Flattening Supply Chains: When do Technology Improvements lead to Disintermediation?
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Abstract (arXiv)
In the digital economy, technological innovations make it cheaper to produce high-quality content. For example, generative AI tools reduce costs for creators who develop content to be distributed online, but can also reduce production costs for the users who consume that content. These innovations can thus lead to disintermediation, since consumers may choose to use these technologies directly, bypassing intermediaries. To investigate when technological improvements lead to disintermediation, we study a game with an intermediary, suppliers of a production technology, and consumers. First, we show disintermediation occurs whenever production costs are too high or too low. We then investigate the consequences of disintermediation for welfare and content quality at equilibrium. While the intermediary is welfare-improving, the intermediary extracts all gains to social welfare and its presence can raise or lower content quality. We further analyze how disintermediation is affected by the level of competition between suppliers and the intermediary's fee structure. More broadly, our results take a step towards assessing how production technology innovations affect the survival of intermediaries and impact the digital economy.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 14 of its 27 references that are in the map. First posted on arXiv 28 Feb 2025.
Builds on
14 of 27 references.
References · 14
- 2022Modeling Content Creator Incentives on Algorithm-Curated Platformscited by 35
- 2018A Game-Theoretic Approach to Recommendation Systems with Strategic Content Providerscited by 33
- 2022Supply-Side Equilibria in Recommender Systemscited by 33
- 2020Optimizing Long-term Social Welfare in Recommender Systems: A Constrained Matching Approachcited by 32
- 2023How Bad is Top-K Recommendation under Competing Content Creators?cited by 27
- 2024Human vs. Generative AI in Content Creation Competition: Symbiosis or Conflict?cited by 13
- 2024The Impact of Large Language Models on Open-source Innovation: Evidence from GitHub Copilotcited by 13
- 2024Competition and Diversity in Generative AIcited by 13
- 2023Learning with Exposure Constraints in Recommendation Systemscited by 8
- 2024Braess's Paradox of Generative AIcited by 7
- 2025Selective Response Strategies for GenAIcited by 6
- 2023Matching of Users and Creators in Two-Sided Markets with Departurescited by 5
2 more
- 2024How to Strategize Human Content Creation in the Era of GenAI?cited by 4
- 2020Supply Network Formation and Fragilitycited by 2
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Citing papers per year: 2025: 1; 2026: 1.
2 papers.