Sep 25, 2026

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

Devansh Tandon, who leads Meta Recommendations Research, argues that recommendation systems scale like LLMs, that the field is early on that curve, and that LLM recommenders will become one of AI's biggest consumer applications. He walks through four S-curves of recommendation, a three-step recipe built on semantic IDs, and why content feeds can be up to 100x cheaper per hour of engagement than AI chat apps.

Sep 25, 2026

Distill the LLM, Don't Serve It: Search & Personalization at DoorDash — Raghav Saboo, DoorDash

Raghav Saboo, Staff ML Engineer at DoorDash, argues that semantic understanding, not engagement, is the real bottleneck in marketplace discovery. He walks through four LLM-driven primitives (graded relevance supervision, semantic IDs, multi-timescale consumer memory, and steerable personalized collections) and explains the core pattern of reasoning offline with LLMs and distilling that into fast serving models.