Today's leading AI systems have achieved one of the field's oldest dreams and promises: They can take in any language as input and produce reasonable responses – often very much like the responses that a reasonable (and knowledgeable and helpful) person would produce. Yet the processes at work inside these systems, and how they are built, do not (at least obviously) have much in common with the mechanisms or origins of the human mind. What would it take to build a model with something like the input-output behavior of ChatGPT but whose inner workings actually instantiated a theory of human cognition – and even our best current scientific theory? I will discuss several possible routes to this goal, and the challenges and opportunities they present. I will argue that now more than ever is the time for a bidirectional exchange between the fields of AI and Cog Sci – fields that grew up together starting in the 1950s, but have followed very different trajectories recently. AI tools and techniques have much to offer cognitive theories, but cognitive science has just as much if not more to offer back to AI. Understanding and using AI tools in a framework guided by foundational thinking in cognitive science represents the best hope to deliver on the theoretical goals, dreams, and promises of both fields.