1 min lesson
The AI-native rule cuts both ways
Tell someone how to act on this idea: "Expect to defend your own reasoning live in the craft screen, then show AI-native impact in the practical build."
Step 1 of 2
Cursor builds an AI coding tool and prizes candidates who instinctively apply AI, agents and MCPModel Context Protocol. A standard that lets an AI agent pull in context from outside the repo, like Jira tickets or internal docs. Press Enter for the full definition. to real GTM work. Yet in engineering screens they famously bar AI assistance to test raw skill. Expect to defend your own reasoning live in the craft screen, then show AI-native impact in the practical build. Confirm the AI policy per stage rather than assuming.
Treat the “paid project” signal seriously. A company that pays you for the round expects production-grade output and real time investment and it tells you the build is where the offer is decided.
Underneath every stage is the same working loop Cursor's own growth team runs on real GTM problems: problem → context-dump → iterate on the approach → test on a few examples → refine. You stay the domain expert who knows what good output looks like and where the edge cases hide; the agent does the repetitive execution. Validate or invalidate fast against your gut, adjust the weights and guidelines, and only extend to a fuller workflow once each small piece feels right.
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Full explanation
The Knowledge-Work Loop Under the Build
Interactive diagram. Step through it with the Next and Previous controls below, or Tab to a region to read its detail.
The loop Cursor's GTM team runs on real problems - you stay the expert, the agent executes. It's what the practical project quietly grades.
George on Cursor's growth team, on why this loop matters for a GTM engineer:
“you go from I know how this should work to I have a tool that does it potentially in an afternoon.”
QWhich stage of the Cursor GTM Engineer loop most decides the offer and what does the “paid project” framing imply?