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

Four of the 10 most-used apps in the world are content feeds, and for an hour of engagement they can be up to 100x cheaper to run than AI chat apps. The reason is that a feed decodes pointers to existing content instead of generating every token itself. Devansh Tandon, who leads Meta Recommendations Research, argues that recommendation systems scale just like LLMs, that the field is still early on that curve, and that the LLM recommender will become one of the biggest consumer applications of AI. Tandon lays out four S-curves the industry is climbing: traditional recsys, LLM-inspired, LLM-native and agentic. He then walks through the recipe for building an LLM recommender. First, tokenize content with semantic IDs, which turns a three-minute Reel from about 10,000 tokens into about 10. Next, make the model bilingual in English and your catalog. Then post-train it to rank, with chain-of-thought reasoning you can read. He also shows how this makes feeds steerable, like Instagram's Your Algorithm, where you can see and edit what the algorithm thinks you like. Speaker info: X/Twitter: @devanshtandon_ (https://x.com/devanshtandon_) LinkedIn: https://www.linkedin.com/in/devanshtandon/ Related links: Meta AI: https://ai.meta.com Timestamps: 0:00 Intro: two big arguments 0:45 About Devansh 1:30 Semantic IDs and generative retrieval go mainstream 2:15 Scaling laws, from LLMs to recommenders 3:45 Real scaling curves at Meta 4:15 Instagram Reels: 30% more watch time 4:55 The tokens in, engagement out flywheel 5:40 Four S-curves of recommendation 6:35 LLM-native recommenders 6:50 Agentic recommenders 8:05 The three-step recipe for an LLM recommender 8:35 The five-layer cake 9:35 Semantic IDs: a Reel in 10 tokens 10:45 Pre-training on English and semantic IDs 11:25 Post-training: an LLM re-ranker with chain of thought 12:25 Steerable feeds: talking to your Instagram algorithm 13:15 Explaining recommendations 13:50 Prompted playlists, custom feeds and Ask DoorDash 14:05 Feeds vs. chat apps: the same flywheel 14:50 Why feeds are 100x cheaper per hour of engagement 16:25 Why LLM recsys is AI's biggest consumer app 17:40 Wrap-up