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

27:29All right. It's a very complicated refrigerator, but it's closer to a refrigerator than a 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, you know, 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, it is. 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, it's 66 pounds.

28:08It can lift about 150 pounds. Which is amazing. I mean, in terms of the weight strength ratio. It is the weight strength ratio of an athletic human. And then it can carry about 50 pounds around. So, hopefully, it's all earlier here. And battery life is about four hours rechargeable in half an hour half an hour or half a week to like a two hours if you use a full battery now actually interesting enough I have one in my house right so I'm starting to get some get some date on this now and it's five foot four five 5 foot 5, what is it? 5 foot 4. 5 foot 4, okay. I think so. That's a perfect height by the way, just in case you were wondering.

28:52Yeah, 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, but I mean, it's, what's very interesting, I can also 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's very quiet, it's fine, you put it in your home, and you're like, first day it's fine, second day it'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?

29:27It's such a requirement for like just dead quiet, right? You'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. Nice. Yeah. Well, and I want to talk about quickly since you said that. Specifications. Like the number of degrees of freedom, right? Yeah. Basically, it's how many joints does the robot have, right?

29:57Yeah. So, like, humans have like six joints 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 a lot of 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. Yeah, so how many do humans have in the hand?

30:3422. So you matched it. Well, okay, depending on how you count your capitol bones, So like the small bones that you have here allow you to cut your hand. Yeah. You could to some extent see that that's like that's more like four, 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.

31:10Well, diversity of your data and your diversity comes from two things, the limit to the diversity you can achieve. It comes from the environment you're deploying in. So 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?

31:41If 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 managed 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 start companies. But at that age, everything is working against you. You have an idea, you're challenged 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.

32:16Both 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? As an 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.

33:00And 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, mydeceos and investors is part of it. We house 66 ,000 square feet of purpose built incubator space and 26 AI startups call LinkXPV 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 I'm 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 and can't do is in the neural net or in the physical construction of the hardware.

33:45Is there any way to decouple that or debug, you know, the two different sizes, or just incredibly impossible? I mean, once it's meshed together, you just can't. Well, we have a pretty good neural net here. Yeah. So usually the way I approach this is, can we do it in tellyup? And if we can, the right neural net can do it with enough data. Interesting. And that's generally been proven to be true. If we manage to do something in tellyup, it's just like, OK, now we need a little diverse data of similar tasks. So we get some transfer learning and we need a lot of data, that specific task, and And almost irrespective of how complicated that task is, you can get it to work.

34:22Now, of course, that doesn't mean you can get everything to work with generalization across. We're not there yet. But you can see that, OK, you can get the neural network to do this. Now we need to scale it. So we kind of get this beautiful transfer of knowledge between tasks and our distribution to the generalization and all these things that we currently see in our labs that we don't see that much in robotics yet. We have some pretty cool stuff in turn. We'll see some signs of what I'm picturing, though, you ask it to make crepes, is that, or you ask it to do microsurgery, and it can't quite do it.

34:51And then you say, well, look, the hardware guy is claiming the hardware is good enough. It must be the software guy. And then software guy is 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, well, we bring in our best teleprater. And we say, he can do it. And then the hardware can do it clearly. It's literally fruitful. 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.

35:30And they have very high fidelity text on feedback. The human hands are so good. No, the robot hands. So they have the humans that hands are still even better. but 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, so, yeah. I mean, back to the X -Price, right? The avatar 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.

36:03So you like actually have the robot like interactively learning real how to have a lot of objects. and you can do things that your operator could just stream off. So that's not where you're going to get your computer. No, we can't do that anymore. I want to talk about three things in sequence. Teleoperations 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, we saw the new gamma out here operating until operator mode, but also in AI full AI mode. Right? And it was able to do both.

36:42And its AI systems are going to increasingly add better and better and more capable. Again, as I'm talking to Gemini 3 or GROC4 or GPT -5 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. Oh, yeah, clearly. And then more than that is already. It is. I'm all that is grounded, right? that actually understands to some extent what this existence is.

37:27Which today's LLMs are kind of like, they have this kind of abstract notion of it, but 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. No, what are the humans doing? they're giving slightly more high level commands. So just guiding like, hey, put your hands over here, like grasp this thing. You don't want like all reconstructed 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.

38:10And then we have the learning of like 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 goes away. You're using for you're using automation and till operations always together in every guard. And so everything that enables the robot to do anything that the operator does, the operator is fully learned and to end. Like it then the network outputs talks to the motors. That's that's very similar to a Tesla and Elon Musk were 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.

38:52And now there's 300 ,000 lines of C++ were eliminated. Yeah, just a few guard rails left and then the rest is just one neural net. So same thing here. It's all weights, right? Yeah. Like the coldest is 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 will be small if you compare it to like two 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 I'm dying to ask, which is, you've seen X -Makana, right?

39:33I want to show that movie and like why don't you just open it up a little bit. Why is the brain, the blue blob in the head? Yeah. Why isn't it in the server room? So learning is shared between all robots. Well, 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 on a battery charge. If you move it over to the server room, and it just communicates. So, right, why did you choose to put it in the head? 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 in there.

40:13Like the resistive. Yeah, like it's 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. No, it's really hard and engineering problem. So it's like, where are we going to put this if we don't put it in the head, if you don't put it on the physical robot? Now there's smaller argument. The very high bandwidth thing that happens in your brain is vision and to some extent audio, smell, right tactile, but vision just dominates. And you just want to minimize the distance between your eyes and the compute.

40:51For real? So the bandwidth between the sensors, they are very high. So I honestly wouldn't make it over the home Wi -Fi. Well, it wouldn't even make it down to the stomach of the robot. Really? Without getting overly complicated on like which physical interface is you would choose for this transfer, it's very high bandwidth. with. I'm I'm I'm shocked by that. But I mean, we're running like no light are no structured light, no wrist cameras, no nothing. We're running pure like emulation of human vision, right? Yeah. So we're relying so heavy on that. So it's a very, very high resolution, very high band with very high frequency.

41:27That'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 intelligence point of view. Just like if you think about your kind of like your neuromuscular system, this runs quite fast, right? It usually runs with like 25 words 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 robot, we have some of our stuff pushed to the power electronics that controls your latency, say just for speed.

42:05Yeah. 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 faxate ten hertz, very low latency. And this runs on the robot. Now, if you're running like more like a one hertz streaming thing that typically in LLM first time to token land, right? Yeah. That runs offward. But 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 NeoGammas then learn that? Are you shared learning?

42:40They 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 NeoGammas. But there's also the distributor models. So, of course, there would be like a nightly checkpoint where like, hey, this model is better, we have more data, we validated this, we give it to safety, which I'll talk about later. when it comes to how to 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, just you need to do like one hop through the server layer and like do the training and propagate this out.

43:13There 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 robbers will not be the same, but they will share an intelligence back. 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.

43:53You 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, to take care of your aging mother and find out that it's, you know, 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 -grama. It's called. Shred. 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.

44:29But 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. Right. We are kind of trading a bit on privacy versus being an ordered it up 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 better. Sure. I mean, 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?

45:10It'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 model. 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 you a notification on your phone or say like, hey, this specific window, we want to review the data and you'll get a video of what that data is.

45:44And 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 like 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 a 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 weights. 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.

46:23It's very old. A lot of them. But there is, so the mode I talked about now is when the robot is what we call best effort autonomy, which is most of the time. So, what you saw earlier today, which we talked to it, you ask it 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 than when it succeeds. Sure, it learns more from failures just as we do.

46:53But in this mode, that's the privacy. Now, with the course of teleop, 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 just see blobs and you just see the object interacting with them. You can do a lot on interaction on the field -drink side to ensure privacy. But the most important thing we do here is that no one goes into teleop in your robot unless you approve it, right? And it's very visible on the robot. like the lighting chain is 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 into their human in an other human 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 in teleoperator.

47:56And so you'll have teleoperators and headquarters that if it needs help or doing something complicated or it gets something wrong, the teleoperator can step in and actually make the task happen. Yeah, it's in the beginning, it's actually there's two different modes. So you have the mode which I call the best effort autonomy that we just talked about. Yeah. And then you have tasks scheduling. It's 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 is like, do my white laundry. And then there's a package coming from Instagram to receive it at a door and unpack it in the fridge.

48:36And it's just like generally tidy. And I've given it when I'm not whole. Like these hours I'm at work. Just get it done, right? Now, I don't care if that happens autonomously or to a teleoperator, right? So a lot of that happens to a teleoperator because some of these tasks are quite complicated and we don't know how to automate them well enough yet. Now, of course, that teleoperator uses autonomy to help improve their efficiency. So it's not all teleoperation, 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 the gray zone kind of in the middle.

49:09If it's like you want to, like, I don't know, having your friends over a party and you want their able to be the bartender and we don't have like a bartender mode yet. Then you can approve a teleoperator to do that. So you can ask all of the videos we see of Optimus at you know Tesla's diner or at their events or teleoperation points. They are. But I think teleoperation has gotten 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 risk that autonomous? But it is just labeled data. It's expert demonstrations.

49:47Right? 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 looked at examples, rolled out question answers, and bootstrapped, kind of like this highly, very high quality data set for this to work, right? So you pre -chain on general information. We also do that just everything that's happened with robot. 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 in what's going on.

50:24Well, I think the objection is if you have a demo, like a video, it makes it look like you can do something and it actually can't because you hand coded it. Well, it clearly can, but it can't do it at all. Yeah, I can't do it. But yeah, but I think you're you're done that the if it can physically do that the 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 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 inside only to find out he's got stage for cancer A few years later, for Tonya brother of mine dies in his sleep.

51:06He 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 3 or stage 4. And the sad fact is that we have all the technology we need to detect and prevent this 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.

51:41So 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. Tuneer gigabytes of data about my body, head to 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.

52:18You can go to foundlife .com to talk one of my team members there, that's foundlife .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 wanna make sure like body language comes across crystal clear because that's like the magic of the device, of the companion.

53:07But 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. So 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 to understand this 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 a different threshold here.

53:44So we're trying to hit in a middle of that and ensure that as many people as possible, this is just 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 this knowledge barrier, right? does a barrier to entry, even using a phone, there's a barrier to entry. And 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 it always crappin'.

54:35The robot can in real time access the information and learn how to do it and just do it. Yeah, from the word. There is, I can't do that. It would take me an hour to study. And there's no laborer that's going to come into the house and do it for under like 400 books. And so it's like, there's 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, and just go do it.

55:12And you know, it's like, this is micro units of work, essentially, right? Like you need five minutes of, but 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 really hard to get a 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 want to get like this crap off the garage floor. The robot will know how to the shop back works because somebody else's robot One of the other 10 ,000 made me done it make my perfect teriyaki salmon on the grill.

55:45Yeah, so much skewer mixing some food Which brings us to something that you talked about earlier. It's got a dog's scotch program. I'm so I Do hope to make you a perfect Salmon teriyaki. Thank you But I have to do it myself. I'm not gonna let the robot do it because that's one of the things we're actually not doing when we're launching now. And that is due to safety. Yeah. Because what I worked so hard on 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 it hits you, that might be painful, but it's not going to be likely to severely harm you.

56:22Illuminous, right? And once you pick up a kettle of boiling water, there's no more guarantee that you're are safe. 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 the things. So we're doing internal products on this, but we're not going to be rolling it out to the customers in the beginning just due to safety concerns. Yeah. Yeah. Cooking and safety is a real problem. I mean, it's a really problem for humans too.

57:01It is. But there's the notion of intrinsic safety. 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 might 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 models. They are, of course, the currently best known path to work. or say TI, 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.

57:43So 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 your 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 want to do that with a physical system, right? You can't do that with Altogamous car idler. What 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 what 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 a video but how the world feels like the forces everything for the robot.

58:29So it's essentially like the robots in the matrix. We take the robot, we put it in a role model and it doesn't know that it's in a role model, it thinks it in a role and it does its things and we ask you 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 to ensure or both that is performing better, but also that is not doing anything that can be deemed unsafe. So it's really like this incredibly important and powerful evaluation tool that starts to solve in the problem right. Do you think that's why you guys in figure and Tesla are getting monster evaluations?

59:03Because is the valuation just purely, hey, we're gonna sell 10 ,000, 20 ,000 and 200 ,000? Whereas it know 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 both a digital and a physical. So yes, it will be productized. still a bit out, but I guess this will be privatized. It seems like no matter how much factory capacity you build, it wouldn't be till like 2028, 2029 that you could diversify into all these like microsurgery and warehouses and drones, all that.

59:58But that same world model could apply to those much sooner, but you'd have to somehow get it into the hands of men. I actually, I think revenue from the robots will dominate forever. I do think like the real physical world has way higher value done 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'll want what we're doing. Yeah, it's going to be so much bigger. You've attracted some incredible early investors.

1:00:32Do 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, equity, Nvidia, OpenEye, system good names in there. 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.

1:01:17You 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 going to get there without that. You're just running out of these basic constraints. And I think humanoid's will be surprisingly useful, surprisingly fast. Not perfect, but it's going to be surprisingly useful, surprisingly early.

1:01:57I have a question on the half of the Selimis male who's our typical or third moon shot mate here I have to ask that's gonna be so So Selim is constantly saying why two arms why two legs why not six arms? Why you know why we need to have a 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 is kind of right like humanoid is indulging that will work I do think that I'm going to look a lot like I don't know of any form factor that is as general as a human and 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 complex like this you 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, obviously, your goal is the most important part of the equation there.

1:02:58It's very important. And then you're not going to transfer to a six -armed robot learnings from a human. It's at least harder. And then, I mean, the world is made for humans. It's like, Jensen says, right? Like, it's brownfield deployment. It's very true. And then, I think, lastly, you want to live with a sex like a robot in your kitchen. 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 started with big mainframe computers, solving very specialized tasks, the equivalent in robotics would be industrial robotics, right?

1:03:47Now comes the PC or even before the PC, like the kind of like VIX or Atari or 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 too many, it's already complicated for a 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.

1:04:25Like, 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. Humanoid's 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 compute for AI, specialized compute for physics, especially for all kinds of things, right? And this is because it becomes so big.

1:04:59Now 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. I mean, 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?

1:05:36Well, I wouldn't have thought of data, but now that you said data, that's a, that's top of the food chain. Everybody loves RTD too, because for some reason, RTD too has no voice, even though they have voice technology or 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. And so I ask you to do things that are rational and not irrational because I know what a person could do.

1:06:12If I had a six -legged thing that Celine came up with, I'm not quite sure, like, should it be able to climb on the roof and fix the shingles or not? I don't know, like, what this thing's capable to do. So it breaks the whole, kind of, like, complex zone of execution. 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 that 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 Stooges right? Like when you see the equivalent act with two robots, they're just in phase and they just do it seamlessly.

1:06:48So I think I think there's a very high probability that this standard in the home is going to be like four or six. You get the price point down a lot, but they work so well in and concert with each other, it's almost a crime not to have that teamwork synergy. Yeah, it seems like a bit much to me, but in terms of maybe I could like, if I need to have a mover, I can ask my neo -gammon, he'll invite some friends over. But you're not taking everything into account, Peter, because you have to remember that by the time you have these many aerobiles in your home, everyone's homes are really freaking big.

1:07:22Yeah. We have an abundance of labor.

1:07:27You're, you're, you're, your, your, your house is not going to be this small. So labor, labor is going to, is going to continue to demonetize and democratize. Everybody, there's not a week that goes by when I don't get the strangest accomplishments. 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.

1:08:09I 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 U .S. and China. There's some in Europe. You started in Norway. There are some in India, parts in Japan and in Korea, but China by far I think is dominating.

1:08:55And 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 realize it maybe as much, but of course we need the same thing. 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.

1:09:39It's just, right, you need something and you go over and get machine on the corner here, something, 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 incredible ecosystem. And I think the the the the bay, I said the bay now, but I know it's also like, I mean, the Silicon Valley, like the hardware bay in Chen Stenaire is 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.

1:10:14I 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 a lot. We have great material scientists that know how magnets work. I 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. There's just so much of that, right? And this is just so disseminated in China. There's so much process knowledge. How do that evolve in China and not here?

1:11:03What's the cause? Top down incentives. Just one day? I think it's the government saying, you're a robot city, you're a neo -denium magnet city and just capital and people and just communist directed 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 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 Huawei and the leadership of all these And there was a super vibrant entrepreneurial community, right?

1:12:05The mindset was 9 .96 you'd work 9 a .m. to 9 p .m. 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 re -emerge, but I think the government is really pushing hard on supporting AI and you know, accumulated robots is a embodiment of AI. It's it 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.

1:12:51But I think like what I wanted to say was just like I think the most likely the most genius thing that it was the economic zones, like the free economic zones. Sure. Like it's not that people here don't want to build stuff. We want to build stuff. 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 use should just like spin up some free economic zones. Like here you have like expedited permitting and like California in particular, there'd be no brainer to do that. It's the simplest best idea ever, but I don't know what it would take to get it through.

1:13:24No, 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 Lahn, for example, it's very similar 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, the US tested software software software for forever. And we were not only not doing hardware, we just didn't do chips. Like chips can be this. Well, not forever, we're in Silicon Valley. Yeah, we're in Silicon Valley. We're in Silicon Valley. Yeah, yeah, yeah. So there was a phase in between here where people kind of like they lost the way.

1:13:56They lost the plot. And now we have to find the whole market. Well, 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 be like, well, I'm looking for the next meta or Google hardware. I don't really care. All I've been doing is keeping this company close for 10 years so I can go on and on. You can go on and on. How much people are afraid of hardware? But it's going to kill us if we don't find a solution. I think also it's going to kill VC, to be honest. If you do get return of ENTURE, it used to be incredible.

1:14:33If you got to be an LP in ENTURE, you're like, oh man, I'm like set. right? I'm going to make the big box. Now it's 10 years and no return. It's not. In now it's more like a philanthropic thing. You want to start a entrepreneur's because venture doesn't really make that much money. And I think it has a lot to do with... Accept it, Link. You guys are doing amazing. We are doing amazing. That is good. That's good. You guys touched a hard stuff. The point is if you don't touch the parts of what's your own. We touch the early stuff. right? So it's first checks into companies that then are scaling rapidly versus companies that are doing.

1:15:09We're doing first checks into hard stuff at the seed stage, but we're not doing hardware. So I'm as much part of the problem as we were talking about. 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, E -line 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 wanna work on the really hard problems.

1:15:47They'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. Things here are actually defensible. The mode we have on hardware, 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. So let's say we're three months ahead. Right? Exactly because like, wait. But you know, this way ahead, you're incredibly rare.

1:16:26And, and all props to Elon, he's, he's incredible, but, but Elon's pathway to getting too hard where was through his head. 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. I need to borrow money in 2008, in and of course, with SpaceX having its third failure. That is my first approach. That is a wonderful funding model. You know, we still have, I was very lucky, I had a very good early founding investor.

1:17:03I said like the company didn't start in a garage because we're not Silicon Valley. We started on barn because we were in a region. And at some point, two years later, he sold the farms, we had to move. He sold the farm to fund the company. So you would this cost me the Insurgent Valley wouldn't exist without an Norwegian investor. I don't think we would exist because we wouldn't like we wouldn't have had the run my right. 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 mission?

1:17:38Or are they believing in you? Yeah, I think it's all three. All three. Yeah, and it turned out pretty well. Yeah. Yeah, well, yeah, but I mean, it wasn't here. That's the point of making it. I mean, they're 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 I mean, in Texas and Dallas, there's just, there's, there's something to the talent water or something like it, the talent pool, it's a talent pool.

1:18:23Yeah. 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. At 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 tables. And those people are still the ones that are also these companies, right? And those people, they don't know how to make a great product.

1:18:57They 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 field 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 Silicon Valley, right?

1:19:36But there's been an incredible inflection point in human underbottles. I remember we had the Avatar Express, right? Our NA Avatar Express that had teams build robotic avatars that you could tell at 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 next 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.

1:20:20I do think we saw kind of like the bed -red crumbs and we were like on the path already then, but it wasn't kind of working yet. And 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 that 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.

1:20:54So 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 escapeable. 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 net for 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.

1:21:31On the physical hardware side, what's something that you used a day that you couldn'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 it. So, bro. Yeah, you literally make your own motor. Like, we have to run the wires. Yeah, so, while we crap, we do it kind of special. It's the one next version of this.

1:22:05So, motor is one of the things we really innovate in. And this is actually how I started like, you know, when I sat down a decade ago, the first thing I did was to sign a different kind of motor. Okay. And the motor we have now in Neo, they are five and a half times the world record in Torque de Weit. Wow. Yeah, 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 human muscles. That's why it's so light. It's also why it's so like a travel compliant. It's why it's achieved by the manufacturer. Everything kind of comes from this.

1:22:41Now, 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 and millions and millions of cycles. And these are really hard research problems, right? They're not even engineering problems, they're hard research problems. And we spend 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.

1:23:20So that kind of like, there's a lot of things that come together. You couldn't have designed the motors we do today without some of the innovations that had happened in Magnetics. And of course you couldn't have done it with OAA, I had either. The first thing I did when packing the day when I sat down was to program a network to learn how to make motors. Oh, you're kidding. You made them you designed the motors VAI. How long ago was that? It's big more than 10 years. Wow. Okay. I mean, it wasn't transformers, but it doesn't matter. Yeah. Well, yeah, for that kind of use case, but it was an early year than.

1:23:56Yeah. Wow. You think about robots in the world probably more than anybody else. What's your vision 10 years from now? 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 you cut corners to shape costs, right? If you actually have a abundance of energy and labor, why would you not do things sustainably?

1:24:41And 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 need to do all the experiments. And also I think it's almost existential to us for human happiness. I don't want the God -like AI in the sky to be directing all of the planets, inhabitants around with their glasses to do experiments for it to solve science.

1:25:24That's not the future we're aiming for. Yeah, we want to have these beautiful symbiosis of like Coin mentioned between mad and machine Mm -hmm, and that particular use is so acute where you know Demisosavis is working on the full cell simulator to try and close the loop But you know that you're gonna 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 not only can they do the work I think this is a common miscalception. Juvenile robots will do a lot of work initially, but once it gets to a certain scale, the humanoid robot will make the automation system that will do the work.

1:26:06Because Juvenile 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 carrying it with 30 humanoids. Generally, this does not make sense, right? We have existing automation system and we will build more. What you know it will do for you is to build all 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. How are you going to do it? How are you going to do it in a vacuum? I want my new gamma to help me set up my space station or mine.

1:26:40Well, my asteroids. I think first of all, we have a huge advantage because the robot is so light. Yes. And kind of like, I guess Elon's working on this, but payload to orbit is still expensive. it. 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 zero G, one of my companies is a company called Zero Gravity Corporation. This parabolic lights. Yeah. We flew Stephen Hawking in zero G, maybe Neo Gamma should come next. That would be great. And I actually do think it's like, this is real use cases for this.

1:27:20And 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 tell you up. 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.

1:28:03And of course, when you've done this for a while, you have the data to automate all this, which is 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, yeah, you're going to have to somehow bleed of your heat, right? Right. It's really hard. Yeah, that's right. That's a you must be looking to hire people. We are. What kind of 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 in abundance of labor and like to solve really hard problems.

1:28:43People have 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 with respect to work life balance and then this we're not quite Chinese, but it's a hard problem and we're into in but But probably the place on the planet with the most experts across all different disciplines in science. So 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.

1:29:24And 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 these 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 -eating manufacturing. I can calibrate that away. Or like, you don't need to calibrate. I can just doesn't cost more. Yeah. I like to see that actually when you're walking around the building here, Dean Kamehman's lab in New Hampshire is very, very similar where you use the segue inventor and everybody's just happy.

1:29:58Like all the MIT people that we know that work with him, they're just happy. And the reason is because when you do software, where 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 just, it energizes you all day long. It's just such a fun work environment around it. It's so obviously tangible, just walking around talking to people. Though it's a good, it's a good lifestyle. And it helps when there's a lot of robots walking around with you. Yeah, for sure. And people go to one X technologies as the website to you.

1:30:29That's true. To go and find out what positions are open. Yeah, yeah, and fall falls 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 Neogamas are gonna be out the abundance Summit 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 superintelligence 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 Neogamas there.

1:31:08It's sort of like interacting and and sort of living and hanging out with the abundance members. Yeah. How did I get there? You buy on an airplane seat? They just walk off. Yeah. Yeah. We're down in LA. Yeah. We're probably going to drive down to LA. It's easier than getting them on a plane. Do you put them in the seats since Trappin and... Yeah, we do. Actually, at 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 an interesting story. Interesting news. Funny story, because we put one of the first robots on a plane back in the day.

1:31:44We were rushing back home from China. It was a proper startup story where we were like, it's way back in the day. We were running out of money and we hadn't kind of like gotten to 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, deciding and manufacturing kind of like as we go. No, the sign until I then denied 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 electronics market or it's magical, right?

1:32:16And then we have to go back and we're just like giving the rushback on the plane to meet some investors. So we check, we take the robot and we fold it off, right? Yeah. And we put it in a briefcase. And then when it goes through the... The scanner. Oh, 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, he's just a robot. And he's like, yeah, it's a robot. That's hilarious. Well, yeah, I really, really throw that. Love your, your Ted talk and excited to have, but Neogama, they're hanging out with all our abundance members.

1:32:56And hopefully, you'll be ready to make some, some sell some robots. So in the early days of making them to home, no promises, but you're going to have sort of an application process to get the robots in and start to build data assets. that's when, when do you think you'll be ready to take pre -writers and orders for Neogamo? It'll be kind to my team and I'll say a specific date. Okay. But it is happening this year. Okay. It's this year. This year, this year. This year, this year. It's 25. Yeah. Now, we're going to talk a lot about this in the pre -order, but the most important thing we do here is expect, is expectation management.

1:33:39Yes. 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 he, or into your family, help us teach it. It's going to be a lot of fun. It's going to be useful. I love that framing. Perfect. It's going to be useful, but it's not going to be perfect. It's going to be a lot of rough edges. And we're going to treat you really well. We're going to figure it out together. It's going to be an incredibly fun journey. And that's kind of like the early adopter program that we're launching this year. Yeah. You're going to have a long waiting list.

1:34:10You know, we need millions and millions of days and we need to get the price point. And when you think about the constraints to human happiness globally, a lot of them are going to be solved through regular AI, but another big chunk. Most of them are related to houses and food and physical happiness. Give the jobs they're dull, dangerous, and dirty to the robots. And then create a lot more of the things that make people happy, the parks and the homes and 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.

1:34:51Let me ask you a numbers question. So I interviewed Elon at FII Summit. You're gonna be there in October as well. And also Brett Adcock and they both gave a number around 10 billion humanoid robots by 24. Do you believe that number? 10 billion by 24? Yeah, I think it's probably roughly correct. I think it might happen before. I think it really comes down to What kind of 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 with with robots into labor?

1:35:36or how you build out a power infrastructure, we need more chipfabs, by the way. Yeah, I mean, like, we're not going to be able to build 10 billion humanoids without way more chipfabs. We can help with having robots build this 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 automobiles on the planet. You know, you think there's more, but I don't know how many sure how many iPhones are. Well, there's on the order of 8 billion smartphones on the planet.

1:36:19I'm really glad you said what you just said though, because the numbers are so wildly out of balance. Each of each one of these robots uses a full GPU. You could probably use two. 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 that's if you just go one layer deep. Like look at ASMR. Yeah, I'm really sure. Right, right. So like the supply chain for chipfabs, that's even more brittle.

1:36:58Yep. 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. 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. That's the problem that like with only China can really make high -grade magnets. It's not just the rare earth. It's the process to produce. I think now finally people are opening their eyes and like, real problem. We need a lot of government officials and they're completely unaware of these bottlenecks and it's funny.

1:37:38If you point them out, they're still in a reaction. It's so acute and so urgent. You're in a perfect position to actually identify those bottlenecks. It's really great that you set it on this podcast because then we can take that material and say, look, you would know, this is what we need. This is going to be a crisis very quickly. Yeah. 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 abundance360 .com. Check it out. Again, it's one xtechnologies .com to come and learn about the positions here and following you on x.

1:38:26Great, but he's here. Onex .tech. Onex .tech. And by the way, the reason you named the company Onex, I think that's worth closing out as the story here. Well, you know, there's all of these videos on YouTube. Yeah. Robots. And there's always like these A -dex or 4x in a corner. And all we do is wheel time because we build proper robots. There you got it. What you're seeing is real Onex speed. And we had fun today with YoGamma. And it's also amazing because if you apply for our careers and you come here you get to be a one -exigent hair Okay Well, I'm really glad you're a friend Awesome The things I get to do because of this podcast Fun, yeah, yeah, yeah, awesome.

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