Before building an AI code editor, the Cursor founders were working on a copilot for CAD. The idea was to help mechanical engineers in tools like SolidWorks and Fusion 360 by predicting the next geometry change while designing a part.
They explored two directions:
- A pure 3D approach.
- A text-based approach, where CAD actions were converted into sequences of method calls.
That second path sounded clever, but it was very hard in practice. The model had to do more than predict the next action. It also had to mentally reconstruct the geometry from a sequence of operations, which is difficult because CAD kernels and 3D geometry are complex.
The bigger issue was data. There was far less CAD data on the open internet than code, so training useful models was much harder. The science also was not ready yet. Pretrained models were still weak for 3D tasks, and the team had to do a lot of scraping and data work just to improve performance.
They did many user interviews with CAD users, but later realized that interviews were not enough.
What is interesting is that the project was not wasted. While working on CAD AI, they learned how to train large models, run inference at scale, and build infrastructure around behavior cloning and model deployment. Those lessons became very useful later.
The real reason for the pivot was simple: they were more excited about coding than mechanical engineering. They were programmers themselves, believed AI would reshape software development, and decided to work on the domain they understood best.
Well, sometimes the first idea (2, 3, 4, 5, 6, 7, 8, ha-ha) fails, but the skills, infrastructure, and clarity you gain from it become the foundation for the future. Yes?
Image - Flux Schnell
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