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a16zWhat iPhone Packaging Reveals About the Robotics Opportunity
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45 min read · 9,314 words- 0:00 – 0:49
Intro
- JGJosh Gruenstein
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.
- ETErik Torenberg
We see a lot of robot pitches. Very few people are thinking about what the robot is supposed to do at the end of all of this.
- JGJosh Gruenstein
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.
- ETErik Torenberg
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.
- JGJosh Gruenstein
So-
- 0:49 – 2:49
Tutor Intelligence at a glance
- MCMartin Casado
Josh-
- JGJosh Gruenstein
Yes.
- MCMartin Casado
Thanks for, uh, for joining us. So, uh, we're with Josh Gruenstein, who is the CEO and founder, co-founder of-
- JGJosh Gruenstein
Co-founder
- MCMartin Casado
... of, um, Tutor Intelligence, and we're gonna be talking about robots today.
- JGJosh Gruenstein
Mm-hmm.
- MCMartin Casado
And so maybe before we jump in, just give a quick background maybe of yourself and then the company.
- JGJosh Gruenstein
Yeah. So I'm Josh. I started Tutor, uh, about five and a half years ago, uh, 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 datasets, the models, and the supply chain that's needed to proliferate robots, uh, 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.
- MCMartin Casado
And again, 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.
- JGJosh Gruenstein
Yeah.
- MCMartin 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.
- JGJosh Gruenstein
Yeah. Uh, 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-
- MCMartin Casado
Wow
- JGJosh Gruenstein
... to American consumers. So, like, presumably, most American consumers have... are a Tutor customer a couple layers down. Um, 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-
- MCMartin Casado
Right
- JGJosh Gruenstein
... that can't scale.
- MCMartin Casado
Yeah.
- JGJosh Gruenstein
Um, yeah, so we, we started the company with this thesis that if we could just start deploying robots, we could build, uh, the label datasets that help us train these models and deliver this value to the American consumer.
- 2:49 – 4:32
Start with the job, not the model
- ETErik Torenberg
We see a lot of robot pitches. 99% [chuckles] of them start with some insights about the model and how you're gonna build the model or some insight about the data, uh, shape of the data, that is, and very few people are thinking about-
- JGJosh Gruenstein
Yeah
- ETErik Torenberg
... 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, uh, you could actually tutor the robot, so to speak.
- JGJosh Gruenstein
Yeah.
- ETErik Torenberg
Hence the name of your company. Where did that, uh, insight come from?
- JGJosh Gruenstein
Uh, it came from kind of this thought experiment where, yeah, we, we had this insight that, hey, there are eight 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. 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.
- ETErik Torenberg
Yeah.
- JGJosh Gruenstein
Robots are rounding error even in the most automated industries like manufacturing and 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 – 9:08
Robotics as the ultimate systems problem
- ETErik Torenberg
Yeah. Internally, we talk about robotics being the ultimate systems company-
- JGJosh Gruenstein
Yep.
- MCMartin Casado
Yeah
- ETErik Torenberg
... as opposed to being one, one or the other.
- MCMartin Casado
But you know what's funny about this, though, which is, um, it was a question I wanted to ask you is, uh, I mean, you've got a pretty deep technical background, right? You know, and like, uh, I mean, MIT robotics-
- JGJosh Gruenstein
Yeah
- MCMartin Casado
... is that fair? Um, but whenever I talk to you, I feel [chuckles] like I'm talking to an operations guy that talks about like [laughing] u-
- JGJosh Gruenstein
Yeah.
- MCMartin Casado
That talk about unit economics and operations and supply chain. So I mean-
- ETErik Torenberg
And even a go-to-market guy. [laughs]
- MCMartin Casado
[laughs] And even a go-to-market guy. So it's kind of you've done this, like the transformation is complete. And so-
- JGJosh Gruenstein
Yeah
- MCMartin Casado
... 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?
- JGJosh Gruenstein
No. [laughs] But, but I think you have to do all of it is kind of the thing.
- MCMartin Casado
Okay.
- JGJosh Gruenstein
And like I... When I was just a roboticist, right?
- MCMartin Casado
Yeah.
- JGJosh Gruenstein
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 i-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.
- MCMartin Casado
Yeah.
- JGJosh Gruenstein
And if you throw out one of the layers of the stack and you're like, "Well, we're not gonna touch that," you've eliminated 90% of your trade-off space to make progress.
- MCMartin Casado
Oh, interesting.
- JGJosh Gruenstein
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-
- MCMartin Casado
Go to market.
- JGJosh Gruenstein
Right? It's like-
- MCMartin Casado
Cask. Yeah.
- JGJosh Gruenstein
Exactly. And similarly, like-
- ETErik Torenberg
Business model
- JGJosh Gruenstein
... 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, um- Why is that? A-a-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, um, unit economics of the system to a certain level. Um, but two, it's that it's the super sort of disaggregated market.
- MCMartin Casado
Yeah.
- JGJosh Gruenstein
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 trade-offs across the full stack.
- 9:08 – 14:54
Why it’s called Tutor, and whether tutors go away
- JGJosh Gruenstein
is big enough.
- ETErik Torenberg
Why is this thing called Tutor Intelligence?
- JGJosh Gruenstein
Yeah. So Tutor was founded, uh, 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-
- ETErik Torenberg
Yeah
- JGJosh Gruenstein
... and nobody knows, right? Like nobody recorded all of the pressures of, of my fingertips and where they were and the poses. And so we don't have this asset. Um, 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 robot intelligence. And for us we were like, "Yeah, that, 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, uh, 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, 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. Um, and so when we thought about, okay, what are we gonna name this business? Um, 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.
- ETErik Torenberg
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-
- JGJosh Gruenstein
Yeah
- ETErik Torenberg
... 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've made those very, very seamless too.
- JGJosh Gruenstein
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 five cents cheaper than it was." If the feedback is like, "The robot is pretty," it's like, "Oh, this is probably a really bad robot."
- MCMartin Casado
[laughs] I- is the goal to not have tutors in the future or does it matter?
- JGJosh Gruenstein
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 map 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? Um, and then it becomes a unit economics question, right? Like how much engineering do you have to do to get that last nine, uh, out of your models versus using tutors and really, you know, going back to what we were talking about, you wanna be able to make as many trade-offs here as you possibly can to make a business like this work.
- MCMartin Casado
I mean, the, you know, um- It's tempting to say that, like, the economic marvel that happened with generative AI can also happen here. And the, kind of the, the marvel that happened with generative AI is like you, you train these, you know, these models with s- you know, some distribution of data, and it turns out basically the same distribution is economically viable.
- JGJosh Gruenstein
Yeah.
- MCMartin Casado
Right? It's like you train it on images and people want images.
- JGJosh Gruenstein
Yeah.
- MCMartin Casado
You train it on text and people like... You know, so that kind of distribution in, distribution out. It's not clear that this is actually the case- [chuckles]
- JGJosh Gruenstein
Yeah
- MCMartin Casado
... 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-
- JGJosh Gruenstein
Yeah
- MCMartin Casado
... the convergence is and whether you can actually remove tutors. Like what is your intuition on, on that?
- JGJosh Gruenstein
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?
- MCMartin Casado
Yeah.
- JGJosh Gruenstein
And the magic of the internet, right, is like if you are, um, 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, um, then you're probably also making data as a business, right?
- MCMartin Casado
Yeah.
- JGJosh Gruenstein
Those, like, economic incentives are aligned. And similarly for robots, if you're like, "Okay, we're just gonna strap cameras on everybody's heads and send them throughout Indonesia to collect data doing X, Y, and Z," you, you have no guarantee.
- MCMartin Casado
Yeah.
- JGJosh Gruenstein
Right? You, you can guess, um, but there's no, you know, invisible hand of the market that's guiding you to ensure that that data is actually economically valuable.
- MCMartin Casado
Right.
- JGJosh Gruenstein
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.
- MCMartin Casado
Doesn't it kind of depend on also the dimensionality of the solution set, though? Like it could-
- 14:54 – 16:57
How frontier models are accelerating robotics
- ETErik Torenberg
And talk a little bit more about the simulations and these other tools and the impact, uh, models like Astra are having on that.
- JGJosh Gruenstein
Yeah.
- ETErik Torenberg
That seems to be another cottage industry of excitement for the robotics-
- JGJosh Gruenstein
Yeah
- ETErik Torenberg
... world, right? How are you using that?
- JGJosh Gruenstein
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?
- ETErik Torenberg
Yeah.
- JGJosh Gruenstein
They didn't have that last mile of spatial reasoning to connect the dots from the stuff that like, uh, you know, a GBT model knows to something that a, an action diffusion transformer head can know. Um, and that's changed, right? The labs are now post-training their models on 3D spatial data like CAD and-
- ETErik Torenberg
Mm-hmm
- JGJosh Gruenstein
... um, like 3D modeling, which by the way, they're doing because there's this direct economic incentive 'cause there's a real market for it. Um, 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 'cause we're starting to be able to use all of that general intelligence that lives in these big frontier models for robots.
- ETErik Torenberg
And because it's a systems problem, then that effect for you is accelerating-
- JGJosh Gruenstein
Yeah
- ETErik Torenberg
... as opposed to slowing you down or saying somebody's gonna come and eat your lunch.
- JGJosh Gruenstein
Yeah. No, we, uh-- And we've seen a couple of these, uh, happen, right? We started this business in 2021. We were doing this initially with convolutional neural networks.
- ETErik Torenberg
[laughs]
- JGJosh Gruenstein
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.
- MCMartin Casado
Yeah.
- JGJosh Gruenstein
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. Um, but, but ultimately we think the thing that has kind of the longstanding generational value in a business is the flywheel and loops themselves.
- 16:57 – 23:17
Building robots in-house: Cassie and Sonny
- MCMartin Casado
Can, can you talk a little bit about what you mean when you say you build robots?
- JGJosh Gruenstein
Yeah.
- MCMartin Casado
Like I think on one end of the continuum it's like-
- JGJosh Gruenstein
Yes
- MCMartin Casado
... you buy a Unitree.
- JGJosh Gruenstein
Yeah. [laughs]
- MCMartin Casado
You know, and you like prompt it. On the other end, you know, like whatever, you're like machining parts. Like where are you in this?
- JGJosh Gruenstein
Yeah. So, um, for context for everybody, in 2025, the United States deployed, uh, 38,000 industrial robot work cells, which is tiny compared to roughly a million-
- ETErik Torenberg
Like you're not counting the Fanucs and the old world robots.
- JGJosh Gruenstein
The Fanucs, right. But, but that's it, right? Nobody, nobody deployed any Unitrees in the United States into any-- They would've shown up in those numbers, but there were probably zero. Um, 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 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. Um, and so we're kind of starting from scratch on the hardware side. Um, 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-
- ETErik Torenberg
Mm-hmm
- JGJosh Gruenstein
... can do all the tasks, we actually are building, uh, at scale, uh, in Watertown, Massachusetts.
- MCMartin Casado
Can you talk about the morphology of the robot? Is that what-- Do you... Is that called morphology? Anatomy? Physiology?
- JGJosh Gruenstein
You, you can use whatever word you want. That's, uh-
- ETErik Torenberg
It's called anatomy. [laughs]
- MCMartin Casado
What do they look like? [laughs]
- JGJosh Gruenstein
What do they look like? We make two. Um, so one robot we make-
- ETErik Torenberg
Look at his website, Martin. Yeah, go ahead. Sorry.
- JGJosh Gruenstein
Yes. Uh, so Cassie. Cassie is kind of, uh, 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. Um, 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, um, by hand. And so Cassie is kind of the result of that co-design, and we manufacture it at very large scale, and we deploy it to a big diversity of businesses across manufacturing logistics. Our second robot is called Sonny, and Sonny 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 three to ten years in a factory at human rates and reliability." And so you end up with a slightly different set of design decisions. Sonny is wheeled, not legged, because most indoor industrial settings have polished ground floors. Um, it has a much higher payload capacity and can move much faster and has, uh, industrial collaborative safety standards that enable it to operate around humans. And so we have 100 Sonnys 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 Sonnys out in the real world, uh, doing more dexterous bi-manual manipulation.
- ETErik Torenberg
And do you see-- How broad do you see this product line of different robot form factors? Is it-- Can you capture-
- JGJosh Gruenstein
Yeah
- ETErik Torenberg
... a big chunk of the useful market with three of them, or do you need 30 of them?
- JGJosh Gruenstein
Depends how you define the useful market. Like, where do you wanna go? If you wanna just live inside of manufacturing logistics, Sonny and Cassie today can probably do 80% to 90% of the work, right? These are designed for those cuts. Uh, there-
- MCMartin Casado
Well, maybe just describe what that work is.
- JGJosh Gruenstein
Yeah.
- MCMartin Casado
Because, yeah, I'm not sure it's broadly understood.
- JGJosh Gruenstein
Yeah. So most people have this mental picture of what sort of the industrial base or manufacturing logistics looks in the United States, which comes from, um, the TV show How It's Made. I don't know if either of you, you have seen this?
- MCMartin Casado
Yes, of course. Of course, yeah.
- JGJosh Gruenstein
And you see these, like, Rube Goldbergian machines-
- MCMartin Casado
Yeah
- 23:17 – 29:04
Selling to first-time robot buyers
- JGJosh Gruenstein
Yeah.
- ETErik Torenberg
Yeah. Let's talk about the customer adoption side. How's, how has that been? Obviously not strictly from a, "I don't have people, so I'll take whatever I can get"-
- JGJosh Gruenstein
Yeah
- ETErik Torenberg
... to once they have it in their assembly line or whatever it is, how does adoption go, uh, in terms of their mindset?
- JGJosh Gruenstein
Yeah. It's an interesting challenge 'cause almost everybody we're selling a robot to has never purchased a robot before. Um, and that kinda 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. 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. Um, 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, um- From, uh, 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. Uh, we stand up robots in production within a day, uh, often within the first couple 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, um, but we're not putting any sort of economic risk on the customer, so we're able to get them on board.
- ETErik Torenberg
And once they deployed, how do their attitudes about labor versus machines change?
- JGJosh Gruenstein
The operations community is really, really fantastic 'cause they see a thing work and it's like, "I... It works." [laughs]
- ETErik Torenberg
[laughs]
- JGJosh Gruenstein
Right? It's... 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, um, 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.
- MCMartin Casado
It, it is interesting though, 'cause like, 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-
- JGJosh Gruenstein
Yeah
- MCMartin Casado
... I mean, listen, you probably have more experience deploying robots than most people alive at this point. Like, are you seeing the same thing? Like, is it... Are they viewed like equipment or are, like, they starting to be viewed like beings and coworkers?
- JGJosh Gruenstein
Yeah. So I saw a very funny tweet which was, um, "A robot is a robot unless it works, in which case it's a machine." Um-
- MCMartin Casado
[laughs]
- ETErik Torenberg
[laughs]
- JGJosh Gruenstein
I gotta figure out the attribution.
- MCMartin Casado
But is it real? Like, is it-
- JGJosh Gruenstein
It's, it's pretty real [laughs]
- MCMartin Casado
Is that, is that what it is? Like, they just view it as, like, a piece of equipment once it works?
- JGJosh Gruenstein
Certainly at, like, the factory manager level, yes.
- MCMartin Casado
Oh, okay.
- JGJosh Gruenstein
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, a factory worker-
- MCMartin Casado
Yeah
- JGJosh Gruenstein
... 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 a warehouse. Um-
- MCMartin Casado
And do you have a philosophy on, like, how you even approach this? Like, do you, like, try-
- JGJosh Gruenstein
Oh, yeah
- MCMartin Casado
... to make them look friend... Do you, like-
- JGJosh Gruenstein
Yeah
- MCMartin Casado
... kind of make them look more like coworkers or less like coworkers? Do you make them talk? Like, like, how do you kind of view this philosophically?
- JGJosh Gruenstein
Yeah. Uh, so very, very luckily there is a whole school of academic research called HRI, Human-Robot Interaction.
- 29:04 – 38:19
Why small and mid-sized businesses
- ETErik Torenberg
Well, 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?
- JGJosh Gruenstein
Yeah.
- ETErik Torenberg
The big Fortune 50 type companies that seem to have ex- the budget for lots of experiments, et cetera.
- JGJosh Gruenstein
Yeah.
- ETErik Torenberg
Whereas I think a significant majority of your customers are all these local businesses almost.
- JGJosh Gruenstein
Yeah.
- ETErik Torenberg
And what does that... I mean, is there a message in that or, uh, was it a conscious strategy or...?
- JGJosh Gruenstein
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. Um, so if you wanna serve the market, that's what it takes to serve them. I think we also started this company, um, 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. Um, 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 gonna expand with it. We're gonna spend two years building a very custom system to solve their problem. We'll deploy it." Chances are it won't work 'cause, like anybody who's built products knows that you can't just one and done a product, right?
- ETErik Torenberg
Yeah.
- JGJosh Gruenstein
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 ten times in a row to the point of like, "Oh my God, if this happened to us, like that would just be embarrassing."
- MCMartin Casado
[laughs]
- JGJosh Gruenstein
Um, 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 wanna build a Rube Goldberg machine that works really well for big industrial customer A or B. We wanna build a, a platform that is generally capable robot bodies running generally capable robot models that can immediately get to work. Um, 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.
- MCMartin Casado
How, how do you think in the limit, um, what the industry looks like, assuming you continue to be successful? Is it just that, um, you know, this work gets cheaper faster, or does like somehow quality go up-
- JGJosh Gruenstein
Yeah
- MCMartin Casado
... or can they do more things? Or like kind of like what, what is like the economic convergence or the productive convergence?
- JGJosh Gruenstein
Yeah. I think everything gets better. So there's a whole set of goods that, um, if they are cheaper, we will buy more of them, and if you believe those goods are valuable, that's good for everybody.
- MCMartin Casado
Yeah.
- JGJosh Gruenstein
There are also a bunch of discontinuities in the world. Like I was talking with somebody, um, 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 five percent more expensive, uh, to build them in a factory than to do traditional construction on site. But if these houses were even just one percent 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 Tutor's 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, uh, a cheaper and better place to live in.
- MCMartin Casado
Will you ever go in the home?
- JGJosh Gruenstein
Um, yeah, we're, we're not opinionated. We'll go anywhere.
- MCMartin Casado
Yep. Um, but I, I would imagine that would be kind of a much tougher technical problem just from like, I don't know-
- JGJosh Gruenstein
Yeah
- MCMartin Casado
... complexity of task and safety and-
- JGJosh Gruenstein
And the economics of it, right? Like how many-- what is the economic value a Tutor robot delivers, uh, in a factory, and how many consumers are willing to pay for that in their home? And it doesn't quite work out. Um, 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.
- MCMartin Casado
Yeah.
- ETErik Torenberg
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. W- what is it differ-- how is it different than like digital product innovation or other categories?
- JGJosh Gruenstein
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 have built a huge amount of infrastructure to support that level of iteration. We've built, I think, a really unique shape of product function, where our product function and our organization actually sits right next to our customer success and implementation functions-
- ETErik Torenberg
Mm-hmm
- JGJosh Gruenstein
... 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, um, 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 i- in a way that I think most companies couldn't and get that out into the field much quicker.
- MCMartin Casado
If I, if I'm, uh, listening to this and I have a warehouse-
- 38:19 – 42:04
Factories, China and the US supply chain
- ETErik Torenberg
I have a question on, uh-
- MCMartin Casado
Great
- ETErik Torenberg
... 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-
- JGJosh Gruenstein
Yeah
- ETErik Torenberg
... these factories and, and, you know, these one town factories and one, uh, one factory town, whatever the expression is.
- JGJosh Gruenstein
Yeah.
- ETErik Torenberg
All of that fundamentally changes now, right?
- JGJosh Gruenstein
Yeah. Uh, 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-
- ETErik Torenberg
Yep
- JGJosh Gruenstein
... 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 build of automating our industrial capacity using generally capable robots. In China, they're gonna have to tear down, right? We're not gonna have to do that in the United States in the same way. I think another thing that's gonna shift, which is gonna be really interesting, is we're just gonna 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. 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.
- MCMartin Casado
You, you brought up China. Our, you know, uh, you know, 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.
- JGJosh Gruenstein
Yeah.
- MCMartin Casado
So can you talk a little bit about your dependency on it and then how that evolves over time, and how much of it there, is there risk in China to the supply chain?
- JGJosh Gruenstein
Yeah. Uh, I think ultimately, no matter what anybody says, today in twenty twenty-six, we have a highly interconnected, interdependent global supply chain, and this is one of many examples of this.
- MCMartin Casado
Yeah.
- JGJosh Gruenstein
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.
- MCMartin Casado
Yeah. I've, I've noticed that, you know, you've gotten, as, as far as I can, um, tell, you've, um, verticalized more. Is that right?
- JGJosh Gruenstein
Yes.
- MCMartin Casado
Like just taking more in-house. And do you continue, or do you plan to continue to do so?
- JGJosh Gruenstein
Yeah. Well, there's this tough thing in supply chain where, and again, because robotics is such a cottage industry, is you might start, um, 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, 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 wanna be using a command or do I wanna be manufacturing it in-house?
- MCMartin Casado
Yeah. I mean, Tutor must be o-o-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.
- JGJosh Gruenstein
I think that's right.
- MCMartin Casado
Does that mean taking it in-house or just telling them what to do? I mean, is it just, like are they still suppliers?
- ETErik Torenberg
Some combination, I guess.
- MCMartin Casado
Is it?
- JGJosh Gruenstein
Some combination. I think ultimately for Tutor to be successful, we will need a mature robotic supply chain in the United States.
- MCMartin Casado
Yeah.
- JGJosh Gruenstein
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.
- 42:04 – 44:42
Real-world reinforcement learning at scale
- ETErik Torenberg
Again, going back to when we first started talking, I remember it was probably the second meeting when we actually started about the research, right? Most of the meeting, because we had such a great business. But that's, uh, 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 and so forth. Uh, how, how are you approaching the robot, uh, the learning bit-
- JGJosh Gruenstein
Yeah
- ETErik Torenberg
... in research?
- JGJosh Gruenstein
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? Um, and so we built this 100-robot fleet called Data Factory One-
- MCMartin Casado
Yeah
- JGJosh Gruenstein
... 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 test bed of how do we do real world reinforcement learning at scale with robots? Um, you know, reinforcement learning is a technique family where, uh, it doesn't really work at small batch sizes. Um, 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.
- MCMartin Casado
So I, I presume like building a, a team for this is quite different, right-
- JGJosh Gruenstein
Yeah
- MCMartin Casado
... than like a, you know, lab. So maybe talk about kind of like the construction of the team and maybe if there's anybody you're looking to, to hire.
- JGJosh Gruenstein
We're looking to hire everybody. Um-
- MCMartin Casado
Everybody.
- JGJosh Gruenstein
So we're looking to hire researchers and engineers, and that's across every single layer of the stack.
- MCMartin Casado
Wow.
- JGJosh Gruenstein
Uh, we are also looking to hire, uh, technicians to build robots.
- MCMartin Casado
By the way, every layer of the stack, you mean like software-
- JGJosh Gruenstein
Hardware-
- MCMartin Casado
... hardware-
- JGJosh Gruenstein
Materials-
- MCMartin Casado
Electronics
- JGJosh Gruenstein
... electronics.
- MCMartin Casado
Materials, everything.
- JGJosh Gruenstein
Like there-- Yeah, there's nowhere that you can abdicate responsibility, right?
- MCMartin Casado
It's robotics [laughs] .
- JGJosh Gruenstein
You're gonna engage with it whether you want to or no. The, the question is whether you understand it. Um, 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-
- MCMartin Casado
Yeah
- JGJosh Gruenstein
... 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, in the afternoon, like, you know, looking at a factory line and seeing how a robot is doing and talking.
- MCMartin Casado
Is this primarily in Massachusetts or?
- JGJosh Gruenstein
So we have a team across the United States.
- MCMartin Casado
Okay.
- JGJosh Gruenstein
Um, 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.
Episode duration: 44:56
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