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As AI generates code faster than humans can write it, good taste in programming moves upstream—from judging individual lines to asking the right questions about what to build. The programmers who'll get most out of AI tools are those who already know what they want.
- Human taste during coding happens at typing speed, which constrains damage from bad decisions. AI removes that slowness, so taste becomes a reviewing filter—but reviewing is harder than writing and happens too late to catch fundamental design mistakes.
- The most important choices (what to build, how to split it, what to omit) happen before the model writes anything. A well-executed answer to the wrong question is still wrong.
- Models will get good at local code judgment (idioms, naming, structure) but can't learn domain-specific taste—knowing which complexity matters, how users will behave, where the system will change. That knowledge lives with the people who own the problem.
A staff engineer argues that AI coding tools are improving fast enough to make most software engineering jobs obsolete within a decade, and that past predictions of industry doom don't apply because those technologies never actually replaced programmers while AI likely will.
- AI agents are already better than many engineers at code maintenance and debugging, not just writing new code, which undermines the argument that humans will always be needed to clean up AI-generated code.
- Junior and mid-level engineers will face job losses first; senior staff engineers supervising AI agents may survive longer, but this represents a massive contraction in total engineering headcount, not job security.
- The Jevons effect (increased demand offsetting productivity gains) probably won't save the industry because AI can handle both code creation and maintenance equally well, unlike past automation waves that only eliminated specific tasks.