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a16z Podcast

Designing the Physical World with AI

50 min episode · 2 min read
·
Alex Boden,Davide Asnagi

Episode

50 min

Read time

2 min

Topics

Relationships, Investing, Fundraising & VC

AI-Generated Summary

Key Takeaways

  • Code-as-hardware abstraction: Diode Computers bypasses the scarcity of PCB training data by reframing circuit board design as code generation. Their open-source compiler (diodeinc/pcb on GitHub) gives AI models enough structural hints that designing a circuit board resembles writing Python — leveraging the vast existing code training data LLMs already possess.
  • Design-for-manufacturing as the automation bottleneck: Electronics manufacturing is already 80% robotic via surface mount technology, but the remaining 20% — oversized transformers, board-to-enclosure assembly — stalls full automation. Diode's strategy is to constrain AI-generated designs so tightly to manufacturing specs that existing robotic lines can handle 100% assembly without waiting for robotics hardware improvements.
  • Parametric modeling eliminates construction redesign costs: Unlimited Industries builds all project designs as fully parametric code models, meaning a change six months into a year-long design process updates as a variable rather than triggering a full restart. This directly compresses project schedules by three to six months, which materially improves project IRR for infrastructure investors.
  • Vertical integration as the entry strategy for entrenched industries: Both companies conclude that selling software tools to traditional industries fails due to high switching costs and misaligned incentives. The viable path is owning enough of the end-to-end workflow — design through manufacturing or procurement through construction — to deliver a finished product customers already know how to buy, faster and cheaper.
  • Data generation, not model architecture, is the primary constraint: Asnagi argues the core barrier to fully automated PCB design is not a missing AI breakthrough but a lack of training data, currently siloed at companies like Apple, Meta, and SpaceX. His strategy is to become the open manufacturing infrastructure so that designs flowing through Diode's platform generate the proprietary dataset needed to compound model accuracy over time.

What It Covers

A16z General Partner Aaron Price Wright speaks with Alex Modin of Unlimited Industries and Davide Asnagi of Diode Computers about applying AI to physical world design — specifically large-scale construction engineering and custom PCB manufacturing — and the timelines, data challenges, and vertical integration strategies required to automate both industries.

Key Questions Answered

  • Code-as-hardware abstraction: Diode Computers bypasses the scarcity of PCB training data by reframing circuit board design as code generation. Their open-source compiler (diodeinc/pcb on GitHub) gives AI models enough structural hints that designing a circuit board resembles writing Python — leveraging the vast existing code training data LLMs already possess.
  • Design-for-manufacturing as the automation bottleneck: Electronics manufacturing is already 80% robotic via surface mount technology, but the remaining 20% — oversized transformers, board-to-enclosure assembly — stalls full automation. Diode's strategy is to constrain AI-generated designs so tightly to manufacturing specs that existing robotic lines can handle 100% assembly without waiting for robotics hardware improvements.
  • Parametric modeling eliminates construction redesign costs: Unlimited Industries builds all project designs as fully parametric code models, meaning a change six months into a year-long design process updates as a variable rather than triggering a full restart. This directly compresses project schedules by three to six months, which materially improves project IRR for infrastructure investors.
  • Vertical integration as the entry strategy for entrenched industries: Both companies conclude that selling software tools to traditional industries fails due to high switching costs and misaligned incentives. The viable path is owning enough of the end-to-end workflow — design through manufacturing or procurement through construction — to deliver a finished product customers already know how to buy, faster and cheaper.
  • Data generation, not model architecture, is the primary constraint: Asnagi argues the core barrier to fully automated PCB design is not a missing AI breakthrough but a lack of training data, currently siloed at companies like Apple, Meta, and SpaceX. His strategy is to become the open manufacturing infrastructure so that designs flowing through Diode's platform generate the proprietary dataset needed to compound model accuracy over time.

Notable Moment

Asnagi recounts visiting a Chinese engineer whose boards were deliberately cramped to avoid a second SMT line pass — not because it was required, but because he personally knew the manufacturer. This cultural proximity between designer and factory floor, largely absent in US hardware development, is what Diode is attempting to encode into AI models.

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Episode Transcript

I want to be able to spin up a hardware company the same way that my friends spin up b two b 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. Mhmm. 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 boards 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 twentieth 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. 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. A sixteen z general partner, Aaron Price Wright, speaks with Alex Boden, cofounder and CEO at Unlimited Industries, and Davide Asnagi, CEO at Diode Computers. We're thrilled to be here today with Davide Asnagi 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 holding 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. Yeah. Excited to be here. Thank you. Yeah. …

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  • diodeinc/pcbBy guest

    by Diode Computers

    Diode Computers bypasses the scarcity of PCB training data by reframing circuit board design as code generation. Their open-source compiler (diodeinc/pcb on GitHub) gives AI models enough structural hints that designing a circuit board resembles writing Python

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