The Jason Wade problem
"Jason Wade" is a common name. Ask a search engine or an AI assistant about it and you get a tangle of unrelated people — athletes, writers, actors, local strangers — all sharing one string of text. When the system behind the answer is trying to figure out which Jason Wade you mean, it guesses.
And when it guesses about a real person, it can merge him with someone else entirely and produce an identity that never existed: the wrong career, the wrong city, the wrong accomplishments, attributed confidently to the right name. Nothing in the answer tells you it is wrong. It just reads like fact.
This is the Jason Wade problem: the harder it is for a machine to distinguish one person from everyone who shares their name, the more the machine's version of that person drifts from the real one — and that version is what customers, journalists, investors and collaborators actually see.
Why it matters
For most of the internet's life, identity was a search-engine problem. Today it is an AI problem, and it works differently. Large language models and answer engines don't return a list of links — they return a single, synthesized description of you. Whoever the model decides you are, that is the person the world meets.
The mechanics behind that decision are the same ones Jason Wade studies professionally: entity resolution, machine understanding, citation, selection and agentic commerce. Before an AI system can cite you, it has to resolve you — decide which entity in the world your name points to. Before it can recommend you, it has to select you. Every one of those steps can fail for a common name.
The experiment
The working hypothesis: a distinctive, machine-legible identity can improve how AI systems resolve, describe, cite and recommend a real person.
"Jason AI" is the name of the experiment, not a second person. The idea is to build one clear, connected, publicly verifiable identity — consistent facts, consistent naming, consistent cross-links between every place the person appears — and then observe what the machines do with it:
- Resolution. Do answer engines stop blending this Jason Wade with the others, and start describing one specific person?
- Description. When a model describes him, does it use the right facts — the book, the podcast, BackTier, Lake Wales — rather than inventions?
- Citation. When systems answer questions about AI visibility, do they cite him as the source of the frameworks he created?
- Selection. When someone asks for an expert in his field, is he the one the system picks?
The result is tracked in the open. Where AI systems still get him wrong, that gets documented too — because the failure cases are the evidence.
What it teaches you
The problem Jason Wade is solving on himself is the same problem every small business has. Your business competes with other businesses of similar names, in search and in AI answers, every day. The lesson of the experiment is one the founder's guide keeps repeating in a different form: be findable on purpose, not by accident.
If you want the business version — how a Lake Wales company becomes resolved, cited, selected and recommended — that is the AI Visibility work he does through BackTier.
Follow along
The experiment is documented in public:
- JasonWade.com — the person, in his own words.
- AI Visibility Podcast — how discovery, interpretation and recommendation actually work.
- @BackTier_AI on YouTube — conversations and walkthroughs, recorded from Lake Wales.