How OpenAI's Sora 2 goes beyond video generation.

The speaker discusses how increasing computational power and data in AI systems, particularly for predicting the next token, leads to the development of robust internal world models. This concept is extended to video data, where these models explicitly simulate reality using "space-time patches" as a reusable representation across diverse data types like video footage, anime, and cartoons. The speaker emphasizes that this approach allows a single neural network to build powerful, generalizable representations of the world, which is useful for predicting how various scenarios, from cartoons to conversations, might unfold.