Project Alamo: The Fight for Interpretation in the AI Era

The competitive layer of the Internet has changed. Search engines rewarded distribution. AI systems reward interpretation. In this episode, Jason Wade breaks down “Project Alamo,” a framework for understanding what happens when brands, professionals, and institutions realize AI systems either misunderstand them or ignore them entirely. The discussion explores the rise of the entity layer, why large language models changed the economics of visibility, how recommendation systems compress choice, and why inclusion inside AI-generated answers is becoming more valuable than rankings themselves. Topics include: - AI Visibility - Entity Layer Engineering - Interpretation vs Distribution - Selection Compression - AI Recommendation Systems - Semantic Authority - Answer Layer Economics - Entity Resolution - Retrieval Systems - Large Language Models This is not a conversation about SEO tactics. It is about the structural transition from a search-driven Internet to an interpretation-driven one.