AI coding assistant Cursor now indexes large codebases in 21 seconds instead of over four hours. The trick: instead of building an index from scratch for each new user, Cursor reuses existing indices from team members. According to the company's blog post, copies of the same codebase within a team are 92 percent identical on average, making this approach highly efficient.
A Cursor study found that the semantic search enabled by these indices improves AI response accuracy by 12.5 percent. The technology relies on Merkle trees - a data structure using cryptographic hashes - to ensure users only see code they're authorized to access. For typical projects, wait times for the first search query drop from nearly 8 seconds to just 525 milliseconds. The startup behind Cursor shipped version 2.0 with its own coding model in October 2025 and now generates around $500 million in annual revenue.
