How to build an AI-Native Health Company — Dan Feng, Maven Clinic
The transition to an AI-native company requires a fundamental shift in process and culture, moving away from lengthy planning cycles toward rapid, iterative development. While building software is now fast (minutes), the expense lies in arguing requirements. Build engineers must adapt by adopting short-cycle planning (2–4 weeks) and implementing rigorous, multi-layered testing strategies to manage AI-specific risks like hallucination. Key process changes include limiting Pull Request (PR) size (capped near 500 lines) and running integration tests multiple times to ensure reliability.
Key takeaways
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Shift Planning Focus
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Instead of spending weeks or months finalizing requirements, focus on delivering value in the next two to four weeks. Long-term plans (1 year) should only serve as directional inspiration, not rigid commitments.
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Redefining Code Review
Due to increased code output from AI tools, traditional code review must change. Engineers can self-certify simple PRs, and large features should be stacked into multiple smaller PRs (capped near 500 lines) to maintain meaningful review quality.
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AI Reliability Testing
For GenAI solutions, failure modes must be categorized (tolerable vs. not acceptable). Critical processes require consensus among multiple models (e.g., using different LLMs to review the same receipt) and running integration tests many times, rather than just once.
