1 link tagged with all of: software-engineering + codebase-expertise + ai-coding + ai-replacement
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As AI models get better at coding, engineers need to stop pretending it's not happening and instead identify what they can do better than the models—mainly deep knowledge of their specific codebase and the ability to write clearly about technical decisions.
- AI models fail on "errors of ignorance" (not knowing which module to use, missing company conventions) and "errors of paranoia" (over-engineering simple systems), mistakes only caught by someone deeply familiar with the actual codebase and willing to confidently disagree with the AI.
- LLMs are paradoxically getting worse at writing even as they improve at coding, because writing quality can't be automatically verified and labs prioritize capability over communication—making human-written technical docs increasingly rare and valuable.
- Being a "meat proxy" who just runs prompts through AI and submits the output is worse than not using AI at all, since you're providing zero value and will be replaced once those workflows get automated into enterprise tools.