The Breakthrough For Home Robots with Kyle Vogt, CEO of the Bot Company | Ep. 32

You think it's like cooking a steak at some point?

>> Yeah, why not? If you think about at the end of the day, you you have pick and place and simple manipulation. That's

what cooking is. They're just like a much higher degree of reliability. And

there's other things around food safety and bacteria and other things that come in cookie cooking and temperature sensing and what like that. So, it's all doable. It's just like I

doable. It's just like I >> think at some point it's like, "Hey robot, I'm at work right now. There's a

steak in the fridge. Please cook it and clean up everything by the time like 15 years from now. That's that's doable."

>> Less than five.

>> Less than five.

>> Yeah. All right, I'm really pumped to be here with Kyle Vote. Kyle, thanks a ton for making time for this.

>> Thanks for having me.

>> So, I want to start with talking about like why robotics seems to be having such a moment. You know, it's obviously been really important for a long time, but in the last few years, it seems like a lot of really good entrepreneurs, a lot of good investors have started to

pour a bunch of time, money, resources, and effort into this. And I guess I'm curious just to start with sort of laying a foundation of like can you put this in some context and like what used to be the case and what has changed

that's like making people so energized right now?

>> Yeah, it is. It's like the most excited I've ever seen people in robotics. And

you know, I I guess as an engineer, there's something like romantic about building machines to do the stuff that we don't want to do. And that's that's why I've been doing this for so long.

First with, you know, a decade on self-driving cars. But for me, even

self-driving cars. But for me, even going back to like teenage years doing battlebots and then going to MIT to basically build more robots. But, you

know, during that entire spectrum, it's als it's always been this niche thing.

And frankly, like robots have never really lived up to their promise.

There's always something. They're always

overly fragile. Like in a factory environment, we put them in cages and if things don't line up like within a millimeter, the whole thing doesn't work.

>> Except the battlebots. Those were good actually. Now I'm thinking back

actually. Now I'm thinking back >> battlebots were Yeah.

>> You made them with like the saws and everything.

>> Ours had like a hydraulic axe which was which was pretty cool. But the calling these robots is a bit of a stretch.

They're basically glorified RC cars, right? With a weapon.

right? With a weapon.

>> Yeah. So So with a weapon. What's

different now is, you know, for the first time you have robots that are powered by essentially they have all the brains of an LLM built into this robot and we're controlling them with neural

networks instead of classically engineered algorithms. And so the difference was before if you have a robot that's like in a room like this, uh even saying like go to the whiteboard is almost like an impossibly hard

computer science problem. It's like,

okay, I have to build an exact 3D map of the world, like have a detector that can figure out what a whiteboard is, train it on millions of examples of what whiteboards look like just to be able to do this. And even then, the failure rate

do this. And even then, the failure rate would be high if you put it in a different room and it doesn't have a map.

>> But now it's almost like cheating. You

can take all the >> the common sense that's on the internet and inject it into a robot brain. And so

if you're like, where's the whiteboard?

It knows instantly.

>> You like open the chip, you know, if you open like video, you can like show it anything and it like knows what it is.

>> Yeah. Uh and so imagine like robots before started with zero knowledge of the world and now like suddenly have this kind of knowledge of the world >> better than us. Like they can look around a room and see stuff better than we can.

>> Yeah. And then on the motion side uh you used to have to have a PhD to compute these complex um trajectories. You have

like 12 joints on a motor or on a robot.

How do you get all 12 joints to move in tight coordination to like move an arm to a place? And this is a very difficult and computationally intensive problem.

And now we just kind of jump over that whole thing. And now if you have a way

whole thing. And now if you have a way to to teleyoperate a robot or to put it in a simulation, it can just learn how to move all those joints to mimic the human operator or to accomplish, you know, some reward function or to

maximize it. Um, and so you can skip

maximize it. Um, and so you can skip that whole computational challenge. And

so those two things together basically mean that everything we thought we knew about robotics or like what kind of businesses were good businesses or bad businesses, all like that slate has wiped been wiped clean. Yeah. And so I think you're going to see this Cambrian

explosion of different robots for different applications that now suddenly just work whereas before they would like really struggle to do the most basic things.

>> And when you say for different applications is that are you saying it won't be terribly generalized? Will it

be medium generalized? Like what made you say for different applications or like different environments maybe?

>> Yeah, I mean classically like a lot of robot businesses like try to re get really really narrow the successful ones. we're going to focus on this one

ones. we're going to focus on this one problem like factory automation for 3PLs for putting things in box boxes and putting them on a conveyor belt like very specific and that's just so you could narrow the problem enough to be

good at it. Now I think you're going to see people broaden the horizons a little bit um because it's much much easier to go from you know a piece of dumb hardware to something that's performing a useful task. You know I say multiple applications too because it's my view

that there's going to be a whole bunch of different shapes and sizes of robots each optimized for different types of work. you know, as opposed to maybe like

work. you know, as opposed to maybe like a humanoid robot, which you know, is very, very expensive, but in theory could do everything. I think we're probably going to see some of those, but the vast majority of robots will be more special purpose in nature.

>> Feel like there was a moment in AI where the researchers who were sort of closest to the work were like very sure it was going to work before the rest of the world sort of knew. Is there an

equivalent thing in robotic? Like have

people crossed a similar threshold to like whatever that was at like the pre chatbt moment in robotics where like some people who are at the very front have been working on it for decades are like this is definitely happening now.

>> Yeah. If you had like uh secret microphones in like robotics labs across the country right now, you'd just be hearing holy holy holy It's like constantly happening. And I

think finally like the light bulb moments are happening. And it all like in the early days of this stuff it all looks very rudimentary and uh kind of simple but uh if you know what you're looking at you see the signs of life

that mean over the next three to five 10 you know even less uh years of devel development this will go from an interesting technology in a research organization to like you know broad mainstream appeal and yeah those those

signs of life are happening those light bulb moments are happening all over the place right now. So what are the components? Like there's obviously, you

components? Like there's obviously, you know, we talked about like there's like vision. There's like the ability for the

vision. There's like the ability for the robot to do like manipulation the right way for it to have the right sort of dexterity. There's got to be something

dexterity. There's got to be something around like reliability. I don't know about like decision- making if that's its own sort of like what are the components basically to like this up level?

>> Yeah, you've touched on a bunch of good ones. Um, depends on the type of robot,

ones. Um, depends on the type of robot, but the ones we're building like robots that operate in your home, they need to navigate through a home. They need to remember where things are in the home.

uh they need to uh interact with and manipulate these objects like you said and um probably have some way of incorporating your user preferences into all of this and so you've got a reasoning component like I see a certain

thing in a home plus I know you know the preferences you've told me in the past about how you like things organized or or how you run things in your home and then I'm going to take that and reason about what the next steps I should take as a robot and then once you have those

next steps you're going to take you know drive to the oven put the towel on it then go over here once you have those discreet steps then you and move to more one of these um you know end to-end

models that basically given a simple task can go execute it. my uh implicit assumption here is that on some time scale you're like extremely confident this will all work but like what are you unsure about in the next let's say like

five to 10 years or like what will drag >> you know one of the uh biggest challenges for something like this it's a brand new product is like how do I use it like how does my life change and how do I adapt the way I live to best you

know make use of a robot like this and that could be you know in a home environment or maybe it's like a manufacturing business that you know its entire workflow is is organized around people standing in work cells doing a

task and handing on the conveyor below how does all this change and so I think that the technology part will come pretty fast uh and and I'm pretty confident in that the part that I think traditionally takes longer is the world

has to adapt to basically now that this new thing exists how does everything about how I run my business or how I live in my home or how do I operate my hotel or whatever it is need to change or should change to best make use of this new thing

>> like this is like where like AI software is where like it's obviously much better than like what's currently being deployed and used it like takes time to get from like the tech is good to now it's like implemented everywhere. So

you're basically saying it's like the robots will be good enough at some point soon but then figuring out how to use them in daily life and like where does it actually fit into like a life workflow that kind of thing.

>> Yeah. And I think the companies building these technologies have a responsibility to help us figure that out. they're

closest to the technology and I think they need to think not just about like what what is what does technology building do or you know what's the fancy new thing I built in there but like you know three steps removed from that how how do businesses actually make use of

this and like you know what do they need to know about it and like what things do you need to build in so that it's as easy as possible to sort of go on this adoption curve and make it happen >> are you more uh in a mindset of like we

are Apple and we're going to like we'll tell you the product kind of and like this is how it's going to work and this is what the robot will be or is it more of like the YC like let's just get it into some homes and iterate type like

which mindset do you think you feel closer to?

>> Well, it was a frustrating answer but a little bit of both. It's one of those things like strong opinions weakly held.

So, I think you have to have an opinion.

You have to have your taste and your preferences built into the design of a product or it feels bland. Like a

product with no opinions is just like, you know, you wouldn't even notice it.

So, I think you have to start off with strong opinions and then be willing to put those in people's hands and then quickly abandon them if it's not, you know, if it doesn't work the way you want to. I think if you're not stubborn

want to. I think if you're not stubborn enough, you end up with a just product no one is interested in. And if you're too stubborn, then you end up with a flop in the market once it's out there.

And so, you know, uh I think it's like a careful balance.

>> Why did you feel compelled to go for the home? Like you obviously even with this

home? Like you obviously even with this generalized robot sort of idea. There's

a lot of things that you could do that aren't just like pack a box in a warehouse type of thing that's sort of like more dynamic than that. But like

you picked home for some reason.

>> Yeah, for some reason. Um so I just turned 40. This is my third, you know,

turned 40. This is my third, you know, company that I'm working on. Uh, big

one. Yeah. I bring this up because like at this point in my career, I kind of know how I want to spend my time and like what's important to me. And, uh,

first of all, I want to have a lot of fun and working on home robots that I could use, all my friends can use. Like

couldn't think of anything more interesting that uh, or more fun, especially compared to like robots that are hidden in a factory that no one would ever see. Totally. I also think that, you know, one of the great promises of working on really cool technology is, you know, certainly you

get some dopamine hits when you're solving a problem and you make it work.

But like 10 times that or a hundred times more is when you see your hard work go in someone's hands and they use it for the first time and they come back to you and say, "Oh, this is so cool."

Or, "My life changed because of this." I

remember, you know, one of my favorite stories from or examples of this was when we were working on Twitch and there was this guy who was like a carpet cleaner in Minnesota or something who started streaming on the side and like became had a really popular channel and

he was making you know he was like the first one of the first streamers to make six figures just playing video games online and uh he's like this completely changed my life and so like moments like that when you uh build some cool technology but then it actually like

moves the needle for someone and they tell you their stories that's that's the really motivating thing for me and you're just not going to get that you know if you don't have millions of people using the product.

>> I mean, the idea that you could get like a robot in everyone's home is um it's really it's actually totally believable to me. Like I could see a future, which

to me. Like I could see a future, which I guess this is what you're building towards if it's the right form factor and price point and it does the right set of things. It seems very believable.

And I guess like you probably had some range of considerations where you're like we could make the smallest possible, cheapest possible thing all the way up to we could try to make a

$50,000 humanoid and you picked something at some point along that spectrum trying to be somewhere there.

Did you think about it in sort of like a range of like what was technologically possible, what future you thought kind of made the most sense? Like how'd you pick what sort of like complexity and price point to live along cuz you're not

doing humanoid >> from day one. Uh my concern is that there's always going to be an expectation for what the Home Robot product can deliver and what reality is, especially in the early days. And that

expectation versus reality kind of goes into value, how much value you perceive you get from this product. There's a

scale. There's like cost on one hand, value on the other. And we want to do everything possible in our favor to tip the scale towards like value. And so

that means like being really aggressive on cost um to get the price down and make these affordable. that has the dual benefit of on one hand um you know making it so that people are delighted by the product because it's not

something they spent as much as a new car on. They spent something much much

car on. They spent something much much less um and they're pleasantly surprised hopefully. And the other is if you get

hopefully. And the other is if you get the cost low enough you can sell these to a lot of people because lots of people can afford them. And at this day and age data real world data is one of the biggest biggest bottlenecks in robotics. And so if you can get lots of

robotics. And so if you can get lots of robots out there, you're going to have lots of data much sooner, which then creates this feedback loop where the product gets better and then it's worth more to people and then more people buy it. There are a lot of trades where you

it. There are a lot of trades where you can build a cooler robot or add more capabilities or you can reduce the cost and we've almost always been in the reduce the cost kind of thing.

>> Yeah. Do you think that the like the humanoid vision, which is obviously extremely sci-fi and cool, like does it make sense? Obviously, you could build a

make sense? Obviously, you could build a robot a bunch of ways and like one way you could choose to do it is just like shape it like a person, but it's a robot. You know, maybe there's some

robot. You know, maybe there's some reason for it, but like when you think about like the humanoid question, like does it intuitively make sense as something that like ought to exist or is it kind of random?

>> First of all, when when I see the videos of of human humanoid robots these days, having worked in the field for a long time, it is just so cool. It's so

amazing to see what people are able to come up with these days and how fluid the mo movement uh looks and you know how dextrous they're getting in terms of the things that they can do and so I think they're amazing machines and I

think they need to exist in the world. I

think the the you know the question for me is uh if we're talking about putting these robots to work or like people owning them uh the question is like at the end of the day is this the most cost-effective way to deliver the most

value I can you know to that customer to that person and I think for humanoids there are very few uses for which the answer is yes most of the time the answer is no I can build a simpler machine that works in this environment

if it's a factory thing where the floors are all flat and you're just moving things from one place to another that robot should probably have wheels >> if you're in a home environment And you know like a humanoid presents all these safety issues like with walking upstairs. If it slips on a banana peel

upstairs. If it slips on a banana peel and falls it becomes a you know ballistic missile basically going down your stairs. These are not good things

your stairs. These are not good things for the home.

>> That's true. Actually like a big heavy robot falling down your stair is a huge problem.

>> Yeah. So for the home you probably want to optimize more on like low mass, low cost and try to like you know maximize what you can do but you know not not running into some of the challenges of a humanoid. That said like there are some

humanoid. That said like there are some things that be really hard for um a non-humanoid robot to accomplish. like

if you're on a construction site and you're climbing up and down ladders and using hand tools designed for humans and all these things. I buy that argument that there are some uses where we'll want humanoids but I think it is currently I think people um advertising

humanoids are trying to get hype in the space get more investment in the space which we need um but I think the actual practical uses of them it will be a little bit smaller than what is being portrayed currently.

>> It also could make sense that they don't make the most sense in a home but they live other places. Like it would be good for example if like a lot of like defense was carried out by machines like cuz that could in some world you know

hopefully that could like save lives for example or like you could imagine it sort of like you know guarding at like you know a stadium or like taking care of like you know big sort of like uh

patrol areas and things like that. So I

could see that cuz it is like a very mobile thing in the home that example that you just gave you know it slips and it falls on the stairs and it you know hurts a kid or an animal or something like that. I mean maybe maybe in the

like that. I mean maybe maybe in the distant future we can solve these problems. I think just near-term you're less likely to see them in the home first.

>> Along that curve though between now and of course you know 50 years out like obviously these things are going to be I think it's like cars where it like gets safer than people you know one day I you know I assume but on the way up what's

the regulation going to be like for robotics like do you need to be like really involved with the government to like put these robots in a home or is part of what you're doing with the design like to avoid a lot of that stuff >> right now? Um, you know, it's very

different than some of the industries I've worked in or defense things or automotive things where they're very very heavily regulated industries and for good reason. I think you're going to see um a lot of products in the home and it depends on your view. On one hand, we have these little robot vacuums going

around today. You could make an argument

around today. You could make an argument that this is kind of just a step up from that. But you don't see for consumer

that. But you don't see for consumer products a whole lot of targeted regulations for individual products. We

have, you know, general product liability laws and other things that are generally applicable to everything from chainsaws to blenders or other things that you might have in your home that carry some risk associated with them.

Um, but I think there's an immense responsibility on the develop developers of these products to try to make them safe and to do everything possible following best practices um, regardless of whether or not there's regulation.

One thing that we may see more of is looking at how the data is used from these products. Um, you know, the

these products. Um, you know, the security of these products. I think

that's really important. Obviously, like

the home is one of the most intimate spaces in your life. You know, there needs to be a great degree of trust and responsibility that goes with companies who are, you know, have these machines that are likely covered with cameras, you know, running around our homes. Uh,

and most of us don't even think about today, like when we buy a robot vacuum, where does it come from? Like who is the company behind it? Are they trustworthy?

Are they going to do the right thing in my home? And that's where I'd like to

my home? And that's where I'd like to see a lot more scrutiny.

>> So, what does that mean you're going to need to do? Cuz you're right. It's like,

you know, I remember, you know, people got comfortable with it at some point, but like the Alexa problem where there's like a microphone in your home. Like now

there's like a microphone and a camera and like whatever else in your home. So,

like what what does that mean you need as like a company to sort of be, you know, a trusted brand there? Like you

have to go from day one pretty hard at that, I guess.

>> Yeah. Yeah. You have to have some principles and opinions and be able to talk about it publicly, I think. But,

you know, all these products are going to every every new category of product like this goes through weird snafu in the early days. When you mentioned uh Alexa, I was thinking to mine when when those first came out, wasn't there something where there's like a TV commercial that came on and said, "Hey,

Alexa, something something." And then like across the United States, like thousands of people bought toilet paper, >> you know, and then recently with the uh meta meta glasses, Zuckerberg was on stage and he said something and all the people in the audience their device

pinged the server at the same time and the demo failed, you know. So there's

going to be these weird moments and things that um that that come along in the early days that um but anyways on on the uh on the data side for us we have like two things we care about. One is

transparency. So if there's data being collected in your home like what was it?

I want to be able to know that that you know what that data was and what's going from the robot to anywhere else. And the

second is control. You you know if this product is in your home you own it. You

need to have the onoff switch and be able to to control um what that data is used for.

>> Yeah. And I think if you have those two things and you are principled about those things and hold true to them uh and and basically fulfill your promises uh and you give the control to the user I think that's the best you know sort of starting position for something like

this is just establish those principles up front.

>> One last question on robotics and we can go to uh another topic AI models behind robotics. How how distinct is the

robotics. How how distinct is the concept of like robotics AI versus like other AI? So I mean there's a lot of lot

other AI? So I mean there's a lot of lot of similarities and I think uh in a way like LLMs that started off as uh you know like chat bots that exist purely in the text world and robots which are like

physical machines very multimodal in nature nature you can see these things kind of converging because the latest uh models are multimodal they can take in audio images other things in the same way that your robot is expecting that

and so over time I think they're converging a little bit and in fact a lot of the training approaches pre-training post training those concepts exist in the robotics world um however there's still a lot of things that are unique to robots robotics that

you would never do if you're working purely on an LLM. And that's a lot um basically like mixing in real world data, different ways of collecting it, different ways of using simulation um and figuring out how to tie that to all the intelligence that's embedded in like a modern LLM.

>> And then the data is like super important here. Obviously,

important here. Obviously, >> data is important uh today. I think this is like a now problem. You know, if you look at LLMs, I think the reason that you can see uh so many different companies like like 20 different

companies all building foundational models and get, you know, within a stones throw of each other in terms of performance, small teams, large teams, whatever it is, is because essentially they're all starting from the same data set, which is the internet and

everything that can be downloaded from it. And you know, that data kind of

it. And you know, that data kind of determines the quality of the the model that you can get. And there's certainly some alpha on top of that from individual teams. But in the robotics world, there's no corpus of data like the internet that exists. There aren't

there isn't an entire internet of point clouds or camera images of, you know, robots manipulating objects. And so,

right now, we're in this early days where you've got to either bootstrap that data yourself, you've got to pay people to collect it for you, or you've got to try to interpret um you know, or generate robot data from other things

like watching YouTube videos and trying to infer from hand motions how a robot should do the same thing. And so, we're just kind of in the early early days of that for robotics. Do you think there should be like a scale AI for robotics

data or will it be that a company like yours just generates its own data and gets smarter as a result of that?

>> I don't know. I think there'll probably be both. In fact, I' I've probably

be both. In fact, I' I've probably talked to at least a dozen companies who want to be the scale AI for robotics.

And I think that there's going to be plenty of customers for that in the near term. Um especially as you know as this

term. Um especially as you know as this data void exists. But when that starts to be filled and we start to see useful robots in the world, I do think the majority of data collection will come from robots and less from people getting data.

>> I would also think that for like for your product for example, any data set that is not your products in the wild is going to be approximating the data. And

the perfect data set I would imagine would be if you had armies of robots out in homes giving you data. If your

technology is sufficiently advanced that you can um do transfer learning from other forms of data, other robots, YouTube videos, whatever it is, any source of data and you can use that uh to train your robot, that's like an

advantage because you don't you know then that total size of that data set may be much larger than just the data set that would be collected on your specific robots. However, where we are

specific robots. However, where we are today, it's much easier to get robots to do amazing things if the data collected came from the exact robot that you're trying to deploy a model on.

>> Totally. Yeah. Y and we'll see if that that changes over time.

>> Yeah, it makes sense. So, um we alluded to this before, but you obviously started Twitch, you started Cruz, you're doing it again. First of all, why uh why

are you so motivated to keep doing these hard companies? And so like many people

hard companies? And so like many people after this much success wouldn't go back to the beginning. And um you've had two really successful companies which I want to talk about particularly Cruz cuz I

think it's related. But I guess to get started like what's driving you now to do this again?

>> I mean I had you know a very very shortlived existential crisis after Cruz. I was like oh my gosh I'm I'm done

Cruz. I was like oh my gosh I'm I'm done with this company. This this is like you know practically my identity for a full decade. What next? I spent some time

decade. What next? I spent some time thinking about that there's you know you could retire. or you could become a

could retire. or you could become a venture capitalist.

>> Are those the same thing? I think I don't know. Just kidding. Um we work

don't know. Just kidding. Um we work super and then after after thinking about that for a while, I realized like the thing that you know outside of spending time with my family and friends, the thing that brings me the

most joy is solving really hard pe or really hard problems with really smart people. And so like that is retirement

people. And so like that is retirement for me. That's like the the most fun,

for me. That's like the the most fun, you know, satisfying thing that I could possibly think of to do. And it also ends up being you can do more of that and do it at a larger scale if you work with like a big team of people and you do it in the form of a company as

opposed to a hobby or or something that you're doing it on your own. And so to me I think there's no better thing and maybe at some point I'll run out of energy to to go hard like I am right now. Uh but for now like this is this is

now. Uh but for now like this is this is great. We have a brilliant team. We're

great. We have a brilliant team. We're

going on this you know building this exciting new product and a big market.

Uh and that is energizing to me.

>> I want to talk about a couple of the things that you've said about how you want to build it this time. One that

stuck out to me was that you never want to be more than 100 people. Yeah.

>> And first of all, I actually didn't know is that like literal or is that directional?

>> To be to be seen. Yeah. I think right now we're taking it very seriously. So

if if that is actually your belief and talk about why, but if that's actually your belief, then you make very different hiring decisions. It's like,

well, you know, if I think about the future company having 100 people in it, I can allocate this many people to this type of role. That means every person in every seat has to be the best in the world at this for the company to be successful. And so you end up, you know,

successful. And so you end up, you know, passing on a lot of people that are great people, really talented, but they're not they're not at that specific level we want for that particular role.

And I think, you know, if you're successful in doing that, you end up with this, there needs to be a name for it. But like in the early days of a

it. But like in the early days of a startup when everyone is like on the same page, like maybe just the founders, they're all in it 110%. They're all

usually like brilliant working together.

They're almost mind-melted. Um and and then you have like insane productivity for some period of time until you until you get bogged down by the organization growing and adding more functions and you know teams of people and management

layers and all this kind of stuff disconnected you have issues.

>> And so you get this drift away from this like pure like force of energy that is in the beginning stage of a company. And

so the reason for trying to have a cap on the size of the company is to keep it so that we're always in that pure high output zone. Uh, and you can't get that

output zone. Uh, and you can't get that if you have like too much of a range of of people in the company. I I really think of it more like a pro sports team.

Like you're not going to have, you know, the Lakers. You're gonna have like

the Lakers. You're gonna have like LeBron LeBron James and a bunch of high school kids on the team. It's like

they're all players that are the best in the world. So that, you know, when they

the world. So that, you know, when they work together as a team, they can outperform a team that is like a mix of of talents.

>> I think like if you have like the LeBron with the high school players, like the LeBron are like, "What are we doing here?" And they want they want to go

here?" And they want they want to go play with the best people in in the world against the best people in the world. And uh and that's how you get

world. And uh and that's how you get better and grow. And you know, people who are people who are the best in the world at what they do typically got there because they have this growth mindset that constantly want to get better. And you know what better way to

better. And you know what better way to do that than to surround yourself with people of different skill sets that are all the best in the world at what they do and sort of absorb from that.

>> Like so much of what gets hard is as you start getting into like scaling operations and you get into like that side of things, it just gets so hard to keep it really small. You know, like even you think about like let's say you

only had like 10 non-engineering roles.

It's like, well, someone's got to run finance. They probably can't do it

finance. They probably can't do it alone. You've got like you you're going

alone. You've got like you you're going to have all these like physical parts.

You're going to have to have buildings for like, so how do you think you'll actually try to keep a limit on that?

Like will you partner? Do you go sort of like work with people outsource or do you actually think that like you know maybe with like new AI tooling you could just go way further with people and it's just sort of like a demandial place?

>> Yeah, it's a good question. That's

that's part of why I said to be seen like this is a great mental model now and it may not approach and by the way I think most of the one of the healthiest changes I feel like I've seen from 5 years ago is the shift from thinking

that like big teams are cool to thinking big teams are lame.

>> Yeah I mean things seem to eb and flow right like I'm taking an extreme position here but I do think if that has the effect of you know causing a small shift in that direction that's probably net good for the industry uh and good for these companies. So it'll be a

question for us. Do we partner or outsource things? I think it, you know,

outsource things? I think it, you know, keeping the team small also forces you to focus on like what are our core competencies, the things that we need to do uniquely because we think we can actually do them better than any other company that we could potentially work

with. And you know for things like a lot

with. And you know for things like a lot of operations or facilities or you know buildings, these are things where maybe we have no reason to think we would be the best in the world at this so we should partner. And a lot of companies

should partner. And a lot of companies like they have lots of funding, they have lots of teams. like it's almost like they take on these responsibilities because they can, not necessarily because they should. I feel like one of the most important things which I think

you've obviously shipped in self-driving in a way that like you know very few have but I think in a lot of these um sort of more sci-fi areas it's very easy

to not be in like shipping mindset and like I think you did this really well at cruise obviously like opening I was doing this like well before chat GPT and so you basically probably are in a

mindset I assume of figuring out like how quickly can we ship and like iterate and like that's got to be the mindset rather than just like hang in a warehouse building the perfect robot forever. I think for that it's it's

forever. I think for that it's it's starting with the thing you want to build and then working back to what is the m what is the constraint what are what are the constraints or bottlenecks that we need to be that we need to make our number one priority because it

cannot go faster than you know what that one bottleneck or constraint would dictate and for self-driving that's a combination of uh safety trust and public acceptance and so you know those are different work streams where basically like unless those are all

green you don't have a product it doesn't matter how good the technology is and there are similar things you know for a home robot or really any business and so like you know mapping out what those are and basically making that the company's top priority like it you know

cruise for example safety metrics were the single thing we talked about every week week over week over week making progress towards those and I think for any company like what you talk about what you design your metrics around kind of sets the tone for the company and it's got to be aligned with that you

know whatever the constraints are >> what do you think you can do in a home first reli like what do you think will be the first activity that can like really be done well in a home and then like what are the things that you think are close but maybe follow in the you

know next 12 to 24 months or something.

Yeah, I mean there are hierarchies I think of of tasks for a home robot. Um

and if you look at uh I think two like a classic 2 by two grid I guess one is maybe the um technical complexity of the task like how hard is it to get a robot to do this successfully. Uh and then the second is like what is the success rate

that is acceptable to a customer of a product like this and I'll give you an example. if you are, you know, in the

example. if you are, you know, in the the easy side of things from the technical capability and also the very forgiving side of things in terms of success rate is probably like picking up your kids toys. So, I have, you know,

two kids, um, a one-year-old and a seven-year-old and they're between the two of them are constantly making messes and toys are all over the house, running around picking up toys.

>> Same.

>> And so, if you have a product that you can buy, you can go to the store, buy this thing, put it in your house, push a button, turn it on, and then when you're gone for the day, all the toys are magically put away by the time you get home. This is like a mind-blowing

home. This is like a mind-blowing experience. And let's say it screws up

experience. And let's say it screws up and like two out of the hundred toys are still on the floor when you get home.

>> That's okay. Yeah. So that so that like you think about >> nines of reliability for for engineering like maybe one nine is fine for that particular task.

>> There are other things like putting a wine glass in a dishwasher where the technical complexity is a little higher and the the um >> what's hard about that by the way? Is it

like the grabbing or is it like >> Yeah. So, so if you think about picking

>> Yeah. So, so if you think about picking up objects, this microphone, which is going to make a noise when I squish it, is uh is is compliant. And so, if I'm off a little bit on where I grip it or like how much I squeeze it, I'm not going to shatter this microphone into a

million pieces. For a wine glass, the

million pieces. For a wine glass, the the margin is is very thin. And so, from a dexterity standpoint, it's a little more fragile. Actually, sometimes I

more fragile. Actually, sometimes I think about that's like a good example of a thing where I'm like, it's amazing that people can do certain things like squeeze a wine glass the right amount or like hit, you know, a ball, you know,

with a racket or a golf club with the right angle or something like that or like catch something that's flying while you're moving. Like, it's actually

you're moving. Like, it's actually pretty crazy what you can do like mechanically.

>> It is. And and the evolution to how we get there is interesting, too, cuz my one-year-old daughter, her her hands are like open, closed. There's nothing in between. She grabs objects. The wine

between. She grabs objects. The wine

glass is shattering. And at some at some point along the way, we we developed much more nuance skills and abilities.

But so wine glass is another one. The

other thing is challenging is if you're putting a wine glass on a rack and and you know it's a thin stem or something and you nick uh you like bump into something, you might break the stem off, right? And so not only is it more

right? And so not only is it more difficult from a technical standpoint, but if you shatter a wine glass in someone's dishwasher, they're probably not going to be your customer anymore.

That's right. And so that's like several nines of reliability. And so I think that this this sort of spectrum of technical difficulty and uh basically forgivability is going to dictate the

types of things you see home robots do first. And um and I think we'll work our

first. And um and I think we'll work our way up towards you know I think the holy grail of a home robot which is like dishes, laundry, end to end maybe cooking. These things all of them have

cooking. These things all of them have like all of these little uh it's like a minefield. You do one thing wrong and

minefield. You do one thing wrong and you ruin the whole process. Like for

laundry, if you put the red sock in with the whites, you know, have pink laundry, that's like game over, right? And it's,

you know, so there's things like that.

For cooking, it's the same thing. You

put too much salt or pepper in there and the dish is ruined, you know? So, these

are things that I think we'll get to and it'll happen pretty quick. But, you

know, I think >> you think it's like cooking a steak at some point.

>> Yeah. Why not? If you think about at the end of the day, you you have pick and place and simple manipulation. That's

what cooking is.

>> Yeah.

>> They're just like a much higher degree of reliability. And there's other things

of reliability. And there's other things around food safety and bacteria and other things that come in cookie cooking and temperature sensing and what like that. So, it's all doable. It's just

that. So, it's all doable. It's just

like I >> think at some point it's like, "Hey robot, I'm at work right now. There's a

steak in the fridge. Please cook it and clean up everything." By the time like 15 years from now, that's that's doable.

>> Less than five.

>> Less than five.

>> Yeah. This stuff is going fast. Again,

if you saw if you look at the robots you can buy today in the world, like the nice robot vacuums, um you may not think that. If you see what's happening behind

that. If you see what's happening behind closed doors at the best robotics companies in the world, you might think that. And if you're the leadership of

that. And if you're the leadership of these companies, the technical leadership, and you kind of know where things are going, you absolutely believe that.

>> The hand seems really, as we're talking about this, I was sort of like stupidly like I was like, actually, a hand's pretty good. Like your fingers are like

pretty good. Like your fingers are like pliable. You have like a lot of degrees

pliable. You have like a lot of degrees of freedom. You have like multiple grip

of freedom. You have like multiple grip points. Is the hand the optimal thing?

points. Is the hand the optimal thing?

>> The hand is really important to get right because it is the robot's interface to every object that it interacts with. If you make it too

interacts with. If you make it too simplistic or not enough um sensing capabilities or whatever, then you have to have a much much smarter brain to figure out how to use this primitive

tool to accomplish a complicated task.

And so the more mechanical complexity or capability that you add to a hand, the more sensing ability in theory would require less, you know, sort of rocket science to figure out how to do a task um with that hand. The trade of course

is the more technology, the more degrees of freedom or motors that you pack into a hand, the more complicated it becomes, which impacts durability and also cost.

Yeah. And so there's push and pull there to find that sweet spot where you can basically come up with the simplest hand possible to do the tasks you want to do uh at the lowest cost while while also being able to accomplish everything in a

fairly straightforward manner. But I

think in the limit, you know, there's a lot of uh, you know, we think by analogy a lot and it we have two hands and two arms and so like a lot of the robots you see today have two hands and two arms. But it is really interesting thought

experiment like what does the ultimate handto arm thing look like? And I think it was Rodney Brooks who said this the other day, but I actually do kind of think maybe it ends up being some crazy octopus tentacle looking thing in the future that's like very adaptable and

can reach into small spaces.

>> Interesting. Well, I think that, you know, the human hand was ended up where we are due to probably some impossible to unravel sequence of evolutional pressures right?

>> Well, it's like you start down some path and then you do your best, you know, evolution does its best given some somewhat random starting point, I suppose right?

>> Yeah. So, if you could go back like a million years and hit the reset button on human evolution, maybe a new fork would emerge and it would be more tentacle-like or who knows what. But I

am skeptical that the way that human hands and arms evolved is the ultimate.

And so that the challenge will be like, can we figure out what that is?

>> I have a couple stupid questions about the robot at home. One is how strong could it be? Like is it is a 100 pound robot like it's must be ridiculously stronger than a person, right?

>> I would think uh for a 100 pound robot you could certainly make it uh maybe stronger in some dimensions. There are

some things that like our sort of soft biological muscles are pretty good at >> are stronger than like like a physical robot pound-for-pound kind of thing.

>> Yeah, it's it's really hard to say. I

think so. I think probably the state-of-the-art Boston Dynamics robot seems like it's on par if not, you know, more capable than a human. And if not now, I'm sure the next couple generations will be. So, that's kind of interesting.

>> Surprising that a soft muscle is stronger than like I don't know why I would think a robot could be dramatically stronger. Yeah, I've been

dramatically stronger. Yeah, I've been going down the rabbit hole on this a little bit, thinking about like, you know, as as again our focus on affordability and cost. Like is a electromagnetic gear motor where you've

got a magnets and copper winding and a bunch of gears in a housing is that the most cost effective durable way to generate motion for a robot? And the

answer in the short term is probably yes. But I think there are some

yes. But I think there are some interesting things happening where we're trying to mimic either some of the chemical processes or electrostatic actuators or other things that are similar in how they work to like a human muscle. And the benefit there is you can

muscle. And the benefit there is you can get a higher cycle count, more silent operation and potentially more power density. Like how much strength can you

density. Like how much strength can you get into a physical volume than what we have today in gear motors and then potentially much much beyond what humans have in our muscles.

>> Isn't like hydraulic is pretty strong like a hyd like hydraulic pressure is pretty strong.

>> Hydraulics can be extremely powerful but they have other trades. Typically noisy

the valves and things are pretty expensive can be harder to control and get you know high fidelity motion. And

so in terms of power density may be good, but you know there are other trades and the reasons you don't see this on a lot of robots.

>> That makes sense. Another question I had that's sort of like probably offsp spec.

But while we're talking, is this going to be something that would have like home security applications as well? Or

does that then take you into weird territory that's just not worth going to?

>> Yeah, I think so. I mean, one of the challenges with a with a home robot is it's kind of general purpose. And so

like, you know, what are people going to use this thing for? And I think it's it's hard if you just have a laundry list of 50 different items that the thing can do and security is one of them. But I do think a lot of people

them. But I do think a lot of people will be out and about and know with their home robot at home be like, "Oh, I wonder if I forgot to turn off the gas on the stove." And send the robot over there to just, you know, tell you or even take take it on for yourself. In

the same way, um you could be like, "Hey robot, like you know, if you see any person in my home or any doors open, like let me know."

>> Yeah. If you see me getting burglarized, like do something. But I I don't know if you will think of it as a security robot so much as like this is just one of the many responsibilities of my home robot is to keep tabs on my home.

>> Totally. Alerting probably is good.

Taking actions probably not.

>> I hadn't thought about that side of that. I you know that's not really in

that. I you know that's not really in our >> That's good. That makes sense. I'm just

thinking cuz like you know in my head I'm like okay if there's this brilliant capable robot in the house my guess is you're going to have a lot of people want it to start doing a ridiculous number of things for them. the arc of

time and then you'll have to choose from that set like what goes in.

>> Yeah, I I think so. But for sure, I mean on the security side, I would hope though rather than having like physical deterrence and like you know having your um home robot turn into a >> Yeah.

>> security guard with a baton or something. It's more so that it just

something. It's more so that it just becomes unattractive to rob homes or do you know break in enter into a home.

Maybe in the same way that >> you know a world full of cars where everyone has like that Tesla sentry mode there's very little incentive to break into cars. It's not worth the risk.

into cars. It's not worth the risk.

>> Well, I mean, even like a security system just makes a loud sound and calls the police, you know.

>> I think that's pretty effective, isn't it?

>> I think it's extremely effective. Yeah.

And I think those systems are pretty old and, you know, they're deeply embedded and but yeah, >> it's like you may figure out how to disable the alarm and sneak into the house, but if there's a robot, you know, rolling around >> and then a siren's blaring and all that

stuff. I just think it'll be interesting

stuff. I just think it'll be interesting where if this gets in there, my guess is people will start to I could see a future where people expect a ridiculous amount from these things. Well, I mean, it touched on something interesting I've

thought about is like when you ask people um or we ask people, "What would you do with a home robot?" You know, they immediately what comes to mind is like the thing that's most annoying to you today to do in your home. And I

think that's good. We want to help with the annoying stuff. And

>> what comes out most like laundry probably?

>> Yeah. Laundry, dishes, picking up after my kids, um you know, wiping surfaces, cleaning, like these are the things you would expect. And so we're going to chip

would expect. And so we're going to chip away at those things for sure. But what

I also like to think about is the things that we don't do because we value our time more than that. Um, you know, the example is if you ever gone to like a really nice hotel, you know, the slippers are laid out for you, there's a glass of water on the nightstand, a

little chocolate on the pillow, all these like little flourishes. Like, I

don't know. I I I think that, you know, robots should not only automate the things that we don't want to do, but also like elevate our standard of living to some degree.

>> And so, I love the idea that if you can afford a really affordable home robot, we're going to give you a lifestyle that, you know, would otherwise be inaccessible to you.

>> Totally. I mean, that's actually a really interesting point that like a lot of the types of things you're talking about don't require any new inputs. It's

just about taking care of your home in a certain way that's like beyond what you would normally need, but it's like you got a bunch of towels that are like sitting in the laundry room that are clean, but can you like put those by, you know, the shower and like roll them up nicely?

>> Yeah. And maybe you don't need all these things, but the point is like, you know, your your time is more valuable than that. It's very scarce. Like humanity's

that. It's very scarce. Like humanity's

time, I think, is really important. But

for a robot that's got 24 hours to sit around in your home and like try to make your life better, what could we come up with for it to do? What what could it come up with to do for you? That's

that's an interesting question.

>> Wow. I'm curious about reflecting on what you've learned from the way self-driving cars played out and how it might matter here. Maybe one interesting

sort of case study is, you know, the Tesla verse Whimo approaches. Do you

think in any way that how that played out or any learnings there that poured over to like what could, you know, be impactful in the robotics land?

>> Well, it's hard to say that, you know, very different approaches to getting to market, but it does seem like they're both trying to converge at the same thing, which is, you know, self-driving cars everywhere. I think one thing that

cars everywhere. I think one thing that uh was really brilliant about Tesla's approach, they found a way to sell the product essentially before it was fully complete, if we're looking purely at the self-driving side, and generate billions

of dollars of cash flow, which they could use to bolster the core business, but also continue to invest in R&D to to make this self-driving product. Whimo by

comparison uh you know has taken uh almost a couple decades at this point um maybe not quite that long and probably tens of billions of dollars of investment and the revenue relative to that has been fairly meager right

compared to that total investment over time >> which basically means that the only companies in the world who can do this are the ones with that kind of capital on their balance sheet to basically fund this crazy amount of R&D year-over-year

>> and I think it's no coincidence that the only companies who succeeded in that approach or or on track to succeed in that approach are owned by, you know, Amazon, Google, or um, you know, like a major car car

company. And that was even a struggle

company. And that was even a struggle for a company like General Motors. And

so, in the home robot space, I hope we don't repeat that. I hope it doesn't become the case that the only companies that make it are ones that are basically kept alive through billions or tens of billions of dollars from, you know, a

corporate uh, benefactor. And instead,

we can find clever ways to get to market that uh, >> which I guess is why you need to get to market and be selling something along the way to fund all of this. Well, I

think if if if your development cycle means you don't get to meaningful revenue for 5 to 10 years after the company has started, >> it means you're entirely dependent on either being acquired or the capital markets, you know, being pointed in the

right direction. And, you know,

right direction. And, you know, historically things tend to cycle back and forth >> in a 5 to 10 year timeline, you're getting awfully close to almost guaranteeing that you straddle like a down cycle as well as an up one. And

that can be a killer for these companies. I we don't need to talk about

companies. I we don't need to talk about sort of Cruz and GM too much, but um I am curious about sort of you know I saw you share on Cheeky Pine about like not

wanting to sell and I'm just curious like your mindset about the sense of autonomy and how you think about selling a company since you've been through it you know a couple times and everything.

I think uh you know and I said this before but my conclusion is like if you if you are selling a company um it should be because the reason you started the company or the thesis that you had in mind or the thing you wanted to build

something has changed and maybe like you're no longer interested in it your life circumstances have changed whatever but I think it is a uh a fantasy to believe that you can sell your company like have your cake you need it too like sell >> your company in order to further the mission

>> and further the mission I think in theory this can happen sometimes but it is so so rare it is rare and I think more likely than not you'd be disappointed with that outcome and therefore Like for me, I uh I can't imagine being in a situation where I would trade, you know, the opportunity

to build this amazing thing and control it and make sure it, you know, happens in the way that I want it to uh for for some kind of partnership or liquidity.

Uh so that just doesn't make sense to me. Maybe not for everyone. And maybe

me. Maybe not for everyone. And maybe

that's just because I'm so excited about this thing and bringing this new idea of a home robot into the world that like it just wouldn't even cross my mind the thought of like handing over the rand to someone else. You're also at a point now

someone else. You're also at a point now where like this type of company is such a forever project and like you're now able to start a company that's like you know it's not it is not some little thing like if this works it's just such

an important thing which also I guess that probably also drives you to want to sort of hold on to it indefinitely.

Yeah, perhaps. But I think I I I I'm not selfish about it. Like if I, you know, I I feel like I have an obligation to stay true to, you know, our our investors, the employees, and the mission. So, you

know, even though uh I certainly want to hold on to this, I'm not treating it like a pet project. I actually do want to like fulfill this broader vision that that we all share.

>> Yeah. Maybe as a final uh thing to touch on, you did this crazy like marathon around the world experience. What was

that? And like why did you do something that seemed so, you know, hard >> deep in the in the middle of cruise? I

was I think I was frankly like kind of frustrated that like we were putting in all this energy and sometimes there would just be periods where the metrics wouldn't always go up and to the right.

We dig a regression and then go back and forth and so you know the result wasn't always proportionate to the energy going in and uh for running for me at least that was not the case. You put in the time better. Yeah. And so that's very

time better. Yeah. And so that's very deterministic and satisfying. And so I needed something to balance that I feel like in my life.

>> Um and as I do I went down a rabbit hole reading about like extreme marathons that you can do. I was like sort of an amateur marathon runner and came across the world marathon challenge. It's this

thing you can sign up for. They take you to each continent, one continent per day and you run a marathon on each one and then like the next day you fly to the next continent. And I thought that is

next continent. And I thought that is insane. And then in fine print on the

insane. And then in fine print on the website it's like the world record is like 5 days and 10 hours by this this one guy. And I got the the wheels

one guy. And I got the the wheels turning. It's like well I wonder what

turning. It's like well I wonder what the theoretic engineer brain clicks on.

I wonder what the fastest theoretical time you could do is if you if you optimize the it's like the traveling salesman >> of the continent >> like yeah where you land optimize for customs in and out and logistics and

like really dial it up to 11 >> and that turned into an 18-month obsession got stuck in my head and I ended up writing some software to find the shortest route between the seven continents >> spending a

>> my problem is that I couldn't run a half marathon that's where I would struggle >> this sort of stubbornness and attachment to this idea meant that part of this was I had to train my body physically to be able to do this >> cuz like you're running a marathon and

then you're not resting afterwards.

>> Yeah. So the the the cycle for example is you know you start in Cape Town so that you fly to Antarctica. You have to start there because the weather is so the Antarctica one that's tough.

>> You need like at least a call it a 6-hour window of decent weather and you're like looking at the weather forecast when it clears then you fly in and you land you run the marathon and you get out cuz that can throw off the >> Where in Antarctica do you do this?

>> On like the most temperate outer part of Antarctica. So it's on the continent but

Antarctica. So it's on the continent but we're not talking like South Pole.

>> Yeah. I mean, so it's cold, but it's not it's not like >> but it's not snowy.

>> I mean, it's icy and you can't see. It's

like barren, all ice. You land the plane on ice, you run on ice.

>> You're running on ice.

>> On ice. It's like kind of like crunchy ice. Like we there was a there was like

ice. Like we there was a there was like a ski slope groomer that did a course down there. Uh it's like a six mile loop

down there. Uh it's like a six mile loop or something. And so it was like running

or something. And so it was like running on it was like trail running.

>> Got it.

>> It's still pretty crazy.

>> That's crazy.

>> Anyways, yeah. So organizing the logistics for the training, training my body and everything, doing it at the end. ended up doing this in about three

end. ended up doing this in about three and a half days, which blew the world record aside. And then after 18 months

record aside. And then after 18 months doing this and finishing it, you know, I gotta say like it was it was you ever like finish the last item on your to-do list and it's just like a dopamine hit.

You're like, "Oh, that feels so great."

That's what it was. It was like relief.

It's like check the box now. I can my tormented brain, which wouldn't let this go for 18 months, can finally relax.

>> Where do you go? Cape Town, Antarctica, South America.

>> Yeah. Southern tip of South America and then to Panama City and then think to Madrid and then uh Oman. And you're just like by the last one, are you just crazy fried or were you in shape to just not

be, you know, so beaten down?

>> I was pretty fried. Pretty fried.

>> Yeah.

>> But it's like uh, you know, the training I did peaked at at doing three marathons within a 24-hour period in three different cities. That was like the the

different cities. That was like the the stress test for this, if you will. And

my coach was like, you know, I was like, only three marathons? That's not seven.

Like, is that actually the right amount of training? And he said, "I promise you

of training? And he said, "I promise you when you're doing the really real thing and you have this whole crew of people with you and everything is on the line and the adrenaline going, you're if you can do three in 24 hours, you can do the rest." And he was right.

rest." And he was right.

>> That's wild. That's like the hardest physical task imaginable.

>> It you know, mental toughness is important for startups and I feel like it it really helped me quite a bit in that domain.

>> Yeah, totally. All right, Kyle, this was really fun. Thanks for making time for

really fun. Thanks for making time for it.

>> Thank you.