AI coding tools can produce a striking burst of productivity — but the initial surge may not last. A Carnegie Mellon study of public repositories adopting Cursor found a three- to five-fold increase in lines added during the first month, with the gains dissipating after roughly two months. The study, which used SonarQube (Sonar’s core platform) to measure code quality, also found that static analysis warnings rose by 30% and code complexity by 41%. The short-term velocity boost therefore comes with a persistent increase in technical debt.

Tom Howlett, Sonar’s Director of AI Engineering Engagement, tells The New Stack that while coding has sped up, verification, which many teams still rely on manual code review to do, hasn’t kept pace. If developers generate 50 times more code while relying on the same review process, time spent on code review rises and becomes a bottleneck, Howlett tells The New Stack.

There’s another complication. Agents don’t always know how to make the smallest change needed to deliver a feature. Without sufficient context about the codebase, they can make assumptions, generate unnecessary code, and add to technical debt. As Howlett puts it, you end up with more complex, verbose code, which makes the problem worse.

Explore how SonarQube can help your organization adopt agentic software development with confidence.

The lesson for engineering leaders is that faster code generation doesn’t necessarily mean greater value. Teams also need confidence that the code delivers the intended outcome and meets their standards. Sonar describes this through its code confidence formula : intent x guiderails x verification x evidence.

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