LLMs succeed remarkably on tasks with abundant training data where consequences are low (e.g., recipe generation); they struggle with tasks requiring real-world embodied action (e.g., a robot clearing a table and loading a dishwasher) because no LLM is good at robotic AI, and because real-world action carries real consequences.