The Importance of Knowing What Not to Write in Software Engineering
This article discusses how the role of software engineers is evolving as AI and large language models (LLMs) become more capable. The key point is that the value of an engineer lies increasingly in their judgment and ability to make decisions about what not to build, rather than just raw coding output.
Why it matters
This article highlights the evolving role of software engineers as AI capabilities advance, emphasizing the importance of judgment and decision-making over raw coding output.
Key Points
- 1Code output is no longer the main bottleneck in software projects
- 2With inexpensive code, the harder decisions are about what to build, not just how to build it
- 3Experienced engineers are valuable for their judgment in knowing which features to say no to
- 4Understanding the real-world constraints and deeply knowing the codebase is more important than rapid coding
- 5The skill that separates senior engineers is pattern recognition and architectural thinking, not just typing speed or prompting ability
Details
The article argues that as AI and LLMs make code generation cheaper and easier, the role of software engineers is shifting. The bottleneck is no longer just about the ability to write code, but about making the right decisions on what to build. Experienced engineers who can recognize hidden complexities, understand when to use third-party services instead of building in-house, and make tough decisions to cut features are becoming more valuable than those who can simply generate a lot of code. The article emphasizes that ownership and deep understanding of the codebase are critical, as 'vibe coding' that lacks this context will lead to issues down the line. Ultimately, the article concludes that the practice of engineering, involving judgment, pattern recognition, and a deep understanding of what works and what doesn't, is the skill that will separate senior engineers in the age of AI-assisted coding.
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