1X Technologies CEO on deploying 100 NEO humanoid robots into homes to build the data loop that scales

start talking to him about humanoid robots. How you doing? There he is. What's happening? Welcome to the show. Doing good. Doing good. Thanks for having me. Excited. Fantastic. Um, yeah. Give us some context on on your background on 1X and Neo. And then we'll just get into a bunch of different questions.

I was about to tell John uh Neo was actually blessed by Lil B the base god a while ago, much like Timothy Shalomé was before the generational run. So, it's very possible that that Neo is is on a similar run itself. I'm very excited. So, anyways, uh, great to have you on, Burnt. And uh yeah, give us give us some color.

Awesome. No, and I think like uh let's see. Thank you. Whoa. Hey over here. Wow. Wow. Okay, that's convenient. I think um there has to be something through it, right? It's it's been going pretty well lately like uh lately. So um we have been blessed. Okay. Um you've clearly made one.

How many How many have you made of these things? It's about 100. 100? Wow. Okay. different versions though, right? They're not still all operational. Like we're very quickly cycling through hardware revisions to ensure we actually get to the launch by end of year. So um we do quickly deprecate them as new ones come out.

Sure. What what does launch mean? You want to be able to sell these to companies. This is business to business, business to consumer. I've seen them on streams. They seem like kind of just like companions. Uh it's less industrial, but what what's your thesis for the early adopter here?

Yeah, let me just jump in straight into it. like who am I, who's the company, what are we doing, why are we doing this? So, I'm well, I'm Burnt. I'm the founder, um CEO. Come to this from a robotics background. U been on this journey all my life.

Uh picked apart all of my mom's kitchen appliances since they had motors when I was a kid. Always liked building stuff, programming. Started 1x 10 years ago and like the mission stayed pretty constant.

We want to create an abundance of artificial labor and we're doing this through creating these intelligent machines that can live and learn among us. And that last part is so important.

I think what's really differentiates 1x from the rest of the pack here is the focus on making robots that are safe among people and that are very affordable. Right? First principles engineering.

How do you make something that's like super lightweight, doesn't need very special uh raw materials, doesn't need like that tight tolerances in manufacturing. Make something that's like closer to a refrigerator than a car, but still as capable as a human. Mhm.

Um, and when I say getting this out the door, what I mean is literally like getting it into your homes, right? So, we're going through in-home testing among employees now. I have one in my house in Redwood.

It's uh amazing to get to finally live with the product and there's going to be some uh customers having them quite soon, but like under NDA early testing and then if we play our cards right, it should be in the main market by end of year. That's the goal. Wild moving quick.

Um, how has a company evolved since 10 years ago? Uh, humanoids have always been uh in people's minds to some degree.

They haven't been in the headlines as much as they've been, let's say, in the last couple years, but kind of walk us through maybe those different stages, how your vision evolved and and uh then I have a bunch of other questions. Yeah, I mean it's the kind of product that we all know will happen, right?

and we've known it for like I don't know last 50 years like at some point this will happen and the impact is tremendous right so I think what's really changed uh the last couple years is uh to be very blunt that it works uh for the for the first eight years it was kind of a grind and it's a kind of a problem that's really worth just like doubling down on like grinding and pushing on until it works uh and I don't think the impact of this type of technology can be exagregated right It's we're we're years not decades now away.

It's it's very hard to like better put a specific date, but we're years not decades away from robots building robots, robots building out our data centers, our energy infrastructure, our chip manufacturing, like our mining in general, like this this kind of hard takeoff moment, right?

Where we can actually get to an ab abundance of artificial labor. And it doesn't really even matter if it's like physical or digital, right?

Because once you have this engine running, you will just in general like have extremely intelligent agents whether they're in the digital or physical space and kind of will redefine what it means to be human. Like what do we value in humans? Like how do we see our own worth?

And I'm just really freaking lucky to be alive, right? To be part of that. It's been my dream all my life. And uh yeah, the timing is amazing. What about what about getting getting into more specifics with Neo?

Do you have any sort of specific early use cases that you're focused on in all of the sort of uh sort of marketing that I've seen so far? It it hasn't seemed focused on certain use cases like manufacturing or certainly not mining or things like that. It feels like you're maybe more, you know, focused on the on the home.

Uh is that is that correct? Uh am I off? No, no, no. It's it's correct. But really important there to point out that like you take a step back, right? It's like we're years, not decades away from actual abundance of artificial labor.

So as a company, the only thing that makes sense here is to get there as quickly as possible. It's all we care about. What's the shortest path? So we're not a consumer company. We're shortest path to artificial labor.

And it just happens to be that the shortest path is through consumer because you need to live and learn among people. Like if it's one thing that's incredibly clear in AI, it is that the diversity or like the variance in your data set basically equals your intelligence, right?

That's that that's where intelligence emerges from. And in a factory there just isn't enough diversity. Think about like if you're if you're in a factory all your life moving something from A to B and that's all you're doing, then like you could ask like okay, how much do you actually learn, right?

And we actually have the answer. you learn about you we need like 20 to 30 hours of data from that and then we don't learn anything more. Interesting. So like that that's just like that's not where to deploy robots.

We want to deploy robots there but we have to deploy them first where we actually get the data that gets you through a true truly intelligent machine that doesn't just repeat a motion. It understands deeply the task it's doing.

And this brings you into like something interesting that you said in your intro here which is companionship, right? Because everything we do is social. Like work is social. Like whenever you do something, usually it's because of someone wants something.

And usually someone's next to you or even like communication, the social context of objects, like hey, is this cup on the table? Someone using it. Is it yours? Like is it is it half full, half empty? Is it dirty? Like there's liquid in it? But if it's coffee and it's cold, it's probably like not in use anymore.

It's actually dirty. like everything has this like social context and this is what creates this incredible variance and diversity in the home. Not only is every home different, but like everything happening in the home is different every day and everything has a social context.

So I think we're seeing pretty strong proof now that like a lot of model intelligence comes from this diversity and we really want to double down on that.

And then the other part is I think it's just going to be incredible to live with these companions in our life that can not only give us back time in our everyday life with like household chores but actually be a trusted friend right and we're talking to our phones with chat bots and being like these are our new companions but it's not the right interface right the right interface is something that you can really deeply connect with and that gets you away from a screen bsentennial man a movie I know you haven't seen but is a fantastic story of a humanoid robot that that stays with the family for 200 years and and evolves and changes and uh Robin Williams it's a beautiful movie uh I want to talk to you about kind of the progression of the software um tea operation super valuable in a bunch of different ways simulation uh we're seeing lots of promising data uh there's the sim toreal gap there's uh deterministic algorithms for walk cycles there's end to end systems.

What is your view on the path that we need to go down to get to something that's truly generalizable so that you can put a humanoid robot in a completely novel situation, just talk to it and say, "Hey, clean up this table. " And it will remove all of these cans one at a time.

It knows exactly how to do it and it's never actually been trained on that specific task. But it can generalize just like any other human can. I think I there's two interesting answers. The first one is no one knows how to solve that yet. And then the second answer is we actually know really well how to solve that.

Uh which which is it's going to be some big transformer and you just need enough high quality data. Okay. So we kind of know like it's an engineering problem at this point because even though we don't know the exact solution, we know what we need, right?

We just need an enormous amount of relevant data and this data is very different. All models that you see today, they're trained on what I call like only observations. So if you think about the internet where there's video or pictures or text or whatever um it's static, right?

So you're like given that I saw or read this, what's the next thing? Yeah. But what you're actually missing and where in my opinion like a lot of like our ability to reason comes from is that you have some hypothesis about what's going to happen. Like you have your own mental model of like this conversation. Yeah.

So when you're saying something, if I'm training just on what you're saying, that's not the same as if I'm training on what you were thinking and planning and then what you said and then what the result of that was with respect to like how I reacted to that. Yeah. And robots actually allow you to do this, right?

Because you have what the robot is thinking and what it was planning to do, what it actually did, and what it resulted in. And this data is what is needed to get to this. And that's what we're gathering at scale. Yeah. there's no real like massive data set like the web that you can just scrape.

Um folks are using tea operation to generate data. There's simulation data. Like what are the richest sources of data for you? Uh what do you see as being like the real trove that you're going to be doubling down on over the next few years? I think it's all of them, but they're all just like a crutch.

It's like you're bootstrapping your way to where robots are learning in the real world. Yeah. And this is really the direction we're moving in because teleoper operation does not scale, right? It's super valuable and we need it, but it doesn't scale. Um, simulated data very useful for simple things like walking. Yeah.

But for like peeling a shrimp or whatever, right? It's like it's not nearly detailed enough like it doesn't work. So um you just what you need is to use the internet data, some teleoperation, simulation, synthetic data, all of these things to get to where your model is able to sometimes succeed.

So when you say get me a Coke in the fridge, maybe there's a 50% chance that a robot comes back with a Coke. Yeah, maybe it gets stuck somewhere on the way and didn't manage to do so. But once this happens, then you can basically just tell it, "Hey, good job. " Or like, "Hey, weren't you supposed to get me a Coke?

" If it's just, I don't know, staring out the window instead. Uh, and that's the data loop that scales, right? And that's what we're deploying. So, when we're saying like getting this into your home, what we mean is almost more like adopt a Neo to help us teach it. It's not going to do everything in your home day one.

We're not there yet, but we're able to deliver a product that is really fun, very engaging, somewhat helpful, and really the ability to be part of this generational journey from the beginning, right? That's the product. And that's how we gather that data. And this comes back to how we design robots that are safe.

Because if you want the robots to actually be learning in the real world, they need to be safe not just with respect to you, but with respect to itself and your environment, right? You don't want your kitchen to look like a robot has been ravaging around there when it's done.

You want your kitchen to look nice just like it did before the robot went there, even though the robot failed a few times opening. The challenge is you don't have the luxury of of hallucinations long term.

Like if the robot is unloading the dishwasher and puts a bunch of knives in in the wrong cupboard, you know that it's like that's not safe, right? Like if you have, you know, kids at home and, you know, so it's like the the bar for acceptable is is very high. I want to ask about the supply chain.

Dylan Patel was on the show saying that no matter what happens with humanoid robotics, all the parts are going to be made in China. Um I there were some actuator startups in the comments saying like that's not true. We're going to build it here. Uh we're going to fix the supply chain.

What's your view on the short, medium, and long term of the robotic supply chain and you know how to ask the hard questions.

Um let's uh let's start by saying um to us this has been all about how do you don't like over constrain the system too early right so juvenid robots don't exist at scale yet so like if we were making a car if my new startup was a car I would clearly utilize the existing supply chain of course what we've done is we've made everything ourselves and when I say literally here like I mean we're undergoing some patenting process for a new type of diecasting of aluminium at the same time that we're launching AI models.

So we're like definition of vertical integrator including all the manufacturing technology. Um that puts us in a position where we can actually kind of manufacture anywhere. It just depends on access to raw materials, power and general like tariffs and everything else.

Um we are actually building a factory in the US right now. M um there are challenges because like sourcing simple things in quotes like copper, aluminium, steel, these kind of things in the US for example is a lot more expensive than in China.

Logistically it's also kind of like a nightmare because you don't have kind of like the economic zones with like basically your metal refinery sits next to your forge. Um and there so there's like pros and cons but I think there's a path.

I think what's the most important here to realize is that when people talk about like things need to be built in China, we're not necessarily talking only about this. We're talking about actual access to knowledge about how to do manufacturing. Mhm.

And I think that that is the thing that takes the longest to kind of onshore, right? So, uh sure there are things that needs to happen with rare earths. There's things that need to happen with all these things.

But I think the main thing to really work on also is just making sure that people understand how incred incredibly exciting manufacturing is and make sure that our new generation actually really wants to work on the hard problems in manufacturing. Yeah.

Yeah, I mean, we talked to the the the author of Apple in China, and he really reframed the conversation about Apple's impact in China, not just as being a buyer, but actually being an educator in China and and and sending over massive teams of manufacturing designers to to create an to create a supply chain that didn't even exist, but was enabled by the scale of the population and the economic zones and everything.

you uh everything you articulated. So uh yeah, I'm optimistic that in a new boom you could potentially set up trade deals such that you know if your business is scaling you would have the choice to to create a new generation of of manufacturing engineers in America or anywhere else that might be advantageous.

Um but this has been fantastic. We we I'd love to have you back and go way deeper. Uh last question. Congrats on all the new launches this week. Uh excited to follow you and and and the whole team's work. It's It's been awesome to see. It was great talking to you guys and stay tuned. There's actually more coming.

Amazing. Wow. And we'll uh we'll pull up another chair for Neo whenever you're ready. We'll adopt Happy Happy to Happy to adopt one. Bring us another energy drink. Thank you so much for joining. We'll talk to you soon. Cheers, B. Bye.

Have a good Next up, we have John Doyle from Cape coming in the studio talking to us about wireless networking, cellular technology. very excited to talk to him, especially with the backdrop of what happened in Ukraine two weeks ago. Uh anyway, welcome to the studio, John.