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Before LLMs, CAD automation meant Python scripts, iLogic rules, and macros that broke every update. Now it means LLMs that write those scripts for you. Both approaches have the same failure mode: nobody tests the edge cases.
If you want a custom bracket for your 3D printer, a replacement knob, or a case for your Raspberry Pi and you don't want to learn Fusion 360 first, text-to-CAD is genuinely useful. Just check the dimensions before you print.
Text-to-CAD could be a great teaching tool. It could also produce a generation of engineers who can't sketch a rectangle without a prompt. The answer depends on how you use it.
Automotive design involves surfacing, Class A continuity, packaging constraints, crash analysis, and a review process that makes aerospace look relaxed. Text-to-CAD fits into approximately none of that.
Aerospace has certification requirements, material traceability, stress analysis dependencies, and tolerances measured in fractions of a thou. Text-to-CAD doesn't know any of that exists.
I prompted five parts with specific dimensions, exported the output, and measured everything in Fusion 360. The results were educational, in the way that watching someone parallel park into a fire hydrant is educational.
Neural CAD is the idea that neural networks can learn to produce CAD operations, not just final geometry. Autodesk is the loudest about it. The research is real. The production readiness is not.
The real prize in AI CAD isn't geometry. It's generating a parametric model with a feature tree you can actually edit. Almost nobody can do this yet.
Topology optimization was doing AI-adjacent geometry generation before text-to-CAD existed. It's still more useful for structural parts, and it's still a pain to manufacture.
Medical device design lives under FDA 21 CFR Part 820, ISO 13485, and a documentation burden that would make your feature tree weep. AI-generated geometry has no place in that workflow. Yet.
