How to Get an Audience to Laugh at a Live Generative Story: Crowd-Sourced Humor and Shared Agency

Large language models can generate joke-like text, but making a live audience laugh within an audiovisual story requires more than isolated humor generation. We present a live crowd-to-story system that automatically groups and prioritizes pseudonymous audience comments and integrates selected threads into approximately one-minute audiovisual story blocks. The system clusters related comments, prioritizes them by narrative fit and audience response, preserves recognizable audience phrasing through near-verbatim ``whispers,'' and stores successful ideas in memory so they can return as recurring jokes and callbacks. This design creates a rapid feedback loop in which participants can see their contributions acknowledged by both the crowd and the story. Across 133 stories, 401 participants, and 793 person-in-story observations, early inclusion of a participant's comment in played story content was associated with a 51.7\% adjusted difference in the geometric mean of subsequent commenting. Comment-linked dialogue and on-screen whispers showed positive adjusted differences of 38.1\% and 28.1\%, respectively. These observational results do not establish causality, but they support the design principle that rapid, recognizable inclusion can reinforce participation. We argue that comic payoff in live generative storytelling emerges through distributed agency among the audience, AI writers, platform, and human creators.