“Make me a car” is an impressive demo until you ask where the braking hydraulics, controls, and safety logic went.

We let Viral Shah, CEO and founder of JuliaHub, and Chris Rackauckas, tell us about physical AI and its use for systems analysis.

Its a different AI story than the popular one: physical AI for engineers, where models must respect governing equations, compile, and validate against known test cases. Along the way we unpack why Julia, a programming language, was created and how it led to Dyad. Hint: to do systems analyses. Any system. Also how open source shaped its growth, and why that foundation matters when you want AI to do more than autocomplete code.

We then get concrete with Dyad, JuliaHub’s domain-specific language for systems modeling and multiphysics simulation. Rather than building another CAD tool, Dyad focuses on function over form, the system and subsystem level where real products live. That unlocks fast iteration in the engineering V-model: requirements, architecture, integration, and ultimately digital twin workflows, without forcing every engineer to become a full-time programmer.

The highlight is a demo where an agent ingests NASA HL-20 lifting body documents and aerodynamic data, generates a working systems model, runs a documented pitch-pulse test, and produces plots you can compare to the original validation figures. We also talk about the trust problem with AI and why physics-aware compilers, transparent artifacts, and test cases change the conversation from “wow” to “verify.” If you care about engineering simulation, systems engineering, agentic AI, and digital twins, subscribe, share this with a colleague, and leave a review with the tool you want AI to tackle next.