Building a Mass Market Robot
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As a general matter, I'm a fan of technological progress. But I'll admit that humanoid robots kind of creep me out for, you know, obvious, normie, uncanny valley type reasons.

And yet there is an exchange that you'll hear near the end of today's show that's the most compelling argument I've ever heard for humanoid robots. And it's not just an intellectual argument. It's an emotional argument, if that's a phrase. It's really a very human argument for humanoid robots. I'm Jacob Goldstein, and this is What's Your Problem? The show where I talk to people who are trying to make technological progress.

My guest today is Jeff Cardenas. He's the co-founder and CEO of Aptronic. Jeff's problem is this. Can you make a safe, reliable humanoid robot for less than $50,000? We started our conversation talking about the DARPA Robotics Challenge.

DARPA, of course, is the government agency that helped to create the Internet and that gave a big push to early self-driving cars, among other things. And Jeff says the agency's Robotics Challenge, which happened a decade ago, happened in 2015, played a key role in launching a bunch of the companies that are now working on humanoid robots. The DARPA Robotics Challenge was a challenge that was created in the wake of the Fukushima disaster.

Lukashima happened and there was a meltdown in the nuclear reactors and it was unsafe for people to go in. And essentially what happened was they needed a robot to go in to sort of prevent a broader catastrophe. And as they went out to the robotics community, the idea was calling all roboticists, we need a robot to go in and to help out here.

And there was no robots that could do all the different tasks that were required to actually get to the melted down reactors. So you had to go down steps, you had to open doors, you had to do a whole range of things. And in the wake of that, basically what DARPA said was, certainly there's got to be the technology to enable us to have much more versatile robots for natural disaster relief so this never happens again. So out of that, DARPA created something called the DARPA Robotics Challenge,

And so there's a variety of teams around the world that were put together to build these general purpose robots. And the team that we came out of was the NASA Johnson Space Center team working on a robot called Valkyrie. So I want to go back to this moment when the DARPA challenge ends, there's this big final contest. And like what we have from it is not some incredible breakthrough, but like a blooper reel of robots happening.

What are they doing? Falling down stairs or driving cars into walls or something? Pretty much. I mean, the blooper reels make it seem worse than it was. But yeah, basically the realization was the technology's not there. It's going to take time to continue to mature until it can get to the point where it's actually commercially viable. And so it's interesting. It's super interesting, right, that this moment is not the like...

beginning of some humanoid robot winter, but rather the beginning of this humanoid robot industry. Like, how does that work? Like, how do people, whatever, start companies and get money out of this seemingly disappointing moment?

Well, I think it actually was a winter. When we started in, the company was incorporated in 2015 and we started in 2016. And for the most part, nobody wanted to talk about humanoids and nobody was really paying attention to it. There was a handful of folks that I sort of think of as the true believers that were really working on this problem and thought, we don't care how long this is going to take. We're just going to keep working on this no matter what. And

But for the most part, the entire robotics industry was very anti-humanoids coming out of the DARPA Robotics Challenge. And in fact, there was many people that were saying they'll never be viable. Like, why would you ever use a humanoid robot? They're too complicated. They're way too expensive. And you'll always use a simpler robot. So actually, most of the people that we met when we decided to start Uptronic were doubters. And we're saying, humanoids will never make sense. We'll use these special purpose robots. Right.

You know, maybe in 50 years, humanoids will make sense, but not for a long time. I mean, special purpose robots is a pretty compelling case, right? Like, whatever, we all have, you know, dishwashing robots and clothes cleaning robots in our houses and like...

You know, wheels seem way easier than legs for lots of things. And obviously there have been robot arms for what? I don't know, 70 years now or something like so robots in a way are all around us. Why would you build a machine that looks like a dude when that's like wildly hard? Right.

Yeah, I mean, I was naive coming out of graduate school, and to me it seemed obvious. And the way I used to think about it was you could either have thousands of robots that do one thing, or you could build one robot that could do thousands of different things.

And when I would talk about this with Nick, my co-founder, Nick would say, look, you can either invest all of this engineering in each of these sort of narrow solutions. Or yes, a humanoid robot, a viable humanoid robot could take you years. It could take you a decade. But once you invest all this time in this single platform, then you can reap the benefits of that across – you can spread –

the research of that across many different applications. I mean, is there not a middle case where there's like some core

kind of functionality that you develop that works across many different types of robots? Is that a less strawman-y version of the non-humanoid robot kind of argument? I think there could be, but I came into robotics and basically just saw a lot of challenges with the business models. So you build this special purpose robot, you custom program the robot. In the industrial space, you can spend six times the price of the robot on just systems integration and

And then the robot just does one thing. So this idea that you could have a much more versatile robot, to me, seemed obvious. Like if robotics is going to scale, we have to have much more versatile robots than we've had in the past. So if you sort of think of that as the premise is we need more versatile robots, then the question is, well, how do you get there? And what does versatility mean?

And that's where it led me to the humanoid making a lot of sense because if you have to modify the environment for every new task that the robot can do, you run into the same problem that we had in sort of classical robotics. But if the robot can retrofit into the environment such that you don't have to change or modify the environment for every new task that the robot can do,

then it seemed to me that this would maybe be the key unlock for robotics to actually scale to the masses. The demand would be infinite if you had a thing that was the size and shape of a person with arms and legs.

like scale would be off the charts and presumably that's what drives costs down. And that's like the good flywheel, right? Yeah, exactly. So, okay. So you had this big idea about humanoid robots and you started a company, but at the moment you started the humanoid robot company, the prevailing sentiment was like deeply skeptical. What happened? What did you do? Well, a handful of us kept working on it. So I didn't know any better. You know, sometimes it's better that you don't know any better. I thought humanoids were,

really cool. And I thought that it just seemed it made sense to me that, you know, how are we going to get to millions of robots that are working and, you know, with and around humans and all these environments? And to me, this seemed like the only way that that was going to happen. And the way I looked at it was, you

even if we fail, this was a worthy pursuit. And I would be proud that I tried to do it. And so the way that we did it was we bootstrapped the company. There was no investors that were willing to invest in humanoid robots at the time that we got started, especially for hardware, which we can talk about that as we move forward. And so we bootstrapped the company and we basically got paid to build robots for a

And for the first five years of the company, we just built the company on revenue. We would get project after project and somehow never died. Like what kind of jobs were you taking at that time? What's one example? Well, we had our first contract was with NASA. So we had a contract with NASA to build Valkyrie 2 to take the lessons learned from the DARPA challenge and build the next iteration of Valkyrie 2.

We were really kind of pioneering new ways of building these systems. So US Special Forces ended up coming to us about a year in and said, "Hey, we want to do Iron Man suits." And our view was this was kind of a humanoid robot that you wear. We worked in automotive. We helped build humanoid robots for a couple major companies that are still working on these things today. And we would help sort of pioneer new ways of building their platforms.

So we've done 15 unique robots since we got started. And we're now on our ninth iteration of humanoid and have only raised money in the last couple of years. So where did the idea to build a robot for $50,000 come from?

The idea of where to build a robot for $50,000 was what will it take for these robots to be economic and reach mass market? So, you know, when we got started, sort of my view was, you know, what will a truly viable commercial humanoid look like? And, and what would the bomb cost need to be for this to make sense? And if you sort of just do that bottoms up about

About $50,000 for the robot, you're in the money for mass market. You can still do some tasks in a very economic way at even $100,000 or $150,000, but $50,000 was the goal. This has now been blown by by some of the new entrepreneurs that are coming out that are talking about sub $20,000. But it never made sense to me that robots were as expensive as they were at the time.

If you look at a humanoid compared to a car, there's about 4% the raw material by weight. So in one of our robots, there's about $300 of raw aluminum, which is the base metal of the system. And so it never made sense to me that these robots would need to be any more than $50,000 as you could reach scale and as you could start to think about new ways of building them and similar ways that we build other machines.

So you decide you want to build a $50,000 robot. What do you actually do to do that? How do you go from having an idea of building a $50,000 robot to having a $50,000 robot? Well, you iterate. That's how you solve any problem. So what we would do is basically we would get a project or a contract to build a robot.

And we would put a lot of different ideas into those designs. In early days, it was all about performance. How can you get the robot to just do these tasks, to stand and have a battery life that's long enough? And then as we kept evolving, we started to really focus on cost and addition and scalability and assemblability and robustness. And the key building block to drive cost and performance is the actuator.

So I mentioned we've done nine iterations of humanoids, but we've done 60 iterations of electric actuators. Actuators are basically the thing that makes the robot move, right? Yeah, the muscles of a robot. So where are you now? Tell me about Apollo.

Yeah, we're now at an exciting point. We have about 170 employees at UpTronic. We are piloting these robots right now. So I think the entire industry is still in the pilot stage overall. There's some commercial orders that are happening, but still early days for humanoids. We're working with a handful of really great partners, folks like Mercedes and GXO, and

And we're getting the robots out into the real world in pretty big ways. So we'll have more announcements over this year.

We have a big partnership with Google DeepMind, which is something that I always dreamed of coming out of graduate school. We had a lot of respect for the folks at Google, and they have a whole history and legacy in the humanoid space as well. And basically right now we're getting these robots out into the world and gearing up for real commercialization, which we expect to happen in 2026.

What's the robot look like? The robot kind of looks like a superhero, maybe. That's been kind of the idea that we've had from the beginning. It's got two eyes and a face. It's 5'8", weighs 160 pounds, has four-hour swappable batteries. Yeah, it's got a screen on its chest and a face, and that's about it. Two arms, two legs. What's it have in the way of hands?

So it has hands, it has five-fingered hands. You know, there's these debates that I think of as false debates in the humanoid space. So a lot of people, when they sort of knock humanoids and the viability of humanoids, it usually has to do with, do they need legs and do they need hands?

And the answer to that question for me is no, they don't. It's a robot and robots are modular. So we can put Apollo on any mobility base. We can put it on wheels. We could put it on tracks. We could stationary mount the upper torso of Apollo. And the same thing's true for the hands of the grippers. We can use parallel grippers or we can use five fingered hands. Hands are like a whole thing, right? Like hands are, is it partly because they're so hard? Like what's going on with robots and hands?

It turns out hands are a whole thing. This is another one of those things that, you know, it's almost better that you don't understand the complexity before you get into it or else you might not have done it in the first place. 98% of all tasks that humans do are done with our hands. So there are narrow things that humanoids can do without more dexterity, but it's very limited relative to the whole sort of

you know, all the different types of tasks that humans do. Most of the things we do involve our hands and certainly in the industrial space, most of the work is done with hands. So solving the end effector or the hand problem is a big deal. There's a lot of different debates about what you need and how you get something that can actually perform industrial work. You know, we've chosen the 550

finger hand route and we're working across the space to really make some big advancements there overall. It's part of the trade-off. Like I could build whatever, two, what do you call them? Prongs. Like if you had two fingers, basically like a claw, like you could do a lot of things with a claw. Presumably it would be way easier to,

But you couldn't do everything. Is it kind of like, what are you optimizing for and sort of how much payoff now versus how much payoff later? Yeah, I think that's exactly right. It's, you know, versatility, you know, compared to robustness and cost, basically. How much complexity do you want to have on the system? And, you know, for these robots to be really viable in the long run, especially in the industrial space, they've got to be able to operate efficiently.

Two shifts a day minimum, really, you know, 22 hours a day, seven days a week. But solving that problem in a hand, so just getting the performance of the hand first, but then the robustness for them to do that type of work is the next piece. And that's a tradeoff of performance and complexity and cost. Because it gets delicate, right? Presumably the fingers, so to speak, would be...

Fragile, right? Yeah, they can break. Easy to break. Yeah. Yeah. Yeah. And you got to maintain it and you got to support those systems and fix them out in the field. And so what's the trade off there? And that's a whole trade space that we've been working on over a long time. So we've been talking about hardware. Let's talk about the software side. What's happening with that?

A lot's happening on that side. I think we're really in a really, you know, we're in an exciting point for robotics overall. Think of the AI as really the last piece of the puzzle. So, you know, we've had the ability to build robots

complex robots for a relatively long time. We're just now really figuring out how to take the lessons from automotive and consumer electronics and build much more economic systems. And we've had some advancements in things like motors and batteries and compute and sensors that have all sort of built up to this moment. But the final piece of the puzzle was the AI and the intelligence.

and essentially the way to think about it, and I think Jensen does a great job of explaining this, but the advance... Jensen Wong from NVIDIA. Yeah, that's right. I feel like Jensen is not quite to Elon level of one name, household name, but sorry, go on. He's getting there. Yeah, he should be. He should be. He should be. Yeah.

Basically, the advancements in generative AI turn out to apply very well to robotics and particularly to humanoid robotics. So you can basically map human movement and trajectories from humans doing things and build big data sets and use that to train robots to do similar tasks in similar environments.

And these transformer architectures that we're using in generative AI actually apply very well to robotics. And so this has been a big sort of breakthrough moment for robotics. And so I think as an industry as a whole, everybody's really excited right now because we're reaching new heights and we're able to do things that we dreamed about doing with robots only even a few years ago are now possible. And we're seeing a really rapid advancement in performance overall. Yeah.

What's an example of a thing that you could only dream of a few years ago that robots can do now? I think it's more dexterity and versatility. So just the range of things that you can do. So the challenge for robotics was that each new task, even if you build something like a humanoid robot, and this is true for us, and say you build an application to pick boxes off of a pallet and place those boxes onto a conveyor.

Well, you hand build that application and you, you know, maybe takes you 18 months to sort of wring that out and get it to a certain amount of robustness.

Well, now you want to do the reverse of that and pick off of a conveyor and palletize something. That will take you the same amount of time that it took you to build the initial application. You have to basically have to write a whole other piece of software. You have to start from scratch almost. Yeah. Yeah, exactly. And so basically what is happening now is that we now have these much more sort of general models of

that where you can collect a lot of data at the top layer. And so each new task that you want to perform actually takes less and less incremental amount of work. So what is opening up now is more dexterous applications. Still to come on the show, how Jeff's grandparents inspired his work on robots.

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When I'm reporting a season of Revisionist History, I'll admit I rack up a lot of expenses. There's archival fees, travel expenses, meals, research materials, books, my very own Pushkin Plus subscription, so I can hear the episodes early on Apple Podcasts, of course. It all adds up. And that's where my no annual fee Apple card comes into play.

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Okay, business leaders, are you playing defense or are you on the offense? Are you just, excuse me, hey, I'm trying to talk business here.

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breakthrough that has driven large language models in training robots, essentially. I mean, of course, a key sort of serendipitous thing that happened with language models was there is this crazy large data set of words and pictures, which is the internet. And there's not an analogous data set for the physical world, right? It seems like that is, is that the rate limiting step? Is that the big problem in sort of AI for robots? Yeah.

Yeah. I mean, there, there's a lot of work that's still happening at the research level for, you know, how can you pull that kind of data from, uh, from videos? So you can think of big data sets of humans doing things that can be really interesting to train robots in the future. Um, and that, that will come into play over time, but yeah, it's the chicken or the egg problem. And, and data is the, um, one of the key things that we need to enable the next

wave of breakthroughs and this is kind of the race is

Can you get robots out into the real world, into the field, and collecting data in very high volumes? Whoever does that will have better models. This is the data flywheel. So this is kind of the race that's on right now where you hear a lot of other humanoid CEOs talking about getting a lot of robots out into the world. Largely, those are going to be under teleoperation, collecting data, and then training and building these models of the future.

Aha. So it's like whoever gets there first will win just because that'll be the accelerant. Like once you have robots out in the world and you're collecting data, then you're immediately getting ahead of whoever has fewer robots out in the world because they're collecting less data. Yeah. So tell me about teleoperation.

Teleoperation is basically just remotely controlling the robot. So you're taking over the robot. You can see through the robot's eyes with a VR headset, and then you're controlling the robot's arms and hands to do a particular task. It's like a video game and you're controlling a robot. It's a simple idea. Yeah.

There's a couple reasons it's important. The first thing is that it tells you what the robot's physically capable of doing. So if I'm completely controlling the robot and I can't do a task under teleoperation, then that means the robot's not physically capable of doing it. So it'd be very difficult for an AI control system to control the robot to do that. So this is how we understand the physical capabilities of the robot.

As these new models have come along, the simple idea is that if you can teleoperate the robot to do a task, then you should be able to automate that task on the other end. So if you can collect enough data under teleoperation, then you can automate it by running it through these similar architectures that we talked about. So it's the basic idea that like...

You use remote control to drive the robot to do a thing, whatever, a thousand times, some number of times. And in doing that, you're training the robot, you're training the software, you're training the AI. Yeah, that's exactly right. What's an example of a thing that you've done that way? And how many times did you have to remote control it before the robot could do it? So each is picking is a good example, or you're taking objects and you're putting them into a box. To do that in a simple context...

Thousands of demonstrations is what you need.

And we think of this as generally hours. So how many hours of data collection have we done? And thousands of iterations can get you to, let's say, 80% of human rate. If you want to get to 95% or better of human rate, then you need more and more data. But it's in the thousands. It's not millions. Yeah, thousands makes it seem totally tractable.

Yeah, I was actually surprised by how well these models work and actually how little data they need to get relatively good performance. And you're seeing a lot of demonstrations of this out there today. And presumably that'll get better and better, right? As the software side of AI gets better and better, it'll learn faster, essentially. And the other obvious thing, but I'm just going to say it, is like, once you have done it once, then you...

Then it works for every robot. Then you can make a million robots and they all know how to do the thing, right? Yeah, that's exactly right. And one of the interesting things about these models is actually the diversity of data is almost more important than task-specific data. So we want to go wide across a range of tasks. Interesting. And then you're basically building all these skills into the robot. And then it becomes better at doing any one particular task.

It really is like learning. It really is human-esque. Yeah, that's right. So I know you're in a few pilot projects with Mercedes and with, what is it, GXO, big logistics company. When do you want to start selling robots for real? Like when do you think that might happen? 2026. Okay. Yeah. Suddenly that's next year? Almost now? A year's a long time in, you know, these are dog years. It's a long time in this space. Yeah.

And 2026 could be almost two years for now. - Yeah, exactly. - Like who are you gonna sell robots to and how much are you gonna charge? And what are they gonna do? - So initially in manufacturing and logistics, so folks like Mercedes and GXO, these are the initial customers of these systems.

We are not announcing pricing yet, but you can think of it as, you know, take what it costs to do these tasks today and with some discount to what it costs to do these tasks today.

We have a RAS model that we use. So you basically... Robot as a service? Yeah, robot as a service model, where you're paying the robot basically by the hour effectively to do a particular task. And that's at a discount to what it costs to do that task today. How far are you from the $50,000 robot?

We're not there yet. So not very far. So we have the architecture to be able to do this. So getting the cost down on these robots is a two-step process. So first step is new architectures. So if you still require this very high precision in the system and you're using bespoke components that are only used for robotics...

These robots will still be expensive. The challenge of humanoid robots is they have a lot more motors than traditional robots. So a traditional robot has six or seven motors. A humanoid robot has 30 to 40 plus. Okay, so that means it's expensive or you got to figure out how to get...

Yeah, so we're there. So for us, that was a $500 actuator that we – and we have a $500 actuator now today. And so once you solve that problem and once you solve the architecture problem, now it's about scale and manufacturing. So a lot of where we spend – a lot of where the cost is –

at low volumes is in just the structures of the robot where we're CNCing, we're milling out of big blocks of metal, you know, parts in very small quantities. But there's other techniques that are

are much more cost-effective, like casting or stamping. And these will allow these robots to be much cheaper. As I mentioned, look at automotive and look at the scale of automotive. There's 4% the raw material by weight in a humanoid robot as compared to a car. So once you solve the architecture problem such that you can build a lot of these systems and they're simpler to make,

Then the next piece is just applying mass manufacturing approaches to this to make them a lot cheaper as you scale. Well, I mean, that's a hard leap to make, right? Like, what do you do? You get a ton of capital and just...

build a factory and hope there's demand on the other end? Like, how do you go from this bespoke expensive thing to a mass produced, you know, much less expensive thing? Well, it's a gradient. So like I said, step one is you have new approaches that allow you to make them cheaper just inherently on a unit to unit basis. So the early humanoids were like millions of dollars.

And now we're in the hundreds of thousands of dollars range for building one. So you just got to get one more order of magnitude out of it. Yeah, so we've already dropped the price by an order of magnitude. And then now as we build more, and even as you add a zero, as you go from 10 to 100, the price drops pretty dramatically. So you don't need the volume that you need.

that you might think like my view is we can get to the sub $50,000 price point in the thousands of unit quantity. So without hundreds of thousands or millions of these. So one big buyer, one, one big car company or logistics company might place an order of thousands of units, right? Yeah. And you, you made a comment that you said, and you hope that there's demand. One of the things that I think is important to note is the demand for these robots is enormous. Right.

Uh, we have demand for hundreds of thousands of units already today with the customers that we're working with. So the demand is enormous. So we're, we're ramping up, you know, we've got to get the robustness and the safety of the system and really bring out the design. And, you know, we're, these are, you know,

Really credible, thoughtful people that are coming from other industries that are now joining us that now see that we've crossed this threshold of of, you know, technical viability and now taking lessons from, you know, how you scale and manufacture other things and bringing that into the robotic space and the humanoid space overall.

So in a year, or at least next year, you want to be selling robots for real. Where do you want to be in five, say, years?

My view is that where this evolves is it's going to start in logistics and manufacturing. And then as we solve safety as an industry, I'm really interested in health care and particularly in elder care over time. So, you know, how can these robots be used to improve the way we live and work? That was really the lens that I came into this on.

And so I think over the next five years, you'll start to see the early stages of the next three years. You'll start to see early applications for robots entering the home. There's some folks that are really working hard on this. I think we're about three years out from that being really viable. But I hope people prove me wrong. I hope it's faster than that. Three years seems fast. What's the sort of first step?

Use case, first job you imagine a robot doing for real in somebody's house in three years.

Well, everybody wants laundry. If everybody I talk to says, when is this thing going to do my laundry? And I want that as well. There's literally already a machine to do your laundry. All you have to do is put it in one machine and then put it in another. The remaining work is trivial. Yeah. I mean, look, I'm not the person to talk about the home. I think we're still a ways out. But there's humanoid companies like One X that are really focused on

uh, on the home and we've got a lot of respect for what they're doing over there. And so, um, I hope they do it. I know that they're, they're working hard on it and, um,

You know, I want a robot for the home as well. So a lot of the things that are happening with these models that I talked about, these more generic models, the things that we're learning in the industrial base can apply to the home over time as well. In terms of the AI models, sure. I mean, the AI models are basically teaching a robot how to deal with the physical world. That's right. How to move around, how to pick things up, how to... Yeah.

Put things down. Yeah, the task in the... I mean, the home's tough because how much is it... Even a robot does your dishes, your laundry, cleans and cooks for you. How much are you willing to pay for that on a yearly basis? I'm imagining the first household tasks. I would have thought you would have said like...

people who are quadriplegic, right? Like there are a lot of people who have various kinds of mobility problems, who can't do very basic things around the house, where essentially a robot could do it for them. Like I would think that would be the first use case. I think that's a great use case. And, you know, for me, that's sort of in the realm of what I say is elder care, which is like the assistive robots that help you with

Just base tasks, right? Like, you know, my granddad, one granddad went to a home, the other granddad had in-home care. And the one that had in-home care, it was very simple things. Remind you to take your medication and bring the medication over. Get you a glass of water. Help you to get up and out of bed, you know, to go to the bathroom. Just help you stabilize to go to the bathroom. And so...

That's not something that we're largely paying attention to industrial applications right now, but that is the dream long term. So I'll be excited to see how it shakes out. Rationally, what you were saying is it makes sense. I understand most people would rather stay at home. I understand that in-home care is impossibly expensive for most people.

At the same time, like, my emotional response to a robot taking care of, say, my parents is, it makes me feel sad. And I recognize that that's perhaps irrational, but that is at some level my emotional response. But, you know, the happy thing is, like, I should take care of them. But, like...

That's hard and it's probably not going to happen for its own set of reasons, right? It's more than we bargained for in this conversation. I don't know. There is something, though. A humanoid robot starts to get to some weird places in that way, right? Yeah, I've thought a lot about this. And I think it's a great place to go to. I'm happy to talk about it. I think what we want is we want humans taking care of other humans. That's what we want, right? But we don't have that today.

Yeah.

you know, these were people that both of them were war heroes. They contributed to society. They did all these amazing things. And then at the end of their life, they felt like they were a burden to their family. And, and they, they had this feeling like they never had to rely on anyone for anything. And now they're completely reliant on people for everything. And, and what I saw them do as they aged was they lost their dignity and,

And for me, this idea that you could have a machine that carries your secrets, that is your machine, that does things for you, allows you to keep your dignity such that then you as a human that's aging, you're fresher. You don't have to rely on your son or your daughter or your spouse anymore.

to get you a glass of water or to do things for you, you still have your own agency and your own autonomy through a machine. And that then helps your family to be much fresher because they don't have the burden of having to do all these things to support you, where then they can be fresher. And so my hope is that, you know, this is not designed to replace what humans do for each other. This is designed to augment and enhance that.

Remember just like my granddad, as he was getting older, I was working and I was busy and I would try to go over as many days as I could, but it was always really tough and I didn't want to be alone. And it was this whole battle that I think everybody goes through. And my hope for the future, I actually think it's a much more optimistic version, is that hopefully my parents have a robot and that robot is basically programmed for their happiness.

And it's designed to remind them when they're down of their favorite song and play it, right? Remind them that of the movies that they like watching or whatever it might be, right? And, and,

I think that's more optimistic. I think that's exciting. And that makes me hopeful about the future. And, you know, I think that's the worst part of the human experience is the way that we age. And I think that these robots and AI embodied AI and AI in general, um, can hopefully allow us to take better care of each other. So I don't, I don't think it is, um,

creepy. I think it can actually be pretty beautiful if properly done. And that's what, you know, you asked at the beginning, how is uptronic different and what are we focused on?

For me, I say human-centered robotics, but what that means is that we want this to be an optimistic future for humanity. We are toolmakers and we want to build tools for humans to enable us to live in better ways. And I think that if we really focus on that, I think that there can be really amazing ways of doing this. And I think elder care is a great example of how this can be used in that way.

We'll be back in a minute with the lightning round.

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after mile. So outlast every adventure and outlive the moment. Buy a Tundra or Tacoma today. Visit buytoyota.com, Toyota's official website for deals, or stop by your local Toyota dealer to find out more. Toyota, let's go places. And now we are back, as promised, with the lightning round. What's the biggest difference between Austin today and Austin 10 years ago?

Oh, man. It's changed quite a bit. So one of the things that made Austin really a great place to live and work was just how small it was and how accessible everybody was. We used to have these house parties and somebody would bring a violin and someone would bring a sitar and these world instruments and you'd have just all sorts of eclectic, creative people doing really interesting things. And so I think...

One of the things that I am worried about is that was kind of what made Austin special. And the things that make you special, people want to kind of commercialize, right? And they want to take this and they want to sort of scale it. And it's almost special because it's not commercialized. It's just this raw, organic thing. And so how does that, as more tech and more money comes into Austin, how does that...

How does what make Austin, what made Austin great, how does that continue to evolve, um,

So I think though I welcome it, you know, I'd rather be in the place where everybody's coming and everyone wants to build the future. So I, I want, I'm not one of those that, uh, is sort of resisting the changes. I think it's really exciting. And I think more people with new ideas about the future and the world and kind of a free place to do it. There's a, you know, there's a real ability here in Texas and in Austin to, uh,

kind of do what you want. And there's a real culture around, you know, the freedom to do the things that you want to do. And so it's a kind of a unique place where all that's coming to the creativity and the, you know, the, the capitalism and all that's all coming together. Is Austin still weird? Still there's pockets of weird. Yeah, certainly. There's still weird. Austin is still there. It's all growing up, but yeah, certainly.

What's your favorite humanoid robot in fiction, in books, in movies? C-3PO, for sure. Okay, you were ready with that one. You had that one on deck. Yeah, I want to make C-3PO, the human helper, right? What's one thing that you've learned about the human body from building robots?

Oh, man. At a high level, what I've learned is how amazing the human body really is. I think there's this fear from humans that as we sort of continue down this pursuit of replicating humans and building machines that can do what humans do, that that diminishes what it means to be humans.

But what it's actually done for me and most of the people working on this is it just makes you appreciate even more how amazing humans are. So the hand is something that you think a lot about. You just do all these things and you don't appreciate how incredible your hands are. Um, and you just, when you, when you start to try to build a hand for a robot, you just appreciate all the limitations, how we walk, how we move. Um,

The fact that we can- So hard, right? All the things we do, just like pick up an egg or open a door, like that's wildly difficult. It's amazing. Or you eat that egg and it powers you for a day. It powers this neural network in your brain that's billions of parameters, right? I mean, humans are amazing. And I think as we continue to learn more about what it means to be human, what does it mean to be conscious?

All of these kind of big ideas, I think, will only grow to appreciate what we actually have here. Last one. Tell me about your grandfather. Oh, man. So, you know, two grandfathers. One, Gilberto Cardenas. The other one, George Smith.

Both of them were great. My granddad, Gilberto Cardenas, came from Puerto Rico when he was 17. He joined the army and fought in the Korean War. He spoke five languages. He was self-educated. And he had the American dream and dreamed of what he could do. He was in the army. He was actually a field medic but became a hospital administrator later.

And he's a big sort of driving force in our family. And I watched him age and watched all the things he went through. He actually fell and lost his vision. So his brain was still intact and his body largely, but he couldn't see. And so he had to have around the clock care when he was in his 90s and in the home.

And he wasn't wealthy by any stretch, but he'd done okay and it saved his money. And it was $17,000 a month for in-home care. And it was like this revolving door of people that would rather be doing anything else than sitting in a room with my granddad and taking care of him.

And so, you know, for me, I admired my granddad so much and just seeing sort of that as the end of his life, sitting in a room, you know, counting the days down. I just thought there's got to be a better way than this. And that was a big driver for me doing all this. My other granddad actually ended up getting George Smith. He went to, he had to go to a home. He had colon cancer.

And his brain still functioned. He had a great sense of humor and he lost control of his bowels. And so you can imagine how humiliating that is as you age to be fully aware of what's going on, never rely on anybody, but not be able to control your bowels. And so he had to get multiple showers a day. And every time I would go see him, you know, just...

It was a humiliating experience. And so these are the things that are just my story, but everybody has their own story of, you know, taking care of an aging parent or grandparent and just what that looks like. And,

And my hope is that as humans, as tool makers, I think we can do better than that. And I think that these machines we can create will allow us to take better care of each other. And my parents are already naming their robots. They can't wait to get them. They're almost 70 now and they're like,

You've got to have these ready for whenever our time is there so that we can age more gracefully than our parents did. Jeff Cardenas is the co-founder and CEO of Aptronic. Today's show was produced by Gabriel Hunter-Chang. It was edited by Lydia Jean Cott and engineered by Sarah Bruguier. You can email us at problem at pushkin.fm. I'm Jacob Goldstein, and we'll be back next week with another episode of What's Your Problem? ♪

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