Handling Millions of Small Indices Efficiently

What happens when one of the architects of modern vector search asks whether AI can remember like a seasoned engineer, not a gold‑fish savant? In this episode, Edo Liberty – founder & CEO of Pinecone and one‑time Amazon scientist – joins me to discuss true memory in LLMs. We unpack the gap between raw cognitive skill and workable knowledge, why RAG still feels pre‑ChatGPT, and the breakthroughs needed to move from demo‑ware to dependable memory stacks.

Edo explains why a vector database needs to be built from the ground (and then rebuilt many times), that storage – not compute – has become the next hardware frontier, and predicts a near‑term future where ingesting a million documents is table stakes for any serious agent. We also touch the thorny issues of truth, contested data, and whether knowledgeable AI is an inevitable waypoint on the road to AGI.

Whether you wrangle embeddings for a living, scout the next infrastructure wave, or simply wonder how machines will keep their facts straight, this conversation will sharpen your view of “memory” in the age of autonomous agents.

Let’s find out when tomorrow’s AI will finally remember what matters.

(CORRECTION: the opening slide introduces Edo Liberty as a co-founder. We apologize for this error: Edo Liberty is the Founder and CEO of Pinecone.)

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Guest:

Edo Liberty, CEO and founder at Pinecone

Website: https://www.pinecone.io/

Additional Reading:

https://www.turingpost.com/

Chapters

00:00 Intro & The Big Question – When will we give AI true memory?

01:20 Defining AI Memory and Knowledge

02:50 The Current State of Memory Systems in AI

04:35 What’s Missing for “True Memory”?

06:00 Hardware and Software Scaling Challenges

07:45 Contextual Models and Memory-Aware Retrieval

08:55 Query Understanding as a Task, Not a String

10:00 Pinecone’s Full Stack Approach

11:00 Commoditization of Vector Databases?

13:00 When Scale Breaks Your Architecture

15:00 The Rise of Multi-Tenant & Micro-Indexing

17:25 Dynamically Choosing the Right Indexing Method

19:05 Infrastructure for Agentic Workflows

20:15 The Hard Questions: What is Knowledge?

21:55 Truth vs Frequency in AI

22:45 What is “Knowledgeable AI”?

23:35 Is Memory a Path to AGI?

24:40 A Book That Shaped a CEO – *Endurance* by Shackleton

26:45 What Excites or Worries You About AI’s Future?

29:10 Final Thoughts: Sea Change is Here

In Turing Post we love machine learning and AI so deeply that we cover it extensively from all perspectives: past of it, its present, and our joint-future. We explain what happens the way you will understand.

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