Human vs. Generative AI in Content Creation Competition: Symbiosis or Conflict?
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Abstract (arXiv)
The advent of generative AI (GenAI) technology produces transformative impact on the content creation landscape, offering alternative approaches to produce diverse, high-quality content across media, thereby reshaping online ecosystems but also raising concerns about market over-saturation and the potential marginalization of human creativity. Our work introduces a competition model generalized from the Tullock contest to analyze the tension between human creators and GenAI. Our theory and simulations suggest that despite challenges, a stable equilibrium between human and AI-generated content is possible. Our work contributes to understanding the competitive dynamics in the content creation industry, offering insights into the future interplay between human creativity and technological advancements in GenAI.
Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 Feb 2024.
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Citing papers per year: 2024: 1; 2025: 8; 2026: 4.
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- 2025Game Theory Meets Large Language Models: A Systematic Survey with Taxonomy and New Frontierscited by 14
- 2025Towards Transparent and Incentive-Compatible Collaboration in Decentralized LLM Multi-Agent Systems: A Blockchain-Driven Approachcited by 11
- 2024How to Strategize Human Content Creation in the Era of GenAI?cited by 4
- 2025Foundations of GenIRcited by 1
- 2025Flattening Supply Chains: When do Technology Improvements lead to Disintermediation?cited by 1
- 2025Heterogeneous Data Game: Characterizing the Model Competition Across Multiple Data Sourcescited by 1
- 2025Data Sharing with a Generative AI Competitorcited by 0
- 2025Entry Barriers in Content Marketscited by 0
- 2025Human-AI Collaboration Mechanism Study on AIGC Assisted Image Production for Special Coveragecited by 0
- 2026LLM Active Alignment: A Nash Equilibrium Perspectivecited by 0
- 2026Market Games for Generative Models: Equilibria, Welfare, and Strategic Entrycited by 0
- 2026Content Creation with Spillovers: An Incentive Design Approachcited by 0
- 2026Guiding Worker Self-Selection in Crowdsourcing Contests: An LLM-Augmented Algorithmic Approach7 Sep 2026
- 2026Content Creation with Spillovers: An Incentive Design Approach15 Mar 2026
- 2026Market Games for Generative Models: Equilibria, Welfare, and Strategic Entry19 Feb 2026
- 2026LLM Active Alignment: A Nash Equilibrium Perspective6 Feb 2026
- 2025Human-AI Collaboration Mechanism Study on AIGC Assisted Image Production for Special Coverage14 Dec 2025
- 2025Towards Transparent and Incentive-Compatible Collaboration in Decentralized LLM Multi-Agent Systems: A Blockchain-Driven Approach20 Sep 2025
- 2025Entry Barriers in Content Markets2 Sep 2025
- 2025Data Sharing with a Generative AI Competitor18 May 2025
- 2025Heterogeneous Data Game: Characterizing the Model Competition Across Multiple Data Sources12 May 2025
- 2025Flattening Supply Chains: When do Technology Improvements lead to Disintermediation?28 Feb 2025
- 2025Game Theory Meets Large Language Models: A Systematic Survey with Taxonomy and New Frontiers13 Feb 2025
- 2025Foundations of GenIR6 Jan 2025
1 more
- 2026LLM Active Alignment: A Nash Equilibrium Perspectivecited by 0
- 2026LLM Active Alignment: A Nash Equilibrium Perspective6 Feb 2026
- 2025Game Theory Meets Large Language Models: A Systematic Survey with Taxonomy and New Frontierscited by 14
- 2024How to Strategize Human Content Creation in the Era of GenAI?cited by 4
- 2025Foundations of GenIRcited by 1
- 2025Flattening Supply Chains: When do Technology Improvements lead to Disintermediation?cited by 1
- 2025Heterogeneous Data Game: Characterizing the Model Competition Across Multiple Data Sourcescited by 1
- 2025Data Sharing with a Generative AI Competitorcited by 0
- 2025Entry Barriers in Content Marketscited by 0
- 2025Human-AI Collaboration Mechanism Study on AIGC Assisted Image Production for Special Coveragecited by 0
- 2026Market Games for Generative Models: Equilibria, Welfare, and Strategic Entrycited by 0
- 2026Content Creation with Spillovers: An Incentive Design Approachcited by 0
- 2026Guiding Worker Self-Selection in Crowdsourcing Contests: An LLM-Augmented Algorithmic Approachcited by 0
- 2026Guiding Worker Self-Selection in Crowdsourcing Contests: An LLM-Augmented Algorithmic Approach7 Sep 2026
- 2026Content Creation with Spillovers: An Incentive Design Approach15 Mar 2026
- 2026Market Games for Generative Models: Equilibria, Welfare, and Strategic Entry19 Feb 2026
- 2025Human-AI Collaboration Mechanism Study on AIGC Assisted Image Production for Special Coverage14 Dec 2025
- 2025Entry Barriers in Content Markets2 Sep 2025
- 2025Data Sharing with a Generative AI Competitor18 May 2025
- 2025Heterogeneous Data Game: Characterizing the Model Competition Across Multiple Data Sources12 May 2025
- 2025Flattening Supply Chains: When do Technology Improvements lead to Disintermediation?28 Feb 2025
- 2025Game Theory Meets Large Language Models: A Systematic Survey with Taxonomy and New Frontiers13 Feb 2025
- 2025Foundations of GenIR6 Jan 2025
- 2024How to Strategize Human Content Creation in the Era of GenAI?7 Jun 2024