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 \emph{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 \emph{accountability gap}, where no addressable principal can be identified, and an \emph{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.

翻译：所有主流人工智能治理框架都预设存在一个可识别的实体——开发者、部署者或运营者——该实体可被问责并被迫遵守规定。去中心化AI（DeAI）消解了这一预设。我们将DeAI分析为六层去中心化堆栈——模型、训练、计算、控制、身份和所有权——并展示各层间的部分去中心化如何叠加形成我们所谓的"治理真空"：一种AI系统影响重大到需要治理，但缺乏现有框架在其治理对象中所预设特性的状况。这种真空具有两种分析上不同的形式："问责缺口"，即无法识别出可对之问责的主体；以及"无力化缺口"，即即使识别出主体也无法改变正在运行的系统。我们证明，这些失败不仅限于管辖权问题，而且通过规范性指令——将规则传达给具有理解力和响应能力的主体——破坏了治理的每一项预设。借鉴莱西格的调节模式理论和塞尔关于调节性规则与构成性规则的区分，我们主张将治理的焦点从政策转向协议，从规范性指令转向架构约束。基于协议的构成性治理并非针对在系统内运作的主体进行指令，而是塑造决定系统内何种行动成为可能的底层基础。我们确定了此类治理必须满足的四项伦理条件——合法性、可争议性、透明性和非支配性——以避免退化为不负责任的专家权力，并主张在去中心化世界中治理AI的核心政治挑战，是在常规政策链条断裂后，为持续存在的架构选择重构民主授权形式。