Sustaining engineering health through your AI transformation

​AI shifted your engineers from writing code to reviewing it, and the new shape of the work is wearing teams down. Review queues outpace reviewers, engineers carry responsibility for code they don't feel they own, and the same shift leaves some engineers energized and others further from the work they love.

​In our recent benchmark data, drawn from more than 8.1 million pull requests since January, AI-generated pull requests merge at 33% while human-written ones merge at 85%. That gap lands on the people doing the review.

​Join us on Thursday, October 8 10am PT and take away a plan for reading these signals in your own engineering data and acting on them before they show up as attrition. You'll learn:

• ​Why review overload, ownership without authorship, and an uneven shift make the strain structural rather than personal

• ​The quantitative and qualitative signals that flag the strain early, and where to find them in your own engineering data

• ​How to rightsize review, rebalance load with gitStream, and rebuild ownership norms for agent-authored code

• ​How the engineering health dashboard and developer surveys in LinearB surface these signals so you can act on them