Manufacturing 50,000 Humanoid Robots Next Year | Bernt Børnich, 1X

Bernt BørnichA well-designed humanoid robot is probably about 1,000 parts.

and a car is like 4 000 pounds a well-designed human or robot is hopefully less than 70 if it's going to be safe uh so it is actually a simpler system in that sense although like the individual complexity and the number of moving parts is larger uh so you get some on that side uh but you're of course entirely correct also that like uh it's a way less constrained environment that you're deploying into so you're going to see a lot of a lot more weird shit that's just the way it's going to be um i think a lot of it comes down to expectation management like it's going to be bumpy it's it's not going to be perfect uh i really hope we can provide a very good customer service and ensure that whatever happens we're treating you well and we really figure this out iterate on this and make sure that you don't have to face the same problem repeatedly but of course We also need to deploy into some quite structured, more like less variance type environments for parts of the volume and not have 50,000 robots into 50,000 different tasks.

That would not be manageable. Some employees have the robots in their own home, and I think you're one of them that's just got this robot walking around this environment and like experiencing it. You're kind of like a few years ahead of everyone else. The other thing, too, is you probably had multiple versions of your robot in your home. And so I'd like to know what it feels like on just some personal experience level to have the first evolution of that bot in your home environment, and also how that's progressed, and how the trust has been built over time. Yeah, yeah. So it's actually kind of interesting, because it's about three years now since I kind of had the first, like, let's call it pilot at home, where I actually had our previous robot, eve the wheeled humanoid which is a bit like bigger and more heavy but very capable at home and it was a lot of fun and we learned a lot and it was also one of the main inspirations for why like we had to kind of speed run getting to like the full biped to be able to do all these tasks around the home and also in like other diverse environments legs are actually very useful also manipulation uh it was a hard lesson learned um but um i think the evolution over time is

surprisingly slow like it takes a long time right so and and this is often because you find something that doesn't really work and you need some significant data on this then you need to iterate on your hardware and you need your hardware to kind of redeploy and now you get new data and we're not in that many homes so like it takes time to kind of run that loop but luckily we started very early so we've had a lot of time to to think about these things but i think mainly to me the kind of like important barrier that has to be crossed on the trust side is is safety and that's something that we're still working really hard on and we really have a big goal to kind of like prove out safety this year in a more formal manner so I'm very excited about that, and hopefully we can share some more about that towards the end of the year.

How do you kind of decide on, there may be, you know, a thousand tasks that the robot can do in like a lab environment, but then, like you said, kind of there's this safety bar that you want to hit before you ship it to, you know, your kit is around it. How fast do you go from, you know, creating a new task to actually having that in the environment and like testing it in your own home? so one of the beautiful things about the system is that since it's so close to a human in how it operates it's actually pretty straightforward to also operate the robot as a human so this concept of kind of like an expert in place or like an expert operator allows you to really test a lot of tasks way before you're able to automate them which is very useful um gives you kind of like the kind of customer impression how is it to live with this how um how well capable is the hardware to do these tasks so i think like largely most of the things we've already tested it out and really the big thing now is how do you automate it and here we follow a very different approach than most of the players in the space i'm a very big believer and we can solve the

Bernt Børnichgeneral problem we don't need to go and like automate every single task individually there's

a lot of tasks and there's a lot of complexity and also there's a lot more variability than you kind of like would think it is so it's kind of like deep rabbit hole right to solve every task and i see humanoids almost more like this bridge between human data and human intelligence and the machines we've designed robots that are so similar to us as possible even in how not just how they move but how they interact with the world how stiff they are how compliant they are all these things so that we can take all this data that exists of us and just video and everything else out there on the internet and train models that allows our system to be very generally capable across essentially all tasks but less capable than if you specialize right now but it allows you to solve the full problem way sooner and i think that's generally what's also needed for the home so i'm very excited about that the new 1x world model labs that we stood up amazing team and we're kicking off kind of like the first new big training run ...now, later this month, and I'm very excited to see how the new model is going to turn out.

It's a bit early yet, but there's some very, very promising results there, and I think we have a pretty good shot at solving the general problem. On this training run? No. Okay. But getting to... It's kind of like a bootstrap problem, right? Because you train a model that is very capable of essentially attempting all these tasks. and it does a good job of attempting them and sometimes succeeds sometimes fails and this gives you more data and you kind of fold this into the training and now you get more data so now the model is even more capable and then you kind of hill climb like this but the really important thing here is to have models that are so capable that you don't need the human in the loop to generate the data you just need a large fleet of deployed robots and then you will get very capable data of both successes and failures and you close the loop in the real world essentially to learn right and then you need the capable model you need a safe robot because if it's not safe then it will harm itself for the environment when it's trying to do things it doesn't quite know how to do and then you need a lot of deployed robots so that's one more reason to see if we can get 50 000

of them out by the end of next year on the kind of scaling the manufacturing side what has the process been like going from the first one to like the first 10 to the first 100 and what have been the biggest bottlenecks along that journey so to some extent we're a bit lucky and like the system has been designed from day one for manufacturability so even if you think about like this is a tech tree and how do we choose to build robots it's a bit different so while you normally would have like kind of normal motors which aren't that strong so now you need very complex gear systems these gear systems introduces a lot of like energy and other artificial dynamics into the system that nature doesn't have so now you need a lot of sensors and high speed control and all things to kind of mask this you're kind of like fighting physics we've taken a very different approach so we have some very capable motors we develop in-house and also manufacture that allows us to do these tendon drives and they're drastically simpler actually so not just more performant but drastically simpler like less parts less complexity less tolerances less sensors and all things are going to go wrong so the system is quite manufacturable

in that sense now the downside of course is that since this is a new type of system there's no supply chain for this so we have to manufacture all of it ourselves um and that's taken us a decade right like the company is 11 years old now so we we really had to kind of like climb the maturity ladder of these production processes but now that that is getting quite mature it's really a superpower because we do it all in-house here so whenever anything happens we can just go back and iterate very fast and make sure we fix it upstream so i think the most

Bernt Børnichimportant metric we have right now is we use about four weeks from we do like major changes on the full system in cad until a new robot walks off the line and it took us a decade to kind of build that

momentum where we can iterate so fast on the hardware but that allows us to take the feedback from the line you know and feed that back into engineering and iterate in about four weeks so So then you can really, really, really kind of like tune in your manufacturing process. So you pick off any mistakes that's done in like assembly or anything that's hard to do with respect to quality, yield, calibration, all these things down the line. But there's no like secret sauce. You just need to like grind through all these problems. And in the end, you really cross your fingers that you're going to run out of problems.

If you just fix enough of them, you're going to run out of problems. Do you still believe? Yeah, I still believe. but like since that's kind of the only way to do it then you just need to be able to really benchmark yourself on like how quickly can I iterate because that's how quickly I can kind of like turn through these problems and we see less and less problems so there's seems to be going in the right direction so sooner or later we'll run out of the problems when you're like starting to put these in people's homes that aren't inside of the company I think the bot at the beginning of next year is going to look significantly different than the bot at the end of next year. And the feedback that you get from January customers are going to be very different.

You know, you can incorporate that for the December people. How do you kind of as quickly as possible incorporate new information and new feedback from the robots in the field and then basically spin up new manufacturing lines in order to scale that? There's a lot you can do now with just like fleet monitoring and data and analytics that was very very harder to do before um so i think that that's a very important part of it but there's nothing really that replaces the need to just like stay close to your customer and really listen to your customer and get that feedback uh and then also like when we talk about iteration here right you you iterate on the entire fleet so uh we do have a big goal in the beginning at least for like the first units uh that we will do do a lot of remanufacturing also.

So if you find things that we need to fix, then we fix it not just for new robots. We should also fix it for the existing robots. Would that be literally, quote unquote, recall back end? Well, not necessarily recall. Most of it is the product is pretty small and light. So generally, you can service it in the field. And if you figure out that we need new left feed on a robot, right we we replaced place the left field in the field we don't necessarily recall the robot but technically you could call it a recall i was looking at the hand video and i was thinking to myself this reminds me of this idea from steve jobs where if you look at the inside of like an apple computer it's gorgeous and people were kind of wondering to themselves like why is this and it's like because steve wanted to design something that was both beautiful on the inside of the outside and also functional and i think when i look at your robot it's that has that same feel where it's, like, beautiful to look at even when it doesn't have its, like, clothes on or skin on.

What's your kind of, like, philosophy on designing something where even the internals look good? I'm probably, like, blunted here in, like, a special way because I think sufficiently advanced engineering is art. And this is clearly advanced engineering.

Bernt BørnichBut I think the beauty of the machine kind of comes from the simplicity, not the complexity.

So you take something that's extremely complex and you manage to boil it down to something is like it is the simplest form the simplest implementation that can fully solve the problem and there's a real beauty to that i think often you can see this in systems you can kind of see that like oh first principles wise this is just like the minimum implementation of the system and there's a real beauty to that simplicity and that's really hope what i hope we can deliver that kind of comes from itself right you don't need to design for it to be beautiful in inside if you've done your job on like how do you design the system then this just automatically happens because it should always be the minimum complexity implementation that solves the problem that's how you achieve scale do you ever have a feeling where you're looking at something and you say to yourself like it is clearly not there yet because it doesn't look simple it doesn't look beautiful oh yeah and then like every day every time i look at the robot all i see is everything that's wrong so I'm not that lucky in that sense like everyone else sees something as like this is beautiful I'm like no no no no like this is wrong this is wrong this is wrong and I think

that's probably going to be like that forever so I'm not the right person to ask that but I do think like I find beauty in every time we can do simplifications and I think the biggest simplification that's happened over the last year is for example wire harnessing and just ensuring like uh the complexity of the cabling really going down tremendously uh like minimum number of connectors minimum number of wires and this is just something we drive top down like we literally have like a cable and connector budget for example there's a certain number of connectors that you have to have from like a first principles point of view but it's not that many.

And every connector that you have more than that, that's an exception that really needs to be defended and needs to be approved, because it shouldn't be there, and we should have a plan for how to get rid of it. And this is the same for moving parts, right? Like, you want the number of parts in your robot to be the number of moving parts. If two parts don't move with respect to each other, like, why are there two parts? Like, they should be one part. you can't always achieve this but you should absolutely know what is the first principles minimum complexity and how far away are you and how are you driving it down towards that because the ultimate goals should be to reach that has there been any specific example where there was like a very obvious this should just be one part you can think of this is very niche but my favorite is actually how the rotational part and shaft on the motor is now one part less than what it's always been because some very smart people figured out how to do the bearing assembly of the shaft without needing two parts, which usually it's always two parts because you kind of like need to sandwich it in, but it's actually one part.

And to me, that's very beautiful. It's half the number of parts for that. If you took Bernt from five years ago and you teleported him to today or teleported yourself back, would you make any decisions drastically differently than you have?

Bernt BørnichI'd keep the team way smaller and leaner way longer.

i think a lesson you learn again and again and again as you do development is that the beautiful products are the simple products and the beautiful simple products are made by very small extremely talented teams the more people you have the more complexity you will get in your product and this type of engineering just does not scale with a number of people like you can scale your manufacturing with having lots of people doing manufacturing processes testing automation all these things but like the core product design once you have multiple people you need interfaces and once you get interfaces uh you kind of introduce artificial complexity there's a real very material advantage to how much of the system can you have in your head and understand so that you as a single person can make the decisions across the full system usually i would say like across the subsystem but ideally there are no subsystems it's just the system right now this not doable for this complex of a product um but you want to get as close to that as possible when you are trying to keep the entire idea of like this thousand part product in your head how do you kind of come up to speed as fast as possible with and have the most like accurate representation

of what this thing is in your head at any given time i think it just comes down to like have you understood the fundamental principles of how the machine operates and why and if you really understand the fundamentals then everything else is just logical like if you have a good design so then the complexity kind of goes largely goes away um and then you look at it in the end and you're like actually how do we spend this much time like this isn't that complicated and like that should really be the goal right and maybe other people look at it and they're like oh man this is complex but once you really know how it works then the magic goes away right and it's just like this pretty simple machine i think that takes a lot of time so you also need to create an environment where kind of people stick around and get that intuition and under deep understanding of the system but you can also do a lot on the tooling side which is quite important like how do you build the design tools on the electromechanical side that allows you to iterate on the product in a way that's like verifiably correct and this is increasingly becoming more and more productive because you have so good tools especially on the coding side but that's always been a challenge

like finding people who are very deep on electromechanical but are still general enough that they can kind of develop their own tools to solve the problem because most people that are very deep on the electromechanical side they're not necessarily software people but by far the most valuable people are the people who are very All across all these domains, preferably even down to like material science and everything else, right? Like how many domains have you actually mastered? And can you start to see kind of like the correlation between them? So kind of like the boundaries disappear. The best people generally end up building a lot of tools for how to do your job.

If long-term millions of people are going to have these things in their homes, ideally this is something that humans like form a connection with and they start to love. One of the videos that I saw, you were like hugging Neo. And I was thinking to myself, how do you go through the process of trying to design something that is fundamentally something that humans can learn to love or decide to love yeah so there's there's the obvious answer which is like oh you do the right things with respect to like warmth and materials and it's easy to design things that are scary and then they look kind of science fiction and modern because they're scary like that's kind of like the simple cheat code we don't do that we try to design things with warmth like technology that just blends into society and you shouldn't even think about it as technology like that would be the success so that's kind of like on the hardware side um and that's hard to do but i think it's pretty straightforward um i think on the intelligence side it's incredibly interesting and one of the things i'm so bullish on with the world models and what we're doing there is the robot actually learns social behaviors also through all this data

and a good example is just like if you get the robot to like i mean there's some bags over there and we have the robot to go and like give you a goodie bag of swag um it hands it to you and it doesn't let go right and then you grab the handle and then the robot lets go and there's there's no

Bernt Børnichdata in the training there right the robot doesn't have data of the specific tasks it's just these kind of like social behaviors and how to interact with people just falls out of the model body

language falls out of the model like everything kind of collapses into one big omni model that understands everything from how to do the task but also how to like how to be social um how to simulate other agents like other people like if you want to move around and do things in an efficient manner then you need to understand and predict what other people do right uh and we do of the intelligence is very very interesting but also very very hard to get right i think ultimately that will matter a lot right because that's how you create the trust that's how you kind of like create that warmth and i think it's also going to be quite individual like people don't like it's personalizable like people don't necessarily want the exact same thing but i do think that these machines will they will learn and last for a long time and they will become a part of the family and a part of society and it's going to be very interesting to see how that evolves.

I think it's going to be quite beautiful to have this companion throughout your life that is always on your side, remembers everything that's happened and it's always there for you and I think we could all need that. I think there's very few products that most people would describe as delightful to have in their lives and I think this is going to be one of them where when people think of the experience of interacting with humanoid robots and NEOs, it's going to feel like delight, like things just are happening. You didn't really tell it to do something, just like magically did some tasks that you weren't expecting and you have this little surprise and joy.

How do you think about designing something that is just going to be like a delightful product? I think ultimately it's all in the data, right? So it becomes a data problem of how do you kind of like create the desired behaviors through how you tune your data i think there's still a lot of unsolved research there just like a lot of work to do i don't have the solution um like everyone else we're working on it but i think it's something that will really really really greatly improve the enjoyability of using the models. And it's been a lot of discussion lately, right? When you look at the best LLMs now, they're kind of becoming worse, personality-wise, because you have to be so careful to make sure they don't do anything wrong. And we're clearly going to have the same problem here. Like, we want to make sure these robots are caretakers of humanity and, like, they help us um and finding the balance there i think is going to be be very hard um so uh yeah that's that's something we're going to work a lot on and i do hope that through kind of exposing the robot to the right kind of like behaviors that we would like to see in other people uh this is emergent behaviors it actually just occurred to me um if you have a robot in a

home environment it doesn't not all homes are actually happy um and there might be learned behaviors that are not good well you know remember like most of like the llms are mainly like trained on i don't know like discord forums reddit right like worse forums than that like it's like all over the place all over the place right so like it's like it's like the dark side of humanity um and then you take that and you know there's this like great meme right where you have like the big cthulhu monster and then someone like slaps a smiley on it and says like now it's ready uh and that's kind of like what a modern model is right you have this like beast and then you're just like no no you're gonna behave well um and i think there's better ways to do that uh long term as you get more data uh right now everyone is extremely data constrained so are so are we right um but as you get this incredible tool to learn about how to operate in our world maybe you can be more selective on your data uh i don't know but uh maybe just encourage everyone to like be be kind to our robots and your robots will be kind to you but there is a balance here because the willingness to kind of like break rules and explore is a large

part of how we learn right it's how you get new ideas and creativity and maybe that's one of the reasons you don't see that much creativity in models today you don't really kind of encourage and expose that kind of behavior how do you expose more of it like if you were to try to drive the robots to do creative things how do you like seed that i think play is just incredibly important and uh i dream of a future where like robots kind of like curious and playful and if they don't have anything else to do they'll go and figure out how something works i don't know go shuffle your feet in the sand and figure out how the dynamics of sand works i don't know uh it Of course, it's pure speculation, but I think the ability to kind of like verify your hypothesis because you literally have the real world, so you're grounding things, hopefully will give you some more ability to explore because you can actually figure out whether or not your hypothesis result in what you thought it would.

When we were on the drive over here, I was watching this kid just randomly. We were stopped at a stoplight, and this kid was just feeling around and like touching the world. I think it was like some handrail and they were touching it and they're just feeling it around. And then it just occurred to me, there's all the tasks that you want your robot to do in your home. Maybe you want it to do the laundry, make you coffee, all this stuff. But then probably most of the time, if you're at work for eight hours, this thing is just there sitting idle. But you could potentially like have it collecting data by taking actions and just literally like a child experiencing the world, you know, experiencing the home, doing things.

and i think also like we haven't talked about this yet but you know there's the concept of the neo platform where we open this up to developers and everyone to like build on it which i think is going to be incredibly important because it's such a big problem um and i don't think we can solve it alone uh we really need to enable as many people as possible to do the development and like partake in the journey and these robots will get exposed to very different environments That's a very different data. and i think that's also very important because you want like as wide a distribution as possible if you think about the home i think you're partially right but i think also actually as these systems become generally very capable our standard will just go up right so like everything is going to be like perfectly full all of your clothes will be ironed like even you even your sheets will be ironed and they will be like perfect on your bed and like Like, all the glasses will be on the line in the cabinet.

And, like, there will be no dust anywhere. And, like, you can create a lot of work if you want to. Like, if there's, like, no cost to the labor, you can come up with a lot of things that are, like, marginally useful. But I'm going to do it anyway because, like, why not? So I'm not sure there will be that much idle time. We'll see. We'll see. You know, when you were talking about perfectly arranging the glasses, it was, I was thinking, like, most humans, like, my laundry. is not perfectly folded so if you emulated me like it's it's it's folded but it doesn't like you see creases you know if most people's environments are imperfect and the way that humans actually operate in the real world is just constantly imperfections how do you collect like the best real world perfection data i don't think you do um i think you just learn how the world works and and like we learned that like there will be wrinkles in your t-shirt if you do like you do and sometimes you will have the like random example where like it's actually straight right and and and both of them are equally important because how you learn how to get from one transition from one stage to another um and it more becomes like how do you ensure like that

have the intelligence emerge over this so that if i say actually i want my laundry to be like perfectly straight then the robot understands how to do that it's a pretty good path there uh it's a pretty well explored problem especially self-driving is a good example of this like most drivers are terrible drivers for driving it's a very it's a very thing you you want the robot to do exactly you know perfect job every time yeah but like if you just clone human behavior in traffic you're not gonna get that no not at all uh so you can't actually just do that right

Bernt Børnichyou learn from all the data about how people drive it's very useful data uh you learn what

happens if you do something you don't learn how to drive you learn the consequence of driving right then when you have that understanding of how to operate a car and how traffic works and how typically like other cars move and like how pedestrians like might move and like predicting all these things now you can learn how to operate in a manner that is desirable given that someone tells you what's the desired behavior but i don't think models necessarily should explicitly learn through cloning human behavior that's that's kind of like a very um that's how we used to do things like years ago but like that's not how you do these things anymore you learn how the world works and And then you can essentially search in that space for like whatever optimal solution that you would want to your problem.

If you had to think forward a few years and you guys are not developing all the like models or whatever for your robots. And there's kind of like this almost mod store where people can create things and create like new actions or tasks that a robot can do. And then someone can just like download it onto their Neo and suddenly a new skill is unlocked. How do you like do that? i think it's going to be essentially exactly the same as you see in large models today so there will be a few dominant models just because they have the best data and evals there's like this not so well kept secret in ai that there are no secrets in ai it's all just data and evals like why are the best models the best models is because Because these companies have the best data.

They were early, had huge... User bases, they got enormous amount and still get like an enormous amount of data. And since they have so much user data, they are able to train better models. And when you have better models, you get better user data because you get more users and it's kind of like the self-enforcing flywheel. There are other models where you can kind of like specialize. You can download an open source model, you could try to like do some fine tuning on it to like get it better on your specific task. But it has to be a very hard, very niche task before this makes more sense than just using data.

model and i don't think this is going to be different we see the same trends the same scaling laws the same patterns uh it's just robotics has until now been like kind of like a toy problem everyone's been working on extremely small sets of data and they've been working mostly on like robotics data where it's like oh i gathered 4 000 hours of like these specific behaviors with my robot and like this is a very big data set no it's not like 4 000 hours is nothing even like a few hundred thousand hours is nothing and if you see the models that people train now of like equipping people with sensors right and then like having that happen at scale and then they're saying like oh we trained this foundation model at 200,000 hours of data which is huge compared to what used to be in robotics it's actually still tiny like general intelligence emerges

Bernt Børnichat like hundreds of millions of hours a day so you're not gonna go gather that data that's why I deeply believe that you need to use these machines as kind of like the bridge between human data and machines.

And I do hope that through doing this, the models that we train can operate a lot of robots, not just Neo. And you can generally go in that direction, right? You can take a large, complicated, like very capable model that's trained on this enormous diversity of data, and you can distill it down on a smaller problem. And then that model will be very good at the smaller problem. You can't go the whole way around. you can't take this model from like this smaller problem that only saw that data and distill it up to solve a general problem that doesn't work it's a one-way thing so you kind of have to have the most general system that has the most diverse data and like has experienced the most things and then you can take that intelligence and distill it down to like more specific applications so the question is when that happens right because we're not there yet and in the meantime there will be very good applications for a lot more kind of like specialized models and that's what we're seeing today right if you want to fold laundry then actually if you go and just gather a lot of data on folding laundry with our robot and trade in the VLA that will work better than our robot but it won't be good at doing anything else it it doesn't have a general understanding of how

the world works or like it's essentially intelligent it won't exhibit kind of like social behavior and like understanding how to interact with people it'll just fold the laundry and that's kind of like the regime we are in today but it's quite quickly evolving so I think 2027 will look very different. Maybe not 2026. I think 2026 to specialized models will still be better. But I think 2027, you're going to see a shift towards more like general intelligence. So initially, you kind of have to use like teleoperation in some form or another to like, I don't know, bridge edge cases and stuff like that.

You need all the data. That's a simple answer. So like you use the web data, which is going to 99 of the data you use simulation synthetic data you use sensors that you put people with to get some of that data you use eccentric video data you're going to use robot data with teleoperation and you're going to use robot data where the robot is just learning by doing uh you use all of it um i think the specific data of like oh i have an intervention and in the sense of like the robot failed and like now i correct the robot um not that important that scale it's extremely important that small scale like if you want to like solve a specific problem with a very small amount of data this data is essential and the technical term here would be that your distribution is very small so like you know how to be within this kind of realm here Then the robot knows what to do. If it gets outside, you need something that tells it how to get back on distribution, so it knows what to do.

But if you have a general model that is very capable and diverse, then actually, it's very hard to get out of distribution, and then you don't need this. You can always kind of like find your way back to where you want because you're still in distribution. It has to do with the robustness of the model. first you train on the internet data then you get a little bit of your own data but eventually in order to really get the data that you need to train a super genius robot you just have to have a whole bunch of these um in operation like if you think about if you have 50 000 next year suddenly and they're operating let's say 16 hours a day uh you're getting 16 times 50 000 every single day of new data i assume when you're thinking about like your bottlenecks for scaling your data is like one of the biggest bottlenecks, just we need more robots in the field.

Yeah, and they need to be in very diverse environments and very diverse applications. In reality, you're almost never data-bound. You're diversity-bound. So when people say like, oh, you need very large amounts of data, that's true, but you don't actually need just large amounts of data. You need an extreme diversity of different experiences and tasks and things that you haven't seen before and if you have enough of that that becomes a lot of data because you have so many different things but lots of data of the same thing doesn't really help you right let's say you ship 50 000 robots next year what is the breakdown of where those different robots are going to go because like maybe you know initially i was thinking like 50 000 to homes but maybe you don't want that maybe you only want like 10 000 to homes and you know 2 000 that'll just go into the woods and build a cabin or something i don't know i think you just that's like a couple years more out but um i one of my dreams is to be able to leave my house and say like hey neo i'm having a part party this weekend and i would like my garden to be japanese and then just like it'll like go and reconfigure the entire garden and like build a nice pavilion

and like you come back like a week later and it's like oh it's ready that's nice um but that's going to be a couple years out um so so so this year there will be a couple of uh enterprise applications uh where we have very large customers so we can deploy a lot of robots into specific verticals and then there's going to be some to the home and it's going to be a lot to the platform it's going to be a good good mix of all of them um i think it's important to get like information from all of them and like get the lessons learned from all of them But I also think it's important to have some applications where solving the task to actually be truly useful in like a return on investment for your customer point of view is very important because that's how you drive adoption, right?

And you need scale to get the cost down and to get the reliability up and everything else. So a good mix of these. We're not quite ready to announce yet what the specific enterprise applications and customers are, but that's in the works. Do you kind of think about it also in the same way that maybe you need a bunch of like folding data? And so one way to get that in an enterprise setting might be like, hey, old Navy, you know, here's 5000 robots and they just spend all day folding clothes. And then you get, you know, 5000 times 16 hours a day of. So that would be one application. We're not going to do folding as our main application.

So I can I can debunk that myth. Everyone else is doing folding. So there's already a lot of folding there. okay so everyone else is doing folding what is the one thing that no one is doing that seems fucking obvious to you is that you could you say no someday i'll reach out to you again someday being just a few months and tell you like here's what i thought about okay next year we can we can know okay we we think we've figured this out uh we we we think we have a couple things we're gonna do that it's gonna be uh very material i remember this one situation where one like oil company bought a fertilizer.

Fertilizer company, and then suddenly, like, all these other oil companies started buying fertilizer companies. Like, the first guy was just like, I just bought it, you know, for shits and gigs, and then a bunch of other people started doing it for no reason. And is it like, some guys started on folding, and then everyone else is like, well, they're working on folding, maybe we should too? No, it's actually, folding is kind of, we also did the folding. Like, you can go on our YouTube channel, like, and you can see us using Eve actually a few years ago to, like, fold shirts. So I guess we were early in folding. It's been this incredibly hard problem because it's it's soft goods right like so it's it's you never know how it's gonna look like you crumple it and it looks completely different like a cup the same cup looks like the same cup every time might be slightly dirty but it's the same shape clothing is a different shape every time you look at it so it's incredibly hard to simulate because it's like soft and deformable so it was just this large open problem in robotics how do you do folding extremely hard to do in the classical sense and just happens to be one of those things that are

very easy to do with AI you need very little data and it just works I don't know maybe someone's working on like a very nice academic paper that can explain to you exactly why that is the case I don't actually know but it is one of the simplest things to do but it's something that used to be very complex and now it's very simple so it's like the most overused demo because it looks like you're extremely capable because you can fold these clothes but it's actually not that hard it's actually quite simple it's one of the simplest problems to solve i love this line starting a startup is like chewing glass and staring into the abyss and being a founder is being someone that enjoys that process when was the last time that you experienced utter disappointment i mean it happens every week yeah i mean there's always things that don't go well uh so what i've learned is you have to enjoy the journey like that's the secret like there's because there is no end right like there's it's not like oh if i just fix these things that i know is wrong then everything's gonna be good no no then you get to play on the next level where more stuff is wrong and if you fix that you get to the next level where even more shit is wrong

and it's just worse every time right because things become bigger and more complex and like so you're never gonna get to the end of it so if you don't think that's fun then you're not gonna last like you you have to really enjoy the journey and i've always just been a fan of like i'd rather have the highs and then endure the lows then kind of like just be in the middle uh so i feel very fortunate that i get to have the highs and the lows every week it's never boring at least i can it's never been boring sometimes i'll admit i've woken up and thought why the heck didn't i just design an app like why did i decide to build a robot like there's easier ways to make money uh but but you weren't really in it for the money but no uh i wasn't uh and that wouldn't have been as fun