Generative AI (GenAI) will have significant impact on content creation
platforms. In this paper, we study the dynamic competition between a GenAI and
a human contributor. Unlike the human, the GenAI's content only improves when
more contents are created by human over the time; however, GenAI has the
advantage of generating content at a lower cost. We study the algorithmic
problem in this dynamic competition model about how the human contributor can
maximize her utility when competing against the GenAI for content generation
over a set of topics. In time-sensitive content domains (e.g., news or pop
music creation) where contents' value diminishes over time, we show that there
is no polynomial time algorithm for finding the human's optimal (dynamic)
strategy, unless the randomized exponential time hypothesis is false.
Fortunately, we are able to design a polynomial time algorithm that naturally
cycles between myopically optimizing over a short time window and pausing and
provably guarantees an approximation ratio of $\frac{1}{2}$. We then turn to
time-insensitive content domains where contents do not lose their value (e.g.,
contents on history facts). Interestingly, we show that this setting permits a
polynomial time algorithm that maximizes the human's utility in the long run.

通过研究人工智能生成模型与人类贡献者之间的竞争，本文探讨了动态竞争模型中人类贡献者如何在一系列主题的内容生成中最大化效益。在时间敏感的内容领域中，研究表明在没有多项式时间的优化策略的情况下，人类的最优策略无法找到，但通过设计一个多项式时间算法可以保证近似性能。而在时间不敏感的内容领域中，我们展示了一种可以在长期内最大化人类效益的多项式时间算法。