OpenAI’s CPO on how AI changes must-have skills, moats, coding, startup playbooks, more | Kevin Weil (CPO at OpenAI, ex-Instagram, Twitter)

Apr 10, 2025 1h 31m 18 insights Episode Page ↗
Kevin Wheel, Chief Product Officer at OpenAI, discusses the rapid pace of AI development, the importance of evals for product building, and how AI will transform work and creativity. He also shares insights on where startups can thrive in the AI ecosystem.
Actionable Insights

1. Mindset for AI Builders

Adopt a mindset that current AI models are the worst you’ll ever use, as capabilities advance every two months. This encourages continuous innovation and rethinking product strategies, as technology changes underneath you so quickly.

2. Build on the Edge

If your product is currently pushing the limits of AI models, continue building. Models improve so rapidly that what barely works today will “sing” in a few months, allowing you to innovate ahead of the curve.

3. Master Writing Evals

Learn to write “evals” (tests or quizzes for AI models) to understand model capabilities and guide product development. This skill is crucial for knowing when a model is 60% vs. 99.5% accurate for a use case and for fine-tuning models to improve performance on specific use cases.

4. Leverage AI for Prototyping

Use AI tools like Cursor or Windsurf for “vibe coding” to rapidly create prototypes and proofs of concept instead of traditional design tools. This allows for quick exploration of ideas and demos, even if the code isn’t production-ready.

5. Build AI Startups in Verticals

Focus on building AI-based products in specific industries and verticals, leveraging company-specific or use-case-specific data. Foundational model companies like OpenAI won’t pursue most of these niche applications, creating immense opportunities for startups.

6. Integrate Researchers into Teams

Build product teams that include researchers or machine learning engineers, especially for fine-tuning models. This collaborative approach, where research, engineering, and product work as a single unit, leads to more novel and effective AI products.

7. Design AI Like Humans

When designing user interfaces and experiences for AI, consider how humans interact with each other. For example, when an AI needs to “think,” it can provide small updates or “babble” like a human, rather than just pausing silently.

8. Prioritize Iterative Deployment

Adopt an “iterative deployment” philosophy by shipping products early and often, even when not fully polished. This allows for co-evolution with users and society, gathering feedback and iterating in public to learn about model capabilities together.

9. Embrace Model Maximalism

Don’t over-engineer scaffolding around AI models to fix minor imperfections. Instead, embrace “model maximalism” by building for the capabilities that are almost there, trusting that future model improvements will quickly address current limitations.

10. Structure Teams PM-Light

Aim for a PM-light organizational structure where PMs work with slightly too many engineers. This empowers engineers to make decisions, fosters product-focused engineering, and encourages rapid movement by preventing micromanagement.

11. Hire for High Agency

When hiring for AI product roles (especially PMs), prioritize candidates with “high agency” who proactively solve problems without waiting for permission, and who are comfortable with massive ambiguity due to the rapidly changing nature of AI.

12. Be Decisive as Leader

As a product manager or leader, cultivate decisiveness. Understand when to empower your team to innovate and when to step in to make a clear call on ambiguous issues to ensure progress and avoid stagnation.

13. Utilize Fine-Tuned Models

Break down complex problems into specific tasks and use fine-tuned models or ensembles of different models for each. This allows for specialized performance, cost optimization, and better overall problem-solving, similar to how human teams work.

14. Teach Kids Core Skills

Focus on teaching children fundamental skills like curiosity, independence, and self-confidence, along with how to think. These qualities are considered essential for navigating an unpredictable future shaped by rapidly advancing AI.

15. Use Examples in Prompts

Improve AI output by providing examples within your prompts, effectively performing “poor man’s fine-tuning.” Include “Here’s an example, here’s a good answer” multiple times before asking the model to solve your problem.

16. Frame AI Prompts Effectively

Enhance AI responses by framing your prompts with role-playing (e.g., “You are the world’s greatest marketer”) or by emphasizing the importance of the task (e.g., “This is very, very important to my career”). This can shift the model into a more effective mindset.

17. Practice Consistent Good Work

Adopt the philosophy that consistent, good work over a long period, rather than seeking a “silver bullet,” leads to compounding gains and significant excellence. This applies to personal growth and professional achievement.

18. Provide Feedback to Developers

Actively provide feedback to AI developers about what works well and what fails in their products. This helps them understand user needs and improve models and features.