Why LLM Recommenders Will Be AI's Biggest Consumer App — Devansh Tandon, Meta

This talk introduces the concept of LLM-native and agentic recommenders, proposing a three-step recipe for building them: 1. Tokenize content with semantic IDs (e.g., a Reel into ~10 tokens). 2. Pre-train the model to be bilingual in English and the catalog of semantic IDs. 3. Post-train the model as an LLM re-ranker with readable chain-of-thought reasoning. It also demonstrates steerable feeds (like Instagram's Your Algorithm) and explains the cost advantages of feed-based engagement over generative AI chat apps, positioning LLM recommenders as a significant future consumer AI application.