Meta's new Autodata paper, published last week, proposes a shift in AI training where data creation becomes an agentic process. Instead of treating data as a static upstream resource, Autodata treats data generation like a miniature research loop: an AI agent creates examples, tests them, studies the failures, updates its recipe, and tries again.