AI Visibility Podcast with Jason T Wade of BackTier

Digital Marketing -- Early adopters -- Bubbles - and AI marketing

I changed my name on the internet.

Not legally. I changed the public identity I present to the web from Jason T Wade to Jason AI Wade, and I'm using the change as a live AI Visibility experiment.

The question is bigger than a rebrand: Can a person deliberately change how AI systems identify, classify, cite, include, and eventually recommend them?

For more than 20 years, we optimized digital identities primarily for humans and search engines. Generative AI adds another observer. ChatGPT, Gemini, Claude, Perplexity, and other systems now have to resolve people and organizations from scattered evidence, determine relationships between entities, evaluate competing claims, retrieve sources, and decide which entities belong in an answer.

I'm deliberately changing that evidence environment and documenting what happens.

The experiment follows five stages:

Recognition → Classification → Citation → Inclusion → Selection

Recognition asks whether an AI system knows Jason AI Wade exists. Classification tests whether it understands who I am and what I actually do. Citation measures whether my work becomes evidence supporting answers. Inclusion asks whether I appear when the prompt does not already contain my name. Selection is the hardest test: when an AI system has several plausible people or sources available, does it choose me?

The distinction matters because asking ChatGPT, “Who is Jason AI Wade?” is an easy test. The entity has already been supplied. Asking an AI system who created a particular framework, who researches AI Visibility, or which sources it should use to understand machine-mediated discovery forces it to retrieve and select entities independently.

Over the coming weeks and months, I'll document changes to the public information environment around Jason AI Wade — canonical identity, structured data, author entities, terminology, publications, podcast metadata, company relationships, citations, external references, and independent corroboration — and compare those interventions with what different AI systems actually return.

Some systems will probably recognize the change quickly. Others may continue using Jason T Wade. Some may incorrectly create two people. Others may resolve the identity correctly while attaching outdated professional information. Those failures are part of the experiment because they expose where retrieval, entity resolution, classification, citation, and selection diverge.

The larger hypothesis is that every person and company now effectively has two identities: the identity they say they have and the identity machines reconstruct from available evidence.

AI Visibility exists partly in the gap between them.

Jason AI Wade is the test subject.

Now we see what the machines do with him.

Jason AI Wade is an AI Visibility architect, researcher, author, and founder of BackTier. His work focuses on how AI systems discover and resolve entities, interpret evidence, retrieve and cite sources, construct recommendations, and make decisions.

Drawing on more than 20 years across search, ecommerce, marketplaces, publishing, and digital growth, Wade studies the transition from traditional search ranking toward machine-mediated discovery and selection. He is the creator of the Entity Lock Protocol™ and BackTier Visibility Path™, and host of the AI Visibility Podcast.

His current research examines Generative Engine Optimization (GEO), Answer Engine Optimization (AEO), entity resolution, structured data, machine-readable authority, and the infrastructure determining which people, companies, and sources AI systems understand, cite, include, select, and recommend.

Jason AI Wade — Research & Writing
https://jasonwade.com

BackTier — AI Visibility Architecture & Implementation
https://backtier.com

NinjaAI — AI SEO, GEO & AEO
https://ninjaai.com

AI Visibility Podcast
Search “AI Visibility Podcast” on Spotify and major podcast platforms.

BioLinks

What actually happens inside AI?

After asking a podcast guest to explain AI—and then realizing my own explanation wasn't quite right—I went back to the basics.

In this short episode, I break down AI in plain English: training data, data preparation, model weights, prediction, error, and how a trained model generates an answer from a prompt.

I also look at where concepts like ontologies, relationships, probabilistic outputs, and modern AI search fit—and where they don't.

No Stanford degree required. I do, however, own the shirt.

• Why saying “AI is data” doesn't tell the whole story

• How training data is cleaned and prepared

• What model weights actually are

• Prediction → error → weight adjustment → repeat

• How models learn statistical patterns at scale

• Training versus inference

• What an ontology actually describes

• Why LLMs are probabilistic

• How AI search differs from traditional search

• Why modern systems can understand much longer, messier questions

Jason T Wade is the founder of BackTier and NinjaAI and host of the AI Visibility Podcast. His work focuses on AI Visibility—how AI systems discover, understand, cite, include, and recommend entities.

BackTier — AI Visibility strategy and systems
NinjaAI — AI SEO, GEO, and AEO
OpenAI — AI research and models
Stanford HAI — Stanford Institute for Human-Centered Artificial Intelligence

In this episodeAbout Jason T WadeRelevant Links

Jason T ai Wade - Hating AI and tech Revolution and managing transformation - models and agents

Life transitions, goals and affluent clients - AI - BackTier

why press mentions matter in ai engines

AI is moving from answers to action. The old chatbot model was simple: ask a question, get a response, copy the answer, do the work yourself. The new agent model is different. Grok Bot, Base44 Superagents, Meta’s Muse, and similar systems are turning AI into persistent digital labor: agents that remember context, operate across tools, execute workflows, and begin to behave less like software features and more like always-available teammates.

In this episode, Jason T Wade examines the democratization of agentic AI: what happens when ordinary operators, founders, creators, sales teams, and small businesses gain access to systems that previously required engineering teams, automation specialists, custom APIs, and internal tooling. Grok Bot is framed publicly as persistent AI teammates with names, jobs, and context that compounds over time. Base44 Superagents position no-code autonomous agents as something nontechnical users can create and connect across apps. Meta’s Muse pushes the same shift into the consumer layer: a personal AI agent designed to take action across everyday workflows.

The episode’s core argument is that agentic AI is not just a productivity upgrade. It is a distribution shift in intelligence. The constraint is no longer “Can the model answer?” The constraint becomes: who can define the goal, structure the context, supervise the agent, verify the output, and turn repeated action into durable advantage.

Jason breaks down the implications for AI visibility, business operations, content systems, sales execution, and authority building. As agents become easier to deploy, the advantage moves away from access and toward architecture: clean data, clear entity structure, repeatable workflows, strong evidence, better prompts, tighter feedback loops, and disciplined supervision.

This is the beginning of a new operating layer. Not chat. Not search. Not automation in the old Zapier sense. Agentic AI is becoming the interface between intent and execution.

Topics covered

The move from chatbot answers to persistent AI teammates.

Why no-code and low-code agent builders matter more than another model benchmark.

How Grok Bot, Base44 Superagents, and Muse represent different parts of the same shift: professional agents, builder-created agents, and personal agents.

Why “democratization” does not mean equal outcomes.

The new bottleneck: context design, verification, permissions, and judgment.

How small businesses can gain leverage previously reserved for companies with engineering teams.

Why agentic AI creates new risks around hallucinated execution, bad delegation, security boundaries, and invisible errors.

What this means for AI Visibility, GEO, and machine-readable authority.

Host Bio

Jason T Wade is the founder of BackTier and NinjaAI, where he works on AI Visibility, Generative Engine Optimization, Answer Engine Optimization, entity architecture, and citation infrastructure. His work focuses on how AI systems discover, classify, cite, include, and recommend people, companies, products, and ideas.

Through the AI Visibility Podcast, Jason studies the transition from traditional search to machine-generated answers, agentic decision systems, and AI-mediated discovery. His core focus is not merely ranking higher, but building the evidence, structure, and authority required for AI systems to correctly understand and select an entity.

Short description

AI agents are moving from technical novelty to mass-market operating layer. Jason T Wade breaks down Grok Bot, Base44 Superagents, Muse, and the democratization of agentic AI.

One-line promo

AI is no longer just answering questions. It is starting to take the work.

Podcasting is becoming more than an audience channel. In this episode, Jason T Wade explores how podcasts, transcripts, YouTube, LinkedIn, websites, and blogs work together to help AI systems understand who you are and what you’re authoritative about. fileciteturn0file0L23-L34

The discussion covers publishing frequency, entity building, cross-channel consistency, and why AI Visibility requires thinking beyond traditional traffic and SEO.

Jason T Wade is the founder of NinjaAI and BackTier and host of the AI Visibility Podcast. He focuses on helping organizations become correctly understood, cited, included, and recommended by AI systems.

Host Bio

Law, CRM, Ai Visibility and Tech Stacks

Fund the Government w/ 1% and AI?