Search and recommendation systems — the engines powering web search, short-video feeds, music, news, advertising, and e-commerce — are undergoing their most significant paradigm shift in a decade. Multi-stage discriminative pipelines are giving way to unified generative foundation models that directly produce items, queries, and entire user trajectories, and in doing so eliminate cascaded error propagation, improve hardware utilization, and unlock optimization objectives that reach far beyond next-click prediction. Driven by rapid progress in large language models and the discovery of scaling laws for search and recommendation, this transition is quietly reshaping how the field thinks about retrieval, ranking, and personalization at the largest scales.

Realizing the paradigm in production, however, is a different challenge entirely: modern discovery platforms must serve billions of users, reason over catalogs that are approaching billions of candidate items, and respond within hundreds of milliseconds, all while training models that consume orders of magnitude more compute than their discriminative predecessors. This Expo workshop brings together leading researchers and industrial practitioners through invited talks and a moderated panel to confront the questions the community is asking right now:

- Are search and recommendation finally converging into a single foundation model?
- What are the right scaling laws for discovery, and where do they bend?
- How do generative systems remain efficient at trillion-item scale while meeting sub-50-millisecond latency budgets?
- And how should they handle non-stationary catalogs, continuous learning, cold-start items, fairness, and multi-objective trade-offs in real production traffic?