According to God of Prompt on Twitter, a new approach called Grassmann flows is being proposed as an alternative to traditional attention mechanisms in AI models. This method replaces attention with a controlled geometric evolution on a manifold, specifically by reducing hidden states from 256 to 32 dimensions, encoding token pairs as two-dimensional subspaces on the Grassmannian Gr(2,32), and using Plücker coordinates to generate geometric features. The approach gates and fuses information without relying on attention weights, focusing purely on manifold geometry. This development highlights a mathematically elegant path for AI architecture innovation and may present significant business opportunities for companies focused on efficient and interpretable neural networks.