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Kevin Weil

Chief Product Officer

OpenAI

🎯 Product Strategy (1)⚡ Execution (1)🔍 User Research (1)

Key Takeaways

  • 1.The AI models you use today are the worst you will ever use; build for where the puck is going, not current limitations.
  • 2.Writing 'evals' (evaluations) is becoming a core competency for Product Managers to measure model performance on specific use cases.
  • 3.Adopt 'Iterative Deployment': ship early to co-evolve with society rather than waiting for perfection.
  • 4.Treat AI models like humans: use 'chain of thought' for reasoning and 'ensembles' of models like a team of experts.
  • 5.PMs need to be 'High Agency' and comfortable with extreme ambiguity; OpenAI keeps the PM-to-Engineer ratio low to prevent micromanagement.
  • 6.Future product teams will embed researchers and ML engineers to handle fine-tuning for specific industry data.

Methodologies(3)

🎯 Product Strategy

A strategic mindset that assumes AI models will improve drastically every few months. Instead of building complex workarounds for current flaws, teams should build products that push the edge of current capabilities, knowing the next model update will likely solve the friction points.

Core Principles

  • 1.Build on the edge: If your product barely works today, it will 'sing' with the next model update.
  • 2.Ignore temporary flaws: Don't spend excessive resources fixing errors that model scaling will naturally solve.
  • 3.Iterative Deployment: Ship continuously to learn how users interact with the model as it improves.

"If the product that you're building is kind of right on the edge of the capabilities of the models, keep going because you're doing something right."

#maximalism#strategy#product
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⚡ Execution

Product Managers must define 'hero use cases' and translate them into specific evaluations (evals)—essentially quizzes for the model. Development becomes a process of hill-climbing on these eval scores, often using fine-tuning to improve performance on specific tasks.

Core Principles

  • 1.Define Hero Use Cases: Identify the specific complex queries or tasks the product must solve.
  • 2.Create Custom Evals: Build a dataset of questions and 'perfect' answers to grade the model.
  • 3.Fine-tune & Hill Climb: Use the data to teach the model and measure progress against the evals continuously.

"Writing evals is quickly becoming a core skill for product builders."

#evals-driven#development#cycle
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🔍 User Research

Design interfaces and workflows by asking 'How would a human handle this?' This informs decisions on latency (showing thinking process vs. silence), error handling, and using chat as a universal interface because it matches human communication flexibility.

Core Principles

  • 1.Show the Work: Just as a human says 'Let me think about that,' the UI should show the model's reasoning steps (summarized) to manage wait times.
  • 2.Ensemble Intelligence: Treat different models like a team of experts (one for coding, one for writing) and use an orchestrator to combine their outputs.
  • 3.Chat as Universal Interface: Chat handles the nuances of intelligence better than rigid buttons.
  • +1 more...

"You can often reason about it the way you would reason about another human and it works."

#human-analog#interaction#design
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