The pursuit of Artificial Superintelligence (ASI) requires a shift from narrow objective optimization towards embracing Open-Endedness—a research paradigm, pioneered in AI by Stanley, Lehman and Clune, that is focused on systems that generate endless sequences of novel but learnable artifacts. In this talk, I will present our work on large-scale foundation world models that can generate a wide variety of diverse environments that can in turn be used to train more general and robust agents. Furthermore, I will argue that the connection between Open-Endedness and Foundation Models points towards automating innovation itself. This convergence is already yielding practical results, enabling self-referential self-improvement loops for automated prompt engineering, automated red-teaming, and AI debate in Large Language Models, and it hints at a future where AI drives its own discoveries.