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The Impact of Generative Artificial Intelligence on Market Equilibrium: Evidence from a Natural Experiment

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  • Kaichen Zhang
  • Zixuan Yuan
  • Hui Xiong

Abstract

Generative artificial intelligence (AI) exhibits the capability to generate creative content akin to human output with greater efficiency and reduced costs. This groundbreaking capability, however, has ignited a debate regarding its potential to displace human creators. In light of these discussions, this paper empirically investigates the impact of generative AI on market equilibrium, in the context of China's leading art outsourcing platform. We overcome the challenge of causal inference by identifying an unanticipated and sudden leak of an advanced image-generative AI as a natural experiment. This leak precipitated a notable reduction in the production costs of anime-style images compared to other genres, thereby providing a unique opportunity for difference-in-differences comparisons. Our analysis shows that the advent of generative AI led to a 64% reduction in average prices, yet it simultaneously spurred a 121% increase in order volume and a 56% increase in overall revenue. This growth is primarily driven by the rising demand for "low-end" personal orders, rather than commercial orders. Moreover, incumbent creators retain the majority of the market share and reap the most benefits of generative AI. Our research highlights the potential of generative AI to benefit all stakeholders across the platform economy, yielding both scholarly contributions and practical implications.

Suggested Citation

  • Kaichen Zhang & Zixuan Yuan & Hui Xiong, 2023. "The Impact of Generative Artificial Intelligence on Market Equilibrium: Evidence from a Natural Experiment," Papers 2311.07071, arXiv.org, revised Oct 2024.
  • Handle: RePEc:arx:papers:2311.07071
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    References listed on IDEAS

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    4. Vandith Pamuru & Warut Khern-am-nuai & Karthik Kannan, 2021. "The Impact of an Augmented-Reality Game on Local Businesses: A Study of Pokémon Go on Restaurants," Information Systems Research, INFORMS, vol. 32(3), pages 950-966, September.
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