Andy Pavlo: What Happens When Billions of AI Agents Hit Your Database

AI agents are becoming the biggest users of databases: creating them, querying them, and sometimes deleting them. Andy Pavlo, Carnegie Mellon's database professor and now VP of Database Research at ClickHouse, explains what changes when billions of agents hit the data layer, and why most of the AI world's mental model of databases stopped at vector databases and RAG. Andy's CMU courses taught a generation of engineers (and, it turns out, the AI models); he writes the year-in-review on databases the whole industry reads; and this summer he joined ClickHouse to found ClickHouse Labs. We cover why vector databases were "just an index," what it means for an agent to create a database, why agents could mean 10 to 100x more queries, whether files or databases should hold an agent's memory, how text-to-SQL went from 60% to 99.5% accuracy, why 60% of open-source databases now have commits from coding agents, and what ten years of self-driving database research taught him. Plus ClickHouse, Postgres, the vector/graph/GPU database verdicts, Larry Ellison, and the Wu-Tang Clan. Disclosure: FirstMark, where Matt is a General Partner, is an investor in ClickHouse. Andy Pavlo LinkedIn — https://www.linkedin.com/in/andy-pavlo/ X — https://x.com/andy_pavlo ClickHouse Website — https://clickhouse.com X — https://x.com/ClickHouseDB Carnegie Mellon University Website — https://www.cmu.edu X — https://x.com/CarnegieMellon Matt Turck (General Partner) Blog - https://mattturck.com LinkedIn - https://www.linkedin.com/in/turck/ X - https://x.com/mattturck FirstMark Capital Website - https://firstmark.com X - https://x.com/FirstMarkCap Listen on: Spotify - https://open.spotify.com/show/7yLATDSaFvgJG80ACcRJtq Apple - https://podcasts.apple.com/us/podcast/the-mad-podcast-with-matt-turck/id168623872 00:00 Cold open & Intro 01:21 From vector databases and RAG to the age of agents 04:09 Neon's stat: agents create 80% of databases? 07:25 Why agents keep deleting production databases 08:19 Guardrails: the toddler-and-stairs rule 10:51 The four eras of database volume: 10–100x more queries 15:10 Agent memory: files vs. databases ("everything is a database") 18:24 Which database do AI models recommend? The new SEO 19:25 "It cites me back to myself" 23:14 MCP for databases, and text-to-SQL from 60% to 99.5% 27:09 Trust an agent the way you'd trust a junior developer 27:47 Can AI build an entire database? Opus 4 and the CMU projects 29:57 60% of open-source databases now have AI commits 30:43 Ten years of self-driving databases, Peloton to today 33:52 What LLMs changed: 85% of the tuning in 15 minutes 35:42 Should anyone still study databases? 38:27 Free CMU courses, the DJ, and the Wu-Tang final exam 42:09 Why start a research lab inside ClickHouse? 45:53 Why ClickHouse looked like vaporware in 2016 47:46 What makes ClickHouse fast: columns, vectors, Snowflake's lineage 52:22 Why Databricks, Snowflake and ClickHouse all added Postgres 58:15 Vector, graph and GPU databases: thumbs up or down? 1:08:34 Is the database market stagnant? "A cheetah on cocaine in a Ferrari" 1:10:14 The relational model is arithmetic; SQL as the new assembly 1:11:59 Larry Ellison, Linux, and why databases still matter #AI #AIAgents #Databases #ClickHouse #Postgres #DataEngineering #MachineLearning #LLM #MADPodcast #AIPodcast

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