Advances in Generative AI are making us rethink the impact of technology on the human creative process. How can we elicit creativity not through imitation but through interaction? First, I’ll illustrate how we can design generative models to better support music composition and performance synthesis. Coconet, the ML model behind the Bach Doodle, supports a nonlinear compositional process through an iterative block-Gibbs like generative procedure, while MIDI-DDSP supports intuitive user control in performance synthesis through hierarchical modeling. Second, I’ll propose a common framework, Expressive Communication, for evaluating how developments in generative models and steering interfaces are both important for empowering human-ai co-creation, where the goal is to create music that communicates an imagery or mood. Third, I’ll introduce the AI Song Contest and discuss some of the technical, creative, and sociocultural challenges musicians face when adapting ML-powered tools into their creative workflows. Looking ahead, I’m excited to co-design with musicians to discover new modes of human-ai collaboration. I’m interested in designing visualizations and interactions that can help musicians understand and steer system behavior, and algorithms that can learn from their feedback in more organic ways. I aim to build systems that musicians can shape, negotiate, and jam with in their creative practice.