Arvid Kahl(@arvidkahl):This analogy is the best I've ever heard when it comes to how LLM-generated code looks, works, and what the role of developers is morphing into. Pre-AI codebases are sturdy, with scaffolding that allows for human comprehension and, at the same time, prevents hyper-optimization. We need to "get" the code, not just run it. Post-AI codebases let go of all pretense to be both accessible by interpreters/compilers/runtimes AND humans, and contain JUST what is needed to provide the expected outcomes. And that is what developers are realizing they must measure, review, and verify. Not the code, but the constraints and tests around it.

2026.10.08 13:25

This analogy is the best I've ever heard when it comes to how LLM-generated code looks, works, and what the role of developers is morphing into. Pre-AI codebases are sturdy, with scaffolding that allows for human comprehension and, at the same time, prevents hyper-optimization. We need to "get" the code, not just run it. Post-AI codebases let go of all pretense to be both accessible by interpreters/compilers/runtimes AND humans, and contain JUST what is needed to provide the expected outcomes. And that is what developers are realizing they must measure, review, and verify. Not the code, but the constraints and tests around it.

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