Bounded Sovereignty and the Control Tax: Pricing AI Oversight When the Deployer Does Not Own the Model

The research introduces the concept of 'bounded sovereignty' in AI deployment, addressing challenges faced by regulated organizations using frontier models via third-party APIs or managed endpoints. It highlights that the deployer often lacks full control over critical AI stack components (model weights, infrastructure, traces, updates, logs), which are typically assumed for effective AI control protocols. This limited access, both technical and contractual, dictates the feasibility of implementing safety and control measures, thereby influencing risk management and regulatory compliance.

Why it matters

This research is strategically important because it redefines the scope of AI control in real-world deployment scenarios, particularly for organizations reliant on external AI services. Understanding 'bounded sovereignty' is crucial for developing robust governance frameworks and risk mitigation strategies, ensuring responsible AI integration without full ownership of the underlying technology.

Key insights

• Traditional AI control research often assumes that the deployer has full control over the AI model and its operational pipeline, enabling direct instrumentation.
• Many regulated organizations deploy frontier AI models through external APIs or managed endpoints, leading to a disconnect where the deployer manages business processes but not core AI components.
• The concept of 'bounded sovereignty' describes this partial technical and contractual access across data, model, infrastructure, and interaction layers of the AI stack.
• The specific conditions of this bounded sovereignty directly determine which AI control protocols can be practically implemented by the deploying organization.

Source

arXiv — Computers and Society — https://arxiv.org/abs/2608.19216