This article explores the rise of Physical AI through three complementary perspectives. First, it examines the technical shift from language-based systems to action-oriented models, focusing on world models as internal simulators that enable agents to perceive, anticipate, and interact with their environment. Second, it analyzes the transformation of training data, moving beyond text toward multimodal, synthetic, and spatial datasets that capture the structure and dynamics of the physical world. Finally, it addresses the legal implications of this evolution, highlighting the convergence of software and machinery regulation and the resulting challenges for designing, deploying, and governing embodied AI systems.