There is a moment in the adoption curve of any powerful tool when the skill of using it becomes widely distributed. The tool becomes table stakes. At that point, the competitive advantage shifts — not to the heaviest users, but to the people who have developed the judgment to know when the tool is the right instrument for the job and when it isn't.

We are approaching that moment with AI. The ability to produce a well-formatted document, a competent marketing email, or a structured analysis using a language model is no longer a differentiator. Millions of people can do it. The differentiator — increasingly, and over the next three to five years almost certainly — is knowing which tasks you should hand to the tool and which tasks you should keep for yourself.

This is not a sentimental argument about preserving human work. It's a practical one about output quality. AI produces its worst results in exactly the situations where most people reach for it first: when they don't know what they want to say, when the subject requires recent information the model wasn't trained on, or when the task depends on a specific relationship or institutional context the model has no access to. Those are not AI problems. They are application judgment problems.