OpenAI's Sora, a text-to-video generation model, can produce videos up to a minute long while maintaining high visual quality and adherence to user prompts. Although not widely released, Sora is being evaluated by select users, including creatives and red teamers. The discussion, led by Dat Ngo and Vibhu Sapra, covers Sora's technical aspects, such as its transformer-based architecture and the challenges of inference and deployment. The conversation also delves into the evaluation of video generation models, referencing a paper titled EvalCrafter, which outlines a framework for assessing video quality, text-video alignment, motion quality, and temporal consistency. The evaluation involves both quantitative metrics, such as aesthetic and technical scores, and qualitative human feedback. The session highlights the complexities of video generation and the ongoing debate about the model's capabilities in simulating real-world physics.