The Exchanges

Q Yeah. Can you talk a little bit about the N, uh, the NVIDIA announcement today? That's very exciting. Uh, seems like it might be a little bit of like a side quest or, or is this like in the critical path to, you know, mass, uh, mass production?

A Yeah, no, NVIDIA's today announcement was, was kind of a little fun thing we did. NVIDIA is an investor in us. So fundamentally, they are very interested in what we are trying to do, you know, being able to capture data from physical phenomenon and build models that can manipulate the physical world. Um, but I think we work with their artists in residence. It's actually those open AI artists in residence that we work with NVIDIA collaboratively to really turn in just the artist speaking to a system into a piece of art, right? So Alex, the artist that will work with us, Basically spoke what he wanted to build, what sculpture he wanted to build, and the full stack of generating the code, running the robots, all were done autonomously. So from speech, from intent, all the way to the physical part, without anybody ever touching anything.

AI assessment note: “NVIDIA's today announcement was, was kind of a little fun thing we did.”

Q which one do you guys fit in? I think you're in the third, right? Changed the way the part is made, uh, developed a way to make stampings, complex geometry form sheet metal parts without dies. And so, uh, can you break us down? Like what is actually going on when this massive robotic arm is, uh, like pushing into this metal? Explain why this is important, how it works.

A Yeah. So we wanted to build a system. We call it the RoboCraftsman, right? And the idea was like, where do we get started? Um, um, and we started sheet forming. Sheet forming is the largest metal processing sector today. I think it's like a two hundred eighty billion dollar industry. You know, most of the metal parts you see day-to-day are sheet metal parts. Like, you know, you're sitting in your car, you know, you're in a sea of sheet metal. Every other car body is sheet metal, for sheet metal parts. Or aircrafts are basically sheet metal cans. Um, but today it takes a very long time to get your first batch of parts in sheet metal work, right? You know, you have to go make dyes, put them in giant stamping presses, like four-story tall buildings. Um, and then stamp your, your, you know, your parts out. Um, so our first kind of application of being sheet forming, um, we have two robots that form, um, you know, start from a flat sheet of metal between two robots. They have these giant fingers that are super strong, but they can basically push and pull on the metal that form it into a very complex shape without the need for any, any dives or tooling. Um, basically, you know, you get your, from idea to, to the first part in matter of hours, right? Um, it's similar to how a potter forms a clay bowl with their fingers. They're coming into the sheet, deform it, um, and, and, and shape…

AI assessment note: “we have two robots that form... start from a flat sheet of metal”

Q then getting, like, a sculpture out, like, that's, that sounds really awesome and futuristic. Um, yeah, yeah, go. How, uh, a lot of investment in humanoid robots lately, many of those promising to revolutionize manufacturing, replace human labor, automate the production of lots of things. As somebody who's been doing it with robots since How do, how do you think of that form factor in the context of, um, manufacturing?

A Yeah, I'm actually super bullish on here. That's right. I think the question is, to your point, is it going to be, is the first application going to be in, um, in manufacturing? I don't think so. Right. Um, I think the biggest problem, if you want to like think of a startup as like, what do you need to do risk first? The first thing you gave an example of like Nike figuring out how to do the shoe manufacturing. The first thing is actually intelligence. We have kinematic frameworks for a long time. You know, we could do what humans does in terms of kinematic freedom with industrial robots. We can actually do it more precisely with higher force with industrial robots, which was what we need in manufacturing setting. So the missing piece really was intelligence. So we're kind of a little bit intelligence first, right? We don't need to solve the joints. We don't need to solve People walking around, like robots walking around and, you know, having the human four factor. If you solve the intelligence, um, you have enough kinematic frameworks, which is industrial robots, to do what you need to do. But that being said, I think, you know, humanoid is a huge opportunity, maybe not in manufacturing, but, but downstream in homes and all other places that, that, that, that we can use humanoid.

AI assessment note: “is the first application going to be in, um, in manufacturing? I don't think so.”

Q actual machine side, are you, are, you know, you and the industry seeing challenges of like, you know, how, how much of like the actual robots that you guys are leveraging are, are sourced, uh, outside of the country? And Chris and Hadrian was saying like, yeah, my capex goes up by 10%, but The demand has been way, way higher. So what's been your experience? I mean, that's true.

A I mean, like, what we can, what we do here, the challenge is, for a lot of work that we do, and especially in the defense, aerospace defense, There is no industrial base, so we have to do it. So the demand is always there. Now, 70% of our bomb is off the shelf, and we intentionally tried to do that so that we can actually finance it easily, right? Use, you know, multi-push slippers, off-the-shelf equipment so that we can easily finance, um, the full hardware stack. That being said, you know, a lot of the hardware that we use is either produced in America or it's in very, like, allied countries. For example, uh, robots were produced in, in, um, Japan. Um, so the tariffs are a little bit more digestible there, but that being said, you know, we are one of the very few manufacturing companies that are like doing almost suffer like margins. So there is enough room for us to, to be able to absorb that cost and still, you know, have a very high value, high margin list.

AI assessment note: “a lot of the hardware that we use is either produced in America or it's in very, like, allied countries.”

Q software investors kind of poke fun at, at VCs that are investing in manufacturing, you know, expecting, uh, software-like margins, and, and there's sort of this sense that, uh, well, manufacturing hasn't, for most things, hasn't historically had software-like margins. You're seeing it today. Um, how, how do you think about the margin profile for advanced manufacturing over time? Even, you know, even in things that are like non-chips, right?

A Yeah. Yeah. So. Um, so, so it's, it's interesting arbitrage. I think there's a lot of people thinking about manufacturing solutions in different ways. I think you can think of it as automating what traditionally has been done. And I think that's usually end up being very low margin, right? But if you're creating something new that, you know, has some kind of arbitrage on either neighbor, Or has some kind of arbitrage on equipment, right? In our case, whereas you don't have to make dyes, right? And a dye, a single dye for, um, you know, a car door can be up to a million dollars, right? So for us, we're faster, but we get rid of that asset. Um, so it allows us to have the, you know, at the same cost parity, have higher margin. But I think, yeah, down the road, um, the margins could erode, and that's why we're thinking about it as a platform, right? It's a robotic system that can do forming today. Tomorrow it's going to be your next operation. It's going to do bending. It's going to do hemming. It's going to do forging, right? So you're constantly expanding the capabilities of the system, which allows you to sustain a very, uh, larger volume business, um, uh, as some of the margins of the older processes kind of erode, right?

AI assessment note: “down the road, um, the margins could erode, and that's why we're thinking about it as a platform”

Q down to temperature. Um, out of all, you know, when you look at manufacturing broadly or I'm assuming you have some type of framework for evaluating whether something like can be automated to the degree that people would like to see out of manufacturing or areas that, you know, are basically shouldn't be touched, right? Like something like, like shoes, which have infinite sizes and a bunch of different factors.

A Yeah, I think it's like a combination of like three things, like the market size opportunity and how, how technology ready it is to, to, to be, to be kind of disrupted, right? So, um, uh, but also I think there's a lot of conversation around automation that, that is in the previous paradigm. I think for now, for the first time, we have this concept of element, this concept of We can actually reason very complex sequence of operations as long as we can train the robots on that sequence of operations. So it comes down to what data do we have available to train the robots? We already figured out, okay, you know, neural networks, LLMs, uh, these, uh, transformers, if you give it enough data, it can actually learn a very complicated, complicated task. The real key was, okay, what, where do we generate enough data? Where do we have enough data to train it? Um, so it means, and unfortunately for a lot of manufacturing tasks, the data is not out there, right? You cannot train a very complicated model on it. For, you know, for ChatGPT, the internet had trove of free data that you could use to create a very complex, um, kind of chatbot. For us, coming up with right sequence to make car doors, um, or shoes doesn't exist. So the key is, can you actually provide a solution That can scale with limited amount of data, with human intervention and limited amount of data. So you can deploy it in…

AI assessment note: “combination of like three things, like the market size opportunity and how, how technology ready”