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.