World Models Move Beyond Robotics Into Real-Time AI Video Generation

World models, a concept in AI research describing systems that learn how environments change over time, are gaining renewed attention beyond their traditional use in robotics and self-driving vehicles. Unlike large language models, which predict sequences of text tokens, world models learn causal and physical patterns by processing video and simulated interactions. AI researcher Yann LeCun has described this approach as analogous to the internal mental maps humans build through repeated experience and cause-and-effect reasoning. Video generation company Runway has now applied this technology in its GWM Worlds 2 model, which allows users to steer and shape a video environment in real time rather than simply prompting a finished output. The development signals a broader shift in generative AI toward systems capable of understanding and simulating physical reality, not just language.