The loudest worry in robotics is data scarcity: we have nothing like the internet-scale corpus that trained language models. Tedrake reframes it. Nobody trains a robot from scratch. You start from a model that already has common sense about the world, a vision-language or world-model backbone, and you learn only the bridge that adds one new output: actions. This is the shape of the 2026 stack, from Physical Intelligence's pi-0.5 generalizing to new homes, to NVIDIA's GR00T, to Google's Gemini Robotics. The data you need is not everything ever; it is enough to map an already-understood scene into robot trajectories. This lesson makes that concrete: the same demos that are hopeless in a raw 64-dimensional space fit cleanly on top of the 4 features the backbone already provides.