StudioOpinion: In the AI era, are we keeping humans in the wrong part of the loop?
Original title: Coding in the AI era: What if we're keeping humans in the wrong part of the loop?
The author argues AI agents can already build features, write APIs, refactor codebases and fix bugs, so the real question is how humans should work with them. Instead of the popular "AI proposes, human approves" model, he suggests defining goals, constraints and coding conventions in plain language, letting the agent propose architecture, implement the plan and run unit, integration and end-to-end tests, static analysis and security checks, while humans focus on verifying actual behavior, challenging assumptions and testing edge cases. He notes LLMs draw on a broader range of technical material than any individual developer, so human review doesn't automatically yield better technical decisions, though security, data integrity and architectural choices still deserve human scrutiny.
The author argues for shifting human involvement from reviewing every AI change to testing and verification, offering a reusable division of labor for AI coding workflows.
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