The physical AI industry is divided between data-driven scaling approaches and architecture-first designs. Data-driven methods rely on large-scale training, while architecture-first methods prioritize real-world reliability and Bayesian uncertainty handling. Experts suggest that architecture-first systems currently show more commercial traction in complex, unstructured environments.