TL;DR — This post is a deep reflection on insights shared by YouWare’s founder (a user-generated software platform) during a recent podcast conversation. It explores what it really means to build AI-native products, rather than simply wrapping LLMs in traditional software shells.

Key ideas: - Do less, align with the biggest variable — AI: feature restraint is a virtue when model progress is the true leverage. - Token speed as a core metric: DAU may mislead; the faster and more effectively a product consumes tokens, the more tightly it aligns with model intelligence. - Wrappers vs. Containers: great AI products don't just function — they shape user behavior and learning (e.g., Midjourney's use of Discord). - Coding as a new creative medium: coding should evolve from expert work to point-and-shoot creativity, like photography did.

Curious to hear thoughts from others building AI-native tools — or grappling with similar product trade-offs. Really, any thoughts are welcome.