How to Strategize Human Content Creation in the Era of GenAI?

TL;DR

This paper models the competitive dynamics between human creators and GenAI in content generation, proposing algorithms for humans to maximize utility in different content domains, with theoretical guarantees and simulation validation.

Contribution

It introduces a novel dynamic competition model between humans and GenAI, providing polynomial algorithms with approximation guarantees for different content valuation scenarios.

Findings

In time-sensitive domains, no polynomial algorithm finds the optimal human strategy unless a major hypothesis fails.

A polynomial algorithm with a 1/2 approximation ratio is proposed for time-sensitive content.

In time-insensitive domains, an optimal polynomial-time strategy for humans is developed.

Abstract

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 the human over 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. Finally, we conduct simulations that demonstrate the advantage of our algorithms in comparison to a collection of baselines.