How long before we stop reading the code?

In the face of AI-generated code outpacing human code review capabilities, the bottleneck in software development is shifting from reviewing code to checking the intent before code is written. AI tools have increased the volume of code being merged, but they have also increased review times, creating a need for a more efficient system. The current reliance on AI for code reviews is flawed due to issues like non-determinism, missing intent, and duplicate blind spots, leading to ineffective quality gates. To address this, the proposed solution involves moving human checkpoints upstream to focus on intent, leveraging deterministic methods and AI for execution tests, and using Large Language Models (LLMs) for judgments where necessary. This approach emphasizes verifying the intent and behavior of the code against predetermined criteria, thus ensuring quality without overburdening the review process. This shift in methodology prioritizes understanding the problem and constraints before coding, ultimately aiming for a more efficient and reliable software development workflow.

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