In early 1963 the United States and the Soviet Union negotiated a nuclear test ban. The Soviet Union proposed three on‑site inspections per year, while the United States requested seven. Neither side had technical experts to measure seismic signatures, and the talks ultimately produced a treaty that excluded underground tests. The episode illustrates how verification assumptions shape arms‑control outcomes.

The same verification challenges appear in contemporary discussions of artificial intelligence. Analysts propose monitoring the computing resources used for AI model training as a transparent indicator. Data‑center power use and hardware logs could be recorded and shared under an agreement. Additional safeguards would involve cryptographic attestation of hardware use without revealing proprietary training details. Such a regime presumes that both the United States and China value reciprocal warning over capability caps.

A concrete example would require advance notice of any AI training run that exceeds a set computation threshold, with monitoring of the run. Participation would depend on both sides expecting continued development and believing that a temporary lead would not create a lasting monopoly. If a durable lead is considered likely, warning may be unattractive to the leading state.

Verification must balance the need for assurance against the risk of exposing sensitive information. Increased monitoring can reveal not only compliance but also strategic capabilities, supply‑chain details, and research directions. Leakage can be minimized by designing mechanisms that separate compliance data from other information and by allowing the inspected party to audit what is collected, retained, and disclosed. However, complete separation against a nation‑state adversary remains unproven.

Three warnings emerge from arms‑control history. First, transparency and intrusive access can reveal vulnerabilities that an adversary might exploit. Second, monitoring technology can generate collateral inferences that provide strategic insight beyond the stated compliance claim. Third, the adequacy of verification is determined through political negotiation, not technical limits alone; actors may use uncertainty to justify tougher standards, creating a feedback loop between technical and political pressures.

Designing a verification framework for AI therefore requires filterability, red‑team testing with transparent methods, and a shared technical baseline before negotiations solidify positions. Short, revisable testing cycles involving governments, industry, and independent scientists can evaluate detection capability, data leakage, and response speed. Implementation would involve international commitments, national laws binding cloud providers and chip manufacturers, and rapid challenge procedures to address violations.

The history of nuclear verification shows that technical solutions must be paired with political agreement. AI verification will likewise depend on mutual trust, clear definitions of compliance, and mechanisms that make cheating detectable early while keeping residual uncertainty explicit.