A new approach to world simulation is emerging, driven by multimodal intelligence that learns to predict how the world evolves over time. Odyssey-2 Pro exemplifies this shift, using vast amounts of video and interaction data to train models that grasp the laws of physics, human behavior, and cause-and-effect relationships. Instead of relying on pre-defined rules, these models learn directly from observations, enhancing their predictive capabilities. The article emphasizes the importance of next-state prediction tasks, which help models internalize how systems function with minimal initial knowledge. As models process longer sequences, they can better maintain an internal state, allowing them to make sensible predictions based on past events. This is particularly relevant in complex scenarios, such as predicting the water level in a bathtub after a person leaves the room. As training data expands in temporal scope, models will likely achieve significant improvements in accuracy and coherence. Traditional simulations are limited by their reliance on hand-crafted rules that can only model specific behaviors. In contrast, world models learn from generalized data, providing a framework that adapts to different scenarios without needing manual adjustments for each case. This approach allows for interactive simulations that maintain state over time, which could transform applications in education, healthcare, and other fields. The potential for continuous interaction with these models opens up new avenues for learning and engagement, moving beyond the static nature of current simulation tools.