What is a data scientist for when AI does the modeling?

The modeling was never the whole job, though it felt like the hard-won center of it. The model is an answer to a question. The harder work has always been asking the right question, knowing which data lies, and telling the difference between a number that is true and a number that matters. The machine can fit a curve. It cannot know that the data was collected wrong, or that the business is asking the wrong thing, or that a technically perfect answer would still lead people astray. That is your ground now. Not the mechanics, but the meaning. The model does the modeling; you do the thinking about whether it should be trusted and what it is really saying. So shift your weight. Spend less pride on the code and more on the questions. Next project, before you touch a model, write down what decision it will drive and how it could mislead. That framing is what a data scientist is for, and no model can do it for you.