AI models have achieved remarkable capabilities across various domains, but their potential is often constrained by the static nature of training data and environments. Open-endedness offers a transformative approach by enabling systems to continuously generate novel, learnable challenges, fostering perpetual learning and broader generalization to unseen tasks. In this talk, we explore how open-ended methods can drive progress beyond traditional approaches, leading to systems that are not only robust in the face of new tasks and environments but also capable of continuous, boundless learning.