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

(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 10% off! Which is also available on Amazon: https://amzn.to/43URLff (Code: ALLIN10X) 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

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Jason Calacanis?

  Hey everybody, it's your boy J Cal. I'm here in Paris, France, at a conference called Maquina. Basically, AI in the real world.

• 0:14Jason Calacanis?

  Pardon my robot.

• 0:17Jason Calacanis?

  Thanks for tuning in, and let's get started.

• 0:23Jason Calacanis?

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• 0:58Jason Calacanis?

  All right, everybody, our interviews with the number one companies in robotics today continue here in Paris. Really excited to have Dr. Peter Funkhauser on the program. You're the co-founder and CEO of Antibiotics. You make the "any-mo," get it? 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, you're, what do you call these? Dog-based robots?

• 1:30Speaker 01

  Well, it's an inspection solution, right? It's about data collection and understanding and critical infrastructure.

• 1:35Jason Calacanis?

  But the form factor is...

• 1:36Speaker 01

  A four-legged robot, a four-legged, or a dog, as you've just said.

• 1:39Jason Calacanis?

  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?

• 1:59Speaker 01

  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.

• 2:07Jason Calacanis?

  So dexterity and balance.

• 2:09Speaker 01

  Mobility, right? Balance, but also stability. Four legs, if you're wide footprint, a lot of footholds hold on to. Because we work in nasty environments, slippery floors, there's, you know, rain forming, this snow falling down, grass growing.

• 2:22Jason Calacanis?

  Got it.

• 2:22Speaker 01

  So four legs is a real good format.

• 2:24Jason Calacanis?

  Well, now, this is a silly question, but why don't we make cenotars 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 gonna fall over, they constantly fall over in demos, and if they fall over, they're gonna break somebody's ankle. Why not put four legs on those?

• 2:47Speaker 01

  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 a tube, and there's narrower spaces, right? You wanna work at eye level, maybe a humanoid is better. In the facility that we work, four legs, stability, there's enough space to go around.

• 3:01Jason Calacanis?

  Yeah.

• 3:02Speaker 01

  It's the perfect format.

• 3:03Jason Calacanis?

  I don't buy it, I think all the cafes should have... Are they, were they cenotars in Greek mythology?

• 3:08Speaker 01

  Yeah.

• 3:09Jason Calacanis?

  It's a cenotar.

• 3:09Speaker 01

  So four legs and...

• 3:10Jason Calacanis?

  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?

• 3:24Speaker 01

  Hundreds, yeah.

• 3:25Jason Calacanis?

  Hundreds forward deployed over the last five years.

• 3:27Speaker 01

  Yeah.

• 3:28Jason Calacanis?

  These are expensive, they're low hundreds of thousands of dollars to buy them.

• 3:32Speaker 01

  Yeah.

• 3:33Jason Calacanis?

  And to operate them, I'm assuming tens of thousands a year in service contracts?

• 3:38Speaker 01

  Yeah.

• 3:38Jason Calacanis?

  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.

• 3:51Speaker 01

  Right.

• 3:52Jason Calacanis?

  These things can go out in any weather, and they can sense things on that pipeline that a human can't, correct?

• 4:00Speaker 01

  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, 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, 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. So that's why we can afford having really expensive sensors, really expensive GPUs on top of a robot.

• 4:38Jason Calacanis?

  Yeah, these have seriously powerful compute on them.

• 4:41Speaker 01

  Right.

• 4:42Jason Calacanis?

  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?

• 4:48Speaker 01

  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.

• 4:55Jason Calacanis?

  14?

• 4:56Speaker 01

  40, five, four, zero.

• 4:57Jason Calacanis?

  Four, zero.

• 4:58Speaker 01

  Interested in a specific point. When the electric arc furnace goes up, they wanna know in that minute what's happening. Too dangerous to send in a person, thermal cameras burned, they need a robot that's right at that moment.

• 5:08Jason Calacanis?

  Got it, and they have to charge, not hot-swapping the batteries?

• 5:11Speaker 01

  No, you want hands-free autonomy. Nobody should even be bothered that there's a robot. 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 an end to collect the data precisely.

• 5:23Jason Calacanis?

  At what point can you upload the very power-hungry compute and put it in the cloud?

• 5:29Speaker 01

  We also do that, there's always two parts. There's parts that need to run real-time on the robot, because you also cannot guarantee connectivity, obstacle avoidance, data quality, making sure you have the right thing. If you upload a blurry image to the cloud, it's too late.

• 5:42Jason Calacanis?

  Yeah.

• 5:43Speaker 01

  But in the cloud, of course, you do contextual analysis, historic downtime analysis, et cetera.

• 5:47Jason Calacanis?

  Are people asking for these to be able to operate for 24 hours yet, or 12 hours?

• 5:53Speaker 01

  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, they will 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.

• 6:09Jason Calacanis?

  Yeah.

• 6:09Speaker 01

  Without putting people into harm's way, plus the quality is so much higher.

• 6:12Jason Calacanis?

  What's the most fascinating science fiction deployment you have currently with these?

• 6:19Speaker 01

  Yeah, I mean, what's really exciting, anything offshore, right? 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?

• 6:29Jason Calacanis?

  Got it.

• 6:30Speaker 01

  It needs to work, there's almost no people around, it needs to be flown.

• 6:32Jason Calacanis?

  These are oil rigs?

• 6:33Speaker 01

  Oil and, and wind energy offshore as well.

• 6:36Jason Calacanis?

  Ah, but wait a second, these things don't operate in the water, so how do they work with windmills in the ocean?

• 6:42Speaker 01

  There's windmills, around hundreds of them, they go together to a transformer station.

• 6:47Jason Calacanis?

  Ah.

• 6:47Speaker 01

  That transforms to AC to DC before it transforms, that's a manned facility typically.

• 6:51Jason Calacanis?

  Got it.

• 6:51Speaker 01

  They convert all, this is where the robot operates.

• 6:53Jason Calacanis?

  Got it. Can they operate, like, in severe conditions like the Antarctic and stuff like that, and have you deployed them there yet?

• 6:59Speaker 01

  Well, in Norway, for sure, so that's minus 20 degrees. In deserts, plus 40, 50, 60 degrees, right? So that's exactly the point where you wanna send in a robot. Temperatures, dust, humidity, most importantly, we have a robot now that goes into explosive atmospheres. Where there's, you know, in oil and gas and chemicals, methane in the air, you're not allowed to, you know?

• 7:19Jason Calacanis?

  Got it.

• 7:20Speaker 01

  Create a spark. So we built a special robot that guaranteed not to create a spark. This is where you don't wanna have people, but for a machine, that's the perfect case, right? Dangerous environment, this is where we're sending robots in.

• 7:30Jason Calacanis?

  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 die.

• 7:42Speaker 01

  And you don't want, you wanna know when it's happening?

• 7:44Jason Calacanis?

  Yeah.

• 7:44Speaker 01

  Or you don't wanna create a problem, so that's the perfect case.

• 7:46Jason Calacanis?

  I mean, I'm gonna keep going sci-fi, but dropping these things into the bottom of the ocean seems like a no-brainer at some point.

• 7:52Speaker 01

  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? Boring, repetitive task, but this is what we...

• 8:02Jason Calacanis?

  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.

• 8:09Speaker 01

  Right.

• 8:09Jason Calacanis?

  Robots that are doing essentially, not inspections, but monitoring systems for, obviously, the military.

• 8:18Jason Calacanis?

  Well, if you're out there inspecting, and there's a gas leak, and it's dangerous to send humans out there, when are you gonna 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?

• 8:36Speaker 01

  Yeah, once you can detect a problem, customers, can you solve it? Can you fix it?

• 8:39Jason Calacanis?

  Can you?

• 8:40Speaker 01

  Turn, not today.

• 8:41Jason Calacanis?

  Okay.

• 8:41Speaker 01

  You know, in a demo, yes, but in reality, getting into 99.9% reliability in explosive atmosphere?

• 8:47Jason Calacanis?

  Yeah.

• 8:47Speaker 01

  That's still in development. First step is closed levers, open cabinets, eventually you wanna have bi-manual manipulation, maybe three, four arms to fix the machine, right? It'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 humanoids 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.

• 9:10Jason Calacanis?

  What percentage of your robot is sourced from China?

• 9:15Speaker 01

  Zero.

• 9:16Jason Calacanis?

  Zero percent?

• 9:17Speaker 01

  Yeah.

• 9:17Jason Calacanis?

  And is that because in the EU and Norway it's banned, or that's a choice?

• 9:22Speaker 01

  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 ones are active, which ones are just metals. Um, so for sure, it's a, it's a hard word to navigate, but tapping into the commoditization of certain hardware. That makes sense for us.

• 9:47Jason Calacanis?

  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?

• 9:57Speaker 01

  For sure, China is number one pushing. There's good companies in Europe, right? Uh, in the US as well, so these three regions. 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.

• 10:10Jason Calacanis?

  Got it. And how do you look at China now? They've been stealing the IP. I'm assuming they've stolen yours already. Um, 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?

• 10:35Speaker 01

  If you look at the robot from China today, that device is a piece of hardware that can walk beautifully. Great engineering, love it, two backflips. But they're not solving the problem. Our customers don't compare a platform to the full solution that we have. Do you need autonomy, inspection, intelligence, the workflow integration, so much more, right? It's just a hardware difference.

• 10:51Jason Calacanis?

  So the harness, the wrapper, the services around it, they're not providing yet.

• 10:56Speaker 01

  The trust in the data, right? We call it very sensitive data. We have ISO certification for cybersecurity, all these topics, right? So that's how we compete.

• 11:03Jason Calacanis?

  So you might not want to send the nuclear power plant's latest uh data to the Chinese Communist Party, you're saying?

• 11:11Speaker 01

  You don't want to have 15 cameras in your critical infrastructure that somebody else controls.

• 11:16Jason Calacanis?

  Yeah, I'm being a bit facetious, but uh.

• 11:18Speaker 01

  But it's happening today.

• 11:19Jason Calacanis?

  But it's, there is data leakage. Talk to me about military applications.

• 11:24Speaker 01

  Yeah.

• 11:24Jason Calacanis?

  NATO is uh having to arm itself. I apologize on behalf of the United States uh 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 uh 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?

• 11:57Speaker 01

  Yeah. So I think there's a responsibility in Europe to build technologies to be able to.

• 12:02Jason Calacanis?

  You believe that personally?

• 12:03Speaker 01

  Yes. However, for any botics, we built and we went down one track, this tremendous pole. So today we're not doing it, not intend 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.

• 12:24Jason Calacanis?

  Is it never say never for you? Uh, or is it you're dead set on like you have a mission, you're not going to build military product?

• 12:32Speaker 01

  For us, today, the mission is clear. We started with non-military, this is where we're headed.

• 12:36Jason Calacanis?

  Got it. But if the EU asked you and you were forced.

• 12:40Speaker 01

  And we get, you know.

• 12:41Jason Calacanis?

  Oh, you do get the request.

• 12:42Speaker 01

  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. We really need to go deep. So you would need a different team to do that.

• 12:51Jason Calacanis?

  Really, you need a different team? Well, it seems like you could do the same team and build military applications.

• 12:57Speaker 01

  No, autonomy is very different, right? So for example, we do autonomy, you have time to set up a robot and that it does inspections, all of that. In military, it's about millisecond being in, right? Remote controlled human in the loop, different communications, different autonomy. Then everything on top, application, software, very different. Yes, you could lose a four-legged robot to also go into a house. That's about it, right? The rest is different.

• 13:18Jason Calacanis?

  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?

• 13:54Speaker 01

  I personally don't like it, I hate it.

• 13:56Jason Calacanis?

  Yeah, you're concerned.

• 13:57Speaker 01

  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, in harm's way.

• 14:11Jason Calacanis?

  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, you know, what do you know that we don't know about what's happening in those authoritarian countries with robotics and the military?

• 14:33Speaker 01

  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 active. Drones, yes, Ukraine, that came out of necessity. Uh, that was a mature category that was used. In robotics, actually, to the people I speak to, I mean, four years ago, we wrote a letter together with our friends at 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.

• 14:59Jason Calacanis?

  Yeah. All right, listen, continued success. All right, everybody, really excited to have Bert Bornek 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-orders 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?

• 15:27Speaker 02

  You've got to keep your promises. So we will ship in 2026.

• 15:31Jason Calacanis?

  Okay.

• 15:31Speaker 02

  Now, expectation managing here, it'll be slow in the beginning. We want to do it right. But there will be a handful of customers that get their Neo in 2026 and I'm so excited and I can't wait.

• 15:43Jason Calacanis?

  What is the cost of the Neo?

• 15:45Speaker 02

  So that's an interesting one because it depends a bit. Um, I mean, when we launched the preorder, we had two different payment models. You had a kind of like early adopter upfront, full payment. Um, 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. 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.

• 16:23Jason Calacanis?

  Yes, 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?

• 16:38Speaker 02

  That will be part of it. But to me, Neo and OneX is about so much more than just consumer, right? 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, not just in homes.

• 17:06Jason Calacanis?

  Right.

• 17:06Speaker 02

  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?

• 17:29Jason Calacanis?

  What was the preorder, 20K or something?

• 17:31Speaker 02

  And so we have, we haven't given out official numbers, but it's pretty significant. We sold, we sold out the first 10K in the first two days.

• 17:40Jason Calacanis?

  Oh, so people put a deposit down for that. They'll have the ability to fully, uh, so sort of like the Tesla $500 deposit. Or 500 a month, 1,000 a month, something in that range?

• 17:51Speaker 02

  Yeah, 500 a month.

• 17:52Jason Calacanis?

  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?

• 18:08Speaker 02

  A hundred percent. I mean, we try to be very transparent about this. Getting a home humanoid in 2026 is going to be rough around the edges.

• 18:15Jason Calacanis?

  Right, they're going to fall.

• 18:16Speaker 02

  They're going to fall. Uh, 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 is 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 the thing that really excites me these days is that we're, we're seeing the path now to actually shipping something that if you wanted, it can be a fully autonomous experience and it's getting pretty darn good.

• 18:57Jason Calacanis?

  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 um a cashier. So they hired somebody in Manila in the Philippines for, you know, $3 an hour, which is a huge salary for a, for a cashier in the Philippines. And they had her on a Zoom call. They just popped up Zoom, 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 task?

• 19:40Jason Calacanis?

  Is, is that what I'm envisioning here correctly or incorrectly?

• 19:44Speaker 02

  I think it will all happen. So, so, so, so back to how the platform works, right? 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, then, okay, you buy a bunch of Neos, 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 shop. Fine-tune our model within our system where we kind of like, we do all the cap, dense captioning of the data for we, you like, we do all that. You fine-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.

• 20:30Speaker 02

  Maybe that does quite work. So you say, ah, I'm gonna have someone intervene sometimes in teleop. And then your data gets better. That's one way of doing it, right?

• 20:39Jason Calacanis?

  Yeah.

• 20:39Speaker 02

  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 teleop. That's also fine. Depends on how you wanna 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 gonna allow that, I think.

• 21:06Jason Calacanis?

  So you're gonna 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 OpenAI has a world model or Claude or some of the other independent world models. They'll be able to be plugged in, yeah?

• 21:21Speaker 02

  Hundred percent. Now, I sincerely believe that our model will be the best one.

• 21:25Jason Calacanis?

  Sure.

• 21:26Speaker 02

  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.

• 21:35Jason Calacanis?

  Yeah.

• 21:36Speaker 02

  Uh, but will we allow other peoples to build on this? Hundred percent. And one of the big reasons for this is that currently, if you look at where the, where the field is, there is no one general model that solves everything for robotics. It's not there yet.

• 21:49Jason Calacanis?

  Right.

• 21:50Speaker 02

  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 wanna do is to work on the general problem. How do we solve embodied AGI? So we can actually create an abundance of labor.

• 22:07Jason Calacanis?

  Got it.

• 22:07Speaker 02

  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.

• 22:23Jason Calacanis?

  Yeah, and I could see some applications where one teleoperator, let's say this was a convenience store robot, that just help 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?

• 22:49Speaker 02

  Personally, actually, I'm, I'm like, I have a use case for Neo.

• 22:52Jason Calacanis?

  Okay.

• 22:53Speaker 02

  In teleop, 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.

• 23:07Jason Calacanis?

  Yes.

• 23:08Speaker 02

  Put the hat on Neo, I am Neo. And that's actually pretty magical. And you can, 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.

• 23:25Jason Calacanis?

  Yeah, your avatar at your factory in Shenzhen.

• 23:28Speaker 02

  Hundred percent.

• 23:29Jason Calacanis?

  Yeah.

• 23:29Speaker 02

  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 like flip the old switches and you do the things. Like, you're likely not gonna automate that because it's kind of like a one-off thing that happens every few months, right?

• 23:51Jason Calacanis?

  Right.

• 23:52Speaker 02

  So.

• 23:53Jason Calacanis?

  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, and work remotely.

• 24:06Speaker 02

  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 tele, teleport them to anywhere in the world to help solve a situation.

• 24:15Jason Calacanis?

  Like a surgeon, yeah.

• 24:16Speaker 02

  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.

• 24:28Jason Calacanis?

  Yeah.

• 24:28Speaker 02

  That teleoperation does not fully use the hardware. Like, you're not able to get the teleoperation to be good enough to fully utilize the hardware.

• 24:35Jason Calacanis?

  Ah, so the fidelity of the hand is greater than a teleoperator is able to leverage?

• 24:40Speaker 02

  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 gonna slow you down and be slow and clunky and like. 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 like, 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 made, which is this decade-long bet in OneX, is if you get the robot to be similar enough to a human, then you can train on all of the available video data out there of humans.

• 25:17Jason Calacanis?

  Yes.

• 25:18Speaker 02

  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 laws on that and we're seeing that this.

• 25:27Jason Calacanis?

  Take us inside that, take us inside the lab. You, are you having people in factories, wear glasses, wear your hands and do their tasks over and over again? Uh, are you working with the micro ones of the world to go do, you know, real world stuff and outsourcing like unique proprietary data that you can have that other companies don't? How, how does the world model get built at scale?

• 25:50Speaker 02

  So, so, so first of all, 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 gonna be as good as the data.

• 26:00Jason Calacanis?

  Yeah.

• 26:00Speaker 02

  And if you think about the data pyramid, then on the top, you have like teleoperation 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? We don't need a lot of that data, it's just align your model. Then you have the data which is what you're talking about with like put the sensors on the human, go and gather data.

• 26:32Jason Calacanis?

  Yeah.

• 26:33Speaker 02

  You have more of that and it's very close to the robot, but it's not the robot. The teledata is the robot. This is not the robot, but it's close. Then you have egocentric video, video from humans' 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 like it looks the same and so it's quite close. And then you have general video data.

• 26:56Jason Calacanis?

  Yes. Of the world.

• 26:58Speaker 02

  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.

• 27:17Jason Calacanis?

  It's YouTube, it's everything.

• 27:18Speaker 02

  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 few years with egocentric data or with this sensor data.

• 27:34Jason Calacanis?

  Got it.

• 27:35Speaker 02

  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.

• 27:51Jason Calacanis?

  Well, you've got a lot of people out there trying to find data. Like, that is like one of the number one jobs.

• 27:55Speaker 02

  Years. So it's, it's like a catch-22. So our big bet is you have to be able to utilize the general video data out there.

• 28:02Jason Calacanis?

  Yeah.

• 28:03Speaker 02

  And the only way to do that is you have to care about every single tiny detail of the robot to be as close to human as possible. Like, you know, like the flesh and tissue and skin.

• 28:13Jason Calacanis?

  Yeah.

• 28:14Speaker 02

  Is highly nonlinear. So like how much force for it to deform? What's the friction? Like, what is the impact energy when touching the table?

• 28:22Jason Calacanis?

  And people have different sized 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.

• 28:39Speaker 02

  That, that's a really good way of saying it, wingspan. We've always, we've always called it for the, the, the gorilla coefficient.

• 28:45Jason Calacanis?

  Yes, long arms.

• 28:46Speaker 02

  Yeah.

• 28:47Jason Calacanis?

  Yeah.

• 28:47Speaker 02

  But anyway, yeah, so my point is, yes, we do all of these things. But ultimately, what differentiates OneX 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.

• 29:00Jason Calacanis?

  Yes.

• 29:01Speaker 02

  And that our cross-embodiment is not another robot. Our cross-embodiment is the human.

• 29:06Jason Calacanis?

  Right.

• 29:06Speaker 02

  And we wanna 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.

• 29:29Jason Calacanis?

  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, 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, uh, 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.

• 30:14Jason Calacanis?

  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. Um, and here are the outcomes. And it says, okay, I've just gotta get better at, you know, uh, poaching these eggs to, to really be great at this. It's kind of sci-fi.

• 30:40Speaker 02

  No, no, it's not sci-fi. It's actually, it's 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 along we would be today on the AIs. And like every time I kind of sample, things have moved 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 chip fabs, doing the mining and refining, actually a true abundance of labor, a self-sufficient system that is just scaled.

• 31:23Jason Calacanis?

  Under 10 years.

• 31:24Speaker 02

  Under 10 years. My current bet would be three years.

• 31:27Jason Calacanis?

  Got it.

• 31:28Speaker 02

  But like if it takes 10, like in, in the history of humanity, right? It's still like a blip. It doesn't really matter. So that gets back to like what is OneX, right?

• 31:36Jason Calacanis?

  And you call this, the industry term hard launch or?

• 31:39Speaker 02

  Hard takeoff.

• 31:40Jason Calacanis?

  Hard takeover.

• 31:41Speaker 02

  Takeoff, not takeover. We're gonna do it right. It's gonna be hard take off, not takeover.

• 31:46Jason Calacanis?

  Yes.

• 31:47Speaker 02

  Uh, but you know.

• 31:49Jason Calacanis?

  I've heard the term, right? This is an industry term, hard take on.

• 31:53Speaker 02

  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.

• 32:00Jason Calacanis?

  Right.

• 32:01Speaker 02

  And I think also this is gonna 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.

• 32:15Jason Calacanis?

  Yeah.

• 32:15Speaker 02

  Because if your AI model can't actually build and carry out its experiments and observe the results, how are they gonna 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.

• 32:31Jason Calacanis?

  Yeah, I mean, 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 gotta catch a flight? This is amazing, continued success. If people wanna 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?

• 33:01Speaker 02

  Well, you go to our website and you order a Neo.

• 33:03Jason Calacanis?

  That's it, that's simple.

• 33:05Speaker 02

  It's that simple. It's 2026, it should be that simple.

• 33:07Jason Calacanis?

  It kinda should, right? If you can order a Tesla online, you can order a Neo online.

• 33:11Speaker 02

  Transparent pricing.

• 33:12Jason Calacanis?

  I like it.

• 33:13Speaker 02

  Yeah.

• 33:13Jason Calacanis?

  Burnt, continued success.

• 33:21Jason Calacanis?

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• 33:45Jason Calacanis?

  All right, everybody, we're really lucky. We have Amanda McMasters here. Not McMasters, McMaster.

• 33:52Speaker 03

  Just McMaster, no S.

• 33:53Jason Calacanis?

  Just McMaster, no McMasters. 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 Son owned it at some point, but I believe Hyundai owns it now.

• 34:25Speaker 03

  That's correct.

• 34:26Jason Calacanis?

  Did I get that whole history correct?

• 34:27Speaker 03

  You did, you nailed it.

• 34:28Jason Calacanis?

  Okay, so apparently I read way too much industry news, but now you're in charge of this.

• 34:34Speaker 03

  Yes.

• 34:34Jason Calacanis?

  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 fierce competition, yeah?

• 35:05Speaker 03

  Yeah, we are big on deploying robots. So it's no longer an AI lab.

• 35:10Jason Calacanis?

  It's not a lab.

• 35:10Speaker 03

  Experiment, 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 quadruped in industrial.

• 35:21Jason Calacanis?

  Famously in Black Mirror chasing people down.

• 35:25Speaker 03

  I hate that.

• 35:26Jason Calacanis?

  Not yours. Well, you can own it, right? There's always gonna be a dystopian version and a utopian version. You're obviously pursuing the utopian. But that is a really cool robot that has been for deployed.

• 35:37Speaker 03

  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.

• 35:47Jason Calacanis?

  Wow.

• 35:47Speaker 03

  It is the mobile autonomous robot that's used more than any other on the planet right now.

• 35:54Jason Calacanis?

  Wow, so it is the most deployed and most utilized?

• 35:57Speaker 03

  Yes, so real customer.

• 35:58Jason Calacanis?

  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?

• 36:06Speaker 03

  Yes.

• 36:07Jason Calacanis?

  Or pony? What do you like to call it? Pony dog?

• 36:10Speaker 03

  We like to think of it as a dog.

• 36:11Jason Calacanis?

  I love it.

• 36:11Speaker 03

  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 wanna monitor them, this allows for them to do that. Now it 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. 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 past dancing at this point, it's now doing real work. And customers need to see your ROI in under two years.

• 36:54Jason Calacanis?

  And those inspections, if they were even being done, were being done by humans?

• 36:58Speaker 03

  Yes.

• 36:59Jason Calacanis?

  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 acoustics you mentioned.

• 37:24Speaker 03

  Yep.

• 37:25Jason Calacanis?

  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 or dozens of customers deploying hundreds of these?

• 37:44Speaker 03

  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 with the humanoid form factor, folks may wanna spin up at different times and have the ability 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 wanna 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, where fully loaded with services, integration deployed.

• 38:20Jason Calacanis?

  So it's the price of a Tesla to a Ferrari, depending on how you equip it?

• 38:25Speaker 03

  Yep.

• 38:25Jason Calacanis?

  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 it can run 20 hours a day, 22 hours a day with charging?

• 38:37Speaker 03

  Yep, so we're at, we think about it in terms of mean time between intervention, and we're at over 3,000 hours.

• 38:43Jason Calacanis?

  Oh, okay.

• 38:44Speaker 03

  Only a couple of 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 and charges, and the next one can take over.

• 38:55Jason Calacanis?

  Does it automatically swap the batteries?

• 38:57Speaker 03

  It just sits down onto its charging pod.

• 38:59Jason Calacanis?

  Perfect.

• 39:00Speaker 03

  Yes. Atlas has swappable batteries, though.

• 39:02Jason Calacanis?

  Yes, but the hot swap is a human has to do it?

• 39:06Speaker 03

  No, Atlas does it itself.

• 39:07Jason Calacanis?

  Oh, it does it itself?

• 39:08Speaker 03

  So Atlas will have two batteries, so it turns its torso around, and you replace one and put it with the other one, and always has a backup.

• 39:13Jason Calacanis?

  Perfect.

• 39:14Speaker 03

  So battery lifespan.

• 39:15Jason Calacanis?

  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?

• 39:25Speaker 03

  Yeah.

• 39:25Jason Calacanis?

  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 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.

• 39:47Speaker 03

  We haven't necessarily looked at labor replacement for Spot. 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 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 inspections. So, yes, one of the metrics the customer might look like for ROI is labor replacement. We're leaning more into how much do we save you? So we found an air leak in your facility, and that was a, would have been $3 million a day.

• 40:22Jason Calacanis?

  Yeah, the outcomes matter.

• 40:23Speaker 03

  Yes, so what is the value that we're driving?

• 40:25Jason Calacanis?

  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 in time?

• 40:34Speaker 03

  And let's be honest, I mean, there's gonna be an element of labor replacement for this as a metric, because it's easy.

• 40:39Jason Calacanis?

  Yeah.

• 40:39Speaker 03

  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 we should be having, right? Just an element of it.

• 40:48Jason Calacanis?

  And hopefully, we're getting rid of the dangerous jobs and the ones people might find oppressive.

• 40:56Speaker 03

  Yeah. Dull, dirty, dangerous.

• 40:59Jason Calacanis?

  Dull, dirty, dangerous.

• 41:00Speaker 03

  Yeah, we don't want them doing that. Hurting their body.

• 41:03Jason Calacanis?

  Yeah, we only get one human body.

• 41:05Speaker 03

  Yeah.

• 41:05Jason Calacanis?

  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?

• 41:15Speaker 03

  Yep.

• 41:16Jason Calacanis?

  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, etc. And do you offer that yet? Or is it all, hey, you gotta 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?

• 41:43Speaker 03

  Yeah.

• 41:44Jason Calacanis?

  So how do you grok that?

• 41:45Speaker 03

  Yeah.

• 41:45Jason Calacanis?

  Or how should we think about it?

• 41:46Speaker 03

  We think about two brains, right? My simplified version of telling the stories is two brains.

• 41:51Jason Calacanis?

  Okay.

• 41:51Speaker 03

  There's the brain that controls the physicality of the robot, which is 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 DeepMind, or we may partner with other AR partners, or we'll build some of this ourself. 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 in which space. So it could be on robot if you needed it to. It could be in the cloud. We'll figure out the wrapper for that.

• 42:36Jason Calacanis?

  What percentage of the robot is built in the United States or outside of China and Taiwan today?

• 42:42Speaker 03

  Hundred percent of the robots.

• 42:43Jason Calacanis?

  Hundred percent. 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.

• 42:51Speaker 03

  Yeah.

• 42:52Jason Calacanis?

  Your personal opinion, as the CEO of this company and as an American, under any circumstances, should we allow humanoid robotics from China and the United States?

• 43:00Speaker 03

  No.

• 43:01Jason Calacanis?

  No, why?

• 43:02Speaker 03

  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 it's being backchanneled 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.

• 43:18Jason Calacanis?

  Right.

• 43:18Speaker 03

  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 in some of these discussions. I'm hoping that more companies in the US join us in taking up this mission.

• 43:41Jason Calacanis?

  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 these? 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?

• 44:23Speaker 03

  Yeah, so we've been pretty public about the fact that we have an anti-weaponization stance.

• 44:27Jason Calacanis?

  Yeah.

• 44:28Speaker 03

  But...

• 44:28Jason Calacanis?

  Why?

• 44:29Speaker 03

  I think that for what we're trying to do right now in industrial use cases, it's a distraction for our business.

• 44:36Jason Calacanis?

  So focus.

• 44:37Speaker 03

  It's focus.

• 44:38Jason Calacanis?

  It's not philosophical.

• 44:39Speaker 03

  It's, I mean, it depends on who you're asking there. As the CFO, CEO, you know, I'm gonna 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 gonna 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.

• 44:58Jason Calacanis?

  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? Just bombing a bomb, you're okay with that?

• 45:08Speaker 03

  EOD. EOD is one of, you know, explosive ordinance disposal is something, it's a great use case for robots.

• 45:13Jason Calacanis?

  And you're doing that currently?

• 45:14Speaker 03

  We do that currently. So we're okay with that. What we don't want is Terminator robots, right?

• 45:19Jason Calacanis?

  Right.

• 45:19Speaker 03

  Not good for the market.

• 45:20Jason Calacanis?

  But China's building them. So if China's building them and we don't...

• 45:23Speaker 03

  Right.

• 45:24Jason Calacanis?

  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?

• 45:34Speaker 03

  I think that's a tough, that's a tough question, and I think we're gonna have to answer it when the time comes, and hopefully it never comes.

• 45:40Jason Calacanis?

  The time is gonna come, I can assure you.

• 45:42Speaker 03

  I know.

• 45:42Jason Calacanis?

  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 gonna deploy them, and they already have shown, you've seen them put AK-47s on these.

• 46:03Speaker 03

  Yes, not on our robots.

• 46:04Jason Calacanis?

  Not on yours, on theirs.

• 46:05Speaker 03

  Yes, and listen, it's terrifying.

• 46:08Jason Calacanis?

  Terrifying.

• 46:08Speaker 03

  I think, listen, I know that we have the best robot, 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 gonna keep everyone focused on the application space that makes a lot of sense for us to make money.

• 46:25Jason Calacanis?

  I'm gonna tell you a secret. 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 gotta go. Continued 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.

• 46:47Speaker 03

  I'm right now.

• 46:47Jason Calacanis?

  So I wish you great luck with it. If people wanna come work at Boston Dynamics...

• 46:52Speaker 03

  Please come.

• 46:52Jason Calacanis?

  Where are you based?

• 46:53Speaker 03

  So we're in Waltham, so we're right outside of Boston.

• 46:55Jason Calacanis?

  Yeah.

• 46:56Speaker 03

  But we're open to some remote work, and we're considering coming to the West Coast.

• 47:01Jason Calacanis?

  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 gonna need to pop up a space there, yeah?

• 47:11Speaker 03

  Yeah, we're considering it.

• 47:12Jason Calacanis?

  All right, listen, continued success. Thank you so much. All right, everybody, our next guest is Professor Jonathan Hirst. He's the co-founder and Chief Robotic Officer, or Chief Robot Officer at Agility Robotics. You have a PhD in robotics from 2008.

• 47:29Speaker 04

  Yeah.

• 47:30Jason Calacanis?

  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.

• 47:51Speaker 04

  Yeah, I mean, 20 years ago when we were doing this, it really was an unknown in industry, right? Robotics was more about automation systems.

• 48:00Jason Calacanis?

  Yeah.

• 48:00Speaker 04

  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, into having direct impact beyond being a research topic.

• 48:18Jason Calacanis?

  Yeah.

• 48:18Speaker 04

  And then the universities have seen this demand and this growth, and people love robots. There's a lot of demand from students who wanna do it. So the number of programs has grown, and it's just exponentially growing. Very, very exciting, very exciting.

• 48:32Jason Calacanis?

  We've had a lot of false starts with humanoid robotics, which you're specializing in.

• 48:37Speaker 04

  And AI.

• 48:38Jason Calacanis?

  And AI.

• 48:39Speaker 04

  They call it the AI winters, you know?

• 48:40Jason Calacanis?

  Yes, multiple ones. This time is real.

• 48:44Speaker 04

  Yeah.

• 48:44Jason Calacanis?

  Quite obviously. Explain to the audience why this time is different, and why you believe this time we're gonna 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 Earth?

• 49:05Speaker 04

  Very impactful.

• 49:06Jason Calacanis?

  Yeah, why? Why is this time different?

• 49:08Speaker 04

  Yeah, well, I would say generally, it is very easy to make a robot that looks like a person.

• 49:14Jason Calacanis?

  Okay.

• 49:14Speaker 04

  And that's why we've seen humanoids for 100 years and what. 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.

• 49:30Jason Calacanis?

  And because of large language models, a lot of things have now become free. When these robots look at a table here...

• 49:39Speaker 04

  Yeah.

• 49:39Jason Calacanis?

  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.

• 49:47Speaker 04

  Yeah.

• 49:48Jason Calacanis?

  If it 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?

• 49:57Speaker 04

  Yeah, perception is incredibly difficult. And the fact that perception is all that's 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 awareness for these robots, so people can see that this is gonna be useful, generally doing many useful things very soon.

• 50:20Jason Calacanis?

  So there's perception. The robot has to understand the world.

• 50:24Speaker 04

  Yep.

• 50:24Jason Calacanis?

  But then there always seemed to be this blocker with getting the robot out of a very confined, narrow task, like, you know, in a factory. And I, my perception is it was the communication 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 gonna make a cup of coffee. We have a company, I invested in Cafe X, 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,

• 51:08Jason Calacanis?

  every coffee recipe, that also seems to be for free. Am I wrong, or?

• 51:13Speaker 04

  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 this 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.

• 51:51Jason Calacanis?

  Right.

• 51:51Speaker 04

  Now, in order to control a robot, you can do these things by learning from demonstration. You can tele-operate the robot, 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 Sim2Real transfer, and all of these kinds of things.

• 52:16Jason Calacanis?

  And world models are the next frontier. People are literally putting gloves on humans, and...

• 52:26Jason Calacanis?

  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.

• 52:36Speaker 04

  Yeah, it is.

• 52:36Jason Calacanis?

  The world models will solve this problem, or?

• 52:41Speaker 04

  They are part of the, part of the solution. As with all of these things, there is no silver bullet.

• 52:45Jason Calacanis?

  Right.

• 52:45Speaker 04

  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 computational 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.

• 53:21Jason Calacanis?

  Yeah, so is there going to be a singularity or a crossing over moment? Where recursive learning, just putting the robot in the kitchen,

• 53:31Speaker 04

  Yeah.

• 53:31Jason Calacanis?

  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 at 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 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 souffle?

• 54:13Speaker 04

  I mean, that's, it's not a very satisfying answer maybe, but it's all of the tools.

• 54:17Jason Calacanis?

  Got it.

• 54:17Speaker 04

  All of them, right? There's not, um, 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.

• 54:28Jason Calacanis?

  Got it.

• 54:28Speaker 04

  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.

• 54:37Jason Calacanis?

  All right, so.

• 54:38Speaker 04

  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 iterate. Robots are not very good at learning yet. Robots take so much more data and so many more examples than a person. We're still figuring out how to teach robots how to learn. Um, 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, know how to play the violin based on your learnings. Robots will be.

• 55:07Jason Calacanis?

  One robot learns to play violin, all robots know how to play violin.

• 55:12Speaker 04

  Or all robots of that type know how to play the violin, right?

• 55:14Jason Calacanis?

  Yeah.

• 55:14Speaker 04

  And then minor variations for the next type and the next piece of hardware.

• 55:17Jason Calacanis?

  So you are actually deploying, your product is called Digit. Digit is, I think, 4.0, you're going to release 5.0, you've got, let's say, dozens uh, in different applications out there in the real world. Give us an idea of what the forward deploy looks like today and where you think it will be in a year or two.

• 55:37Speaker 04

  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 to the top of a tall shelf in narrow spaces. 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, you know, fill a bin and carry it somewhere and palletizing and de palletizing and are expanding out into more and more use cases, is 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,

• 56:22Speaker 04

  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 is out there, that's kind of the scaling moment for us.

• 56:32Jason Calacanis?

  Yeah, this is a key moment that maybe people don't appreciate.

• 56:36Speaker 04

  Right.

• 56:36Jason Calacanis?

  But if you've ever been to one of Elon's factories or Toyota's factories, there are lines.

• 56:42Speaker 04

  There's a line.

• 56:42Jason Calacanis?

  And if you cross that line, the.

• 56:44Speaker 04

  Everything shuts down.

• 56:45Jason Calacanis?

  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 at the Tesla factory. Because it's, it's cranking.

• 56:56Speaker 04

  Yeah.

• 56:57Jason Calacanis?

  We're starting to feel comfortable enough that these robots are not going to fall over and break somebody's ankle.

• 57:03Speaker 04

  Well, it's been a very, very intentional process over the past two or three years.

• 57:07Jason Calacanis?

  Right.

• 57:07Speaker 04

  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, it solves all the R and D, uh, you know, goals we had. And we're like, great, let's go deploy. And they're like, oh, no, no, we can't deploy. Um, because, you know, they, they're not, they don't, 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, because the whole, every system of the robot is touched to figure out how to make it safe.

• 57:33Jason Calacanis?

  When we look at an industrial shrank robot like yours.

• 57:36Speaker 04

  Yeah.

• 57:37Jason Calacanis?

  Bill of materials.

• 57:38Speaker 04

  Uh huh.

• 57:39Jason Calacanis?

  Tens of thousands of dollars each, yeah?

• 57:41Speaker 04

  I mean, we're not discussing bills of materials. We know that the costs are coming down and down and down over time. We'll be selling robots, you know, in the vicinity of costs of cars and things like that. Um, the, 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.

• 58:02Jason Calacanis?

  Yeah, it would be, uh, if we were to think about it from first principles. They can reasonably run 20, 22 hours a day.

• 58:11Speaker 04

  Yeah.

• 58:11Jason Calacanis?

  And then they have to charge and just.

• 58:12Speaker 04

  That's right.

• 58:13Jason Calacanis?

  So we take 20 hours a day.

• 58:15Speaker 04

  That's exactly right, by the way. 20 out of, 20 out of 24 hours for our Digit V5 robot because of the very fast charge iteration that's gone on this pattern.

• 58:22Jason Calacanis?

  So we get, we have 20 hours, 365 days a year.

• 58:26Speaker 04

  Yeah.

• 58:26Jason Calacanis?

  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.

• 58:32Speaker 04

  It adds up.

• 58:34Jason Calacanis?

  Yeah, and people tend to think these things are going to cost 20, 30, 40,000 dollars.

• 58:38Speaker 04

  They will at some point.

• 58:39Jason Calacanis?

  Yeah.

• 58:39Speaker 04

  It's going to need to go through the scaling and have 100,000 robots out there before that actually is real.

• 58:45Jason Calacanis?

  So that's a dollar an hour. These people are being paid in factories currently 40 dollars an hour.

• 58:50Speaker 04

  Yeah.

• 58:50Jason Calacanis?

  Maybe in, uh, some other countries, 10 dollars an hour. But let's put it at 20 bucks an hour. You've got 90% compression in 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.

• 59:08Speaker 04

  Yeah.

• 59:08Jason Calacanis?

  Is that the current plan to charge per hour of utilization? You own the robot, they.

• 59:14Speaker 04

  We do both. We do a CapEx for customers 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.

• 59:22Jason Calacanis?

  What's the price of a robot per hour these days?

• 59:26Speaker 04

  We're not talking about that right now.

• 59:27Jason Calacanis?

  Not talking about that either.

• 59:27Speaker 04

  But I will say like, as, 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 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.

• 59:45Jason Calacanis?

  So between a, a, 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.

• 59:59Speaker 04

  This is a pretty big market.

• 1:00:00Jason Calacanis?

  Yeah, pretty big market. Plenty of room for you to save them money and for you to make enough profit.

• 1:00:06Speaker 04

  Build an actual business, you know?

• 1:00:08Jason Calacanis?

  To build an actual business, yeah. So let's take the conversation to, what do you think the timeframe 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.

• 1:00:25Speaker 04

  Mm-hmm.

• 1:00:26Jason Calacanis?

  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?

• 1:00:35Speaker 04

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

• 1:00:40Jason Calacanis?

  Sure.

• 1:00:40Speaker 04

  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.

• 1:00:46Jason Calacanis?

  Sure.

• 1:00:46Speaker 04

  And this is just a new form of automation like all of the others, that's, uh, helping to increase and build that productivity. So like, how do, you know, how do we, in the United States anyway, how do we build our GDP? It's not a growing population. It's increased efficiency and capability and the only way we could do that is more and more.

• 1:01:02Jason Calacanis?

  Especially not with the anti-immigration vibes we have in the, in the country right now, or even in the Western hemisphere.

• 1:01:09Speaker 04

  Yeah.

• 1:01:09Jason Calacanis?

  Um, but let me phrase the question another way. At what point, if there were a million people working in factories sorting packages, does it go down to 500,000? Is that a three, four, five year?

• 1:01:21Speaker 04

  I think we've already done that.

• 1:01:23Jason Calacanis?

  Right, but looking, but with these new.

• 1:01:25Speaker 04

  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? 20 yards, uh, and that kind of thing.

• 1:02:08Jason Calacanis?

  That's going to be an interesting one.

• 1:02:10Speaker 04

  Yeah.

• 1:02:10Jason Calacanis?

  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 robot taxi, an Uber self-driving car and a robot.

• 1:02:33Speaker 04

  Right.

• 1:02:33Jason Calacanis?

  Getting out.

• 1:02:34Speaker 04

  Yeah.

• 1:02:34Jason Calacanis?

  And bringing the packages to your doorstep. That's going to happen.

• 1:02:39Speaker 04

  Absolutely, going to happen.

• 1:02:40Jason Calacanis?

  Are you working with folks on that? You don't have to say who, but.

• 1:02:43Speaker 04

  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.

• 1:02:53Jason Calacanis?

  Yeah.

• 1:02:53Speaker 04

  Stairs and everything. So we could do that, like, this was seven years ago, something like that. Uh, but I don't think it's the best first use case or the best first market.

• 1:03:03Jason Calacanis?

  Yeah.

• 1:03:03Speaker 04

  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.

• 1:03:09Jason Calacanis?

  How do you, when, when you look at applications,

• 1:03:14Jason Calacanis?

  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, a use case or two that are non-obvious, but that would be incredibly world positive?

• 1:03:30Speaker 04

  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.

• 1:03:43Jason Calacanis?

  Dull, dirty, and dangerous.

• 1:03:44Speaker 04

  The 3Ds of robotics.

• 1:03:45Jason Calacanis?

  Yeah.

• 1:03:46Speaker 04

  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.

• 1:03:55Jason Calacanis?

  Yeah.

• 1:03:55Speaker 04

  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.

• 1:04:14Jason Calacanis?

  You're still a professor of robotics?

• 1:04:16Speaker 04

  Yes.

• 1:04:16Jason Calacanis?

  You have hundreds of people in this graduate program or over 100?

• 1:04:20Speaker 04

  Yes, we do.

• 1:04:22Jason Calacanis?

  For young people who are listening to this, who are worried about their future in careers, this seems like an incredible career path.

• 1:04:32Speaker 04

  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.

• 1:04:48Jason Calacanis?

  Yeah.

• 1:04:48Speaker 04

  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 and useful.

• 1:05:01Jason Calacanis?

  So there's the PhD, masters version of robotics. Is there another version that is, let's say, a little more Generation Toolbelt, blue collar? The equivalent of being an electrician or working on HVAC or a carpenter or a contractor?

• 1:05:18Speaker 04

  Yes, absolutely.

• 1:05:19Jason Calacanis?

  What is that and what will that be?

• 1:05:20Speaker 04

  Robot operators assembling and building robots. The robots can't assemble all themselves yet, you know? So there's a lot of manufacturing and, and again, your robot operations and deployments, there's a lot.

• 1:05:31Jason Calacanis?

  Maintenance, clank, clanker, maintenance.

• 1:05:34Speaker 04

  Absolutely.

• 1:05:35Jason Calacanis?

  Is clanker a derogatory term?

• 1:05:36Speaker 04

  I don't know. It's a Disney, you know, trademark term, so.

• 1:05:40Jason Calacanis?

  Oh, is it really?

• 1:05:41Speaker 04

  Probably.

• 1:05:42Jason Calacanis?

  Probably. Final question. I think we're of the same Gen X. You, you know General Grievous from the Star Wars characters?

• 1:05:49Speaker 04

  Yeah.

• 1:05:50Jason Calacanis?

  Trained in the Jedi dark arts by Count Dooku.

• 1:05:53Speaker 04

  Right.

• 1:05:54Jason Calacanis?

  Able to yield three or four, six lightsabers at a time. Is, a half-serious question. Why not have four or six arms facing all directions?

• 1:06:04Speaker 04

  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 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 where 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.

• 1:06:30Jason Calacanis?

  Yeah.

• 1:06:30Speaker 04

  Maybe we'll see that, but it's going to have to be driven by a real need.

• 1:06:34Jason Calacanis?

  All right. Favorite robot in science fiction history?

• 1:06:37Speaker 04

  Probably Wally.

• 1:06:39Jason Calacanis?

  Wally.

• 1:06:39Speaker 04

  And Eve. 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.

• 1:06:45Jason Calacanis?

  Yeah.

• 1:06:45Speaker 04

  I love Baymax too. Baymax is pretty fantastic.

• 1:06:48Jason Calacanis?

  Wait, wait. Who's, who's Baymax?

• 1:06:49Speaker 04

  Baymax from, uh, what is it? San Fransokio from, uh...

• 1:06:53Jason Calacanis?

  Oh, yes, of course. Um, I do know who you're talking about.

• 1:06:56Speaker 04

  This robot that's very clearly there to help and I, 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, your software, you have to have the safeguards in place. You gotta have the e-stop on these things.

• 1:07:10Jason Calacanis?

  So you think about the prime directives?

• 1:07:13Speaker 04

  Yeah, basically.

• 1:07:14Jason Calacanis?

  Yeah.

• 1:07:14Speaker 04

  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, a e-stop on every robot, all of these things that make, make the robots, they can just really never harm a human.

• 1:07:27Jason Calacanis?

  Jonathan, I know you're hiring Agility Robotics is the company, uh, and if people are looking for a gig, fun place to work.

• 1:07:34Speaker 04

  Agility is great. And we have a location in Salem, Oregon, where, where the, we started, where I am. Uh, we have a, uh, 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.

• 1:07:53Jason Calacanis?

  Oh, right.

• 1:07:54Speaker 04

  Right by Carnegie Mellon.

• 1:07:55Jason Calacanis?

  Amazing. Yeah, three great centers. Uh, so if you're a young person or you're in the robotics field, pretty great place to work. And, uh, if you're worried a little bit about your future, go get a PhD or a masters in robotics. Skate to where the puck is going, folks.

• 1:08:10Speaker 04

  Right.

• 1:08:11Jason Calacanis?

  Great to have met you and thank you for sharing all your knowledge.

• 1:08:14Speaker 04

  Thank you.