taste has a scarcity problem. a model can learn to reproduce a beautiful aesthetic, then make it so common that it stops feeling like a distinctive choice. nothing has changed but the supply. if part of taste comes from finding something others haven’t found yet, a universally “tasteful” default starts consuming its own advantage. i wonder whether a model should notice when its best answer has become everyone’s answer. when distinctiveness matters, candidate ranking could combine quality and user preference with a style similarity penalty against a rolling sample of recent outputs. the hard part would be distinguishing aesthetic repetition from conventions that make a design usable. a choice could keep scoring well in isolation while ranking lower as the system makes its visual language more common. the model’s own output distribution would become part of the objective.