Long Context Isn't Enough: AI Needs Memory
Latent Space · youtube · 2026-07-14
This is an in-depth Latent Space interview with Engram co-founder and CEO Dan Biderman, focusing on "why long context isn't enough and how AI memory should actually work."
Key discussions include:
- Relying solely on long context, RAG, and compaction will eventually hit performance and cost bottlenecks.
- Engram aims to compress knowledge into "cartridges" and model weights to drive continuous learning and longer-term memory.
- They emphasize that token efficiency and intelligence are inseparable, and personal AI can continuously update like a Tamagotchi.
- The interview also covers the research and infrastructure required for continuously updating memory systems, and how enterprise knowledge queries can move beyond traditional RAG.
The video info also mentions Engram's $98M launch and the team's investment in research and engineering.
Related event: Engram CEO: Long Context is Not Enough for AI Memory(2 posts)→
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