Creating virtual interactive worlds can revolutionize media, making it more accessible and affordable, thus expanding its use beyond just video games.

Video generation, simulation, and games are converging, as they involve world-building where you define the stage and assets before shooting.

Architecturally, there's a significant similarity among various AI models, including video diffusion models like Veo and text language models, which share a lot of core elements.

Recent advancements in video generation models have drastically reduced the time needed to simulate complex scenes, demonstrating the power of data-driven processes.

We are not just a video model company; our goal is to achieve multimodal general intelligence, enabling users to create entire worlds.

To train robots effectively, we must simulate environments at 10,000 times faster than real-time and vary parameters like gravity and friction to ensure adaptability.

By running 10,000 simulations in parallel, we can transfer training to real robots without any fine-tuning, showcasing the power of simulation in robotics.

In simulation, we can replay trajectories and vary motions without needing to demonstrate them repeatedly with a real robot, enhancing efficiency in training.

By training neural networks in diverse simulated environments, we can prepare them to handle real-world scenarios, effectively creating a digital twin of the robot and its environment, as demonstrated by humanoid robots that trained for 10 years in just two hours of simulation time to learn walking, showcasing the efficiency of simulation in robotics training.