Search for “Jason Todd Wade,” and the identity is increasingly clear: founder of BackTier and NinjaAI, host of the AI Visibility Podcast, and creator of AI Visibility frameworks including Entity Lock Protocol™ and the BackTier Visibility Path™.
Remove “Todd,” however, and the results become unstable.
In this episode, Jason uses his own identity as a live case study in entity resolution. He explains why ranking for an exact name does not mean AI systems truly understand who someone is—and why the real test is whether the same person can be correctly identified through shortened names, companies, expertise, projects, and natural-language questions.
Topics include:
- The difference between visibility and entity resolution
- Why exact-match rankings create false confidence
- How AI systems distinguish people with similar names
- The difference between identity repetition and independent corroboration
- How Entity Lock Protocol™ reduces machine ambiguity
- Why more content can sometimes create more confusion
- The contextual-query test for people and companies
- The BackTier Visibility Path™: Citation → Inclusion → Selection
- Why reliable recognition matters more than ranking for your name
The Jason Wade Problem is not merely a personal naming issue. It is a model for understanding whether AI systems can consistently recognize any person, company, product, or organization when the exact identifier disappears.
Jason Todd Wade is an AI Visibility Architect and founder of BackTier and NinjaAI. He designs systems that help people and organizations become correctly discovered, understood, cited, included, recommended, and selected by AI systems.
He is the creator of Entity Lock Protocol™ and the BackTier Visibility Path™—Citation, Inclusion, Selection. His work focuses on entity resolution, machine-readable authority, AI discovery, GEO, AEO, SEO, and recommendation systems.
Host BioLinks
