The Hidden Corruption Tax of AI Delegation
Read OriginalThis article examines a Microsoft Research paper showing that frontier LLMs corrupt an average of 25% of document content in long delegated workflows, with errors that are sparse but severe and compound with document size and turn count. The author argues against abandoning AI tools, comparing the issue to onboarding junior developers without proper feedback loops. The proposed solution is to apply proven software engineering practices like linters and continuous integration to catch AI-induced errors, rather than reverting to manual coding.
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