Topics

Deploying AI personalization in enterprise environments: trust, compliance, and scale.

Self-models are persistent, structured representations of individual users that give AI products compounding understanding.

Strategic decisions for AI product teams: moats, positioning, and compounding advantages.

System design patterns for AI products that need to remember, understand, and evolve.

Testing AI quality beyond accuracy: rubrics, judges, and alignment evaluation.

Moving beyond generic AI to products that adapt to each user's beliefs, goals, and preferences.

Measuring what matters in AI: alignment scores, retention, and user understanding.

Building, scaling, and debugging AI products from prototype to production.

Ensuring AI output matches what each user actually needs, not what the average user wants.

Building agents that maintain context, coordinate handoffs, and remember users across sessions.

Why users stay or leave, and how understanding compounds into organic switching costs.

Step-by-step guides for implementing AI personalization, self-models, and evaluation systems.