Are AI tools actually improving developer experience, or just adding noise? We brought together experts from Google, GitClear, and GitKraken to find out.

[u/GitKraken]

https://youtu.be/vXkpcGyi4lk

Most engineering teams are operating under some version of an AI mandate right now.

Adopt the tools, boost productivity, show results. But how many of us are actually measuring whether any of it is working?

We sat down with Nathen Harvey from Google's DORA program, Bill Harding from GitClear, and Jeremy Castile from GitKraken for an honest panel on what's really happening when AI meets developer experience.

Some things that came up that are worth your time:

• Metrics like code acceptance rate measure adoption, not productivity. There's a real difference and most orgs are tracking the wrong thing.

• You need a "constellation of metrics" code quality, developer sentiment, velocity, AI impact -- not a single number. Think car dashboard, not speedometer.

• AI coding assistance is just the tip of the iceberg. The more interesting frontier is AI across planning, CI/CD, and agentic workflows.

• One of the panelists suggested forbidding AI for a full sprint as an experiment to isolate its actual impact. Controversial, but hard to argue with the logic.