# The Closing Window: How Governments Could Lose Their Ability to Restrain Advanced AI

arXiv: https://arxiv.org/abs/2608.05173 · HTML: https://mlsanity.com/paper/2608.05173/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 30 of its 72 references that are in the map.
- First posted on arXiv 26 Jun 2026.

## Builds on (most cited first, 30 of 30)

- [DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligence](https://mlsanity.com/paper/2606.19348/) (2026)
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- [Gradual Disempowerment: Systemic Existential Risks from Incremental AI Development](https://mlsanity.com/paper/2501.16946/) (2025)
- [Computing Power and the Governance of Artificial Intelligence](https://mlsanity.com/paper/2402.08797/) (2024)
- [A Rosetta Stone for AI Benchmarks](https://mlsanity.com/paper/2512.00193/) (2025)
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- [Communication-Efficient Language Model Training Scales Reliably and Robustly: Scaling Laws for DiLoCo](https://mlsanity.com/paper/2503.09799/) (2025)
- [Who's in Charge? Disempowerment Patterns in Real-World LLM Usage](https://mlsanity.com/paper/2601.19062/) (2026)
- [It's the Thought that Counts: Evaluating the Attempts of Frontier LLMs to Persuade on Harmful Topics](https://mlsanity.com/paper/2506.02873/) (2025)
- [Verifying International Agreements on AI: Six Layers of Verification for Rules on Large-Scale AI Development and Deployment](https://mlsanity.com/paper/2507.15916/) (2025)
- [Governing Through the Cloud: The Intermediary Role of Compute Providers in AI Regulation](https://mlsanity.com/paper/2403.08501/) (2024)
- [A Practical Introduction to Side-Channel Extraction of Deep Neural Network Parameters](https://mlsanity.com/paper/2211.05590/) (2022)
- [Mechanisms to Verify International Agreements About AI Development](https://mlsanity.com/paper/2506.15867/) (2025)
- [Measuring AI R&D Automation](https://mlsanity.com/paper/2603.03992/) (2026)
- [A Frontier AI Risk Management Framework: Bridging the Gap Between Current AI Practices and Established Risk Management](https://mlsanity.com/paper/2502.06656/) (2025)
- [Superintelligence Strategy: Expert Version](https://mlsanity.com/paper/2503.05628/) (2025)
- [Verifying LLM Inference to Detect Model Weight Exfiltration](https://mlsanity.com/paper/2511.02620/) (2025)
- [Covenant-72B: Pre-Training a 72B LLM with Trustless Peers Over-the-Internet](https://mlsanity.com/paper/2603.08163/) (2026)
- [An International Agreement to Prevent the Premature Creation of Artificial Superintelligence](https://mlsanity.com/paper/2511.10783/) (2025)
- [Reasons to Doubt the Impact of AI Risk Evaluations](https://mlsanity.com/paper/2408.02565/) (2024)
- [Declare and Justify: Explicit assumptions in AI evaluations are necessary for effective regulation](https://mlsanity.com/paper/2411.12820/) (2024)
- [Trends in AI Supercomputers](https://mlsanity.com/paper/2504.16026/) (2025)
- [AI Researchers' Views on Automating AI R&D and Intelligence Explosions](https://mlsanity.com/paper/2603.03338/) (2026)
- [Decoupled DiLoCo for Resilient Distributed Pre-training](https://mlsanity.com/paper/2604.21428/) (2026)
- [Distributed and Decentralised Training: Technical Governance Challenges in a Shifting AI Landscape](https://mlsanity.com/paper/2507.07765/) (2025)
- [Near-Term Enforcement of AI Chip Export Controls Using A Firmware-Based Design for Offline Licensing](https://mlsanity.com/paper/2404.18308/) (2024)
- [What Information Should Be Shared with Whom "Before and During Training"?](https://mlsanity.com/paper/2501.10379/) (2024)
- [Technical Requirements for Halting Dangerous AI Activities](https://mlsanity.com/paper/2507.09801/) (2025)
- [Aggressive Compression Enables LLM Weight Theft](https://mlsanity.com/paper/2601.01296/) (2026)

## Abstract (arXiv)

As AI capabilities advance, AI systems will pose greater risks to national security and potentially humanity as a whole. Governments may eventually conclude that these risks warrant restraining AI development. This motivates the question: will governments still be able to restrain AI development in the future, should they want to do so? In this paper we analyze which world events and changes to the state of AI development would make future governance more difficult or even effectively impossible. Our analysis surfaces likely pathways that would lead to these difficulties, including hardware proliferation, continued algorithmic progress, and the release of catastrophically dangerous AI models. Due to the field's lack of understanding of AI development, it may be difficult or impossible to know when we will hit a "point of no return", and we therefore recommend a conservative approach. The window may be closing, but governments currently have an opportunity to preserve their optionality if they act soon. Our policy recommendations would enable governments to restrain AI development in the future, while imposing relatively small costs today.