WhoBuilt

Inside the Best AI Model for Coding and Writing

Mar 9, 2025· 48 min· 7.7K views·Watch on YouTube

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Scott White, head of product for Claude at Anthropic, joins Peter Yang to discuss Claude 3.7 Sonnet, AI product development, and how Claude may evolve into a collaborator.

Claude 3.7 and practical work

White describes Claude 3.7 Sonnet as Anthropic’s first hybrid reasoning model, with a fast response mode and Extended Thinking for harder tasks. He says the model is tuned for workplace needs such as production coding, and that its coding ability also helps Anthropic build products faster.

Dogfooding and new ways to work

White says customer feedback and internal use shape the problems Anthropic prioritizes for training and product work. He uses Claude to summarize meetings, draft PRDs, connect information across documents, and build diagrams or React prototypes. He describes teams across design, engineering, product, and sales contributing more directly to product ideas.

Personality and research collaboration

White says companies may use different models for different tasks, and that Claude’s character is another point of distinction. He names curiosity, openness to multiple perspectives, truthfulness, and self-awareness as traits Anthropic encourages. He also describes research as a partner throughout product development, with researchers embedded in product teams to connect user problems to model improvements.

Evals as part of product development

White walks through building Styles, which tailors Claude’s responses to preferences such as concise or explanatory writing. He turns use cases into evaluations, using grounded examples or Claude-graded outputs with human oversight. Those evals caught a problem before launch: Claude 3.7 initially disrupted user preferences, so Anthropic made adjustments before release.

From assistant to collaborator

White describes a shift from Claude as an assistant that needs precise instructions toward a collaborator that can understand more context and take meaningful tasks off a user’s plate. He expects this to come from many model and product capabilities working together. He advises people interested in AI product work to build something themselves to develop practical intuition.

Takeaways

  • Use customer feedback and internal use to identify which model capabilities matter in real work.
  • Turn product use cases into evaluations, then keep those evaluations in the regression process as models change.
  • Use Extended Thinking for complex tasks such as connecting information across a large set of documents.
  • Prototypes can help teams make progress and share ideas before a full implementation exists.
  • Build a small AI product to learn how prompting, evaluation, and model behavior interact.

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