From training a world-action model on over one million hours of human video to deploying robots that fold thousands of napkins daily at Din Tai Fung, Dyna Robotics is pushing physical AI beyond impressive demos and into real-world production. In this episode, Dyna Robotics co-founder and Chief Scientist Jason Ma joins swyx on-site to unpack how robots can learn new tasks from as little as 45 minutes of demonstrations, why predicting the future of the physical world may be the key to general-purpose robotics, and what it actually takes to build robots that work reliably for real customers.

We go deep on Dyna-2, Dyna's million-hour world-action model that combines future video prediction with robot actions, the scaling laws behind human-to-robot transfer, diffusion transformers and flow matching, and why synthetic environments and self-generated training data could unlock the next wave of robotics progress. Jason also explains why Dyna builds its own hardware instead of relying on off-the-shelf humanoids, how agentic harnesses enable robots to execute hour-long workflows, why dexterous hands and missing sensory data remain major bottlenecks, and what separates real commercial deployments from the graveyard of robotics startups. We also cover world-action models versus vision-language-action models, GPT-6 and coding agents in robotics, and Jason's journey from AlphaGo and Penn to NVIDIA, Google DeepMind, and founding Dyna.