If AI coding is lowering your code quality, you're not managing quality right
The author argues that AI-assisted coding does not inherently lower quality if managed through a rigorous, layered process. By utilizing spec-driven development and AI-assisted requirement reviews, teams can reduce bugs while increasing output.
Why it matters
As AI tools become standard in software engineering, shifting from 'blind' usage to structured quality management is becoming a critical skill for developers.
One common take on the coding agents that I see goes something like this: “Sure, AI helps you output more code, but won’t the quality suffer?”
It certainly will if you just blindly merge the PRs and send them off to prod. But if you take a thoughtful, layered approach to managing quality, I find that it’s possible to not just keep the number of bugs stable but actually reduce it—while still increasing the output by 2-2x.
Many of these defensive layers are pretty much the same as before Claude/Copilot/Codex/etc. (though they’re made easier now by AI), while others are new. Here’s a defensive setup that I’ve seen successfully used in practice, both on my team and elsewhere.
Get smarter about the news
Sign up free for a feed built around what you actually care about, Dive Deeper research on any story, and the full text of every article.
Create free accountAlready have an account? Sign in