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

24:48And 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 1x, is if you get the robot to be similar enough to a human, then you can train on all the available video data out there of humans. Yes. And we're starting to see some very good proof that this is actually working incredibly well.

25:22And that's the reason we started the 1x Road Model Lab, because we now finally have the scaling laws on that. and we're seeing that this... Take us inside the lab. Are you having people in factories wear glasses, wear your hands and do their tasks over and over again? Are you working with the micro ones of the world to go do, you know, real world stuff and outsourcing like unique proprietary data that you can have that other companies don't? How does the world model get built at scale? So 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?

25:58The model is going to be as good as the data. Yeah. And if you think about the data pyramid, then on the top you have like teleoperation data, very high quality, small, fine-tuned data set, where actually what we do is you will have the operator try to do the task very well and very fast, and they will often fail and then just try again. And then we pick the good samples where they did the task as good as a human would, right? Yes, we don't need a lot of that data. It's just to align your model Then you have the data which is what you're talking about with like put the sensors on the human go and gather data Yeah, you have more of that and it's very close to the robot, but it's not the robot the teleplater 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 It's quite close and then you have general video data.

26:56Yes, of the world. Oh the world in general and of people, right? And because Neo is so similar to a human We can actually utilize all that data now the bottom layer in the pyramid, which is this video data general video data is absolutely Ludicrously immense compared to anything YouTube it's everything So if you look at what is needed to actually achieve true intelligence, you need multiple orders of magnitude more data than anyone is even close to collecting over the next few years with egocentric data or with this sensor data. Got it. And all of the major breakthroughs that we've seen as far as I'm aware of in AI have been because someone figured out how to use a huge new data source that previously we were not able to use.

27:47You unlock some new set of data and now your model capability greatly improves. Well you've got a lot of people out there trying to find data like that is like one of the number of times. But it's still going to take years. So it's like a catch-22. So our big bet is you have to be able to utilize the general video data out there. Yeah. And the only way to do that is you have to care about every single tiny detail of the robot to be as ghost or human as possible. Like, you know, like the flesh and tissue and skin is highly nonlinear. So like how much force for it to deform? What's the friction?

28:19Like, what is the impact energy when touching the table? And people have different size hands. I mean, literally in the NBA, there's a wingspan as a concept. And people with a wide wingspan, longer arms than the average person, get paid 20 % more for having that extra two or three inches of wingspan. It's pretty fascinating when you think about it. That's a really good way of saying it wingspan. We've always called it for the gorilla coefficient. Yes, long arms. Yeah, yeah, but anyway, yeah, so my point is yes, we do all of these things, but ultimately what differentiates One X from all the other Robotics companies is that we are all in on pre-training our own models on this video data on the internet.

29:00Yes, and that our cross embodiment Is not another robot our cross embodiment is the human right now we want to be as close to that as possible because that solves the catch-22. In the end, all the data will be robotics data because the robotic data has the actions, it has the tactile, it has the forces, it's better. But the only way to get all of that data is to create a base model that is good enough that you can deploy all these robots across society and they will do useful things that people pay for and also gather the data. When do the robots become recursive in nature and they are teaching themselves, building themselves.

29:37And like we're seeing with large language models now where people creating agents, instead of giving it prompts and instructions, we're now starting to say, well, here are the goals. Here's a loop. You are one agent that identifies for a business potential customers. Okay, you're the agent that does customer success. And here's what that looks like. You're the agent that does pricing of products, and those agents start working in content. 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.

30:20You've given them the goal. Hey, you're working in a Michelin-starred restaurant. your goal is to make the most delightful food with this level of fidelity and perfection and here are the outcomes and it says okay I've just got to get better at you know poaching these eggs to really be great at this it's kind of sci-fi no no it's not sci-fi it's actually something we think a lot about but it's also incredibly hard to answer because you know the development now is going like this and you're here on the curb so when you asked me a year ago I was way more bearish on how far along we would be today on the AI.

30:58And 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 under 10 years under 10 years my current bet would be three years got it 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 one next right because you call this the industry term hard launch or hard takeoff hard takeover takeoff not takeover we're gonna do it right so it's gonna be hard take off hard takeover yes yeah but you know So that is the term, right?

31:51This is an industry term, our take on it. 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 if your AI model can't actually build and carry out his 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.

32:29What is the years not decades? Yeah, I mean if you if you believe it's three and I know you're an optimist You have to be to do what you're doing a crazy optimist for sure and you think the outer you know, estimated 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 NIO and give you$500 a month to be part of this absolute lunacy that you're doing, what do they do? How do they get in? Well, you go to our website and you order a NIO. That's it. It's that simple. It's that simple. It's 2026. It should be that simple.

33:07It kind of should, right? If you can order a Tesla online, you can order any online. Transparent pricing. I like it. Yeah. Bernd, continued success. I'm going all in.

33:20In your world, the exact words matter. The number on the diligence call, the commitment in the board meeting. Plod captures a conversation and turns it into searchable intelligence you can pull up in seconds. Ask Plod a question and get the answer with no receipt. Stop scrolling recordings or trusting your memory. Capture the conversation, keep the signal. That's Plod. Learn more at Plod.ai. All right, everybody, we're really lucky. We have Amanda McMaster here. Not McMasters, McMaster. Just McMaster, no us. Just McMaster, no McMasters. You're the interim CEO of Boston Dynamics, the OG, the original robotics company.

34:00The robots we've seen for decades doing backflips, doing kung fu, getting kicked and beaten and getting back up. We have been having a hard time remembering who owns this company now because it was an independent company venture back. Then Sergey and Larry bought it. It was part of Google. Then it got sold. I think Masayoshi-san owned it at some point. But I believe Hyundai owns it now. That's correct. Did I get that whole history correct? You did. You nailed it. Okay. So apparently I read way too much industry news. But now you're in charge of this. Yes. It's changed hands many times, and you went from being essentially one of one, really, in humanoid robotics to one of many.

34:43We're here at this Machina Summit in Paris, and you see many contemporaries now. So what is Boston Dynamics working on now? Is it still a research project, or are you going into the real world and applying these robots? because I think you guys got there early, but you have to now deal with fierce competition, yeah? Yeah, we are big on 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 quadribed in industrial. Famously in Black Mirror chasing people down.

35:25I hate that. Not yours. Well, you can own it, right? There's always going to be a dystopian version and a utopian version. You're obviously pursuing the utopian. But that is a really cool robot that has been for deployed. Yes, it has been deployed in real customer sites. It's providing really customer value. At this point, we have over 500 customers, over 46 countries. Wow. It is the mobile autonomous robot that's used more than any other on the planet right now. Wow. So it is the most deployed and most utilized. Yes. Why? Why and what is the number one use case for it? Is it security? Is it inspections?

36:04What do people use that dog format for? Yes. Or pony. What do you like to call it? Pony dog? We like to think of it as a dog. I love it. I think it moves like that. But we're using this. Customers are finding a lot of value in industrial inspection. So they're using it for both acoustic, gauge reading, vibration detection. So assets that they have expensive assets in their facility and they want to 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.

36:39We 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. And those inspections, if they were even being done. We're being done by humans. 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.

37:24Yep. 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? 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 through the humanoid form factor, folks may want to spin up at different times and then have the ability to decrease.

38:00With Spot, it's been pretty effective in CapEx. It's the way these industrial customers think about industrial tools. So they generally want to spend CapEx for this. It depends on their configuration. You know, it ranges anywhere between, you know,$100 ,000 for the base robot all the 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 is greater than five years, I would think. Like these are, you're known for industrial. So if it can run, I'm assuming you run 20 hours a day, 22 hours a day with charging.

38:37Yep. So we're at, we think about in terms of mean time between intervention and we're at over 3 ,000 hours. Oh, okay. Only a couple times a year does a human have 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. Does it automatically swap the batteries? It just sits down onto its charging pod. Yes. Atlas has swappable batteries though. Yes. But the hot swap is a human has to do it. No, Atlas does it itself. Oh, it does it itself. So Atlas will have two batteries. So it turns its torso around and you replace one and put it with the other one.

39:12It always has a backup. Perfect. So battery lifetime. So for the humanoid one, it can do it itself, obviously. The dog gets charged. So realistically, they could be in the field for close to 24 hours, maybe 18, 20. And so that puts the operations at a couple of dollars an hour. And has that changed how people look at the use case, the dramatic lowering of costs? Because I'm assuming union workers inspecting pipelines, they're getting paid 40, 50, 60 bucks an hour, fully baked. with their benefits, their pension, whatever else. It's quite expensive. We haven't necessarily looked at labor replacement for spot.

39:50While that is a metric you might look at, we thought about 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, we're just trying to figure out ways that humans can do more knowledge worker tasks as opposed to going and doing inspections. So yes, one of the metrics a customer might look like for ROI is labor replacement. we're leaning more into how much do we save you so you we found an air leak in your facility and that was a would have been three million dollars a day yeah the outcomes matter yes so what is the value that we're driving but it's a is it still delicate in the industry to talk about labor replacement so you have to be very thoughtful about that in this moment and let's be honest i mean there's going to be an element of labor replacement for this as a metric because it's easy yeah you know how many bodies are in the world and how can you imagine a total addressable market relative to that.

40:44I just don't think it's the only conversation we should be having, right? Just an element of it. And hopefully we're getting rid of the dangerous jobs and the ones people might find oppressive. Yeah. Dull, dirty, dangerous. Dull, dirty, dangerous. Yeah. We don't want them doing that. Hurting their body. Yeah. We only get one human body. Yeah. The Atlas. How do you think about onboard compute versus remote, when you put the amount of brains, my understanding is you have the brains on the robot. Yep. That means crazy battery drain. What do you think about the option of having, you know, the brains in the cloud and having these be more lightweight if they're in an area that has extremely high speed Wi-Fi, et cetera?

41:32And do you offer that yet? Or is it all, hey, you got to have a robot with a lot of brains on it because that's what the customers want. And that seems to be a paradigm shift that's occurring now. Yeah. So how do you grok that? Yeah. How do we think about it? 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.

42:07That's things that we might partner with Google Define 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 going to live somewhere image-based. 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. What percentage of the robot is built in the United States or outside of China and Taiwan today?

42:42100 % of the robot. 100%. So there's no issue with the sovereignty of robots in the United States. We're seeing a lot of cheap robots coming out of China. Yeah. Your personal opinion as the CEO of this company and as an American, And under any circumstances, should we allow humanoid robotics from China in the United States? No. No. Why? It's not safe, right? We've already heard about leaks that are happening with some of the quadrupeds that you're seeing in the United States and being back-channeled back to China. Listen, we have seen what happens if we let China win in the semiconductor space.

43:16You know, we can't do that with robotics. 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 in taking up this mission. Yeah, we have to be pretty serious about this. This is 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.

43:52How 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? Yeah, so we've been pretty public about the fact that we have an anti-weaponization stance. Why? I think that for what we're trying to do right now in industrial use cases, it's a distraction for our business.

44:36So focus. It's focus. It's not philosophical. I mean, it depends on who you ask in there. As the CFO CEO, I'm going to look at this and say, I'm all about focus right now. We need to be focused on the markets that we think we're going to win in. And certainly, we have great ties with the government, and we're happy to do any non-weaponization work with them. And we do do that today. Okay, so you'll have them, or you do have them in the field, maybe, if it had to go collect a soldier or bring a med pack, you'd be okay with that. Disalming a bomb, you're okay with that. EOD is one of, you know, explosive ordinance disposal is something that's a great use case for robots.

45:13And 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. But China's building them. So if China's building them and we don't, you're kind of obligated if you're Boston Dynamics to build them. So if China puts these into the field, will you build them to protect America? I think that's a tough question, and I think we're going to have to answer it when the time comes, and hopefully it never comes. The time is going to come, I can assure you. I know. And I can assure you what your answer will be when President Trump calls.

45:46You will say, sir, yes, sir, or else your company will be nationalized. I mean, this is the reality of it. I mean, I'm being a little facetious and playful with you, but they're going to deploy these, and they're going to deploy them, and they already have shown. you've seen them put AK-47s on these. Not on our robots. Not on yours, on theirs. Yes, and listen, it's terrifying. It's terrifying. I think, listen, I know that we have the best robot and 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.

46:19So I'm going to keep everyone focused on the application space that makes a lot of sense for us to make money. I'm going to tell you a 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 got to go. Continued success. This is such an important American company. And I hope you take the job and become full time. I know you're interim right now. So I wish you great luck with it. If people want to come work at Boston Dynamics, please. Where are you based? So we're in Waltham.

46:54So right outside of Boston. Yeah. But we're open to some remote work. And we're considering coming to the West Coast. I was about to say, you know, I know it's in the name Boston Dynamics, but I assume with all that talent accumulating in the Bay Area, you're going to need to pop up a space there. Yeah? Yeah, we're considering it. All right. Listen, continued success. Thank you so much. All right, everybody. Our next guest is Professor Jonathan 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.

47:34Things seem to have heated up in the last 36 months. Maybe you could, for the audience, before we get into your product line, level set what you've seen in the past 20 years. Yeah. and how the last two years compares to the previous 20. Yeah. I mean, 20 years ago 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.

48:18And then the universities have seen this demand and this growth, and people love robots. There's a lot of demand from students who want to do it. So the number of programs has grown, and it's just exponentially growing. Very, very exciting. Very exciting. We've had a lot of false starts with humanoid robotics, which you're specializing in. And AI. And AI. They call it the AI winters, you know? Yes, multiple ones. This time is real. quite obviously. Explain to the audience why this time is different and why you believe this time we're going to see robotics and humanoid robotics specifically deployed at a scale that I think we can both agree will be maybe in the next 20, 30 years, one-to-one with humans on the planet.

49:05Very impactful. Yeah. Why? Why is this time different? Yeah. Well, I would say Generally, it is very easy to make a robot that looks like a person. That's why we've seen humanoids for 100 years and one. It's very hard to make a robot that can do useful things in human spaces. And we're starting to see that today. And that's the difference. So even if it doesn't look exactly like a human, but maybe a little bit humanoid, but it's doing useful work, that's where the impact matters. And because of large language models, a lot of things have now become free. when these robots look at a table here and you say, what's on the table?

49:41It knows that's a phone. It knows this is paper, tea, water. It probably knows how many ounces are in each. If you were sitting here three or four years ago, it wouldn't actually know what was in the world. You would have to program it in a very narrow way. Yeah. Yeah. Perception was incredibly difficult. And the fact that perception is all but 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 going to be useful, generally doing many useful things very soon.

50:20So there's perception. The robot has to understand the world. But then there always seemed to be this blocker with getting the robot out of a very confined, narrow task, like, you 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 going to 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, every coffee recipe, that also seems to be for free.

51:12Am I wrong? Not yet. It's actually quite different. So language models, think of it like it's now becoming kind of a commodity, like the internet. It's available to everybody. It's 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.

51:43And 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 by learning from demonstration. You can give it, you can tele-operate the robot and start to train from that data. You can give it animation input or motion capture input or any number of different things. But that's also got a real hard limit because a person controlling a robot is not really getting to what the robot can do if it were optimal and how its behavior could work. That robot needs to practice, you know?

52:12And that's where you get into world models and sim to real transfer and all of these kinds of things. And world models are the next frontier. people are literally putting gloves on humans and having them control robots remotely to actually chop and make a salad to pour water. And that's being done today by many different companies. 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 world model without breaking things in the real world and, you know, compress so you can do, you know, a million iterations within days and computationally, things like that.

53:05But 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 and real life with robots. Yeah, so is there going to be a singularity or a crossing-over moment where recursive learning, just putting the robot in the kitchen, letting it make its own mistakes, and then saying, do the next test, do the next test, which is how we taught it how to win a chess or go.

53:39We 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 breaks a lot of China, or is it going to be these world model companies very refinely working human alongside robot in a Michelin starred kitchen to make that souffle? 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.

54:23I do believe that things are going to get better and better. Think of it more like a snowball picking up steam going down a hill. Got it. But the reason that it's snowballing like this is because people are putting money and resources and engineering time and engineering effort in as they explore everything and start to figure all of this stuff out. All right. 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 and iterate. Robots are not very good at learning yet. Robots take so much more data and so many more examples than a person.

54:53We're still figuring out how to teach robots how to learn. But then one of the benefits that robots have in the long run is they've got Wi-Fi. You know, when you learn how to play the violin, you can't just load that to somebody else and then they learn how to play the violin, know how to play the violin based on your learnings. Robots will be able to do that. One robot learns to play violin. All robots know how to play violin. 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. So you are actually deploying, your product is called Digit.

55:21Digit 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. So today it's doing these sort of multi-purpose 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 space.

55:58So 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 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 to maintain safety in this warehouse. So when Digit V5 is out there, that's kind of a scaling moment for us.

56:32Yeah, this is a key moment that maybe people don't appreciate. Right. But if you've ever been to one of Elon's factories or Toyota's factories, there are lines. There's a line. And if you cross that line. Everything shuts down. Everything shuts down. And I've taken many of these tours with Elon and they're like, seriously, please don't cross that line because it's going to cost a million dollars if you do at the Tesla factory because it's cranking. We're starting to feel comfortable enough that these robots are not going to fall over and break somebody's ankle. Well, it's been a very, very intentional process over the past two or three years.

57:07Right. 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 &D goals we had. And we're like, great, let's go deploy. And they're like, oh, no, we can't deploy because they don't meet our safety requirements. It's like, okay, how do we meet that? Well, it turns out that's super hard. And so it's been a bottom-to-top design of this machine, holistic through the whole, every system of the robot is touched to figure out how to make it safe. When we look at an industrial shrink robot like yours, bill of materials, tens of thousands of dollars each, yeah?

57:41I 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. The real, like, what is the value that they produce is the question to ask. When you have a robot that's working 24 hours a day and has a five-year life, you know, what's the value? And it's quite a lot. Yeah, it would be, if we were to think about it from first principles, they can reasonably run 20, 22 hours a day. and then they have to charge and just be taken. So we take 20 hours a day, 365 days.

58:16That'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 got on this pattern. So we have 20 hours, 365 days a year. 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. They will at some point. Yeah. It's going to need to go through the scaling and have 100 ,000 robots out there before that actually is real. So that's$1 an hour. These people are being paid in factories currently$40 an hour.

58:50Maybe in some other countries,$10 an hour. But let's put it at$20 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? You own the robot. 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. What's the price of a robot per hour these days? We're not talking about that right now.

59:27Not talking about that either. But I will say, 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. So between a bill of materials, tens of thousands of dollars, currently people in factories getting paid$20,$30, or$40 per hour in the Western hemisphere in the modern world. Which is a pretty big market.

1:00:00Yeah, a pretty big market. plenty of room for you to save them money and for you to make enough profit. Build an actual business, you know? To build an actual business, yeah. So let's take the conversation to what do you think the time frame is if I were to ask you in Amazon factories, or if we want to take Amazon out because they're a partner 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? I mean, already in a lot of these applications, the majority of the workers are robots.

1:00:40Sure. 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 our GDP? It's not a growing population. No. It's increased efficiency and capability, and the only way we could do that is more and more automation. Especially not with the anti-immigration vibes we have in the country right now, or even in the Western atmosphere.

1:01:10Let 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? I think we've already done that. Right, but looking forward. But with these new... It's going to just continue. You know, someday there's going to be an autonomous truck that drives up and they have a completely lights out autonomous package sortation factory. And then, you know, an autonomous truck leaving again. And at that point, it's probably specialty automation doing those things because it's just 24-7 doing it.

1:01:41And 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, 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? Print yards and that kind of thing. That's going to be an interesting one.

1:02:10Yeah. 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 robo taxi and Uber self-driving car and a robot getting out and bringing the packages to your doorstep. that's gonna happen. Absolutely, going to happen. Are you working with folks on that? You 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.

1:02:53Stairs 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. So it's on our roadmap for sure. but such a big market for deploying with what we're doing right now we're gonna start there how do you when you look at applications we know applications that seem obvious to us not being in the industry but knowing what you know over two or three decades what do you think is a use case or two that are non-obvious but that would be incredibly world positive i don't know what to to say what's not obvious.

1:03:32I mean, just picking up stuff and putting them somewhere else is such a huge use case that frees people from the classic three Ds of robotics, the dull, dirty, dangerous kind of stuff. Dull, dirty, and dangerous. The three Ds of robotics. Yeah. And I really hope that we look, you know, like our children look back on now and look at some of the jobs that people are doing today that I really think of as robot jobs. The same way we look back on like coal miners in the 1900s and say, I can't believe people did that work. And, you know, the number of roles and things that people do today are so much better.

1:04:05The quality of life is so much better. The jobs that people have today that you couldn't have imagined in 1900 often are just so much better. I think that that's how the future is going to look for us. You're still a professor of robotics. Yes. You have hundreds of people in this graduate program or over 100. Yes, we do. For young people who are listening to this, who are worried about their future and careers, This seems like an incredible career path. It's a massive opportunity. We live in a time of change. Anytime there's a time of change like this, students coming out have an advantage because all the people who have this 20, 30-year career and know how the way things were done, they have to learn how the way, you know, the way things are coming up now too.

1:04:47Yeah. 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. So there's the PhD master's version of robotics. Is there another version that is, let's say, a little more generation tool belt, blue collar, the equivalent of being an electrician or working on HVAC or a carpenter or a contractor? Yes, absolutely. What is that and what will that be? Robot operators assembling and building robots.

1:05:23The robots can't assemble all themselves yet, you know? So there's a lot of manufacturing. And again, your robot operations and deployments, there's a lot. Maintenance. Of course. Clanker maintenance. Absolutely. Is clanker a derogatory term? I don't know. It's a Disney, you know, trademark term. Oh, is it really? Probably. Probably. Final question. I think we're of the same Gen X. You know General 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. To have serious question, why not have four or six arms facing all directions?

1:06:04That'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 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. Maybe we'll see that, but it's going to have to be driven by a real need.

1:06:34All right. Favorite robot in science fiction history? Probably WALL-E. WALL-E. And Ease. I love kind of that vision of these robots just continuing to try and build and create and do what they were designed to do. I love Baymax too. Baymax is pretty fantastic. Wait, wait, who's Baymax? Baymax from, what are the San Fransokyo from? Oh yes, of course. I do know who you're talking about. This robot that's very clearly there to help, and I love how they kind of show that it does what it's programmed to do. I mean, at one point they remove all its memory and it turns red and now it's dangerous. Well, that's very real.

1:07:06You know, you have to have the safeguards in place. You've got to have the e-stop on these things. So you think about the prime directives. Yeah, basically. How do you make sure that these things going through kind of the industrial safety process to make sure that, boy, there's a supervisory circuit, there's an e-stop on every robot, all of these things that make the robots, they can just really never harm 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. Agility is great, and we have a location in Salem, Oregon, where we started, where I am.

1:07:40We have a new facility we're opening in Fremont, California, which is just a beautiful place, and that's where we're doing a lot of robot behavior development. So there will be robots working all day long and you can come in and be working on it. And we have a Pittsburgh location as well. Oh, right. Right by Carnegie Mellon. Amazing. Yeah. Three great centers. So if you're a young person or you're in the robotics field, pretty great place to work. And if you're worried a little bit about your future, go get a PhD or a master's in robotics. Skate to where the puck is going, folks. Right. Great to have met you and thank you for sharing all your knowledge.

1:08:13Thank you.