Creative text is inherently subjective: any opinion can be equally valid, and there is no objective ground truth. The only way to quantify such subjectivity is to model the crowd of opinions that produces it. Large language model (LLM)-as-a-judge has emerged as a scalable substitute for human raters, but whether an LLM can stand in for the human crowd on AI creativity remains unexplored. We develop a decomposition framework (four main components, 11 sub-components) and a three-step rating procedure, applied identically to human raters and to different LLM judging conditions on AI-generated stories. Humans rate AI creativity with the same subjectivity that creativity research documents for human-authored creative work. Across three structural comparisons (disagreement on the same story including ranking; revision from impressionistic to reflective; sub-component weighting of the final rating), no LLM reproduces all three human patterns. We frame the contribution as a methodological framework plus a quantification of where LLM judges currently fall short of the human ratings in our study. We release all stories, ratings, and code at https://github.com/ku-nlp/creativity-evaluation-artifact.