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 does 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. 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 whereas you've got two versions of the robot and you continue to iterate really fast.
So 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.
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 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. 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.
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 has 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 Tutor Robots have helped manufacture and ship over a quarter of a billion products.
To American consumers. So like presumably most American consumers are a Tutor customer a couple 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 engineered machines that can't scale. 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.
We see a lot of robot pitches. 99% of them start with some insights about the model and how you got to build the 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. Yeah, that's the name of your company. Where did that insight come from?
It came from kind of this thought 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 ASI mocking of like suddenly you have unlimited infinite robot intelligence. 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 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, in my view, a successful platform at any one of those layers.
Yeah. Internally, we talk about robotics being the ultimate systems company.
As opposed to being one or the other.
Well, you know what's funny about this though, which is, and it was a question I wanted to ask you, is you've got a pretty deep technical background, right? You know, and like, I mean MIT Robotics, is that fair? But whenever I talk to you, I feel like I'm talking to an operations guy that talks about unit economics and operations and supply chain. So I mean, and even the go-to-market guy. So it's kind of, you've done this, like the transformation is complete. And so like, 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 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 FPGAs and ASICs. 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.
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 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 this 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.
You know it's interesting. So this is an observation that we have had and 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, 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 even in 20 years you will have to verticalize just because the economy of movement or something requires that?
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 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 that full stack platform really works is to actually have successful business units operating on top of it.
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, 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 case, right? But it's general enough that you can run most computing workloads.
Oh, but that's so that's your horizontal component within the humanoid. But for example, would you use the same, you know, brain or, you know, like sub components 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 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, you know, that.
Why is this thing called Tutor 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, 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 where from no data we could build robot intelligence. And for us we were like, yeah, 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 emerging 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. 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 was ultimately like this tutor intelligence that we're building that was connecting and controlling these robots.
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 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. It's now 5 cents cheaper than it was. If the feedback is like the robot is pretty, it's like a probably really bad robot.
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? And then it becomes a unit economics question, right? Like how much engineering do you have to do to get that last 9% out of your models versus using tutors? And really, you know, going back to what we were 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, you know, it's tempting to say that like the economic marvel that happened with generative AI could 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, 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. It's not clear that this is actually the case 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, 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 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 are you getting it from? Yeah. 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 the inflow 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. 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 be the case that like the economically valuable stuff is so high dimensional that you could solve 90% of it but the last 10% you know 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 a period of time.
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.
And talk a little bit more about the simulations and these other tools and the impact models like Astra are having on that. Seems to be another cottage industry of excitement for the robotics world, right? How are you using that?
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. 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 robotics.
And because it's a systems problem, the net effect for you is acceleratory.
As opposed to slowing you down or saying somebody's going to come and eat your lunch.
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 gone through every sea change and the thing that is always valuable is the flow of data, right? And the flow of data is always the thing that uniquely you can do. And it's like, yeah, those like the a 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. 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 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 you buy a Unitree.
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?
Yeah. So for context for everybody, in 2025 the United States deployed 38,000 industrial robot workcells, which is tiny compared to roughly... counting the FANUCs and the old... the FANUCs, right? But that's it, right? Nobody deployed any Unitrees in the United States and to 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 robot spots that FANUC and ABB are building, they're mostly not building in the United States, 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 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.
Can you talk about the morphology of the robot? Is that what is that called? Morphology, anatomy.
What do they look like? We make two.
Look at his website. 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 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 Unitree 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 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 in the real world doing more dexterous bimanual manipulation.
And do you see, how broad do you see this product line of different robot form factors? Is it, can you capture a big chunk of the useful market with three of them or do you need 30 of them?
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 80% to 90% of the work, right? These are designed for those cuts.
What, maybe just describe what that work is because, yeah, I'm not sure it's broadly understood.
Yeah. So most people have this mental picture of what sort of the industrial base or manufacturing logistics look 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 Goldberg machines of levers and it's like, yeah, you don't see any humans in those pictures, right? Somebody built a machine that is the factory, 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 and 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 55 years old and about to retire. And it's like, okay, 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. Anything that requires any degree of assembly, right? Like even, you know, 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.
And then to his question, which is so like you have like the single arm with the sucker, you've got the two-armed humanoid.
What can't they do? And where do...
