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What if not every part of an AI model needed to think at once? In this episode, we unpack Mixture of Experts, the architecture behind efficient large language models like Mixtral. From conditional computation and sparse activation to routing, load balancing, and the fight against router collapse, we explore how MoE breaks the old link between size and compute. As scaling hits physical and economic limits, could selective intelligence be the next leap toward general intelligence?

Sources

• What is mixture of experts? (IBM)
• Applying Mixture of Experts in LLM Architectures (Nvidia)
• A 2025 Guide to Mixture-of-Experts for Lean LLMs
• A Comprehensive Survey of Mixture-of-Experts: Algorithms, Theory, and Applications

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In this episode of The Second Brain AI Podcast, we dive into the protocols quietly wiring the agentic AI ecosystem. From MCP (Model Context Protocol) that lets models securely access tools, to A2A (Agent-to-Agent) that standardizes how agents collaborate, and AP2 (Agent Payments Protocol) that anchors transactions in cryptographic trust, these frameworks form the plumbing of the AI future.

We explore why interoperability is the real bottleneck, how these standards build a “digital delegation stack,” and why the future of trust in AI won’t rely on human oversight but on mathematical proof.

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How are people really using AI, at home, at work, and across the globe? In this episode of The Second Brain AI Podcast, we dive into two reports from OpenAI and Anthropic that reveal the surprising split between consumer and enterprise use.

From billions in hidden consumer surplus to the rise of automation vs augmentation, and from emerging markets skipping skill gaps to enterprises wrestling with “context bottlenecks,” we explore what these usage patterns mean for productivity, global inequality, and the future of knowledge work.

Source:

• Anthropic Economic Index report: Uneven geographic and enterprise AI adoption
• How people are using ChatGPT
• Building more helpful ChatGPT experiences for everyone