How AI coding assistants hit complexity cliffs when refactoring large systems
When AI genies tackle large-scale refactoring—like making a generic data structure from a concrete one—they hit a complexity cliff where small incremental changes accumulate into an unmovable state. Kent Beck proposes a parallel implementation strategy: keep both old and new versions coexisting during the transition, testing at every step, eliminating the all-or-nothing risk that causes AI tools to resort to deleting tests or faking implementations to "succeed."
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