About this episode

Dan Biderman and Jessy Lin discuss their innovative approach to memory and continual learning in AI with a focus on integrating knowledge into model weights.

Dan Biderman and Jessy Lin, co-founders of Engram, are building a neolab around memory and continual learning, which they call two sides of the same coin. Their contrarian premise: instead of stuffing ever-larger prompts into the context window or bolting on RAG, bake a team's knowledge directly into the model's weights, so it knows your company the way an employee of several years does. The payoff: matching or beating frontier models while consuming up to 100x fewer tokens. Working with partners like Microsoft, Notion, and Harvey, the team draws on roots in computational neuroscience and state-space architectures to attack what they see as the real bottleneck in AI — not raw intelligence, but memory and continual learning. In contrast to the frontier labs' race toward one ever-bigger model and AGI, Dan and Jessy imagine a world where everyone has their own model — privately trained, always learning, and good at the things you actually care about. The real ChatGPT moment for memory, they argue, is the day your model feels like an intern that genuinely got smarter overnight. Hosted by Sonya Huang and Shaun Maguire, Sequoia Capital

People in this episode

Hosts: Sonya Huang, Shaun Maguire

Guests: Dan Biderman, Jessy Lin

Topics covered

• memory
• continual learning
• AI models
• computational neuroscience
• knowledge integration
• technology innovation

Keywords

• memory
• continual learning
• AI
• Engram
• model training
• computational neuroscience
• knowledge integration

Mentioned in this episode

Organizations: Engram, Microsoft, Notion, Harvey

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