AI speeds up code generation, but PR reviews can’t keep up. Teams ship more than ever, yet review queues grow and cycle times slow down. Learn what’s breaking in the process and how shifting verification to specs fixes the bottleneck.
AI code reviewed by AI can look correct, tests pass, style is fine and reviews approve, but still miss real requirements. The issue is not ability, it is circular validation. Both systems rely on the same patterns. Without a human-checked spec as the source of truth, correct can still be wrong.
AI coding is crazy fast, but without structure, things fall apart quickly. Templates turn one-off wins into something your whole team can actually rely on.
Learn how defining simple rules and goals for AI can turn it into a tool that cleans up code instead of copying old problems.
The response to my original piece about killing the code review was loud. This is my attempt to address the comments.
Compare Aviator, Cursor, GitHub Copilot, and Tabnine to find the right fit for your team’s workflow and scale.
Teams that treat AI as a code printer will drown in slop and debt. Those who become the best at specifying what they want and verifying that they got it will get the promised 10x productivity boost.
Agents already write code faster than teams can review it. The way through is to move the critical decisions upstream, into specs that humans review before any code is written and acceptance criteria that agents can verify after.
Learn how AI coding standards prevent codebase fragmentation by turning individual knowledge into shared, reusable guidelines for both human and AI contributors.
AI coding agents work in isolation. Multiplayer AI coding uses shared specs so teams and their agents stay aligned across sessions and roles.