The author argues that three major AI researchers—Yann LeCun, Demis Hassabis, and Fei-Fei Li—are converging on world models as the next frontier in machine intelligence. A world model, loosely defined, is a system that builds a representation of its environment, predicts what happens next, and decides what actions to take. The fact that these three figures, who've shaped AI in completely different ways, are now focused on the same problem suggests something significant is shifting. The author sees this as a signal worth paying attention to, even though experts can't agree on a single definition of what a world model actually is. The real value isn't theoretical—it's about what companies actually need to spend billions on AI for. Businesses don't want better text generation. They want better decisions: which experiment to run, whether a code change will break production, which route a robot should take, what inventory decision prevents shortages weeks out. Current AI excels at generating likely continuations, but that's different from understanding environmental state, testing possible futures, and choosing actions. The next phase will probably blend these capabilities rather than replace one approach with another. The author is restructuring their publication around this framework, using world models as the editorial backbone. This doesn't mean covering only systems explicitly branded as "world models," but rather tracking how machines represent, predict, simulate, plan, and act across physical, digital, and scientific domains. They'll still cover OpenAI, robotics, and infrastructure, but only when those developments change the larger story. The author frames this as a hypothesis to test, not a declaration—acknowledging that the term could become too broad or that some promises might collapse.