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

TL;DR

This paper tackles how the strategic openness of AI foundation models shapes competition, investment, and welfare in a two-period AI value chain with an incumbent, a deployer, and an entrant. It develops a formal game-theoretic model that introduces the data flywheel—where deployer engagement lowers future fine-tuning costs—and shows openness has a non-monotonic effect on the incumbent's optimal strategy, generating Harvest, Defend, and Dominate regimes and an openness trap where mandates can backfire. The analysis extends to vertical integration and government subsidies, showing outcomes depend critically on the strength of the flywheel and can lead to welfare gains or losses for different agents; policy conclusions advocate nuanced, condition-specific interventions (e.g., private model registration) over blunt openness mandates. Overall, the work provides a robust framework for understanding competition and governance in the FM ecosystem and offers concrete implications for strategy and antitrust policy.

Abstract

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.

Figures (15)

• Figure 1: Timeline of the Two-Period AI Value Chain Model
• Figure 2: Deriving the Incumbent's Optimal Strategy: Profit Trade-Offs with Respect to Openness ($\theta=10$, $c=0.5$, $w_H=2$, $w_L=0.8$, $\bar{\eta}=0.55$)
• Figure 3: Equilibrium Strategy and Outcomes as a Function of the Data Flywheel Effect $k$ ($\theta=5$, $c=1$, $w_H=2.5$, $w_L=0.5$, $\bar{\eta}=1.5$)
• Figure 4: Welfare Implications of the Incumbent's Strategic Regimes ($\theta=5$, $c=1$, $w_H=2.5$, $w_L=0.5$, $\bar{\eta}=1.5$)
• Figure 5: Impacts of Openness Regulation on Each Party's Profit and Social Welfare ($\theta=5$, $c=1$, $w_H=2.5$, $w_L=0.5$, $\bar{\eta}=1.5$)

Theorems & Definitions (8)

• Lemma 1
• Proposition 1
• Proposition 2
• Proposition 3
• Proposition 4
• Proposition EC.1
• Lemma EC.1
• Proposition EC.2