Publication details
Ex Ante Regulatory Framework for Access to Foundation AI Model Markets
| Authors | |
|---|---|
| Year of publication | 2026 |
| Type | Peer-reviewed scientific article |
| Magazine / Source | JUSLETTER IT |
| MU Faculty or unit | |
| Citation | |
| web | https://jusletter-it.weblaw.ch/issues/2026/juni-2026/ex-ante-regulatory-f_d31489ffee.html__ONCE&login=false |
| Doi | https://doi.org/10.38023/e3365c97-d687-4f61-83ff-44d93e3e3e8e |
| Keywords | Ex ante regulation; foundation model; access regulation; big tech; DMA; Telecommunications; essential facilities |
| Description | Foundation AI models are a vital resource for artificial intelligence downstream applications. Yet their development exhibits natural monopoly characteristics, with market control concentrated among a small number of companies connected to Big Tech. This concentration threatens innovation in downstream AI applications and raises systemic risks such as technological dependence, foreclosure of specific markets and reduced market competition. While competition law mechanisms have proven mostly inadequate for addressing the structural challenges of foundation model markets, the EU's regulatory innovation through the Digital Markets Act demonstrates that ex ante regulation, complementary to traditional antitrust enforcement, could effectively preserve contestability in concentrated markets. This paper proposes an ex ante regulatory approach aimed at foundation AI models, drawing on DMA principles and ex ante obligations common in the telecommunications sector. The proposed approach focuses on mandatory access obligations and non-discrimination principles for systemically important foundation models. It is intended to enable competitive downstream markets. By extending ex ante model access obligations to foundation AI model providers, the proposed approach addresses vertical foreclosure risks, ensures fair downstream access, and facilitates a systemic protection of competition in downstream markets. The paper will outline the problem of market concentration in foundation AI model markets and the possible impacts of it. After that, the paper will provide a summary of proposed regulatory approaches, which include a public utilities approach to foundation models and making accessible training data from dominant companies. These approaches will be contrasted with a new possible accessibility approach broadly proposed in this paper. The proposed approach lies in ex-ante model access regulation following the examples of the DMA and telecommunications regulation. |
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