Poor AI Coding Outcomes Reflect Quality Management Failures, Not AI Limits

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A recently published opinion piece argues that declining code quality from AI-assisted development is a management and process problem rather than a flaw in the AI tools themselves. The author contends that teams using AI coding assistants must actively maintain and enforce quality standards to see good results. The piece suggests that without proper oversight, review processes, and clear expectations, AI-generated code will naturally reflect those gaps. The argument positions AI coding tools as amplifiers of existing engineering culture — good practices yield good output, and poor practices yield poor output.

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