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An analysis of how AI-assisted autonomous software translation works, using LLMs as proposal generators paired with human-designed testing harnesses to validate semantic equivalence. The article argues current translations are economically viable because models sample from better code distributions than random generation, with correctness defined through differential testing against observable behavior.