In this episode, Katherine Forrest and Scott Caravello break down three generative AI architectures—transformers, JEPA, and diffusion models—exploring what sets each apart and how they overlap. They also discuss Manifold-Constrained Hyper-Connections, a recent innovation aimed at improving how transformer layers communicate during training. For the sources referenced in this episode, please see the links below: DeepSeek AI: mHC: Manifold-Constrained Hyper Connections   ## Learn More About Paul, Weiss’s Artificial Intelligence practice: https://www.paulweiss.com/industries/artificial-intelligence