Text Diffusion — Brendon Dillon, Google DeepMind

GPT-4o answered 40. Gemini 2.5 Flash answered 42 and stuck to it even after working through the reasoning incorrectly. The Gemini Diffusion model, considerably smaller than both, answered 60 on the first forward pass, then 49, then corrected itself to 39 once it finished reasoning. Bidirectional attention means it can see future tokens and go back to fix mistakes. Autoregressive models cannot do that. Brendon Dillon covers why text diffusion is fast (24 denoising steps to generate 256 tokens means roughly 10x fewer memory transfers than autoregressive generation), what the tradeoff is (lower throughput at large batch sizes makes it expensive to serve at scale today), and what gets unlocked when latency drops to 2,000 tokens per second. The demos include a fake Wikipedia generated on the fly, a Reddit clone with AI generated comments and images, an operating system where every click generates the next screen, and a todo app built in 15 seconds by voice.