Doubts that current models can truly handle novel problems or large codebases, concerns about reconstituting training data only
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Skepticism toward AI coding often centers on the fear that models merely reconstitute training data rather than solving novel problems, potentially trapping long-lived codebases in a "perpetual MVP" state. Critics argue that while AI excels at routine tasks like scrapers, it frequently struggles with the rigorous demands of reliability, legibility, and the specialized complexity found in large-scale engineering projects. However, this view is challenged by instances of emergent generalization where models transfer knowledge to unfamiliar domains, suggesting capabilities that go beyond simple mimicry. Ultimately, the discourse reflects a sharp tension between those dismissing the technology as marketing hype and those navigating a shifting landscape where AI-led engineering may eventually replace traditional human-AI collaboration.
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