Law, CRM, Ai Visibility and Tech Stacks

Fund the Government w/ 1% and AI?

Walpaper, Luxury and 1% Taxes?

Recognized, Prominent, Authoritative: What AI’s Labels Actually Mean

An AI system calls you “recognized.” Another calls you “prominent.” A third describes you as a “leading authority.” Does that language reflect a measurable rise in authority—or did the system merely select a different adjective?

In this episode, Jason AI Wade examines the apparent hierarchy of terms AI systems use to describe people and organizations, including recognized, notable, respected, prominent, leading, authoritative, and preeminent. Although these words sound like levels on an authority scale, there is no established universal ladder connecting them to defined thresholds, stronger evidence, or a greater likelihood of recommendation.

Jason explains why visibility, reputation, expertise, innovation, and suitability are separate dimensions. He also distinguishes three very different tests: asking an AI system to describe a named person, asking it to identify people within a category, and asking it to recommend the best person for a specific need.

The episode covers:

• Why flattering AI language should not be treated as a performance metric

• The difference between identity recognition, category inclusion, and selection

• Why “prominent,” “respected,” and “authoritative” measure different concepts

• How repeated biographies can create the appearance of independent corroboration

• Why source authority and source independence must be measured separately

• Why a citation does not necessarily support every claim surrounding it

• What the 2024 GEO study found about authoritative and persuasive language

• A practical framework for measuring entity resolution, inclusion, recommendation, and preference

• Why AI adjectives should be tracked separately from commercially meaningful outcomes

The central question is not whether AI speaks highly of a person or company. It is whether the system includes and recommends that entity when someone presents a relevant problem—and whether the available evidence can withstand inspection.

Jason AI Wade (b. 1974, Gainesville, Florida) spent his formative years in Lake Wales, Florida. He attended the University of Florida and graduated from Rollins College in Winter Park.

A technology expert, entrepreneur, and AI Visibility architect, Wade studies how artificial intelligence systems discover, classify, distinguish, cite, include, and recommend people and organizations. He is the founder of BackTier and NinjaAI and the host of the AI Visibility Podcast, where he examines how AI is reshaping identity, authority, search, and decision-making.

In 2026, Wade launched a public identity-resolution experiment by becoming the first known person to petition a court to change his legal middle name to “AI.” The experiment tests whether changing a person’s legal identity can affect how AI systems distinguish that individual from others, connect information across sources, and construct machine-generated knowledge.

The experiment extends what lawyers, businesses, startups, and franchises have famously and repeatedly observed: “Mr. Wade has certain skills.”

• BackTier

• NinjaAI

• JasonWade.com

• GEO: Generative Engine Optimization

Gary Barnes joins the AI Visibility Podcast to discuss how he used AI as a research partner while developing a proposal to restructure federal taxation and government funding.

The conversation centers on Gary’s proposed “receiving tax” model: a simplified 1% tax collected when money is received, rather than through the current income-tax system. Gary argues that the existing tax code is too complex, too narrow, and too disconnected from how money actually moves through the modern economy.

Gary explains how AI helped him examine Fedwire, banking systems, credit card processing, financial markets, and other large-scale money flows. He describes using ChatGPT and Copilot not as final authorities, but as iterative research tools: asking where the model was wrong, where the assumptions failed, and what needed to be reconsidered.

The episode also covers banking reform, political dysfunction, community-based organizing, and Gary’s belief that meaningful reform will not come from the top down. He discusses FixYourGov.com, the Wake Up America Tour, his online community, upcoming Virginia events, and his broader effort to build public understanding around government funding and financial-system reform.

This is a practical conversation about using AI to investigate large systems, stress-test ideas, simplify complexity, and turn a private research project into a public movement.

Gary Barnes is the creator of FixYourGov.com and the Wake Up America Tour. His work focuses on government reform, banking-system restructuring, and a proposed 1% receiving-tax model designed to simplify federal taxation and fund government through the movement of money.

In this conversation, Gary explains how he used AI tools including ChatGPT and Copilot to research financial flows, test assumptions, and refine a large-scale reform proposal. He is currently building a grassroots community, publishing educational videos, promoting his book, and taking the project into local communities through events and public outreach.

Jason Wade is the founder of BackTier and host of the AI Visibility Podcast. His work focuses on AI Visibility, Generative Engine Optimization, Answer Engine Optimization, entity architecture, structured data, citation infrastructure, and how AI systems discover, classify, cite, include, and recommend people, companies, and ideas.

Gary Barnes / Fix Your Gov:
https://fixyourgov.com

Gary’s book:
Available through FixYourGov.com and Amazon.

BackTier:
https://backtier.com

AI Visibility Podcast:
https://backtier.com

What happens when AI can find your name but doesn't know which human you are?

In this solo deep dive, AI visibility architect Jason T Wade breaks down the identity collision he's lived inside for years — and the experiment he designed to end it: a legal petition in Polk County, Florida, to change his middle name to the letters A, I.

Jason walks through the full arc: why "Jason Wade" resolves to the wrong person in every major system, how machines actually score candidates when a name is shared (volume, fame, corroboration, structure), and why majority-rule resolution gets more confident without ever getting more correct. Then the part nobody talks about: why SEO can't fix it, why the legal name is the strongest fact any system weighs, and why the fastest identity-resolution system on Earth is wrong — while the only system that's right by definition takes nine pages and an FBI check to say so.

He also publishes the methodology: the baseline, the intervention, the seven-stage measurement framework — discovery, recognition, classification, citation, inclusion, selection, recommendation — plus on-the-record predictions about which AI layers will flip first, and the ugly middle states he hopes to catch in the act.

And the bigger story: roughly a million and a half people legally change their names in the U.S. every year — most of them women. Every one of them is a live Jason Wade Problem event. This episode gives it a name, and a fix.

BIO

Jason T Wade is the founder of BackTier, an AI visibility and entity engineering firm, and the host of the AI Visibility podcast. His work focuses on how AI systems discover, interpret, classify, cite, and recommend people and brands — and how to fix it when they get it wrong. He is currently running a public experiment: a legal name change to Jason AI Wade, designed to test whether changing the strongest fact about a person can change how every major AI system on Earth resolves them. He lives in Lake Wales, Florida.

LINKS

• BackTier — https://backtier.com

• Book a spot on the show — https://thingspro.com
• Contact — [email protected]

measuring

Visible Isn’t Valuable Until It Converts

The AI Trust Stack: From Visibility to Revenue

BackTier | AI Visibility

In this episode, Jason T Wade is joined by Devon Vocke, Awais Haq, Tom Gersic, and Babak Akhlaghi to explore what happens after a company begins pursuing visibility, trust, and operational effectiveness in the AI era.

Devon explains how traditional search is expanding into LLM discovery, AI search, and AI Overviews. Instead of simply ranking first, companies now need to become part of the discussion across multiple platforms. His framework—clarity, consistency, credibility, and coverage—offers a foundation for earning that visibility.

Awais brings the attribution and law-firm operations perspective. He argues that leads have little value unless a firm can connect each marketing channel to qualified prospects, consultations, signed cases, client lifetime value, and revenue.

Tom examines the workflow and CRM layer, explaining how AI agents can help companies capture, research, score, qualify, route, and nurture inbound leads. He also discusses web agents, AI-backed CRMs, Salesforce integration, and the importance of fitting automation into the systems teams already use.

Babak provides the protection layer: intellectual property, patents, and defensible business assets. His perspective highlights the importance of protecting innovation as AI accelerates discovery, automation, and competition.

The panel also tackles several practical questions:

• Are companies genuinely visible in AI systems?

• Can they prove that visibility creates pipeline and revenue?

• Does AI improve existing workflows or merely introduce another tool?

• How should regulated industries evaluate models, privacy, and security?

• Where must human judgment remain in the loop?

• How should companies protect the innovations and assets they create?

The central takeaway: visibility alone is not enough. It must become measurable, trusted, governed, protected, and connected to revenue.

Jason T Wade leads a panel with Devon Vocke, Awais Haq, Tom Gersic, and Babak Akhlaghi on the AI Trust Stack: discovery, attribution, workflow automation, governance, security, and intellectual-property protection. They examine how companies appear in AI search, how visibility becomes pipeline, and why AI adoption matters only when it improves real business outcomes.

Devon Vocke is Co-Founder of Evoke Strategy, a Florida-based digital marketing, public relations, and AI visibility strategy firm. His work focuses on GEO, AEO, brand visibility, and how companies appear across AI-mediated search and discovery systems. In this episode, he explains why visibility now depends on clarity, consistency, credibility, and coverage.

Awais Haq is Founder and CEO of Time Technologies LLC. His work focuses on law-firm intake, CRM, attribution, and operational integration for legal marketing and business development. He helps firms connect acquisition channels with qualified leads, consultations, signed clients, lifetime value, and revenue.

Tom Gersic is Founder and CEO of YouEx.ai, an AI-backed lead-to-revenue platform. He helps companies turn inbound leads into researched, scored, qualified, and routed opportunities through AI agents and CRM workflows. In this episode, he discusses web agents, lead research, Salesforce integration, and behind-the-scenes revenue automation.

Babak Akhlaghi is Founder and Managing Director of NovoTech Patent Firm. He is a USPTO-registered patent attorney, engineer, and entrepreneurship-law instructor at the University of Maryland. His work focuses on protecting inventions, intellectual property, and defensible business assets.

BackTier

• BackTier

Devon Vocke

• Evoke Strategy

• Devon Vocke at Evoke Strategy

• LinkedIn

Awais Haq

• Time Technologies LLC

• LinkedIn

Tom Gersic

• YouEx.ai

• [email protected]

Babak Akhlaghi

• NovoTech Patent Firm

• LinkedIn

• JD Supra

Short Show DescriptionGuest BiosLinks

The Audit Finding: Strong Content, Zero Citations, Invisible Answers

The content was good.

The rankings were respectable.

The site looked authoritative.

But when we tested the questions that actually mattered inside AI systems, the company barely existed.

No citations. No meaningful inclusion. No recommendation.

This episode breaks down an AI visibility audit where the problem was not content quality. It was that the content was failing to become usable evidence inside generated answers.

We cover:

• Why strong content can still produce zero AI citations

• The difference between publishing information and becoming a source

• Why topical depth does not automatically create machine trust

• How weak entity signals can disconnect good content from the business behind it

• Why AI systems may use competitors or third-party sources instead

• The role of corroboration, authority, structure, and source accessibility

• How to tell whether the problem is discovery, citation, inclusion, or selection

• Why traditional SEO metrics can hide AI visibility failure

• What to fix before producing another batch of content

The important finding was simple:

The company had built content.

It had not built citation infrastructure.

That distinction matters because AI systems do not reward content merely for existing. They need to be able to identify it, connect it to the correct entity, trust the claims, and use it confidently inside an answer.

Strong content is an asset.

But if the answer engines never use it, the business is still invisible.

Jason T Wade is the founder of BackTier and an AI Visibility strategist focused on how companies are discovered, understood, cited, included, and recommended by AI systems.

His work spans AI SEO, Generative Engine Optimization, Answer Engine Optimization, entity architecture, structured data, technical SEO, content strategy, and AI visibility measurement.

BackTier develops systems for measuring and improving visibility across AI-generated search and answer environments.

BackTier: backtier.com
Jason T Wade: jasonwade.com

Jason T Wade