Pattern #1 in the Four Patterns of AI Native Development series. The trend is clear: the more AI generates, the bigger the pull requests become. Developers are shifting from code producers to code reviewers and decision-makers.
AI produces, you review
What began with simple IDE autocompletion has evolved into AI generating complete application scaffolding. Developers now spend less time writing and more time validating AI-generated code — mirroring traditional peer code review, but at a different scale.
Cognitive load and acceptance fatigue
Reviewing massive AI-generated changes creates mental strain. Three approaches help: enhanced code diffs with contextual annotations, breaking reviews into smaller chunks, and using diagrams to visualize change impacts. The concept of “Moldable Development” environments — tools that adapt to specific review tasks — points toward future solutions.
A critical risk: developers approving code without full understanding due to review volume. Research shows junior developers and weekend reviewers are more likely to accept code they don’t comprehend.
Situational awareness
As AI auto-commits and potentially auto-deploys, observability becomes crucial. Developers must understand systems during failures to evaluate AI-suggested solutions.
The manager role
Ultimately, developers become architects and decision-makers, setting rules and creating safe experimentation environments — fundamentally managerial responsibilities.
Full article at tessl.io. Part of the Four Patterns of AI Native Development series.