A team adopts a coding agent. A couple of engineers get noticeably faster. Everyone celebrates the velocity win.
Three weeks later, the same team is complaining that code review has become a bottleneck. Nobody connects the two.
The math nobody does upfront
If two engineers double their output using AI and the rest of the team keeps reviewing at the same pace as before, total code volume goes up without a matching increase in review capacity. Something has to give: either reviews get rushed, or the review queue backs up.
This is not a hypothetical. It is the most common side effect of concentrated AI adoption, and it is why some teams report that AI coding tools made things feel slower, not faster, even though individual engineers were clearly shipping more.
Heroic usage versus shared usage
"Heroic" usage is a single engineer using AI aggressively to personally ship more, without the rest of the team's habits changing. It produces a visible individual win and an invisible team cost, because the reviewer on the other end of that PR is now reading more code, written in a style specific to that one engineer's AI habits, without any change to how much time they have to review it.
Shared usage looks different. When the whole team follows a consistent, scoped workflow, PRs from different engineers start to look more alike in size and structure. A reviewer builds a consistent mental model of what to expect from an AI-assisted PR on this team, instead of relearning each engineer's personal approach.
What actually slows review down
It is rarely the AI-generated code itself that is the problem. It is usually one of these:
The diff is too large. AI makes it easy to generate a lot of code quickly, and without a habit of scoping changes narrowly, PRs balloon past what a reviewer can carefully read in one sitting.
The description does not explain intent. A reviewer trying to evaluate a large AI-assisted diff without a clear description of what changed and why has to reverse-engineer the intent from the code itself, which takes far longer than reading a good summary.
There is no shared expectation. When every engineer has a different personal relationship with the AI tool, reviewers cannot build intuition for what "normal" looks like, so every PR requires fresh scrutiny.
The fix is scoping, not slowing down
The instinct when review gets overloaded is often to add process: more required reviewers, stricter gates, mandatory waiting periods. That treats the symptom.
The actual fix is making AI-assisted PRs as scoped and reviewable as any other good PR:
- one clear change per PR, the same discipline you would want regardless of who or what wrote the code
- a description written or edited by the author explaining what changed and why, not just what the agent was asked to do
- flagging anything the reviewer should pay closer attention to, so the reviewer's effort goes where it is actually needed
This is not a new standard invented for AI. It is the same PR hygiene good teams already expect, applied consistently instead of skipped because "the AI wrote most of it."
What good looks like
Review time per PR stays roughly stable even as more AI-assisted work ships, because PRs are scoped consistently and reviewers know what to expect. If your team's review queue has gotten worse since AI adoption picked up, that is not evidence the tools are not working. It is evidence usage spread faster than the review workflow did.
If you want help fixing this without slowing the team back down, book a call. If I'm not the right person, I'll say so.