Key Takeaways:

More Code ≠ More Productivity

AI is increasing code output, but not overall efficiency. Gains in generation speed are being offset by downstream friction—debugging, test failures, and CI reruns—resulting in flat or even declining net productivity.

Hidden Costs Are Becoming the Dominant Constraint

The biggest impact of AI coding isn’t just speed—it’s cost and complexity. CI infrastructure strain, rising token consumption, and lack of cost visibility are introducing a new layer of operational overhead that most teams are not equipped to manage.

Organizations Are Not Aligned on the Problem

Developers, managers, and executives are experiencing AI-driven development differently. While leadership focuses on velocity and investment, developers are dealing with trust issues, rework, and self-imposed usage limits—creating a growing gap in perception, ownership, and governance.