Physical artificial intelligence (AI) is becoming an important paradigm for embodied intelligence, where agents must reason, imagine, and act under real-world physical constraints. However, current physical reasoning model (PRM) and physical generation model (PGM) may still produce physically inconsistent judgments, scenes, and motions, exposing the lack of a verifiable truth anchor for reliable physical intelligence. Existing simulation-based methods can provide local physical supervision, but their limited coverage makes them insufficient for open and evolving physical environments. To address this bottleneck, this letter proposes parallel physical intelligence (PPI), a truth-anchored co-evolution framework based on parallel intelligence and the artificial systems, computational experiments, and parallel execution (ACP) methodology. PPI introduces the physical fidelity anchor (PFA) to calibrate both PRM and PGM, while enabling PRM and PGM to co-evolve through mutual data augmentation and physical plausibility feedback. During parallel execution, embodied agents and disembodied agents interact through a virtual-real closed loop, where real-world failures and novel physical phenomena update the PFA and trigger subsequent co-evolution. This provides a scalable and adaptive pathway for physically grounded embodied intelligence in complex environments.