Taking collective artificial musical improvisation as a case study, we address software AI agents’ limitations due to their lack of embodiment and explore how machine learning resources may overcome them, without turning to robotics. Provided generative models are trained on embodied data, it is conceivable that the outputs of software agents may indirectly inherit some of the advantages of embodied cognitive processes. Drawing on empirical studies involving the software Somax2, we formulate an Embodiment via Machine Learning hypothesis and articulate the notion of embodiment that it involves.