I ve been puzzled by something: AI generation can produce an elaborate figurine, a cartoon character, even a convincing Super Mario — yet it can t reliably make a simple wedge ramp so my robot vacuum can climb a step. For context: I bought a Bambu P2S but can t model. I tried the describe it and get a model AIs — the output is unusable, you can t adjust it, it s never quite what I meant. I tried having an agent write Python to build geometry directly — it tops out at simple primitives. What finally worked: geometric decomposition. I break a complex part into ordered, grouped steps, describe each as a small spec, and let an agent execute them in Blender (via blender-mcp). That process turned out to abstract into a small engine — the key insight being it converts the 3D spatial reasoning LLMs are bad at, into the structured code they re good at. I wrote it up here: https://github.com/zhuchaokn/spec-3d-model My questions: - Why is functional part generation so much weaker than figurine/aesthetic generation? Is it data (no parametrized-CAD training sets), representation (mesh vs B-rep), or evaluation (nobody benchmarks does it print / is it watertight )? - Is turn 3D modeling into code for an LLM the right framing, or am I missing something better?