Software engineering teams are experiencing a decline in code review efficiency as the volume of pull requests (PRs) has grown alongside the widespread use of automated AI coding agents. While these tools are intended to assist developers, their integration has led to an influx of verbose, AI-generated commentary that often forces human reviewers to spend excessive time parsing through filler content. This trend has resulted in a communication breakdown where team members struggle to maintain context, sometimes finding it faster to ping colleagues directly rather than relying on automated review threads that provide low-quality, redundant, or incorrectly confident feedback.

The core issue identified by leadership is the depletion of human attention, which has become a scarce and valuable resource. Christine Seeman, the author, notes that her company's CEO introduced the concept of "Return-on-Attention" (ROA) to address this problem. ROA functions as a metric evaluating whether the time cost of reading a piece of communication is justified by the value it provides. Instances of poor ROA include technical documents filled with repetition or simple code changes, such as a ten-character update to a configuration file, accompanied by a 1,430-character AI-generated description. This dynamic mirrors the "slop grenade" phenomenon, where massive, unedited AI outputs are inserted into human workflows, shifting the burden of effort from the writer to the reader.

Beyond the strain on reviewers, developers report that over-reliance on AI models has weakened individual workflow discipline and thinking processes. There is a tendency to delegate problem-solving to models prematurely rather than performing the initial analysis. Furthermore, AI agents often inject unprompted commentary into codebases—such as narrating design decisions—which creates obsolete noise that future maintainers must manage. The consensus is that while automated agents can provide value by catching edge cases or offering fresh perspectives, their utility is negated when they consume more attention than they save. To mitigate these issues, developers are encouraged to treat all automated review comments as their own personal contributions. By applying the standard of editing and verifying every comment for length, tone, and accuracy before submission, teams can ensure that automated tools remain a benefit to collaborative code review rather than a hindrance to productivity.

Software engineering teams are experiencing a decline in code review efficiency as the volume of pull requests (PRs) has grown alongside the widespread use of automated AI coding agents. While these tools are intended to assist developers, their integration has led to an influx of verbose, AI-generated commentary that often forces human reviewers to spend excessive time parsing through filler content. This trend has resulted in a communication breakdown where team members struggle to maintain context, sometimes finding it faster to ping colleagues directly rather than relying on automated review threads that provide low-quality, redundant, or incorrectly confident feedback.

The core issue identified by leadership is the depletion of human attention, which has become a scarce and valuable resource. Christine Seeman, the author, notes that her company's CEO introduced the concept of "Return-on-Attention" (ROA) to address this problem. ROA functions as a metric evaluating whether the time cost of reading a piece of communication is justified by the value it provides. Instances of poor ROA include technical documents filled with repetition or simple code changes, such as a ten-character update to a configuration file, accompanied by a 1,430-character AI-generated description. This dynamic mirrors the "slop grenade" phenomenon, where massive, unedited AI outputs are inserted into human workflows, shifting the burden of effort from the writer to the reader.

Beyond the strain on reviewers, developers report that over-reliance on AI models has weakened individual workflow discipline and thinking processes. There is a tendency to delegate problem-solving to models prematurely rather than performing the initial analysis. Furthermore, AI agents often inject unprompted commentary into codebases—such as narrating design decisions—which creates obsolete noise that future maintainers must manage. The consensus is that while automated agents can provide value by catching edge cases or offering fresh perspectives, their utility is negated when they consume more attention than they save. To mitigate these issues, developers are encouraged to treat all automated review comments as their own personal contributions. By applying the standard of editing and verifying every comment for length, tone, and accuracy before submission, teams can ensure that automated tools remain a benefit to collaborative code review rather than a hindrance to productivity.