This paper proves that if an untrained quantum circuit is used as a resource in a machine learning model, then on average no resource quantum circuit is better than any other that can achieve the same set of computational effects. The paper also proves a supporting theorem that even if certain mild idealisations are omitted, the average quantum advantage remains negligible at best. These results cast serious doubt on several proposals to use untrained quantum circuits in machine learning models: at best such claims should be substantiated empirically, as this paper proves there is no a priori theoretical reason to suppose that introducing an untrained quantum circuit will increase performance.