All-In with Chamath, Jason, Sacks & Friedberg - The $1/Hour Worker: Four Robotics CEOs on Humanoids at Home, China's Threat, and the End of Dangerous Jobs

Episode Date: July 28, 2026

(0:00) Intro: Humanoids, Robots, & AI+ (0:57) ANYbotics' Dr. Péter Fankhauser: Why ANYbotics Bet the Company on Four-Legged Robot Dogs, Not Humanoids (13:18) Dr. Péter Fankhauser: China's Armed Robot Dogs Are "Stupid and Risky" — Inside the Terminator Debate (15:01) NEO's Bernt Børnich: Neo Ships in 2026 and Becomes an Open Robot Platform (22:18) Bernt Børnich: Robots Building Robots in Just 3 Years — "Hard Takeoff" (33:45) Boston Dynamics' Amanda McMaster: Spot Is Now the Most Deployed Robot on Earth — Atlas is the Most Capable (42:36) Amanda McMaster: Chinese Robotics Security Risks (47:14) Agility Robotics' Jonathan Hurst: Why "This Time Is Different" for Humanoids After 100 Years of False Starts (58:45) Jonathan Hurst: When Do Robots Outnumber Humans on the Factory Floor? Thanks to our partners for making this possible! AppLovin Ads is AppLovin's AI advertising platform reaching over a billion daily active users across mobile games. Full-screen video ads with a 35-second median watch time. Advertisers are profitably spending hundreds of thousands of dollars a day. AppLovin Ads is now open to all advertisers. Sign up at https://applovin.com/ALLIN  If your work depends on conversations — meetings, deal flow, interviews, customer calls — Plaud helps you capture and organize everything with highly accurate AI-generated notes that are not just simple summaries, but also highlight pain points, key decisions, next steps, and customizable summary templates.  Check out Plaud at https://www.plaud.ai/allin and use code ALLIN for up to 20% off! Which is also available on Amazon: https://amzn.to/43URLff (Code: ALLIN20X) Follow the besties: https://x.com/chamath https://x.com/Jason https://x.com/DavidSacks https://x.com/friedberg Follow on X: https://x.com/theallinpod Follow on Instagram: https://www.instagram.com/theallinpod Follow on TikTok: https://www.tiktok.com/@allin  Follow on LinkedIn:  https://www.linkedin.com/company/allinpod Intro Music Credit: https://rb.gy/tppkzl https://x.com/yung_spielburg Intro Video Credit: https://x.com/TheZachEffect

Hey everybody, it's your boy J-Cal. I'm here in Paris, France, at a conference called Machina. Basically AI in the real world. Pardon my robot. Thanks for tuning in, and let's get started. App Loven started with an $8 domain and no VC funding and became one of the largest ad platforms in the world. Now that same engine powers App Loven ads for e-commerce.

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Continue here in Paris. Really excited to have Dr. Pee. Peter Funkhouser on the program. You're the co-founder and CEO of Ennebotics. You make the Eni-mo, get it. 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, you're, what do you call these, dog-based robots? It's an inspection solution, right? It's about data collection and understanding, and critical infrastructure. But the form factor is. A four-legged robot, a quad-legged, or a dog. We like to call it a dog-obot.

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 did that one become the first one to hit, you know, relative scale and forward deployment? Yeah. In nature, you know, a lot of animals have four legs. So there's a 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're wide footprint, a lot of footholes hold onto, because we work in nasty environments.

Slippery floors, there's, you know, rain farming, this snow falling down, grass growing. 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 Optimus, 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 case. If you need to work, bring, you know, I don't know, in a coffee shop, bring it to you. And there's narrow spaces, right? You want to work in eye level.

Maybe a humanoid is better. In the facilities that we work, four-leg 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 they, were they senators in Greek mythology? Yeah, it's a senator.

So four legs and body on the other. 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, hundreds forward. Hundreds forward deployed over the last five years. 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 a year in service contracts. 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, 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. For us it's not about labor replacement, right? It's what can we do better? What can we do superhuman? Inspection is a great example. Our eyes and ears don't perceive all the signals. Microgas leakages, temperature equipment overheating,

with the cameras on the robot, thermal cameras, acoustic microphones, gas concentrations and all of that. We pack it full of sensors and AI, and 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 robot. And that's why we can afford having really expensive sensors, really expensive GPUs on top of a robot. Yeah, these have seriously powerful compute on them. Right. And they have to have a significant amount of battery power then. So these things can do a mission of what, an hour or two?

Two hours. Two hours. Two hours. But now a docking 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. 40, 4 is 0.

Because they're interested in a specific point when the electric art furnace goes up. They want to know in that minute what's how. minute what's happening. Too dangerous to send in a person. Thermal cameras burned. They need a robot at that right at that moment. Got it. And they have to charge, not hot swapping the batteries. No, you want 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 for an end to collect the data precisely.

At what point 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 is that. need to run real time on the robot because you also cannot guarantee connectivity, obstacle avoidance, data quality, making sure you have to write in. If you upload a blurry image with the cloud, it's too late. Yeah. But in the cloud, of course, you do contextual analysis, historic downtime, analysis, etc. 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 has enough time for charging. If you need to go beyond that, that's rare. There's diminishing returns to more frequently 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, right? So 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, right?

People fly out with helicopters. Every helicopter flight costs in the tens of thousands. But if you're offshore, it's very tricky, right? Got it. It needs to work. There's almost no people around. It needs to be flowing. These are oil rigs. Oil and wind energy offshore as well.

Ah. Well, 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 come together to a transformer station. Ah. That transforms to AC to DC before it transit.

That's a manned facility typically. Got it. Big converter rolls. This is where the robot operates. Got it. Can they operate like in severe conditions like the an arctic and stuff like that? And have you deployed them there yet? Well,

In Norway, for sure. So that's minus 20 degree in deserts plus 40, 50, 60 degree, right? So that's exactly the point where you want to send in a robot. Temperatures, dust, humidity. Most importantly, we have a robot now that goes into explosive atmospheres. We're just, you know, in oil and gas and chemicals, methane in the air. You're not allowed to, you know, create a spark. So we build a special robot that's guaranteed not 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 and gas leaks. This is where people seriously dies. And you don't want to know when it's happening,

or you don't want to create a problem, so that's a perfect case. 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 submarines, right? We don't do that right now, but I agree, right? Robots should work in environments where people shouldn't be. Dangerous, remote, right?

Voring repetitive tasks. This is what we... 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. For, obviously, the military.

Well, if you're out there inspecting, and there's a gas league, and it's dangerous to send humans out there, When are you going to put some equipment on these to fix the goddamn leak while you're out there? And that must be the holy grow, is it not? Yeah, once you can detect a problem? Customs muscles, can you solve it? Can you fix it? Can you?

Not today. Okay. You know, in a demo, yes. But in reality, getting it to 99.9% reliability in explosive atmosphere. Yeah. That's still in development. First step is closed levers, open cabinets. Eventually, you want to have by manual manipulation, maybe three, four arms to fix the machine, right?

That's still, you know, AI will help us. There's still 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 outdoor in a hailstorm, 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 to that. Zero percent. And is that because in the EU and Norway, it's banned or that's a choice? That happened just historically that we source locally. and you get chips from the US, et cetera. 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 hard word to navigate. But tapping into the commoditization of certain hardware, that makes sense for us cost-wise. 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 other... Well, short, China is number one pushing. There's good companies in Europe, right? In the US as well, so these three regions, for sure. If it's just about labor assembly, you can go elsewhere as well. But you want to get the core 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% 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 can walk. Beautiful, great engineering. Love it, do backflips. Yeah. But they're not solving the problem. Our customers don't compare a platform to the full solution that we have.

Got it. Do you need autonomy, inspection, intelligence, the workflow integration, so much more, right? It's just a hardware difference. So the harness, the wrapper, wrapper, the services around it, they're not providing yet. And then the trust in the data, right? We call it very sensitive data.

We have ISO certification with cyber security, all these topics, right? 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 your critical infrastructure at somebody else controls. Yeah, I'm being a bit facetious, but... How is happening today? But there's data leakage.

Talk to me about military applications. Yeah. NATO 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 and in NATO that you may have to go it maybe without the United States.

You may need to build your own military products and services. is 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 be independent.

You believe that personally. Yes. However, for any botics, we built and we went down one track, there's tremendous polls. So today we're not doing it, not intent to do it, right? And it's also a different product at that stage probably, right? It sounds very easy. Just take four legs and do military. You need to go a 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 others. Is it never say never for you? Or is it your dead set on like you have a mission, you're not going to build military products? For us, today the mission is clear. We started with non-military. This is where we're headed. Got it.

But if the EU asked you, and you were for us. I mean, we get, you know. Oh, you do get the requests. But it's also honest truth. Are we solving actually the problem? Just shipping a robot to the military doesn't solve the problem yet. really need to go deep. So you would need a different team to do that. Our team. Really, you need a different team?

Well, it seems like you could do the same team and build military applications. No, autonomy is very different, right? So for example, we do autonomy, you have time to set up a robot. And it does inspections, all of that. Yeah. In military, it's about millisecond being in, right? Remote controlled human in the loop, different communication, different autonomy. Then everything on top, application software, very different. Yes, you could lose it for like a robot to also go into a house. Yeah, 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. They're being built in China already. We've seen drones on the battlefield in Ukraine. Norway is not far away from Russia. 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?

I personally don't like it. I hate it. You're concerned. Right. I mean, as an engineer, you should have pride, right, to build technology for good. Defense is one part, the active attack, putting a gun on it, it's just risky. These technologies getting mature, but they're not that mature that you would put 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 it. What is the buzz inside the industry about this? When you're out with other people in the industry,

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. I've not seen these types of robot. Drones, yes, Ukraine, that came out of necessity. That was a mature category that was used.

In robotics, actually, to the people, I speak. I speak to. I mean, four years ago, we wrote a letter together with our friends, Boston Dynamics and others, right, who condemned the weaponization of robots for exactly that reason that as engineers, we don't want to see it being used. And we think it's just dangerous and risking stupid. Yeah. All right, listen, continued success. All right, everybody, really excited to have Bert Bornyck here. He is the founder and CEO of OneX. If you know OneX, they make the Neo. The Neo is a household robot. You've sold a lot of pre-order and you guaranteed people this would make it and would ship in 2026 into their homes.

What does it cost? And are you going to hit your self-imposed deadline? You've got to keep your promises. Okay. So we will ship in 2026. Okay. Now, expectation managing here, it'll be slow in the beginning. We want to do it right.

Yes. But there will be a handful of customers that get their 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 of it. I mean, when we launched a pre-order, we had two different payment models. You 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. So how this subscription model will look and these things are kind of like still evolving. Got it. 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 really announced this yet, but I've dripped it in a bit, which is we are going to allow a lot of people to build on Neo. So we are also launching Neo as a platform. Yeah, it's happening in the coming weeks. It's going to be like an app store of such or 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 consumer, right? Yeah. So consumer is an incredible, 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, 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 targeted 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 the autonomy and accelerate the path to actually having a fully autonomous agent at home? What was the pre-order? 20K or something? I'm trying to remember. So 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 So sort of like the Tesla $500 deposit, or $500 a month, $1,000 a month, something in that range? Yeah, 500 a month. $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 early adopters, yeah?

100%. I mean, we tried to be very transparent about this. Getting a home humanoid in 2026 is going to be rough around. the edges. Right. They're going to fall. They're going to fall. 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 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 tele-operation 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 tele-operating 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, act at 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 task. 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, okay, you buy a bunch of Nios, you get NEOs, you get the robot operating system with like the fleet management and all that.

You also get the data collection equipment, which is gloves that have the same tactile sensors as NEOs, the same vision system, and you can gather data in your your shop, fine-tune our model within our system where we kind of like, we do all the dense captioning of the data for we do all that. You find you in 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 does quite work. So you say, I'm going to have someone intervene sometimes in teleop, and then your data gets better. That's one way of doing it, right? 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 tell you up. That's also fine. Depends on how you want to apply this. And the platform goes all the way from like 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 a world model or Claude 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 can't make the best model, then we kind of failed. Yeah. But will we allow other people to build on this 100%. And one of the big reasons for this is that, Currently, if you look at where the field is, 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 could see some applications where one teleoperator, let's say this was a convenience store of 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 3,000, 30, 40 NEOs, and they control them remotely and help people move the groceries to their car.

Yeah? Personally, actually, I'm like, might have a use case for NEO in Tallyop, which is I'm part of the time in Norway, mostly in San Francisco area now, 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. Put the hat on Neo, I am Neo.

And that's actually pretty magical. I 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 teleoperation that I think actually will never go away. Like no matter how good your autonomy is, that will still be there.

Yeah, your avatar at your factory in Chen Zhen. 100%. Yeah. And you know, there are other applications like this where remote power stations, where there's no one within like 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 things. Sure. 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 or power converters. They can go out within, you know, minutes and... Yeah, 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 tell all, teleport them to anywhere in the world to help solve a situation. Like a surgeon, yeah. Yeah. It's super useful. I do think that what we've experienced over the last year is, first of all, that NEOW has become so capable, especially with the new hands, that teleoperation does not fully use the hardware.

Like you're not able to get the tele operation to be good enough to fully utilize the hardware. 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. Then you need to build full haptic systems, and they're going to slow you down and be slow and clunky. So we're increasingly seeing that gathering data with humans just wearing the sensors of the robot

in as transparent a manner as possible. So they should not disturb what you are doing, right? That's the most useful data to solve kind of baseline dexterity on the robot. But even more importantly, the big bet that we've made, which is this decade-long bed in 1X, is if you get to the robot to be similar enough to a human, then you can train on all the available video data out there of humans. Yes.

And we're starting to see some very good proof that this is actually working incredibly well. And that's the reason we started the Onex World Model Lab, because we now finally have the scaling loss on that, and we're seeing that this... Take us inside the lab. Are you having people in factories, wear glasses, wear your hands, and do their tasks over and over again? Are you working with the micro ones of the world to go do, do you know, 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, so very 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 pyramid, then on the top you have like tell operation data, very high quality, small, fine-tuned dataset, where actually what we do is you will have the operator try to do the task very well and very fast, and they will often fail and then just try again. And then we pick the good samples where they did the task as good as a human would, right? Yes. We don't need a lot of that data, it's just to align your model.

Then you have the data which is what you're talking about, we'd like put the sensors on the human go and gather data. Yeah. You have more of that and it's very close to the robot. But it's not the robot. The teleplata is the robot. This is not the robot, but it's close. Then you have egocentric video, video from human's point of view.

So that is further away from the robot, but it's still quite close, because the robot's hands is the same as human hands and it looks the same, and so it's quite close. And then you have general video data. Yes. 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 else. It's YouTube, 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 egocentric data or with this sensor data. Got it. 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, you've got a lot of people out there trying to find data.

Like, that is, like, what are the number of jobs? So 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 of, about every single tiny detail of the robot to be as goes to human as possible. Like, you know, like the flesh and tissue and skin is 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, literally in the NBA, there's a wingspan as a concept and people with a wide wingspan, longer arms than the average person, 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 called it for the gorilla coefficient. Yes. Long arms. Yeah. Yeah. But anyway, so my point is, yes, we do all of these things, but ultimately what differentiates one X from all of 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-enbordial. embodiment is the human. Right. And we want to be as close to that as possible, because that solves the catch-22. In the end, all the data will be robotics data, because the 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 these robots across society and they will do useful things that people pay for and also gather the data. When do 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 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, 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 accurate, but they know what their mission is. You've given them the goal, hey, you're working 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, you know, poaching these eggs to really be great at this.

It's kind of sci-fi. No, no, it's 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 asked me a year ago, I was way more bearish on how far how long we would be today on the AI. And, like, every time I kind of sample, things have moved faster than I think. So it's easy to get carried away, right?

But I think if I try to answer it broadly, I am extremely sure there were less than a decade away from hard takeoff. And when I say hard takeoff, I mean, robots building the robots, the data centers, the chip fabs, doing the mining and refining, actually a true abundance of labor, a self-sufficient system that is just scaled. Under 10 years. Under 10 years. My current bet would be three years.

Got it. But like if it takes 10, like in the history of humanity, right, it's still like, like a blimp. It doesn't really matter. So that gets back to like what is 1X, right? And you call this the industry term a hard launch or? Hard takeoff. Hard takeover. Take off. Not take over. We're going to do it right. It's going to be hard take off. Hard take over. Yes. But you know, that is the term, right? This is an industry term, hard take on. 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 a...

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. Yeah. Because if your 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, but it's years, not 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 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 and you order a Neo. That's it. It's that simple. It's that simple. It's 2026. It should be that simple.

It kind of should, right? If you can order a Tesla online, you can order an EO online. Transparent pricing. I like it. Yeah. Burnt, continued success. In your world, the exact words matter. The number on the diligence call. The commitment in the board meeting. Plaud captures a conversation and turns it into searchable intelligence you can pull up in seconds.

Ask Plot a question and get the answer with no receipt. Stop scrolling recordings or trusting your memory. Capture the conversation, keep the signal, that's Plaud. Learn more at plod.a.I. All right, everybody, we're really lucky. We have Amanda McMaster here. Not McMaster's. McMaster.

Just McMaster. No, Ms. 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 and 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 and Larry bought it. It was part of Google. Then it got sold, I think. Masayoshi-san owned it at some point. But I believe Hyundai owns it now. That's correct. Did I get that whole history correct? You did.

You nailed it. Okay. So apparently I read way too much industry news. But now you're in charge of this. It's 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 fear. competition, yeah? Yeah, we are big on deploying robots. So it's no longer an AI lab. It's not a lab. It's not a research and development company anymore. We're now focused on real world deployment. So we started with our spot robot, which many people know, that's our mobile quadru bed in industrial. Famously in Black Mirror chasing people down, not yours. You can own it, right? There's always going to be a dystopian version and a utopian version. You're obviously pursuing the utopian. But that is a really great. cool robot that has been for a deployed. Yes. It has been deployed in real customer sites. It's

providing really customer value. At this point, we have over 500 customers, over 46 countries. Wow. 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 customer. Why? Why? And 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 it? Pony dog? We like to think of as a dog. I mean, 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, they have expensive assets in their facility and they want to

monitor them, this allows for them to do that. Now, I can do that directly. 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. We want customers to find value in this, to do really useful work. It's not just about, yes, it's cute and it dances, but it's long-cost 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, were 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, infrastructure pipelines, and it can record many different sensors, video, obviously, vibrations, radar, I'm assuming, all different Thai acoustics you mentioned. Yep. What are those robots costs? What's the range of the hardware costs?

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 or dozens of customers deploying hundreds of these? Yeah, so we went with a CAPEX model to start with Spot. We'll be doing probably a robot as a service model likely with Atlas.

We understand the humanoid form factor. Folks may want to spin up at different times and have the ability to do 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 deployed. So it's the 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 would think.

Like, these are, you're 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, we think about in terms of meantime between intervention, and we're at over 3,000 hours. Oh, okay. Only 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. Usually we'd have two, comes back, sits down in charges, and the next one can take over. Does it automatically swap the batteries? It just sits down onto its charging pod. Perfect. Yes.

Atlas has swappable batteries, though. Yes. But the hot swap is a human has to do it. No, Atlas does it itself. Oh, it does itself. So Atlas will have two batteries, so it turns its torso around. And you replace one and put it with the other one. It always has a backup.

Perfect. So battery life is too. So for the humanoid 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 18, 20. 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 cost?

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's quite expensive. We haven't necessarily looked at labor replacement for a spot. While that is a metric you might look at, we thought about, you know, how do we bring spots in there to all. human labor. One, humans weren't doing the task, even if they were tasked with it.

They weren't actually doing it. And two, like, we're just trying to figure out ways that humans can do more, you know, knowledge worker tasks as opposed to going and doing inspection. So, yes, one of the metrics the customer I look like for ROI is labor replacement. We're leaning more into how much do we save you? So you found an air leak in your facility and that was a, would have been $3 million a day. Yeah, the outcomes matter. Yes. So what is the value that we're But is it still delicate in the industry to talk about labor replacement so you have to be very thoughtful about that in this moment of time? And let's be honest. I mean, there's going to be an element of labor replacement for this as 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 dangerous jobs. And the ones people might find oppressive. Yeah. Dull, dirty, dangerous. Dull, dirty, dangerous. Yeah, we don't want to do that.

hurting their body. Yeah, we only get one human body. Yeah. The Atlas, how do you think about onboard compute versus remote, when you put the amount of brains, my understanding is 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 brains in the cloud,

and having these be more lightweight if they're in an area that has extremely high speed, Wi-Fi, et cetera. 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 grok that? Or how should we think about it?

We think about two brains, right? It's my simplified version of telling the stories. There's two brains. There's the brain that controls the physicality of the robot, which is a very big, which what Boston Dynamics is known for. You know, the dynamic movement, reliability, the way it manipulates things in the world, that lives on the robot.

The reasoning layer that gives you the semantic understanding of its environment, that can be in the cloud. That's things that we might partner with Google Devine or we may partner with other AR partners or who'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 job processes that they have and the tools that exist in their facility and how this robot will interact with it, that's gonna live somewhere image-speed. So it could be on robot if you needed it too. It could be in the cloud. We'll figure out the wrapper for that. What percentage of the robot is built in the United States

or outside of China and Taiwan today? 100% of the robot. 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 the CEO of that, the CEO of this company and as an American, under any circumstances, should we allow to

humanoid robotics from China and the United States? No. 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. Listen, we have seen what happens if we let China win in the semiconductor space. You know, we can't do that with robotics. Right. So we need to have a consorted effort to protect our IP, to make sure that. that we are bringing manufacturing of this ecosystem into the United States, we're 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 in some of these discussions. I'm hoping that more companies in the US join us and 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 this, obviously military is, you know, the field has been changed with drones in a way and at a velocity, no pun intended, that I don't think anybody anticipated 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 are they at in terms of deployment in the military? Yeah, so we've been pretty public about the fact that we have an anti-webonization stance.

But why? 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 focused. It's not philosophic. I mean, it depends on you asking 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. We're happy to do any non-weaponization work with them. And then we do do that today. Okay. So you'll have them 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. Desaumming a bomb, you're okay with that. EOD is one of, you know, explosive ordinance disposal is something that's a great use case for robots.

And you're doing that currently? We do that currently. So we're okay with that. 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,

you're kind of obligated if you're Boston Dynamics to build them. So if China puts these into the field, will you build them to protect America? 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 and I can assure you what your answer will be

when President Trump calls, you will say, sir, yes, sir, or else your company will be nationalized. 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. Not on our robots. Not on yours.

Yes, and listen, it's terrifying. Terrifying. I think, listen, I know that we have the best robot and the most capable robot in the world. 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 a singer. 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've got to go. Continued success. This is such an important American company. And I hope you take the job and become full-time. I know your interim right now. So I wish you great luck with it.

If people want to come work at Boston Dynamics, where are you base? So we're in Waltham, so right outside of Boston. But we're open to some remote work, and we're considering coming to the West Coast. 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, continued success. Thank you so much. All right, everybody, our next guest is, professor Jonathan Hearst. He's the co-founder and chief robotic officer or chief robot officer at agility robotics. You have a PhD in robotics from 2008. 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 and how the last two years compares to the previous 20. I mean, 20 years ago when we were doing this, it really was an unknown in industry, right? Robotics was more about automation systems. And in the research community, we're doing things like humanoid robots, like autonomous, 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 to having direct impact beyond being a research topic. And then the universities have seen this demand and this growth, and people love robots. 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 fall 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, 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 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 humanoids for 100 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, perception was 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, you know, context, aware. 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. Yep. But then there always seem to be this blocker with getting the robot out of a very confined, narrow task, like, you know, in a factory. And my perception is it was the communication and the training level. Maybe we can unpack that a bit, because my understanding was previously, it was, you know, it was, you know,

Basically had to hard-code the robot if you were going to make a cup of coffee. We have a company I invested in CafeX, and it is a robotic arm. 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? Not yet.

It's actually quite different. So language models, think of it like it's now becoming kind of a commodity like the internet. It's available to everybody. It's 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 tense of many different, very different, useful computational tools. Right. On order to control a robot, you can do these things by learning from demonstration. You can give it, you can teleoperate the robot and start to train from that data. You can give it animation input or motion capture input or any 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 its behavior could work. That of the robot needs to practice, you know? And that's where you get into world models and SIM 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 pour water. And that's being done today by many different companies. It is. 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, compressed so you can do a, you know, a million iterations within days and computation and 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 are not perfectly modeled. 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, letting it make its 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 force 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 builds 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-starred, you know, kitchen to, to make that suflay.

I mean, it's not a very satisfying answer, maybe, but it's all of the tools, 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 gonna 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. But 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 practice and practice to iterate. Robots are not very good at learning yet. Robots take so much more data and so many more examples at 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 load that to somebody else and then they learn how to play the violin based on your learnings. Robots will be able to do that. One robot learns to play violin, all robots know how to play violin, know how to play a violin. Or all robots of that type know how to play the violin, right?

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 going to release 5.0. You've got, let's say, dozens in different applications out there in the real world.

Give us an idea of what the Ford Deploy looks like today and where you think it will be in a year or two. So today, it's doing these sort of multipurpose workflows that are still reasonably well scoped, like 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 in how you're manipulating and moving those. You need to be balancing to lift them the top of a tall shelf and narrow space. So it kind of justifies the form factor for this one use case. But the real useful aspect of a humanoid is its versatility.

So when we do the each picking and fill a bin and carry it somewhere and pallet, and depalotizing and are expanding out into more and more use cases. So when 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 which is balancing, can step out of a work cell and does not need a physical barrier between the robot and the person to maintain safety in this warehouse.

So when Digit V5's out there, that's kind of a scaling moment for us. Yeah, this is a key moment that maybe people don't appreciate. Right. 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, 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 gonna cost a million dollars if you do at the Tesla factory. Because it's cranking. We're starting to feel comfortable enough that these robots are not gonna fall over

and break somebody's ankle. Well, it's been a very, very intentional process over the past two or three years. Right. Where, you know, this is our experience with Amazon, when we deployed and the robots are doing the task 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 deploy and they're like, oh, 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 make it safe. When we look at an industrial shrank robot like yours, fill of materials, tens of thousands of dollars each, yeah? I mean, we're not discussing bills and materials. We know that the costs are coming down and down and down over time. We'll be selling robots 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? And it's quite a lot. Yeah, it would be, if we were to think about it from first principles, they can reasonably run 20, 22 hours a day, and then they have to charge and just be jail-old. So we take 20 hours a day, 365 days. That's exactly right, by the way.

20 out of 24 hours for our Digit V5 robot because of the very fast-charge generation that's gone on this pattern. So we have 20 hours, 365 days a year. Now you're in that 7,000, 8,000 hours a year. Let's put it at 8,000, 5 years, 40,000 hours of work. It adds up. Yeah, and people tend to think these things are going to cost

20, 30, $40,000. 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. Maybe in some other countries, $10 an hour.

But let's put it at $20 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 or 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 CAPX for a customer that prefer that. We also do robot as a service for customers that prefer that. It's really a lower barrier to entry and lower risk for them. What's the price of a robot per hour these days? We're not talking about that right now.

Not talking about that either. But I will say like, obviously, as the robots get better and better and better at what they do, their value goes up and up and up. And that's at the same time that the cost 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 dollars per hour in the western hemisphere in the modern world. Which is a pretty big market. Yeah, a pretty big market. Plenty of room for you to save them money and for you to make enough profit. Build an actual business, you know? 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, don't want to get you 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, John? I mean, already in a lot of these applications, the majority of the workers are robots. Sure. Right? There's a lot of AMRs. There's a lot of conveyor belts. There's a lot of industrial robot arms. 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 in the United States. So like 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 automation. Especially not with the anti-immigration vibes we have in the country right now, or even in the

Western Hemisphere. Let me phrase the question another way. At what point, if there were a million people working in factory sorting packages, does it go down to 500,000? Is that a three, four, five year? I think we've already done that. Right, but looking for it. But with these new...

It's going to just continue. You know, someday, there's going to be an autonomous truck that drives up and they have a completely lights out autonomous package sortation. factory and then, you know, an 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. So 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. And 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 Waymo robotaxie and Uber self-driving car and a robot getting out and bringing the packages to your doorstep. That's going to happen. Absolutely. 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 that we explored with Ford. 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, stairs and everything. So we could do that. 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. So it's on our roadmap for sure. 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 not obvious. I mean, just picking up stuff and putting them somewhere else is such a huge use case that frees people from the classic 3Ds of robotics, the dull, dirty, dangerous kind of stuff. Dull, dirty, and dangerous.

The 3Ds 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. Yeah. The same way we look back, on coal miners in the 1900s and say,

I can't believe people did that work. And 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 gonna look for us.

You're still a professor of robotics. Yes. You have hundreds of people in this graduate program or over 100. Yes, we do. For young people who are listening to this, who are more, worried about their future and careers, this seems like an incredible career path.

It's a massive opportunity. We live in a 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 know how the way things were done, they have to learn how the way, you know, the way things are coming up now too. Yeah. 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 to use for. 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.

Of course. Clanker maintenance. Absolutely. Is Clanker a derogatory term? I don't know. It's a Disney, you know, trademark term. Oh, is it really? Probably.

Probably. Final question. I think we're of the same Gen X. You know General Grevis from the Star Wars characters. Yeah. You trained in the Jedi Dark Arts by Count Ducous. able to yield three or four, six lightsabers at a time. To have 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 how to make the simplest possible robot to do the task, 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. fitting it in and then 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. Maybe we'll see that, but it's gonna have to be driven by a real need.

All right, favorite robot in science fiction history? Probably Wally. 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? Who's Baymax? from what are the San Fransokio from... Oh, yes, of course. I do know who you're talking about. 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 e-stop on these things. So you think about the prime directives. Yeah, basically. Yeah.

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 an e-stop on every robot, all of these things that make the robots. They can just really never harm you here. Jonathan, I know you're hiring Agility Robotics is the company. And if people are looking for a gig, fun place to work. 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 you can come in and be working on. And we have a Pittsburgh location as well. Oh, right, yes. Right by Carnegie Mellon. 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. Great to have met you, and thank you for sharing all your knowledge. Thank you. I'm going on.