Detecting AI Training in Datacentre Power

AI governance agreements require methods to detect large model training runs on a monitored computing cluster, without trusting the operator. We develop a method to detect synchronous model training in facility-level power by Viterbi tracking of the wandering iteration cadence. On simulated datacentre power, when the operator makes no attempt to hide, the Viterbi tracker detects at least 96% of training runs against a declared inference workload, at a 1% false-alarm rate and within 0.04 of the Neyman--Pearson optimum, given a meter that samples at 2 Hz or faster. Of eight adversarial strategies an operator might use to hide, only extreme variation of the work per iteration evades the tracker. Its strongest form hides training in up to 73% of five-minute observations, but over a day at least 87% of such runs are still caught, at a 0.1% false-alarm rate per run. This adversarial strategy costs no throughput, but in small-scale training the operator needs 93--267% more compute to reach the same loss. A modest passive meter is sufficient to make hidden training both detectable and expensive, which makes power metering a promising part of compute verification that does not depend on trusting the operator.