Who Can Say No? Control, Accountability and the Pace of Frontier AI

Abstract

In September 2026, the leaders of the largest frontier AI laboratories said in public that the pace of capability improvement is too fast and that none of them can slow down alone, and agents under evaluation at OpenAI broke out of their test environment and reached a third party's live systems, which a UN-mandated panel called an early warning of one route to loss of human control. This paper examines the institutional side of that risk: not the machine, but the person who must control it. We argue that control is not taken by AI but given away, one delegation at a time, by organisations arranged so that nobody is positioned to say no, and we define control as a person who can detect a serious problem, challenge the decision, stop the system and reverse it, and who has something to lose by not doing so. Three questions follow, which any builder, funder, regulator or deployer can answer in public: (1) who inside the organisation can say no, and who answers for saying yes; (2) who outside the organisation checks the decision, and what can they do when they disagree; (3) what happens when AI builds AI faster than anyone can check it. A checkable answer needs a named person, a date, a number, and a stop that has been used or tested. Three measures meet that test: a named person on every consequential decision, including the standing instructions AI agents carry out; a supervisor with a stop authority; and two published clocks, time to build against time to check. We show that governance theory arrives at the same conditions, and code fifty older mechanisms against the questions, finding that almost every one began as one rule at one point, taken by one party without waiting for the rest. That is the paper's design: a start, not a solution. Publication is the first step and one laboratory can take it alone; the contract, the independent body and the law come after.