In this episode, Ryan and Luca sit down with Michael Lazarenko, co-founder of Embedd, to discuss the real-world challenges of using AI in embedded systems development.

Michael shares his journey from manufacturing physical devices to building AI-powered tools that parse datasheets and generate hardware abstraction layers. The conversation dives deep into when AI should—and critically, shouldn't—be used in embedded development.

Michael offers a refreshingly pragmatic perspective on AI adoption, explaining how Embedd uses AI to extract information from messy, unstandardized PDFs and technical manuals, while deliberately avoiding AI where deterministic approaches work better. The discussion covers the technical challenges of building RAG systems for embedded documentation, the importance of creating stable intermediate representations, and why accuracy matters more than speed when generating safety-critical code.

The episode also explores broader themes around AI adoption in conservative industries like automotive and aerospace, the gap between AI hype and reality in embedded development, and Michael's vision for a unified embedded development platform. Throughout, the conversation maintains a healthy skepticism about AI's current capabilities while acknowledging its potential—a balanced perspective that's rare in today's overheated AI discourse.

Key Topics:

• [02:30] The problem of hardware-software coupling and why embedded documentation is such a mess
• [08:45] When NOT to use AI: deterministic parsing vs. probabilistic approaches
• [15:20] Building RAG systems for technical documentation: chunking, context windows, and accuracy challenges
• [22:10] Creating stable intermediate representations (digital twins) for hardware components
• [28:40] The verification problem: why AI-generated embedded code is harder to validate than web applications
• [35:15] AI adoption in conservative industries: automotive, aerospace, and defense taking risks
• [42:30] The gap between AI hype and reality in embedded development workflows
• [48:20] How AI forces better testing and requirements engineering practices
• [54:00] The future of embedded development: unified platforms and model-based design

Notable Quotes:

"I would be slightly contrary and say at this point at least, I probably wouldn't use it in every possible place, especially in this specific problem set, given the context size that we're facing. AI performs best when the results are as defined and as limited as possible." — Michael Lazarenko

"If there is a register list in an SVD file that I can parse with 0% chance of probabilistic error, why would I use RAG? If there isn't one, then I have to use it, and then I need to find a way of using it that gives me the highest possible accuracy." — Michael Lazarenko

"The number of VCs that I've talked to in the past year who have told me that they don't need testing frameworks because the AI is just going to generate all the code for us. That's exactly why you need more thorough testing. That's why you need more guardrails." — Ryan

"Since I've been using AI seriously to generate code, I've become such a stickler for tests. It's quite remarkable. AI can be a forcing function to really force you to get your development processes in order." — Luca

"I'm seeing dumps of AI code going in that no one read. People are outputting requirements and then code that AI spits out, and it's really soul destroying for those who actually review the code." — Michael Lazarenko

Resources Mentioned:

• Embedd - Michael's company that creates stable representations of embedded hardware and generates hardware abstraction layers using AI