Speaker 1Hey everybody, it's your boy J-Cal. I'm here in Paris, France at a conference called Makina. It's basically AI in the real world. Pardon my robot. Thanks for tuning in and let's get started. App Lovin' 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 Lovin' ads for e-commerce. Your ads run inside mobile games reaching over a billion people with full screen distraction-free attention. The platform finds buyers and optimizes for profit. You set the target, it does the rest. One cookware brand went from $4 million to $16 million, turned profitable and is on pace for $80 million this year. Visit applovin.com slash all in to launch your first campaign today. 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 Anybotics. You make the Anymo, 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 robot?
Speaker 2It's an inspection solution, right? It's about data collection and understanding in critical infrastructure. But the form factor is...
Speaker 1A four-legged robot or a dog, as you've just said. We like to call it a dog-o-bot. 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?
Speaker 2Yeah. Well, you know, 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 footholds to hold onto, because we work in nasty environments. Slippery floors. There's, you know, rain forming, there's snow falling down, grass growing. Got it. So four legs is a real good format.
Speaker 1Well, 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 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?
Speaker 2You 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 them. There's narrow spaces, right? You want to work an eye level. Maybe a humanoid is better. In the facilities that we work. Yeah. There's enough flexibility. There's enough space to go around. Yeah.
Speaker 1It's the perfect format. I don't buy it. I think all the cafes should have, are they, were they cenotars in Greek mythology? Yeah. It's a cenotar. So four legs and body on four legs. 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, hundreds, hundreds forward deployed over the last five years. Yeah. These are expensive. They're low hundreds of thousands of dollars to buy them 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. So you're saying that a human can't, correct?
Speaker 2Yeah, that's right. For us, it's not about labor replacement, right? It's what can we do better? What can we do superhuman? Yeah. Inspection is a great example. Our eyes and ears don't perceive all the signals, micro gas 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. Yeah. Hundreds of thousands per hour. Got it. 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 the robot.
Speaker 1Yeah. These have seriously powerful compute on them. Right. And they have to have a significant amount of battery power then. As well. So these things can do a mission of what? An hour or two?
Speaker 2Two hours. Two hours. An hour 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? 40. 40. 50. 50. And they're not used to it in a specific point. Wow. When the electric arc furnace goes up, they want to know in that minute what's happening. Got it. Too dangerous to send in a person. Thermal cameras burned. They need a robot that's right at that moment.
Speaker 1Got it. And they have to charge, not hot swapping the batteries.
Speaker 2No. You want hands-free autonomy. Right. Nobody should even be bothered that there's a robot. Yeah. They don't care about the robot. Actually, they don't even want the robot. They want the data. They want the insights. The robot is a means to an end to collect the data precisely.
Speaker 1At what point can you offload the very power-hungry compute? And put it in the cloud?
Speaker 2We 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. But in the cloud, of course, you do contextual analysis, historic downtime analysis, et cetera.
Speaker 1Are people asking for these to be able to operate for 24 hours yet or 12 hours?
Speaker 2No, for sure. So the maximum is in the eight-hour range, so it has enough time for charging if you need to go. If you need to go beyond that, that's rare. There's diminishing returns to what 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. So the frequency goes massively up without putting people into harm's way, plus the quality is so much higher.
Speaker 1What's the most fascinating science fiction deployment you have currently with these?
Speaker 2Yeah. I mean, what's really exciting, anything offshore. People fly out with helicopters. Every helicopter flight costs in the tens of thousands. Yeah. 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 flawless. These are oil rigs? Oil and wind energy offshore as well.
Speaker 1Ah. Yeah. Well, wait a second. These things don't operate in the water. So how do they work with windmills in the ocean?
Speaker 2There's windmills around hundreds of them. They come together to a transformer station. That transforms to AC to DC before it transforms. That's a manned facility typically. Got it. Big converter holes. This is where the robot operates.
Speaker 1Got it. Can they operate in severe conditions like the Antarctic and stuff? Yeah. Yeah. Can they operate in severe conditions like the Antarctic and stuff like that? And have you deployed them in the air yet?
Speaker 2Well, in Norway, for sure. So that's minus 20 degree. Pretty severe. 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, which is in oil and gas and chemicals, methane in the air. You're not allowed to create a spark. So we built a special robot that's guaranteed not to create a spark. This is where you don't want to have people. Wow. But for a machine, that's a perfect case, right? Right. Dangerous environment.
Speaker 1This 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 die. Yes. And you want to know when it's happening, but 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.
Speaker 2Well, there's submarines, right? We don't do that right now, but I agree, right? Robots should work in environments where people... Yeah. ... shouldn't be. Right. Dangerous, remote, right? Voring, repetitive tasks.
Speaker 1But 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... Right. ... robots... Yeah. ... that are doing essentially... Yeah. ... not inspections, but monitoring systems... Right. Right. ... for obviously the military. Well, if you're out there inspecting and there's a gas leak and it's dangerous to send humans out there... Yeah. ... when are you going to put some equipment on these to fix the goddamn leak while you're out there? Absolutely.
Speaker 2And that must be the holy grail, is it not? Yeah. Once you can detect a problem, customers ask, "Can you solve it? Can you fix it?" Can you? Not today. Okay. It's 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 bi-manual manipulation, maybe three, four arms... Yeah. ... to fix the machine, right? That's still... There's still a lot of work ahead of us, so 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.
Speaker 1What percentage of your robot is sourced from China? Zero. Zero percent. Yeah. Yeah. And is that because in the EU and Norway it's banned or that's a choice?
Speaker 2That happens just historically that we source locally and you get chips from the U.S. 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, so for sure it's a hard work to navigate, but tapping into the commoditization of certain hardware, that makes sense for us cost-wise as well.
Speaker 1Who's specializing in that outside of China now? Is it Vietnam, India, Taiwan? Where can you source the actuators and all that?
Speaker 2For short, China is number one pushing. companies in Europe, right? 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
Speaker 1builds 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
Speaker 2of Chinese robotics? If you look at the robot from China today, that device is a piece of hardware that can walk beautifully, 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. You need autonomy, inspection, intelligence, the workflow integration, so much more, right? It's just a hardware difference. So the harness, the wrapper,
Speaker 1the services around it, they're not providing yet. And then the trust in the data, right? We call
Speaker 2it very sensitive data. We have ISO certification for cybersecurity, all these topics, right? So
Speaker 1that's how we compete. So you might not want to send the nuclear power plant's latest data to the
Speaker 2Chinese Communist Party, you're saying? You don't want to have 15 cameras in your critical
Speaker 1infrastructure that somebody else controls. Yeah, I'm being a bit facetious. But it's 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. 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.
Speaker 2So, I think there's a responsibility in Europe to build technologies to be able to. You believe that personally? Yes. However, for antibiotics, we built and we went down one track, this tremendous poll. 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
Speaker 1for others. Is it never say never for you? Or is it? You're dead set on like, you have a mission, you're not going to build military products.
Speaker 2For us, today, the mission is clear. We started with non-military. This is where we're headed.
Speaker 1Got it. But if the EU asks you, and you- Well, they did ask. I mean, we get, you know.
Speaker 2Oh, you do get the requests. We get plenty of 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. Our team-
Speaker 1Really? You need a different team? Well, it seems like you could do the same team and build military applications.
Speaker 2Well, 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. In military, it's about millisecond being in, right? Remote control, 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. How do you think about robots that
Speaker 1are armed? Clearly, China has done demonstrations of these same type of, you know, four-legged robot 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?
Speaker 2Yeah. I personally don't like it. I hate it. I think it's stupid. Yeah, 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
Speaker 1going 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 what's happening in those authoritarian countries with robotics and the military?
Speaker 2I think these are all very early tests. If I look at those videos, these are demonstrations. I've not seen these types of robots active. Drones, yes, Ukraine, that came out of necessity. That was a mature category that was used. In robotics, actually, to the people I speak to, four years ago, we wrote a letter together with our friends at Boston Dynamics and others who condemned the weaponization of robots for exactly that reason, that as engineers, we don't want to see it being used. As engineers, we don't want to see it being used. And we think it's just dangerous and risky and stupid.
Speaker 1Yeah. All right, listen, continued success. All right, everybody, really excited to have Bert Bornick here. He is the founder and CEO of 1X. If you know 1X, 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? You've got to keep your promises.
Speaker 3Okay. 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 guess 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 a bit. 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. 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. That's happening in the coming weeks.
Speaker 1Yes, 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?
Speaker 3That will be part of it. But to me, NEO and 1X is about so much more than just consumer, right? Yeah. So consumer is an incredibly important market. But 1X has always been about how do we create an abundance of labor across society through these humanoids. And I sincerely believe that we have a platform now. Which is so uniquely capable and so well situated that allowing people to build on this will open up how to use NEO across all of our society and not just in homes. Right. But it will also benefit the consumer because this will mean there will be more things developed on NEO. And part of that will be an app store 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.
Speaker 1Oh, 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, $1000 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?
Speaker 3A hundred percent. 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. Right. But I am very happy to say that I think we will actually be able to ship something that's very close to full autonomy, which we didn't want to promise when we launched this because it was too early. 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 teleoperation involved or some guidance of the system. But the thing 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 good. Pretty darn good.
Speaker 1The teleoperating is fascinating to me. I don't know if you saw this, but in New York, there was a chicken sandwich shop, couldn't find a cashier. So they hired somebody in Manila, in the Philippines for, you know, $3 an hour, which is a huge salary for a cashier in the Philippines. And they had her on a Zoom call. They just popped up Zoom, hacked it themselves. And you could order. And if you had a customer service issue, you just talk 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?
Speaker 3I think it will all happen. 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 NIO as a platform, so if you want to build your orange shop. shop around us, orange juice shop that, okay, you buy a bunch of Neos, you get Neos, you You get the robot operating system with 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 do all the dense captioning of the data for you. 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. Just one path. Maybe that does quite work, so you say, "Ah, I'm going to have someone intervene sometimes in TallyUp," and then your data gets better. That's one way of doing it, right? Yeah. There's many ways of gathering data. Or maybe you're just saying, "You know what, this is super complicated. I just want it fully TallyUp." That's also fine. Depends on how you want to apply this, and the platform goes all the way from these kind of developers that just want to automate their workflow, all the way to the more foundation labs. Yeah, exactly. Yeah. I think that's a really good point. I think that's a really good point. I think that's a really good point. Yeah. I think that's a really good point. I think that's a really good point.
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Speaker 1I think that's a really good point. I think that's a really good point. I think that's a really good point. I think that's a really good point. I think that's a really good point. I think that's a really good point. I think that's a really good point. I think that's a really good point. I think that's a really good point. I think that's a really good point. I think that's a really good point.
Speaker 3I think that's a really good point. I think that's a really good point. I think that's a really good point. I think that's a really good point. I think that's a really good point. I think that's a really good point. I think that's a really good point. I think that's a really good point. I think that's a really good point. I think that's a really good point. I think that's a really good point. I think that's a really good point. I think that's a really good point. I think that's a really good point. I think that's a really good point. I think that's a really good point. I think that's a really good point. I think that's a really good point. I think that's a really good point. I think that's a really good point. I think that's a really good point. I think that's a really good point. I think that's a really good point. I think that's a really good point. I think that's a really good point. I think that's a really good point. I think that's a really good point. I think that's a really good point. I think that's a really good point. I think that's a really good point.
Speaker 1I think that's a really good point. I think that's a really good point. I think that's a really good point. I think that's a really good point. I think that's a really good point. I think that's a really good point. I think that's a really good point. I think that's a really good point. I think that's a really good point. I think that's a really good point. I think that's a really good point. I think that's a really good point. I think that's a really good point. I think that's a really good point. I think that's a really good point. I think that's a really good point. I think that's a really good point. I think that's a really good point. I think that's a really good point. And here's what that looks like. You're the agent that, you know, does pricing of products. And those agents start working in concert. 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.
Speaker 3No, 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 curve. So when you asked me a year ago, I was way more bearish on how far along we would be today on the AI. And, like, every time. I kind of sample things that 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 scale. 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 a blip. It doesn't really matter. So that gets back to, like, what is 1X, right?
Speaker 1And you call this, the industry term, hard launch? Or hard takeoff.
Speaker 3Hard takeover. Takeoff. Not takeover. We're going to do it right. It's going to be hard takeoff. Hard takeover, yes. But, you know, that is the norm. I've heard the term, right? This is an industry term, hard takeoff. And you can't really get this without the physical part, right? Like, the digital intelligence can never create its own substrate. You need the physical part. Right. And I think also this is going to have incredible impact on humanity with respect to, for example, progressing science, right? Like, a lot of the demand that we're seeing now on our platform is people who want to automate lab work. Yeah. Because if your AI model can't actually build and carry out its experiments and observe the results, how are they going to progress science, right? So, all of these things will happen in the coming years as AI becomes physical. And exact timeline is a bit hard. But it's years, not decades.
Speaker 1Yeah. 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. Bernd, you've got to catch a flight. This is amazing. Continued 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?
Speaker 3Well, 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.
Speaker 1It kind of should, right? If you can order a Tesla online, you can order a NEO online. Transparent pricing. I like it. Yeah. Bernd, continued success. I'm going all in. In your world, the exact words matter. The number on the diligence call. The commitment in the board meeting. Plot 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 Plot. Learn more at Plot.ai. I'm going all in. All right, everybody. We're really lucky we have Amanda McMaster here. Not McMasters. McMaster. Just McMaster. No S. Just McMaster. No McMasters. You're the interim CEO of Boston Dynamics. The over... O.G., the original robotics company, the robots we've seen for decades doing back flips, 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, Ventureback, 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. Yes. 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? I think you guys got there early, but you have to now deal with fierce competition, yeah?
Speaker 4Yeah. 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 quadrobed in industrial.
Speaker 1Famously in Black Mirror, chasing people down. I hate that. Not yours. Well, you can own it, right? Yeah. There's always going to 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.
Speaker 4Yes. 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.
Speaker 1So, it is the most deployed and most utilized.
Speaker 4Yes. So, real customers.
Speaker 1And why and what is the number one use case for it? 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?
Speaker 4We like to think of it as a dog. I love it. I think it moves like that. But, you know, we're using this. 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. So, 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. Got it. So, the answer is yes. We do all of that. And the real inflection point was customer ROI. And 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.
Speaker 1And those inspections, if they were even being done, were being done by you? Yes, 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 tie acoustics you mentioned. Yep. What do those robots cost? What's the range of the hardware? What's 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?
Speaker 4Yeah. 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 for the humanoid form factor, folks may want to spin up at different times and have a little bit of flexibility 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. It ranges anywhere between $100,000 for the base robot all the way up to $300,000 when we're fully loaded with services, integration deployed. So it's the price of a Tesla to a Ferrari depending on how you equip it. Yep. But what people need to understand is the lifespan of these robots is going to be a lot longer than the price of a Tesla. Yeah. So it's going to be a lot longer than the price of a Tesla. Yeah. So it's going to be a lot longer than the price of a Tesla. Yeah. So it's going to be a lot longer than the price of a Tesla. So it's going to be a lot longer than the price of a Tesla. Yeah. So it's going to be a lot longer than the price of a Tesla. Yeah. So it's going to be a lot longer than the price of a Tesla. Yeah.
Speaker 1So it's going to be a lot longer than the price of a Tesla. Yeah. So it's going to be a lot longer than the price of a Tesla. Yeah. So it's going to be a lot longer than the price of a Tesla. Yeah. So it's going to be a lot longer than the price of a Tesla. Yeah. So it's going to be a lot longer than the price of a Tesla. Yeah. So it's going to be a lot longer than the price of a Tesla. Yeah. So it's going to be a lot longer than the price of a Tesla. Yeah. So it's going to be a lot longer than the price of a Tesla. Yeah.
Speaker 4So it's going to be a lot longer than the price of a Tesla. Yeah. So it's going to be a lot longer than the price of a Tesla. Yeah. So it's going to be a lot longer than the price of a Tesla. Yeah. So it's going to be a lot longer than the price of a Tesla. Yeah. So it's going to be a lot longer than the price of a Tesla. Yeah. So it's going to be a lot longer than the price of a Tesla. Yeah. So it's going to be a lot longer than the price of a Tesla. Yeah. So it's going to be a lot longer than the price of a Tesla. Yeah. So it's going to be a lot longer than the price of a Tesla.
Speaker 1Yeah. So it's going to be a lot longer than the price of a Tesla.
Speaker 4Yeah. So it's going to be a lot longer than the price of a Tesla. Yeah. So it's going to be a lot longer than the price of a Tesla. Yeah. So it's going to be a lot longer than the price of a Tesla. Yeah. So it's going to be a lot longer than the price of a Tesla. Yeah.
Speaker 1So it's going to be a lot longer than the price of a Tesla. Yeah. So it's going to be a lot longer than the price of a Tesla. Yeah. So it's going to be a lot longer than the price of a Tesla. Yeah. So it's going to be a lot longer than the price of a Tesla. Yeah. So it's going to be a lot longer than the price of a Tesla. So it's going to be a lot longer than the price of a Tesla. Yeah. So it's going to be a lot longer than the price of a Tesla. Yeah. So it's going to be a lot longer than the price of a Tesla. Yeah. So it's going to be a lot longer than the price of a Tesla. Yeah. So it's going to be a lot longer than the price of a Tesla. Yeah. So it's going to be a lot longer than the price of a Tesla. Yeah. So it's going to be a lot longer than the price of a Tesla. Yeah.
Speaker 4So it's going to be a lot longer than the price of a Tesla. Yeah. So it's going to be a lot longer than the price of a Tesla. Yeah. So it's going to be a lot longer than the price of a Tesla. Yeah. 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 should we save you? So we found an air leak in your facility and that would have been $3 million a day.
Speaker 1- Yeah, the outcomes matter.
Speaker 4- Yes, so what is the value that we're driving?
Speaker 1- 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?
Speaker 4- 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, 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.
Speaker 1- And hopefully we're getting rid of the dangerous jobs and the ones people might find oppressive.
Speaker 4- Yeah, dull, dirty,
Speaker 1dirty, dirty, dangerous. - Dull, dirty, dangerous.
Speaker 4- Yeah, we don't want them doing that, hurting their body.
Speaker 1- 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 wifi, et cetera, and do you offer that yet? Or is it all, hey, you gotta have a robot with a lot of brains on it 'cause 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?
Speaker 4- We think about two brains, right? My simplified version of telling the stories, there's two brains. - Okay. - 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 Devine, 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 there. The way that they think about the robots in their facility and how this robot will interact with it, that's going to live somewhere image-based. So it could be on robot if you needed it to, it could be in the cloud.
Speaker 1We'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 robots outside. - 100%. So there's no issue with the sovereignty of robots in the United States. We're seeing a lot of cheap robots coming out of China. - Yeah. - Your personal opinion as the CEO of this company under any circumstances, should we allow to humanoid robotics from China and the United States? - No. - No, why?
Speaker 4- 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 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 U.S. join us and taking up this mission.
Speaker 1- 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, 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?
Speaker 4- Yeah, so we've been pretty public about the fact that we have an anti-weapon and anti-weaponization 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.
Speaker 1- So focus. - It's focus. - It's not philosophical.
Speaker 4- I mean, it depends on who you ask in there. As the CFO CEO, 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 we do do that today.
Speaker 1- Okay, so you'll have them in, or you do have them in the field, maybe if it had to go collect a soldier or bring a med pack, you'd be okay with that. - Yeah. - Disarming a bomb, you're okay with that?
Speaker 4- EOD. EOD is one of, you know, explosive ordnance 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 want is Terminator robots, right? - Right. - Not good for the market.
Speaker 1- But China's building them. So if China's building them and we don't- - Right. - You're kind of obligated, like your Boston Dynamics, to build them. So if China puts these into the field, will you build them to protect America?
Speaker 4- I think that's a tough question, and I think we're gonna have to answer it when the time comes, and hopefully it never comes.
Speaker 1- The time is gonna come, I can assure you. - I know. - 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- - Yeah. - This is the reality. - Yeah, this is the reality. - It's the reality. - Yeah, it's the reality. - Yeah, it's the reality. - Yeah, it's the reality. - Yeah, it's the reality. - Yeah, it's the reality.
Speaker 4- Yeah, it's the reality. - Yeah, it's the reality. - Yeah, it's the reality. - Yeah, it's the reality. - Yeah, it's the reality. - Yeah, it's the reality. - Yeah, it's the reality. - Yeah, it's the reality. - Yeah, it's the reality. - Yeah, it's the reality. - Yeah, it's the reality. - Yeah, it's the reality. - Yeah, it's the reality. - Yeah, it's the reality. - Yeah, it's the reality. - Yeah, it's the reality. - Yeah, it's the reality. - Yeah, it's the reality. - Yeah, it's the reality. - Yeah, it's the reality. - Yeah, it's the reality. - Yeah, it's the reality. - Yeah, it's the reality. - Yeah, it's the reality. - Yeah, it's the reality. - Yeah, it's the reality.
Speaker 1- Yeah, it's the reality. - Yeah, it's the reality. - Yeah, it's the reality. - Yeah, it's the reality. - Yeah, it's the reality. - Yeah, it's the reality. - Yeah, it's the reality. - Yeah, it's the reality. - Yeah, it's the reality. - Yeah, it's the reality. - Yeah, it's the reality. - Yeah, it's the reality. - Yeah, it's the reality. - Yeah, it's the reality. - Yeah, it's the reality. - Yeah, it's the reality. - Yeah, it's the reality. - Yeah, it's the reality.
Speaker 4- Yeah, it's the reality. - Yeah, it's the reality. - Yeah, it's the reality. - Yeah, it's the reality. - Yeah, it's the reality. - Yeah, it's the reality. - Yeah, it's the reality. - Yeah, it's the reality. - Yeah, it's the reality. - Yeah, it's the reality. - Yeah, it's the reality. - Yeah, it's the reality.
Speaker 1- Yeah, it's the reality. - Thank you so much. All right, everybody, our next guest is Professor Jonathan Hurst. He's the co-founder and chief robotic officer, or chief robot officer at Agility Robotics. You have a PhD in robotics from 2008. - Yeah. - So you've been at this for over 20 years, well over 20 years. Things seem to have heated up in the last 36 months. Maybe you could, for the audience, before we get into your product line, level set, what you've seen in the past 20 years, and how the last two years compares to the previous 20.
Speaker 5- 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. - Yeah. - 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. - Yeah. - And then the universities have seen this demand and this growth, and people love robots. - Yeah. - There's a lot of demand from students who want to do it. So the number of programs has grown, and it's just exponentially growing. Very, very exciting. Very exciting.
Speaker 1- We've had a lot of fall starts with humanoid robotics, which you're specializing in. - And AI. - And AI.
Speaker 5- They call it the AI winters, you know?
Speaker 1- Yes, multiple ones. - Humanoids, yeah. - This time is real. - Yeah. - 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.
Speaker 5- Very impactful.
Speaker 1- Yeah, why? Why is this time different?
Speaker 5- 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 in 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.
Speaker 1- 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 probably knows this is paper, tea, water. It probably knows how many ounces are in each. 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?
Speaker 5- Yeah, perception was incredibly difficult. And the fact that perception is all that's solved at this point is a really, really huge inflection point. I mean, I said, yes, robots doing useful things, but also people can now see the future of generality. AI is really enabling that much more broad context awareness for these robots so people can see that this is gonna be useful, generally doing many useful things very soon.
Speaker 1- So there's perception, the robot has to understand the world, but then there always seem to be this blocker with getting the robot out of a very confined, 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, 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 bee, you can have a coffee and 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.
Speaker 5Am I wrong or? - 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. That data does not exist for robot control. What's the example for your robot of all the torques, all the torque commands to every motor, given all the sensor input? There is 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. In order to control a robot, you can do these things by learning from demonstration. You can give it, you can tele-operate the robot, start to train from that. - Right. - And that data, you can give an animation input or motion capture input or any number of different things. But that's also got a real hard limit 'cause 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 other 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.
Speaker 1- And world models are the next frontier. People are literally putting gloves on humans and letting them control robots remotely to actually chop and make a salad, to pour water. And that's being done today by many different companies. - Yeah, it is.
Speaker 5- The world models will solve this problem or? - They are 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 real world model without breaking things in the real world and, you know, compressed so you can do, you know, a million iterations within days and computationally 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.
Speaker 1- Yeah. So is there gonna be a singularity or a crossing over mode at the 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 breaks a lot of China. Or is it gonna be these world model companies, very refinely working human alongside robot in a Michelin starred kitchen to make that souffle?
Speaker 5- 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 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. And one of the benefits that robots have in the long run is they've got Wi-Fi. 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 able to do that.
Speaker 1- One robot learns to play violin. All robots know how to play violin.
Speaker 5- Or all robots of that type know how to play the violin, right? - Yes. - And then minor variations for the next type and the next piece of hardware.
Speaker 1- So you are actually deploying, your product is called Digit. Digit is, I think, 4.0. You're gonna 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 forward deploy looks like today and where you think it will be in a year or two.
Speaker 5- 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 from the top of a tall shelf in 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 palletizing and depalletizing and are expanding out into more and more use cases, it's 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. And that's why it's so important to maintain safety in this warehouse. So when Digit V5's out there, that's kind of a scaling moment for us.
Speaker 1- 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, the- - Everything shuts down. - Everything shuts down. And I've taken many of these tours with Elon and they're like, seriously, please don't cross that line 'cause it's gonna cost a million dollars if you do at the Tesla factory 'cause it's cranking. - Yep. - We're starting to feel comfortable enough that these robots are not gonna fall over and break somebody's ankle.
Speaker 5- 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 deploy it and the robots are doing the task and they're like, great, you know, it solves all the R&D goals we had. And we're like, great, let's go deploy. And they're like, oh no, we can't deploy 'cause they don't meet our safety requirements. It's like, okay, how do we meet that? Well, it turns out that's super hard. And so it's been a bottom to top design of this machine, holistic through the whole, every system of the robot is touched to figure out how to make it safe.
Speaker 1- When we look at an industrial shrank robot like yours, bill of materials, tens of thousands of dollars each, yeah?
Speaker 5- 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 cost 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.
Speaker 1- Yeah, it would be, if we were to think about it from first principles, they can reasonably run 20, 22 hours a day. - Yeah. - And then they have to charge and just. - That's right. - So we take 20 hours a day, 365 days.
Speaker 5- That's exactly right, by the way. 20 out of 24 hours for our Digit V5 robot because of the very fast charge iteration that's gone on this pattern.
Speaker 1- So we have 20 hours, 365 days a year. You know, now you're in that seven, 8,000 hours a year. Let's put it at 8,000, five years, 40,000 hours of work. - It adds up. - Yeah, and people tend to think these things are gonna cost 20, 30, $40,000.
Speaker 5- Yeah, they will at some point. - Yeah. - It's gonna need to go through the scaling and have 100,000 robots out there before that actually is real.
Speaker 1- 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 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. Is that the current plan to charge per hour of utilization?
Speaker 5You own the robot, they- - We do both. We do a CapEx for customers that prefer that. We also do robot as a service for customers that prefer that. - Got it. - There's really a lower barrier to entry and lower risk for them. - What's the price of a robot per hour these days? I'm not talking about that right now. - Not talking about that either. - 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.
Speaker 1- So between a bill of materials, tens of thousands of dollars, currently people in factories getting paid 20, 30 or $40 per hour in the world. Western half his fear in the modern world 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. 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 and 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, Jonathan?
Speaker 5I 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 and build that productivity. So how do we, in the United States anyway, how do we build? 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.
Speaker 1Especially not with the anti-immigration vibes we have in the country right now, or even in the Western Hemisphere. Well, 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?
Speaker 5Is that a three-, four-, five-year? I think we've already done that.
Speaker 1Right, but looking forward a little. But with these new…
Speaker 5It's going to just continue. Someday, there's going to be an autonomous truck that drives up and have a completely lights-out autonomous package sortation factory, and then 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 and 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 legacy, and still 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. Yards, and that kind of thing.
Speaker 1That'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. Imagine a Waymo robo-taxi, an Uber self-driving car, and a robot getting out and bringing the packages to your doorstep. That's going to happen. Absolutely, going to happen. Are you working with folks on that?
Speaker 5You don't have to say who, but... 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. Yeah. Stairs and everything. So we could do that. Like, this was seven years ago, something like that. But I don't think it's the best first use case or the best first market. Yeah. So it's on our roadmap for sure. But such a big market for deploying with what we're doing right now. We're going to start there.
Speaker 1How 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.
Speaker 5I 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 robots. Robot jobs. The same way we look back on like coal miners in the 1900s and say, I can't believe people did that work. And, you know, the number of roles and things that people do today are so much better. The quality of life is so much better. The jobs that people have today that you couldn't have imagined in 1900 often are just so much better. I think that that's how the future is going to look for us.
Speaker 1You're still a professor of robotics. Yes. You have hundreds of people in this graduate program or over a hundred. Yes, we do. For young people who are listening to this. Who are worried about their future and careers, this seems like an incredible career path.
Speaker 5It'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 career is going to look like. 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.
Speaker 1So there's the Ph.D. 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?
Speaker 5Yes, absolutely. What is that and what will that be? Robot operators assembling and building robots. Robots can't assemble all of themselves yet, you know, so there's a lot of manufacturing. And again, your robot operations and deployments, there's a lot.
Speaker 1Maintenance clanker maintenance. Absolutely.
Speaker 5Is clanker a derogatory term? I don't know. It's a Disney, you know, trademark term. So is it really?
Speaker 1Probably. Probably. Final question. I think we're of the same Gen X. You know, General Grievous from the Star Wars characters. Yeah, you trained in the Jedi Dark Arts by Count Dooku. Right. Able to yield three or four or six lightsabers at a time. Half serious question. Why not have four or six arms facing all directions? That's a good question.
Speaker 5So 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 worth fitting it in. And then, you know, go to four to five. There's a lot to coordinate and a lot of extra complexity. But what else does it make you do? I don't know. Yeah, maybe we'll see that. But it's going to have to be driven by a real need. All right. Favorite robot in science fiction history? Probably Wall-E and Ys. I love kind of that vision of these robots just continuing to try and build and create and do what they were designed to do. Yeah, I love Baymax too. Baymax is pretty fantastic. Wait, wait. Who's Baymax? Baymax from, what is it, San Fransokyo from? Oh, yes, of course. I do know this robot that's very clearly there to help. And I love how they kind of show that it does what it's programmed to do. I mean, at one point they remove all its memory and it turns red and now it's dangerous. Well, that's very real. You know, your software, you have to have the safeguards in place. You've got to have the e-stop on these things.
Speaker 1So you think about the prime directives?
Speaker 5Yeah, 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 make the robots so they can just
Speaker 1really never harm a human. Jonathan, I know you're hiring. Agility Robotics is the company. And if people are looking for a gig, fun place to work.
Speaker 5Agility 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 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. And we have
Speaker 1an iceberg location as well, 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 Ph.D. or a master's in robotics. Skate to where the puck is going, folks. Right. Great to have met you and thank you for sharing all your knowledge. Thank you. Thank you.
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