AI-Native Product Discovery and Design

What this part inherits from Part I:

• The economic reality check (50x cost reduction) reframes discovery as finding problems where AI's cost structure creates new viable markets
• The capabilities/limitations map (Ch 2) directs discovery toward AI-appropriate problem spaces
• The Human-AI Product Stack (Ch 3) provides the design lens for how users and AI will collaborate

What this part changes retroactively:

• Part I's "what AI can do" checklist gets refined through discovery: real user needs expose where the capabilities taxonomy was too coarse or too fine
• The build/buy/bake framework from Ch 1 gets stress-tested against actual product opportunities

Artifacts that now need updating:

• The AI product taxonomy introduced in Part I may need extension based on discovery patterns found in Ch 6-7
• The Human-AI Stack interaction modes (Ch 3) may need new patterns from AI-native UX findings (Ch 8)

Earlier artifacts updated by this part:

• Part I, Ch 1: Build/buy/bake framework gets refined through product strategy lens (Ch 6)
• Part I, Ch 2: AI capabilities list gets filtered by what's discoverable as user needs (Ch 5-7)
• Part I, Ch 3: Human-AI Stack interaction modes get extended with new UX patterns (Ch 8)

Later chapters this part prepares for:

• Part III, Ch 10-14: Vibe-coding is most valuable for problems found through these discovery methods
• Part IV, Ch 15-20: Engineering decisions are constrained by what discovery revealed as valuable
• Part V, Ch 21: Eval-first requirements (Ch 9) become the foundation for eval pipelines
• Part VI, Ch 26: GTM strategies depend on accurate problem framing from discovery