The hidden cost of automated PR noise

Automated review bots are turning our pull requests into a battlefield of "confident wrongness." What started as a way to scale our team's throughput has devolved into a bizarre cycle where a remote bot leaves a hallucinated comment, only to have a developer's local LLM agent try to argue the logic back. Is the time we save on manual reviews actually being eaten up by the friction of managing these digital arguments?

We are facing a massive deficit in what my CEO calls "Return on Attention" (ROA). The assumption in most dev workflows is that LLM-generated text is free, but that's a fallacy. Producing a thousand characters of fluff is cheap; consuming it is incredibly expensive. When a PR for a single-line fix comes with a massive, citation-heavy wall of text that fails to explain the actual why, the ROA hits zero. You end up spending more mental energy parsing the "AI filler" than you would have spent just auditing the raw code.

As an indie dev, I'm hyper-aware of cognitive load. If your tooling is increasing the amount of noise a teammate has to filter through, you aren't increasing productivity—you're just increasing the tax on their focus. If we use tools like Cursor or Claude Code, why aren't we aiming for higher precision instead of just higher volume?

The goal shouldn't be to offload the thinking entirely, but to use these tools to sharpen the context. We need to stop treating attention as an infinite resource and start guarding it.

If you want to see the breakdown of how this attention tax works, the source is here:

https://christine-seeman.com/return-on-attention-ai-code-review/

For anyone building in this niche, the challenge is moving beyond the "verbose bot" era. We need tools that respect the developer's time, not just tools that can write a lot of text.

# Stop checking for fluff, start checking for logic
grep -v "LLM-generated" pull_request_logs.txt