In a recent Substack publication, computer scientist Li Fei-Fei introduces a functional taxonomy to clarify the fragmented and heavily hyped concept of artificial intelligence world models. With the term increasingly applied to video generation engines, embodied robotics, and industrial infrastructure platforms, Li argues that industry confusion stems from conflating distinct computational roles. Drawing on the decades-old Partially Observable Markov Decision Process framework, she categorizes current world models into three functional projections: renderers, simulators, and planners. Renderers output visual observations by translating inputs into pixel sequences optimized for human perception. While commercially mature, their focus on aesthetic fidelity rather than physical accuracy imposes a hard ceiling on utility for engineering or robotics. Planners, conversely, operate at the opposite end of the loop by generating actionable policies based on sensory inputs. This category holds the greatest promise for autonomous systems yet remains the least deployment-ready, as current demonstrations fail to bridge the substantial gap between controlled laboratory environments and real-world operational complexity. Positioned between these extremes, simulators represent the critical but undervalued nexus of the field. Unlike renderers that project appearances or planners that dictate movements, simulators construct structured representations of geometric, physical, and dynamic states. This foundational layer enables both visual projection and action prediction, serving as the necessary infrastructure for digital twins, autonomous vehicle testing, and robotic training. Li emphasizes that true breakthroughs require simulating physical laws rather than merely approximating visual outcomes. Highlighting World Labs Marble architecture, the publication outlines an industry trajectory toward convergence. The model simultaneously generates photorealistic Gaussian splats for visualization and collision meshes for physics engines, effectively dissolving the traditional boundaries between rendering and simulation. As research efforts increasingly align, the sector is moving toward a unified world foundation model capable of dynamically switching between visual, structural, and procedural outputs based on downstream requirements. While significant hurdles remain regarding 3D data scarcity, sim-to-real transfer gaps, and computational scaling, the proposed taxonomy provides a necessary analytical framework. By decoupling hype from function, the industry can better prioritize infrastructure development. The convergence of rendering, simulation, and planning ultimately signals a strategic shift toward spatial intelligence, positioning world models as the foundational mechanism for machine interaction with physical reality.