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Mike Krieger

Chief Product Officer (CPO) at Anthropic

Anthropic

⚡ Execution (1)🎯 Product Strategy (2)

Key Takeaways

  • 1.90% of code at Anthropic is written by AI, shifting bottlenecks from engineering implementation to decision-making and merge queues.
  • 2.Product managers should focus less on specs and more on strategy, comprehensibility, and 'opening eyes' to what's possible.
  • 3.The 'Make the Other Mistake' prompting technique: If the model is too nice, explicitly ask it to be brutal or roast your ideas.
  • 4.Successful AI products require the convergence of three elements: Model Intelligence, Context/Memory (MCP), and Application UI.
  • 5.Don't build features that just create dependency; build for user agency and augmentation.
  • 6.Founders can survive by focusing on deep vertical workflows (e.g., legal, biotech) or differentiated go-to-market strategies.

Methodologies(3)

⚡ Execution

When AI writes 90% of the code, the traditional PM-Designer-Engineer handover breaks down. The bottleneck shifts from 'writing code' to 'decision making' (upstream) and 'merge queues' (downstream). Teams must re-architect their infrastructure and review processes to handle this velocity.

Core Principles

  • 1.Shift Prototyping Left: PMs and designers use tools like Claude Artifacts to build functional prototypes, not just mockups.
  • 2.Re-architect Critical Paths: Optimize merge queues and CI/CD pipelines as the volume of Pull Requests (PRs) explodes.
  • 3.AI-Assisted Review: Use AI agents to review AI-generated code, focusing human effort on acceptance testing rather than line-by-line syntax checks.

"We really rapidly became bottlenecked on other things like our merge queue... Over half of our pull requests are Claude Code generated. Probably at this point it's probably over 70%... or 90%."

#ai-native#development#execution
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🎯 Product Strategy

To get high-quality strategic critique from an AI, you must explicitly push it to the opposite extreme of its training. If it's too nice, ask it to be brutal. If it's too shallow, force it to 'think hard' about reasoning before answering.

Core Principles

  • 1.Roast the Strategy: Explicitly instruct the AI to be brutal, critical, or 'roast' the user's ideas to break its politeness filter.
  • 2.Request Reasoning: Ask the model to 'think hard' or output its reasoning chain before giving the final answer.
  • 3.Meta-Prompting: Use the model to write its own system prompts (using tools like Prompt Improver) because AI understands its own XML tag structures better than humans do.

"With Claude sometimes I'm like, 'Be brutal, Claude, roast me. Tell me what's wrong with this strategy.'... make the other mistake."

#'make#other#mistake'
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🎯 Product Strategy

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.

Core Principles

  • 1.Model Intelligence: The raw capability and reasoning power (Research team's output).
  • 2.Context & Memory: The bridge connecting the model to proprietary data (solved via MCP - Model Context Protocol). Without this, answers are generic.
  • 3.Application & UI: The workflow layer that makes integrations discoverable and results actionable.

"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."

#utility#equation#strategy
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