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Physical AI aims to extend artificial intelligence from digital reasoning to perception, prediction, simulation, planning, and action in the physical world. While recent progress has advanced through vision-language models, vision-language-action models, world models, policy learning, and embodied agents, existing discussions are often organized from robotics-centric, vision-centric, or cyber-physical perspectives. This survey instead studies Physical AI through the lens of LLM-based world knowledge. We argue that LLMs encode implicit semantic, commonsense, procedural, and causal priors through large-scale pretraining, making them useful high-level sources for physical reasoning, multimodal grounding, action grounding, and embodied decision making. However, language-mediated knowledge is sparse and lossy for dense physical states, such as geometry, motion, contact, force, high-frequency dynamics, and long-horizon temporal evolution. VLMs and MLLMs ground LLM-derived priors into perception, but often expose physical understanding through language outputs. VLAs connect perception and language to executable actions, yet typically lack predictive models of how the world evolves under actions. This motivates a roadmap from LLM-based world knowledge to multimodal grounding, action grounding, world modeling, policy learning, and embodied deployment. We review how these components provide perceptual, actionable, predictive, and simulative substrates for Physical AI, and discuss open challenges in grounding, world modeling, closed-loop evaluation, safety, and generalization. A curated repository of related papers and resources is available at https://github.com/Hai-chao-Zhang/Awesome-Physical-AI.