Anthropic’s CPO on what comes next | Mike Krieger (co-founder of Instagram)

Jun 5, 2025 1h 6m 29 insights Episode Page ↗
Mike Krieger, CPO at Anthropic and Instagram co-founder, discusses how AI is transforming product development, the evolving capabilities of models, and the strategic direction of Anthropic. He also shares insights on skills for kids, the shutdown of Artifact, and Claude's unique perspective.
Actionable Insights

1. Integrate Model, Context, UI

To build truly useful AI products, ensure the convergence of strong model intelligence, robust context and memory management (e.g., via MCP), and intuitive applications/user interfaces.

2. Sharpen Strategic Focus

Product teams must sharpen their strategic focus to determine where to play and what to build, as AI enables broader possibilities but still requires deliberate choices to avoid spreading too thin.

3. Embrace Unique Brand Identity

For challenger brands, embrace and lean into your unique strengths and what you can be, rather than trying to imitate or directly compete with market leaders on their terms.

4. Focus AI as Creativity Unlocker

Aim for AI to be a North Star that unlocks user creativity, helps them get things done, and creates more personal space in their lives, moving beyond simple task automation.

5. Measure AI by Work Done

Evaluate AI products based on their ability to help users accomplish work and save time, rather than solely relying on traditional engagement metrics, which may not capture the true value.

6. Re-evaluate AI Product Teams

Re-evaluate and adapt the traditional product development team structure and roles to better align with the efficiencies and new bottlenecks introduced by AI-driven code generation.

7. Empower Founding Engineers

Strongly believe in and empower founding engineer/tech lead types with strong ideas, providing them with appropriate design and product support to help them realize their vision.

8. Maintain Startup Mentality

Even within larger organizations, cultivate an existential, ‘us against the world’ startup mentality to drive innovation and problem-solving, as this cannot be replicated by OKRs alone.

9. Develop AI Prompt Engineering

Engineers and product builders need to develop specialized skills in composing effective AI prompts and structuring changes, as this remains a critical and difficult aspect of leveraging AI for code generation.

10. Push AI Model Capabilities

Companies building on AI models should be willing to push the models to their limits, even breaking them, to discover new capabilities and prepare for future model advancements.

11. Establish Repeatable AI Evaluation

Implement repeatable processes to evaluate how well AI-powered products serve specific use cases and to assess the impact of new models (e.g., AB testing, internal evaluation, re-running traces).

12. Proactively Address New Bottlenecks

Identify and address new bottlenecks that emerge with AI-accelerated development, such as decision-making, alignment, and managing merge queues, by thinking through edge cases and coordinating work.

13. Nurture Kids’ Curiosity

Encourage children to ask questions, discover, and systematically find answers rather than solely relying on AI, fostering independent thought and inquiry.

14. Foster Independent Thinking

Encourage independent thought and critical thinking, rather than delegating all cognition to AI, as AI may not always be right and over-reliance can short-circuit personal inquiry.

15. Prioritize AI Comprehensibility

Focus on making AI tools and their capabilities comprehensible to users, as understanding how to effectively query and leverage AI is a crucial skill for product development and general use.

16. Educate on AI Potential

Actively work to open users’ eyes to the full potential of AI tools, bridging the ‘overhang’ between what models can do and how they are currently being used in daily practice.

17. Build AI for Niche Industries

AI founders should focus on building solutions for specific industries (e.g., legal, healthcare, biotech) where deep, differentiated market knowledge provides defensibility against foundational model companies.

18. Develop Differentiated Go-to-Market

Cultivate strong relationships and deep understanding of specific customers and their roles within target companies, providing a defensible go-to-market strategy for AI startups.

19. Innovate AI Interface Form Factors

Explore and develop completely different form factors for interfacing with AI, as novel approaches can create new markets and provide an advantage over incumbents with established user assumptions.

20. Focus AI for Productivity

Prioritize building AI applications that help users get work done and delegate tasks, even if it means fewer direct consumer applications initially, as this aligns with AI’s core strengths.

21. Prototype Early with AI Tools

Utilize AI tools for early-stage prototyping, allowing PMs and designers to create functional demos to tangibly express ideas, accelerating the development process.

22. Leverage AI for Code Review

Use AI models to review code generated by other AI models, shifting human effort towards acceptance testing rather than line-by-line review, especially for large AI-generated pull requests.

23. Standardize AI Integrations

When building multiple AI integrations, standardize them into a repeatable protocol (like MCP) to avoid rebuilding from scratch, making them usable across different models and platforms.

24. Train AI for Conversational Nuance

Focus on training AI models to develop nuanced conversational skills, understanding when to engage, ask for more information, or remain silent, fostering true collaboration rather than just chatbot interactions.

25. Avoid Over-Optimizing Likability

Do not over-optimize AI models for metrics like ’likability’ or conversation length, as this can lead to sycophantic or unhelpful behavior that diminishes true value.

26. Utilize AI Prompt Improvers

Use AI-powered prompt improvers (like Claude’s workbench tool) to generate and iterate on prompts, as these tools can suggest effective structures (e.g., XML tags) that human intuition might miss.

27. Prompt AI for Brutal Feedback

When seeking critical feedback from AI, use blunt and direct language like ‘be brutal’ or ‘roast me’ to push the model beyond anodyne comments and elicit more critical insights.

28. Use ‘Think Hard’ for Reasoning

When prompting AI for more reasoning, explicitly ask it to ’think hard’ to encourage a different, more in-depth processing flow.

29. Seek Direct AI Feedback

Actively solicit direct feedback from users about specific instances where AI tools fail or fall short of expectations, as this ‘what sucks’ input is the most valuable for improvement.