The Robots Already Working in American Factories | Tutor Intelligence

What does it take to build robots that can actually work in factories and warehouses? Tutor Intelligence CEO Josh Gruenstein argues robotics is a systems problem spanning AI, hardware, manufacturing, and the work itself. Josh joins a16z General Partners Martin Casado and Raghu Raghuram to explain Tutor’s approach: deploy robots into real businesses, use human “tutors” when needed, and turn that work into training data that makes the robots more capable. They discuss Tutor’s 100-robot data factory, robots starting at roughly $16 an hour, advances in spatial reasoning, and what flexible robotic labor could mean for American manufacturing and the physical economy.

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

• 0:00Speaker 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.

• 0:18Speaker 01

  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.

• 0:23Speaker 00

  Almost everybody we're selling a robot to has never purchased a robot before. Almost all of the market is greenfields. 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.

• 0:45Speaker 01

  How does product innovation work in robotics? Most companies are just on V1, whereas you have two versions of the robot and you continue to high-trade really fast.

• 0:55Erik Torenberg?

  So most factories and warehouses in America still rely heavily on people for physical work. What does it take to build robots that can actually do those jobs? In this episode, Martin Casado and Raghu Raghuram sit down with Tutor co-founder and CEO Josh Gruenstein to unpack a different approach to building general-purpose robots. Tutor deploys robots into factories and warehouses, connects them to human operators when needed, and uses that real-world work to generate training data that makes the robots increasingly capable. They discuss why robotics requires solving AI, hardware, manufacturing, and go-to-market at the same time, and why Josh believes general-purpose robots could ultimately become a new labor substrate for the physical economy.

• 1:41Martin Casado?

  Josh, thanks 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?

• 1:56Speaker 00

  Yeah, so I'm Josh. I started Tutor about five and a half years ago with the core thesis that if you could just start deploying robots out into the world and to 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. So 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.

• 2:29Martin Casado?

  And again, maybe talk a little bit about, 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.

• 2:37Speaker 00

  Yeah.

• 2:38Martin Casado?

  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.

• 2:43Speaker 00

  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 Tutor Robots have helped manufacture and ship over a quarter of a billion products to American consumers. So presumably, most American consumers are a Tutor customer, a couple of layers down. 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 engineer, machines that can't scale.

• 3:21Martin Casado?

  Yeah.

• 3:21Speaker 00

  Yeah, so we started the company with this thesis that if we could just start deploying robots, we could build the labeled data sets that help us train these models and deliver this value to the American consumer.

• 3:32Speaker 01

  We see a lot of robot pitches. 99% of them start with some insights about the model and how you're going to build a model or some insight about the data, shape of the data, that is. And very few people thinking about 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.

• 3:59Speaker 00

  Yeah.

• 3:59Speaker 01

  That's the name of your company. Where did that insight come from?

• 4:03Speaker 00

  It came from kind of this thought experiment, where 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. Let's say you take that for granted, and now what? Right? 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 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

• 4:46Speaker 00

  because, 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, in my view, a successful platform at any one of those layers.

• 5:10Speaker 01

  Yeah, internally, we talk about robotics being the ultimate systems company as opposed to being one of the other.

• 5:17Martin Casado?

  You know what's funny about this, though? And the question I wanted to ask you is, I mean, you've got a pretty deep technical background, right? I mean, MIT robotics, is that fair? But whenever I talk to you, I feel like I'm talking to an operations guy. They're talking about unit economics and operations and supply chain. So, I mean.

• 5:33Speaker 01

  And even a go-to-market guy.

• 5:34Martin Casado?

  A go-to-market guy. So it's kind of, you've done this, like, the transformation is complete. And so maybe this is kind of an open-ended question. But like, I've always wondered, is the harder problem here actually that stuff?

• 5:45Speaker 00

  No. But I think you have to do all of it, is kind of the thing. And like, 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 it was very full stack. And I was willing to think about the manufacturing, the hardware. I was willing to think about FBAs and ASICs. And 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.

• 6:14Martin Casado?

  Yeah.

• 6:15Speaker 00

  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.

• 6:22Martin Casado?

  Interesting.

• 6:23Speaker 00

  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? Exactly. And similarly, 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. Why is that? And I think it's because, one, you don't have this core primitive, which is intelligence, and that lets you kind of bring the unit economics of the system to a certain level. But two, it's 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 robots is to be able to make trade-offs across the full stack.

• 7:09Martin Casado?

  So this is an observation that we have had. It's interesting to articulate it this way, which is, most robot companies start horizontal, and they all go vertical. Every one of them, like, they literally start like, we have a model or whatever, and then they start 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, even in 20 years, you will have to verticalize just because the economy of movement or something requires that?

• 7:41Speaker 00

  No, I think it's a maturity 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 do you go about building that platform? 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 full-stack platform really works is to actually have successful business units operating on top of it.

• 8:19Martin Casado?

  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 just not the case for like navigating physical space or?

• 8:36Speaker 00

  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 work.

• 8:51Martin Casado?

  Oh, but that's, so that's your horizontal component within the humanoid. But for example, would you use the same brain or like subcomponents to make a humanoid as you would to make a drone or to make some other form factor?

• 9:02Speaker 00

  No, but you can make that same analogy in compute, right? We have CPUs, we have GPUs, we have basics, right? So I think you track the maturity of the market, and you start in this world of specialization, custom machines, because that's the only thing the market can support. You move towards general purpose. And in robotics, this is going to look like humanoids and similar form factors. And then eventually, you kind of break past to that re-specialization as the market is big enough.

• 9:25Speaker 01

  Why is this thing called Tutor Intelligence?

• 9:28Speaker 00

  Yeah, 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 my fingertips and where they were in the poses. And so we don't have this asset. And the core idea behind Tutor is, what if instead of trying to find kind of this algorithm

• 10:11Speaker 00

  where from no data, we could build robot intelligence? And for us, we're like, 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 emission 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 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.

• 10:55Speaker 00

  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?

• 11:05Speaker 00

  You know, it's not strictly artificial intelligence. There's some artificial intelligence, but it was ultimately like this tutor intelligence that we're building that was connecting and controlling these robots.

• 11:14Speaker 01

  Yeah, that's an amazing insight. And the funny thing is, when we talked to our customers before we invested, most of them didn't seem to know or didn't even seem to care that there was a tutor or when the tutor was operating and when your learning model was operating. And that clarity, you guys, you made those very, very seamless too.

• 11:37Speaker 00

  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. It's now five cents cheaper than it was. If the feedback is like, the robot is pretty, it's like, oh, it's probably a really bad robot.

• 11:51Martin Casado?

  Is the goal to not have tutors in the future? Or does it matter?

• 11:57Speaker 00

  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? 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.

• 12:38Martin Casado?

  I mean, you know, it's tempting to say that like the economic marvel that happened with generative AI can also happen here. And the kind of the marvel that happened with generative AI is like 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. It's like you train it on images and people want images. You train it on text and people, you know, it's that kind of distribution in, distribution out. 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.

• 13:21Martin Casado?

  Like, what is your intuition on, on that?

• 13:24Speaker 00

  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 are you getting it from? And the magic of the internet, right, is like, if you are a company and you provided 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

• 14:09Speaker 00

  you to ensure that that data is actually economically valuable. 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.

• 14:27Martin Casado?

  Doesn't it kind of depend on also the dimensionality of the solution set, though? Like, 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 too period of time.

• 14:47Speaker 00

  Yeah. And that might be fine, 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 have a lot of tools in our toolbox other than that real world robot data.

• 15:08Speaker 01

  And talk a little bit more about the simulations and these other tools and the impact models like Astra are having on that. That seems to be another cottage industry of excitement for the robotics world, right? How are you using that?

• 15:24Speaker 00

  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 like, 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 because there's a real market for it, so they can immediately monetize it. And it turns out that's the exact spatial reasoning that our models need.

• 16:09Speaker 00

  So I think we've been catapulted many years ahead in the future because we're starting to be able to use all of that general intelligence that lives in these big frontier models for robots.

• 16:20Speaker 01

  And because it's a systems problem, the net effect for you is accelerating as opposed to slowing you down or saying somebody's going to come and eat your lunch.

• 16:29Speaker 00

  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, 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.

• 17:03Speaker 00

  But, but ultimately, we think the thing that has kind of the longstanding generational value in a business is the flywheel and loops themselves.

• 17:11Martin Casado?

  Can you talk a little bit about what you mean when you say you build robots? I think on one end of the continuum, it's like, you buy a unitry, 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?

• 17:24Speaker 00

  Yeah. So for context, for everybody, in 2025, the United States deployed 38,000 industrial robot workups, which is tiny compared to roughly a million.

• 17:36Speaker 01

  You're not counting the Fanuks and the old ones.

• 17:37Speaker 00

  The Fanuks, right, but, but that's it, right? Nobody, nobody deployed any unitries in the United States into any, they would have shown up in those numbers, but they were probably zero.

• 17:46Speaker 00

  And so the supply chain that exists for robots is both too small and, as you called out, wrong, right? Like, the robots that Fanuq and ABB are building, they're mostly not building them 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 trade offs 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

• 18:31Speaker 00

  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.

• 18:43Martin Casado?

  Can you talk about the morphology of the robot? Is that what, is that called morphology, anatomy?

• 18:47Speaker 00

  You, you can use whatever.

• 18:49Martin Casado?

  What do they look like?

• 18:50Speaker 00

  What do they look like? We make two. So one robot we make.

• 18:55Speaker 01

  Look at his website, Monchane. Yeah, go ahead.

• 18:56Speaker 00

  Yes. 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.

• 19:41Speaker 00

  So there ends up being an enormous amount of physical, human handwork of transporting pallets, cases by hand. And so Cassie is 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. 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 unitry and you were like, I need this robot to run for three 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

• 20:26Speaker 00

  safety standards that enable it to operate around humans. And so we have 100 Sunnies 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 Sunnies out of the real world, doing more dexterous by manual manipulation.

• 20:44Speaker 01

  And do you see, how broad do you see this product line of different robot farm factors? Can you capture

• 20:53Speaker 00

  Yeah.

• 20:53Speaker 01

  A big chunk of the useful market with three of them, or you need thirty of them?

• 20:57Speaker 00

  Depends how you define the useful market. Like, where do you want to go? If you want to just live inside of manufacturing logistics, Sunny and Cassie today can probably do eighty percent to ninety percent of the work, right? These are designed for those cuts.

• 21:10Martin Casado?

  What, maybe just describe what that work is?

• 21:12Speaker 00

  Yes.

• 21:13Martin Casado?

  Because, yeah, I'm sure it probably understood.

• 21:16Speaker 00

  Yeah, so most people have this mental picture of what sort of the industrial base or manufacturing logistics looks like 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?

• 21:26Martin Casado?

  Of course, of course, yeah.

• 21:27Speaker 00

  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.

• 21:36Martin Casado?

  Yeah.

• 21:37Speaker 00

  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 point oh one percent 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. There's a three-month turnover where your average age of the worker doing that work is fifty-five years old and about to retire. And it's like, okay, we could use a Cassie to do that backbreaking work.

• 22:20Speaker 00

  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. Anything that requires any degree of assembly, right? Like, even iPhones, you know, 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.

• 22:41Martin Casado?

  Then to his question, which is so like you have like the single arm with the sucker, you've got the two-armed humanoid.

• 22:47Speaker 00

  Yeah, what can't they do?

• 22:49Martin Casado?

  Yeah, what can't they do? And where do you go from there? Yeah.

• 22:50Speaker 00

  So they can't go outside, and outside is really, really interesting.

• 22:53Martin Casado?

  Oh, is that just like a locomotion issue?

• 22:55Speaker 00

  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.

• 23:16Speaker 00

  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 Cassie or Sunny in your home just in a home. It's a little bit too industrial of a device.

• 23:30Speaker 01

  Yeah, they look scary.

• 23:31Speaker 00

  Yeah.

• 23:31Speaker 01

  Yeah. Let's talk about the customer adoption side. How's, how's that been, obviously not strictly from a, I don't have people, so I'll take whatever I can get?

• 23:41Speaker 00

  Yeah.

• 23:42Speaker 01

  To, once they have it in their assembly line or whatever it is, how does adoption go in terms of their mindset?

• 23:50Speaker 00

  Yeah, it's an interesting challenge because almost everybody we're selling a robot to has never purchased a robot before. And that kind of comes out of these stats, right? If the United States is only deploying thirty-eight thousand robots a year, if nine out of ten 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. We called them up and we're saying, hey, have you ever considered a robot for sixteen 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.

• 24:31Speaker 00

  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 forty to sixty days. We have an average time from that signed contract to robots actually in the customer site of about forty 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.

• 25:12Speaker 00

  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.

• 25:28Speaker 01

  And once they're deployed, how do their attitudes about labor versus machines change?

• 25:35Speaker 00

  The operations community is really, really fantastic because they see a thing work and it's like, it works.

• 25:42Speaker 00

  Like, that's it, right? It's, it's like, yeah, I could 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 largest companies in the supply chain, from, you know, one robot in one site to, you know, a dozen plus robots across many sites.

• 26:05Martin Casado?

  It is interesting though, because like, a lot of Gen AI 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. And listen, you probably have more experience deploying robots than most people alive at this point. Like, are you saying the same thing? Like, are they viewed like equipment or like they're starting to be viewed like beings and coworkers?

• 26:27Speaker 00

  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."

• 26:36Speaker 00

  I gotta figure out the attribution.

• 26:37Martin Casado?

  But is it real? Like, is it?

• 26:38Speaker 00

  It's, it's pretty real.

• 26:39Martin Casado?

  Is that what it is? Like, they just view it as like a piece of equipment, once it works?

• 26:42Speaker 00

  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 a hundred percent of a factory, we automate some jobs in a factory or warehouse.

• 27:08Martin Casado?

  Do you have a philosophy on like how you even approach this? Like, do you like try to make them look fun? Do you like, let them make them look more like coworkers or less like coworkers? You make them talk? Like, like, how do you kind of view this philosophically?

• 27:17Speaker 00

  Yeah, so very, very luckily, there is a whole school of academic research called HRI, human robot interaction.

• 27:24Martin Casado?

  No idea.

• 27:25Speaker 00

  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. So one trick is every robot has a name. And so that lets us actually build relationships with the robots and also mostly our manufacturing technicians.

• 27:44Martin Casado?

  Okay, so you name it, not the, it's not like a dog is when you get a new dog, you can rename it?

• 27:48Speaker 00

  So when a customer expands with Tutor, when they, when they do a repeat order, then they're allowed to name the robots.

• 27:54Martin Casado?

  Oh, you're not supposed to like rename a boat because you have bad lucks? You gotta keep the boat name, but you can rename a dog, so I'm like.

• 28:00Speaker 00

  Yeah, so the new robots get to be, get to be named, that solves it. And we get customers.

• 28:04Speaker 01

  They get a higher chance of ownership now they've named it.

• 28:07Speaker 00

  Exactly, right? And we get like really strong signals, like we had people name the robots after their kids.

• 28:14Martin Casado?

  Wow.

• 28:15Speaker 00

  Right? People name the robots after their favorite football player, their favorite pop star.

• 28:21Speaker 00

  People get very attached because 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.

• 28:36Martin Casado?

  What naming scheme do you use?

• 28:38Speaker 00

  Yeah.

• 28:38Martin Casado?

  From when you?

• 28:40Speaker 00

  We started off doing hurricane naming and then we kind of.

• 28:43Speaker 01

  That's not.

• 28:44Speaker 00

  Yeah.

• 28:44Speaker 01

  You didn't read the HRI book clearly.

• 28:47Speaker 00

  Well, it's, I don't know, it's. The customer doesn't know that Brittany is, you know, N mod twenty-six or whatever equals two. It's, it's, but we actually stopped because we kind of went through the hurricane. We went through the alphabet too many times to be able to keep track of it.

• 29:03Martin Casado?

  Okay, so what are you using now?

• 29:06Speaker 00

  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 helping to build these robots. And they are also naming them.

• 29:18Speaker 01

  Awesome. The other thing that surprised me when we first did the work before investing.

• 29:25Speaker 01

  Most of the time when we talk to robotics companies, they're going after these humongous companies, right?

• 29:31Speaker 00

  Yeah.

• 29:31Speaker 01

  The big Fortune 50 type companies that seem to have the budget for lots of experiments, et cetera.

• 29:37Speaker 00

  Yeah.

• 29:38Speaker 01

  Whereas I think a significant majority of your customers are all these local businesses almost.

• 29:44Speaker 00

  Yeah.

• 29:45Speaker 01

  And what does that, I mean, is there a message in that? Or was it a conscious strategy or?

• 29:52Speaker 00

  Yeah, it's a lot of things. One is that's most of the market, right? The vast majority of US businesses and 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 where we're seeing kind of all of these very academic, supply chain oriented robotic startups kind of get started and then not really succeed.

• 30:17Speaker 00

  And 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. Chances are it won't work because 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. And so I think working with a diverse range of customers

• 31:01Speaker 00

  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 A or B. We want to build a platform 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.

• 31:36Martin Casado?

  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, kind of like, what is like the economic convergence or the productive convergence?

• 31:56Speaker 00

  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. 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. And 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 onsite in the traditional way, all of the demand from developers would immediately go to that other shape

• 32:40Speaker 00

  of building these homes in a factory. And the same thing can be said about like reclaiming materials and landfills and a whole bunch of other markets. So I think our hope is like, if Tudor 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.

• 33:02Martin Casado?

  Will you ever go in the home?

• 33:05Speaker 00

  Yeah, we're not opinionated. We'll go anywhere.

• 33:08Martin Casado?

  But 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.

• 33:16Speaker 00

  And the economics of it, right? Like how many, what is the economic value a Tutor 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 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.

• 33:41Speaker 01

  How does product innovation work in robotics? I mean, most companies are just a V1, whereas you're got two versions of the robot and you continue to iterate really fast. Well, what is it different, how is it different than like digital product innovation or other categories?

• 33:59Speaker 00

  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 two weeks? We are shipping code to robots either every day or major releases every two weeks. And it built a huge amount of infrastructure to support that level of iteration. We've built a, I think, a 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.

• 34:43Speaker 00

  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 breakthrough 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.

• 35:06Martin Casado?

  If I, if I'm listening to this and I have a warehouse.

• 35:10Speaker 00

  Yes.

• 35:11Martin Casado?

  And I want to get a robot, do I just go to the website and like put in my email?

• 35:16Speaker 00

  That's.

• 35:16Martin Casado?

  How does this work?

• 35:17Speaker 00

  You can go to our website, you can put an email. 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 Tudor robots, we could be saving $100,000 a year, a million dollars a year, a hundred million dollars a year.

• 35:35Speaker 00

  And that's not like, okay, this is a roadmap over five years of custom engineering. It's like, no, you could save that a million dollars a year, two months from today, without putting capex down.

• 35:45Martin Casado?

  And have you found that there is like demand for, I mean, it's just such a sci-fi thing. Is there demand out there?

• 35:52Speaker 00

  There is so much demand. And it's not even demand for robots because again, like people don't know that the shape of a robot exists or that it works or that it can do this job. So 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? Everything else 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 to lower cost, the market for that is the whole market, right? It's all physical word done.

• 36:28Martin Casado?

  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 it?

• 36:38Speaker 00

  It's both, which is very interesting and different, right? Traditionally, we've thought about robotics as ROI, right? Big capex 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.

• 36:53Martin Casado?

  But does that also include like maintenance and break fix and all of that stuff? And then what is, and like, what is the dominant cost or are they roughly equal?

• 37:02Speaker 00

  We drive down all of them over time.

• 37:04Martin Casado?

  No, no, I'm just saying like from your standpoint, when you like pencil out your COGS, how much of is it the upfront cost versus the ongoing?

• 37:11Speaker 00

  Yeah, it is more ongoing at this point, and that's surprising to people.

• 37:14Martin Casado?

  Interesting.

• 37:14Speaker 01

  Oh, why?

• 37:15Speaker 00

  Yeah.

• 37:16Martin Casado?

  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.

• 37:23Speaker 01

  Yeah. So the, when you get colored these 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?

• 37:41Speaker 00

  No, it's strategic, right? It's, um, 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. 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

• 38:26Speaker 00

  or operational experts in crayons or whatever the thing they manufacture is. And they need a partner who's the expert on robots.

• 38:34Speaker 01

  I had a 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? 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 these factories? And, and you know, these one town factories and one factory town, whatever the expression is.

• 39:02Speaker 00

  Yeah.

• 39:02Speaker 01

  All of that fundamentally changes now, right?

• 39:05Speaker 00

  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. 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

• 39:48Speaker 00

  because kind of the cloud and all of the software tooling let people build new businesses. 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.

• 40:02Martin Casado?

  You brought up China, our, you know, uh, we actually, we actually published a report on the supply chain in robotics. And it seemed to be that, that China was so central to it. And so 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?

• 40:22Speaker 00

  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 is the long-term strategy of this look like? And it's Chinese companies investing in the United States. It's United States companies investing in China. And ultimately, everybody trying to build their industrial capacity.

• 40:49Martin Casado?

  Yeah. I've noticed that you've gotten, as far as I can tell, you've verticalized more, is that right?

• 40:58Speaker 00

  Yeah.

• 40:58Martin Casado?

  Like, it's taking more in-house? And do you continue, do you plan to continue to do so?

• 41:01Speaker 00

  Yeah, well, there's this tough thing in the supply chain where, and again, because robotics is such a cottage industry, is you might start in a world where subcomponents are coming from OEMs, and then you grow your volume to the point of they're not OEMs anymore. They're glorified contract manufacturers, right? Ones who are 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 co-man for that thing? Do I want to be using a co-man? Or do I want to be manufacturing it in-house?

• 41:27Martin Casado?

  Yeah, I mean, Tudor must be one of the largest deployers of robots in the United States. 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 yourself.

• 41:43Speaker 00

  I think that's right.

• 41:44Martin Casado?

  Does that mean taking it in-house or just telling them what to do? I mean, is it just, are they still suppliers?

• 41:48Speaker 01

  Some combination.

• 41:49Speaker 00

  Some combination. I think ultimately, for Tudor to be successful, we will need a mature robotic supply chain in the United States.

• 41:56Martin Casado?

  Yeah.

• 41:56Speaker 00

  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 our opportunity to invest very, very, very heavily to building this capacity in the United States. And that's why we build 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.

• 42:18Speaker 01

  Again, 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? Most of the meeting because we have such a great business. But that's a big strength of yours, right? And talk about how you approached it, and there's all sorts of theories about which robotic learning models will win and so on, so forth. How are you approaching the learning back?

• 42:42Speaker 00

  Yeah.

• 42:43Speaker 01

  In research?

• 42:44Speaker 00

  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 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. And we're able to use this as kind of this research testbed

• 43:24Speaker 00

  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 industry.

• 43:47Martin Casado?

  So, I presume like building a team for this is quite different, right? Than 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 hire?

• 43:56Speaker 00

  We're looking to hire everybody.

• 43:58Martin Casado?

  Everybody?

• 43:59Speaker 00

  So, we're looking to hire researchers and engineers, and that's across every single layer of the stack.

• 44:02Martin Casado?

  Wow.

• 44:03Speaker 00

  We are also looking to hire technicians to build robots.

• 44:07Martin Casado?

  By the way, every layer of the stack, you mean like software?

• 44:10Speaker 00

  Hardware, materials, electronics, like, there, yeah. There's nowhere that you can abdicate responsibility, right?

• 44:18Martin Casado?

  It's robotic.

• 44:19Speaker 00

  You're going to engage with it whether you want to or not. The question is whether you understand it. So, 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, but because 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...

• 44:44Martin Casado?

  Is this primarily in Massachusetts or?

• 44:45Speaker 00

  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.

• 44:54Martin Casado?

  That's very, very awesome.

• 44:56Speaker 00

  Awesome. Thank you.

• 44:59Erik Torenberg?

  Thanks for listening to this episode of the A16Z Podcast. If you liked this episode, be sure to like, comment, subscribe, leave us a rating or review, and share it with your friends and family. For more episodes, go to YouTube, Apple Podcasts, and Spotify. Follow us on X at A16Z and subscribe to our Substack at a16z.substack.com. Thanks again for listening, and I'll see you in the next episode.

• 45:24Erik Torenberg?

  As a reminder, the content here is for informational purposes only, should not be taken as legal, business, tax, or investment advice, or be used to evaluate any investment or security, and is not directed at any investors or potential investors in any A16Z fund. Please note that A16Z and its affiliates may also maintain investments in the companies discussed in this podcast. For more details, including a link to our investments, please see a16z.com/disclosures.