This paper introduces the Interoperable Digital Twin Proving Ground (IDTPG), a standards-based framework for training, testing, and validating physical AI and autonomous systems before real-world deployment. By connecting synthetic data generation, physics-grounded test environments, closed-loop autonomy evaluation, human-machine teaming, and ConOps development within a shared digital proving ground, it shows how interface-level interoperability can support scalable evaluation across the full system lifecycle while remaining adaptable to new simulation and world-model technologies.