Pushkin.
AI works amazingly well. It works terrifyingly well even for virtual things, for words, for pictures, for videos. This is true in large part because of the Internet. The Internet provides this wildly abundant, readily available source of words, pictures, and videos to train AI models. But there is no analogous, wildly abundant, readily available data set for the physical world. There is no gargantuan Internet like repository of data that describes how things move and bend and break in real
physical space. And as a result, we do not yet have robust AI for the physical But people are working on it, and if they succeed, they'll change the way the world works, not just the world as it appears on our screens, but the actual physical world, the world where if you drop something on your foot it hurts. I'm Jacob Goldstein and this is What's Your Problem, the show where I talk to people who are trying to make technological progress. My guest today is Edward Mayer. He's
the co founder and CEO of Machina Labs. Edward's problem is this, how can you use AI to turn robots from dumb, inflexible machines into skilled versatile craftsmen. Before he started Machina Labs, Edward worked in the rocket ship business, first at SpaceX and then at a company called Relativity Space. And in the rocket business, the word solw firsthand the problems of traditional manufacturing. It's the kind of problem he's now trying to solve with AI and robots. It's a
problem called the rigid factory problem. So I've heard you use this phrase that's interesting to me, and it's the rigid factory problem. What's the rigid factory problem?
That main problem with the factories today is that rigidity, meaning that if you have to build a physical product, you pretty much have to build a factory that's designed for it and built for it. There's a lot of components that goes into the factory, from machinery all the way to the tooling that is required to build products that are specifically designed for the geometry for the material that you're trying to use. The moment you want to change that, you have to change your factory, which is
a huge investment. You know, I always give an example from when I was at SpaceX. You know, you think of SpaceX as a very innovative and it is you know on the edge of a hardware space in terms of innovation in the past twenty four years twenty three four years that they have existed, they have two rocket families. There's Starship and there's Falcon, right, because at the moment you decide on diameter of for example, Falcon nine or the Falcon family in general, the diameter of that core,
it's very hard to change it. A lot of tooling and machinery specifically built for that diameter. And that's why for Starship they had to start from scratch.
Start from scratch, meaning like not just design, but like the factory itself, like the Factorily had to build a whole new factory because they wanted to make a different sized rocket.
Yes, different size, different material, All the tooling has to change, right almost almost, Yeah, you have to basically assume building from scratch, ground up factory. Why does it need to be there for us to build this new product?
I heard you describe was this from your own experience the sort of era at SpaceX when the fact that you couldn't make the rocket wider led to all these kind of difficult things people were trying to do to be like, how can we do all these things under this fundamental constraint, Like, can you talk a little bit about that.
Yeah, this is a lot of conversation happening in twenty twelve, twenty thirteen, twenty fourteen time when the diameter of the Falcon nine could not get any larger, and if you look at actually different Falcon versions, the height of that vehicle kept going higher, the diameter could not change. So it was about where what space can you find to put new features and new designs that exist within the vehicle.
So there was a lot of stuff basically being crammed into the space that you have already got.
So that's true for building rockets. I mean, what are some other just you know, different kinds of manufactured products where that kind of rigidity is a problem.
Yeah, I think it is just common almost in all manufacturing. That's why this phenomenon. I think it's kind of funny. People take it for granted that a thing called economies of scale, uh huh, Like people take it for granted as if it's rule of nature. It's actually not.
Just to be clear, it's basically, the more you build of a thing, the cheaper each one of those things gets. If you build one, it's really expensive. If you build a million each one's.
A lot cheaper, yes, exactly, But then people don't think about it's like, oh, okay, intuitively makes sense, but why it's actually not that intuitive. It's actually a limitation of technology. Right. Why why economies of scale is a thing is because you have to make a huge amount of investment to make the first thing, and the moment you make the second thing and the third thing, then you can break even your investment onto more products that you're going to
come out of it. But that's only true if the second product can be built for the first investment. You had to turn this concept and say, oh, this is a given. This is an axiom of the world that economies of scale is a thing, but in reality it
is a technological challenge. Right. It means that you build a car, you ask what application you Once you build a factory for a car and all the toolings and dies that goes into stamping of the panels of that car, that's one hundred and fifty million dollar investment just for stamping. And this is a number for example from Tesla. Tesla spens one hundred and fifty million dollars in a stamping plant they have in Giga factory in Texas, right, and that can only make Model Y or Model three right
the moment you have to change that. That means go through every eighty two hundred and thirty sheet metal panels that exist on that car and design a new tool for it. And each of these tool is going to be a few hundred thousand dollars to sometimes a million dollars or a million and a half million dollars. And you're talking about like eighty t one hundred and thirty tools per vehicle.
And like all you're doing you're not reinventing the car there, you're just making a car that's a slightly different shape.
Basically, yes, maybe you may get sedan, and you're not trying to do a slightly longer version, a slightly bigger version. And that's why economies a scale of the thing you saying, Okay, I made a fact. Now it only pays back if I make a million of this car, right, because I had to just drop one hundred and fifty million dollars on just a stamping plant. So yeah, it's all over manufacturing. We abstract this whole concept and gave it the name, says economies, of scale.
Yeah, so you left SpaceX and you went to Relativity Space, right, a company that was also in the space business that was using three D printing.
Right.
That was the idea of the company, which seems like an approach to this problem that you're talking about.
Right.
An advantage of three D printing is that it is much more flexible and less rigid than traditional manufacturing. Right, So tell me about that.
Yeah. So, yeah, we saw this challenge at SpaceX and I joined Relativity very early on. I was the fourth person on that team. And the goal over there was, Okay, let's just think about this fundamentally, can we build a rocket that all built with flexible technology and a time? Three D printing was that forefront of everybody's minds because
people were already starting to build that. NASA SpaceX people were already starting to build engines out of three D printing, and the concept was like, well, that's great, it's very flexible. Three D printing has this promise of geometry agnostic, material agnostic. You can just feed it a design and can build a product for you. And it worked very well with
rocket engines. I think probably the future all rocket engines would be would be three D printed, And the concept was, can we take this and scale it to the whole vehicle, right, can we build the whole vehicle with a process like three D printing so that it is flexible Today, if you want to build a rocket with twelve foot diameter,
we can do it. And then if our calculation changes and we wanted to go to another orbit or do a different type of emission, then we can change that diet twelve diameter to twenty diameter twenty fie.
Don't have to build a new factory, don't have to build new machinery, just change three D printer.
Yeah, exactly. So that was a concept behind relativity. That's a thesis behind relativity, and that was the goal there. The goal was, you know, three D print a whole rocket so they can be flexible.
But it hasn't. It hasn't worked at least in the kind of maximalist version, right, Like, they just haven't been able to do it. They've they've sort of backed off of that that big dream, as I understand it.
Yeah, yeah, So I think the challenge was that three D printing is just one process and it's necessarily not good for every type of part. You know, manufacturing is very versatile. You do different types of geometries, different types of material, and three D printing has a very small reach. There's certain type of parts like rocket engines, very good fit. You're building a tank, maybe not so right. So yeah it's good for certain type of parts, but there as a
whole lot of other parts. Like I said, you know you're building a fuel tank, which is basically large sheet metal or thin walled structure, then maybe three D printing is not as good as a fit because it takes a long time, and also because it's thin, you have a lot of physical challenges in terms of controlling the geometry and the tolerances. So we realize soon that maybe other processes are also need to be automated the same
way three D printing is. We need to have more flexible processes that are not just one process, more flexible platforms. They can do different types of processes, not just three D printing, to be able to cover a whole variety of products in a flexible manner, the same way the three D printing that's for certain type of products. And that was actually the thinking behind MARKETA Labs is that, okay, can we step back and say, what do we need
to build? What is this flexible platform that can do three D printing if needed, or it can do sheet forming if it's needed, It can do machining if it's needed, but chooses the right operation, right flexible operation for the right part, but still very agile and doesn't require a lot of tooling and it's not inflexible.
So it's it's sort of zooming out more. It's saying three D printing is not going to do everything the way manufacturing works now. It's just too rigid, too hard to change things, to rely on on scale to make the economics work out. So like that's a very big, very abstract thought. To start a company, you got to make something or you got to make something that makes something like what do you what do you actually do?
Yeah? So it was interesting, right, you know we actually the solution was in our past. Right if you look at like.
The lesson in a movie, it's like the Wizard of Oz or something exactly.
If you look at manufacturing, I mean up to Industrial Revolution, it was arts and crafts. Right, it was basically humans trying to figure out how to conquer nature, right, Like, how am I gonna use my hands? On my brains and very few primitive tools to deform a product or shape a product from raw material. Right and to this state, if you are in a very high mixed manufacturing still a lot of that creativity exists. There is a person at Space IX. His name is Big John. I don't
think he's there anymore, but there was this guy. It was like, you know, a very skilled maker, a craftsman. You could figure out how to use simpler tools to build different things in a creative way. Maybe it's not a repeatable way like you know a stamping works or ejection molding works, but you can be flexible. You can do different types of things. You can be creative about
it and do different type of things. So the inspiration came from how actually humans used to do manufacturing but realized in order to be flexible, you actually need two components. You need intelligence and you need set of simple tools with a lot of kinematic freedom. Now you can pick up those simple tools, and as long as you have the intelligence on how to use tools and what sequence and what kind of a process how to use those tools, you can actually do a whole variety of projects.
And so when you say kinematic freedom, you basically mean like robot arms that can move in lots of different ways. Is that practically what kinematic freedom means in this context?
Yes, basically can apply these tools in a lot with a lot of freedom to the material, right the same way humans humans do it, right, you know as a human, you know, if you think about it, you can pick up a welder and weld something, and then you drop the welder, and you pick up a drill and you put a hole in it, and you drop to drill and you pick up a you know, hammer and maybe
hammer it into shape. So you actually have a few set of tools, but you have a lot of good kinematic freedom and most importantly, very creative mind too tells you how to apply these tools to the material, so they can actually get very complex set of products and a lot of diversity.
So plainly, instead of big John, you want a robot, right, That's where that's the kinematic freedom. The tools are kind of like old tools, but optimized for the root. And then when you say intelligence, that's the one where it's like feels more frontier ish, like does that mean like clever engineers figuring out how to automate the robots doesn't mean AI? Does it mean both?
Yeah? So I think yeah, you're basically getting to the crux of how do you scale it? Right? You need to have those three components, and how does the intelligent piece, which is the most important piece, comes into play in an automated fashion. So early days we started from basic intelligence of humans. But then we had a plan to capture data and train AI so that you can replace the thinking and the creativity that human had to put in.
What's the first thing you decided to try and build. What's the first sort of problem you want to solve?
Yeah? I think so. I left relativity in twenty eighteen, and the idea when I left relativity was there, right. I was like, okay, we need to build basically what I had in My mom called it robot craftsmen. Robocraftsman, we call it a time. How can you build a robot system? To your point, you can pick up different tools, has the same king mean, but also have to have the intelligence. The challenge is you know you said, in order to train these robots with AI, you need to
have a lot of data. And this is not the data you can find on internet.
Right, this is the AI robotics problem, it seems right, like unlike with large language models, like that's why we have large language models and not AI robots, right because because we have the data just sort of randomly sitting around on the Internet, and we don't have that physical world data for robots.
Right exactly. So basically the problem narrowed down into Okay, how can I generate enough data? How can I create a business that has a sustaining way of generating data so I can actually build these models, I can build this intelligence for these robots. And the thinking was, Okay, I need to create a solution that can scale in the industry with limited amount of data and some heuristic. But then because it's scaling, we can generate a lot of data and it starts building AI mods.
Right. You need a first thing that you can actually do before you really have AI, to generate the data that will get you to.
AI exactly exactly. So we're thinking about, Okay, it needs to be a large enough market right where we can get mass adoption, and we need to solve a problem that's big enough it's ten times at least better than the current solution so it can actually get adoption, right.
Meaning you can't just do something as well, you have to do it ten times better.
Yeah, Because I think what we realize is that through the last two companies, if something is not ten times better, cannot overcome the inertia that exists in an industry for adoption because you know, if you're doing something for the same way, and in manufacturing, people have been doing things the old way for hundred of years, right.
Yeah, and it's a risk, right if they're going to try working with you, they're immediately taking a risk. And if it's only going to be a little better, why should I take that risk?
Exactly? So the idea was, Okay, we need to find it large enough market for our first application, and we need to have a solution that at least ten times better. So that landed us. We actually looked at a lot of things, from three D printing to forging to a lot of things, and then landed on sheet metal. So sheet metal is the largest metal processing sector out of all It's a two hundred and eighty billion dollar industry today, and forming complex sheet metal shapes is very tool intensive.
So so what we started to do was, okay, can we make our robot craftsman's first operation to be forming sheet metal, basically forming sheet metal the same way a sheet shaper hammer is a sheet into shape.
And when I think about sheet metal, I mean I don't know anything about sheet metal. I think of like I think of cars, I think of planes, right, I think of like you know, detroit, like stamping.
Is that?
Am I thinking about the right thing? Am I missing huge or huge sheet metal universe?
Like?
What's the sheet metal universe?
Yes? So sheep metal almost is everywhere. I think is the most common metal part that you see on day to day, right, because most of the time we use metal to be a container for other things. So it's usually a thin metal structure that's formed in complex shape to hold something else. Now you know it can be from case of a computer. Uh, you know, to a car, right, you know you're sitting in a freeway you're in to see a sheet metal or to a to a airplane you're in a sheet metal can to a rocket body.
Uh.
For for a lot of rocket someone will composites with a lot of a machine metal. And to agricultural heavy equipment machinery you think of combines, tractors to even building equipments. You look at your h ract ducts are all sheet metal, right, because it just makes sense. It's we mostly use metal parts to contain other things, and we give it complex shapes and that's where she forming comes into play. So
you pretty much see it everywhere. But the challenge is that in almost in all cases, you have to create tooling. It goes back to that first problem. He said, you have to create tooling for each of those geometries. And that's why you know a Ford needs to make sure they can sell a million of an FUN fifty before they can invest in a plant that makes a new version of FN fifty, right.
Because you basically have to build a bespoke factory just to shape sheet metal in a new way exactly for a new geometry, for for a new design. Exactly where is that a particular problem? Like where is it? Where is that? Where does that acutely bind the fact that sheet metal is so hard to do if you're not working at.
Scale so expensive? Yeah, so I think now you're coming to the even the third stage of how do you scale this technology? You need to first find you know, you said you need to be ten ex better we need Right, you're in an area that has a lot of pain with today's time.
I was like, oh my god, thank god, you've walked through the door. We've been waiting for you.
Yeah, So end up being very much defense in airspace. Right, So think of you know, think of our military for example, right today, they we have fifty sixty different weapon system or defense systems you can basically think of like aircrafts that they're maintaining. And some of these systems have been built from sixty seventy eighty years ago, like think of B fifty two C one thirty like World War.
Two planes still flying kind.
Of still flying, yes, exactly, and they have like you know, thirty of one, fifty of another, one hundred of another one. And these things get break down, right, and unlike a Ford factory, there is no factory for seventy different products that they're carrying.
Right, and presumably the factory they built in nineteen forty one to build this plane doesn't.
Exist any It doesn't exact. Even the vendor might completely have disappeared, right, that made that misspecific component. So they're constantly battling with this challenge of an aircraft goes down, how can I fix it? How can I find the part? And there are thousands of parts in each of these aircrafts, right, so any of them can go down, and that's a
huge challenge. I mean, if you look at you know, government of a government accountabilit The office put this report out, I think it was a couple of years ago or a year ago about how ready each weapon system is to defend the United States. Out of the forty eight to forty nine weapon systems they look into only one, only one in the past eleven years, every year was ready, right. I think only top four had like at least half
of the years ready right. So that means in most years these weapons are not ready to fight, like.
They're waiting for parts.
They're waiting for parts. Something is broken, something is damaged, and we used to go deeper. Some of these components take four years to be replaced. So if a plane gets damaged, it needs to sit on the ground for four years before it can be it can be replaced, and the cost of replacement is building another factory basically, So some of these parts, and think of it, a landing gear door that goes on a plane will cost them eight hundred thousand dollars for example, because they have.
To go make it because it's bespoke essentially, like buying a bespoke suit or something. It's just like it's gonna cost a lot.
Yeah, yeah, So the idea started there. I think that was one of our first customers. Can we make defense manufacturing more agile? Directly affects our national readiness for military conflict and it's a huge problem. But then you know, even in a broader sense, any defense product or aerospace product usually has very low volume but high mix of products.
You know, even you know, you're building a missile, you make like, you know, a few thousand a year, and you might make five, six seven different versions of right, So it's very unlike cars, where you know, you make a million of the same car over and over all. So that ends up being our first application, which we've got a lot of traction with. But but you know, even outside of that, you know, you look at companies like Caterpillar, like John Deere's of the world. These folks
also are in the same book. You know, they make two hundred combines, right, but they need to support them in the field. And these folks have the exactly same problem, right, you know, do I need to run a large factory to support all these models at all time, and that's will be very expensive to support, like one hundred vehicles out.
There still to come on the show. We'll talk about the future of AI and robotics at Mocking the Labs and beyond. And so you got the right market. Now you've got to make a thing. You got to figure out how to actually do the thing, how to make your idea come true? Like how does that work?
So the idea originally was can we get rid of a die right and do it the same way a sheet shaper forms a sheet of metal? And what does a sheet chaper do? Sheet chaper get starts from a flat sheet of metal and it slowly hammers it into shape. So what we wanted to do was have a robot do that, right, have robotic system basically do that incremental deformation into shape. We call it romophor.
So you're sort of bending it, right, I mean you're hammering it. It's sort of like if you take a whatever, cut open a luminum can and kind of bend it into shape. Like that's a version of what's happening here, right, Yes, exactly complicated way, yeah.
Exactly, you're right, I mean the same way a potter forms a clay bowl. That's basically what our robots do. They start from a flash sheet of metal and slowly deforming in the shape the same way a potter, which form it clay bowl or a sheet shape of hammets and hammers a sheet into shape. Yeah, so I've seen it right.
So there will be a sheet of metal like hanging hanging up in whatever above the ground. And then you have a robot arm on either side, right, like one on one side, one on the other. And then what.
Happens Basically the robots come together from both sides the sheet and they pinch the sheet in a certain way so that that location that they're pinching slightly stretches and deforms. Right. And if you start applying this pinching all over the sheet and incrementally, you slowly start to form it into a shape. Right. So instead of traditionally would use it die and with sheer pressure of the press pushing the
sheet against the dye to give it a shape. Now the robots are like a craftsman, like a trades person coming in to slowly deform the sheet into shape by just applying pressure. So one robot is pushing it the other robot is supporting it, and by applying a pinch you slightly stretch the material and you form it into a shape.
So I mean, the way you describe it, it makes sense and it sounds easy. I'm sure it wasn't easy, Like were there things that just didn't work for a while.
So you should have been here when the first time we actually tried to form a part, the part looked like it was like a ghost of the geometry that they wanted to make, and actually in the end it tore right. So think about it. You have this very flimsy sheet applying pressure to it, and if features apply pressure slightly wrong right, it can potentially tear it. It can form it into a different shape. And also the whole sheet is moving the whole time you're trying to
move to form it. The whole sheet is moving because it's very flimsy. It's not a rigid structure, right. So the main challenge was how do you get this accurate? Right? How do you get this process accurate? How do you get accuracy? And the idea was what does the robot need to do given all of these chaotic nature of the process where the sheet moves and if you apply it too much pressure, it will deform in a bad way or in my tear. If you're probably not enough pressure,
it might just not form. So how do you come up with the right set of robot movements and process parameters to form the part? And that was the problem we want to solve with AI right, but we didn't have the data right right in the beginning. Right. The idea was that if I form enough parts with this process, and I can capture all the data throughout the process, where did the robot go, how much pressure did it apply,
and what was the resulting geometry? That can start building a model that says that correlates the inputs to the outputs, and I can explore this and say, okay, in order to get to the right output, I need these inputs. But we didn't have them in the beginning. So the idea was two things. One was maybe we can simulate the data right and very early on we started doing some simulation, physics based simulation, and we soon realized in order to get an accurate result, the simulations are going
to be very computationally intensive. A simulation of a part that took only fifteen minutes to form took us one week on twenty seven core machine. Wow. Right, so okay, stimulation not only is not accurate, it takes forever. So we realized, okay, so that's not the right route. The right route was like, Okay, we can also form a lot of parts and gather the data. But in order to do that we go back to that same problem.
We need to have a scale. We need to have a lot of these machines for these parts and get that data.
I mean, one of the big AI insights of the last whatever decade is like, you need a ton of data, which is easy if it's words, but hard if it's metal. Right.
Yes, we ended up doing was created a hybrid model. We said, okay, what if we keep the humans in the loop, so the human can give an instruction initially based on herostricts, and then we look at the data and human can adjust and then iterate on that. But while we are capturing all these data, and over time, as we're capturing the data, we start building the models
that will help the human do less trials. Right. It's basically guided reinforcement learning, right, and a humans are actually guiding it where to go, but it's exploring those areas but after a while, once we started forming south thousands of parts, then you can start feeding this data into model. Then the model will be like, okay, human, you don't
need to do twenty five different trials. Now you can do with five trials, you're going to get to the right place, which is actually the number we are at right now.
And that's happening in the physical world largely those iterations like you're trying a piece of metal and it's bad and it tears, and you do another piece of metal and it's a little less bad and eventually.
Exactly exactly, and that initially would take twenty five parts, like you know, before we find a recipe for that design. But twenty five parts still was better than traditional alternative.
When you say twenty five parts, I mean twenty five tries twenty five pieces of metal before you make the part the right way exactly.
And that was like, you know, they would sit down basically twenty five days in a row, so in a month they could actually define a recipe where traditionally making a mold would take at least three four months. Right, So we were still better. But then now with over time, when we generated the data and now the model can tell the engineer, okay, maybe you want to choose these parameters, is now becoming an advisor with down to five trials.
In five trials, we can actually get to the right part and then hopefully in the future we get to a point where you know, the machine will tell the romans what to do and the human can be completely out of the loop. Yeah. But the idea was like, how do you kind of create that hybrid model that's efficient so that we can generate the data until the model is good enough to do the job itself.
And you find that the data is sort of generalizable, I mean clearly, like making one kind of part makes the model the AI smarter about making another kind of part.
Yes, you know, yeah it is. It's kind of interesting. I think people don't think about it. I used to do sheet shaping by hand, right, That was one of the hobbies I had. I was working with this shop in Pomona that we were actually hammer sheets into shape, and we used to say, you know, if you spent five years doing it, you're really good. You get really
good at it. I was used to think, you know, okay, after five years of doing this, yes, you have this intuitive understanding of you look at the sheet and be like, okay, this this place needs to be hammered more. This place needs to be hammered. It was, it was, it was. It was intuitive. It was like you couldn't explains why you're thinking this need to happen. There was no physical explanation. None of these people who were she shaping got PhDs
in material science. Yeah, they just learned over time seeing the pattern of how the sheet formed. Yes, craftsmanship, that's craftsmanship right. Yeah, but really reminded me of Okay, these people can know how to do it, but without really being able to explain it, to do it for five years.
It's that kind of tacit knowledge.
Yeah, and reminded me of the same challenge we had early machine learning challenge where they were like, okay, a human can look at two pictures and say, okay, this is a cat and this is a dog. Something happens in their brain that knows which is a cat, but they cannot really define why they're calling this cat and this sort of dog. So that was where it starts to click for me. If I can capture enough data, five years worth of data right of a human, then I should be able to get to a very good
sheet shaper, right, And you know it's funny. Back at the end, I was like, okay, humans are you know, receiving x amount of megabytes a second? Okay, how five years worse of data? Is that much? So roughly, I think once we get a certain amount of data, I think we have enough data to be able to basically replace a like not replace replace the mentality or the model that the sheet shaper has in their mind.
So how how how many years of kind of human level craftsmen sheet shaping data does the model have at this point?
Yeah? No, so I think rastam I check? One year ago? I checked around, we were like three fourths of the way there in terms of the data that we have for just she shaping. Right. So once we get to I think full, I think and these at that point we have no excuse. We have enough data. The model should be good. We just need to figure out how why it's not. Maybe far from it is.
It is interesting to analogize it to like human craftsmanship, right, And I mean even if you want to zoom out even more, the like fifty year history of AI, where first everybody was like, oh, you just got to teach the machine all the rules for to use your example, like what's a cat and what's a dog? But then you realize it's actually wildly hard to make a list of rules that can reliably distinguish a cat from a dog. And the weird thing that has happened in AI is like, oh,
you don't actually have to make a list. You just need like image that you just need like a giant database of images and a giant neural network and you just throw it at it like and say figure it out, and it figures it out, and you're sort of doing that. But for shaping metal.
For metal, and then the only challenge was, like you know, cats and dogs pictures were Internet and sheet metal forming data wasn't. And so that's that was an additional problem we have to solve, as you pointed out, which is a big problem in physical AI.
So I want to talk a little bit more about AI and robotics. Jansen Wong has been talking about it, as I'm sure you know in video and videos vc ARM as an investor in your company, other people are working on what you're working on. I mean, I'm curious what does the sort of AI and robotics path look like to you? For the next few years, and what like, what do you understand about it now that you didn't
understand whatever five years ago? Like what what have you really come to realize by working on it all the time?
I think the biggest problem for physical AI is data generation for now to train models. So we need to either there's two things need to happen. Either new types of models needs to be created, new architectures, new new algorithms basically, which I'm sure it's going to happen that can learn more with less data basically and the same way humans kind of learn more with less data. Right. But at the same time, I think, you know, we only exposed our models to categorically to ten percent of
type of data that humans receive. You know, you think about you know, human intrictions. You and I are now talking, if it was AI, AI is probably only listening to the words we're saying, right, But that's only ten percent of communication. I can see your lips moving, I can see your eyebrows moving. I can see like maybe you're folding your arms and okay, I know that like okay, maybe there's all these ninety percent of the signals are
not captured. That that's used for learning. You know, you look at if you ask chat GPT or Dolly or you know any of the you know, even even you know Grock say okay, draw me a clock that is shows five thirty. It cannot show you draw you a clock. It will draw you a clock, but it doesn't show five thirty. Actually, most the time it shows ten ten.
Ten ten, because that's where watchhands, like analog watchhands look good.
Right, it's a nice little v because those are all the images that they're seen on internet because they watch it.
It's almost always ten ten. It's the classic watch photo.
It's like five thirty is also ten ten, because.
It's always ten ten right to a generative AI, it's always ten ten somewhere.
So I think, but that humans, you know, receive this data of movement. When you grow up you look at the clock on the wall as a kid, you're like, okay, now I intuitively get it. I think I know what's going on, so I can actually make it work. So even though we train it a lot of data, I don't think we trained it on the right categorically right data yet right to get all the intuitive understanding that we have today. So I think we have a data problem and that exists the physical AI. So I think
the applications will win. There's a lot of people are working in this. I think the applications will win who can either synthetically generate that data or they can actually scale in the physical world in a way where they can actually generate the day for themselves. But the scaling needs to happen with less data, and I think that was That's why I'm like, for example, like very bullish on manufacturing. So I think the data is going to
be the biggest challenge. And I think, you know, in order for us to massively change this space, we need to be able to get to the data. I don't think algorithms is a bottleneck there yet. It's just a data for us.
And is it just a matter of people doing what you're doing and like finding little wedge places to start and having people sort of hold the hand of the model and training up the models. I mean that seems slow on a certain level, like not you know, obviously it's working for you, but like, is there some kind
of breakthrough move people can make? Can you put sensors somewhere in the world to you know, train AI without having to you know, have a human stand next to it as it messes up one piece of sheet metal after another.
Yeah, I think I think that there is there's another path, which is simulation path. Make physics based simulations faster and kind of learn. Let the robots just go play in a digital playground as opposed to deploy it in real role, and that becomes a computation problem. And then you know, as long as you have enough computation, you can train
to robots. But I think, you know, I think you know the good examples that we have had such success so far as like autonomous cars, right, did the same thing we were doing, but in the car like Okay, Tesla, you know, deploy the fleet of robots that are capturing data still be driven by humans, but the data can be used later on to kind of automate it.
I mean, that's an interesting case because it has been much harder clearly than many people thought. Maybe most people thought, right, Like, I know, that's a particular instance where you're really worried about edge cases. I don't know, is autonomous cars like a good model or not. It seems complicated.
I think the model of capturing data is there, but then the the task at hand is very hard. Yeah, right, so I think that's the challenge, right so where it says like with us, it's still much more structured environment, And I think that's that was the thinking we're thinking. I think the hardest problem right now in physical AI is finding the business model of how do you scale data capture without requiring billions of dollars in investment?
So what do you make in today?
I imagine you know, so last time I checked in the facility, one four of the sales are working on a defense application.
Is it secret? Can you tell me what it is?
It's a missile? And two of them were working on an aerospace application. This is components of an aircraft or a drawing. And one of them, as an interesting one, was working on an architectural component, which is a roof tile for a specific building that's used by the Department of the by Bureau of Water Recognition.
Oh, I was I was going to say, what is it? Something like Frank Gary, like nightmare weirdo metal park.
Oh, those we have had those in the past two but this one is actually very practical. Well, it's this building. It's actually very interesting. Exactly these buildings, these large industrial buildings that built they built in the sixties or fifties, and they use these type of roof tiles that the manufacturer doesn't exist anymore. And anybody else who they went to quoted them hundreds of thousand dollars to make those tiles, and we're like, oh no, we can make it for you.
But also that show is kind of the diversity. I mean, like like I say, in the morning, we have like aerospace parts. In the afternoon, roof tiles for a industrial complex, for you know, for a dam.
Now you're in the sheet metal business. I know you're large. Dream is much larger than that, right, but like what like that, tell me where you are now? Tell me where you are now? Like what are you doing? What are you selling? And then kind of what's the next big step.
So some of our systems are now operating out in the wild and working for the customers. And but I think the next phase of growth for us is getting into each of these applications and own more of the process so we can teach the robocraftsmen the future processes not just sheet for me, but also maybe how to assemble it, how to weld it, how do you surface
finish it right. So what we are doing now in the next phase is actually instead of selling parts or components or systems, we're actually saying, Okay, can we get this robocraftsman to actually build you a subassembly or a full product, not just a component of it, but a full product. So that's something we're describing with folks. Can we have the robocraftsmen build the full drone for you?
Can we have the robocrafts and build you a full missile as opposed to just build missile you know missile scans.
Is there that seems like a leap? Is there not an intermediate step?
Like yes? Yes? So I mean how we're doing it is we're gradually stepping into it right the same way she metal was our first application. So we're putting a facility that maybe makes drones, but the main component that we automate today is sheet for me, which is the bottleneck. And then we do the welding in a traditional way on the same robots, but we actually instruct them to do it.
So that way, the robot is kind of back where it was on sheet metal five years ago, but it's learning how to weld now exactly.
I used to work in a you know, a shop that we will do custom cars, build the custom cars with hand, and so it was also near near and dear to my heart. So what we realize is that with our technology, for the first time, we can actually enable a product that didn't exist in automotive, meaning that instead of buying a car that's mass produced and every single one of them look the same, you can now
let the customer design a custom car for them. You know, right now, if you go buy a car, you can you have options of what the what the seat color would be, or maybe the color of the car would be, and what some trim options. But you can't really choose the design of your car. You can't say, oh, I want a different hood and I want a different fender because going to the back same problem you have to make tooling and mold for the vender of certain designs.
It cannot easily change it. So with our technology you can. So what we started doing was like, okay, applying this freedom that this technology provides to now automotive is the ability of the customers to be able to go to a website design a fully customized car for themselves. It can be either from already design panels round car designer or adding a specific customer customizations they want to do, for example, logo of their company to their door of the car or the hood of the car, and actually
get a completely unique car right manufactured for them. And we're actually working with this with some of our automotive partners Automotive Aims as well. Right, we actually showed some of this work in the biggest aftermarket show in the United States is called SEMA with our partner Toyota. So I think this is going to be, in my opinion,
one of the new product categories in automotive. We have had a time of his cars, we have had you know, electric cars, and I think now for the first time, with technologies like ours, you can have custom to order cars, like cars that are like, you know, the same way you choose what T shirt you wear and your T shirt is different than mine. We also don't have to drive the same you know model S or you know Model three. We can actually have our own customized Model three is and modelesses.
So what's the I mean is that the if you think sort of long term for Makeina is like that what you think about like give me the give me the five year vision yeah, or ten year or whatever.
Yeah. So I think the long term motivation behind our company is can you grant this democratization of ideas for people who want to build anything? Right? Can I express myself if I'm a builder, can I go build something with not having to build a factory for So that's
really the long term goal. So I imagine in the next five to ten years, you can as a designer, somebody who has an idea, you can go to a website, get guided through your ideas on how to make and design a physical product, hit a button and say, okay, I want twenty of these, and I want in Chatsworth, California, and the right facility programs the right number of robots to actually do those operations without any hardware or investment that needs to be made for those of specific parts
and ship it to you two days later in the right location. That is the future we're building towards cars is just you know, one of the products that could be built. But I imagine that you know this technology or technology like these, technologies like these can be used to do the myriad of designs. I think the moment you open up this possibility of any designs could be a reality. I think so many things will be created
that we're not even thinking of right now. You know the fact that we have cars today and they all look the same as limitation of technology. But the moment you can open up this creativity of turning ideas into physical reality without a without an initial investment or huge barrier to entry, then I think we're going to have all kinds of drones, all kinds of satellites, all kinds of rockets, all kinds of cars that you're going to be this like you know, Cambrian explosion of different designs
that's going to come into our world. And I think that's what future is about. The future is about you know what I call it, Like future is custom Like future is about being able to make these all these ideas in reality. We had this explosion happening in digital world. Yeah, you know, now we have even models generating images and videos and there's this you know, explosion of different ideas
and content being created using the technology. But the link is broken to the physical world and the physical work is still pretty uniform because it's very hard to make things in the physical world. Can we bridge that gap? Can we connect the digital world of creation to physical world of creation and create the same variety in the physical world as we have in the digital world. I think that's the goal in our company.
We'll be back in a minute with the light Year Round. Let's finish with the Lightning Round. Do you drive a customized car?
I don't actually yet. Well, if I am, what have I.
Seen on your Instagram? What's that truck you keep posting on your Instagram?
So I so I have a truck that's customized. I don't drive it around as much, but maybe this year I'll start taking it out. This year I've been you know, we have been kind of stelf about it, talking about it, but we haven't talked about it in a big way because we have a big release coming soon.
I mean, you're literally posting it on Instagram. It's not that stuff. Tell me about Tell me about that truck you keep posting on Instagram? What's going on with that?
So it's a truck is fully the full body is fully customized.
It says anvil in the back when you post it? Is it called anvil?
Dumb question and call it and call it anvil? I think it's the idea was actually the shape design of it was inspired by amil. If you look at the front fender, it actually looks like a the front bumper looks like an anvil. But also the idea is that, like, you know, we're actually forming shits on an anvil. So yeah, it was very fitting.
Tell me about that truck, like, tell just tell me what's it look like.
Yeah, so for example, like you know, we put a lot of form and sharp edges in the in the hood. Right. Most vehicles have a very hard time if you look at most of the you know, hood of the vehicles, they are very smooth because it's very hard to actually put sharp angles in the hood. So if you look at this truck, this truck has a lot of angles, a lot of sharp detail right in the hood. Right, And and and that's very expressive of the type of person for example, that I am, right, I like things
that are edgy, and and that truck is certainly edgy. Right. It's bare metal, right, you know, there is no blemishes being hiden and hidden under the under the vehicle. You know, a lot of people when cyber Truck came out, we got very excited about you know, oh, it's bare metal. It looks like a metal, but then there was no form in it because it's actually very hard to make it for metal look nice. And so that's one of
the things we wanted to show. We want to show that, Okay, you can actually have a form metal with a lot of detail in it and still keep it bare metal because it will look nice. Right, So, yeah, a lot of design features of it. For me kind of represents the type of personality and character that I have. But I think that's how every car should be. You know, people should be able to have that freedom to choose what their cars look like.
How many skull tattoos do you have?
I've got three? Why it's it's so, yeah, it's an interesting thing. So a skull for me represents kind of and it's an abstract for death of ego. So I have a tattoo on my thumb which is a skull that's holding a microphone to his ears. And this was a time where you know, I felt like, you know, I had a good platform and I could talk a
lot and people would listen. But then I realized I should yes, that's right, but I should maybe keep them keep the mic close to my ears and also listen as opposed to talk all the time, right, So I think.
Skull microphones don't work that way for the record, but.
I like it as a metaphor exactly. But I think the idea of is around, you know, kind of reminders of you can see a lot of my tattoos on my on my hands, so it's a really reminder for myself to know that, you know, be present and make sure that you know you're not involved with your ego too much and you can see others people's perspective.
Is there any tension between ego death and custom cars?
Tension between ego death and custom cars? I don't know.
I'm just playing, but like you know, custom car kind of seems like, hey, look at me, I'm special, and ego death seems like, oh, don't look at me, I'm not so special.
Yeah, no, I think I think the difference is I think, yeah, if you have attachment to your custom card, then maybe there's tension. But I more think of it in terms of expression. Right. You know, you can be an artist, you can. You can you can design your home the way it expresses you. You can design the theme of your podcast the way it expresses you. You can design your car. Also, the way it expresses you. I think it's leus so about oh look at me, I'm special.
It's more like, here's my expression to the world for the people to see. But I think that expressiveness is it is pretty amazing. I think that's uniquely one of the unique things about humans that like, you know, we we I think all we do when we come to this world is expressing ourselves right, expressing uself through our work, expressing through ourselves through our relationships. And if you can enable people to express themselves better better, I think that's great.
But if you get attached to your expressions and your ideas and your thoughts and think, oh, I'm better than everybody else, and I think that that becomes that becomes a little bit of an ego driven trip.
Edward Mayer is the co founder and CEO of Mocking Labs. Today's show was produced by Gabriel Hunter Cheang. It was edited by Lyddy jeene Kott and engineered by Sarah Bruginner. You can email us at problem at Pushkin dot FM. I'm Jacob Goldstein and we'll be back next week with another episode of What's Your Problem.