The AI Utility Equation
by Mike Krieger • Chief Product Officer (CPO) at Anthropic at Anthropic
Co-founder and former CTO of Instagram. Currently leading product at Anthropic (makers of Claude), where he oversees the development of AI models and products like Claude and Artifacts.
🎙️ Episode Context
Mike Krieger discusses the radical shift in software development at Anthropic, where 90% of code is now written by AI. He explores how product management evolves when engineering barriers vanish, the strategic importance of MCP (Model Context Protocol), and how to compete as a 'challenger' brand against OpenAI. He also shares lessons from shutting down his news app, Artifact, and advice for AI founders on avoiding being crushed by foundational models.
Problem It Solves
Defining why an AI product fails to be useful despite having a strong model.
Framework Overview
Useful AI products are not just about the model. They require a convergence of three distinct layers: Model Intelligence, Context/Memory, and Application/UI. Focusing only on one leaves the product incomplete.
🧠 Framework Structure
Model Intelligence: The raw capabilit...
Context & Memory: The bridge connecti...
Application & UI: The workflow layer ...
When to Use
When evaluating why an AI feature isn't getting traction or when planning a roadmap for an AI-native application.
Common Mistakes
Thinking better models alone will solve product problems, ignoring the 'Context' gap where the model doesn't know the user's specific data.
Real World Example
Anthropic realized that building one-off integrations wasn't scaling. They introduced MCP (Model Context Protocol) to solve the 'Context & Memory' variable universally, allowing Claude to connect to Google Drive, Slack, etc., without custom code each time.
For utility of AI products, it's three part. One is model intelligence, the second part is context and memory, and the third part is applications and UI.
— Mike Krieger