Cursor requires engineering and design candidates to spend two days in the office working on a real project. This unorthodox process tests for agency and culture fit and has been maintained despite scaling to over 200 people, proving its value in assessing true on-the-job performance and mutual fit.
Counter to the adage that "startups shouldn't buy startups," Cursor successfully uses M&A as a core recruiting strategy. They acquire small, talented teams working on complementary problems, viewing acquisitions as a way to onboard the best people who happen to already be working on their own companies.
Cursor's rapid scaling caused them to become a double-digit percentage of their API providers' revenue, forcing those providers into major capacity and financing decisions. This illustrates that at extreme scale, success shifts from pure technical problem-solving to strategic relationship management and diversifying dependencies across multiple providers.
In the early AI coding wars, many startups pursued ambitious, "science fiction" goals like creating autonomous agents. Cursor's success came from a deliberately narrow focus: building a dramatically better user experience within the existing VS Code ecosystem, a market already matured by GitHub Copilot. This pragmatic approach gained them immediate traction.
Cursor's initial failed attempt at a 3D CAD tool highlights the "blind man and the elephant" problem. Despite interviewing engineers, the founders lacked an intuitive, first-hand feel for the user's daily workflow. This failure underscores that deep, personal domain experience is critical for founder-market fit, and cannot be replaced by secondhand research.
While starting with a focused editor, Cursor's CEO sees a larger opportunity to become the single AI coding provider for its customers. This involves a deliberate multi-product strategy to build a "bundle" of tools that addresses the entire software development lifecycle, from individual coding to team collaboration, creating a powerful ecosystem.