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What does a world model actually do—and why might a robot learn to fold your laundry before it can make perfect scrambled eggs?

Nate sits down with Ming-Yu Liu, VP of Cosmos Lab at NVIDIA, to explore how world models turn observations into predictions and physical actions. They discuss where the analogy to language models helps, where it breaks down, and why knowing whether an action worked can be harder than generating the action itself.

• How simulation lets builders test many kitchens and situations before returning to a real robot.
• Why robots in the field face different timing and compute constraints from services in a data center.
• What researchers can—and cannot—conclude about a model’s understanding of physics.
• How verifiable results change the pace of learning, from folding laundry to judging food.

For builders and operators, this conversation connects the models to the practical questions: what can be tested, what must happen immediately, and where human judgment is still needed.

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