Laurie Voss describes a growing mismatch between rapid AI code generation and the much slower process of establishing whether a change should ship. He contrasts code-output growth with delivery gains and argues that asking people to review ever larger diffs is neither scalable nor sustainable.
Laurie Voss distinguishes passing tests from being safe and maintainable enough to merge. He discusses benchmark gaps, automated reviewers' false-positive filtering and feedback from human acceptance, while warning that these mechanisms still depend on what the review system can observe and evaluate.
Laurie Voss examines attempts to remove direct human review and argues that the work often moves into human-designed tests, harnesses and oversight instead. He highlights security, prompt-injection and untested-context risks, then recommends encoding organizational standards and domain knowledge into a reliable review system and monitoring behavior after deployment.
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