  Creativity serves as a cornerstone for societal progress and innovation. With
the rise of advanced generative AI models capable of tasks once reserved for
human creativity, the study of AI's creative potential becomes imperative for
its responsible development and application. In this paper, we prove in theory
that AI can be as creative as humans under the condition that it can properly
fit the data generated by human creators. Therefore, the debate on AI's
creativity is reduced into the question of its ability to fit a sufficient
amount of data. To arrive at this conclusion, this paper first addresses the
complexities in defining creativity by introducing a new concept called
Relative Creativity. Rather than attempting to define creativity universally,
we shift the focus to whether AI can match the creative abilities of a
hypothetical human. The methodological shift leads to a statistically
quantifiable assessment of AI's creativity, term Statistical Creativity. This
concept, statistically comparing the creative abilities of AI with those of
specific human groups, facilitates theoretical exploration of AI's creative
potential. Our analysis reveals that by fitting extensive conditional data
without marginalizing out the generative conditions, AI can emerge as a
hypothetical new creator. The creator possesses the same creative abilities on
par with the human creators it was trained on. Building on theoretical
findings, we discuss the application in prompt-conditioned autoregressive
models, providing a practical means for evaluating creative abilities of
generative AI models, such as Large Language Models (LLMs). Additionally, this
study provides an actionable training guideline, bridging the theoretical
quantification of creativity with practical model training.
