According to @godofprompt on Twitter, while AI video diffusion models such as Sora are gaining significant attention for their high rendering quality, they still struggle with understanding basic human actions and maintaining temporal coherence (source: @godofprompt, Twitter). Common issues include characters freezing mid-action, getting stuck in objects like revolving doors, and breaking physical logic—problems similar to glitches seen in video game NPCs. The core challenge, as highlighted in recent arXiv papers, is that these models predict frames without simulating real-world physics or continuous actions, leading to unreliable outputs for professional use. For businesses aiming to use AI-generated video in scaled content production or product demos, these glitches are deal-breakers, limiting the commercial viability and reliability of current solutions. The focus on visual quality often overlooks the more critical need for models to understand causality and physical logic, which is essential for creating practical, scalable AI video applications (source: @godofprompt, Twitter).