The potential of artificial intelligence (AI) in biology is immense, yet its success is contingent on interfacing effectively with wet-lab experimentation and remaining grounded in the system, structure, and physics of biology. In this talk, I will discuss how we have developed biophysically grounded AI algorithms for biomolecular design. I will share recent work in creating a diffusion-based generative model that designs protein structures by mirroring the biophysics of the native protein folding process. This work provides an example of how bridging AI with fundamental biophysics can accelerate design and discovery in biology, opening the door for sustained feedback and integration between the computational and biological sciences.