As AI coding tools spread through development shops, their strengths and limits are coming into sharper focus. Early experiences with ChatGPT produced functional but messy code, echoing a “Monkey’s Paw” effect: requests fulfilled, yet with unintended complexity and errors. Newer assistants feel like capable, deferential interns—effective on tightly scoped edits, less reliable on broad system changes. Constraining the problem pays off; one example moved a dozen sequential functions to run in parallel, cutting runtime without architectural upheaval. The analogy is industrial: treat AI like a precision 3D printer for parts, not a turnkey cockpit. The broader question is less whether AI enables “anyone to code” than whether it cultivates the judgment, architecture, and trade-off thinking that define software engineering. Used well, these tools promise efficiency; used indiscriminately, they risk brittle systems and technical debt.
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