# Is Decentralized AI Governable? From Regulative Policy to Constitutive Protocol

arXiv: https://arxiv.org/abs/2605.24538 · HTML: https://mlsanity.com/paper/2605.24538/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 29 of its 103 references that are in the map.
- First posted on arXiv 23 May 2026.

## Builds on (most cited first, 29 of 29)

- [Efficient Large Language Models: A Survey](https://mlsanity.com/paper/2312.03863/) (2023)
- [Multi-Agent Risks from Advanced AI](https://mlsanity.com/paper/2502.14143/) (2025)
- [On the Societal Impact of Open Foundation Models](https://mlsanity.com/paper/2403.07918/) (2024)
- [Open Problems in Technical AI Governance](https://mlsanity.com/paper/2407.14981/) (2024)
- [Understanding accountability in algorithmic supply chains](https://mlsanity.com/paper/2304.14749/) (2023)
- [Computing Power and the Governance of Artificial Intelligence](https://mlsanity.com/paper/2402.08797/) (2024)
- [Society-in-the-Loop: Programming the Algorithmic Social Contract](https://mlsanity.com/paper/1707.07232/) (2017)
- [Agents of Chaos](https://mlsanity.com/paper/2602.20021/) (2026)
- [Open-Sourcing Highly Capable Foundation Models: An evaluation of risks, benefits, and alternative methods for pursuing open-source objectives](https://mlsanity.com/paper/2311.09227/) (2023)
- [The Future of Large Language Model Pre-training is Federated](https://mlsanity.com/paper/2405.10853/) (2024)
- [INTELLECT-2: A Reasoning Model Trained Through Globally Decentralized Reinforcement Learning](https://mlsanity.com/paper/2505.07291/) (2025)
- [OpenDiLoCo: An Open-Source Framework for Globally Distributed Low-Communication Training](https://mlsanity.com/paper/2407.07852/) (2024)
- [Eliza: A Web3 friendly AI Agent Operating System](https://mlsanity.com/paper/2501.06781/) (2025)
- [Autonomous Agents on Blockchains: Standards, Execution Models, and Trust Boundaries](https://mlsanity.com/paper/2601.04583/) (2026)
- [Trustless Autonomy: Understanding Motivations, Benefits, and Governance Dilemmas in Self-Sovereign Decentralized AI Agents](https://mlsanity.com/paper/2505.09757/) (2025)
- [Can We Govern the Agent-to-Agent Economy?](https://mlsanity.com/paper/2501.16606/) (2025)
- [The Agent Economy: A Blockchain-Based Foundation for Autonomous AI Agents](https://mlsanity.com/paper/2602.14219/) (2026)
- [A Taxonomy for Blockchain-based Decentralized Physical Infrastructure Networks (DePIN)](https://mlsanity.com/paper/2309.16707/) (2023)
- [SoK: Blockchain-Based Decentralized AI (DeAI)](https://mlsanity.com/paper/2411.17461/) (2024)
- [Spore in the Wild: A Case Study of Spore.fun as an Open-Environment Evolution Experiment with Sovereign AI Agents on TEE-Secured Blockchains](https://mlsanity.com/paper/2506.04236/) (2025)
- [Covenant-72B: Pre-Training a 72B LLM with Trustless Peers Over-the-Internet](https://mlsanity.com/paper/2603.08163/) (2026)
- [The Artificial Self: Characterising the landscape of AI identity](https://mlsanity.com/paper/2603.11353/) (2026)
- [How to Count AIs: Individuation and Liability for AI Agents](https://mlsanity.com/paper/2603.10028/) (2026)
- [Privacy-Preserving Decentralized AI with Confidential Computing](https://mlsanity.com/paper/2410.13752/) (2024)
- [Protocol Learning, Decentralized Frontier Risk and the No-Off Problem](https://mlsanity.com/paper/2412.07890/) (2024)
- [Giving AI Agents Access to Cryptocurrency and Smart Contracts Creates New Vectors of AI Harm](https://mlsanity.com/paper/2507.08249/) (2025)
- [A Review on Building Blocks of Decentralized Artificial Intelligence](https://mlsanity.com/paper/2402.02885/) (2024)
- [Self-Sovereign Agent](https://mlsanity.com/paper/2604.08551/) (2026)
- [The Impact of Sanctions on decentralised Privacy Tools: A Case Study of Tornado Cash](https://mlsanity.com/paper/2510.09443/) (2025)

## Abstract (arXiv)

Every major framework for governing artificial intelligence presupposes an identifiable entity -- a developer, deployer, or operator -- who can be held responsible and compelled to comply. Decentralized AI (DeAI) dissolves this presupposition. We analyze DeAI as a six-layer decentralizing stack -- model, training, compute, harness, identity, and ownership -- and show how partial decentralization across layers compounds into what we call the governance vacuum: a condition in which AI systems are consequential enough to require governance but lack the properties that existing frameworks presuppose in their targets. This vacuum takes two analytically distinct forms: an accountability gap, where no addressable principal can be identified, and an incapacitation gap, where even an identified principal cannot alter the running system. We demonstrate that these failures are not merely jurisdictional but defeat every presupposition of governance through normative address -- the communication of rules to a comprehending, responsive agent. Drawing on Lessig's modalities of regulation and Searle's distinction between regulative and constitutive rules, we argue for a shift in the locus of governance from policy to protocol, from normative address to architectural constraint. Protocol-based constitutive governance does not address the agents operating within a system but shapes the substrate that determines what kinds of actions are possible within it. We identify four ethical conditions -- legitimacy, contestability, transparency, and non-domination -- that such governance must satisfy to avoid degenerating into unaccountable technocratic power, and we argue that the central political challenge of governing AI in a decentralized world is reconstructing forms of democratic authorization for architectural choices that persist after the ordinary chain of policy has broken down.