[2511.04427] Speed at the Cost of Quality: How Cursor AI Increases Short-Term Velocity and Long-Term Complexity in Open-Source Projects

An empirical research paper using a difference-in-differences causal design to measure the effect of Cursor AI adoption on open-source GitHub projects. The study finds that Cursor adoption causes a statistically significant but short-lived boost in development velocity, while simultaneously producing a substantial and persistent increase in static analysis warnings and code complexity. Panel GMM estimation further shows these quality degradations are the primary driver of long-term velocity slowdown. The authors conclude that quality assurance is a critical bottleneck for AI coding tool adopters and should be a first-class concern in agentic AI workflow design.