Explainer: How AI Video Generators Work, from Diffusion Models to World Simulation

A technical explainer traces the architectural evolution of AI video generation from early diffusion-based frame synthesis through current approaches that begin to model underlying world physics and temporal coherence. The piece covers how latent diffusion models are adapted for video by adding temporal attention layers, how models like Sora and its contemporaries approach consistency across frames, and where 'world simulation' framing diverges from traditional generation. For developers integrating video generation APIs or evaluating models for creative pipelines, understanding these architectural distinctions clarifies which model families are best suited to different task types — short clips vs. long-form coherent sequences. The explainer also touches on the compute scaling properties of video diffusion and why inference costs remain substantially higher than image generation. This serves as a useful reference for teams onboarding engineers to video AI or briefing non-technical stakeholders.