. So we're trying to hit kind of like in the middle of that and ensure that for as many people as possible, this is just like an incredibly easy to understand product. But at the same time, that is not creepy. And I think adoption here, by the way, talking about scale, adoption is so important. And adoption of new technology usually takes some time because there's just like this knowledge barrier, right, there's a barrier to entry. Even using a phone, there's a barrier to entry. - Yeah. - This interface is just so natural. Like there is no barrier to entry. It's something you just talk to. Like at first, it's, you know, what's incredibly cool to me is that there are like 50 things around the house that I don't know how to do, including the frickin, the other way to backwash the pool, like all this crap. The robot can in real time access the information, learn how to do it and just do it. - Yeah, from the world. - I can't do that. It would take me an hour to study. And there's no labor that's gonna come into the house and do it for under like 400 bucks. And so it's like, like there are so many things that are in that category where I'm not trying to replace a human being. I'm doing something that there literally was no other option for because the knowledge is obscure. And there's so many of those things around a house now. Like resetting the water heater keeps going out and the reset process. But you can look it up, the robot can look it up. - Yeah. - And just go do it. - And it's like, this is micro units of work essentially. - Right. - Like you need five minutes of, but hyper- - But hyper- - You need hyper- - hyper-specialized micro units of work. - Yeah, I do need like five minutes of whatever now and then it's super high value to you. - Yeah. - And it's just too hard to do. - Because I can shop back. You know, the shop back, you can run it forward or backward. There's a manual there. You could read the manual. I just wanna get like this crap off the garage floor. The robot will know how to shop backwards because somebody else's robot, the other 10,000. - They've made my perfect teriyaki salmon on the grill. - Yeah, so much. - So much skewer mixing some food. - Which brings us to something that you talked about earlier. - That was my dog. - That was my dog. - Scotch program. - Yeah. - So I do hope to make your perfect salvent teriyaki. - Thank you. - But I have to do it myself. I'm not gonna let the robot do it. (laughs) Because that's one of the things we're actually not doing when we're launching now. And that is due to safety. Because what I worked so hard on, right? For this decade is to make robots that are safe intrinsically. And what I mean by that is just like, if something goes really wrong and accidentally hits you, that might be painful, but it's not gonna be likely to severely harm you. - Illures, right? - And once you pick up a kettle of boiling water, there's no more guarantee that you're safe, right? So we generally avoid any kind of dangerous objects so that we can ensure safety in the beginning. Now of course over time, as the AI improves and we get more and more certainty on all behaviors being safe, we will allow cooking and all of things. So we're doing internal projects on this, but we're not gonna be rolling it out to the customers in the beginning, just due to safety concerns. - Yeah. - Yeah, cooking and safety is a. - It's a real problem for environments. - It's a real problem for humans too. - It is. But there's the notion of intrinsic safety. So this is incredibly important, and then it's the safety of the AI. And this is the reason we have a white paper out on this, that I'd recommend if you guys are interested, read it, but why we have started very early, betting extremely heavily on role models. - I'm on role models. - World. - A world model. - World models. They are of course the currently best known path towards the AI, but even more importantly for us, like short term, as we progress here on data collection and model training, they give us this incredible opportunity to automate evaluation of models, including safety and red teaming and all these things. So you can think about like if you train a new model, and now you wanna know if it's better than the previous one, and you can deploy it to all their customers, and you can get some vibe check a few days later, like, "Hey, are people more happy now?" It's generally how it's done. You don't wanna do that with a physical system, right? You can't do that with a autonomous car rider. Well, the world model actually is, it is a model that is able to generate what will happen if you take specific actions. So you can think of it like a video model where you ask it to do something, and then it actually gets not only like the question of what to do, gets the actions to do so, and it gives you back not just the video, but how the world feels, like the force is everything for the robot. So it's essentially like the robots in the matrix. You take the robot, we put it in a world model, and it doesn't know that it's in a world model, it thinks it's in a world, and it does its things, and we ask it to do the things we're usually doing around the house, and we see what it does, and we can put in lots of automated checks, so I'm sure that both that is performing better, but also that is not doing anything that can be deemed unsafe. So it's really like this incredible, important and powerful evaluation tool, and that starts to solve in the problem, right? Do you think that's why you guys and figure, and Tesla are getting monster evaluations? 'Cause is the valuation just purely, hey, we're gonna sell 10,000, 20,000 and 200,000? Where is it, no, the world model is such a unique asset, and so valuable in thousands of different ways, and that becomes a very much a self-feeding buried entry, and that could also explain, like do you plan to productize that core capability? - Yeah, so to me, it's back to our mission is to create an abundance of artificial labor, and that goes across, well, the digital and the physical, so yes, it will be productized, still a bit out, but I guess this will be productized. - It could seem like no matter how much factory capacity you build, it wouldn't be till like 2028, 2029 that you could diversify into all these microsurgery and warehouses and drones, all that, But that same world model could apply to this. as much sooner, but you'd have to somehow get it into the hands of Manning Action. - Well, I think revenue from the robots will dominate forever. I do think that the real physical world has way higher value than people think. - I mean, just for folks to realize, right, we're at $110 trillion global GDP and labor is half of that, right? So the TAM, total addressable market here is like 50 plus trillion dollars. - Just if you keep doing what we already do, but you do what we're doing. - You will be able to do what we're doing. - Yeah, it's gonna be so much bigger. - You've attracted some incredible early investors. Do you mind just sharing who's come into your cap stack? - I think maybe like we have some big classical ventures, like soft bank, target global, - Envidia. - Ekt, Nvidia, Open Eye. - Yeah. - System good names in there. - I mean, it's a lot more. - That's damn good. - I think it's becoming increasingly clear, right? That the bottleneck in society to super intelligence is not better algorithms or scraping the internet in a more thorough way. It's better data. It's, yeah, it's better data and then you need a robust to generate this data, but even more importantly, it's the physical parts, right? You need more data centers. You need more power. You need like, to do this, you need more labor and it's kind of like this bootstrapping problem. And if you just break down the pyramid and you say like super intelligence consists of this incredible amount of data and it consists of this substrate of compute and power, then you see that humanoid is a solution to both of them. And if you just do the math, you'll see that you're probably not gonna get there without that. You're just running out of these like basic constraints. And I think humanoid's will be surprisingly useful, surprisingly fast. - Okay, not perfect, but it's gonna be surprisingly useful, surprisingly early. - I have a question on the half of the Selimis male who's our typical third moon shot mate here. I have to ask, could I ask who's that? - That's gonna be. - So Selim is constantly saying why two arms, why two legs, why not six arms, why do you need to have the humanoid form? I mean, in the kitchen wouldn't be better to have an extra pair of arms. So what's the definitive answer to him? - Well, I think first of all, it's kind of right. Like humanoid is an donizing that will work. I do think that I'm gonna look a lot like, I don't know of any form factor that is as general as a human in doing kind of like any kind of labor in any kind of environment. And we've tried to simplify, we've tried to increase complexity. Like the humanoid is pretty good machine. So if your goal is to just be as general as possible, then you need a humanoid. Now, if your goal is to transfer knowledge from humans, it's a lot easier if you have a humanoid. Now, if your goal is the most important part of the equation there. - It's very important. - And then you're not gonna transfer to a six armed robot learnings from a human. - Yeah, it's at least harder. And then, I mean, the world is made for humans. It's like, Jensen says, right? Like it's brown for deployment. It's very true. And then I think, lastly, it's just, you wanna live with a sex like a robot in your kitchen? (laughing) But I view humanoids as kind of like the pinnacle of general technology. But there is this kind of repeat pattern through history of this happening with like zero to one level products. So if you think about the, say, the computer, it started with big mainframe computers, solving very specialized tasks. The equivalent in robotics would be in the sort of robotics, right? And then now comes the PC or even before the PC, like the kind of like a VIX or Atari, so whatever, like more general computers. And this gets produced at such a scale that it just becomes generally available. And now it's super high quality and it's incredibly reliable. It's a huge ecosystem. And it just becomes the best way to solve any problem. Even though, and here's the argument I get to humanoids, it's overly complicated for the task. Even though it's overly complicated for the task, when you take your beautiful apple here and you write a word document, I mean, that's the most complicated typewriter I can think of. Like you, you manage to like master nano scale chip manufacturing for you to have a typewriter. Like, but it's still actually the cheapest, most reliable typewriter because it's just like made at such a scale. Humanoids, exactly the same. Now, if you see what happens to computers now, because the market has become so big, it starts to actually become segmented again. And now you see you can carve out niches in computing and they're still so large that it has scale. So now you get specialized computer for AI, specialized computer for physics, specialized for all kinds of things, right? And this is because it becomes so big. Now, the same will happen in robotics. So we will get to where we have Star Wars. There will be different drones doing different tasks and they will look kind of like more specialized to like my repair drone with like six arms and like, see their hands and I don't know. Like, it'll get there, but you have to go through this humanoid face first. So let's just say humanos is a face. - Yeah. My favorite robot is still data from Star Trek. It's a great robot. - Yeah, and it's kind of the closest thing I think of to what you're building. You know, lovable, happy robot that you can give a hug to. - Do you have a favorite robot? - Well, I wouldn't have thought of data, but now that you said data, that's top of the food chain. Everybody loves R2D too, 'cause for some reason R2D too has no voice, even though they have voice technology. - That's weeks. - All those visions though are built around what Hollywood could easily get on a set. - Yeah. - I think the humanoid form factor though, there's another aspect that you kind of touched on, but when I bring it into my house, I have a vision of what it can do and what it can do based on humans. - All right. - And so I ask you to do things that are rational and not irrational because I know what a person could do. If I had a six-legged thing that Selene came up with, I'm not quite sure, should it be able to climb on the roof and fix the shingles or not? I don't know what this thing's capable to do. - Interesting. - So it breaks the whole kind of like, comfort zone of exaggeration. - The thing it does surprise me though, about the robots are unbelievably coordinated between themselves. And there's some good demos of this at MIT, they're just mind blowing. But when you have two movers, trying to take a couch up the stairs. And they're like, it's like the stoogees, right? Like, now we move a little down a little bit. When you see the equivalent act with two robots, they're just in phase. And they just do it seamlessly. So I think there's a very high probability that this standard in the home is gonna be like four or six. If you get the price point down a lot. But they work so well in concert with each other. It's almost a crime not to have that teamwork synergy. - Huh. That seems like a bit much to me, but in terms of, maybe I could like, if I need to have a movers, like, you know, I can ask my neo-gammon, he'll invite some friends over. - But you're not taking everything into account, Peter. - Yeah. - Because you have to remember that by the time you have these many robots in your home, everyone's homes are really freaking big. - Yeah. - We have an abundance of labor. - Mm. - You can do an intellectual math on this. - You mean like, your house is not gonna be this small. - So labor is gonna be huge. - So labor is gonna continue to demonetize and democratize. - Hey everybody, there's not a week that goes by when I don't get the strangest of compliments. Someone will stop me and say, "Peter, you got such nice skin." Honestly, I never thought, especially at age 64, I'd be hearing anyone say that I have great skin. And honestly, I can't take any credit. I use an amazing product called One Skin, OS01 twice a day, every day. The company was built by four brilliant PhD women who have identified a 10 amino acid peptide that effectively reverses the age of your skin. I love it, and like I say, I use it every day twice a day. There you have it, that's my secret. You go to oneskin.co and write Peter at checkout for a discount on the same product I use. Okay, now back to the episode. - Let's go someplace that I'd love your insight on, which is China. So when I think about the robot industry, you know, I'm tracking 50 plus well-funded humanoid robot companies in different stages around the world. Majority are US and China. There's some in Europe, you started in Norway. There's some in India, parts in Japan and Korea. But China by far, I think is dominating. And what I see there with the robot Olympics and special robot villages is pretty extraordinary. Where the Chinese government is really accelerating this for obvious reasons. You know, they need access to low cost labor to continue the manufacturing boom. They needed for supporting their elderly population. How do you think about China? What do you think of the work coming out of China? - Well, first of all, I think we need the same thing here. - We don't kind of like realize it maybe as much, but of course we need the same thing. - Yeah. - I think the Chinese ecosystem is incredible. I mean, I don't know anywhere else in the world where you can go and like develop hardware as fast. It's just, right, you need something and you go over and get machine on the corner here, something from the Chinese. - It's in the E and you just like go to street and like buy some new components that is someone like, there's someone doing a reflow over on the street corner over there. And like, this is an incredible ecosystem. And I think the Bay, I said the Bay now, but I know it's also like, I mean, the Silicon Valley. in Valley like the hardware bay in Chen Stena, it's also a bay, but Silicon Valley bay, this bay, we have a long way to go if we want to like really get to the same level of rapid iteration of hardware. So that's just incredible. I think the manufacturing part is incredible. It's just so much process knowledge. And I think this is highly underrated. Like, you know, think about magnets. I do. We have so do I have a lot. We have great material scientists that know how miners work and can design very good magnets. But then we like that guy that knows that yeah, you do all that stuff that they told you in the books, but you know, after two hours, you have to stir to the left, not to the right. Like, there's like, there's just so much of that, right? And this is just so disseminated in China. There's so much process knowledge. How's that? A wall of in China and not here. What's the cause? Top down incentives. Just Monday? I think it's it's the government saying you're a robot city. You're a neodymium magnet city. You're and just capital and people and just, you know, communist directed, but then allowing companies to build on top of that. Is that what you see as well or not? I'm sure. I think like the the Chinese startup communities very alive, right? And the capital is quite alive. And I felt it runs very similar to kind of like more of like what we like to think of as like the Bay here. I mean, I used to take a group of investors every year to China and we would go and visit Shenzhen and Shanghai and Hong Kong and Beijing and meet with Baidu and Tencent and and Huawei and the leadership of all these. And there was a super vibrant entrepreneurial community, right? The mindset was 9.9.6. You'd work 9am to 9pm six days a week and that was a great lifestyle. And you considered the 1.3 billion people in China, your market and the 300 million in America, your market as well. But there was a fall off after 2019. And there was a real dip in that in that ecosystem. I think it's beginning to reemerge. But I think the government is really pushing hard on supporting AI and, you know, a human aid robots is a embodiment of AI. It's it they're they're obviously you know this. They're cleanly meshed. So I do think there's a lot more support that the US government needs to give to us 100% of our companies. But I think like what I wanted to say was this was just like I think the most likely the most genius thing that it was the the economic zones like the Vietnam sounds. Sure. Like it's not that people here don't want to build stuff. And we want to build stuff. Yeah. It's just it takes too long and costs too much and it's too convoluted. Right. We do it in spite of the challenges. Yeah. I think the US should just like spin up some free economic zones. Like here you have like expedited permitting and to a California in particular. That'd be a no brainer to do that. It's the simplest best idea ever. But I don't know what we'd take to get it through. No, I mean, but this is some of like what people are working on these days. Right. Like if you look at like Massa stream for project Chris land, for example, it's very similar to to this kind of like a free economic zone in the US. There's another problem you need to solve too, though, which is that, you know, the US just did software software for, you know, forever. And we were not only not doing hardware. We just didn't do chips. Well, no, no, forever. We're in Silicon Valley. Yeah. Yeah. Yeah. We're in Silicon. Yeah. There was a face in between here where people kind of like they lost the way. Yeah. Lost the plot. Yeah. And now we have to find the venture community got on the stuff too. Because they wouldn't find it. If you had a physical component in your business plan, they'd back well, I'm looking for the next meta or Google hardware. It's not really care. Like the hardware software. Well, all I've been doing is for I've been keeping this company close for 10 years. So I can, yeah, you've got to go on and off. Yeah. I'd like to go on and on. Because I can't how how much people are afraid of hardware. Yeah. Right. Yeah. But it's going to kill us if we don't find a solution. I think also it's going to like kill VC to be honest. Yeah. I think people like, like if you do get return of venture, it used to be incredible. Right. If you've got to be an LP in a venture, you're like, Oh man, I'm like, Seth, right? I'm going to make the big box. Now it's 10 years and no return. It's not in and now it's more like a philanthropic thing. You want to start a venture because venture doesn't really make that much money. And I think it has a lot to do with accepted link. You guys are doing you guys are doing amazing. We are doing amazing. That is good. Yeah. I know that you guys touched a hard stuff. The point is if you don't touch the parts of what's you know, we touched the early stuff. Right. So it's first checks into companies that then are scaling rapidly versus companies that are doing. But we're not we're doing first checks into hard stuff at the seed stage, but we're not doing hardware. So I'm almost much part of the problem as we were talking like what if somebody came to me with a seed stage hard physical device problem. And we very rarely will fund that. And as a dysfunction. Well, you should look at what's the biggest companies they all have hardware. Yeah. I mean, listen, Elon cracked the code on that. I mean, he's been able to just make hardware sexy and is generated incredible returns. Yeah. I think Jensen says it really well, right? They want to work on the really hard problems. Yeah. They're super painful that you are uniquely capable of because you know that your competitors have to go through the same pain or more. They're not going to be willing to take as much pain as you. This is how you win. And things here are actually defensible, right? The mode we have on hardware. Yeah. That's years. The mode we have. I'm incredibly proud of our AI team. By the way, we've accomplished some things that are so amazing on such a budget. So we're way ahead of everyone else in what we're doing on world malls. Yeah. So let's say we're three months ahead. Right? Exactly. Because like, wait, you're incredibly rare. And and all props to Elon. He's incredible. But but Elon's pathway to getting to hardware was through. Yeah. Sure. A couple hundred million dollars burned it all himself, got down to near bankruptcy. It was almost dead on both of his big, you know, Tesla and SpaceX barely pulled it out and then made them huge. But the VCs were not touching it. Yeah. They did borrow money in 2008 on the in a divorce with SpaceX having its third failure. That is a wonderful funding model. You know, we still that's not I was very lucky. I had a very very good like early founding investor. I said like the company didn't start in a garage because we're not Silicon Valley. We started a barn because we were in a region. Yeah. And at some point, two years later, he sold the farms that we had to move. He sold the farm to fund the company. So you would this company in Silicon Valley wouldn't exist without an Norwegian investor. No, I don't think we would exist because we wouldn't like we wouldn't have had the runway. Operating this in Norway was just incredibly cheap corporate operate. So your initial Norwegian investor did he or she believe that they were going to make a huge amount of money or did they do it because they're passionate about your vision and your business or are they believing in you? Yeah. I think it's all three. All three. Yeah. It turned out pretty well. Yeah. Yeah. But it wasn't here. There are different phases, right? If you want to scale something, you have to come here. I think you can do deep research in all the parts of the world. There's talent everywhere. But really kind of like hyper scaling that and like getting it across the finish line. That's here. Did you consider LA Austin, Florida versus here in Palo Alto? Yeah. We even had manufacturing for a little time in Texas and Dallas. There's just there's there's something to the water or something like the the talent pool. It's a talent pool. Yeah. Like there's talents that talent everywhere. But like the density of talent. And you know, there's different types of talent because when you have like a zero to one field like this in the beginning, you have a lot of like really passionate people that have been working on this all their life. And they're so good. In this case, like human or robotics, right? And I remember back in the day, if you went to the human or its conference, like everyone could fit around multiple table. And those people are still the ones that are also these companies, right? So and those people, they don't know how to make a great product. They don't know how to just don't know how to scale that to a million or a billion devices. They don't know how to write like the incredibly good APIs for the software to support the ecosystem. They know this thing and they do deep research. And now your feet kind of comes of age. And it's time to actually do this because the timing is right. And we purposefully stayed very small for the first seven years just doing core technology. Now, suddenly you get access to this talent pool of people that just go from field to field. That is the hottest thing right now. And just do it again and again and again and again. And that's Silicon Valley, right? But there's been an incredible inflection point in human or robotics. I remember, you know, we had the Avatar Express, right? Our in a avatar express that had teams build robotic avatars that you could tell a presence. And I remember the finals. We had good teams. I know some of your team members here were parts of those teams. But it's come a thousand X since then. And really in the last five years, right? Is it been the AI models that have made that? What's caused the inflection in the last five years? >> The AI is clearly part of it. There are things we do with AI now that we couldn't do five years ago. I do think we saw the bedbed chrome, so we were on the path already then, but it wasn't working yet. I think it's just, you hit this kind of like critical mass of like a accumulation of like innovations that has happened in hardware. I do think it's important to note though that it's hard to see what is like a real innovation or not in any field and especially in human order robotics. So I want to just point out again, not like you can go on YouTube and find things from the early 2000s that look better than most things you see today that human order robotics companies are doing. So you can't just make a beautiful robot that looks good. You have to actually make a robot that is safe, that you can actually manufacture at scale for a very affordable price and that's still is capable, right? And I think that's been the main unlock on the challenge. Like you need to get those things right. And that just takes a lot of time. >> Let me be, the neural nets are light years ahead of anything anyone would have predicted five years ago. And then the hardware, the Nvidia chip that it runs on is getting pushed as fast as any innovation in history because the demand is through the roof. So that part is well understood. On the physical hardware side, what's something they used today that you shouldn't have used 10 years ago? Like yeah, what's improving in the motors, in the harnesses, in the electronics, batteries? >> Yeah, so I think mostly it's been on like the motors and materials side and side. So we make our own motors, including not only the IP for the motor, but also the manufacturing and automation for all this and everything that goes into the play chain is so brutal. >> Yeah, you literally make your own motor. Like we have to run the wires. >> Yeah, so how do we crap? We do it kind of special, the one next version of this. So motors is one of the things we really innovated in. And this is actually how I started like, you know what I sat down a decade ago, the first thing I did was to sign a different kind of motor. >> Okay. >> And the motors we have now in Neo, they are five and a half times the world record in Torque de Weit. >> Wow. >> And that's why we have something that's so powerful that we don't need gears. We can just pull on these tendons to loosely simulate a loosely simulate human muscles. >> That's what I was thinking. >> Yeah, and that's why it's so light. It's also why it's so like a travel compliant. It's why it's a cheap manufacturer. >> That's interesting. >> Like everything kind of comes from this. Now of course when you have these motors, then you can start using tendons. But then you need to think a lot of time into figuring out how to use tendons. And then comes all the material science to have tendons that can last millions or millions of millions of miles of cycles. >> Yeah. >> And these are really hard research problems, right? They're not even engineering problems. They're hard research problems. And we spent so much time figuring all that out. You can't make the motors that we make without doing some pretty significant innovations in electronics and how you do power amplification and in general just motor drives. So that kind of like, there's a lot of things that come together. You couldn't have designed the motors video today without some of the innovations that had happened in magnetics. And of course you couldn't have done with OAAL either. The first thing I did was pack in the day when I sat down was to program a network to learn how to make motors. >> Oh, you're kidding. You designed the motors via AI. >> How long ago was that? It's big more than 10 years. >> Wow. >> But I mean, it wasn't transformers, but it doesn't matter. >> Yeah, well, yeah, for that kind of use case. But it was an all-nighter than, yeah, wow. >> You think about robots in the world probably more than anybody else. What are we seeing? What is abundance in labor, enable that goes beyond people's initial reaction to how I would use a robot? >> I think that first of all, what will happen is actual abundance means everyone can have whatever they want, but not only can you have whatever you want, you can have whatever you want in a sustainable manner. Because sustainability is something we lose when it cut corners to shape costs, right? If you actually have a abundance of energy and labor, why would you not do things sustainably? And then I think the next frontier that comes after just in general, like building out the infrastructure across the globe that allows everyone to have an incredible quality of life is how do we solve the remaining really hard problems in science? And I think this is not going to happen without humanoids because you need to build particle accelerators. You need to build enormous biotech labs. >> You can experience. >> You need to do all the experiments, right? >> And also I think it's almost existential to us for human happiness. I don't want the godlike AI in the sky to be directing all of the planets, inhabitants around with their glasses to do experiments for it to solve science. That's not the future we're aiming for. We want to have these beautiful symbiosis and like Coenvention between Man and Machine. >> And that particular use is so cute where you know, Dennis of Service is working on the full cell simulator to try and close the loop, but you know that you're going to need people to mix a huge number of chemicals to truly unlock longevity and health and chemistry. And you know, the humanoid robots can do the work because everything in the lab is so cute. >> Not only can they do the work, I think this is a common misconception. >> Humanoid robots will do a lot of the work initially. But once it gets to a certain scale, the humanoid robot will make the automation system that will do the work. >> Because humanoid robots will not be machining new parts with a dermal, right? >> You will use the CNC machine. Humanoid robots will not be moving car chassis around by like carrying 30 humanoids. Generally, this does not make sense, right? We have existing automation system and we will build more. What humanoids will do for you is to build all of these automation systems and get them up and running and then cover the remaining gaps that you currently today can't do with humans. >> Yep. >> Yep. >> How are you going to do it? >> How are you going to do it in a vacuum? >> I want it. >> Yeah. >> My new gamma to help me set up my space station or my asteroids. >> I think first of all, we have a huge advantage because the robot is so light. >> Yes. >> And kind of like Elon's working on this, but payload to orbit is still expensive. Secondly, most of the stuff we have actually works pretty well in space. We have to do some stuff with epoxy on the motors. That's not going to be very vacuum hard. >> If you want to train in 0G, one of my companies is a company called Zero Gravity Corporation. >> I know. >> These parabolic lights. >> Yeah. >> We flew Stephen Hawking in 0G, maybe Neo Gamma should come next. >> That would be great. And actually, do you think it's like, there's a real use cases for this. And one thing is like building a base of Mars or whatever, right? But even before we get there, just in orbit assembly. >> Yes. >> It's this extremely high value task. And I think there actually we will use teleop. And the reason I'm saying that is just like, the cost of mistakes is so hard. That you want to use like the smartest, most expert humans you have. And until we get to super intelligence, that will be a human. And you have people in orbit, you have robots outside. Very low latency. You can tell operate in a very natural manner, as if it was your own body, how to do all of these in orbit assembly tasks. And it can be incredibly complex. And you can still do them with very high accuracy. And you're not endadering people. And of course, when you've done this for a while, you have the data to automate all this. It's very interesting. >> Yeah. >> Most of your weighted energy would be really amazing too, because you can take five, six, seven of these. >> And the energy efficiency. >> You must be, you're going to have to somehow bleed off your heat, right? >> Right. >> It's really hard. >> Yeah, that's right. >> You must be looking to hire people. >> We are. >> What kind of people watching, are you interested in potentially hiring? >> People are just really mission driven. That really believe in the beauty that will be a world where we have an abundance of labor. And like to solve really hard problems. People that really also can demonstrate that they've solved incredibly hard problems. Because that's what we're doing here, right? Everything from material science all the way in the bottom, all the way up to the foundation models at the top. And I think what we offer is just this incredible place to work. Not to disrespect to work-life balance on any of this, we're not quite Chinese, but it's a hard problem and we're in the wind. But probably the place on the planet with the most experts across all different disciplines and science. If you come here as a mechanical engineer, you will learn so much about AI, about electrical engineering, about batteries, about material science, everything else. And like, it doesn't matter which discipline you come from, right? You will learn so much from the people around you. And I think also that's one of our biggest strengths, how we really always work in this multidisciplinary groups. And we find the good solutions between the disciplines. Whereas like, hey, you don't need to do that. That's kind of cost-effective manufacturing. I can calibrate that away. Or like, you don't need to calibrate. I can just just cost more. Yeah. I like to see that actually when you're walking around the building here, Dean Kamehn's lab in New Hampshire is very, very similar. Where you use the segue inventor and everybody's just happy. Like all the MIT people that we know that work with him. They're just happy and the reason is because when you do software you're largely behind a workstation all day You're setting whatever when you're doing physical things you're moving around a lot more and you're building and making and it's just It it energizes you all day long. It's just such a fun work environment around it's so Obviously tangible just walking around talking to people though. It's it's a good. It's a good lifestyle and helps And there's a lot of robots walking around with you. Yeah, for sure and people go to one x technologies as the website to That's true to go and find out what positions are open. Yeah, yeah, and fault fault was on x and you'll learn more about us pretty pretty active there and For sure and one thing I'm excited about to to announce is you and Dar and the neo gammas are gonna be out the abundance Some it in March. Yeah, I'll wait. Yeah, he's a lot of great people. Yeah, so our theme this year is The rise of is digital super intelligence and the rise of humanoid robots because the two are going to get spot on Yeah, I think so. I mean it really is it really is and Without making any promises, I'm hopeful we'll have a number of the neo gammas there sort of like interacting and Enter of living and hanging out with the abundance members. Yeah, how do I get there? You buy on an airplane seat? They just walk off Yeah, yeah You don't want some off do you yeah? We're down in LA. Yeah, we're probably gonna drive down to LA Easier than getting them on a plane. Do you put them in the seats and strap them in yeah, we do This point they're starting to sit into the seat themselves So it doesn't strap itself in yet, but that's calming It's it's an interesting story. They're interesting. It's funny story That's all because we put one of the first robots on a plane back in the day We were rushing back home from China. It's a proper proper startup story where we were like this way back in the day We were running out of money and we hadn't kind of like Got into where the product was good enough to raise more money So I took the entire team and we went to China and we lived in a hotel for five weeks designing and manufacturing kind of like As we go, you know, no, this sign until I didn't deny it and the morning you walked down to the machine shop You help get them some information you get some new parts back and we just kept like iterating on this an electronics market magical right yeah, and then we have to go back and we're just like Okay, we're gonna rush back on the plane to meet some investors. So we check We take the robot and we fold it off right here and we put it in a briefcase And then when it goes through the And you can see the guy just goes all white and he's like shaking his hands with his opening the bag And we're like no, no, it's just a robot and he's like yeah, it's a robot That's hilarious Well, yeah, I really really thrilled that loved your your Ted talk and excited to have But Neogamma they're hanging out with all our abundance members and hopefully You'll be ready to make some some sell some robots. So in the early days of of making getting them to home No promises, but you're going to have sort of an application process to get To get the robots in and start to build data Data assets when when do you think you'll be ready to take pre-writers and orders for Neogamma Gonna be kind to my team and I'll say specific date. Okay, but it is happening this here. Okay. It's this year this year So I'm 25. Yeah now We're gonna talk a lot about this in the pre-order But the most important thing we do here is expect this expectation manager Yes, this is incredibly early right yeah, and what you're buying here is kind of a ticket to be part of this transformation Adopt and heo into your family Help us teach it It's gonna be a lot of fun a lot of useful. I love that framing that perfect. It's going to be useful It's not gonna be perfect. It's gonna be a lot of like rough edges and We'll treat you really well. We're gonna figure it out together. It's gonna be an incredibly fun journey and That's kind of like the early adopter program that we're launching this year. Yeah, you're gonna have a long waiting list You know, we need we need millions and millions of these and we need to get the price point and and and I mean when you think about The constraints to human happiness globally A lot of them are gonna be solved through regular AI, but another big chunk most of them are related to houses and food and Physical happening. Give the jobs their dull dangerous and dirty to the robots and then create a lot more of the things that make people happy the parks and the And at homes and that you know all of the bigger homes and Better things to play with. It's all constrained by that inability to manufacture through the lack of the humanoid You know, let me ask you a numbers question. So I interviewed Elon at fii summit. You're gonna be there in October as well and And also Brett atcock and they both gave a number around 10 billion humanoid robots by 24. Do you believe that number? 10 billion by 20 point Yeah, I think it's probably roughly correct. I think it might happen before I think it really comes down to What kind of like artificial constraints we put on how we scale yeah At that point you have to actually really think about like how are you refining rear earths? How are you mining more aluminium? How are you ensuring that you're like get your labor bootstrap really well Weed robots into labor how do you build out the power infrastructure we need more chip fans by the way yeah I mean like is it we're we're not going to be able to build 10 billion humanoids without way more chip fans and you yeah We can help with having robots build us out but I do think that time nine depends a lot on How permitting processes go and like how much We kind of allow ourselves to scale and how fast But I do hope we get there. Yeah, I mean for reference. There's like a billion on mobiles on the planet, you know, you think there's more but I don't know how many sure how many iPhones or well there's on the order of 8 billion smart phones on the planet. I'm really glad you said what you just said though because the numbers are so wildly at a balance You know each each one of these robots uses a full GPU could probably use to you And if you're talking about a billion of them by 2040 we're only making 20 million GPUs a year and then TSMC is 66% market share now in the fab So they have literally one point of failure for the entire economy that we're trying to build And so we're desperately short on the fabs and and that's if you just go one layer deep. Yeah, like look at ASMR Behind my head, right. Right. It's like the supply chain for chip fabs. Yeah, that's even more brittle. Yep I'm really surprised that we're not moving much faster Given that Elon is right in the middle of it Elon is or was in Washington They were just letting this bottle neck. Well, how long have we been talking about magnets? How long have we been talking about magnets? We've been talking about magnets for a long time right? That's the problem that like with only China can really make a high grid of magnets. Yeah. Yeah. Yeah. Yeah And he's not just a rare earth. It's the process to produce. Yeah Yeah, and I think now finally like people are opening their eyes and like, wait, man. This is actually a real problem We need we need a lot of government officials and they're completely unaware of these bottle necks and it's funny They if you point them out, they're still in a reaction. Yeah, it's like but it's it's so acute and so urgent Um, it's I mean you're in a perfect position to actually identify those bottle necks So it's really great that you set it on this podcast because then we can take that material and say look Look, you would know, you know, like this. This is what we need. This is going to be a crisis very quickly Yeah Made your own thank you for the tour today. Thank you for the work that you're doing Super grateful Excited to have you at the abundance summit with your with your team of robots If you're interested, it's abundant 360.com check it out Again, it's 1x technologies Dotcom to come and learn about the positions here and following you on x Great, but he's here one extra tech one x dot tech and by the way the reason you named the company one x I think that's worth closing out as the story here Well, you know there's all of these videos on youtube. Yeah robots. Yes, and there's always like these 8x or 4x in a corner And all we do is real time because we build proper robots There you got it what you're seeing is real 1x speed and We had fun today with your gamma and it's also amazing because if you apply for our careers and you come here you get to be a 1x engineer Okay Well, I'm really glad you're a friend also thank you for the day Guys definitely the things I get to do because of this podcast Fun. Yeah, my god. Yeah, yeah, awesome. They're awesome every week my team and I study the top 10 technology meta trends that will transform Industries over the decade ahead. I cover trends ranging from human under robotics AGI and quantum computing to transport energy longevity and more. There's no fluff Only the most important stuff that matters that impacts our lives our companies and our careers If you want me to share these meta trends with you I writing newsletter twice a week Sending it out is a short two-minute read via email and if you want to discover the most important meta trends 10 years before anyone else This reports for you readers include founders and CEOs from the world's most disruptive companies and entrepreneurs building the world's most disruptive tech It's not for you if you don't want to be informed about what's coming Why it matters and how you can benefit from it to subscribe for free go to demandus.com/metatream to gain access to the trends 10 years before anyone else. Alright, now back to this episode. [MUSIC]