A Governance Structure for Safe and Scalable AI: The Foundation Model Utility Proposal

Ensuring Safe and Scalable AI Development: Introducing the Foundation Model Utility Framework

In recent years, artificial intelligence has rapidly advanced, bringing remarkable capabilities that promise to transform society. However, this progress is accompanied by significant governance challenges. The high resource demands for building cutting-edge foundation models have led to a stark concentration of power within a small number of corporate giants. This imbalance raises concerns about innovation stagnation, safety risks, and the long-term sustainability of AI progress.

Understanding the Current Landscape

The creation of state-of-the-art AI models necessitates enormous computational power and access to vast, proprietary datasets. As a result, only a handful of well-funded organizations can afford to develop and deploy these models, leading to the emergence of “data moats” and proprietary ecosystems that hinder broader research and competition. This siloed approach not only risks monopolizing technological advancements but also incentivizes rapid deployment of increasingly powerful models, often at the expense of thorough safety and alignment considerations.

The existing corporate frameworks are ill-equipped to handle these unique challenges, prompting the need for innovative governance models tailored specifically to AI’s complexities.

A New Approach: The Foundation Model Utility Concept

Envision a scenario where leading AI institutions can adopt a novel corporate structure designed to balance scale with safety. Introducing the Foundation Model Utility (FMU)—a specialized form of a Voluntary Utility Corporation (VUC) aimed at managing foundational AI models for the public good.

Under this framework, an AI organization that reaches a dominant market position could choose to reconfigure as an FMU. This transition would offer several key benefits:

• Legal Protections: The organization would gain antitrust immunity, enabling it to scale without fear of regulatory breakup.
• Profit Limitations: Excess profits—beyond a reasonable return—would be legally mandated to fund safety research, model evaluation, and alignment efforts.
• Public Benefit Commitment: The FMU would be legally bound to prioritize safety, transparency, and broad accessibility.
• Access and Open Standards: To prevent misuse and foster a healthy ecosystem, the organization would provide stable, reasonably priced API access to its models and uphold open standards for auditing and evaluation.

Learning from Historical Precedents

This model draws inspiration from past institutions like Bell Labs, which, as a regulated monopoly, produced foundational scientific and technological innovations through stable, long-term funding. Just as Bell Labs balanced monopoly power with a commitment to public benefit, a Foundation Model Utility could fund the deep safety and