A text prompt that “almost” makes a bracket is the fastest way to lose an engineer’s trust. I sit down with Peggy Xia, co-founder and CEO of gNucleus, to find out what, besides almost making a bracket, gNucleus was up to.

Peggy's background is not that of a typical AI startup. She has had a career in the real mechanics of engineering AI, CAD automation and simulation workflows with Siemens. He wrote the code for Solid Edge's Synchronous Technology. At Google, she developed YouTube's most widely used recommendation model, which emphasizes video quality.

We unpack why text-to-CAD goes wrong in ways that feel unforgivable to practitioners: the model is forced to guess when it has not seen enough domain data, and that guessing manifests as hallucinations such as misplaced fillets or incorrect features. Peggy explains the training stack in practical terms: pretraining, post-training with reinforcement-style scoring, and fine-tuning on customer-specific CAD data, so a model can become genuinely strong in a narrow domain such as automotive motors or assemblies. We also talk about multimodal AI, what “sketch to CAD” could look like, and why converting meshes or photogrammetry outputs into clean, manufacturable parametric models is still one of the hardest problems in the pipeline.

From generative design to computational design, we challenge the idea that a cool-looking shape equals an engineering solution. Accuracy, tolerances, benchmarking, token cost, and runtime matter, especially when you want production-ready results, not just visuals.

If you care about the future of CAD, CAE, and manufacturing-grade AI, this conversation will sharpen your mental model of what’s possible now and what still needs breakthroughs.