AI Video Models Struggle with Real-World Physics, Study Reveals Inconsistent Performance on Physical Reasoning Tasks

Recent research reveals that today’s AI video models struggle to accurately represent how the real world functions, particularly when it comes to physical reasoning. Despite significant advances in generating realistic video content, these models often fail to consistently understand or predict fundamental physical principles such as gravity, object permanence, and cause-and-effect relationships. In a series of tests, researchers evaluated leading AI video generation models on tasks involving object interactions—such as whether a ball would fall when dropped, or if a stack of blocks would remain stable. The results showed highly inconsistent performance, with many models producing videos that defy basic laws of physics. For example, objects were frequently seen floating mid-air, passing through solid surfaces, or behaving in ways that contradict real-world expectations. The study highlights a critical gap between the visual realism of AI-generated videos and their underlying understanding of physical causality. While the generated footage may appear lifelike at first glance, closer inspection reveals logical flaws that undermine their reliability for applications requiring accurate simulations—such as robotics, autonomous vehicles, or scientific modeling. Experts suggest that current models rely heavily on statistical patterns from training data rather than internalized knowledge of physics. They learn to mimic the appearance of motion and interaction without truly comprehending the mechanisms behind them. This limitation means that even the most advanced models can produce plausible but physically incorrect outcomes. The findings underscore the need for new training approaches that incorporate explicit physical constraints or reasoning modules. Researchers are exploring hybrid models that combine deep learning with symbolic reasoning or physics engines to improve consistency and accuracy. While AI video models continue to improve in visual fidelity, this research confirms that they still fall short of truly modeling how the real world works—raising important questions about their use in high-stakes, real-world applications.