Teaching AI Engineering not Geometry: Why Text-To-Code-To-CAD, The Next Step In Computational Design

CDFAM Computational Design Symposium — Tokyo 2026 Darren Henry · PTC The race toward AI-generated CAD has largely focused on teaching models to produce geometry directly—from B-reps and meshes to feature histories and sketches. But what if we’re teaching AI the wrong language? This presentation explores an alternative paradigm: Text-to-Code-to-CAD, where large language models generate engineering logic rather than geometry itself. Instead of predicting low-level geometric entities, AI writes executable CAD code that captures design intent, constraints, parameters, and engineering relationships. The result is not merely a finished model, but a native, editable, and reusable engineering feature that integrates naturally into the design workflow. Using real-world examples, including AI-generated CAD features built through FeatureScript, this session demonstrates how modern language models can write, test, debug, and refine engineering code to create complex parametric designs while preserving the transparency and editability required for professional engineering. The discussion contrasts this approach with direct geometry generation and examines why engineering intent—not geometry—may be the more scalable representation for AI-assisted design. Attendees will leave with a new perspective on computational design: the future may not be AI replacing CAD, but AI leveraging programming languages purpose-built for engineering to accelerate design while keeping engineers firmly in control. Links Talk page with full transcript Watch the talk on YouTube Search this talk's transcript Cite this talk This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.designforam.com