The $1/Hour Worker: Four Robotics CEOs on Humanoids at Home, China's Threat, and the End of Dangerous Jobs

At a conference called "Makina", basically AI in the real world! Pardon my robot. Thanks for tuning in and let's get started. Applove and started with an $8 domain and no VC funding and became one of the largest ad platforms in the world. Now that same engine, Paris applove and ads for e-commerce.

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A lot of you have puns, but you've been working in this space for close to 20 years. The company's been around for 10. First five years kind of a research lab. Last five years, your what do you call these dog-based robots? Well, it's an inspection solution.

Exactly, but Asia collection and understanding and critical infrastructure.

But the form factor is a four-legged robot. A core-legged robot. A core-legged robot. A core-legged dog, as you've just said. We like to call it a dog's robot, but why did that dog format become the standard? You're not the only person making it. There's many people making it now. Why does that one become the first one to hit relative scale and for a deployment? Yeah. In nature, a lot of animals have four legs. So this is your reason to that.

So for sure, you have a great mobility. You can climb stairs. You can go anywhere a person can go. So dexterity and balance. Mobility, right? Balance but also stability. Four legs if you white-foot-print, a lot of footholds hold onto because we work in nasty environments, slippery force. There's rain foaming this snow falling down, grass-drawing, got it. So four legs is a real good format. Well, now this is a silly question, but why don't we make senators for the human versions?

When people are making the optimist, the Neo, the Atlas from Boston Dynamics, those stand-up robots,

with two legs, the concern is they're always going to fall over. They constantly fall over in

demos and if they fall over, they're going to break somebody's ankle. Why not put four legs on those?

“You could, absolutely. And it really depends on the use guys. If you need to work,”

bring, you know, I don't know, in a coffee shop, bring a tube and there's not always spaces, right? You want to work an eye level. Maybe you want to know it is better. In the facilities that we work, four legs stability, there's enough space to go around. Yeah. It's the perfect format. I don't buy it. I think all the cafes should have are, are they, are they, are they sanitars in a great mythology? Yeah. It's a sentence for four legs and two. Yeah. I think that should be the new standard.

You found a really effective first use case, which is inspecting really important infrastructure. And now you have thousands of these, hundreds of these, four hundreds, hundreds for deployed over the last five years. Yeah. These are expensive. They're low hundreds of thousands of dollars to buy them. Yeah. And to operate them, I'm assuming tens of thousands of a year in service contracts. Yeah. So they're not for home use. These are industrial. And they have a lot of

sensors on them. So if you were going to inspect, I don't know, a pipeline with natural gas in it. Right. These things can go out in any weather. And they can sense things on that pipeline that a human can't correct. Yeah. That's right. Frost is not about labor replacement. Right. It's what can we do better? What can we do? Superhuman? Yeah. Inspection is a great example. Our eyes and ears don't perceive all the singles. Micro gas leakages, temperature equipment overheating

with the cameras on the robot, thermal cameras, acoustic, you know, microphones, gas concentrations, and all of that. We pack it full of sensors and AI. You can go way beyond what a human can do. So the monetary benefit is avoiding downtime. These assets, if they stop, they lose revenues in hundreds of thousands per hour. So every minute, every hour, we can save them,

“essentially pays for the robots. And that's why we can afford having really expensive sensors,”

really expensive GPUs on top of the robot. Yeah. These have seriously powerful compute on them. Right. And they have to have a significant battery power then. So these things can do a mission of what? An hour or two? Two hours. But our ducking station to come back charge. But they do this over and over. Some of our customers run these missions 40 times a day. 14, 14, five, four, zero.

Four inches. They're interested in a specific point. Wow. The electric art for it

Thermal cameras burned. They need a robot that's right at that moment. God. And they have to charge not hot swapping the batteries. No. You won't hands free autonomy. Nobody should even be bothered that there's a robot. Yeah. They don't care about the robot. Actually, they don't even want the robot. They want the data. They want the insights. The robot is a means to collect the data precisely. But I can you upload the very power hungry compute and put it

in the cloud. We also do that. There's always two parts. This part that need to run real time

on the robot, because you also cannot guarantee connectivity, obstacle avoidance, data quality.

“Make sure you have to write. Yeah. If you upload the blurry image for the cloud, it's too late.”

Yeah. But in the cloud, of course, you do contextual analysis, historic downtime and analysis. That's it. Are people asking for these to be able to operate for 24 hours yet or 12 hours? No, for sure. So the maximum is in an eight hour range. So it doesn't have time for charging. If you need to go beyond that, that's rare. There's diminishing returns to what frequently they do it. But you have to manage. They do it manually today. Maybe once or twice a day.

And they get eight, 15, 20 times now. I thought it's already the frequency goes massively up. Yeah. Without putting people into harm's way plus the quality is so much higher. What's the most fascinating science fiction deployment you have currently with these? Yeah. I mean, what's really exciting anything offshore? People fly out with helicopters. Every helicopter flight costs in the tens of thousands. So, but if you're offshore, it's very tricky.

Right. It needs to work. There's almost no people around. It needs to be floating. These are oil rigs. Oil and wind energy offshore as well.

Ah. Yeah. But wait a second. These things don't operate in the water.

So how do they work with windmills in the ocean? There's windmills around hundreds of them. They go up together to a transformer station. That transforms to ACDD. So before it turns out that's a man's facility, typically. Got it. They convert roles. This is where the robot operates.

“Got it. Can they operate like in severe conditions like the Antarctic and stuff like that?”

Have you deployed them in the area? Well, in Norway for sure. So that's my 2020 degree. In deserts plus 40, 50, 60 degree, right? So that's exactly the point where you want to send in the robot. Temperatures, dust, humidity. Most importantly, we have a robot now that goes into explosive atmospheres, which is, you know, in oil and gas and chemicals, methane in the air. You're not allowed to, you know, go back to spark. So we build a special robot that's guaranteed

enough to create a spark. This is where you don't want to have people. But for a machine, that's a perfect case, right? Dangerous environment. This is where we're sending robots in. That's fascinating. So if you're in the Permian basin and something's leaking, that is one of the most dangerous these oil rigs, gas leaks. This is where people seriously die. And you don't want, you want to know when it's happening. Yeah. You don't want to create a problem. So that's a perfect thing. I mean,

I'm going to keep going sci-fi, but dropping these things into the bottom of the ocean seems like a no-brainer at some point. Well, there's something that means, right? We don't do that right now. But I agree, right? Robots should work in environments where people shouldn't be dangerous remotes, right? Voring repetitive tasks. But this is what we, we're, that's a different form factor right now. But there are people creating on the surface and then under the surface,

slightly under the surface, robots that are doing essentially not inspections, but monitoring systems, right? For, obviously, the military. Well, if you're out there inspecting and there's a gas leak, and it's dangerous to send humans out there when you're going to put some equipment on these

“to fix the goddamn leak while you're out there. And that must be the holy grail. Is it not?”

Yeah, once you can detect a problem, Cosmoso, can you solve it? Can you fix it? Can you turn not today? Okay. You know, in a demo, yes. But in reality, getting it to 99.9% reliability,

in explosive atmosphere, that's still in development. First step, this close lever is open cabinets.

Eventually, you won't have bi-manual manipulation, maybe three, four arms, the extra machine, right? That's so, you know, AI will help us. That's a lot of work ahead of us. It's a lot of the demos you see of humanoid folding laundry. That's a very controlled environment. Once you're out door in a hail storm, right? Freezing temperatures. It's different also for perception. But eventually, we foresee the future that this will be solved. What percentage of your robot

is sourced from China? Zero. Zero percent. Yeah. And is that because in the EU and Norway, it's banned or that's a choice? That happens just historically that we source locally and you get chips from the US, etc. And for some of our customers, it's important. And we built a lot ourselves, right? Because we started 10 years ago. So a lot of the architecture nowadays, you get cheaper components around the globe. So it's about being smart where you get components

from, which one are active, which one are just metals? So for sure, it's a whole work to navigate, but tapping into the commoditization of certain hardware. That makes sense for us costbizes. Who's specializing in that outside of China now? Is it Vietnam, India, Taiwan? Where can you source like the actuators and a lot of this stuff? A short China is number one pushing. There's good companies in Europe, right? I knew US as well. So I did these three regions. For sure.

expertise, somebody who builds that component. Got it. And how do you look at China now? They've

been stealing the IP. I'm assuming they've stolen yours already. And certainly other people's IP is being stolen at scale in China. And they're building robots that are going to be 80 percent cheaper. And they're going to try to deploy them to the same customer base. I am certain. How are you thinking about the threat of Chinese robotics? If you look at the robot from China, today, that device is a piece of hardware that I can walk. Beautifully, great engineering. Love it.

Do backflips. Yeah. But they're not solving a problem. Our customers don't compare a platform to the full solution now. Yeah. Do you need autonomy, inspection, intelligence? They're worth flowing to aggression. It's so much more, right? It's just a hardware difference. So the harness, the wrapper, the services around it. They're not providing the trust in the data. Right. We collect very

“sensitive data. We have ISO certification for cyber security, all these topics. So that's how we compete.”

So you might not want to send the nuclear power plants, latest data to the Chinese Communist

party. You're saying you don't want to have 15 cameras in critical infrastructure. Yeah,

somebody else controls. Yeah, I'm being a bit busitious. But, uh, but it's happening to that. But it's, there's data leakage. Talk to me about military applications. Yeah, low is having to arm itself. I apologize on behalf of the United States. For our stance with NATO, but you guys have to pay up and pay your fair share. You've agreed to do that. But I think there's a perception in Europe. You can tell me if I'm wrong in a NATO that you may have to go it maybe

without the United States. You may need to build your own military products and services. Do you not need to be in the military space and do you not to take the same applications and build military applications and are you doing that yet? Yeah. So I think there's a responsibility in Europe to build technologies to be able to, you believe that personally. Yes. However, for any boutics, we built and we went down one track, this tremendous poll. So today we're not doing it,

not intended to do it. Right? And it's also a different product that that stage probably. It sounds

“very easy. Just take four legs and do military. You need to go couple of steps for what exactly”

you're doing different communications, different autonomy. So we're not doing it. But I mean,

I think there's a responsibility to do it for all of this. Is it never say never for you? Or is it

your debt set like you have a mission? You're not going to build military products? So the mission is clear. We started with non-military. This is where we headed. Got it. But if the EU asks you and you want to ask, I mean, we get, you know. Oh, you do get the last request. But it's also honest, we're resolving actually the problem. Just shipping a robot to the military doesn't solve the problem. We really need to go deep. So you wouldn't need a different team to do that.

Art team. Really, you need a different team. Well, it seems like you could do the same team and build military app. No autonomy is very different. Right. So for example, we do autonomy. You have time to set up a robot and does it. It does inspections all of that. Yeah. In military, it's about millisecond being in right, remote control due when in the loop, the communication is different autonomy. Then everything on top, application software, very different. Yes, you could lose it for

like a robot, also going to a house. That's about it. Right. The rest is different. How do you think about robots that are armed? Clearly China has done demonstrations of these same type of you know, four-legged robots with guns on them. And obviously with AI, these terminator scenarios are here. Yeah. They're being built in China already. Yeah. We've seen drones on the battlefield in Ukraine. Norway is not far away from Russia. Yeah. It's not that close. But it's not that far away,

either. How do you think about the fact that communist countries are building these robots that have weapons on them though? I personally don't like it. I hate it. I think you're concerned.

“Right. I mean, as an engineer, you should have pride to build technology for good. The fence is”

one part. The active attack putting a gun on it. It's risky. These technologies getting mature, but done not that mature that you would put it somebody else in harm's way. Yeah, it is. The enemy we're going to be faced is going to do this and we need to monitor what is the buzz inside the industry about this. When you're out with other people in the industry, you know, what do you know that we don't know about what's happening in those authoritarian

countries with robotics and the military? I think these are all very early tests. If I look at those videos, these are demonstrations. God, I've not seen these types of robots. I can drones. Yes, Ukraine. That came out of necessity. Yeah. That was a mature category that was used. In robotic issues with the people I speak to, I mean, four years ago, we wrote a letter together with our friends, a boss dynamics and all this right, who condemned the weaponization

of robots for exactly that reason. That's as engineers. We don't want to see it being used. And we think it's just dangerous in risking stupid. Yeah. All right, listen, continued success. All right, everybody. Really excited to have bird born here. He is the founder and CEO of

pre-orders and you guaranteed people. This would make it and would ship in 2026 into their homes.

What is it cost? And are you going to hit yourself and hose deadline? You've got to keep your promises. Okay. So we will ship in 2026. Okay. Now, expectation managing here will be slow in the beginning. We want to do it right. Yes, well, there will be a handful of customers. I'm guessing the Neo in 2026. And I'm so excited. And I can't wait. What is the cost of the Neo? So that's an interesting one because it depends a bit. I mean, when we launched a pre-order, we had two different

payment bottles. We had a kind of like early adopter upfront full payment. And then we had a subscription fee. And the product, of course, is going through a lot of development. Yeah. How this subscription model will look and these things are kind of like still evolving. Yeah. And we want to figure that out also a bit together with our customers in the beginning. But another big one now is, we haven't read an analysis yet. But I've dripped it in a bit. God, which is, we are going to allow a lot of people

to build on the Neo. So we are also launching Neo as a platform. Yeah. That's a, I don't think you're like an ass store of such a skill store. So if I have it in my home, and I want to make a salad, you as a hacker could make the salad skill. And I can buy and subscribe to your salad skill. Yeah. That will be part of it. But to me, Neo and Onex is about so much more than just a consumer,

right? Yeah. So consumer is an incredibly important market. But Onex has always been about how

do we create an abundance of labor across society through these humanoids. And I sincerely believe that we have a platform now, which is so uniquely capable and so well situated, that allowing people to build on this will open up how to use Neo across all of our society and not just in homes. Right. But it will also benefit the consumer because this will mean there will be more things developed on Neo. And part of that will be an app store target towards consumer, which we're very

excited about. But also it will just be, in general, how do you create a bigger ecosystem that can just

“accelerate your autonomy and accelerate the path to actually having a fully autonomous agent at home?”

You can do what was the pre order 20k or something? And we haven't given out official numbers,

but it's pretty significant. We sold out the first 10k in the first few days.

Oh, so people put a deposit down for that. They'll have the ability to fully sort of like the Tesla $500 deposit or 500 a month, a thousand a month, something in that range. Yeah, 500 a month, what's 500 a month? So this is for, if I were to think of a parallel Google glasses or the vision pro, this is for high end folks, who are the Vanguard, who are the earliest of the earlier doctors? Yeah, I mean, we tried to be very transparent about this, getting a home

humanoid in 2021 to 6 is going to be rough around the edges. Right. They're going to fall. They're going to fall. Right. But I am very happy to say that I think we will actually be able to ship something that's very close to full autonomy, which we did not want to promise when we launched this because it was too early. But, and I'm not going to fully promise it yet, but the way it's trending now. It looks like we will be able to ship an experience that it's fully autonomous,

and that is still quite useful. Now, if you want everything to just work out of the box day one,

“then there will be some teleportation involved or some guidance of the system, but I think”

that really excites me these days is that we're seeing the path now to actually shipping something that if you want it, it can be a fully autonomous experience, and it's getting pretty darn good. The teleoperating is fascinating to me. I don't know if you saw this, but in New York, there was a chicken sandwich shop, couldn't find a cashier, so they hired somebody in Manila in the Philippines for, you know, $3 an hour, which is a huge salary for a cashier in the Philippines,

and they had her on a zoom call. They just popped up zoom. They acted themselves, and you could order, and if you had a customer service issue, you just talked to her and she was like, "Hey, I'm right here." That is, in some ways, what you'll be able to do with your robot, you'll have somebody in the Philippines, who you'll be able to tap into, who'll be able to turn it on, and when you say, "Hey, pour me a glass of orange juice, that person will be able to remotely do that test,

is that what I'm envisioning here correctly or incorrectly?" I think it will all happen. So back to how the platform works. Let me just back up and spend like two minutes on that.

“So if you think about Neo as a platform, so if you want to build your orange shop around this,”

orange juice shop, that okay, you buy a bunch of Neo's, you get Neo's, you get the robot operating system with like a fleet management and all that. You also get the data collection equipment, which is gloves that have the same tactile sensors as Neo's, the same vision system,

we do all the cap-dense captioning of the data for we do all that. You'll find tune your model,

you deploy this, and you get this working, and now you have a fully automated shop, and you're very happy. That's one path. Maybe that's quite work. So you say, "Ah, I'm going to have someone

“intervenes sometimes in tele, and then your data gets better." That's one way of doing it, right?”

Yeah, there's many ways of gathering data, or maybe you're just saying, like, you know what, this is super complicated. I just want it fully tele. That's also fine. The depends on how you want to apply this. And the platform goes all the way from these kind of like developers that just want to automate their workflow all the way to the more foundation labs that wants to deploy their models. So there's also a world where you can run someone else's model on Neo.

We're going to allow that. I think, so you're going to be an open platform. You'll be, in a way,

headless to the knowledge inside of it. You'll be able to plug in. If open AI has the world model, clawed, or some of the other independent world models, they'll be able to be plugged in. Yeah. 100%. Now, I sincerely believe that our model will be the best one. Sure, and I believe in competition. So if we, that actually control everything from the manufacturing all the way up to the product, I can't make the best model, then we kind of failed. Yeah. But we'll be allowed other

peoples to build on this 100%. And one of the big reasons for this is that currently, if you look at where this, where to feel this, there is no one general model that solves everything for robotics. It's not there yet. Right. And if we are stuck in our customers, kind of like backyards, helping them integrate towards ERP solutions and everything else, the next couple of years, we are not going to get there. What we want to do is to work on the general problem, how do we

solve embodied AI? So we can actually create an abundance of labor. And this requires us to focus on the general problem. And then allow other people to also help apply what is available today and to help build the ecosystem. Right. If we get this enormous robotics ecosystem, we all benefit on. Yeah. And I should see some applications where one teleoperator, let's say this was a convenience store robot that just helped you carry stuff out to your car. That might only happen

once every hour. You could have one teleoperator or maybe you have 10 of them that are monitoring 30, 40 neos and they control them remotely and help people move the groceries to their car. Yeah. Personally, actually, I'm like, I might have a use case for neo. Okay. Intelliop, which is I'm part of the time in Norway mostly in San Francisco area, but part of the time in Norway. And I'm also kind of like conventions like this. Right. And when I'm out traveling,

I want to be able to be present and run my company through neo. Yes. But I don't neo. I am neo. And that's actually pretty magical. And you can go around. I can pick up the parts. I can look at the parts. I can talk to people. I can be in the meetings. Right. And so that's one application

“of teleoperator that I think actually will never go away. Like no matter how good your autonomous,”

that will be there. Yeah. Your avatar at your factory in Xinjiang. 100%. Yeah. And you know, there are other applications like this where remote power stations, where there's no one within an hour of driving. You have a robot standing in the closet and something goes wrong and you go out and you flip the old switches and you do the same thing. Like you're likely not going to automate that because it's kind of like a one-off thing that happens every

few months. Right. Right. So, but it's worth having that robot in that space out in the middle of the forest near, you know, those power lines, your power converters, they can go out within, you know, minutes and and we're essentially like what used to be called like expert in place. Like this concept of like you can take the world's best expert and teleport them to anywhere in the world to help solve a situation like a surgeon. Yeah. Yeah. It's it's super useful. I do think that

what we've experienced over the last year is first of all, that Neo has become so capable,

especially with the new hands. Yeah. That teleportation does not fully use the hardware. Like you're not able to get the teleportation to be good enough to fully utilize the hardware. Ah, so the fidelity of the hand is greater than a teller operator is able to leverage. Yes. Right. The teleoperator will not feel the same as the robot is feeling for example. Right.

“Then you need to build full haptic systems and they're going to slow you down and be slow”

and clunky and like so we're increasing the seeing that gathering data with humans just wearing the sensors of the robot in us transparent the manner as possible. So like they should not disturb what you are doing. Right. That's the most useful data to solve kind of baseline the exterior of the robot. But even more importantly, the big bet that we made, which is this decade long batting one eggs is if you get the robot to be similar enough to a human,

see some very good proof that this is actually working incredibly well. And that's the reason

we started the one next world model lab because we now finally have the scaling loss on that.

And we're seeing that this exercise that take us inside the lab. You are you having people in factories where glass is where your hands and do their tasks over and over again. Are you working with the micro ones of the world to go do real world stuff and outsourcing like unique proprietary data that you can have that other companies don't? How does the world model get built at scale? So, so, so for the, well, yes, we do that. And if you,

“but that's not the main point. So I think ultimately it's very simple, right? The model is going”

to be as good as the data. Yeah. And if you think about the data pyramids, then on the top, you have like tell operation data, very high quality, small fine tuned data set, where actually what we do is you will have the operator try to do the task very well and very fast. And they will open fail and then just try again. And then we pick the good samples where they did the task ask you to see human one, right? Yes. We don't need a lot on that data. It's just online your model.

Then you have the data which is what you're talking about, we'd like to put the sensors on the human going at a data. Yeah. You have more of that and it's very close to the robot. But it's not the robot. The teleplader is the robot. This is not the robot, but it's close. Then you have equal centric video, video from humans point of view. So that is further away from the robot, but it's still quite close because the robot hands is the same as human hands. And like it looks to say,

it means it's quite close. And then you have general video data. Yes, out of the world, all the world in general, and of people, right? And because Neo is so similar to a human, we can actually utilize all of that data. Now, the bottom layer in the pyramid, which is this video data, general video data, is absolutely ludicrously immense compared to anything you do. It's everything. So if you look at what is needed to actually achieve true intelligence, you need multiple orders

of magnitude more data than anyone is even close to collecting over the next two years with equal centric data or with this sensor data. And all of the major breakthroughs that we've seen as far as I'm aware of in AI have been because someone figured out how to use a huge new data source that previously we were not able to use. You unlock some new set of data and now your model capability greatly improves. Well, we've got a lot of people out there trying to find data, like

“that is like one of them. It's like a catch 22. So our big bet is you have to be able to utilize”

the general video data out there. Yeah. And the only way to do that is you have to care about every single tiny detail of the robot to be asked goes to human as possible. Like, you know, like the flesh and tissue and skin. Yeah, it's highly non-linear. So like how much force for it to deform. What's the friction? Like, what is the impact energy when touching the table? And people have different size hands. I mean, yeah, literally in the NBA, there's a wingspan

as a concept in people with a wide wingspan, longer arms than the average person. Yeah. Get paid 20% more for having that extra two or three inches of wingspan. It's pretty fascinating

when you think about it. That's a really good way of saying it. wingspan. We've always

always called it for the the the gorilla coefficient. Yes, long arms. Yeah. Yeah. If you want what anyway, yes. So my point is, yes, we do all of these things. But ultimately what differentiates one X from all the other robotics companies is that we are all in on pre-training our own models on this video data on the internet. Yes. And that our cross embodiment is not another robot. Our cross embodiment is the human. Right. And we want to be as close to as possible because that

solves the catch 22. In the end, all the data will be robotics data because a robotic data has

“it has the actions, it has the tactile, it has the forces, it's better. But the only way to get”

all of that data is to create a base model that is good enough, that you can deploy all of these robots across society and they will do useful things that people pay for and also gather the data. One of the robots become recursive in nature and they are teaching themselves, building themselves and like we're seeing with large language models now where people creating agents instead of giving it prompts and instructions, we're now starting to say, well, here are the goals,

here's a loop, you are one agent that you know identifies for a business, potential customers.

Okay, you're the agent that does customer success and here's what that looks like. You're the

agent that you know does pricing of products and those agents start working in constant. We're starting to see that in knowledge work. When does that come to robotics where you don't have to actually worry about making the robots better, they're sentient enough to use a word. Perhaps not

in a Michelin-starred restaurant, your goal is to make the most delightful food with this level of fidelity and perfection and here are the outcomes and it says, okay, I've just got to get better at

poaching these eggs to really be great at this. It's not always not sci-fi, it's actually

something we think a lot about, but it's also incredibly hard to answer because you know, the development now is going like this and you're here on the curb. So when you ask me a year ago, I was way more bearish on how far along we would be today on the AI and like every time I kind of

“sample things that move faster than I think. So it's easy to get like carried away, right? But I think”

if I try to answer it broadly, I am extremely sure that we're less than a decade away from hard takeoff. And when I say hard takeoff, I mean robots building the robots, the data centers, the chipfabs, doing the mining and refining, actually a true abundance of labor, a self-sufficient system that is just standard ten years, and in ten years, my current bet would be three years, got it. But like if it takes ten, like in the history of humanity, it's still like a blip, it's also really

a matter. So that gets back to like what is one next, right? Because in the history of hard launch, or hard takeoff, hard takeover, takeoff, takeover, we're going to do it right. So it's going to be hard takeoff, hard takeover. But you know, that's the term, right? This is the industry term, hard takeoff. And you can't really get this without the physical part, right? Like the digital

intelligence can never create its own substrate. You need the physical part, right? And I think also

this is going to have incredible impact on humanity, with respect to, for example, progressing science, right? Like a lot of the demand that we're seeing now on our platform is people who want to automate lab work. Because if your AI model can't actually build and carry out its experiments and observe the results, how are they going to progress science, right? So all of these things will happen in the coming years as AI becomes physical. And exact timeline is a bit hard,

what is the year's salt decades? Yeah, I mean, if you, if you believe it's three and I know

“you're an optimist, you have to be to do what you're doing, a crazy optimist for sure, and you think”

the outer, you know, estimate is 10, you know, we'll we'll be fine with five, six, or seven,

burnt, you've got to catch a flight. This is amazing, continuous success. If people want to order

a neo and give you $500 a month to be part of this absolute lunacy that you're doing, what do they do? How do they get in? Well, you go to our website on the order neo. That's him, and that's simple. It's not simple. It's 2020, it's six, it should be that simple. It kind of shouldn't, right? You can order a Tesla online, you can order any of our lines. Trasperon pricing. I like it. Yeah, burnt, continued success.

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All right, everybody, we're really lucky. We have Amanda McMaster here. Not McMaster. Just McMaster. Just McMaster. No, just McMaster. No, McMaster. You're the interim CEO of Boston Dynamics, the OG, the original robotics company. The robots we've seen for decades doing backflips, doing kung fu, getting kicked in beaten and getting back up. We have been having a hard time remembering who owns this company now because it was an independent company venture back

then Sergey Lary bought it. It was part of Google. Then it got sold, I think. Massey Yoshi saw an owner at some point, but I believe Hyundai owns it now. That's correct. Did I get that whole history corrective? You nailed it. Okay. So apparently I read way too much industry news, but now you're in charge of this. Yes. It changed hands many times and you went from being essentially one of one really in humanoid robotics to one of many. We're here at this machina summit in Paris

and you see many contemporaries now. So what is Boston Dynamics working on now? Is it still a research project or are you going into the real world and applying these robots? Because I think

“you guys got there early, but you have to now deal with fierce competition. Yeah. Yeah. We are”

big on point robots. So it's no longer an AI lab. It's not a fair amount. It's not a research development company anymore. We're now focused on real world's limits. So we started with our spot robot, which many people know. That's our mobile quadriben in industry. Famously in

dystopian version and a utopian version here obviously pursuing the utopian. But that is a really cool robot that has been for deployed. Yes. It has been deployed in real customer sites. It's prepped providing really customer value. At this point we have over 500 customers, over 46 countries. Wow. It is the it is the mobile autonomous robot that's used more than any other on the planet right now. Wow. So it is the most deployed and most utilized. Yes. So

“real. Why? And who's what is the number one use case for it? Like, is it security? Is it inspections?”

What do people use that dog format for? Yes, or pony? What do you like to call? How many dogs? We like to think of it as a dog. I love it. I think it moves like that. But you know, we're using this, the customers are finding a lot of value in industrial inspection. So they're using it for both, you know, acoustic, gauge reading, vibration detection. So assets that, you know, if they have expensive assets and their facility and they want to monitor them,

this allows for them to do that. Now, can do that during the day and then it can do security

perimeter work at night. So the answer is yes. We do all of that. And the real

inflection point was customer ROI. I want customers to find value in this to do really useful work. It's not just about yes, it's cuten of dances, but it's long cast dancing at this point. It's now doing real work. And customers need to see your ROI in under two years. And those inspections, if they were even being done, we're being done by humans. Yes. Humans as we all know, being them are fallible. We make mistakes. And these ones were just out there now as little

puppies running around a water treatment facility, a bridge, whatever it happens to be in infrastructure pipelines. And it can record many different sensors, video, obviously, vibrations, or radar, I'm assuming all different types of acoustic, you mentioned. Yep. What are those

“robots cost? What's the range of the hardware cost? And then what's your business model with these?”

People buy them and rent the brain. They rent it by the hour. What do you think of as the CEO will be the business model? And what is the business model with these hundreds of or dozens of customers, deploying hundreds of these? Yeah. So we went with a capex model to start with spot. We'll be doing it probably a robot as a service model likely with that list. We understand the humanoid corn factor folks may want to spin up at different times and then have the ability to

to decrease with spot. It's been pretty effective in capex. It's the way these industrial customers think about industrial tools. So they generally want to spend capex for this. It depends on their configuration, you know, it ranges anywhere between, you know, $100,000 for the base robot, all the way up to $300,000 one where fully loaded with services, integration, so it's a price of a Tesla to a Ferrari depending on how you equip it. But what people need to understand is the

lifespan of these is greater than five years I was saying, like these are your known for industrial. So if it can run, I'm assuming you run 20 hours a day, 22 hours a day with charging. Yep. So we're at, um, we think about in terms of mean time between intervention and we're at over three thousand hours. Okay. Well, there are a couple times a year. It is a human app to be involved and it has a charging station. So battery runs for about 90 minutes. Um, usually we'd have to

“come back, sit sounds and charges, and the next one can take over. Does it automatically swap the batteries?”

Or it just sits down onto its charging plug? Perfect. Yes. Uh, so it has swappable batteries, though. Yes. But that, the hot swap is a human has to do it. No, Atlas does it itself. Oh, it doesn't go. Atlas will have two batteries who turns its torso around and you replace one and put it

with the other one and always says a backup. Perfect. So battery lifestyles. So for the human

I'd one, it can do it itself, obviously. The dog gets charged. So realistically, they could be in the field for close to 24 hours, maybe eight to the end, yeah, 20s. And so that puts the operations at a couple of dollars an hour. And has that changed how people look at the use case, the dramatic lowering of costs because I'm assuming union workers inspecting, you know, pipelines. They're getting paid 40, 50, 60 bucks an hour, fully baked with their benefits, their pension, whatever else.

It is quite expensive. We haven't necessarily looked at labor replacement for spot on a lot while that is a metric you might look at. We thought about, you know, how do we bring spots in there to augment human labor. One humans weren't doing the task, even if they were tasked with that they weren't actually doing it. And two, like, we're just trying to figure out ways that humans could do more, you know, knowledge worker tasks as opposed to going into an inspection. So yes,

one of the metrics that customers look left by look like for ROI is labor replacement. We're leaning more into, how much do we save you? So you we found an air leak in your facility and that was a,

would have been $3 million a day. Yeah, the outcome is matter. Yes. So what is the value that we're

have to be very thoughtful about that in this part of talk. And let's be honest, I mean, there's going

to be an element of labor replacement for this. It adds a metric because it's easy, you know, how many bodies are in the world and how can you imagine a total addressable market relative to that? I just don't think it's the only conversation I should be having, right? Just an element of it. And hopefully we're getting rid of the, the dangerous jobs. And the ones people might find um, oppressive. Yeah. Dull, dirty, dirty, dangerous. Dull, dirty, dangerous. Yeah, we don't want

to want that. Harding their body. Yeah. We only get one human body. Yeah. Uh, the Atlas, how do you think about on-board compute versus remote when you put the amount of brains of my understanding as you have the brains on the robot? Yep. That means crazy battery drain. What

do you think about the option of having, you know, the, uh, brains in the cloud and having these

be more lightweight if they're in an area that has extremely high speed, Wi-Fi, etc. And do you offer

“that yet? Or is it all, hey, you got to have a robot with a lot of brains on it because that's what”

the customers want and that seems to be a paradigm shift that's occurring now. Yeah. So how do you rock that? Yeah. We'll actually think about it. We think about two brains, right? Right. It's my simplified version of telling the stories or two brains. Okay. There's the, the brain that controls the physicality of the robot, which is what Boston dynamics is known for. No, the dynamic movement, reliability, the way of manipulate things in the world, that lives on the robot. The reasoning layer,

that under the gives you the semantic understanding of its environment, that can be in the cloud.

That's things that we might partner with who will define it. We may partner with other AR partners or

we'll build some of this ourselves. And then the wrapper around all of that is the very specific information that a particular customer needs around their own workflows, you know, the way that they think about the, um, the job processes that they have and the tools that exist in their facility

“and how this robot will interact with it. That's going to live somewhere in much speed. So it could”

be on on real bad if you needed it to. It could be in the cloud. I'm well for your other wrapper about what percentage of the robot is built in the United States or outside of China and Taiwan today on the percent of the row of 100%. So there's no issue with the sovereignty of robots in the United States. We're seeing a lot of cheap robots coming out of China. Yeah. Your personal opinion as the CEO of this company and as an American under any circumstances should we allow the humanoid

robotics from China in the United States? No. Why? It's not safe, right? We've already heard about leaks that are happening with some of the quadrupeds that you're seeing in the United States and being back channeled back to China. Isn't we? We have seen what happens if we let China win in the semiconductor space. You know, we can't do that with robotics. So we need to have a concerted effort to protect our IP to make sure that we are bringing manufacturing of this

ecosystem into the United States or into our allied countries. And that means that we need to take our national robotics strategy. We're lucky enough that we get to sit at the table and some of these discussions. I'm hoping that more companies in the US join us in taking up this mission. Yeah, we have to be pretty serious about this. It's an existential issue because these not only do we have to win this, we have to make sure that the rest of the world uses our platform

rather than China's. How do you think about the military application of the isobviously military is, you know, the the field has been changed with drones in a way and out of velocity, no pun intended, that I don't think anybody had dissipated because of what's happened in Ukraine. And now we see in the Middle East with the war with Iran. How do you think about

“Atlas and spot in the battlefield where they add in terms of deployment in the military?”

Yeah, so we've been pretty public about the fact that we have an anti-weaponsization stance. But I think that for what we're trying to do right now in industrial use cases, it's a distraction for our business. So focus. It's focus. It's not the list offer. It's I mean, it depends on you ask in there as the CFO CEO. I'm going to look at this and say, I'm all about focus right now. We need to be focused on the markets that we think we're going to win in. And certainly we have

great ties with the government and we're happy to do any non-weaponization work with them. And then we do do that today. Okay. So you'll have them in or you do have them in the field. Maybe if it had to go collect a soldier or bring a med pack, you'd be okay with that. Assuming a bomb, you're okay with that. No deal. EOD is one of you know, explosive ordinance disposal is something. It's a great use case for robots.

And you're doing that currently. We do that currently. So we're okay with that on what we don't on is Terminator robots, right? Right. Not good for the market. But China's building them. So if China's building them and we don't. Right. You're kind of obligated if you're boss and

I think that's a tough question. And I think we're going to have to answer it when the

time comes and hopefully it never comes. The time is going to come. I can assure you. I know.

And I can assure you what your answer will be when President Trump calls. You will say, "Sorry, yes, sir." Or else your company will be naturalized. I mean, this is the reality of it. I mean, I'm being a little facetious and playful with you, but they're going to deploy these. And they're going to deploy them. And they already have shown. You've seen them put AK-47s on these. Yes. Not on our robots. Not on yours. On theirs. Yes. And listen, it's terrifying.

“So I think, listen, I know that we have the best robot in the most capable robot in the world.”

You know, if and when that time came, that we had to make a tough decision. We would make the right one. But today, we don't have to make that decision. So I'm going to keep everyone focused on the

application space that makes a lot of sense for us to make money. I'm going to tell you this later.

Don't tell anybody. The CIA, the FBI and the Department of War have many of your robots with many weapons attached to them currently. Don't tell anybody. All right, listen. I know you got to go continue to success. This is such an important American company. And I hope you take the job and become full time. I know you're interim right now. So I wish you great luck with it. If people want to come work at Boston Dynamics, please tell me. Where are

you base? So we're in Walthams or right outside of Boston. Yeah. But we're open to some remote book work. And I'm working, considering coming to the West Coast. So I was about to say, you know,

“I know it's in the name Boston Dynamics. But I assume with all that talent accumulating in the”

Bay area, you're going to need to pop up a space there. Yeah. Yeah, we're considering it. All right, listen, continue to success. Thank you so much. All right, everybody. Our next guest is Professor Jonathan Hurs. He's the co-founder and chief robotic officer or chief robot officer at agility robotics. You have a PhD in robotics from 2008. Yeah. So you've been at this for over 20 years. Well, over 20 years. Things seem to have heated up in the last 36 months. Maybe you could for the audience

before we get into your product line. Level set. What you've seen in the past 20 years. Yeah. And how the last two years compares to the previous 20. Yeah. I mean, 20 years ago and we were doing this, it really was an unknown industry, right? Robotics was more about automation systems. Yeah. And in the research community, we're doing things like humanoid robots, like autonomous, you know, mobile robots, really trying to build the intelligence and then build

the hardware that can make it capable. And that's really started to break through now into the real world and do having direct impact beyond being a research topic. Yeah. And then the universities have seen this demand and this growth and people love robots. Yeah. There's a lot of demand from students who want to do it. So the number of programs has grown and it's just exponentially growing very, very exciting, very exciting. We've had a lot of full starts with humanoid

robotics, which you're specializing in and AI and AI. They call it the AI winters, you know? Yes, multiple ones. This time is real. Yeah. Quite obviously. Explain to the audience why this time is different and why you believe this time we're going to see robotics and humanoid robotics

“specifically deployed at a scale that I think we can both agree it will be maybe in the next 20”

30 years, one to one with humans on the planet. Very impactful. Yeah. Why? Why is this time different? Yeah. Well, I would say generally it is very easy to make a robot that looks like a person. Okay. That's why we've seen humanoid for a hundred years and one. It's very hard to make a robot that can do useful things in human spaces and we're starting to see that today and that's the difference. So even if it doesn't look exactly like a human but maybe a little bit humanoid but it's doing

useful work. That's where the impact matters. And because of large language models, a lot of things have now become free. When these robots look at a table here and you say what's on the table, it knows that's a phone, it knows this is paper, tea, water. It probably knows how many ounces are in each. Yeah. If we were sitting here three or four years ago, it wouldn't actually know what was in the world. You would have to program it in a very narrow way, yeah? Yeah,

perceptions incredibly difficult. And the fact that perception is all that solved at this point is a really, really huge inflection point. I mean, you know, I said yes robots doing useful things but also people can now see the future of generality. AI is really enabling that much more broad context awareness for these robots. So people can see that this is going to be useful. Generally doing many useful things, very soon. So there's perception. The robot has to understand the world.

But then there always seem to be this blocker with getting the robot out of a very confined

and the training level. Maybe we can unpack that a bit because my understanding was previously you basically had to hard code the robot. If you were going to make a cup of coffee. We have a company I invested in cafe X and it is a robotic arm. It makes a cup of coffee perfectly every time can draft a beer all that stuff. But it had to be manually coded. Now the instruction set because of perception because of language models having trained on every video on the internet,

every coffee recipe, that also seems to be for free. Am I wrong or not yet? It's actually quite different. So language models think of it like it's it's now becoming kind of a commodity like the internet.

It's available to everybody. It's an amazing rising tide. But these language models are

trained off of the entire data on the internet. And that data does not exist for robot control. You know, what's the example for your robot? Of all the torques, all the torque commands to every

“motor, given all the sensor input. There's no training set of data. So you have to generate and”

create that somehow. And there's a lot of different approaches and ways people are going about this. And some of these AI tools, again, think of AI not as a black box. But as a big tent of many different, very different, useful computational tools. Right. In order to control a robot, you can do these things by learning from demonstration. You can give it, you can tell you operate the robots start to train from that data. You can give an animation input or motion capture

and put on a number of different things. But that's also got a real hard limit. Because a person

controlling a robot is not really getting to what the robot can do if it were optimal and how it's

behavior to work. That of a robot needs to practice, you know. And that's where you get into world models and seem to real transfer and all of these kinds of things. And world models are the next frontier. People are literally putting gloves on humans and having them control robots remotely to actually chop and make a salad to poor water. And that's being done today by many different companies. The world models will solve this problem or they are part of the part of the

solution. As with all of these things, there is no silver bullet. Right. So the world models as I understand it are, you know, can you model an entire warehouse? And all of the physics of all of the objects inside of it so that then simulations of these robots can go practice in the world model without breaking things in the real world and you know, compress. So you can do

you know, a million iterations within days and computationally things like that. But there's always

a massive sim to real gap. Things aren't simulated perfectly. And then, you know, as you pick up something in the real world and there's wave dynamics and there's condensation on the glass and the dynamics of the robot and all perfectly model, all these things are still very, very difficult. That takes real practice in real life with robots. Yeah. So is there going to be a singularity

“or a crossing over moment where recursive learning just putting the robot in the kitchen?”

Yeah. Letting it make it own mistakes and then saying do the next test, do the next test. Which is how we taught it how to win a chess or go. We didn't tell it like here's how to castle. We just brute forced it and said try every computation. And it was able to figure it out. Now with these recursive loops, what will get us there quicker? Somebody built a world model says go get recursive, puts the robots into a kitchen and, you know, breaks a lot of China.

Or is it going to be these world model companies very, refinedly working human, alongside robot, in a Michelin start, you know, kitchen to make that souffle. I mean, it's not a very satisfying answer, maybe, but it's all of the tools. Got it. All of them, right? There's not a silver bullet at all here. I don't believe that there's this singularity. I do believe that things are going to get better and better.

Think of it more like a snowball picking up steam going down a hill. Got it. But the reason that it's snowballing like this is because people are putting money and resources and engineering time and engineering effort in as they explore everything and start to figure all of this stuff out. All right. So humans, for example, we've evolved to learn. We are very good at learning and it takes very little data to show us how to do something.

And then we pack this and practice to iterate. Robots are not very good at learning yet. Robots take so much more data at so many more examples than a person. We're still figuring out how to teach robots how to learn. But then one of the benefits that robots have in the long run is they've got Wi-Fi. You know, when you learn how to play the violin, you can't just blow that to somebody else and then they learn how to play the violin,

know how to play the violin based on your learnings. Robots will be one robot learns to play violin,

“all robots know how to play violin. Or all robots have that type know how to play the violin, right?”

Yeah. And then minor variations for the next type and the next piece of hardware. So you are actually deploying your product. It's called digit. Digit is, I think, 4.0. You're in release 5.0. You've got, let's say, dozens in different applications out there in the world. Give us an idea of what the four deploy looks like today.

workflows that are still reasonably well-scoped by picking up bins and totes and carrying them around.

And the reason we do that is because you need two arms to pick up big things. You need this

“whole body control to be dexterous and how you're manipulating and moving those. You need to be”

balancing to lift them the top of the tall shelf and aerospace. So it kind of justifies the four factor for this one use case. But the real useful aspect of humanoid is its versatility. So when we do the each picking and, you know, fill a bin and carry it somewhere and palatizing and depalatizing and are expanding out into more and more use cases, so what it really starts to escalate. And digit V5, which is coming out later this year,

is the first time that a humanoid robot a robot was balancing, can step out of a work cell and does not need a physical barrier between the robot and the person to maintain safety from this warehouse. So when digit V5 is out there, that's kind of the scaling moment for us. Yeah, this is a key moment that maybe people don't appreciate. But if you've ever been to one of Elon's factories or Toyota's factories, there are lines. There's a line. And if you cross that line,

“the everything shuts down. Everything shuts down. And I've taken many of these tours with”

Elon and they're like, seriously, please don't cross that line because it's going to cost a million

dollars if you do with Tesla factory because it's cranking. We're starting to feel comfortable enough that these robots are not going to fall over and break somebody's ankle. Well, it's been a very, very intentional process of the past two or three years, where, you know, this is our experience with Amazon. When we deploy it in the robots are doing the tasks and they're like, great, you know, it solves all the R&D, you know, goals we had and we're like, great, let's go to

deploy and they're like, oh, no, no, we can't deploy because, you know, they're not, they don't meet our safety requirements. It's like, okay, how do we meet that? Well, it turns out. That's super hard. And so it's been a bottom to top design of this machine, holistic through the whole every system of the robot is touched to figure out how to make it safe. When we look at an

“industrial shrank robot like yours, yeah, Bill of materials, tens of thousands of dollars each,”

yeah. I mean, we're not discussing those materials. We know that the costs are coming down and down over time, we'll be selling robots, you know, in the vicinity of costs of cars and things like that, the real, like, what is the value that they produce is the question to ask? When you have a robot that's working 24 hours a day and has a five-year life, you know, what's the value? It's quite a lot.

Yeah, it would be, if we were to think about it from first principles, they can reasonably

we're on 20, 22 hours a day and then they have a charge and just, that's right. You tell us, so we take 20 hours a day, 30, 65 by the way. 20 out of 24 hours for our digital V5 robot because of the very fast charge generation that's gone on this path. Yeah, so we get, we have 20 hours for just 65 days a year. Yeah, you know, now you're in that 7, 8,000 hours a year, let's put it at 8,000, five years, 40,000 hours of work. It adds up. Yeah, and people tend to think these things are in a cost 20, 30, 40,000 dollars.

But they will at some point. Yeah. It's going to need to go through the scaling and have 100,000 robots out there before that actually is real. So that's a dollar an hour. These people are being paid in factories currently, $40 an hour. Yeah. Maybe in some other countries, $10 an hour. But let's put it at 20 bucks an hour. You've got 90% compression and costs at some point when these things hit the market, which gives you plenty of room to charge an Amazon, a Toyota, other partners on an

hourly basis, is that the current plan to charge per hour of utilization, you own the robot. We do both. We do a cap X for a customer as a prefer that. We also do robot as a service for customers. It's really a lower barrier to entry and lower risk for them. What's the price of a robot per hour? I'm not talking about that. That's talking about that. But I will say like, as obviously, as the robots get better and better and better, what they do, the value goes up and

up and up. And that's at the same time that the costs to build the robot are going down. And the value for these robots is really set by the human labor and what does it cost to pay people to do these jobs. So it's a very inelastic price for a very long time. So between a bill of materials, tens of thousands of dollars, currently, people in factories getting paid $20, $30 or $40 per hour in the western half of sphere in the modern world. It's a pretty big market. Yeah, a pretty big market,

plenty of room for you to save the money and for you to make enough profit to build an actual business. Yeah. So let's take the conversation to what do you think the time frame is if I were to ask you in Amazon factories, or if we want to take Amazon out because they're a partner and don't want to get in trouble. But an Amazon or target like company at what point will the majority of workers in a factory be robotic. When will that flip happen to 51% knowing what you

know Jonathan? I mean, already in a lot of these applications, the majority of the workers are robots.

robots, and that's not changing, that's continuing to grow. Sure. And this is just a new form of automation like all of the others that's helping to increase and build that productivity. So like, how do we, you know, how do we in the United States anyway? How do we build our GDP? It's not

“a growing population. No. It's increased efficiency and capability and the only way we could do that”

is more and more. Especially not with the anti-immigration vibes we have in the country right now or even in the Western Hemisphere. Yeah. Well, let me phrase the question another way. At one point,

if there were a million people working in factory sorting packages, just it go down to 500,000

that three, four, five year. I think we've already done that. Right. But looking for it, but with these new, you're going to just continue. Your Sunday, there's going to be an autonomous truck that drives up and then you have a completely lights out autonomous package certification factory and then, you know, a autonomous truck leaving again. And at that point, it's probably specialty automation doing those things because it's just 24/7 doing it. And a humanoid doesn't make sense.

It's not the most efficient thing for that specific task. A humanoid is useful for walking into human environments, doing human workflows. By the time this, one factory is entirely automated, there's also a whole bunch of other factories that still are, you know, legacy and still, you know, need automation where humans were. But then we're also working now in retail and grocery stores and hospitals and construction sites and delivering packages to your front door,

which is a forever human environment, right? Yeah. That kind of thing. That's going to be an interesting one. Yeah. Because it's fairly obvious to anybody who has even looked at the latest generation of humanoid robots that the factories are going lights out. Most people are incapable at this point of imagining a Wemo Robotaxi and Uber self-driving car and a robot getting out and bringing the packages to your doorstep. That's going to happen. Absolutely going on. Are you working with

folks on that? You don't have to say who? You know what? That was one of our very first use cases

if you spoke before. And there's a nice video online of our very first digit robot getting out of a vehicle walking up to someone's front porch and dropping a package there. Yeah. Stairs and everything. So we could do that like this was seven years ago, something like that. But I don't think it's the best first use case or the best first market. Yeah. So it's on our roadmap for short. But such a big market for deploying with what we're doing right now. We're going to start there. How do you

when you look at applications? We know applications that seem obvious to us not being in the industry, but knowing what you know over two or three decades, what do you think is a use case or two that are non-obvious? But that would be incredibly world-positive. I don't know what to say what's non-obvious. I mean just picking up stuff and putting them somewhere else is such a huge use case that frees people from the classic 3D's of robotics, the doll dirty danger is kind of

stuff. Doll dirty and dangerous. The 3D's of robotics. Yeah. And I really hope that we look, you know, like our children look back on now and look at some of the jobs that people are doing today that I really think of as robot jobs. The same way we look back on like coal miners in the 1900s and say I can't believe people did that work. And you know the number of roles and things

“that people do today are so much better. The quality of life is so much better. The jobs that people”

have today that you couldn't have imagined in 1900 often are just so much better. I think that that's how the future is going to look for us. You're still a professor at robotics. Yes. You have hundreds of people in this graduate program or over a hundred. Yes, we do. For young people who are listening to this, who are worried about their future in careers,

this seems like an incredible career path. It's a massive opportunity. We live in its time of change.

Anytime there's a time of change like this, students coming out have an advantage, because all the people who have this 20, 30-year career and have no other way things were done, they have to learn how the way things are coming up now too. So students have an advantage. And it's hard to predict exactly all the things that people, you know, the way the careers are going to look in 10 years. But if students just build some of the core skill sets around engineering,

it's going to be applicable in use for it. So there's the PhD master's version of robotics. Is there another version that is let's say a little more generation tool belt, blue collar,

“the equivalent of being an electrician or working on HVAC or a carpenter or a contractor?”

Yes, absolutely. What is that? And what will that be? Robot operators assembling and building robots. The robots can't assemble all themselves yet, you know? So there's a lot of manufacturing. And again, you know, robot operations and deployments, there's a lot. Maintenance, clunker, clunker maintenance. Absolutely. Is clunker a derogatory term? I don't know. It's a Disney, you know, trademark term.

You, you know, General Grievous from the Star Wars characters. Yeah. You trained in the Gen I, Dark Arts by Count Ducco, able to yield three or four or six lightsabers at a time.

“Half serious question. Why not have four or six arms facing all directions?”

It's a good question. So I would say that, you know, as we think about the first principles of

what how to make the simplest possible robots to do the tasks, right? One arm is not quite enough to pick up big things. You can only pick up small things. Two arms now you can pick up big things. Adding a third arm, it's hard to see the, enough utility to make it worth fitting it in. And then, you know, go to four to five. There's a lot to coordinate and a lot of extra complexity.

“But what else does it make you do? I don't know. Yeah. Maybe we'll see that.”

But it's going to have to be driven by a real need. All right. Favorite robot in science fiction history? Probably walley. Walley. And ease. I love kind of that vision of these robots. Just continuing to try and build and create and do what they were designed to do. Yeah. I love Baymax too. Baymax is pretty fantastic. Wait, wait. Who's Baymax? Baymax from one of the San Francisco from, oh, yes, of course. I do know this robot that's very

clearly there to help. And I love how they kind of show that it does what it's programmed to do. I mean, at one point, they remove all its memory and it turns red and now it's dangerous. Well,

“that's very real. You know, you're soft. You have to have the safeguards in place.”

You've got to have the east up on these things. So you think about the prime directives? Yeah, basically. How do you make sure that these things going through kind of the industrial safety process to make sure that boy, there's a supervisory circuit. There's a east up on every robot.

All of these things that make make the robots so they can just really never harm human.

Jonathan, I know you're hiring agility robotics is the company. And if people are looking for a gig, one place to work. A agility is great. And we have location in Salem, Oregon, where we started, where I am. We have a new facility we're opening in Fremont, California, which is just a beautiful place. And that's where we're doing a lot of robot behavior development. So there will be robots working all day long. And if you come in and be working on,

and we have a Pittsburgh location as well. Oh, right. Yeah, kind of humility. Amazing. Yeah,

three great centers. So if you're a young person or you're in the robotics field, pretty great place to work. And if you're worried a little bit about your future, go get a PhD or a master's in robotics, skate to where the puck is going folks. Right. Great to have met you. And thank you for sharing all your knowledge. Thank you.