1017: Vector Search, Agentic Memory and Effective RAG, with MongoDB’s Pete Johnson
About this episode
Pete Johnson discusses AI strategies, the embedding model, and agentic memory in the context of organizational AI investments.
In Episode #1017, Pete Johnson (Field CTO of AI at MongoDB) joins Jon Krohn to explain why four out of five organizations have AI steering committees and success metrics, yet only one in five sees a return on the investment. Having made nineteen stops across six countries this year advising more than a hundred companies on their AI strategies, Pete has an unusually wide view of what is actually working in production. In this episode, he traces the history of SQL and denormalization, unpacks why the embedding model is the most underrated choice in a RAG pipeline, explains Matryoshka embeddings and lays out what better agentic memory looks like. Additional materials: https://www.superdatascience.com/1017 Interested in sponsoring a SuperDataScience Podcast episode? Email natalie@superdatascience.com for sponsorship information. In this episode you will learn: (00:06:34) Why the AI ROI gap happens and what to do differently (00:18:21) Jevons paradox, bank tellers and toll booth workers (00:24:05) From Codd’s 1970 paper to denormalization (00:32:31) Why the embedding model is not a commodity…
More episodes of Super Data Science: ML & AI Podcast with Jon Krohn
- 1025: Word Gravity: How Transformers Bend Space, with Dr. Luis Serrano
- 1024: In Case You Missed It in August 2026
- 1023: Agentic AI Skills That Matter Now, with Aishwarya Srinivasan
- 1022: CLAUDE.md, AGENTS.md, Skills, Hooks and Subagents: A Field Guide to Steering AI Agents
- 1021: How dbt Won Analytics Engineering, with dbt Lab’s CEO Tristan Handy
- 1020: How to Choose Model Size and Effort Level: The Two Critical Dials
Explore listener stats, chart rankings, contacts and more on the Super Data Science: ML & AI Podcast with Jon Krohn podcast page.