What iPhone Packaging Reveals About the Robotics Opportunity

0:00 No matter what anybody says, today in 2026 we have a highly interconnected, interdependent global supply chain. Both US companies and Chinese companies are thinking very hard about what is the long-term strategy of this look like and ultimately everybody trying to build their industrial capacity. >> We see a lot of robot pitches. Very few people thinking about what the robot is supposed to do at the end of all of this. >> Almost everybody we're selling a robot to has never purchased a robot before.

0:23 Almost all of the market is greenfield. The reality is you need to build a full stack platform. Tutor robots have helped manufacture and ship over a quarter of a billion products to American consumers. We're training frontier robot AI models, manufacturing robots at scale, and directly deploying into the largest businesses in the supply chain. >> How does product innovation work in robotics? Most companies are just on V1, but as you have got two versions of the robot and you continue to iterate really fast.

0:46 >> So, >> Josh, thanks for for joining us. So, we're with Josh Gruenstein, who is the CEO and founder, co-founder of Tutor Intelligence. >> And we're going to be talking about robots today. And so maybe before we jump in, just give a quick background maybe of yourself and then the company. >> Yeah, so I'm Josh. I started Tutor about 5 and 1/2 years ago with the core thesis that if you could just start deploying robots out into the world, into all the places in the physical economy where robots are needed the most, you could start to build the data sets, the models, and the supply chain that's needed to proliferate robots throughout the physical economy. We're about 160 people based primarily in Boston and we're training frontier robot AI models, manufacturing robots at scale, and directly deploying into the largest businesses in the supply chain.

1:41 >> I can maybe talk a little bit about like, you know, we see a lot of pitches. There's a lot of companies that talk about doing robotics but don't actually deploy robots or have not yet. And you're one of the few companies that's actually deployed robotics at scale. So, maybe just talk about like the state of the business. >> Yeah, we have robots deployed across the United States, everywhere from small mom and pop businesses to Fortune 50, Fortune 100 businesses. Claude told me last night that it estimates two to robots have helped manufacturing ship over a quarter of a billion products to American consumers. So, like, presumably most American consumers have are a Tutor customer a couple layers down.

2:20 I think this comes from kind of the origin story of the business, which is us looking at the state of robot learning. It was obvious to us that robot intelligence and robot software was this bottleneck, right? Robots for 60 years has been this custom engineered machines that can't scale. yeah, so we we started the company with this thesis that if we could just start deploying robots, we could build the label data sets that help us train these models and deliver this value to the American consumer.

2:49 >> We see a lot of robot pitches. 99% of them start with some insights about the model and how you going to build a model or some insight about the data, shape of the data that is. And very few people thinking about the what the robot is supposed to do at the end of all of this. You started from the reverse. And you had the insight that you could actually tutor the robot, so to speak. That's the name of your company.

3:21 Where did that insight come from? >> it came from kind of the start experiment where yeah, we had this insight that hey, there are 8 billion people on Earth who all have this baseline physical intelligence that we want our robots to have. And so, imagine you had infinitely powerful software that could connect any human being on Earth to any robot and kind of embody that robot with intelligence. That's kind of like this deus ex machina of like suddenly you have unlimited infinite robot intelligence.

3:47 Let's say you take that for granted. Now what? Right? You know, the problem isn't actually solved, right? You still need to actually manufacture robots at scale, right? Robots are this cottage industry today in the United States. Robots are a rounding error even in the most automated industries like manufacturing logistics. So, that doesn't exist. Certainly none of the software exists and none of the customers exist because, you know, again, the robots can't do any of the jobs that customers want robots to do. So, robotics is not, I think, any one layer of the stack. It's not just about intelligence, it's not just about hardware, it's not just about the customer. It's about kind of figuring out how to put all these three pieces together and it's the combination of all three that actually lets you build a, in my view, a successful platform at any one of those layers.

4:32 >> Yeah, I mean, totally if you talk about robotics being the ultimate systems company. >> Yeah. >> As opposed to being one one of the other. >> But, you know what's funny about this though, which is you know, the question I wanted to ask you is I mean, you've got a pretty deep technical background, right? You know, and like I mean, MIT robotics, is that that fair? but, whenever I talk to you, I feel like I'm talking to an operations guy that talks about like >> >> Yeah.

4:56 >> They're talking about unit economics and operations and supply chain. I mean, >> And even a go-to-market guy. >> Go-to-market guy. So, it's kind of you've done this like the transformation is complete and so like may- maybe this is kind of an open-ended question, but like I've always wondered like is the harder problem here actually that stuff? >> No. But, but I think you have to do all of it is kind of the thing. And like I when I was just a roboticist, right? And only working on the technology side of this problem, I think my strength as a roboticist was that I was very full stack and I was willing to think about the manufacturing, the hardware, I was willing to think about FPGAs and ASICs and like if you view robotics in the same way that I do, that it's this like ultimate systems problem, the only way you can make forward progress is by figuring out all of the clever trade-offs across as many layers of the stack as you can. And if you throw out one of the layers of the stack and you're like, "Well, we're not going to touch that." you've eliminated 90% of your trade-off space to make progress.

5:52 >> Interesting. >> And then starting a business, it became really clear to me that like, "Oh, okay. Robotics is not actually full stack like model, hardware, software. It's full stack like model, hardware, software, supply chain, customers, product, right? It's like >> yeah. >> Exactly. And similarly, like >> business model >> you ask the question like why hasn't robotics worked, right? There's been a lot of robot startups. I would argue no robot startup has been like really massively economically successful.

6:19 Why is that? And I I think it's because one, you don't have this core primitive, which is intelligence, and that lets you kind of bring the the unit economics of the system to a certain level. but two is that it's the super sort of disaggregated market. And you have infinite players, and they're all trying to work with each other. And the reality of what we need in robotics is to be able to make tradeoffs across the full stack.

6:46 >> You know, it's interesting. So, this is an observation that we have had, and it's it's interesting to articulate it this way, which is most robot companies start horizontal, and they all go vertical. Yeah, every one of them. Like, they literally start like, "We have a model." Or whatever. And then, you know, they start like buying 3D printers and Do you think that that is kind of because the market is so immature, like you had to do in the early days of cars, or like you think it's actually inherent in this domain? Like, like even in 20 years, you will have to verticalize just because the economy of movement or something requires that.

7:21 >> No, I think it's immaturity in the market, and you can see similar things in like the early computing market. And so, to us the question then becomes, okay, how do you build kind of the general platforms that let people build businesses that aren't robotics businesses, right? We have all these incredible software businesses today, and the people who run them don't have to think about chip architectures, right? how do we make that possible in the physical world with robotics, and how you go about building that platform?

7:46 And I agree with you, people have tried to solve that problem by thinking about very narrow layers of the stack. And in our view, the reality is you need to build a full stack platform. And the only way to know that that full stack platform really works is to actually have successful business units operating on top of it. >> I mean, I mean, are you open to like it being the case that actually the physical world is different than the digital world, and the reason we horizontalized compute is because you have clean abstractions that are generally applied, and that's not the case for like navigating, you know, physical space, or >> I think the beauty of something like a humanoid is it's the same, it's that clean abstraction, right? You think about like what is a CPU, and it's this general computing machine, and it's not perfectly optimized for every use cases, right? But it's general enough that you can run most computing workloads.

8:32 >> Oh, but that's so that's your horizontal component would be in the humanoid. But for example, would you use the same, you know, brain, or, you know, like subcomponents to make a humanoid as you would to like make a drone, or to make some other form factor? >> No, but you can make that same analogy in compute, right? We have CPUs, we have GPUs, we have ASICs, right? So, I think you track the maturity of the market, and you as start in this world of specialization, custom machines, cuz that's the only thing the market can support. You move towards general purpose, and then robotics this is going to look like humanoids and similar form factors, and then eventually you kind of break past to you know, that re-specialization as the market is big enough.

9:08 >> Why is this thing called Tutor >> Yeah. >> Intelligence? >> So, Tutor was founded kind of with this thesis that there is a whole internet of human intelligence that is networked, right? That is posting on social media, that's writing emails, and that network of human intelligence for LLMs and other kind of digital world models by natural emission has created this beautiful data set that we can use to train general purpose models. And the issue is our work in the physical world is not similarly networked, right? Like I pick up my coffee cup, and nobody knows, right? Like nobody recorded all of the pressures of of my fingertips and where they were in the poses, and so we don't have this asset.

9:52 and the core idea behind Tutor is what if instead of trying to fill find kind of this algorithm where from no data we could build our own intelligence of us where like yeah, that's that seems like a problem that's rigged against us, right? That's not how this worked in computer vision, it's not how this worked in NLP. What if we could build this network of robots out in the real world, we could connect them with human teleoperators, which we call tutors, that teach the robots how to do new tasks. And in the same way the internet kind of by omission created this data set to train these frontier models, we can do the same with robots, and we can build this internet of robot training data. And there are all these beautiful kind of emergent properties where like, okay, suddenly it's not this massive capital project. It It is to go build this network, but also you're creating incredible economic value as you're doing it, right? You're making everybody's goods cheaper as you deploy these robots and collect this data. And so it's very positive sum, I think in this very unique way. and so when we thought about, okay, what are we going to name this business, you know, it's not strictly artificial intelligence. There's some artificial intelligence, but it's ultimately like this tutor intelligence that we're building that was connecting and controlling these robots.

11:00 >> Yeah, that's an amazing insight. And the funny thing is when we talked to your customers before we invested, most of them didn't seem to know or didn't even seem to care >> Yeah. >> that there was a tutor or when the tutor was operating and when the your learning model was operating. And that credit to you guys that you made this very very seamless, too. >> Yeah, this is to me a signal that you know the robots are good, right? You want the feedback to be like, "My shampoo is that I'm manufacturing at volume is now 5 cents cheaper than it was." If the feedback is like, "The robot is pretty." It's like, " that's probably a really bad robot."

11:37 >> Is the goal to not have tutors in the future or does it matter? >> We think of it like at a very pragmatic level of like, what is the operational complexity of tutors? What is the economic impact of tutors, right? Like think about what is the cost to serve models with an H200, right? And you can math that out. And what's the duty cycle and utilization of that H200? And you can think very similarly about human intelligence, right? What is sort of the heterogeneous profile of intelligence that you really want from humans that you can't get yet from your models.

12:08 and then it becomes a unit economics question, right? Like how much engineering do you have to do to get that last nine out of your models versus using tutors and really, you know, going back to what we're talking about, you want to be able to make as many trade-offs here as you possibly can to make a business like this work. >> I mean, that you know, it's tempting to say that like the economic marvel that happened with generative AI can also happen here. And the the kind of the the marvel that happened with generative AI is like you you train these, you know, these models with, you know, some distribution of data and it turns out basically the same distribution is economically viable. Right? It's like you train it on images and people want images. You train it on text and people like, you know, it's like kind of distribution in, distribution out.

12:49 It's not clear that this is actually the case with with these robots, right? You could train it on like, you know, a distribution of data, but then like actually what you need is just slightly out of that distribution. So, like how I don't know, you probably have the best visibility into how this scales and what the convergence is and whether you can actually remove tutors. Like, what is your intuition on on that? >> I think this is one of the big questions and I think really it ultimately comes down to like what is the data and where you're getting it from. And the magic of the internet, right, is like if you are a company and you provide at the application layer tools for software engineers to accelerate their software development, you are naturally in flow of all of this really, really, really economically valuable work, right? If you can make money as a business, then you're probably also making data as a business, right? Those like economic incentives are aligned. And similarly for robots, if you're like, okay, we're just going to strap cameras on everybody's heads and send them throughout Indonesia to collect data doing X, Y, and Z, you have no guarantee. Right? You can guess, but there's no, you know, invisible hand of the market that's guiding you to ensure that that data is actually economically valuable.

13:58 But if it's actually this like kind of emission byproduct, if you have robots in real customer sites that customers are paying for for those robots to do that task, you know for sure that that data is economically valuable. >> Doesn't it kind of depend on also the dimensionality of the solution set though. Like it could it could be the case that like the economically valuable stuff is so high dimensional that you could like you could solve 90% of it, but the last 10% you know, you'll you'll need a human being to do for a while just because like getting that data set is just going to require too many tutors over two period of time longer.

14:33 >> Yeah. And that might be fun, right? You might be willing to accept that on the economics. That also might be a place where other forms of data matter. I think we're not purists. again, we're willing to take any trade-off, right? We love using simulation to kind of widen these data distributions. We love using like internet scale data to widen these distributions. So, we've a lot of tools in our toolbox other than that real world robot data.

14:54 >> And talk a little bit more about the simulations and these other tools and the impact models like Astra are having on that. >> Yeah. >> That seems to be another cottage industry of excitement for the robotics >> Yeah. >> world, right? How are you using that? >> It's it's a sea change, right? I think we lived in this world for a while where everybody knew that all of this general knowledge about the world was baked into these models, but they couldn't really express it, right? They didn't have that last mile of spatial reasoning to connect the dots from the stuff that you know, a GPT model knows to something that an action diffusion transformer head can know. and that's changed, right? The labs are now post-training their models on 3D spatial data like CAD and like 3D modeling, which by the way they're doing because there's this direct economic incentive cuz there's a real market for it. so, they can immediately monetize it.

15:51 And it turns out that's the exact spatial reasoning that our models need. So, I think we've been catapulted many years ahead in the future cuz we're starting to be able to use all of that general intelligence that lives in these big frontier models for robotics. >> And because it's a systems problem, the net effect for you is acceleratory >> Yeah. >> as opposed to slowing you down or saying somebody's going to come and eat your lunch.

16:15 >> Yeah. No, we and we've seen a couple of these happen, right? We started this business in 2021. We were doing this initially with convolutional neural networks. Right? So like we've we've gone through every sea change and and the thing that is always valuable is the flow of data. >> Yeah. >> Right? And the flow of data is is always the thing that uniquely you can do and it's like yeah, those like the million architectural tricks like do them if it helps you accelerate the flywheel, right? If it helps you deploy more robots, collect more data, manufacture at a bigger scale, do more tasks like they're worth doing.

16:48 but but ultimately we think the thing that has kind of the long-standing generational value in a business is the flywheel and loops themselves. >> Can Can you talk a little bit about what you mean when you say you build robots? Like I think on one end of the continuum it's like yes, you buy a unitree. >> Yeah. >> You know and you like prompt it and the other end, you know, like whatever you're like machining parts. Like where are you in this?

17:09 >> Yeah. So for context for everybody in 2025, the United States deployed 38,000 industrial robot work cells, which is tiny compared to roughly a million >> counting the Fanucs and the old ones. >> The Fanucs, right? But but that's it, right? Nobody Nobody deployed any unitrees in the United States into any They would have shown up in those numbers, but they were probably zero. and so the supply chain that exists for robots is both too small and as you called out wrong, right? Like the robots that Fanuc and ABB are building they're mostly not building in the United States, but but even when they are building them in the United States, they're building these objects that are designed for systems integrators, that are designed for fast repetitive motions. They are not cut from the set of tradeoffs that you would want to co-design with frontier models. and so we're kind of starting from scratch on the hardware side. so Tutor, in order to deploy robots at scale, has actually been forced to design and manufacture our own hardware. For us today, like we're not building actuators, right? So we're not building cameras. There's a whole layer of stuff that we're sourcing from OEMs where they're manufacturing at a much larger scale. But the actual robot itself of, you know, the thing that drives around and has the arm on it and can do all the tasks. We actually are building at scale in Watertown, Massachusetts.

18:29 >> Can you talk about the morphology of the robot? Is that what do you Is that called morphology? Anatomy? What do you call >> You you can use whatever word you want. That's fine. >> What do they look like? >> What do they look like? We make two. so, one robot we make >> Look at his website, Machine. >> Yeah, go ahead. >> so, Cassie. Cassie is kind of a brute, right? Cassie is designed to do heavy-duty work. It has a single arm and a lot of suction cups and it's powered by foundation models in like the 200 to 400 million parameter range and kind of came out of this world that Tutor was started out of in 2021 of like, what can foundation models do for robots? They can predict kind of affordances, grasps that you can go then execute. so, it's this co-design of the hardware that you can manufacture at scale, what your models can actually do, and actually a big market need where in the United States anything in the physical economy has to be transported on a pallet or in a cardboard box. So, there ends up being an enormous amount of physical human hand work of transporting pallets, cases by hand. And so, Cassie's kind of the result of that co-design and we manufacture it at a very large scale and we deploy it to a big diversity of businesses across manufacturing logistics.

19:43 Our second robot is called Sunny. And Sunny is kind of an industrial semi-humanoid, right? It's as if you said you were looking at a unit tree and you were like, I need this robot to run for 3 to 10 years in a factory at human rates and reliability. And so, you end up with a slightly different set of design decisions. Sunny is wheeled, not legged, because most indoor industrial settings have polished ground floors. it has a much higher payload capacity and can move much faster and has industrial collaborative safety standards that enable it to operate around humans. And so, we have 100 Sunnys operating in Boston in our data factory collecting data for internal use and advancing our research agenda. And then we also now have a growing fleet of Sunnys out of the real world doing more dexterous bimanual manipulation.

20:30 >> And do you see how broad do you see this product line of different robot form factors? Is it Can you capture >> Yeah. >> a big chunk of the useful market with three of them or you need 30 of them? >> Depends how you define the useful market. Where do you want to go? If you want to just live inside of manufacturing logistics, Sunny and Cassie today can probably do 80% to 90% of the work, right? These are designed for those cuts.

20:55 >> What Maybe just describe what that work is. >> Yeah. >> Because yeah, I'm not sure it's probably understood. >> Yeah, so most people have this mental picture of what sort of the industrial base or manufacturing logistics in the United States, which comes from the TV show How It's Made. I don't know if either of you have seen this. >> Yes, of course. Of course, yeah. >> And you see these like Rube Goldbergian machines of levers and it's like, yeah, you don't see any humans in those pictures, right? It's somebody built the machine that is the factory.

21:22 >> Yeah. >> And that does exist. and if it exists, they probably filmed it and put it on How It's Made, right? Because that's the top 0.01% of American manufacturing logistics. and so what's below that? And you walk into these million square foot facilities and you see a bunch of people standing around on conveyor belts or folding tables doing things by hand. And it's kind of shocking for anybody who doesn't come from this industry and community. and so you look at a robot like Cassie and it's like you know, a huge amount of work which is backbreaking work that's really hard to hire for, that has a three-month turnover where your average age of the worker doing that work is 55 years old and about to retire. And it's like, okay, we we could use a Cassie to do that backbreaking work. And similarly, Sunny is like basically anything you ever bought that came in a box, a human had to put that in a box, which is a crazy revelation for most people.

22:15 Anything that requires any degree of assembly, right? Like even, you know, iPhones, you know, it you would assume it's a robot, you know, putting that packaging together. No, it's a person even at the highest level of scale. >> Then then to his question, which is so like you have like the single arm with the sucker, you've got the two-armed humanoid. >> Yeah, what can't they do? >> Yeah, what can't they do and where do you go from there? Yeah.

22:36 >> So, they can't go outside. And outside is really, really interesting. >> that just like a locomotion issue? It's >> It's not locomotion, it's the hardware, right? These These robots have wheels and they have like a little bit of traction, you know, they have like these articulating hind legs, but they can't drive over gravel, right? There's huge opportunities of robots in construction, robots reclaiming materials from landfills. we, you know, we need robots with legs for that. So, I think that'll eventually be part of our pantheon of embodiments. And then similarly, robots in the home, you probably don't want a Casey or Sunny in your home just as it's a little bit too industrial of a device.

23:15 >> Yeah, they look scary. >> Yeah. >> Yeah. Let's talk about the customer adoption side. How's How is that being obviously not strictly from a I don't have people, so I'll take whatever I can get. >> Yeah. >> To once they have it in their assembly line or whatever it is, how does adoption go in terms of their mindset? >> Yeah. It's an interesting challenge cuz almost everybody we're selling a robot to has never purchased a robot before.

23:42 and that kind of comes out of these stats, right? If the United States is only deploying 38,000 robots a year, if nine out of 10 US factories and warehouses don't have any robots, then almost all of the market is greenfield. And these people, you know, understand what this old guard of automation looks like and they assume a robot doesn't work for them. And so, the way we break through that actually isn't by talking about AI or models or tutors or anything like that.

24:03 We call them up and we're saying, "Hey, have you ever considered a robot for 16 bucks an hour?" And they haven't, right? That's That's a new proposition for them, right? So, you can book a meeting and then you can kind of explain to them this thing that we're doing and get people interested and involved. and kind of by creating that business model where there is that true incentive alignment between us as the robot vendor and the customer, we're able to run distribution of robots at velocities that, you know, are many orders of magnitude faster than traditional industrial automation industry. We have an average time from that first call to a robot signing contract of 40 to 60 days. We have an average time from that signed contract to robots actually in the customer site of about 40 days doing work. we stand up robots in production within a day, often within the first couple of hours that a robot is on site. So, we're able to move very, very, very quickly because not only is the robot this generally intelligent, generally capable machine that we're not customizing, we're just, you know, shipping directly from the factory to the customer, but we're not putting any sort of economic risk on the customer. So, we're able to get them on board.

25:14 >> And once they're deployed, how do their attitudes about labor versus machines change? >> The operations community is really, really fantastic cuz they see a thing work and it's like, I it works. >> >> They're like, that's it, right? It's it's like, yeah, I I can see obviously it does the job. I know all the places where this job needs to be done. Let's go put it in all those places. So, we've been able to scale up very, very quickly both with small businesses and to some of the large companies in the supply chain from, you know, one robot in one site to, you know, a dozen plus robots across many sites.

25:51 >> It It is interesting though cuz like a lot of gen I I was like there's a social element where like people get very attached to their models and like it's hard to swap models and so I mean listen, you probably have more experience deploying robots than most people alive at this point. Like, are you saying the same thing? Like, is it are they viewed like equipment or like they're starting to be a viewed like beings and co-workers?

26:12 >> Yeah, so I saw a very funny tweet which was a robot is a robot unless it works, in which case it's a machine. >> >> I got to figure out the attribution. >> this it, Neil? I guess it >> It's It's pretty real. >> Is that what it is? Like it just feels like a piece of equipment once it works. >> Certainly at like the factory manager level, yes. I think there's still the layer of like interestingly in robotics, your buyer is not your user, right? Your buyer is like the COO or the general manager and the user is like a factory worker, right? Who's working with the robot all day long and there you actually do need to cultivate the relationship between those workers and the robots because you know, we don't fully automate 100% of a factory, we we automate some jobs in a factory or warehouse.

26:53 >> Do you have a philosophy on like how you even approach this? Like do you like try to make them look friendly? Do you like make them look more like co-workers or less like co-workers? Do you make it them talk? Like like how do you kind of view this philosophically? >> Yeah, so very very luckily there is a whole school of academic research called HRI, human robot interaction. >> No idea. >> go. >> Yeah, a lot of people have spent a lot of time thinking about this including a bunch of people from from my academic background and there are a whole bunch of tricks.

27:18 So one trick is every robot has a name. And so that lets us actually build relationship with the robots. It also Mostly our manufacturing technicians. >> so so you name it, not the It's not like a dog is when you get a new dog you can rename it. >> So when a customer expands with Tutor, when they when they do a repeat order, then they're allowed to name the robots. >> Oh, cuz like you're not supposed to like rename a boat because that has bad luck.

27:43 So you got to keep the boat name but you can rename a dog. So I don't like >> Yeah, so the new robots get to be get to be named and that solves it. And we got customers >> they they have higher sense of ownership now they've named it. >> Exactly, right? And we get like really strong signals. Like we had people name the robots after their kids. >> Woah. >> Right? People name the robots after their favorite football player, their favorite pop star.

28:07 People get very attached cuz the robot ultimately helps, right? It makes their jobs easier. If you're a line worker in the facility, you're not doing backbreaking work as much, you're able to be elevated into a higher position and none of these places can hire and then if you're a factory manager, your job is just a thousand times easier. >> What naming scheme do you use from when you >> We started off doing hurricane naming and then we kind of >> Oh, that's not Yeah. You didn't read the HRI books clearly.

28:33 Well, it's I don't know. It's The The customer doesn't know that Britney is, you know, Nmod26 or whatever equals two. It's It's but we actually stopped cuz we kind of went through the hurricane. We went through the alphabet too many times to be able to keep track of it. >> Okay. So, what are you using now? >> I think the technicians just just pick whatever brings them joy. We We have a really wonderful and privileged to employ a big staff of people in the United States who are public >> To build these robots?

29:02 >> >> And they are also naming them. >> Awesome. Huh. The other thing that surprised me when we first did the work before investing most of the time when we talk to robotics companies, they're going after these humongous companies, right? The big Fortune 50 type companies that seem to have ex- the budget for lots of experiments, etc. >> Yeah. >> Whereas I think a significant majority of your customers are all these local businesses almost. >> Yeah.

29:31 >> And what does that I mean, is there a message in there or or was it a conscious strategy or >> Yeah, it's a lot of things. One is that's most of the market, right? The vast majority of US businesses in manufacturing logistics are small businesses or mid-market businesses. So, if you want to serve the market, that's what it takes to serve them. I think we also started this company at a time we're seeing kind of all of these very academic supply chain oriented robotic startups kind of get started and then not really succeed.

30:02 And we we had the opportunity of being like, okay, what went wrong here? And one of the big things I think that robotics companies get wrong is they're not actually really product companies. They're like, okay, some really big company has a very specific problem. They're willing to write us a shiny piece of paper that says they're going to expand with it. We're going to spend two years building a very custom system to solve their problem. We'll deploy it.

30:24 Chances are it won't work cuz like anybody who's built products knows that you can't just one-and-done a product, right? Doing product requires iteration. And then even if it does work, who's to say that customer still has that problem? And so boom, the company's done. And I feel like I saw this happen like five to 10 times in a row to the point of like oh my god if this happened to us, like that would just be embarrassing.

30:44 and so I think working with a diverse range of customers is this beautiful forcing function for building the right shape of product, which to us is a general robot. Right? We don't want to build a Rube Goldberg machine that works really well for big industrial customer that is generally capable robot bodies running generally capable robot models that can immediately get to work. and if you can, you know, sign a contract in two weeks with a mid-market customer and have a robot running immediately after that, and that's a totally different type of product and business than your other customers, you know you're being successful.

31:22 >> How how do you think in the limit, what the industry looks like assuming you continue to be successful? Is it just that you know, this work gets cheaper, faster, or does like somehow quality go up, or can they do more things, or like can they like what what is like the economic convergence to the productive convergence? >> Yeah, I think everything gets better. So there's a whole set of goods that if they are cheaper, we will buy more of them. And if you believe those goods are valuable, that's good for everybody.

31:52 There are also a bunch of discontinuities in the world. Like I was talking with somebody the other day who worked on the line in a housing factory in I think Wisconsin. They were describing how the houses that they're building are about 5% more expensive to build them in a factory than to do traditional construction on site. But if these houses were even just 1% cheaper than kind of the equivalent quality of house manufactured or constructed on site in the traditional way, all of the demand from developers would immediately go to that other shape of building these homes in a factory. And the same thing could be said about like reclaiming materials in landfills and a whole bunch of other markets. So I think our hope is like if Figure is really successful, we can provide this robot substrate, this this labor force that other businesses can use to transform the physical economy and make the world ultimately a cheaper and better place to live in.

32:48 >> Will you ever go in the home? >> yeah, we're we're not opinionated. We'll go anywhere. >> Yeah, and and a little bit. But I I would imagine that would be kind of a much tougher technical problem just from like I don't know, complexity of task and safety and >> and the economics of it, right? Like how many What is the economic value to the robot delivers in a factory and how many consumers are willing to pay for that in their home and it doesn't quite work out. but again, that that comes back to our our overall philosophy is take trade-offs across as many layers of the stack as possible to get a foothold, start building the platform, and as you build the platform you have to take fewer and fewer trade-offs over time.

33:27 >> How does product innovation work in robotics? I mean, most companies are just on V1, whereas you're got two versions of the robot and you continue to iterate really fast. What What is it different How is it different than like digital product innovation or other categories? >> We're trying to make it as similar as possible. So, we're manufacturing our own robots. This gives us the ability to own that manufacturing process and figure out how do we push changes to the manufacturing process every 2 weeks. We are shipping code to robots either every day or major releases every 2 weeks and have built a huge amount of infrastructure to support that level of iteration. We've built a think I really unique shape of product function where our product function in our organization actually sits right next to our customer success and implementation functions and operate actually as one big team. So, they're as close to the customer as humanly possible, which I think has been really successful for us. And then we also have a research team which is able to be both on the one hand deeply embedded in kind of the customer problem space and we're able to pull all sorts of levers to get specific types of data from customers, but also able to take these moonshots. And you know, if we have a a on our modeling side that lets robots do a whole new shape of thing, we can kind of co-design a product around that in a way that I think most companies couldn't and get that out into the field much quicker.

34:52 >> If I if I'm listening to this and I have a warehouse, >> Yes. >> and I want to get a robot, do I just go to the website and I put in my email? >> That's >> How does this work? >> You can go to our website, you can put an email at you. We actually have cool pricing calculators where you can actually put in some operational data about how your warehouse or factory runs today, and you can get an estimate of, "Hey, with an initial deployment of two to robots, we could be saving $100,000 a year, a million dollars a year, $100 million dollars a year." and that's not like, "Okay, this is a roadmap over 5 years of custom engineering." It's like, "No, you could save that a million dollars a year 2 months from today without putting CapEx down."

35:31 >> And have you found that there is like demand for I mean, it's just such a sci-fi thing. Like, is there demand out there? >> There is so much demand. And it's not even demand for robots cuz again, like people don't know that the shape of robot exists or that it works or that it could do this job. So, it's it's ultimately like, you know, we have this framework of the value of a robot is you take how much work a robot can do, and you divide it by its cost. And that's it. Right?

35:57 Everything else is is commentary. And so, if we're able to compete in these highly liquid labor markets that exist across American physical economy, and we can offer something that delivers greater equivalent capability at lower cost, the market for that is the whole market, right? It's all physical work done. >> And what just one quick wonky question on this, which is how much when you say cost do you mean amortizing the upfront cost versus like the ongoing cost to operate?

36:24 >> It's both, which is very interesting and different, right? Traditionally, we've thought about robotics as ROI, right? Big CapEx as expense and then 10 years to make it back. And now it's actually like, no, more and more of the cost is going into ongoing inference teleoperation. >> But does that also include like maintenance and all of that stuff? And then and all of that stuff. And then And what is the dominant cost? Or are they or are they roughly equal?

36:47 >> we drive down all of them over time. >> No, no, I'm just saying like from your standpoint, when you like pencil out your COGS, how much of it is the upfront cost versus the ongoing? >> Yeah, it is more ongoing at this point. And that surprises people. >> Interesting. >> Oh, why? >> Yeah. Not surprising to me. I mean, you're really competing with like something that you feed rice to and it's very, very efficient, right? And like the the human body.

37:08 >> Yeah. So, the when you get colored thieves customers, is it because Black Friday or something is coming up and they need a bunch of units done in a really short time or is this actually now part of their actual labor planning cycle? >> No, it's strategic. Right? It's these businesses, I think, are faced with a really hard choice, which is they know the age of their workforce, they know that demand is ramping up due to all these secular trends, and the cost basis of fulfilling that work is going up, and they're like, "How does our business pencil out?" Because, you know, these are not software businesses that have these, you know, huge gross margins that you can you can play with and can compress a little bit, and it's like, "It's fine." It's like, "No, the business either works or it doesn't."

37:58 and so, I think our customers are ultimately looking for a long-term partner that can do the robots, right? If one of our customers is manufacturing deodorant, they're doing the deodorant, right? And they're going to be operational experts in deodorant or operational experts in crayons or whatever the thing they manufacture is, and they need a partner who's the expert on robots. >> Another question on the the nature of factories and I mean, you can ship a robot to anybody anytime. Not today because of your shortages, but at some point in the future, right?

38:31 >> >> And if your robots become more and more general purpose over time, what does it mean for where factories are located and how people organize around >> Yeah. >> these factories and and you know, these one-town factories and one-town one-down town, whatever the expression is. Yeah. All of that fundamentally changes now, right? >> Yeah. I think a lot changes. Right, there's a lot of geopolitics in this. I think one big thing is this is going to be a great equalizer between the United States and other company or excuse me, other countries like China invested in the previous regime of industrial automation.

39:06 And in some ways that made them very successful and in other ways I think in hindsight that might end up being an enormous mistake. Because in the United States we just discussed we're doing this greenfield builds of automating our industrial capacity using generally capable robots. In China they're going to have to tear down, right? We're not going to have to do that in the United States in the same way. I think another thing that's going to shift, which is going to be really interesting, is we're just going to have way more factories in the same way that we have way more businesses because kind of the cloud and all of the software tooling let people build new businesses.

39:39 Anybody will be able to start a business in the physical world. Anybody will be able to scale a business in the physical world if we kind of have this infinite flexible labor substrate. >> You you brought up China or you know you know we actually we actually published a report on the supply chain and robotics and it seemed to be that that China was so central to it. So, can you talk a little bit about your dependency on it and then how that evolves over time and how much of there is there risk in China to the supply chain?

40:07 >> Yeah. I think ultimately, no matter what anybody says, today in 2026 we have a highly interconnected, interdependent global supply chain and this is one of many examples of this. And I think both US companies and Chinese companies are thinking very hard about kind of what does the long-term strategy of this look like? And it's Chinese companies investing in the United States, it's United States companies investing China, and ultimately everybody trying to build their industrial capacity.

40:35 >> Yeah. I've I've noticed that you know you've gotten as far as I can tell you've verticalized more. Is that right? Like you're taking more in-house. And do you continue do you plan to continue to do so? >> Yeah. Well, there's this tough thing in supply chain where and again because robotics is such a cottage industry is you might start in a world where sub components are coming from OEMs and then you grow your volume to the point of they're not OEMs anymore. They're they're glorified contract manufacturers, right? Once you're doing a material percentage of their volume and at that point just the relationship and the dynamics change, right? It's like is that the right command for that thing? Do I want to be using a command or do I want to be manufacturing it in house?

41:13 >> Yeah, I mean Tutor must be one of the largest employers of robots in the United States and you know as a result you must be like one of the largest customers for some of these OEMs. You know, as a result I would imagine over time like you just continue to like drive, you know, these components yourselves. >> I think that's right. >> Does that mean taking it in house or just telling them what to do? I mean is it just are they still suppliers?

41:33 >> Some combination, I guess. >> Is it >> Some combination. I think ultimately for Tutor to be successful we will need a mature robotic supply chain in the United States and we're in this position where because we are so much of the demand for that supply chain, I think it's both our obligation and an opportunity to invest very very very heavily to building this capacity in the United States and that's why we built robots in Watertown, Massachusetts. It's why we expect to continue to verticalize and build more and more of our robot componentry in the United States.

42:04 >> Like I going back to when we first started talking I don't remember it was probably the second meeting when we actually started about the research, right? Mostly the meeting because you've got such a great business. But that's a big strength of yours, right? And talk about how you approach data and there's all sorts of theories about which robotic learning models will win and so on so forth. How how are you approaching the robotic learning bet?

42:28 >> Yeah. >> In research. >> I think we're thinking about how this played out in LLMs and what's similar and what's different. And I think one thing that clearly has been very very important in LLMs is there are lots of heterogeneous sources of data, but ultimately there's a top of the data period which is real-world reinforcement learning traces doing the thing you want to be able to do. And so we've built our research program in such a way where we are thinking about how do we get the most out of robot experience out in the real world. and so we built this a 100 robot fleet called Data Factory 1 in Boston. This is to our knowledge the largest sort of semi-humanoid data collection farm in the United States.

43:07 And we're able to use this as kind of this research test bed of how do we do real-world reinforcement learning at scale with robots? you know, reinforcement learning is a technique family where it doesn't really work at small batch sizes. and so if you have a very large fleet out in the real world, you can start to play with these methods which I think people haven't been able to play with either in academia or in industry.

43:33 >> So I I I presume like building a a team for this is quite different, right? Then like you know, labs and maybe talk about kind of like the construction of the team and maybe if there's anybody you're looking to to hire? >> We're looking to hire everybody. >> Everybody? >> So we're looking to hire researchers and engineers and that's across to every single layer of the stack. >> Wow. >> we are also looking to hire technicians to build robots.

43:53 >> way, every layer of the stack you mean like software >> Hardware, materials, electronics, like there yeah there's nowhere that you can abdicate responsibility, right? >> It's robotics. >> You're going to engage with it whether you want to or not. The question is whether you understand it. so so we're we're building that team and I think that's a really unique shape of team not only because it's so interdisciplinary and collaborative across all those layers of the technical stack.

44:18 >> Yeah. >> But because it's I think it's one of the only places in the universe where you can be on a whiteboard talking about model architecture in the morning and the afternoon like you know, looking at a factory line and seeing how a robot is doing and >> Is this primarily in Massachusetts or >> So we have a team across the United States. we have especially folks on the go-to-market and technicians who are kind of engaging, you know, our customers are everywhere across the United States.

44:40 >> That's great. All right. >> Awesome. Thank you.