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Where Engineering Work Goes When AI Writes the Code
AI can make code much faster to produce, but that does not mean the whole software development process speeds up at the same rate. Much of the work moves elsewhere – into review, rework and the judgment required to decide which changes are worth taking forward. This article looks at what that shift means for engineering productivity and how teams should measure it. In the time it used to take me to write and merge one pull request, AI agents now produce about ten candidate changes, and I merge roughly six. Code has become cheap to produce, but software delivery hasn't sped up by nearly as much. Much of the effort has shifted to reviewing changes and deciding which ones to keep, and that work is easy to miss when teams count code output. Code Is Cheaper, but Delivery Is Still a Pipeline A change still has to be reviewed, tested and deployed. Sometimes it also has to be fixed after it ships. AI has shortened the coding step, but the other steps still determine whether the whole pipeline gets faster. Developers' own sense of speed can be a poor guide, so it helps to check that impression against tracked data. In METR's randomized controlled trial from 2025, sixteen experie...
