My conversation with @jerryjliu0. He's building @llama_index: document infrastructure for AI agents, the layer underneath everything models need to work. Did you know he's the guy whose tool half the industry used for RAG? Then he called RAG a hack, two years before the field agreed. Here's the problem he's pointing at: 90% of the world's information is locked inside PDFs, PowerPoints, and Word documents. The models are already smarter than humans. But until an agent can read and reason over all that, it just sits still. RAG was the hack everyone reached for to bridge that gap: chunk, embed, retrieve, done. The easiest thing that kind of worked, so people could ship demos. Anything complex, and it falls apart. We got into: •Why RAG has no first principles, and what's replacing it •Why context, not intelligence, is the real bottleneck now •Why even MCP doesn't fully solve context •How you get agents to follow a company's culture, not just its instructions •What he'd build if he started LlamaIndex today 𝐓𝐈𝐌𝐄𝐒𝐓𝐀𝐌𝐏𝐒 (00:00) The Context Bottleneck (02:00) One Commit, One Tweet: The Origin (05:00) Feeding GPT-3 Your Private Data, Pre-RAG (08:00) From Quora to Uber's Self-Driving Lab (11:00) Meeting His Co-Founder at Uber ATG (14:00) Document Infrastructure for AI Agents (18:00) "RAG Is a Hack" — What He Meant (22:00) Why Production Search Is Still Unsolved (24:00) How a Research Background Changes How You Build (33:00) The Open Problem: Context Across Sources (38:00) Specs, Cultural Context, and Underspecified Tasks (40:00) What He'd Build Differently Today