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

• URL: http://arxiv.org/abs/2311.07071v2
• Date: Thu, 10 Oct 2024 10:19:55 GMT
• Title: The Impact of Generative Artificial Intelligence on Market Equilibrium: Evidence from a Natural Experiment
• Authors: Kaichen Zhang, Zixuan Yuan, Hui Xiong,
• Abstract summary: Generative artificial intelligence (AI) exhibits the capability to generate creative content akin to human output with greater efficiency and reduced costs. This paper empirically investigates the impact of generative AI on market equilibrium, in the context of China's leading art outsourcing platform. 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.
• Score: 19.963531237647103
• License:
• 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.

Related papers

• Raising the Stakes: Performance Pressure Improves AI-Assisted Decision Making [57.53469908423318]
  We show the effects of performance pressure on AI advice reliance when laypeople complete a common AI-assisted task. We find that when the stakes are high, people use AI advice more appropriately than when stakes are lower, regardless of the presence of an AI explanation.
  arXiv  Detail & Related papers  (2024-10-21T22:39:52Z)
• The Influence of Generative AI on Content Platforms: Supply, Demand, and Welfare Impacts in Two-Sided Markets [3.4039202831583903]
  This paper explores how generative artificial intelligence affects online platforms where both human creators and AI generate content. We develop a model to understand how generative AI changes supply and demand, impacts traffic distribution, and influences social welfare.
  arXiv  Detail & Related papers  (2024-10-17T00:14:12Z)
• Hype, Sustainability, and the Price of the Bigger-is-Better Paradigm in AI [67.58673784790375]
  We argue that the 'bigger is better' AI paradigm is not only fragile scientifically, but comes with undesirable consequences. First, it is not sustainable, as its compute demands increase faster than model performance, leading to unreasonable economic requirements and a disproportionate environmental footprint. Second, it implies focusing on certain problems at the expense of others, leaving aside important applications, e.g. health, education, or the climate.
  arXiv  Detail & Related papers  (2024-09-21T14:43:54Z)
• Measuring Human Contribution in AI-Assisted Content Generation [68.03658922067487]
  This study raises the research question of measuring human contribution in AI-assisted content generation. By calculating mutual information between human input and AI-assisted output relative to self-information of AI-assisted output, we quantify the proportional information contribution of humans in content generation.
  arXiv  Detail & Related papers  (2024-08-27T05:56:04Z)
• The Narrow Depth and Breadth of Corporate Responsible AI Research [3.364518262921329]
  We show that the majority of AI firms show limited or no engagement in this critical subfield of AI. Leading AI firms exhibit significantly lower output in responsible AI research compared to their conventional AI research. Our results highlight the urgent need for industry to publicly engage in responsible AI research.
  arXiv  Detail & Related papers  (2024-05-20T17:26:43Z)
• Exploration with Principles for Diverse AI Supervision [88.61687950039662]
  Training large transformers using next-token prediction has given rise to groundbreaking advancements in AI. While this generative AI approach has produced impressive results, it heavily leans on human supervision. This strong reliance on human oversight poses a significant hurdle to the advancement of AI innovation. We propose a novel paradigm termed Exploratory AI (EAI) aimed at autonomously generating high-quality training data.
  arXiv  Detail & Related papers  (2023-10-13T07:03:39Z)
• Human-AI Interactions and Societal Pitfalls [1.6413583085553642]
  When working with generative artificial intelligence (AI), users may see productivity gains, but the AI-generated content may not match their preferences exactly. We show that the interplay between individual-level decisions and AI training may lead to societal challenges. A solution to the homogenization and bias issues is to improve human-AI interactions, enabling personalized outputs without sacrificing productivity.
  arXiv  Detail & Related papers  (2023-09-19T09:09:59Z)
• VerifAI: Verified Generative AI [22.14231506649365]
  Generative AI has made significant strides, yet concerns about its accuracy and reliability continue to grow. We propose that verifying the outputs of generative AI from a data management perspective is an emerging issue for generative AI. Our vision is to promote the development of verifiable generative AI and contribute to a more trustworthy and responsible use of AI.
  arXiv  Detail & Related papers  (2023-07-06T06:11:51Z)
• Fairness in AI and Its Long-Term Implications on Society [68.8204255655161]
  We take a closer look at AI fairness and analyze how lack of AI fairness can lead to deepening of biases over time. We discuss how biased models can lead to more negative real-world outcomes for certain groups. If the issues persist, they could be reinforced by interactions with other risks and have severe implications on society in the form of social unrest.
  arXiv  Detail & Related papers  (2023-04-16T11:22:59Z)
• Building Bridges: Generative Artworks to Explore AI Ethics [56.058588908294446]
  In recent years, there has been an increased emphasis on understanding and mitigating adverse impacts of artificial intelligence (AI) technologies on society. A significant challenge in the design of ethical AI systems is that there are multiple stakeholders in the AI pipeline, each with their own set of constraints and interests. This position paper outlines some potential ways in which generative artworks can play this role by serving as accessible and powerful educational tools.
  arXiv  Detail & Related papers  (2021-06-25T22:31:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.