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Chapter 1: What is the main topic discussed in this episode?
I want to be able to spin up a hardware company the same way that my friends spin up B2B SaaS. Like, you should be able to say, I want to do something that's considered very hard and just go and do it. We basically built a compiler that gives the model enough hints that it feels like it's writing a Python program instead of designing a circuit board.
It's basically this combination of a very model-led approach that allows you to use these agents to write code, which is what they know how to do. Put on Rails.
Everything is code. The last frontier standing is we don't have enough data. The data is like the thing that we need to generate as a society if we want Circuit Boys to be automated by AI.
Making sure that you design the system to actually be fully autonomous and to not be human in the loop. I think for us, at least, it feels like it's driven a very different architecture.
What happens when intelligence gets cheap, but the physical world stays slow? In the 20th century, industrial power came from the ability to design and build at scale. From assembly lines to semiconductor fabs, progress meant compressing time between idea and output. Software accelerated that loop to near zero.
But in construction and manufacturing, timelines still stretch into years, shaped by fragmented workflows, fixed incentives, and systems that resist change.
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Chapter 2: How is AI transforming construction and electronics design?
Now that's starting to shift. AI can write code, run simulations, and generate designs across thousands of permutations. The question is whether that translates into faster builds or just better plans. I want to understand what it takes to actually move atoms, not just bits.
A16Z General Partner Aaron Price-Wright speaks with Alex Bowden, co-founder and CEO at Unlimited Industries, and Davide Asnaghi, CEO at Diode Computers.
We're thrilled to be here today with Davide Asnaghi and Alex Modin. Davide is the CEO of Diode Computers, and they're using AI to design and manufacture custom circuit boards faster and better than before and faster than ever possible in the United States.
Alex is the CEO of Unlimited Industries, an AI-native firm that vertically integrates design, engineering, procurement, and construction for big infrastructure projects. So we're here today to talk about physical world AI. And when I say that, I think a lot of people probably think about things like humanoids and robotics foundation models.
But while I think robotic housekeepers folding your laundry is still a few years away, or maybe if you're really optimistic, a few months. AI is already starting to cross this chasm with use cases that move atoms. So these companies are working on physical world AI at two very different scales from the micro to the macro.
And I'm excited to get your perspectives about where we are and what's ahead. So Alex Davide, welcome to the show.
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Chapter 3: What challenges exist in automating construction workflows?
Yeah, excited to be here. Thank you.
Maybe, Alex, to kick off, you've said that in 10 years, all construction will be fully automated, which feels like a pretty bold claim, very ambitious. But what does that actually mean? What does it take to get there?
Yeah, I think it's probably helpful to level set on what a construction project looks like. And it starts with a developer who's got an empty lot of land and they want to make some sort of big project there.
And then there is, depending on how big the project is, if you're going to build a power plant or a hospital or some large facility, you're going to spend almost a year, sometimes a year and a half just doing design for that. And there's hundreds of engineers that touch this. There's lots of different project managers that touch this. And it's this orchestration of everything.
mechanical and process engineers and electrical engineers and civil and structural folks all kind of working together to do the pre-construction package which is effectively like a giant set of instructions that you can then hand to a general contractor or some builder who will order the things on there and actually construct the facility that first part that's like line of sight today of how we automate end-to-end typically we call that final output an ifc package and issued for construction package
Where you will literally feed in a site, a bunch of different requirements about what you're trying to build and anything you want to stipulate about how it gets built. And AI is going to explore tens of thousands of different permutations about how to optimally design that facility. A button click.
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Chapter 4: How do incentives and manufacturing constraints impact AI adoption?
And then what you get back from that is a globally optimized IFC package issued for construction.
Optimized for what?
It depends what the optimization function is. And so the easiest way to think about that might be like CapEx, like how much does this thing cost? But a much, much better way to think about that is like a total cost of ownership of the project. So if you really care about operation and maintenance of the facility, how constructible is the facility itself?
And I think a lot of things we kind of generally see in industry today is there's so many different segments and slices of people that all optimize for very different things. So being able to
kind of approach designing these giant things the same way you do from a software perspective, which is a very like a parametric, ultimately a super, super flexible approach you can take to optimize on any sort of main metric for that. So I guess that's, I guess, first half of it is how do you automate that end to end, which is what we're working on now and have a product to do.
The second part does look like a bunch of robotics and it's everything from like autonomous earthmovers, which feels more tractable in the short term, to how is a site full of tons of humanoids and drones and all sorts of like autonomous robotics. So, yeah, we very much think that that future is destined to happen over the next like a decade.
And a big piece of that is if the incentives are properly aligned, which I'm happy to talk more about later.
Yeah, I'm excited to talk about that. Davide, what is the kind of equivalent timeline for automation and hardware, and in particular, maybe electronics, because that's where you focus on both design and manufacturing?
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Chapter 5: What role does data play in automating hardware design?
So maybe paint us a picture on what this looks like for you.
So I have to be careful because my timelines are getting shorter and shorter, and I think I need to stay on the reasonable side. I will say it's really interesting to hear Alex describe AI applied to construction because it can draw very immediate parallels to hardware.
And for this discussion, I would like to stick to circuit boards specifically, which is both the design and the assembly of a circuit card. I am reasonably confident. We do a lot of work with Anthropic, for example. And the jump that we see in design capabilities between each model tier, like publicly available model, is wild. We thought it would be five years.
I think that I can probably say two. I think that the caveat here is that there are very, very different types of electronics design. And making a blanket statement about all of them, I think, is not appropriate. But I do think that there is a subsect that I really care about, and I'm going to go into details as to why, that I think will be fully automated in two years in terms of design.
The other reason why it's interesting, for us, it's not just design. It's also manufacturing. And Alex said, OK, we have this very high optimization function on effectively what the plan looks like.
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Chapter 6: How can AI improve efficiency in PCB manufacturing?
And then there will be a bunch of robotics on the manufacturing. We already have the robotics. The electronic industry has had robots for years. The biggest problem is that there is a 80-20 robotic automation versus like manual labor. And so right now, like nobody has really bridged that gap in the United States. So what I am like where we are working on a diet.
What do you mean by that? Can you double click on that 80-20?
So normally, there's a process called surface mount technology. So this means that there is a robot that will basically place every single component on top of a circuit board. And then you bake it in an oven, and you're done. But the 20% that is very, very hard to automate is there are some components that will not fit that very nice mold.
Maybe it's a very big transformer that needs to be soldered with a different process. Maybe it's something that hugs the PCB.
Chapter 7: What are the implications of AI on the future of skilled labor?
Even the assembly of the circuit board itself into an enclosure, that's usually not fully automated as well. So there's been companies like Foxconn, for example, for Apple, or like Pegatron, that have solved that problem with labor.
And that makes complete sense for certain segments and in certain geographies, but you're not going to double the production capacity for data centers in the US by just relying on labor alone.
Also, you're not going to be able to reduce the time cycle that it takes to bring up a data center from four years to two years if you're not able to redesign the boards, redesign them for manufacturing, manufacture them at scale in a constricted timeline. So what we really are bullish on is not really AI completely automating away design work.
It's more AI being able to automate away the type of design that produces these very manufacturable outputs. And then we have a lot of moonshot ideas about how you can improve the robotics on the line. But the core goal is if the design is constrained, you can manufacture it at 100% automation today. I don't even need to wait for robotics to get better. The robots are already here.
It is funny to think about like the design of a data center with all the complex sort of optimizations you need to do and the specs and everything you need to fit into a particular site footprint actually being kind of similar, surprisingly similar probably to the design of a very small circuit board that needs to fit lots of different components and meet lots of different specs.
I don't think I actually fully appreciated that till right now. And Davide, you've said your goal is to transform every software engineer into an electrical engineer. Why? And how?
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Chapter 8: How can we harness AI to enhance physical world automation?
I want to re-qualify that. OK. I think that software has incredibly good properties. And I think that empirically, we are immediately observing this with tools like Cloud Code. Basically, agents have been able to leverage the structured nature of code to do things that are not code-like at all. You can basically use Cloud Code to do things that are completely orthogonal.
the initial comment that I made was basically, yes, like we need to take the current like set of software engineers that we have already in the United States and allow them to do more, like being able to be also electrical engineer. But now like the total set of software engineers or people that are able to produce software is exploding. And it includes agents.
So really, what you want to do is you want to give anything that has the ability to generate code the same ability to generate hardware. That's really what you want to do. And this will include greatly skilled SWEs that are building beautiful cathedrals on a PCB, like a lot of our employees, which I'm very fond of.
But it will also include a lot of smaller designs that are completely automated away by allowing an agent to use code to build the board itself. The complexity of those designs today is limited. But you can extrapolate the derivative and like get to where we want to be in like a year or two.
I think this gets to another parallel in both of the industries that you're working in, which is that you're working in these industries with like a very entrenched sort of sets of expertise, ways of working, tools that people are used to using.
And I'm curious to hear, I mean, which is very different than like software engineering, for example, where even before AI, I think software engineers are just naturally technology curious and used to adopting completely new frameworks
for doing their work like every couple of years so can you talk a little bit about that like how do you how do you essentially change an entire industry's way of working um how much do you have to vertically integrate and own yourself um versus how can you bring people along for the journey i mean on our side there is um
there is a lot of like, you have to earn it. And super traditional industry, this is a lot, again, with the incentives, but it's, you know.
Yeah, I mean, it's hard to think of a more traditional industry than construction.
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