My guest post on Terence Tao's famous blog: Thank you Terry for giving us this opportunity and talking about our work from the very moment we launched on September 7th even before @OpenAI did. In the post, I go into how our method takes a different starting point: designing physics-AI (PINN) to discover singular candidates rather than human constructed ones. Making PINN optimization work for the first time for unforced Euler in R^3, converting that numerical solution to certified bounds which are then used in analytical stability arguments: we develop new tools to bridge numerical computation with analysis, and we believe it has much broader applications in theory. Physics-AI in the form of Neural Operators have already been successful in so many applications, including training the first AI-based high-resolution weather model, and most recently making density functional theory in quantum chemistry quasi-linear time. There is a wealth of new research to be done here!