# The Economics of AI Foundation Models: Openness, Competition, and Governance

arXiv: https://arxiv.org/abs/2510.15200 · HTML: https://mlsanity.com/paper/2510.15200/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 57 references that are in the map.
- First posted on arXiv 17 Oct 2025.

## Builds on (most cited first, 6 of 6)

- [A Note on Shumailov et al. (2024): `AI Models Collapse When Trained on Recursively Generated Data'](https://mlsanity.com/paper/2410.12954/) (2024)
- [On the Societal Impact of Open Foundation Models](https://mlsanity.com/paper/2403.07918/) (2024)
- [The 2024 Foundation Model Transparency Index](https://mlsanity.com/paper/2407.12929/) (2024)
- [Human-AI Interactions and Societal Pitfalls](https://mlsanity.com/paper/2309.10448/) (2023)
- [Generative AI and Copyright: A Dynamic Perspective](https://mlsanity.com/paper/2402.17801/) (2024)
- [Modeling the Economic Impacts of AI Openness Regulation](https://mlsanity.com/paper/2507.14193/) (2025)

## Built on by (most cited first, 4 of 4)

- [Computational Arbitrage in AI Model Markets](https://mlsanity.com/paper/2603.22404/) (2026)
- [From Product Search to Preference Articulation: The Economics of Agentic Commerce](https://mlsanity.com/paper/2608.08395/) (2026)
- [Grounded Scaling: Why Agentic AI Needs Deterministic Environments](https://mlsanity.com/paper/2606.22495/) (2026)
- [Optimal Liability Design for Medical AI](https://mlsanity.com/paper/2608.03114/) (2026)

## Abstract (arXiv)

The strategic choice of model "openness" has become a defining issue for the foundation model (FM) ecosystem. While this choice is intensely debated, its underlying economic drivers remain underexplored. We construct a two-period game-theoretic model to analyze how openness shapes competition in an AI value chain, featuring an incumbent developer, a downstream deployer, and an entrant developer. Openness exerts a dual effect: it amplifies knowledge spillovers to the entrant, but it also enhances the incumbent's advantage through a "data flywheel effect," whereby greater user engagement today further lowers the deployer's future fine-tuning cost. Our analysis reveals that the incumbent's optimal first-period openness is surprisingly non-monotonic in the strength of the data flywheel effect. When the data flywheel effect is either weak or very strong, the incumbent prefers a higher level of openness; however, for an intermediate range, it strategically restricts openness to impair the entrant's learning. This dynamic gives rise to an "openness trap," a critical policy paradox where transparency mandates can backfire by removing firms' strategic flexibility, reducing investment, and lowering welfare. We extend the model to show that other common interventions can be similarly ineffective. Vertical integration, for instance, only benefits the ecosystem when the data flywheel effect is strong enough to overcome the loss of a potentially more efficient competitor. Likewise, government subsidies intended to spur adoption can be captured entirely by the incumbent through strategic price and openness adjustments, leaving the rest of the value chain worse off. By modeling the developer's strategic response to competitive and regulatory pressures, we provide a robust framework for analyzing competition and designing effective policy in the complex and rapidly evolving FM ecosystem.