Code Governance for AI-Native Software Development

Chapter 1. The AI Code Quality Crisis

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A decade ago, writing during a similarly rapid technological inflection point as the one we find ourselves navigating, I wrote a sentence I have never found reason to take back:

Big design up front, exhaustive documentation, architectural review boards, and lengthy regression testing cycles all fly in the face of the speed we’re seeking. Of course, all of these practices were created with good intentions. Unfortunately, none of them have provided consistently measurable improvements in the number of defects that make it into production.

I penned those words in 2015, in Migrating to Cloud-Native Application Architectures (at that time, the first book having the words “cloud-native” in its title). I believed it then. I believe it more now. And for ten years it sat as one of those true things our industry could afford to nod at and then ignore—because something was quietly compensating for it.

Latency. Human latency.

We wrote code at human speed, which is to say slowly, and the slowness was a quiet compensation. ...