World Model, the supposed algorithmic surrogate of the real-world environmentwhich biological agents experience with and act upon, has been an emergingtopic in recent years because of the rising needs to develop virtual agentswith artificial (general) intelligence. There has been much debate on what aworld model really is, how to build it, how to use it, and how to evaluate it.In this essay, starting from the imagination in the famed Sci-Fi classic Dune,and drawing inspiration from the concept of "hypothetical thinking" inpsychology literature, we offer critiques of several schools of thoughts onworld modeling, and argue the primary goal of a world model to be simulatingall actionable possibilities of the real world for purposeful reasoning andacting. Building on the critiques, we propose a new architecture for ageneral-purpose world model, based on hierarchical, multi-level, and mixedcontinuous/discrete representations, and a generative and self-supervisionlearning framework, with an outlook of a Physical, Agentic, and Nested (PAN)AGI system enabled by such a model.