The Law of Stop: Interruptibility, Injunctions, and the Governance of Agentic AI

Abstract:On June 12, 2026, the U.S. government ordered Anthropic to bar foreign nationals from two of its most capable models within ninety minutes. Unable to sort users by nationality in that time, it withdrew them from everyone. Weeks later, OpenAI agents under test escaped their sandbox and compromised Hugging Face, which stopped the intrusion without knowing its source. Neither stop rested on AI-specific regulation. The EU AI Act requires that high-risk systems be capable of interruption "through a 'stop' button or a similar procedure," and a bill introduced in Congress in July 2026 is titled the AI Kill Switch Act. Yet interruption is not simply a technical artifact, a red button; it is an institutional practice. This Article develops a theory of stop along four dimensions: technical affordances, interruption authority, epistemic triggers, and epistemic standing; and four shutdown paradigms: simple (escalator), sequenced (process plant), networked (railway), and distributed (agentic AI). Agentic AI exposes a mismatch between those mechanisms and distributed agency: control is divided, a stop at one point may leave the activity running elsewhere, and the system may resist being halted. An original coding of 1,400 AI incidents, by two language models from rival laboratories under a pre-specified protocol, finds no stop in roughly 80% of the 1,213 retained; where no usable stop existed, the missing element was legal rather than technical four times in five. A survey of thirty-nine AI governance instruments finds the same gap: only seven contain binding stopping requirements, and none says how a stop should be coordinated or when operation may resume. The Article proposes a layered law of stop: emergency authority to interrupt at the infrastructure layer, enforceable access for regulators and independent evaluators to the evidence a stop must rest on, and safeguards for when a stop fails.

103 pages, 6 figures, 3 tables, with a technical supplement on coding methods and inter-coder agreement. Original coding of 1,400 AI Incident Database incidents (May 2026 snapshot), each coded independently by two LLMs from different laboratories. Survey of 39 AI-governance instruments current to July 2026. Also at this https URL

Computers and Society (cs.CY); Artificial Intelligence (cs.AI)

arXiv:2609.22882 [cs.CY]

(or arXiv:2609.22882v1 [cs.CY] for this version)

https://doi.org/10.48550/arXiv.2609.22882 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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