Memory and Continual Learning: Engram's Dan Biderman and Jessy Lin
Sequoia Capital
Jun 24, 2026
Don Beerman and Jesse Lynn of Engram argue that relying on external retrieval (RAG) is a bottleneck for AI utility. They propose that models must move beyond static pre-training and external lookups to internalize company-specific context directly into their weights, enabling them to evolve alongside teams and perform complex tasks with significantly higher efficiency and lower token consumption.
Key insight: Engram’s founders suggest that internalizing context into model weights can reduce inference token consumption by up to 100x compared to traditional RAG-based approaches, as the model no longer needs to repeatedly process massive system prompts or search through external documents.
