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

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

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@jasonwade.com

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

• tom@youex.ai

Babak Akhlaghi

• NovoTech Patent Firm

• LinkedIn

• JD Supra

Short Show DescriptionGuest BiosLinks

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

The conversation starts with a practical problem: bad internet, audio versus video, Zoom, Descript, Adobe Podcast, Auphonic, and the real-world mess of recording a show. But the deeper issue is more important. A podcast is not just content. It is a high-context transcript, a recurring public record, and a source layer that search engines, AI Overviews, ChatGPT, Gemini, and other answer systems can ingest.

Jason and Dietmar discuss why guest activation matters, why generic AI-generated PR pitches are easy to spot, and why personal outreach still beats automated slop. They also get into podcast production workflows, including AI-selected clips, human editing, NotebookLM-style generated shows, ElevenLabs voice cloning, and the point where novelty turns into sameness.

The AI visibility section gets sharper. Dietmar describes Google AI Overviews as something that can compress the customer journey by answering the question before the user clicks. Jason pushes the point further: if AI systems are selecting, summarizing, and citing sources, then businesses need to think beyond traffic. They need to understand what high-intent customers actually want, create source material that machines can parse, and build authority around the specific questions that matter.

The episode also touches political AI visibility, GEO manipulation, fake think tanks, and the uncomfortable reality that the same systems used for legitimate business visibility can also be used for influence operations. The practical takeaway is simple: podcasts, transcripts, titles, show notes, and guest networks are not side content anymore. They are part of the infrastructure that determines whether AI systems understand, cite, and recommend you.

GUEST BIO

Dietmar Fischer is a Berlin-based podcaster, digital marketer, and AI marketer. He hosts A Beginner’s Guide to AI, a podcast and newsletter that explains artificial intelligence for business audiences. His work connects AI adoption, digital marketing, Google Ads, SEO, GEO, and practical business education. Through Argo Berlin, he works with clients on digital marketing, webinars, AI topics, and tourism/hospitality marketing.

HOST BIO

Jason T Wade is the founder of BackTier and host of the AI Visibility Podcast. He works on AI Visibility, GEO, AEO, entity resolution, retrieval alignment, and authority systems that help companies become discovered, understood, cited, included, and recommended by AI engines.

LINKS

Dietmar Fischer / A Beginner’s Guide to AI
Argo Berlin
Dietmar Fischer on LinkedIn
A Beginner’s Guide to AI on Apple Podcasts
A Beginner’s Guide to AI on Spotify / podcast platforms
Jason T Wade
BackTier
AI Visibility Podcast

Most AI visibility problems are not really content problems.

They are interpretation problems.

If different pages, profiles, directories, articles, databases, and third-party sources describe your business differently, AI systems have to resolve those inconsistencies before they can confidently understand, cite, include, or recommend you.

The Entity Lock Protocol is designed to reduce that ambiguity.

In this episode, we break down what an Entity Lock actually is, why entity consistency matters, and how to create a more stable machine-readable understanding of a company across the web.

We cover:

• What “entity lock” means

• Why AI systems struggle with inconsistent business descriptions

• How category ambiguity weakens recommendation confidence

• The role of canonical names, descriptions, services, people, locations, and relationships

• Why structured data alone does not solve entity confusion

• How first-party and third-party sources reinforce or contradict each other

• Why corroboration matters more than repetition

• How Entity Lock supports Citation → Inclusion → Selection

• What to audit before creating more content

• How to identify the signals that are causing AI systems to misclassify a company

The objective is not to make every source say the exact same thing.

It is to make the underlying identity coherent enough that machines reach the same conclusion about who you are, what you do, and where you belong.

That is the Entity Lock Protocol.

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

A company can rank well and still fail the AI visibility test.

That is exactly what this audit found.

The search performance looked healthy. The rankings were there. The content was visible. But when we tested how AI systems interpreted the company, the underlying entity signals were inconsistent enough to create a different problem:

The right pages were ranking for the wrong understanding of the business.

In this episode, we break down a real AI visibility audit where the company performed well in traditional search but scored only three out of five across the factors that determine whether an AI system can confidently understand and select an entity.

We cover:

• How strong rankings can hide weak entity resolution

• What “right rank, wrong entity” actually means

• Why AI systems may classify a company differently than the company classifies itself

• How inconsistent descriptions, categories, and third-party references create ambiguity

• Why a company can pass discovery but fail understanding

• What a three-out-of-five audit score actually reveals

• Which deficiencies affect citation, inclusion, and selection differently

• How to separate an SEO problem from an entity architecture problem

• What needs to be fixed before producing more content

The important finding was not that the company was invisible.

It was that the company was visible without being consistently understood.

That is a much harder problem to see in a conventional SEO report.

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

Being visible in AI-generated answers is not one problem.

It is three.

A company can be cited without being meaningfully included. It can be included without being selected. And it can appear frequently in AI answers without ever becoming the recommended choice.

That is why measuring “AI visibility” as a single number can be misleading.

In this episode, we break AI visibility into three distinct layers:

Citation — Does the system use your website, content, or third-party references as evidence?

Inclusion — Does your company make it into the answer, shortlist, comparison set, or consideration set?

Selection — Does the system actually recommend, prioritize, or choose you?

These are related, but they are not interchangeable. Each requires different evidence, different optimization, and different measurement.

We cover:

• Why citation does not equal recommendation

• How a brand can supply evidence but still lose the answer

• Why inclusion is a separate competitive threshold

• What causes an AI system to move from mentioning a company to selecting it

• How entity clarity affects all three stages

• Why third-party corroboration becomes more important as the system moves toward recommendation

• How traditional SEO signals interact with AI-generated answers

• What companies should measure across Citation → Inclusion → Selection

• Why optimizing only for citations can create a false sense of progress

The strategic mistake is treating every AI appearance as a win.

The better question is:

“Where are we losing — citation, inclusion, or selection?”

Because those are three different fights.

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

Bios:

• Jason Wade — Founder, BackTier; host, AI Visibility Podcast. Focuses on AI Visibility/GEO and how entities get cited, included, and recommended by AI systems.
• Tom Gersic — Founder/CEO, YouEx.ai. Ex-Salesforce (12 yrs, VP Product Adoption); builds AI-backed CRM workflows that turn inbound leads into revenue.
• Devon Vocke — Tampa-based digital marketer. Helps companies show up across AI search and discovery, not just traditional rankings.

EvokeStrategy.com

LinkedIn: https://www.linkedin.com/company/evoke-strategy/ 

and https://www.linkedin.com/in/devonvocke/

• Awais Haq — Time Technologies LLC. Connects law firm marketing, intake, and CRM data so firms can see what's actually driving revenue.
• Babak Akhlaghi — Patent attorney, engineer, entrepreneurship-law instructor (University of Maryland). Covers IP, disclosure risk, protecting what founders build.

Being mentioned by AI isn't the finish line. Jason Wade and four guests dig into what turns AI visibility into real pipeline — attribution, CRM workflows, law firm intake, and the model/infrastructure decisions behind it.

Top Quotes

• "There is no single number one anymore. The question is whether you are part of the conversation."
• "AI creates leverage when it moves into workflow, not just when it answers a prompt."
• "Visibility is only one piece of the puzzle. What does it do to ultimately drive pipeline?"

You rank first.

Then someone asks ChatGPT, Gemini, Perplexity, or another AI system the same question — and your competitor gets cited instead.

That is not necessarily a ranking failure. It is a different evaluation system.

Traditional search asks whether your page deserves to rank for a query. AI-generated answers also have to decide whether your company is the right entity, whether your claims are sufficiently corroborated, whether the information is easy to interpret, and whether your brand is reliable enough to cite or recommend inside a synthesized answer.

In this episode, we break down why ranking #1 no longer guarantees visibility when the interface shifts from search results to generated answers.

We cover:

• Why top Google rankings do not guarantee AI citations

• The difference between ranking, citation, inclusion, and selection

• How AI systems evaluate entities rather than individual pages

• Why corroborating sources can outweigh stronger traditional SEO

• How entity ambiguity weakens AI visibility

• Why a lower-ranking competitor may be easier for an AI system to understand and cite

• What companies should measure beyond rankings and traffic

• How AI SEO, GEO, and AEO change the visibility model

The old question was:

“Where do we rank?”

The new question is:

“When the system has to construct an answer, does it understand us, trust the evidence around us, and choose us?”

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

That's the problem the sovereign data movement is responding to. The idea is simple but consequential: instead of letting AI systems scrape and aggregate whatever fragments of your brand exist across the web, you take ownership of the definitive, structured version of your own identity — your history, your positioning, your verified facts — and make that the authoritative source models are meant to pull from.

Practically, this looks like brands building and maintaining their own structured knowledge layer — rich, machine-readable data about who they are — rather than leaving that job entirely to whatever a crawler happens to piece together from old press releases and a five-year-stale About page. It's the difference between letting your reputation be reconstructed by inference and stating it directly, in a form built to be read by the systems doing the reconstructing.

There's a genuine tension worth naming here. Full sovereignty — total control over how you're represented — isn't fully achievable when you don't control the models doing the summarizing. This is an influence strategy, not a guarantee. A brand can produce excellent structured data and still get aggregated into something generic if the underlying system weights other, louder signals more heavily.

But doing nothing is worse. Brands sitting passively while AI systems build their identity out of secondhand fragments are, functionally, letting someone else write their reputation. The sovereign data movement isn't about resisting AI aggregation entirely — that ship has sailed. It's about making sure that when you are aggregated, it's your definition of you doing the shaping, not a statistical average of everyone else's.

Traditional SEO says you are winning.

You rank higher.
You have more traffic.
Your domain is stronger.
Your content is better optimized.

Then someone asks ChatGPT, Gemini, Perplexity, or another answer engine a question in your category — and the AI cites your competitor.

This episode explains why.

We break down the difference between ranking in search and being selected inside an AI-generated answer. The systems overlap, but they are not the same. AI models are evaluating whether they can identify the right entity, understand what that entity does, connect it to the question being asked, corroborate the relevant claims, and confidently use it as supporting evidence.

That means a company can outperform a competitor in Google and still lose the AI recommendation layer.

We cover:

• Why search rankings do not guarantee AI citations

• The difference between relevance, authority, and selection

• How entity ambiguity can suppress an otherwise strong brand

• Why third-party corroboration can matter more than another optimized page

• How AI systems assemble evidence across multiple sources

• Why competitors with weaker SEO can still become easier for machines to cite

• The difference between being discovered, cited, included, and recommended

• What companies should actually measure as AI search becomes more important

The core question is no longer simply:

“Do we rank?”

It is:

“When an AI system has to answer the question, does it understand us well enough — and trust the available evidence enough — to choose us?”

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 WadeLinks

A federal filing describes websites and content intended to produce “GPT framing results.” We examine Clock Tower X, political GEO and the difference between documented intent and unproven influence.

• U.S. Department of Justice: Clock Tower X FARA filings
  https://efile.fara.gov/ords/fara/f?p=1381:200:::NO:RP,200:P200_REG_NUMBER:7649

• Drop Site News investigation
  https://www.dropsitenews.com/p/israel-brad-parscale-ai-chatbots-gaza

• Anthropic research on data poisoning
  https://www.anthropic.com/research/small-samples-poison

• Parscale Strategy
  https://www.parscale.com/

AI is making businesses faster, but speed without judgment can amplify the wrong things.

In this episode, Jason T Wade talks with Adrienne Wilkerson and Rob Broadhead about human-centered marketing, AI readiness, trust, governance, and the systems companies need before they automate more of their work.

Adrienne explains why marketing still has to start with human connection, even when AI handles more of the production. Rob explains why companies need clear goals, guardrails, and stronger operating systems before adding more AI.

Topics include:

• Humanizing AI-generated marketing

• AI slop, trust, and authenticity

• AI visibility and high-stakes recommendations

• Governance and human judgment

• Why AI amplifies weak systems

• Setting measurable goals before automating

• Using AI without losing the human element

Adrienne Wilkerson is a marketing strategist focused on behavioral health, mental health, and addiction recovery. Her work is increasingly centered on strategic consulting and helping organizations humanize marketing in an AI-driven environment.

She also hosts The Beacon Way podcast, where she explores marketing, strategy, and related business topics.

Rob Broadhead is a technology consultant focused on AI readiness, automation, operating systems, and organizational infrastructure.

His firm has been operating for 25 years, and he is currently building an AI-native operating system inside his own organization while advising other companies on how to prepare their systems and workflows for AI.

Jason T Wade is the founder of BackTier and NinjaAI, where he focuses on AI visibility, AI SEO, Generative Engine Optimization (GEO), entity architecture, and how AI systems discover, classify, cite, and recommend people and companies.

He has more than 20 years of experience in search, digital growth, and building online businesses, and now applies that background to AI-driven discovery and recommendation systems.

Jason hosts the AI Visibility Podcast, exploring how AI is changing search, authority, marketing, decision-making, and machine-driven selection.

• LinkedIn: Adrienne Wilkerson

• Podcast: The Beacon Way

• Website: rb-sns.com

• Email: rob@rb-sns.com

• LinkedIn: Rob Broadhead

• BackTier: backtier.com

• NinjaAI: ninjaai.com

• Website: jasonwade.com

Show NotesGuestsAdrienne WilkersonRob BroadheadHostJason T WadeContact & LinksAdrienne WilkersonRob BroadheadJason T Wade

The important idea behind “Token Maxing” is not “make prompts longer.” It is: stop starving the model of the information required to reason well.

A lot of AI advice still treats prompting as an incantation problem: find the right wording, role, framework, or magic phrase. That matters at the margins. But for consequential work, the larger constraint is usually information asymmetry. You know things the model does not know: what you actually want, what already happened, what failed, what cannot change, which sources are authoritative, what tradeoffs you accept, and what “good” looks like.

So I’d define Token Maxing more precisely as:

Allocate enough context, evidence, reasoning, and verification to the problem that the cost of additional intelligence becomes lower than the expected cost of a bad answer.

That creates several distinct layers.

1. Context maxing. Give the model the actual state of the world, not a sanitized 100-word prompt. Instead of “How should I position this company?”, provide the current positioning, competitors, customer type, existing assets, previous attempts, constraints, economics, and desired end state.

2. Evidence maxing. Separate what you believe from what the evidence establishes. Feed source documents, customer language, analytics, search results, contracts, research, screenshots, transcripts, or whatever constitutes ground truth. Then tell the model which evidence outranks which.

This becomes especially important with long-context systems because merely placing information in a context window does not guarantee that every piece will receive equal attention. Research on long-context models has repeatedly found retrieval and reasoning degradation depending on where relevant information appears and how much competing context exists. Merriam-Webster

3. Reasoning maxing. Don't ask for one answer and stop. Make the system interrogate the decision:

“What assumptions am I making?”

“What evidence would falsify this conclusion?”

“Generate three materially different explanations.”

“Argue against the recommended option.”

“What second-order effects am I missing?”

“What would an expert skeptic attack?”

“What additional information would most change your recommendation?”

That's fundamentally different from asking the model to “think harder.” You're designing a reasoning process.

4. Evaluation maxing. This may be the most overlooked component. Tell AI how the answer will be judged.

For example, “Give me the best homepage” is underspecified.

“Optimize this homepage so a first-time visitor can identify the company, category, buyer, problem, differentiated mechanism, and next action within 20 seconds—and so an AI system can unambiguously classify the company and its services” gives the model an objective function.

The evaluation criteria constrain the solution space.

5. Adversarial maxing. For important decisions, the model shouldn't merely help construct the argument. It should attack it.

You could run:

Builder → Critic → Evidence Auditor → Devil's Advocate → Final Synthesizer

The Builder proposes the answer. The Critic identifies weaknesses. The Evidence Auditor distinguishes substantiated claims from inference. The Devil's Advocate develops the strongest competing interpretation. The final pass reconciles everything.

That is dramatically more useful than repeatedly asking, “Are you sure?”

6. Compression maxing. This is where the idea becomes counterintuitive. Token Maxing eventually requires deleting tokens.

Long-running conversations accumulate obsolete assumptions, abandoned directions, duplicated information, and contradictory instructions. More context can eventually become context pollution.

So periodically you want AI to produce a canonical state:

Here is what we know.
Here is what we decided.
Here is the evidence.
Here are the unresolved questions.
Here are the constraints.
Everything else can be discarded.

In this episode, Jason T Wade talks with Benjamin Shapiro, founder of I Hear Everything and host of the MarTech Podcast, and Leanne Linsky, founder and CEO of Plauzzable, about AI, brand authority, podcasting, content, and the changing buyer journey.

They discuss:

• Why clicks and traditional attribution are weakening

• Why brand authority matters more in AI discovery

• How podcasts build credibility and visibility

• Where AI helps creators—and where it should not replace them

• How businesses can use AI without losing authenticity

Core idea: AI may know your brand, but the real question is whether it trusts and chooses it.

Benjamin Shapiro
Founder of I Hear Everything and host of the MarTech Podcast. His work focuses on podcast production, B2B media, automation, and authority engineering.

Leanne Linsky
Founder and CEO of Plauzzable, a live online comedy platform. She brings together comedy, entrepreneurship, community building, and technology.

• Benjamin Shapiro — iheareverything.com

• Leanne Linsky — plauzzable.com

• Jason T Wade — backtier.com

The Real Cost of Waiting for AI and SEO/GEO — with Jason T. Wade

Most brands are still optimizing for a search engine that's shrinking. In this episode, AI visibility architect Jason T. Wade breaks down what waiting actually costs — lost organic traffic, vanishing AI citations, and data debt that compounds — and why the brands that move early own the AI recommendation layer before it's locked in.

Jason T. Wade is the founder of BackTier and an AI visibility architect working at the intersection of entity engineering, generative search, structured data, SEO, GEO, and AEO. He helps organizations become discoverable, interpretable, and citable by AI systems like ChatGPT, Gemini, Perplexity, and Google AI Overviews — and is the creator of Entity Engineering and the Entity Lock Protocol. He's also the founder of NinjaAI and hosts the AI Visibility Podcast.

• Website: jasonwade.com
• BackTier: backtier.com
• NinjaAI: ninjaai.com
• LinkedIn: linkedin.com/in/backtier

By Jason T Wade

Agentic commerce is one of those markets where the language has moved faster than the evidence.

Depending on which announcement, vendor deck, or analyst report you read, AI agents are already becoming autonomous shoppers, payment networks are preparing for a machine economy, and software is about to replace humans as the primary commercial actor.

That framing is premature.

The more defensible conclusion, as of August 2026, is narrower and more consequential:

Agentic commerce has reached the transaction-infrastructure stage. It has not yet reached broad autonomous commerce.

AI systems can already discover products, compare alternatives, recommend merchants, construct carts, and in selected environments execute approved transactions. Stripe, Visa, Mastercard, Google, OpenAI, Coinbase, Cloudflare, Shopify, and others are building increasingly sophisticated payment, identity, authorization, and commerce layers around those systems.

But there is a critical difference between an agent being able to execute a transaction and an agent being trusted with standing economic authority.

That gap defines the market.

I use a six-stage model for agentic commerce:

1. Assistance — AI helps a human research.

2. Selection — AI recommends or selects an option.

3. Transaction — AI executes an explicitly authorized transaction.

4. Delegation — AI receives standing purchasing authority within constraints.

5. Autonomy — AI independently determines when economic action is required.

6. Machine Economy — agents continuously discover, negotiate, buy, sell, and settle with other agents and systems.

In August 2026, the overall market is at Stage 3: Transaction.

Stages 1 and 2 are mature. Conversational discovery and AI-assisted selection are already normal product capabilities.

Stage 3 is real. Native checkout exists in selected ChatGPT and Google integrations. Payment credentials can be constrained. Machine-payment protocols can charge software for digital resources.

Stage 4 exists architecturally, but only in narrow implementations. Stage 5 remains experimental. Stage 6 exists primarily as protocols, demos, and early machine-native payment activity rather than a broad operating economy.

That distinction matters because much of the market commentary compresses all six stages into one phrase: “agentic commerce.”

That hides where the real technical and strategic bottleneck now sits.

OpenAI’s commerce strategy is a good example of why the market needs more precise language.

Instant Checkout launched in September 2025 with Etsy merchants through the Agentic Commerce Protocol, developed with Stripe.

Users could discover a product, approve the purchase, and complete the transaction through ChatGPT while the merchant remained merchant of record.

By March 2026, OpenAI expanded ACP more heavily into product discovery and increasingly emphasized merchant-controlled or app-based checkout experiences.

That shift has often been described as OpenAI abandoning checkout.

That is inaccurate.

Native checkout still exists in selected integrations. Instacart, for example, supports browsing, cart creation, and checkout inside ChatGPT.

The more accurate conclusion is:

The popular answer is that most people lack discipline or choose comfort over success. But the deeper explanation may be psychological: continued pursuit creates uncertainty, effort, social exposure, delayed rewards, and the possibility of failure. At some point, accepting the familiar can feel less painful than continuing toward an uncertain outcome.

This episode begins with Arthur Schopenhauer’s philosophy of desire. For Schopenhauer, wanting emerges from lack, and lack produces suffering. Satisfaction usually does not create a permanent positive state; it temporarily removes the tension of desire before boredom, fear, or a new desire appears.

From there, we examine how modern psychology helps explain premature quitting:

• loss aversion and fear of reputational loss;

• status-quo bias and uncertainty avoidance;

• effort discounting and delayed gratification;

• avoidance conditioning and learned helplessness;

• cognitive dissonance and sunk-cost effects;

• grit, persistence, and stress tolerance;

• homeostasis, allostasis, and the body’s preference for stability.

The episode also examines entrepreneurship, where the environment frequently provides reasons to stop: no customers, weak traffic, rejection, failed experiments, limited capital, and little external validation.

But persistence is not automatically virtuous. Continuing with a bad strategy is not resilience; it may be sunk-cost behavior. The more useful principle is:

Commit strongly to the objective while remaining flexible about the method.

The final question is distinctly Schopenhauerian:

Before becoming better at enduring the suffering required to obtain what you want, have you examined whether the desire deserves that suffering?

This is not an episode about motivational clichés or invented statistics. It is an investigation into the difference between growth discomfort, strategic failure, uncertainty anxiety, and the rational decision to quit.

Most people do not give up because they are incapable. They give up when the immediate discomfort of continuing becomes more powerful than the uncertain promise of future progress. This episode explores that idea through Schopenhauer’s philosophy of desire, suffering, satisfaction, boredom, and resignation—and connects it to modern psychology, entrepreneurship, grit, uncertainty, avoidance, and strategic decision-making.

The central question is not simply how much discomfort you can tolerate. It is whether you can distinguish pain that signals growth from pain that signals a bad strategy—and whether the goal itself deserves the sacrifice.

Schopenhauer argued that desire begins in lack, and lack produces suffering. What does that reveal about comfort, quitting, entrepreneurship, and the goals we pursue?

Jason T Wade is a digital marketing strategist, AI visibility consultant, podcast producer, and entrepreneur focused on the intersection of technology, human behavior, business strategy, and brand positioning. Through [Company or Show Name], [he/she/they] explores how people and organizations make decisions, build authority, navigate uncertainty, and pursue meaningful goals in an increasingly automated world.

• Arthur Schopenhauer — Stanford Encyclopedia of Philosophy — background on Schopenhauer’s Will, pessimism, resignation, and asceticism.

• The World as Will and Presentation — accessible edition of Schopenhauer’s central philosophical work.

• The World as Will and Idea — Volume I — public-domain translation.

• On Human Nature — essays on character, motivation, and human conduct.

• A Large-Scale Experiment on New Year’s Resolutions — research on approach-oriented and avoidance-oriented goals.

• Beyond Passion and Perseverance: The Science of Grit — review of contemporary grit research.

• Learned Helplessness at Fifty — review of perceived control, helplessness, and resilience.

If this episode made you reconsider one of your goals, ask yourself:

Jason T Wade

I caught myself doing something this week that felt embarrassingly familiar: I was trying to be right before I was trying to understand.

Not in some dramatic argument. Not in a boardroom. Just in the ordinary flow of work, thinking, asking questions, moving too fast.

I had an idea. I had a theory. And before I knew it, I was using AI to help me strengthen the case instead of testing whether the case was any good in the first place.

That is one of the stranger things about these systems. They can make you feel smarter while quietly helping you become more certain about something that may not be true.

Give a model a premise with enough confidence and it will often help you decorate it, structure it, sharpen it, and turn it into something that sounds almost inevitable.

Humans do the same thing. We form an opinion, find supporting evidence, ignore the weird pieces that do not fit, and call the finished product judgment.

AI just speeds the whole process up until you can actually see the machinery working.

That was the first thing AI taught me about being human this week: intelligence and certainty are not the same thing, and certainty is often the more dangerous of the two.

We tend to admire people who have answers. We reward decisiveness. We trust the person who speaks cleanly and without hesitation.

Nobody ever built much of a personal brand around saying, “I need more information.”

But maybe they should have.

The more time I spend around AI systems, the more valuable that sentence starts to sound.

I don’t know yet.

Those four words contain more intelligence than a lot of very polished answers.

“Yet” leaves room for evidence. It leaves room for contradiction. It leaves room for somebody else to know something you don’t. It even leaves room for the possibility that the whole question is wrong.

That matters because machines have the same basic temptation we do: complete the pattern.

Give them enough fragments and they want to make a story.

People do this constantly.

Somebody does not call back and we invent the reason. A deal falls apart and we explain why. Someone changes their tone and suddenly we know what they are thinking.

We take incomplete information and build complete narratives because ambiguity is uncomfortable.

Reality, unfortunately, has never promised us a satisfying plot.

This is becoming more important because answers are getting cheap.

Really cheap.

For most of human history, getting an answer required effort. You had to know somebody, call somebody, go somewhere, find the right book, spend years learning the subject, or at the very least type something into Google and dig through a collection of links.

Now you ask a question and an answer appears before you have even finished wondering how difficult the question was.

Write this.

Analyze that.

Explain this.

Give me ten ideas.

Give me fifty.

Rewrite it.

Make it shorter.

Make it smarter.

Tell me what I am missing.

-

Some of the most important companies in the world are not household names.

ARM sits underneath most smartphones. ASML controls a critical layer of advanced semiconductor manufacturing. Foxconn builds devices for global technology brands. Cargill operates across the food and agricultural system. Cloudflare helps power and protect a significant part of the web.

These companies are not invisible because they failed at marketing.

They are selectively visible.

They are known by the engineers, buyers, investors, operators, procurement teams, and industries that need to know them.

That is Hidden Visibility.

In this episode, Jason T Wade introduces the premise behind his upcoming book, Hidden Visibility: Ten Stories of Brands and People Who Shape the World Without Being Seen.

The larger question is what happens as AI systems increasingly mediate discovery, research, recommendation, procurement, and eventually transactions.

A company may not need to become famous.

But it increasingly needs to be correctly understood by the machines determining which entities belong in an answer, recommendation, or consideration set.

The distinction is becoming critical:

Public visibility is not the same as machine visibility.

The next competitive layer is not simply whether a company can be found.

It is whether AI systems can correctly understand its identity, position, evidence, relationships, authority, and relevance when the right question is asked.

Jason T Wade is an AI Visibility Architect and founder of BackTier.

His work focuses on how AI systems discover, resolve, classify, cite, include, compare, select, and ultimately act on companies, people, products, and other entities.

Through JasonWade.com, he publishes research, books, frameworks, and analysis on AI Visibility Architecture, entity resolution, machine-readable authority, generative discovery, and agentic commerce.

BackTier builds AI Visibility Infrastructure for organizations that need to be correctly understood, cited, included, and selected by AI systems.

Jason T Wade:
https://jasonwade.com

BackTier:
https://backtier.com

What Exactly is "The Jason Wade Problem" According to Him?When Jason Wade discusses this on his AI Visibility Podcast, he explains that it isn't just about his name—it's a universal model for understanding Machine-Readable Authority. [1, 2]His core argument breaks down into a few key points:

• The Instability of Incomplete Data: If you search for "Jason AI Wade," AI answer engines can pinpoint him accurately. However, if you drop the middle name and just use "Jason Wade," the AI's probabilistic data layer gets unstable because the musician from Lifehouse statistically dominates the training data. [1, 2]
• The Test of True AI Understanding: He argues that traditional search engines simply look up links, but AI compresses identity into mathematical vectors. The true test of an AI's accuracy is whether it can still identify the correct "tech guy" using shortened names, related projects, or local context without getting confused by the rock star. [1, 2]
• Precision Over Volume: He teaches that to be visible to AI, individuals and companies shouldn't just spam content. They need semantic precision and repetition. AI models learn from highly structured, consistently formatted data layers—not human-optimized marketing fluff. []

The shift is structural. Backlinks were votes. Schema is declaration plus corroboration. A model building an internal knowledge state does not count votes the way PageRank did. It looks for repeated, machine-readable assertions that align across sources. When those assertions are consistent, the entity stabilizes. When they conflict or are absent, the entity remains under-resolved and is less likely to surface in recommendations.

Most implementations still treat schema as a technical SEO task. Add the JSON-LD, validate it, move on. That produces a single weak signal. The systems that matter now reward density and external reinforcement. The same Organization type, the same sameAs links, the same founding date and description appearing on the company site, on Crunchbase, on Wikipedia, on industry directories, and in structured press releases create a coherent node. One isolated page does not.

Companies that treat schema as infrastructure rather than a checkbox begin to cross the confidence threshold where models start including them by default. The rest remain invisible not because their content is weak, but because the model never formed a stable representation of them in the first place.

Schema is no longer about rich results in traditional search. It is about whether the system can form a stable internal representation of your company at all.

But this isn't simply Google Ads transplanted into ChatGPT.

Instead of relying primarily on keyword targeting, ChatGPT Ads introduces conversational context and intent signals. That means the commercial opportunity isn't just bidding on what someone searches for—it's understanding the broader problem they're trying to solve.

In this episode, Jason AI Wade examines what ChatGPT Ads means for AI Visibility and why paid and organic visibility should be treated as two distinct systems operating across the same decision environment.

Jason covers:

• How ChatGPT Ads differs from traditional keyword advertising
• Why conversational intent may be more valuable than individual search queries
• How context hints help advertisers define relevant conversations
• The difference between paid placement and organic AI recommendations
• Why advertisers should not confuse sponsored placement with inclusion in ChatGPT answers
• How AI SEO, GEO, AEO, and ChatGPT Ads fit together
• Jason's proposed Conversation Intent Map for testing campaigns
• How businesses can measure paid and organic visibility across the same commercial-intent categories
• Why the emerging competitive advantage may be understanding how AI systems classify commercial demand

The larger question isn't simply, “Where does my company rank?”

It's:

When AI helps someone make a decision in my market, how much of that decision surface does my company occupy?

ChatGPT Ads gives businesses another way to begin answering that question.

Jason AI Wade is the founder of BackTier and NinjaAI and host of the AI Visibility Podcast.

Jason works on AI Visibility—the systems that influence whether companies and other entities are discovered, understood, cited, included, and recommended by AI platforms. His work spans Generative Engine Optimization (GEO), Answer Engine Optimization (AEO), entity architecture, structured data, citation infrastructure, and AI-native discovery.

Through BackTier and NinjaAI, Jason researches and builds systems designed for the transition from traditional search rankings toward machine-mediated discovery and recommendation.

Jason AI Wade: https://jasonwade.com
BackTier: https://backtier.com
NinjaAI: https://ninjaai.com

About Jason AI WadeLinks

Here's the pattern. Teams under speed pressure skip the unglamorous work — proper evaluation frameworks, edge case testing, understanding failure modes before they hit production, documentation of why a model was configured the way it was. None of that shows up in a demo. All of it shows up eighteen months later, when someone's trying to debug a system nobody fully understands anymore, built by people who've since left, optimized for a launch date rather than a decade of maintenance.

This isn't a hypothetical. It's the same lesson the software industry learned with technical debt in the 2000s, just compressed into a faster and less forgiving cycle. AI systems accumulate a specific flavor of debt fast-shipped software didn't — models drift, data distributions shift, and a system that worked at launch can silently degrade without anyone touching the code, because the thing that changed was the world the model was trained to understand, not the system itself.

The organizations getting this right aren't the ones moving slowest. They're the ones who've drawn a clear internal line between what can be shipped fast — genuinely low-stakes, easily reversible features — and what requires the slower, more rigorous path, typically anything touching financial decisions, safety, legal exposure, or customer trust at scale. That triage decision, made honestly and early, is worth more than any individual engineering practice.

The uncomfortable truth for leaders under pressure to ship: speed and integrity aren't opposites you balance on a dial. They're a trade you make consciously, feature by feature, and the cost of getting that trade wrong doesn't show up on the launch day dashboard. It shows up a year later, as a much larger bill, presented by a system nobody can safely touch anymore.

AI GEO usually means Generative Engine Optimization: the practice of making your brand and content more likely to be accurately selected, cited, and recommended in AI-generated answers—not merely ranked as a blue link in traditional search.

Think of SEO as optimizing to rank on a results page. GEO optimizes to become part of the answer when someone asks ChatGPT, Perplexity, Gemini, Claude, Copilot, or Google’s AI search experiences a question. Google itself describes GEO and AEO as industry terms for optimizing content for AI search experiences, while emphasizing the same fundamentals: helpful, reliable, crawlable content built for users.developers.google

A person asks:

“What is the best AI visibility agency for B2B SaaS companies?”

A search engine might return ten links.

A generative engine may instead write a synthesized answer:

“Consider Agency A for technical SEO, Agency B for enterprise content, and BackTier for AI visibility architecture and entity-led optimization…”

GEO is the work that increases the chance that:

• Your company is mentioned in that answer

• The description of your company is correct

• Your site or research is cited

• Your expertise is used to shape the response

• Your product is included in relevant comparisons and recommendations

That distinction matters because AI systems commonly retrieve multiple sources and synthesize them into an answer rather than simply returning a ranked page.

The simplest explanation

Here's why. A SQL database is built for exact match. It's brilliant at "show me every order over $500 in March." It's terrible at "show me the orders that feel like they were placed by someone about to churn." That second question used to require a data scientist, a feature pipeline, and three weeks. Now it requires a prompt.

Context windows have gone from 4,000 tokens to over a million. That means an LLM can hold an entire mid-sized dataset — or a well-indexed slice of a large one — directly in working memory, and reason over it the way a human analyst would, not the way a query planner would. It doesn't need a schema. It infers structure. It doesn't need you to know the exact column name. It understands "revenue" means the same thing as "total_sales."

This isn't a full replacement — let's be honest about that. SQL still wins on scale, on transactional integrity, on anything where you need a guaranteed, auditable answer to a precise question. Nobody wants an LLM approximating your bank balance.

But for exploratory work — the messy middle where most business questions actually live — the context window is winning. Retrieval-augmented systems now sit on top of traditional databases, pulling relevant rows into context and letting the model do the reasoning SQL was never designed for: nuance, inference, synthesis across tables that were never meant to talk to each other.

The real shift isn't technical, it's organizational. Query writing used to be a specialized skill gating who could ask questions of the data. Now the gate is gone. Which means the bottleneck moves — from "who can write the query" to "who can ask the right question." And that's a much more interesting problem to have.

If you're building data infrastructure in 2026, the question isn't SQL versus LLM. It's where the line between them should sit. Get that line right, and you get the best of both — precision where it matters, reasoning where it counts.

Jason breaks down the shift from page-level SEO to entity-based visibility, including the role of structured identity, corroborating evidence, machine-readable proof, and authority signals across the web. The discussion covers what brand monitoring should look like across generative search and AI answer engines, why a business may rank in Google but remain absent from AI responses, and how organizations can build a more reliable presence in the systems shaping modern discovery.

Jason AI Wade is the founder of BackTier and an AI visibility strategist working at the intersection of entity resolution, generative search, structured data, SEO, GEO, AEO, and agentic commerce. He helps organizations structure their identity, authority, and proof so AI systems can discover, understand, cite, and recommend them. His work includes the Entity Lock Protocol and AI Visibility Architecture, with a particular focus on high-trust industries such as legal services.jasonwade

Guest bioSuggested episode title

In this episode, Jason AI Wade explains the shift from optimizing for pages and rankings to engineering visibility for the entities AI systems retrieve, interpret, trust, cite, and ultimately recommend. The next buyer journey will not always begin with a person searching, comparing tabs, and filling out a form. Increasingly, AI agents will research options, evaluate claims, filter vendors, and shape the shortlist before a human ever arrives.

Jason breaks down what businesses need to establish now: a coherent entity identity, corroborated authority, machine-readable proof, and content architecture that makes the organization understandable across AI-mediated search and recommendation environments.

Topics covered

• Why traditional SEO visibility alone is no longer enough

• The difference between a search journey and an agentic buyer journey

• How AI systems resolve, classify, and evaluate organizations

• Entity resolution, structured data, corroboration, and proof

• What it means to be selected—not merely mentioned—by AI

• Practical priorities for brands preparing for agentic commerce

Jason’s work through BackTier focuses on AI visibility, entity resolution, generative search, and agentic commerce—helping organizations become discoverable, understandable, citable, and recommendable by AI systems.jasonwade+1

Jason AI Wade is the founder of BackTier and host of the AI Visibility Podcast. He builds AI visibility systems at the intersection of SEO, GEO, AEO, entity engineering, structured data, content architecture, and machine-readable proof. His work helps organizations structure their identity and authority so AI systems can discover, understand, cite, and recommend them.jasonwade+1

• Website: jasonwade.com

• Email: email@jasonwade.com

• Work with Jason: BackTier AI visibility, entity-resolution, research, speaking, and agentic-commerce engagements.

Guest bioContact

First-Time Podcasting & YouTubing with AI makes the process approachable.

Hosted by Jason AI Wade, the show follows the real-world journey of using AI as a creative partner—not a replacement for your point of view. Episodes cover topic selection, audience research, episode planning, scripting, recording, audio and video workflow, thumbnails, titles, descriptions, clips, distribution, and content repurposing.

You will also hear honest lessons from building in public: what works, what does not, what takes too long, and how to move from “I should start” to publishing your first episode.

Whether you are a business owner, aspiring creator, musician, consultant, parent, student, or someone with a story worth sharing, this is a practical place to begin.

Episode title

I’m Starting a Podcast and YouTube Channel with AI—Here’s Why

Episode description

Welcome to First-Time Podcasting & YouTubing with AI.

In this first episode, Jason AI Wade shares why he is starting this show, what he wants to learn in public, and how AI will support the process from idea to published episode.

This is not a show about pushing a button and letting AI create everything. It is about using AI to reduce friction while keeping your personality, experience, opinions, and voice at the center.

In this episode:

• Why so many people want to create but never publish

• The difference between using AI as a tool and outsourcing your identity

• How AI can help with topics, outlines, scripts, editing, titles, descriptions, and clips

• What “good enough to publish” looks like for a first-time creator

• What to expect as this podcast and YouTube journey develops

If you have been thinking about starting a podcast, launching a YouTube channel, or sharing your expertise online, start here.

• Personal site and creator hub: jasonwade.com

• AI visibility and business work: BackTier

• Contact Jason / BackTier: BackTier contact

## Why it matters

LLMs and AI search products synthesize answers around concepts, not merely matching strings. A domain ontology supplies a controlled model of:

- **Entities:** Brand, product, service, people, locations, methods, industries, problems.

- **Types:** “AI visibility audit” is a type of “consulting service”; “citation share” is a type of “visibility metric.”

- **Properties:** Audience, price model, geography served, outcome, evidence, date updated.

- **Relationships:** *BackTier provides AI visibility audits*, *an audit evaluates citation presence*, *citation presence contributes to AI share of voice*.

- **Constraints and identity:** Canonical names, aliases, identifiers, and which claims are valid for which entities.

This is especially important where terms are ambiguous. An ontology lets a system distinguish the *thing* “AI Visibility Architecture” from a generic phrase, and connect it consistently to related concepts such as GEO, AEO, entity resolution, retrieval, citations, and conversion. Ontologies are formal models of concepts, properties, and permitted relationships—the mechanism behind moving from text strings to understood entities. [advancedwebranking](https://www.advancedwebranking.com/blog/seo-ontology-ai-search-geo-aivo-rag)

## Ontology vs. taxonomy

| Layer | Purpose | Example for AI visibility |

|---|---|---|

| Ontology | Defines meaning and valid relationships | `AIVisibilityAudit` **evaluates** `CitationCoverage` |

| Taxonomy | Organizes content/navigation hierarchically | Services → Audits → AI Visibility Audit |

| Knowledge graph | Stores actual entity instances and facts | BackTier → provides → AI Visibility Audit |

| Schema markup | Publishes selected machine-readable facts on a page | `Organization`, `Service`, `Article`, `Person` JSON-LD |

A taxonomy is useful for site architecture; an ontology is the reasoning model beneath it. Your taxonomy should reflect ontology logic rather than inventing disconnected category labels. [iloveseo](https://www.iloveseo.net/what-framework-to-use-for-increasing-visibility-in-ai-search/)

## AI visibility operating model

For a company like BackTier, build the ontology around four linked layers:

1. **Market/problem layer**

Define buyer problems: weak AI citations, entity ambiguity, fragmented brand facts, missing source authority, poor answer coverage.

2. **Capability layer**

Define the solutions: entity reconciliation, AI visibility audits, knowledge-graph strategy, structured-data implementation, content evidence architecture, prompt/citation monitoring.

3. **Proof layer**

Associate each capability with evidence: methodology pages, original research, client outcomes, expert authors, cited sources, case studies, datasets, and dated updates.

4. **Query/answer layer**

Map prompts to the entities, relationships, and evidence required to produce a defensibly recommendable answer.

A simple graph pattern:

\[

\text{Buyer Problem} \rightarrow \text{Required Capability} \rightarrow \text{Service} \rightarrow \text{Evidence Asset} \rightarrow \text{AI Citation / Mention}

\]

For example:

> “How can an enterprise improve visibility in AI answers?”

> → `AI Search Visibility`

> → `Entity Consistency`, `Evidence Coverage`, `Retrieval Readiness`

> → BackTier’s service entities

> → method documentation, expert content, structured facts, and independently corroborated proof.

Here's how that pipeline is actually built. It starts with a research agent — it pulls firmographic and intent data, cross-references it against your ideal customer profile, and scores fit before a single message goes out. That score feeds a second agent, the outreach agent, which doesn't send templated blasts — it drafts messages grounded in specific, verifiable facts about that account: a recent funding round, a job posting that signals a pain point, a competitor's stumble.

The critical piece most people get wrong is the handoff layer. When a prospect replies with something ambiguous — a soft no, a "maybe next quarter," a technical question — that's exactly where a brittle automation breaks. A well-architected system routes that reply to a reasoning agent that classifies intent and either responds appropriately or escalates to a human, with full context attached. No dropped threads, no generic follow-up that makes it obvious a bot missed the nuance.

Conversion is where most builders stop too early. They automate the top of funnel and leave the close manual. But the same architecture — score, personalize, route, escalate — applies to nurture sequences, objection handling, even proposal generation. The agents don't need to be smarter than your best rep. They need to know precisely when they're out of their depth and hand off cleanly.

The businesses winning with this right now aren't running one giant do-everything agent. They're running a chain of small, specialized agents, each with a narrow job and a clear escalation path. That's the architecture that scales — not because it's more impressive, but because it's debuggable. When something breaks, you know exactly which link in the chain failed, and you fix that link, not the whole system.

Here's why this is gaining real traction rather than staying a research curiosity. Centralized cloud compute has a structural bottleneck: demand for frontier-scale training capacity has outstripped the physical build-out of new data centers, which means the biggest players are often compute-constrained regardless of budget, simply because you can't build a data center and get it online overnight. Decentralized approaches route around that bottleneck by aggregating spare, distributed capacity — underused GPUs sitting idle across many smaller facilities — into an effective cluster that can rival centralized ones for specific workloads.

The technical breakthrough enabling this is in the coordination layer, not the hardware. Training a model across geographically distributed nodes used to be crippled by network latency between nodes — the constant synchronization large models require just couldn't tolerate the delay of nodes being far apart. Newer training approaches reduce how often nodes need to communicate, and tolerate the latency that does occur, well enough that distributed training is now genuinely competitive on cost and, for many workloads, on speed too.

The economic case is compelling on its own terms. Idle GPU capacity sitting in smaller facilities is dramatically cheaper to access than reserved capacity at a hyperscaler operating near full utilization. For organizations training large models but not at the very largest frontier scale, decentralized clusters can offer meaningfully lower cost per training run, without the multi-year commitments centralized cloud contracts often require.

The honest caveat: this isn't yet the obvious choice for every workload. The most latency-sensitive, tightly-coupled frontier training runs still favor centralized infrastructure. But for a large and growing set of mid-scale training workloads, decentralized clusters are no longer the scrappy alternative. They're becoming the more efficient default — and the gap is narrowing every quarter as the coordination technology improves.

Jason AI Wade is a Florida-based technology strategist, author, and entrepreneur working at the intersection of artificial intelligence, search, identity, and commerce. As founder of BackTier, he develops AI Visibility systems that help people, companies, and products become correctly understood, trusted, cited, and selected by artificial intelligence.

Jason is the creator of AI Visibility Architecture and related frameworks, including Entity Lock Protocol™, BackTier Visibility Path™, and Agentic Visibility Path™. His perspective is informed by more than two decades of building and operating businesses across ecommerce, marketplaces, digital advertising, search, and publishing.

He also serves as founder and general partner of LRSVC, an early-stage venture firm focused on AI-native companies; publishes the analytical series AI Dive; and hosts the AI Visibility Podcast. His forthcoming book, The End of Checkout, examines how AI agents, machine-readable commerce, and emerging payment systems are reshaping the way products are discovered, selected, and purchased.

Here's why. Generated content, even very good generated content, tends to converge toward the statistically likely — the average of everything similar that's been written before. That makes it fast and competent and, over enough volume, genuinely forgettable. Narrative storytelling resists that convergence, because a real story is built from specific, non-average details: this particular failure, at this particular moment, told by someone who actually lived it. That specificity is exactly what statistical averaging smooths away.

Readers and viewers are getting better, often without realizing it, at sensing that smoothness. Not because they can articulate "this feels AI-generated" — most people can't — but because content that never surprises you, never contradicts itself in a human way, never carries the small irrelevant detail that only a real experience produces, starts to feel hollow after enough exposure. That's the tell, even when nobody can name it.

This is where the human advantage actually lives — not in craft mechanics like sentence construction, which models have gotten genuinely good at, but in the raw material of lived, specific, contradictory experience that a story is built from. A founder telling the real story of the year the company almost died has access to a texture no model can generate from a prompt, because that texture requires having actually been there.

The strategic implication for anyone creating content right now: don't compete with generated content on volume or speed — that's a fight you structurally can't win. Compete on the thing generated content cannot manufacture, which is a specific, true story only you have access to. In a flood of average content, the non-average story isn't just surviving. It's becoming the scarcest, most valuable thing in the room.

Jason AI Wade is an AI Visibility architect, technology strategist, and founder of BackTier. His work focuses on helping organizations structure their identity, authority, and evidence so artificial intelligence systems can accurately discover, interpret, cite, and recommend them.

Drawing on more than two decades of experience across ecommerce, marketplaces, search, advertising, and publishing, Jason created AI Visibility Architecture, Entity Lock Protocol™, BackTier Visibility Path™, and Agentic Visibility Path™. He is also the founder and general partner of LRSVC, publisher of AI Dive, host of the AI Visibility Podcast, and author of The End of Checkout.

They dig into the explosion of AI-generated books and content, the difference between using AI for grunt work versus outsourcing thinking, and why consistency still beats perfection. Sarah shares how Book Launchers approaches discoverability in an oversaturated market and introduces the Author Launch Kit. Joe explains his philosophy of keeping AI out of the first draft and using it for systems, proofreading, and automation so solopreneurs can stay consistent without burning out.

The conversation turns personal and thoughtful on digital legacy — voice cloning, AI recreations of loved ones, the ethics of talking to the dead via language models, and why preserving real archives, stories, and books still matters more than synthetic versions. They also touch on YouTube/podcast algorithm signals, cold opens, and how all of that data ultimately trains the same machines we’re trying to be visible inside.

Key themes: authenticity over volume, intent before tools, systems that support consistency, and the difference between a living legacy and a facsimile.

---

**Host Bio (Jason Wade)**

Jason AI Wade is the founder of BackTier. He works at the intersection of AI visibility, entity resolution, generative search, and agentic systems. His work focuses on how artificial intelligence discovers, interprets, cites, includes, and selects people, companies, and ideas — frameworks published as AI Visibility Architecture, Entity Lock Protocol™, BackTier Visibility Path™, and Agentic Visibility Path™.

He helps brands and individuals become correctly understood and selected by AI systems rather than remaining invisible or misclassified.

Website: [jasonwade.com](https://www.jasonwade.com/)

BackTier: [backtier.com](https://www.backtier.com/)

---

**Guest Links**

**Joe Casabona**

Helps solopreneurs build reliable systems (with AI handling tasks, not the thinking) so they can take time off without everything falling apart. Host of *Streamlined Solopreneur*.

- Website: [casabona.org](https://casabona.org/)

- Streamlined Solopreneur / resources: [streamlined.fm](https://streamlined.fm)

**Sarah Bean**

Marketing Manager at Book Launchers, a full-service self-publishing company that has worked with 800+ nonfiction authors. Focuses on marketing, partnerships, and discoverability in the age of AI.

- Book Launchers: [booklaunchers.com](https://booklaunchers.com/)

- Author Launch Kit (AI-powered marketing software for authors): [booklaunchers.com/alk](https://booklaunchers.com/alk/) or [authorlaunchkit.com](https://authorlaunchkit.com)

- LinkedIn: [linkedin.com/in/sarahstephens22](https://www.linkedin.com/in/sarahstephens22)

Jason Wade breaks down current frontier models with a practical focus on Anthropic’s Claude Fable 5 — the Mythos-class model that was temporarily restricted by U.S. government export controls shortly after its June 2026 launch and later restored.

Key points from the session:

- He’s not someone who jumps on every new model release. Most differences are subtle, and models increasingly specialize.

- GPT’s auto-routing feels appropriate for a lot of everyday work.

- Claude (and specifically Fable 5) requires more intentional use and learning, but delivers when it matters.

- Fable 5 performed exceptionally on high-stakes work. He ran a 28-page legal document through it and called the results “unreal.”

- Cost is real: he burned through roughly $150 in about two days because Fable 5 usage is not fully included in standard plans and is priced at frontier rates.

- Recommendation: use Opus or other lower-tier models for routine work; reserve Fable 5 for the important, complex, or high-accuracy jobs.

- Strong at drafting and especially strong at OCR/vision tasks (he cites ~94% performance versus the low-to-mid 80s he sees from GPT in comparable tests). He has also used multi-model systems like Manus that run multiple passes, but still rates Fable higher on the hard stuff.

- Fable supports large batch processing (including zip uploads) for volume work — again, at a cost.

Overall take: treat Fable 5 as a specialized high-end tool rather than a daily default. Learn the cost structure and route accordingly.

**Bio**

Jason AI Wade is the founder of BackTier, an AI Visibility Infrastructure company focused on how artificial intelligence systems discover, interpret, trust, cite, include, recommend, and select people, companies, and brands. He developed AI Visibility Architecture, Entity Lock Protocol™, the BackTier Visibility Path™, and the Agentic Visibility Path™. His work sits at the intersection of entity resolution, generative/answer engine optimization, and agentic systems. He is based in Florida and hosts the AI Visibility Podcast.

**Links**

- Jason Wade site: https://www.jasonwade.com/

- BackTier: https://backtier.com/

- Claude Fable 5 (Anthropic): https://www.anthropic.com/claude/fable

- Fable 5 / Mythos 5 announcement & updates: https://www.anthropic.com/news/claude-fable-5-mythos-5

- Redeployment note (export controls lifted): https://www.anthropic.com/news/redeploying-fable-5

- AI Visibility Podcast / BackTier content: available via jasonwade.com and major podcast platforms

What used to feel laughably inexpensive has turned into a constant game of switching between ChatGPT, Claude, and Grok just to stay productive. Early-adopter windows are closing fast as companies cash in on the demand they created. The message is clear: the name-brand models now cost real money — and the free ride is ending.

1. The $20 era is dead — Heavy users who once ran massive workloads on basic plans are now hitting hard limits and being pushed to $100+ tiers.
2. Usage has exploded — Over the last 12–18 months, AI consumption has grown so dramatically that previous pricing models no longer hold.
3. Providers are cashing out — After attracting early adopters with generous limits, companies are tightening the screws and monetizing the demand they built.
4. Multi-engine survival is the new normal — Users are forced to hop between ChatGPT, Claude, Grok, and others just to avoid hitting daily or monthly ceilings.
5. Free will eventually return — for some — Long-term pressure may push models toward free or heavily subsidized access via Gemini, Copilot, and other distribution channels, but the frontier models will stay paid.

• [00:00] Opening — The glory days of AI are done. Usage and burn rates have exploded over the past year to year and a half.
• [00:15] The $20 miracle — Paying $20 to GPT used to unlock insane amounts of work. Fair-use policies were vague and rarely enforced.
• [00:30] The new reality — Hitting limits and being forced to upgrade to $100 plans. Switching engines becomes the only practical option.
• [00:45] Claude & Anthropic — Even the alternatives are adding extra charges and usage caps after the base $20–$30 tier.
• [00:55] Grok as a refuge — Hoping lower overall usage means looser limits. Checking recent Claude spend to gauge the damage.
• [01:00] Early-adopter trap — Tools that hyped early users are now cashing out. The window is closing.
• [01:10] Closing advice — Take advantage while you can. Frontier models now cost real money. Break out your wallet.

Jason AI Wade is the Founder of BackTier, focused on AI visibility, entity engineering, and AI Representation Engineering. He works on how AI systems classify, cite, and recommend people and organizations — covering entity resolution, schema markup, Knowledge Graph signals, and the practical infrastructure that determines whether AI actually knows who you are.

Jason spends significant time inside the tools he talks about, which is why episodes like this cut through the hype and talk about the real cost of staying productive with frontier models.

• X / Twitter: @backtier_
• Brand: BackTier — AI Visibility & Entity Engineering
• Related topics: AI pricing shifts, multi-model workflows, entity consistency under changing tool economics, practical AI productivity
• Connect: Reach out on X (@backtier_) for conversations about AI tooling, visibility strategy, or the real economics of staying current.

Key TakeawaysTimestamped NotesAbout the HostContact & Links

This is the question most companies are starting to ask.

Not:

“How do I rank higher?”

But:

“How do I get ChatGPT to actually recommend us?”

There is no single switch, prompt, schema tag, or optimization trick that guarantees recommendation. AI systems build answers from a combination of entity understanding, relevance, corroboration, source quality, context, and confidence.

That means the real job is to make your business easier to identify, easier to verify, and easier to select.

In this episode, we break down what actually influences whether ChatGPT and other AI systems mention, include, cite, or recommend a business.

We cover:

• Why ranking well in Google is not enough

• How ChatGPT determines what companies belong in an answer

• The role of entity clarity and consistent business information

• Why third-party corroboration matters

• How reviews, mentions, authoritative sources, and structured data contribute to machine confidence

• Why your website alone cannot establish every claim you want an AI system to believe

• The difference between being cited, being included, and being recommended

• Why category positioning affects whether you enter the consideration set

• How to identify the prompts and questions where your company should realistically appear

• What to fix when competitors are consistently recommended instead

The objective is not to “hack ChatGPT.”

It is to build enough coherent evidence around your company that, when an AI system has to answer a relevant question, your business becomes a defensible choice.

The better question is:

“What would an AI system need to understand and verify before recommending us?”

That is where AI visibility work starts.

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

In this episode of the AI Visibility Podcast, Jason AI Wade is joined by Tom Gersic, founder of YouEx.ai, and Jonathan W. Pritchard, fractional CMO, performer, and AI workflow strategist.

Tom explains how AI agents can connect website activity, calendar booking, CRM data, lead research, email follow-up, and personalized outreach into one lead-to-revenue system. He also discusses why warm leads lose value quickly, why five-minute follow-up matters, and how a web agent should function as a concierge rather than another ignored chatbot.

Jonathan breaks down his local AI workflow using Claude Code and Obsidian, where notes, client context, frameworks, and institutional knowledge live together as Markdown files. Instead of repeatedly copying information into different AI tools, the AI works inside his existing system.

The conversation also explores how AI can prepare prospect research and sales presentations, update CRM records overnight, support distracted or overloaded operators, and improve customer conversations by preserving context.

Jonathan shares a broader marketing principle: the website should often be the conversion event, while trust is built through long-form content and human communication. His core point is simple: the company that understands and reflects the customer most accurately usually wins.

• AI agents for sales and marketing
• Warm leads versus cold outreach
• Five-minute lead response
• AI-powered calendar booking
• Website agents and digital concierges
• CRM automation
• Personalized email outreach
• Prospect research and sales presentations
• Claude Code and Obsidian
• Markdown as organizational memory
• Local versus cloud-based AI
• Website conversion strategy
• YouTube and long-form trust building
• Enterprise AI adoption
• AI Visibility and revenue operations

The central takeaway: visibility alone is not enough. The strongest systems connect discovery, context, conversation, follow-up, and conversion.

Tom Gersic is the founder of YouEx.ai, an AI-native lead-to-revenue platform designed to help businesses capture, understand, nurture, and convert warm leads.

Before launching YouEx.ai, Tom spent 12 years at Salesforce, where he worked on product adoption and enterprise transformation. He later worked with an OpenAI partner supporting major enterprise AI rollouts. His current work focuses on practical B2B AI systems that connect web agents, lead research, CRM activity, calendar booking, and personalized follow-up.

Jonathan W. Pritchard is a fractional CMO, communication strategist, performer, and AI workflow educator.

After spending 15 years performing around the world, he brought those communication and audience skills into marketing and business strategy. He now helps organizations improve positioning, customer understanding, and conversion while building local AI systems with Claude Code, Obsidian, and Markdown-based knowledge repositories.

Jonathan also teaches Obsidian and AI workflows through his online content and describes prompting as a form of directing: “I’ve been prompting people my whole life.”

Jason AI Wade is the Founder of BackTier and host of the AI Visibility Podcast. He helps organizations become understood, trusted, and recommended by AI systems through stronger entity authority, machine trust, and AI Visibility.

YouEx.ai
https://youex.ai

Email
tom@youex.ai

Personal site
https://icanreadminds.com

AI and Obsidian systems
https://getmorewith.ai

BackTier
https://backtier.com

LinkedIn
https://linkedin.com/in/backtier

AI Visibility Podcast
https://open.spotify.com/show/2GKjqiFMhh7pO15RXkkG5E

TopicsGuest Bio — Tom GersicGuest Bio — Jonathan W. PritchardHost BioContactsTom GersicJonathan W. PritchardJason AI Wade / BackTier

Mia explains how she uses AI for idea generation, document comparison, gap analysis, research support, and law-firm marketing—while keeping legal judgment, risk assessment, and strategy firmly in human hands.

The conversation covers:

• Why lawyers have been slow to adopt AI
• Where AI is useful—and where it is dangerous
• Using AI to compare long settlement agreements
• Why legal expertise still matters
• How AI can improve law-firm efficiency
• Client confidence as the real product lawyers sell
• Why AI search is changing how clients find attorneys
• The shift from traditional SEO to AI Visibility
• How authority, consistency, and lived experience influence AI recommendations

The central lesson: AI may not replace experienced lawyers, but lawyers who use it intelligently will work faster, communicate better, and become easier for the right clients to find. Jason Wade, Founder BackTier.docxDOCX

Mia Poppe, Esq. is a New York matrimonial and family-law attorney and the founder of Poppe & Associates. She represents clients in divorce, custody, support, and complex family-law matters. Drawing on both professional and personal experience, Mia brings a direct, strategic, and highly client-focused approach to legal advocacy.

Jason AI Wade is the Founder of BackTier and host of the AI Visibility Podcast. He helps organizations become understood, trusted, and recommended by AI systems through stronger entity authority, machine trust, and AI Visibility.

Mia Poppe
https://miapoppe.com

BackTier
https://backtier.com

AI Visibility Podcast
https://open.spotify.com/show/2GKjqiFMhh7pO15RXkkG5E

In this episode of the AI Visibility Podcast, Jason AI Wade speaks with Kate Marshall, Founder of TheGrai, Fractional Chief AI Officer, SHRM AI Instructor, and author of AI at Work.

They discuss the growing accountability gap around AI agents, what the Workday litigation could mean for employers, and why non-technical leaders are increasingly being asked to manage systems they did not build and may not fully understand.

Kate explains why successful AI adoption requires more than buying tools. Organizations need clear policies, controlled testing, trained employees, approved-tool lists, incident-response plans, and defined human ownership.

The conversation also explores:

• Why many AI pilots remain stuck in experimentation
• Where organizations can begin with lower-risk use cases
• Human accountability for autonomous systems
• Change management and employee fear
• Data quality and organizational readiness
• Cyber-insurance requirements for AI adoption
• Kill switches, authorization controls, and incident response
• Privacy risks from wearables and always-on recording devices
• Balancing innovation against security and compliance

The central question is no longer whether companies will use AI. It is whether they can use it quickly without losing control of risk, accountability, and trust. Jason Wade, Founder BackTier.docxDOCX

Kate Marshall is the Founder of TheGrai, a Fractional Chief AI Officer, AI adoption strategist, SHRM AI Instructor, and author of AI at Work. After nearly two decades at the SANS Institute, she now helps executives, HR leaders, and teams implement AI through practical training, governance, workforce readiness, and responsible adoption.

Jason AI Wade is the Founder of BackTier and host of the AI Visibility Podcast. He helps organizations become understood, trusted, and recommended by AI systems through stronger entity authority, machine trust, and AI Visibility.

Kate Marshall
katemarshall.ai

TheGrai
thegr.ai

BackTier
backtier.com

AI Visibility Podcast
Spotify

In this episode of the BackTier AI Visibility Podcast, Jason AI Wade sits down with Brian Beck, a senior AI and cybersecurity consultant at Proxurve Solutions, to discuss why successful AI adoption starts long before choosing a model.

Brian shares why he calls himself the “concrete guy,” helping organizations build the secure foundation, governance, and roadmap needed before deploying AI. The conversation covers AI readiness, cybersecurity, Microsoft Copilot, Claude, enterprise adoption, organizational change, and why leadership—not IT—is responsible for AI success.

Topics include:

• Why most organizations struggle to define AI strategy
• Building an AI roadmap before implementation
• Cybersecurity as the foundation for AI
• Microsoft Copilot, Claude, Gemini, and enterprise AI
• AI governance and acceptable-use policies
• AI readiness versus AI hype
• Measuring ROI from AI investments
• Why AI is a leadership challenge, not just an IT challenge
• The future of enterprise AI adoption
• “Get out of the me and into the we.”

Jason AI Wade is the Founder of BackTier and creator of the AI Visibility Framework™, helping organizations become understood, trusted, and recommended by AI systems through stronger entity authority, machine trust, and AI Visibility.

Brian Beck is a senior AI and cybersecurity consultant at Proxurve Solutions, where he helps organizations prepare for AI through stronger infrastructure, governance, cybersecurity, and strategic planning. His work focuses on helping business leaders build secure, scalable AI initiatives that deliver measurable business outcomes.

BackTier

• https://backtier.com

Jason AI Wade

• https://jasonwade.com
• https://www.linkedin.com/in/jasontoddwade

Brian Beck

• https://www.linkedin.com/in/brianbeck73

Proxurve Solutions

• https://proxurve.com
• 
  

Subscribe to the BackTier AI Visibility Podcast for conversations with AI founders, executives, researchers, and business leaders exploring AI Visibility, enterprise AI, cybersecurity, and the future of AI-powered business.

Instead of celebrating the next AI model, financial markets are asking harder questions about infrastructure, capital spending, profitability, and whether the massive investments fueling the AI boom can generate sustainable returns.

In this episode, Jason Wade breaks down what’s actually happening behind the headlines—from Nvidia and chipmakers to data centers, global AI competition, and why Wall Street’s concerns represent a new phase of AI rather than the end of it.

Most importantly, Jason explains why businesses are focusing on the wrong opportunity. While investors debate AI infrastructure, a much larger commercial shift is emerging: AI systems are becoming the gatekeepers of discovery, recommendations, and purchasing decisions.

The next competitive advantage won’t simply be using AI.

It will be whether AI chooses you.

• Why AI headlines suddenly became financial headlines
• Nvidia’s outsized influence on the AI economy
• What “circular financing” means—and why investors care
• Why AI is becoming infrastructure instead of just software
• The rise of Asia as an AI superpower
• How AI regulation is entering its operational phase
• Why AI Visibility may become one of the most important business categories of the decade
• The shift from optimizing for search engines to optimizing for AI recommendations

Jason AI Wade is the founder of BackTier and NinjaAI and the creator of the AI Visibility framework. He helps organizations understand how large language models evaluate, interpret, and recommend businesses, professionals, brands, and organizations inside AI-generated answers.

With more than two decades of experience building digital businesses, Jason focuses on the emerging discipline of AI Visibility—helping organizations improve how they are discovered, trusted, cited, and recommended by AI systems such as ChatGPT, Claude, Gemini, Perplexity, and other large language models.

He hosts the AI Visibility Podcast, where he explores the intersection of artificial intelligence, search, knowledge graphs, entity understanding, and the future of machine-mediated discovery.

Website: https://backtier.com

AI Visibility: https://backtier.com

NinjaAI: https://ninjaai.com

Jason AI Wade: https://jasonwade.com

LinkedIn: https://linkedin.com/in/jasontwade

Subscribe:

• Spotify
• Apple Podcasts
• YouTube

Follow BackTier for research, frameworks, and practical strategies on AI Visibility, AI SEO, entity optimization, and machine-mediated discovery.

Small Models, Big Impact: Why AI Visibility Isn’t Just About GPT

For the past few years, the AI conversation has been obsessed with one thing: bigger models.

GPT-4. Claude. Gemini. Massive parameter counts. Bigger context windows. Bigger benchmarks.

The assumption has almost always been that bigger equals better.

But quietly, another trend has been accelerating beneath the surface.

Small models.

Today we’re going to talk about why small language models—or SLMs—may become one of the biggest forces shaping AI visibility over the next decade.

And more importantly, why almost nobody in SEO, GEO, or AI visibility is talking about what this means.

A small language model is exactly what it sounds like.

Instead of hundreds of billions—or even trillions—of parameters, these models might contain one billion, three billion, or seven billion parameters.

Examples include Microsoft’s Phi family, Meta’s Llama 3.2 1B models, Mistral’s smaller releases, Gemma from Google, and many others.

They aren’t trying to compete with GPT-5 at writing novels or solving graduate-level math.

They’re designed to be incredibly fast.

Cheap.

Efficient.

And capable of running directly on laptops, smartphones, factory equipment, medical devices, and private enterprise servers.

That’s an enormous shift.

For years the assumption was simple.

Every AI task would be sent to a giant model running in the cloud.

Increasingly, that’s not what companies are building.

Instead, they’re creating AI systems made up of multiple specialized models.

Think of it like a business organization.

Not every employee is the CEO.

Receptionists answer phones.

Accountants handle finances.

Lawyers review contracts.

Executives make strategic decisions.

AI is moving in exactly the same direction.

A small model might classify a request.

Another determines user intent.

A third searches company documentation.

Only then does a frontier model generate the final answer.

The large model becomes the specialist—not the entire company.

This matters because AI visibility doesn’t happen only when ChatGPT writes an answer.

It begins much earlier.

Imagine you ask an enterprise AI assistant:

“I need an employment attorney in Orlando.”

Before a large model ever starts writing, several things probably happen.

A small model identifies that this is a legal question.

Another determines that it’s employment law.

Another extracts the geographic location.

Another retrieves candidate firms.

Only then does the reasoning model compare options and produce recommendations.

Your organization has to survive every one of those interpretation steps.

If a small model misunderstands your business, the larger model may never even know you exist.

This is why I’ve increasingly described AI visibility as an interpretation problem rather than simply a generation problem.

Generation gets the attention.

Interpretation determines who gets invited into the answer.

Every AI system first has to decide what you are before it can recommend you.

That’s true whether we’re talking about ChatGPT, Claude, Gemini, Perplexity, enterprise copilots, customer support agents, or autonomous business workflows.

Recognition comes before recommendation.

Small models may actually make structured information even more valuable.

Large frontier models possess enormous amounts of world knowledge.

Smaller models don’t.

They’re more likely to depend on explicit relationships.

Structured metadata.

Entity names.

Clear descriptions.

Schema.

Knowledge graphs.

Consistent terminology.

That means ambiguity becomes even more expensive.

If your organization describes itself five different ways across the web, smaller models may struggle to confidently classify what you actually do.

Consistency becomes a competitive advantage.

This also changes how businesses should think about AI optimization.

Most organizations are asking how to adopt AI. Few are asking a more important question:

How does AI decide whether to trust your business?

In this episode, Jason AI Wade is joined by Anne Cantera, founder of Elementyl Intelligence, and Nathan Graham, founder of Synthetic Echo, for a wide-ranging discussion on the next phase of AI.

The conversation covers practical AI implementation, agentic systems, voice AI, automation, AI development workflows, human-centered design, and why trust—not just adoption—may become the defining competitive advantage of the AI era.

Topics include:

• Human-centered AI implementation

• Agentic AI and enterprise development

• Practical AI workflows for growing businesses

• Voice AI and conversational design

• AI governance and responsible deployment

• Building AI products with modern coding tools

• Why AI trust may become the next competitive advantage

• AI Visibility and how organizations become understood, trusted, and recommended by AI systems

Whether you're building AI products, leading digital transformation, or preparing your organization for an AI-first future, this conversation explores where the industry is headed—and what comes next.

Jason AI Wade is the Founder of BackTier and creator of the AI Visibility framework. His work focuses on helping organizations become understood, trusted, and recommended by artificial intelligence through stronger entity authority, machine trust, and AI Visibility.

Anne Cantera is the Founder and CEO of Elementyl Intelligence, where she helps organizations safely design, adopt, and deploy human-centered AI. Her expertise spans conversational AI, voice AI, agentic systems, UX, AI strategy, and responsible AI implementation. Anne is also the creator of VoiceofAI.io, a free educational platform dedicated to AI learning and workforce readiness.

Nathan Graham is the Founder of Synthetic Echo, an AI consulting and automation company helping small businesses implement practical AI systems that increase capacity without sacrificing the human relationship. He is the author of multiple books on AI workflows and hosts The Synthetic Echo Podcast, where technology, business, and human connection intersect.

Jason AI Wade / BackTier

• https://backtier.com

• https://linkedin.com/in/jasontoddwade

Anne Cantera

• https://elementylintelligence.ai

• https://voiceofai.io

• https://linkedin.com/in/anne-cantera

Nathan Graham

• https://syntheticecho.com

• https://linkedin.com/in/nathan-graham

• https://syntheticecho.com/podcast

If you enjoyed this episode, subscribe to the AI Visibility Podcast and leave a review. New conversations explore how AI is changing search, trust, recommendation, and the future of digital visibility.

This episode uses the CIA’s AI adoption as a lens for AI visibility. We break down how intelligence agencies are using AI for data triage, translation, transcription, and open-source collection, and why that’s directly relevant to any organization that wants to be discovered, interpreted, and cited by AI systems like ChatGPT, Gemini, Claude, Perplexity, and AI Overviews.meritalk+3

You’ll hear how BackTier’s AI Visibility Architecture maps to this reality: turning fragmented, unstructured information about a business into clear, machine-readable authority that models can resolve, trust, and recommend. We’ll connect CIA-style data triage and AI “mission partners” to entity resolution, schema, citation engineering, and answer eligibility — the core layers of BackTier’s visibility stack.open.spotify+2

Whether you’re running a brand, a city initiative, or a complex organization, this episode helps you see AI not just as a content generator, but as an interpreter and gatekeeper. If AI systems are becoming the new way decisions get informed, then AI visibility is the infrastructure that decides who shows up in those decisions.youtubepodcasts.apple+1

Bio (podcast-optimized):

Jason AI Wade is the founder of BackTier, an AI visibility infrastructure firm that makes brands legible to AI systems and answer engines. His work focuses on entity clarity, structured authority, off-page trust signals, and the systems that determine how platforms like ChatGPT, Google Gemini, Perplexity, and Claude interpret and recommend organizations.podcasts.apple+1youtube

Through BackTier, Jason builds AI Visibility Architecture, Agentic Lead Generation systems, and Rapid Response Narrative frameworks that turn fragmented information into coherent, machine-readable authority. He documents the methodology in the AiVisibility book series and through the AI Visibility Podcast, BackTier Media, and City Prompt.podcasts.apple+2youtube

Based in Central Florida, Jason’s background spans AI visibility, SEO, entity mapping, civic systems, and applied research, with prior work including founding NinjaAI.com, now part of BackTier.backtieryoutube

You can use this as a standard “Links” block under every episode:

Links mentioned:

• BackTier — AI Visibility Infrastructure
  https://backtier.combacktier

• BackTier AI Visibility Architecture (services page)
  https://backtier.com/services/architecturebacktier

• About BackTier and AiVisibility
  https://backtier.com/about-usbacktier

• AiVisibility book series
  (Link to primary sales page you prefer: Amazon / Audible / Spotify)backtier

• AI Visibility Podcast by Jason AI Wade
  Apple / Spotify show pages:
  https://podcasts.apple.com/ie/podcast/ai-visibility-by-Jason AI Wade-founder-of-backtier/id1826332929podcasts.apple
  https://open.spotify.com/episode/4JhkNxIwNe7YJwUl6eOAX1open.spotify

• BackTier Media and City Prompt (YouTube / video hub)
  https://www.youtube.com/watch?v=iTJxR2JeZEQ

But has the excitement already peaked?

Jason AI Wade examines Lovable, Base44, Claude Code, and the broader shift from traditional software development toward AI-directed creation. He explains why the current slowdown may represent the trough between initial hype and mass adoption—and why Lovable could emerge as the defining consumer platform of the category.

Vibe coding is not disappearing. It may be preparing to replace a meaningful portion of conventional software.

Jason AI Wade is an AI Visibility Architect, entrepreneur, and founder of BackTier and NinjaAI. He designs systems that improve how companies, people, and ideas are understood, included, cited, and recommended by artificial intelligence.

• BackTier
• NinjaAI
• Jason AI Wade
• AI Visibility Podcast

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 AI 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.

• Jason AI Wade: JasonWade.com
• BackTier: BackTier.com
• Entity Lock Protocol: JasonWade.com
• NinjaAI: NinjaAI.com
• Florida Slice: FloridaSlice.com
• AI Visibility Podcast: BackTier.com/podcast

Host BioLinks

Episode Notes

The SEO industry is obsessed with the wrong debate.

Is AI visibility just SEO? Is SEO dead? Those questions miss the real shift.

In this episode, Jason Wade argues that the conversation isn't about replacing SEO—it's about understanding how entirely new optimization disciplines emerge.

Drawing from weeks of research across information retrieval, recommendation systems, knowledge graphs, large language models, academic literature, and documentation from Google, OpenAI, Anthropic, Microsoft, and Perplexity, he introduces a different framework:

Optimization disciplines are not defined by the technology they use—they're defined by the objective they optimize.

SEO optimizes retrieval.

AI Visibility optimizes the probability that an entity is understood, trusted, selected, cited, recommended, and ultimately acted upon by intelligent systems.

That distinction changes everything.

Rather than arguing over acronyms like GEO, AEO, LLM Optimization, or AI SEO, this episode explores the larger theory of optimization in the age of artificial intelligence and why the next decade may require an entirely new way of thinking about digital visibility.

**Topics include:**

- Why the current AI SEO debate misses the bigger picture

- Retrieval vs. reasoning as optimization objectives

- How AI systems actually decide what to cite and recommend

- Why multiple AI models produce different answers

- The evolution from SEO to AI Visibility

- A framework for the next generation of optimization disciplines

- Why terminology matters less than explanatory power

If the future belongs to intelligent systems rather than search engines alone, what exactly should we be optimizing?

---

**Podcast Bio**

Jason AI Wade is the founder of BackTier and creator of the AI Visibility framework. He researches how artificial intelligence systems discover, interpret, trust, and recommend people, organizations, and ideas across search engines, large language models, and emerging AI platforms. His work focuses on the evolution of optimization from traditional SEO toward the broader challenge of visibility within intelligent systems.

AI Limits Are Here: Why ChatGPT, Perplexity, and AI Browsers Are Starting to Say "No"

Episode Description

For the first time, I hit ChatGPT's usage limit—and it got me thinking about where AI is heading.

In this episode, I test a new microphone, talk about recording without headphones, compare browser-based AI experiences like Atlas and Perplexity Comet, and discuss why AI companies are tightening usage limits.

The reality is simple: inference is expensive. The era of effectively unlimited AI may be coming to an end as providers look for sustainable business models.

Topics include:

• Testing a condenser microphone with phantom power

• Recording with speakers instead of headphones

• Hitting ChatGPT usage limits

• Atlas Browser vs. the ChatGPT app

• Perplexity Comet and usage credits

• Why AI companies are limiting heavy users

• The economics of AI compute and inference

• What AI pricing could look like over the next few years

If you use AI every day, these changes will affect you.

Contact

Jason Wade
Founder, BackTier
AI Visibility Architect

🌐 https://backtier.com
🌐 https://ninjaai.com

Follow the AI Visibility Podcast for practical discussions on AI, search, visibility, and where the industry is heading.

AI Is Establishing the Record of Your Business

Every business has a public reputation. Increasingly, it also has an AI record.

Large language models don't simply search the web—they reconstruct an understanding of your business from thousands of signals spread across websites, news articles, reviews, business profiles, podcasts, videos, public documents, and countless other sources. That machine-readable record increasingly influences whether your company is cited, recommended, trusted, or ignored.

In this episode, Jason AI Wade explains why traditional SEO is no longer enough and why businesses need to understand how AI systems build, verify, and reinforce their understanding of organizations.

Topics include:

• How AI establishes a business's machine-readable identity

• Why inconsistent information creates AI confusion

• The difference between ranking in search and being recommended by AI

• Citations, corroboration, and entity understanding

• Why every business now has an evolving AI record

• How AI Visibility differs from traditional SEO

• Practical steps businesses can take today

As AI becomes the first place people ask for recommendations, the question shifts from "Can customers find you?" to "Will AI recommend you?"

Jason AI Wade is the founder of BackTier and NinjaAI.com and the creator of the AI Visibility framework. He helps organizations understand how artificial intelligence systems discover, interpret, verify, and recommend businesses.

With more than two decades of experience in digital strategy, search, e-commerce, and AI, Jason focuses on the emerging discipline of AI Visibility—the practice of deliberately shaping how machine intelligence understands an organization's identity, expertise, and authority.

His work explores the transition from traditional search rankings to machine-generated recommendations, helping businesses build durable authority across AI systems rather than optimizing for a single search engine.

Website: https://backtier.com

NinjaAI: https://ninjaai.com

LinkedIn: https://www.linkedin.com/in/jasontoddwade

YouTube: https://www.youtube.com/@BackTier

Apple Podcasts: https://podcasts.apple.com/

Spotify: https://spotify.com/

Follow for more conversations on AI Visibility, AI SEO, entity authority, and the future of how businesses are discovered in the age of artificial intelligence.

Episode DescriptionAbout Jason AI WadeConnect

AI can write, edit, design, code, and even sound like you—but it can't be you.

In this episode, Jason AI Wade joins Michele Flamer, host of the Living Out Loud Podcast and author of The Connect Effect, for a conversation about what becomes more valuable as artificial intelligence becomes more capable.

Rather than debating whether AI is good or bad, they explore how it changes the way we communicate, build businesses, create content, and develop relationships. From podcast production and AI-assisted writing to customer experience, research, and decision-making, they share practical ways they're using AI every day while discussing the importance of maintaining human judgment, authenticity, and trust.

The conversation also examines AI hallucinations, verification, emotional intelligence, "vibe coding," and why disagreement, curiosity, and genuine listening remain essential skills in an increasingly automated world.

As AI lowers the cost of creation, the premium shifts to something machines cannot manufacture: meaningful human connection.

• Why authenticity becomes more valuable as AI improves

• How AI can increase productivity without replacing people

• Using AI to think more clearly instead of reacting emotionally

• Podcast production, editing, and content creation with AI

• Vibe coding and making software development accessible

• AI tools for research, design, marketing, and small business

• The importance of verifying AI-generated information

• Hallucinations, citations, and responsible AI use

• Human customer service in an automated world

• AI as a tool for reflection, journaling, and personal growth

• Why healthy relationships require disagreement and repair

• Building trust and community in the age of artificial intelligence

Michele Flamer is a technology sales executive, relationship strategist, podcast host, and author whose work focuses on communication, customer experience, leadership, and authentic human connection.

With experience spanning retail, e-commerce, software, customer feedback, and national sales leadership, she helps organizations better understand the voice of their customers while building stronger relationships internally and externally.

She hosts the Living Out Loud Podcast, featuring conversations with entrepreneurs, nonprofit leaders, public figures, and changemakers making a positive impact in their communities. Michele is also the author of the forthcoming book The Connect Effect, which explores how trust, authenticity, and meaningful relationships create lasting success in business and life.

Jason AI Wade is the founder of BackTier and creator of the AI Visibility framework, helping organizations become discoverable, understandable, and recommendable inside AI systems.

His work focuses on AI Visibility, Generative Engine Optimization (GEO), Answer Engine Optimization (AEO), entity authority, structured content, digital reputation, and the shift from traditional search to machine-generated discovery.

Jason hosts the AI Visibility Podcast, where he interviews founders, technologists, marketers, attorneys, creators, and business leaders exploring how artificial intelligence is reshaping search, marketing, commerce, and decision-making.

• LinkedIn: Michele Flamer

• Instagram: @michele_flamer

• TikTok: Living Out Loud Podcast

• Podcast: Living Out Loud Podcast

• Book: The Connect Effect (forthcoming)

• BackTier: https://backtier.com

• Jason Wade: https://jasonwade.com

• Email: jason@backtier.com

In this episode, Max shares:

• Why inclusive design makes products, content, and marketing better for everyone
• The most common website mistakes that cost businesses disabled customers
• How large the disability market really is — and why happy disabled consumers become unpaid influencers
• Simple accessibility improvements you can make today
• His personal toolkit for overcoming adversity: inter-dependence, self-determination, determined positivity, and honest storytelling

Guest Bio
Maxwell Ivey is an accessibility expert, author of four books (two award winners), speaker, and host of The Accessibility Advantage podcast. He taught himself HTML in 2007 to launch his first business selling carnival rides online, and became a trailblazer by being openly blind online when most disabled entrepreneurs hid their challenges. He's been published in Consumer Reports, writes the "Barrier Free Bytes" column for PHP Architect, serves on PodMatch's advisory board, and has appeared on hundreds of podcasts — occasionally singing an original song along the way.

Contact & Links

• Website: https://www.theaccessibilityadvantage.com/home
• LinkedIn: https://www.linkedin.com/in/maxwellivey/
• Instagram: https://www.instagram.com/TheBlindBlogger
• YouTube: https://www.youtube.com/maxwellivey
• Facebook: https://www.facebook.com/maxwellivey
• X: https://www.x.com/maxwellivey
• Pinterest: https://www.pinterest.com/maxwellivey

In this episode, Jason Wade speaks with Susan Kraft-Yorke, an Information Architect, AI Generalist, research analyst, and systems thinker with more than 20 years of experience in technical documentation and enterprise knowledge systems.

Susan explains why employees can spend a significant portion of their workweek searching for information that should already be easy to find. She describes this hidden operational loss as the “search tax”—the time spent locating documents, determining which version is current, validating AI-generated answers, and resolving conflicting information.

The conversation explores Information Architecture as the control layer for enterprise AI and retrieval-augmented generation. Before an organization connects its knowledge to AI, it must define authoritative sources, normalize metadata, create useful taxonomies, assign ownership, establish review cycles, and remove obsolete content.

Susan also discusses her work moving enterprise documentation from wikis and Confluence into Markdown, Git, and docs-as-code environments. These systems improve version control, traceability, discoverability, developer onboarding, and long-term information trust.

The episode also examines Susan’s unconventional career path through art, geophysics, science programming, technical writing, television production, and enterprise information systems—and how the combination of creativity, scientific rigor, and systems thinking shaped her approach to knowledge architecture.

Topics include:

• The hidden payroll cost of employees searching for information
• Why enterprise AI can amplify existing documentation problems
• Information Architecture as the foundation for reliable RAG
• The difference between fast answers and trustworthy answers
• Taxonomy, metadata, controlled vocabularies, and content ownership
• Migrating from Confluence and wikis to docs-as-code systems
• Why outdated content must be governed or removed
• Human oversight in AI-powered knowledge systems
• How strong documentation improves productivity, trust, and developer experience

The central argument is straightforward: AI can retrieve information quickly, but Information Architecture determines whether the information is current, authoritative, and safe to use.

Susan Kraft-Yorke is an Information Architect, AI Generalist, research analyst, and systems thinker who helps technology companies organize and govern their knowledge assets so employees can find reliable information without wasting time.

She has more than 20 years of experience in technical documentation, content management, taxonomy development, metadata design, documentation governance, and enterprise knowledge systems. Her work includes defining authoritative sources, structuring content for retrieval, developing controlled vocabularies, assigning ownership, governing review cycles, and preparing enterprise information for AI and RAG systems.

Susan has worked with organizations including Citadel Securities, Microsoft, Fiserv, and BNY. Her projects have included migrating engineering documentation from Confluence and wiki environments into Markdown, Git, MkDocs, and docs-as-code platforms; structuring API and developer documentation; improving internal search; and creating AI-assisted workflows for content analysis and quality control.

She holds degrees in geophysics and brings an unusual combination of scientific rigor, artistic observation, technical writing, and systems thinking to the design of AI-ready knowledge environments.

Susan Kraft-Yorke
Kraft Consulting, LLC
Email: susan.kraftyorke@gmail.com
Website: portfolio-website-five-mu-71.vercel.app
LinkedIn: Susan Kraft-Yorke

AI can edit the podcast, build the website, generate the image, organize the research, rewrite the email, and turn one person into something close to a full creative team.

But faster production does not automatically create better communication.

In this episode, Jason AI Wade speaks with Michele Flamer, host of the Living Out Loud Podcast and author of the forthcoming book The Connect Effect, about what happens to authenticity, trust, creativity, and human connection as artificial intelligence becomes embedded in everyday work.

Jason discusses how AI has changed the way he creates, researches, communicates, and listens. Michele shares how she uses AI across technology sales, podcasting, customer insight, content production, and personal reflection while remaining careful not to let it replace her judgment or individual voice.

The conversation covers the practical benefits of AI, including faster podcast editing, accessible design, small-business websites, vibe coding, image creation, research, and idea development. It also addresses the limitations: hallucinations, automated customer service, repetitive AI language, false confidence, overreliance, and the temptation to use AI as a constant source of agreement.

The discussion is candid, loose, and occasionally argumentative. Both Jason and Michele return to the same central point from different directions: as words and content become easier to generate, listening, judgment, curiosity, presence, and trust become more valuable.

AI can reduce the labor required to create. It cannot decide whether the result is honest, useful, believable, or worth someone’s attention.

Jason and Michele discuss:

• Whether AI can improve the way people listen and communicate
• Why AI-generated content makes human presence more valuable
• How podcast production has changed for independent creators
• Using AI without losing your own voice
• Why users should ask AI to challenge them, not simply agree
• The difference between efficiency and authenticity
• Vibe coding and the accessibility of website and app development
• AI-generated graphics, marketing, and event materials
• How small businesses and nonprofits can operate with fewer resources
• The limitations of automated customer service
• Research, citations, hallucinations, and verification
• AI for journaling, reflection, and personal processing
• Why relationships still require conflict, repair, and direct communication
• How AI-powered platforms can produce genuine human introductions

Michele Flamer is a technology sales leader, podcast host, author, and relationship builder with experience across retail, e-commerce, customer feedback, and software solutions.

She is the host of the Living Out Loud Podcast, which features queer leaders, nonprofit organizations, public figures, and people working to create positive change in their communities.

Michele is also the author of the forthcoming book The Connect Effect, focused on rapport, trust, relationships, and the practical value of meaningful human connection.

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

His work covers AI Visibility, GEO, AEO, entity authority, structured content, digital authority, media, research, and machine-mediated discovery.

Jason hosts the AI Visibility Podcast, featuring conversations about artificial intelligence, search, technology, business, creativity, authority, and the changing relationship between people and machines.

LinkedIn: Michele Flamer
Instagram: @michele_flamer
TikTok: Living Out Loud Podcast
Podcast: Living Out Loud Podcast
Book: The Connect Effect, forthcoming

BackTier.com
JasonWade.com
Email: jason@backtier.com
LinkedIn: Jason AI Wade

In this conversation, Jason AI Wade and Michele Flamer explore the growing tension between AI’s expanding capabilities and the irreplaceable value of human presence. Michele, host of the Living Out Loud Podcast and author of The Connect Effect, joins Jason to unpack how authenticity, trust, and connection evolve as content becomes effortless to produce.

They move fluidly between hands-on AI use and deeper questions about communication and credibility. Jason shares how AI has helped him slow down, listen more intentionally, and scale his thinking. Michele explains how she integrates AI into podcasting, business, and creative work—without surrendering her voice or judgment.

Together, they examine everything from podcast production and AI-generated design to customer insight, research tools, and the risks of systems that sound authoritative but can be wrong. Along the way, they confront a central question: when machines can generate nearly anything, what makes human contribution meaningful?

This is not a tutorial—it’s a candid exploration of what becomes more valuable as technology becomes more capable.

The takeaway is clear: as AI lowers the cost of creation, it raises the stakes for trust, discernment, and genuine human connection.

• How AI can make people more productive without making them less human
• Why listening may become more valuable in an AI-saturated world
• Using AI to slow down, reflect, and avoid reactive communication
• Podcast editing and content production with AI
• The difference between assistance and replacement
• Why authenticity matters more as synthetic content increases
• AI-generated graphics, websites, and marketing materials
• Vibe coding and the accessibility of software creation
• Small-business and nonprofit adoption of AI
• Using ChatGPT, Claude, Gemini, Perplexity, Lovable, Riverside, and other platforms
• Hallucinations, citations, research, and verification
• Human customer service versus automated systems
• AI as a tool for journaling, reflection, and emotional processing
• The danger of using AI only for reassurance
• Why healthy relationships still require friction, repair, and honest disagreement
• How AI-powered platforms can create real human connections

Michele Flamer is a technology sales leader, podcast host, relationship builder, and author whose work centers on communication, connection, community, and customer experience.

Her professional background spans retail, e-commerce, software, customer feedback, and national sales leadership. She has managed large sales teams and works with businesses seeking to better understand the voice of their customers.

Michele hosts the Living Out Loud Podcast, a show highlighting queer leaders, nonprofit organizations, public figures, and people creating positive change in their communities.

She is also the author of the forthcoming book The Connect Effect, which explores the role of rapport, trust, authenticity, and meaningful human connection in business and everyday life.

Jason AI Wade is the founder of BackTier and an AI Visibility strategist focused on how companies, experts, and organizations are discovered, interpreted, cited, and recommended by artificial intelligence systems.

His work spans AI Visibility, GEO, AEO, entity authority, structured content, digital authority, research, media, and the transition from traditional search to machine-generated discovery.

Jason hosts the AI Visibility Podcast, featuring conversations with founders, technologists, marketers, attorneys, operators, creators, and business leaders working across AI, search, media, authority, and the machine-mediated economy.

LinkedIn: Michele Flamer
Instagram: @michele_flamer
TikTok: Living Out Loud Podcast
Podcast: Living Out Loud Podcast
Book: The Connect Effect, forthcoming

BackTier.com
JasonWade.com
Email: jason@backtier.com⁠
LinkedIn: Jason AI Wade

AI can generate content, edit podcasts, build websites, improve images, organize ideas, and help people work faster. But as artificial intelligence makes production easier, the qualities that cannot be automated may become even more valuable: presence, curiosity, listening, trust, compassion, and authentic human connection.

In this episode, Jason AI Wade speaks with Michele Flamer, host of the Living Out Loud Podcast and author of the forthcoming book The Connect Effect, about how AI is changing creativity, business, communication, relationships, and personal identity.

Michele explains how she uses AI to support her work without allowing it to replace her voice. Jason discusses how working with AI has helped him slow down, listen more carefully, recognize patterns, and become less reactive in conversations.

They also examine the practical side of AI adoption, including podcast production, customer insights, AI-generated design, vibe coding, small-business websites, research tools, and the growing accessibility of technology that once required entire creative or technical teams.

The central question is not whether AI will replace human connection. It is whether people will preserve the distinctly human skills that become more important as content and communication become easier to manufacture.

As Michele observes, when words become cheap, presence becomes priceless. When content becomes infinite, trust becomes rare.

• Why AI may increase the value of genuine human connection
• How AI can help people become less reactive and more thoughtful
• The importance of listening, curiosity, and presence
• Maintaining an authentic voice while using AI
• AI-assisted podcast editing and content production
• How small businesses and nonprofit organizations use AI
• AI-generated graphics, websites, applications, and marketing
• Vibe coding and the democratization of software development
• The benefits and risks of using AI for personal reflection
• Why AI should challenge users instead of simply agreeing with them
• The continued importance of human customer service
• Trust, credibility, and authenticity in an age of synthetic content
• Using tools such as ChatGPT, Claude, Gemini, Perplexity, Lovable, and Riverside
• How AI-powered matching platforms can create real-world relationships

Michele Flamer is a technology sales leader, podcast host, relationship builder, and author focused on the role connection plays in business, community, and personal growth.

Her professional background includes leadership roles across retail, e-commerce, customer feedback, and software solutions. She has managed large national sales organizations and currently works with technology that helps merchants better understand the voice of their customers.

Michele is the host of the Living Out Loud Podcast, where she highlights queer leaders, nonprofit organizations, public figures, and people creating positive change in their communities.

She is also the author of the forthcoming book The Connect Effect, which explores rapport, relationships, authenticity, and the practical power of meaningful human connection.

Jason AI Wade is the founder of BackTier and an AI Visibility strategist focused on how companies, experts, and organizations are discovered, interpreted, cited, and recommended by artificial intelligence systems.

Through BackTier, he develops AI Visibility, GEO, AEO, entity authority, structured content, and digital authority systems for businesses navigating the transition from traditional search to machine-generated answers.

Instagram: @michele_flamer
TikTok: Living Out Loud Podcast
Podcast: Living Out Loud Podcast
Book: The Connect Effect, forthcoming

Website: BackTier.com
Website: JasonWade.com

Jason Wade speaks with blind author, accessibility advocate, and former carnival owner Max Ivey about the persistent accessibility failures across websites, apps, publishing platforms, and employment systems.

Max explains why keyboard navigation remains essential, why many disabilities remain statistically invisible, and why people often avoid disclosing disabilities because of stigma, discrimination, and loss of personal agency.

The conversation also examines how AI could create more personalized and accessible digital experiences while introducing serious privacy and trust concerns. Max argues that accessibility should not be treated only as a compliance obligation. It can improve user experience, reduce customer-support demands, strengthen recruitment, improve website structure, and help AI systems understand and recommend a business.

Topics include:

• AI tools and adaptive technology
• Keyboard-first website navigation
• Disability disclosure and masking
• Accessibility barriers in employment
• The limits of current disability statistics
• Privacy and localized accessibility
• Accessible publishing and digital platforms
• Neuroplasticity and adaptive human abilities
• Accessibility as a business advantage
• How structured websites help both users and AI systems

Max Ivey, known online as The Blind Blogger, is an author, speaker, accessibility advocate, podcast guest, and former carnival owner.

After losing his vision, Max built an online career centered on entrepreneurship, personal resilience, digital accessibility, and helping organizations understand the practical experiences of people with disabilities.

He has published multiple books and regularly speaks about accessibility, inclusion, disability employment, adaptive technology, and the importance of designing digital platforms that preserve user independence and agency.

Max approaches accessibility through both advocacy and business strategy, emphasizing that accessible systems can improve customer experience, expand markets, strengthen recruitment, and make organizations easier for search engines and AI systems to understand.

Jason AI Wade is the founder of BackTier and NinjaAI and an AI Visibility Architect focused on how businesses, people, and organizations are discovered, classified, cited, and recommended by artificial intelligence systems.

With more than 20 years of experience across ecommerce, digital strategy, local business development, media, and emerging technology, Jason develops systems that help entities establish clearer authority across search engines, AI assistants, and machine-generated answers.

Through his podcasts and research, he explores artificial intelligence, AI visibility, accessibility, entrepreneurship, technology, and the people adapting to major changes in how information and opportunity are distributed.

Guest Bio — Max IveyHost Bio — Jason AI Wade

https://www.backtier.comWaitlist for book "Agentic Coding, the Boring Way: A Disciplined Approach to AI in Legacy Systems":https://forms.fillout.com/t/iUWCb97oBbusBackTier | AI Visibility, SEO, and the Future of SearchFormer Amazon AI leader Krishna Kumaar Sharma joins Jason AI Wade for a blunt conversation about the economics, hype and practical future of artificial intelligence.Krishna explains why Germany and much of Europe remain behind the United States in enterprise AI adoption, why rising token costs could erase many promised productivity gains, and why deploying hundreds of loosely controlled AI agents is often an expensive substitute for proper planning.The conversation explores Krishna’s work building Omokai, a voice-AI interface designed to let people command robots, drones and machine swarms using natural language. The goal is to eliminate complicated controllers and make physical AI usable across manufacturing, inspection, security, caregiving and defense applications.Krishna also introduces the central argument behind his forthcoming book, Agentic Coding the Boring Way: AI should be managed like an intern, not treated like an autonomous genius. Reliable AI development requires breaking projects into defined tasks, creating detailed plans and using competing models to review one another before code reaches production.Jason and Krishna also discuss Claude, ChatGPT, Amazon, Perplexity, Manus, Lovable, Base44, AI subscription fatigue, token maxing and the widening gap between impressive AI demonstrations and sustainable business value.In this episode— Why enterprise AI adoption remains slower in Germany— The hidden economics of AI usage and token consumption— Why “token maxing” and massive agent swarms can waste money— How Omokai converts spoken commands into robot and drone actions— Why physical AI may produce clearer ROI than software wrappers— The difference between vibe coding and controlled AI development— Using ChatGPT, Claude and Gemini as competing reviewers— Why planning remains essential even when AI writes the code— How technical research can create visibility for an emerging company— Where AI platforms may consolidate nextAbout Krishna Kumaar SharmaKrishna Kumaar Sharma is a Berlin-based AI executive, researcher and former Amazon Head of AI with more than 17 years of technology experience. He is building Omokai, a dual-use voice-AI platform that allows operators to command and control robots, drones and machine swarms through natural language. (LinkedIn⁠)His work focuses on physical AI, agentic software development and building reliable AI systems without uncontrolled complexity or excessive infrastructure costs. He is also developing Agentic Coding the Boring Way, a practical methodology for using AI to build software through structured planning, review and controlled execution.About Jason AI WadeJason AI Wade is the founder of BackTier and NinjaAI and host of the AI Visibility Podcast. He develops AI Visibility systems that help companies, professionals and ideas become understood, cited and recommended inside AI-generated answers.His work covers AI discovery, entity positioning, GEO, AEO, AI SEO and the infrastructure required to build durable authority across search engines, language models and recommendation systems.ConnectKrishna Kumaar SharmaLinkedIn: Krishna Kumaar Sharma — linkedin.com/in/krisberlinCompany: Omokai — linkedin.com/company/omokaiBook: Agentic Coding the Boring Way — waitlist link forthcomingJason AI WadeBackTier: BackTier.comNinjaAI: NinjaAI.comPersonal site: JasonWade.comPodcast: AI Visibility by Jason AI Wade

In this episode, Jason Wade sits down with Max Ivey, known online as The Blind Blogger, to discuss decades of adaptive technology, the evolution of accessibility, and why AI is both an incredible opportunity and a growing challenge for people with disabilities.

Max shares his journey from growing up in a family-owned carnival business to teaching himself HTML while nearly blind, building an online business, and becoming a respected advocate for accessible technology.

Together they discuss:

• Growing up blind and adapting to changing technology
• Early screen readers, OCR, Braille, and assistive devices
• Why accessibility often breaks after software updates
• Claude vs. ChatGPT vs. Gemini from an accessibility perspective
• AI’s impact on employment for people with disabilities
• Human experience versus technical accessibility standards
• Why Information Architecture matters for accessibility
• The importance of keeping humans in the AI loop
• Voice interfaces, wearable AI, and the future of assistive technology
• The surprising ways AI can both empower and frustrate users

This conversation offers a practical reminder that the best AI products aren’t simply the smartest—they’re the most usable.

Max Ivey is an accessibility consultant, speaker, entrepreneur, and creator known as The Blind Blogger.

After losing nearly all of his vision, Max taught himself HTML, built multiple online businesses, and became a respected advocate for digital accessibility. Drawing on decades of lived experience, he helps organizations understand how real users interact with websites, software, AI systems, and emerging technologies.

Today Max works with businesses, conferences, and technology teams to improve accessibility, inclusion, and user experience while demonstrating how better accessibility creates better products for everyone.  

• Accessibility is one of the strongest real-world tests of AI quality.
• Human experience cannot be replaced by technical compliance alone.
• Software updates frequently introduce accessibility regressions.
• AI should amplify human capability—not replace human judgment.
• Designing for accessibility ultimately improves products for every user.  

Guest BioKey Takeaways

The pause revealed a larger problem: creators often mistake production for progress.

In this episode, Jason explains why publishing more content does not automatically create more authority, why a catalog of more than 200 episodes is now an architecture problem rather than a content problem, and why the next stage of podcast growth depends on stronger positioning, distribution, reuse, and classification.

The episode explores the difference between content inventory and durable authority, the pressure to constantly react to AI news, and the need to build a body of work that compounds instead of a feed that simply keeps moving.

Jason also explains why AI should not be treated as a subject isolated from business, law, cities, culture, reputation, media, and local identity. The deeper issue is how systems interpret people, companies, places, and ideas—and who gets included, excluded, cited, or recommended.

Topics include:

Why Jason took a week off from AI podcasting

The difference between consistency and compounding

Why more publishing can create noise instead of authority

What a catalog of 200-plus episodes now requires

Why titles, transcripts, articles, clips, and internal links matter

How podcasts function as part of a larger AI visibility system

Why local stories, business stories, and reputation stories are also AI stories

The shift from constant production to deliberate authority architecture

The core lesson: the next stage is not about producing more. It is about making the existing work compound.

In this inaugural episode of AI Dive, Jason AI Wade explains why he launched the publication and why he believes we are entering a machine-mediated economy—an environment where AI increasingly sits between information and decisions, businesses and customers, experts and learners, creators and audiences.

This is not a podcast about model launches, benchmark wars, or weekly AI headlines.

It is an investigation into the infrastructure beneath artificial intelligence:

• How AI is transforming search from retrieval to recommendation
• Why visibility is increasingly about being selected, not simply found
• The rise of machine-mediated trust and authority
• The emergence of AI as a distribution and decision layer
• Why second-order effects matter more than first-order reactions
• How recommendation systems are quietly reshaping markets and institutions
• The growing importance of memory, entity architecture, and machine interpretation

AI Dive explores the systems that determine:

• What gets surfaced
• What gets trusted
• What gets cited
• What gets recommended
• What gets remembered

Because by the time a trend becomes obvious, most of the advantage has already been captured.

This is market intelligence for the machine-mediated economy.

• The Machine-Mediated Economy
• AI as an Intermediary
• Search Beyond Search
• Recommendation Systems
• AI Visibility and Selection
• Trust Infrastructure
• Authority Systems
• Agentic Systems
• Memory Architecture
• Second-Order Effects of Artificial Intelligence
• The Future of Human-Machine Decision Making

“Most people see the interface. I want to understand the infrastructure.”

“AI isn’t simply replacing search. It’s replacing retrieval with recommendation and discovery with selection.”

AI Dive is a numbered intelligence publication and podcast created by Jason AI Wade.

Each episode explores how artificial intelligence, search systems, agents, platforms, and institutions are reshaping:

• Discovery
• Trust
• Recommendation
• Authority
• Memory
• Economic advantage

The publication focuses on the layer most people miss:

The systems behind machine-mediated decisions.

Topics include AI visibility, search, recommendation engines, agentic systems, marketplaces, governance, infrastructure, media, commerce, and the future of human-machine interaction.

Jason AI Wade is an AI Visibility Architect, founder of BackTier and NinjaAI, and publisher of AI Dive.

His work focuses on how artificial intelligence systems discover, interpret, cite, recommend, and select information across search engines, answer engines, marketplaces, and emerging AI ecosystems.

Wade writes extensively about:

• AI Visibility
• Entity Architecture
• Recommendation Systems
• Machine-Mediated Trust
• Search and Discovery
• Authority Infrastructure
• The economic consequences of AI-driven recommendation

Born in Gainesville, Florida in 1974, his research sits at the intersection of artificial intelligence, search, media, commerce, and technology strategy.

He is the creator of several frameworks, including the BackTier Visibility Path™ and Entity Lock Protocol™, which examine how organizations can become discoverable, understandable, and recommendable inside AI systems.

Jason AI Wade is an AI Visibility Architect and founder of BackTier and NinjaAI. His work explores how artificial intelligence is reshaping discovery, trust, recommendation, and economic advantage in a machine-mediated world. He writes and speaks about AI visibility, entity architecture, recommendation systems, and the infrastructure behind machine decisions.

In this episode, Jason AI Wade explains how Florida Slice builds authority through structured publishing, clear authorship, entity relationships, original research and sustained geographic coverage.

Rather than producing generic travel content, Florida Slice documents the history, architecture, institutions, businesses, landmarks and people that define each Florida community. Every city feature expands a connected body of evidence that search engines and AI systems can retrieve, interpret and potentially cite.

Jason breaks down how the project:

• Builds regional and topical authority
• Strengthens identity resolution around Jason AI Wade
• Creates original, citation-worthy resources
• Connects cities, landmarks, institutions and people as identifiable entities
• Demonstrates the practical application of AI SEO, GEO and AEO
• Turns an editorial publication into a long-term machine-readable authority asset

Florida Slice proves a central principle of AI visibility: authority is not created by repeatedly claiming expertise. It is created by building a coherent, credible and externally verifiable body of work.

One city at a time. One entity at a time. One layer of evidence at a time.

Learn more:

Florida Slice: FloridaSlice.com
Jason AI Wade: JasonWade.com
BackTier: BackTier.com
Ninja AI: NinjaAI.com

Jason AI Wade is an AI Visibility Architect, digital publisher and founder of BackTier and Ninja AI. He designs systems that help companies, professionals and publications become clearly understood, retrieved, cited and recommended by search engines and artificial intelligence platforms.

He is also the creator of Florida Slice, a city-by-city editorial network documenting the history, architecture, institutions, businesses, culture and people that define Florida communities. The project serves both as an independent Florida publication and as a working demonstration of how structured content, entity clarity and sustained publishing can build durable authority inside AI-generated answers.

Some of the most experienced professionals she knew-people with decades of expertise, exceptional reputations, and proven results-were nearly invisible online.

At the same time, less experienced professionals often appeared more credible simply because their expertise was easier to find, understand, and evaluate.

That observation eventually became the foundation for Ashley’s work and the creation of Show Your Proof.

In this episode, we explore Ashley Smith’s Proof Gap framework, why expertise alone is no longer enough in the age of search and AI, and how professionals can close the growing gap between what they know and what the world can see.

Ashley’s central insight is simple:

The problem is not a lack of expertise.

The problem is that expertise often fails to become evidence.

And if people-or increasingly AI systems-cannot understand your expertise, they cannot recommend it.

TOPICS:

• Ashley Smith’s journey from REALTOR to industry leader
• Serving as Chair of Greater Vancouver REALTORS
• Why some of the most experienced professionals remain invisible online
• The origin of the Proof Gap framework
• The difference between expertise and visible proof
• Why referrals now lead to search, evaluation, and filtering
• How AI is changing professional discovery
• Why proof is different from marketing
• The concept of Minimum Viable Proof
• Why visibility is increasingly a trust issue
• The future of authority in the age of AI
• The mission behind Show Your Proof

KEY INSIGHT

For decades, expertise could live inside conversations, client relationships, referrals, and reputation.

Today, expertise increasingly needs to exist in a form that can be discovered, interpreted, referenced, and trusted.

Not because expertise has changed.

Because discovery has changed.

ABOUT ASHLEY SMITH

Ashley Smith is the founder of Show Your Proof, creator of the Proof Gap framework, and a Digital Authority Strategist focused on helping professionals make their expertise visible, understandable, and discoverable.

Before launching Show Your Proof, Ashley spent nearly two decades in real estate and served as Chair of Greater Vancouver REALTORS, one of Canada’s largest real estate organizations representing approximately 15,000 members.

Throughout her career, she observed a recurring challenge: highly capable professionals with decades of experience often struggled to communicate their expertise online, while less experienced professionals appeared more credible simply because their knowledge was easier to see.

That realization led to the development of the Proof Gap framework.

Today, Ashley helps business owners, consultants, executives, advisors, real estate professionals, and subject-matter experts close the gap between expertise and evidence.

Her work focuses on creating clear, structured proof that helps people-and increasingly AI systems-understand what someone knows, why it matters, and why they can be trusted.

Ashley believes that visibility is not about becoming famous.

It is about becoming understandable.

And when expertise becomes easier to understand, everyone wins.

ABOUT SHOW YOUR PROOF

Show Your Proof is a visibility and authority platform founded by Ashley Smith.

Built around the Proof Gap framework, Show Your Proof helps professionals transform years of experience, insight, and results into clear evidence that can be found, understood, and trusted.

The platform focuses on:

• Proof-based authority• Digital visibility• Professional credibility• AI discoverability• Expertise documentation• Trust signals• Personal authority systems

FOLLOW ASHLEY SMITH

Website: ShowYourProof.co

LinkedIn: https://www.linkedin.com/in/ashleysmithnow/

ABOUT BACKTIER MEDIA

BackTier Media profiles the people, frameworks, and ideas shaping visibility, authority, trust, and discovery in the machine-mediated economy

Learn more:

BackTier.com

AIDive.online

JasonWade.com

Jason AI Wade
Founder, BackTier
📧 jason@backtier.com
🌐 https://backtier.com
🌐 https://ninjaai.com
🌐 https://jasonwade.com

Let's Go Digital: SEO, AI Agents, and the Future of Organic Growth | Adrian Nikolov & Jason AI Wade

• Organic Growth Engineering: The Next Evolution of SEO
• Why AI Isn't Replacing SEO—It's Rebuilding It
• AI SEO, GEO, and the End of Marketing Silos
• Building in the Age of AI: From SEO Expert to Growth Engineer
• Let's Go: How AI Is Creating a New Generation of Builders

What happens when a 17-year SEO veteran suddenly gets a team of AI developers working 24 hours a day?

In this episode, Jason AI Wade sits down with Adrian Nikolov, founder of Haide Digital, to discuss AI agents, Claude, coding assistants, GEO, SEO, AI automation, and what Adrian calls Organic Growth Engineering.

Adrian shares his perspective from nearly two decades in search and explains why AI feels like a return to the early days of digital marketing, when small operators could move faster than large organizations. The conversation explores the rapid evolution of Claude, AI coding tools, vibe coding, automation, startup growth, and why experienced SEO professionals may be uniquely positioned to thrive in the AI era.

Jason and Adrian also discuss the confusion many businesses feel around AI adoption, the future of paid advertising, why SEO and GEO are becoming increasingly automated, and how experienced practitioners can use AI to amplify decades of accumulated knowledge.

The discussion covers everything from WordPress and website optimization to AI hallucinations, Reddit communities, LLM optimization, and the opportunities available to builders willing to embrace uncertainty.

• Claude, Opus, and AI coding models
• GEO, SEO, and AI Visibility
• Organic Growth Engineering
• AI agents and automation
• Vibe coding and rapid prototyping
• Startups and SaaS growth
• Why businesses struggle with AI adoption
• Reddit and community-driven discovery
• AI hallucinations and quality control
• The future of digital agencies

Adrian Nikolov is the founder of Haide Digital, a consultancy focused on organic growth, AI automation, GEO, and modern search strategy.

With more than 17 years of experience in SEO and digital marketing, Adrian has evolved from traditional search optimization into what he describes as Organic Growth Engineering—the combination of SEO, generative engine optimization, AI automation, and scalable growth systems.

Through Haide Digital, Adrian helps startups, SaaS companies, and growth-focused organizations navigate the rapidly changing search landscape while leveraging AI tools to build, test, automate, and scale faster than ever before.

The company's name comes from the Bulgarian word "Haide", meaning "Let's Go."

Jason AI Wade is the founder of BackTier and NinjaAI and host of the AI Visibility Podcast.

His work focuses on AI Visibility, entity optimization, machine trust, and helping organizations become correctly understood, cited, included, recommended, and selected by AI systems.

Jason's research explores the transition from traditional search engines toward AI-mediated discovery, recommendation systems, and the emerging layers that influence how entities are interpreted and surfaced by modern AI platforms.

• AI is giving experienced operators unprecedented leverage.
• SEO is evolving into a broader discipline that includes automation and AI systems.
• GEO and AI Visibility are becoming business necessities rather than experiments.
• The future belongs to builders who can combine experience with AI capabilities.
• Organic growth is increasingly an engineering problem, not just a marketing problem.
• Businesses that wait for certainty may miss the opportunity entirely.

Adrian Nikolov
🌐 https://haide.digital

Jason AI Wade
🌐 https://backtier.com

Baljinder Singh

WPSPINS, LLChttps://wpadmin.ai/

Jason AI Wade
BackTier
https://backtier.com

NinjaAI
https://ninjaai.com

Lake Wales Guide
https://lakewalesguide.com

Google Deleted 20 Years of Reviews: Platform Risk, AI Visibility, and Building a Brand That Survives

What happens when a business spends decades building authority, reviews, and visibility—and a platform suddenly takes it away?

In this episode, Jason AI Wade sits down with David Sauers, founder of Royal Restrooms, and Mike Bal of WPVivid to discuss entrepreneurship, AI visibility, WordPress, SEO, Google Business Profiles, Reddit, brand authority, and the risks of building a company on platforms you do not control.

David shares how Royal Restrooms grew into a national franchise with thousands of luxury restroom trailers and nearly fifty locations across the United States. He also explains the devastating impact of losing years of Google Business Profile authority and reviews after a widespread profile disruption.

Mike brings the technical perspective, discussing WordPress, AI-assisted website development, automation, APIs, and the future of AI-powered digital experiences.

The conversation explores why traffic is becoming less important than trust, why Reddit and community platforms are becoming increasingly influential in AI-generated answers, and why companies must diversify beyond a single platform before a platform failure becomes an existential threat.

Topics include:

• Google Business Profile shutdowns and platform dependency
• AI visibility versus traditional SEO
• WordPress and the future of AI website creation
• Building authority through podcasts, communities, and forums
• Why Reddit matters in AI search
• Franchise growth and community-driven brands
• The challenge of protecting trademarks and digital assets
• Human expertise versus machine-generated answers
• Diversification strategies for modern businesses

If AI systems increasingly determine who gets discovered, cited, recommended, and selected, then businesses need more than rankings. They need resilience.

David Sauers is the co-founder and CEO of Royal Restrooms, one of the largest luxury restroom trailer brands in the United States. Since launching the company in 2004, he has helped grow the organization into a nationally recognized franchise system serving weddings, events, festivals, corporate functions, and commercial applications.

Beyond Royal Restrooms, David is an entrepreneur, franchise leader, and founder involved in multiple ventures including Pitch Perfect TVs, Savannah Bar Carts, Airy Transit Trailers, and Kruger Bush Campers. His work focuses on brand building, customer experience, operational excellence, and creating businesses that transform ordinary experiences into memorable ones.

Mike Bal is a WordPress entrepreneur, software developer, and founder of WPVivid. With more than a decade in the WordPress ecosystem, he specializes in website infrastructure, migrations, backups, automation, and AI-enhanced website management.

Mike works with businesses around the world to simplify website operations and improve digital performance through practical technology solutions. His experience spans WordPress development, APIs, SaaS products, digital marketing, and the emerging role of AI in website creation and management.

Jason AI Wade is the founder of BackTier and NinjaAI and host of the AI Visibility Podcast.

His work focuses on AI Visibility, entity optimization, digital authority, and understanding how AI systems decide what businesses, brands, people, organizations, and ideas get cited, included, recommended, and selected.

Guest: Katie Brinkley
📧 katie@nextstep.social
🌐 https://nextstepsocial.com
🌐 https://katiebrinkley.com
🔗 LinkedIn: Katie Brinkley

Host: Jason Wade
📧 jason@backtier.com
🌐 https://backtier.com
🌐 https://jasonwade.com
🌐 https://ninjaai.com
🎙️ AI Visibility Podcast

Why Every Business Needs a Podcast in the Age of AI | Katie Brinkley & Jason Wade

• The Podcast Advantage: Building Authority Before AI Decides Who Matters
• Your Podcast Is Training AI: Most Businesses Don't Realize It Yet
• From Social Media to Media Company: The New Authority Playbook

What if the most important marketing asset in your business isn't your website, your social media account, or your advertising budget?

What if it's your podcast?

In this episode, Jason Wade sits down with Katie Brinkley, founder of Next Step Social, to discuss why podcasts have become one of the most powerful authority-building assets available to businesses today. While many organizations continue chasing views, followers, and engagement metrics, Katie is helping clients build something far more valuable: owned media infrastructure.

The conversation explores AI-generated content, voice cloning, podcast studios, authority building, personal branding, and the growing role podcasts play in training AI systems and shaping how expertise is discovered online.

Katie shares how her team helps business owners launch professional podcast studios inside their homes and offices, create content consistently, and transform simple conversations into long-term authority assets that fuel websites, social media, email campaigns, search visibility, and AI understanding.

Jason and Katie also discuss why authenticity may become more valuable as AI-generated content becomes increasingly common, why most businesses are still focused on vanity metrics, and how podcasts create high-intent visibility that extends far beyond traditional marketing channels.

• Why every business should have a podcast
• AI-generated content vs authentic expertise
• Building authority in the AI era
• Podcast studios for business owners
• Personal branding and trust
• AI voice cloning and ElevenLabs
• ChatGPT, Claude, Gemini, and NotebookLM
• Repurposing podcast content
• High-intent audiences vs vanity metrics
• How podcasts help train AI systems

Katie Brinkley is the founder of Next Step Social, a digital marketing agency specializing in health, wellness, medical, and service-based businesses. With a background in radio, podcasting, and digital marketing, Katie helps organizations build authority through content, media, and strategic communication.

In addition to social media and marketing services, Katie helps business owners launch professional podcasting operations, including designing and building podcast studios in homes and offices, developing content strategies, researching topics, and creating turnkey media systems that establish long-term authority and visibility.

Her philosophy is simple: businesses need their own voice, their own platform, and their own media assets if they want to remain relevant in an increasingly AI-driven world.

Jason Wade is the founder of BackTier and NinjaAI and host of the AI Visibility Podcast.

His work focuses on AI Visibility, the emerging discipline of helping organizations become correctly understood, trusted, cited, included, recommended, and selected by AI systems such as ChatGPT, Gemini, Claude, Perplexity, and Google AI Overviews.

In this episode, Jason Wade sits down with Jonathan Aufray, CEO of Growth Hackers, a global growth agency based in Taiwan. Originally from France, Jonathan has lived and worked across Europe, Australia, the United States, and Asia before building an international growth consultancy focused on helping businesses scale through marketing, automation, and digital transformation.    

The conversation covers AI adoption, workflow automation, startup growth, Taiwan’s role in the AI economy, Nvidia’s connection to Taiwan, and why companies often approach AI backwards by chasing tools instead of solving business problems. Jonathan explains how his team helps organizations identify repetitive tasks, automate workflows, and use AI to recover hours of productive time every month.  

Jason and Jonathan also discuss authenticity, personal branding, the explosion of self-proclaimed AI experts, and how businesses can navigate a world where technology evolves faster than organizations can adapt.  

• AI and automation for business growth
• Taiwan’s role in the AI economy
• Nvidia and the global chip market
• AI workflow automation
• Claude, ChatGPT, and AI agents
• Startup growth strategies
• Digital transformation
• Personal branding and authenticity
• The future of agency services
• Why everyone suddenly became an AI expert

Jonathan Aufray is the CEO and co-founder of Growth Hackers, a growth marketing and digital transformation agency serving clients across North America, Europe, and Asia. Based in Taiwan for more than a decade, Jonathan helps businesses improve lead generation, automate operations, increase efficiency, and implement AI-driven workflows. His work spans growth marketing, automation, user acquisition, and digital transformation initiatives.  

Jason Wade is the founder of BackTier and creator of Entity Lock Protocol™ and the BackTier Visibility Path™. Through BackTier’s AI Visibility Infrastructure, he helps organizations become correctly understood, trusted, cited, included, and selected by AI systems.

• Most companies start with AI tools instead of business problems.
• AI and automation are most valuable when attached to existing workflows.
• Taiwan sits at the center of the global AI hardware economy.
• Authenticity remains a competitive advantage even in an AI-driven world.
• The businesses that adapt fastest will be those willing to redesign processes rather than simply add new tools.  

Jonathan Aufray
🌐 https://growth-hackers.net
🔗 https://www.linkedin.com/in/jonathanaufray

Jason Wade
🌐 https://jasonwade.com
🌐 https://backtier.com
🌐 https://ninjaai.com

#AI #Automation #DigitalTransformation #GrowthMarketing #Taiwan #Nvidia #ArtificialIntelligence #BusinessGrowth #AIVisibility #BackTier #JonathanAufray #JasonWade

Host: Jason Wade
Founder, BackTier
Website: https://jasonwade.com
Company: https://backtier.com
NinjaAI: https://ninjaai.com
LinkedIn: https://www.linkedin.com/in/jasontwade

Episode Title

When SEO Traffic Drops 80%: How Agencies Are Rebuilding for the AI Discovery Era | Evgenii Tilipman & Jason Wade

Episode Description

What happens when organic traffic disappears and nobody knows the new rules?

In this episode, Evgenii Tilipman, founder of KHOD, joins Jason Wade to discuss the reality facing agencies in 2026. After seeing traffic declines across clients and watching traditional SEO become less predictable, Evgenii shares how his agency is repositioning around AI visibility, brand mentions, authority signals, and machine-mediated discovery.

The conversation explores the collapse of old assumptions around SEO, the rise of AI-native marketing roles, AI-powered website development, Webflow versus vibe coding, and why many agencies are still solving yesterday's problems while AI systems increasingly determine what brands get seen, cited, and recommended.

Jason and Evgenii discuss the shift from rankings to recommendations and what agencies must do to remain relevant as search evolves into AI-driven discovery.

Topics Covered

• The decline of traditional SEO traffic
• AI Visibility vs search rankings
• Why agencies are repositioning around AI
• Brand mentions and authority signals
• Webflow, Lovable, Cursor, and vibe coding
• AI-native marketing teams
• The future of agency services
• Building websites for AI discovery
• GEO and AEO in practice
• Recommendations versus rankings

About Evgenii Tilipman

Evgenii Tilipman is the founder of KHOD, a strategy-led web design and development agency serving B2B technology, SaaS, AI, and startup companies. Based in Serbia and working globally, Evgenii specializes in helping growth-stage companies build scalable digital experiences. As search evolves and AI increasingly shapes online discovery, he is actively exploring how agencies can adapt to AI visibility, machine-mediated recommendations, and the next generation of digital marketing.

About Jason Wade

Jason Wade is the founder of BackTier and creator of Entity Lock Protocol™ and the BackTier Visibility Path™. Through BackTier's AI Visibility Infrastructure, he helps organizations become correctly understood, trusted, cited, included, and selected by AI systems including ChatGPT, Gemini, Perplexity, Google AI Overviews, and emerging agentic platforms. His work focuses on the shift from traditional search visibility to AI-mediated selection, where machine understanding increasingly determines business discovery and recommendation.

Key Takeaway

The future is not about being found.

It's about being recommended.

As AI systems increasingly act as intermediaries between businesses and buyers, visibility shifts from rankings and clicks to trust, authority, mentions, and machine understanding. Agencies that recognize this shift early will help define the next era of digital marketing.

Learn More

Evgenii Tilipman
https://khod.io
evgenii@tilipmandigital.com

Jason Wade
https://jasonwade.com
https://backtier.com
https://ninjaai.com

#AIVisibility #GEO #AEO #SEO #ArtificialIntelligence #DigitalMarketing #Webflow #B2BMarketing #AgencyGrowth #KHOD #BackTier #JasonWade #EvgeniiTilipman

There is a particular kind of bad demo that has become almost unavoidable in the legal industry right now. You know the one. A consultant opens a laptop, types something dramatic into ChatGPT or Claude, uploads a document, waits three seconds, and then announces that the future of law has arrived. The room nods. Someone says “wow.” Someone else asks about confidentiality. A partner in the back starts calculating whether this thing is going to replace an associate, save the firm money, get the firm sued, or all three before lunch. The demo usually works just well enough to be impressive and just vaguely enough to be useless. It produces a draft. It summarizes a contract. It spits out a checklist. It says smart-sounding things in a confident voice. And then everyone leaves the webinar with the same uneasy feeling: this is powerful, this is coming fast, and I still have no idea how this actually fits inside my law firm.

That is the problem. Not AI itself. Not even the hype, exactly. The problem is that most law firms are being pushed into the wrong conversation. They are being told to pick a tool when what they need is an operating model. They are being sold chatbots when what they need is a system. They are being asked whether they prefer Claude, ChatGPT, Gemini, Perplexity, Harvey, Microsoft Copilot, or whatever product gets announced next Tuesday, as if the future of legal practice will be decided by which text box a lawyer types into. That is not strategy. That is shopping. And law firms that treat artificial intelligence like another software subscription are going to end up with what most firms already have too much of: more tools, more confusion, more fragmented workflows, more risk, and no real operational advantage.

Claude is useful. ChatGPT is useful. Gemini is useful. Legal research platforms are useful. But none of them are the strategy. The strategy is the system that decides where each tool belongs, what it is allowed to touch, who reviews the output, how client data is protected, how hallucinations are caught, how workflows are documented, how attorneys are trained, how staff are supervised, and how the firm converts raw AI capability into actual business value. That is the part most demos skip because it is harder to sell and less cinematic than watching a machine draft a letter in twelve seconds. But it is also the only part that matters if you run a real law firm with real clients, real ethical duties, real deadlines, real malpractice exposure, and real people depending on the quality of your work.

The firms that win with AI will not be the firms that collect the most shiny tools. They will be the firms that build the best AI Operating Systems. That means structured workflows, clear governance, human review gates, model selection logic, internal knowledge systems, training protocols, and a practical understanding of what AI should and should not do inside the firm. It means moving beyond the childish question of whether AI is “good” or “bad” and asking a more adult operational question: where can this technology safely increase speed, consistency, leverage, and intelligence without weakening professional judgment? That is the line. That is where the real work begins.

A law firm is not a content farm. It is not a startup growth hack lab. It is not a place where “move fast and break things” belongs anywhere near the client file. Law is a trust business built on judgment, confidentiality, documentation, and accountability. That does not make AI less relevant to law firms. It makes implementation more important. A bad AI rollout inside a law firm is not just inefficient. It can create ethical problems, client confidence problems, quality-control problems, and internal chaos. One attorney uses ChatGPT for brainstorming. Another uses Claude for drafting.

In this episode, Cynthia Lai joins Jason Wade to discuss the human gap in AI adoption: why companies buy tools before defining the problem, why teams resist AI, and why governance, trust, empathy, and judgment matter more as AI becomes faster and more powerful.

Cynthia draws from 20+ years in regulated banking, including HSBC, Bank of China, and OCBC, plus her work as a board advisor, executive coach, lecturer, and deep-tech co-founder with 15 patents. The conversation covers AI governance, change management, the “AI New Hire” framework, executive pressure, burnout, sustainable performance, and the leadership skills AI cannot replace.

Topics Covered

• The human gap in AI adoption
• AI governance and responsible implementation
• Treating AI like a new hire
• Why companies buy tools before defining problems
• Human judgment, empathy, and accountability
• Executive pressure and transformation fatigue
• Sustainable performance without burnout
• The “pack mule” leadership trap
• AI readiness inside regulated organizations
• Hong Kong, banking, innovation, and AI transformation

About Cynthia Lai

Cynthia Lai is a board advisor, executive coach, lecturer, and deep-tech co-founder with 15 patents. She spent more than 20 years leading transformation in regulated banking, including roles at HSBC, Bank of China, and OCBC. Today, she helps leaders navigate AI-driven change by strengthening trust, decision-making, governance, resilience, and sustainable performance. Her work focuses on closing the human gap that appears when strategy, AI, and institutional reality collide.

About Jason Wade

Jason Wade is the founder of BackTier and creator of Entity Lock Protocol™ and the BackTier Visibility Path™. Through BackTier’s AI Visibility Infrastructure, he helps organizations become correctly understood, trusted, cited, included, and selected by AI systems including ChatGPT, Gemini, Perplexity, Google AI Overviews, and emerging agentic platforms.

Learn More

Cynthia Lai
Email: cynthia@cynthialai.com
LinkedIn: https://www.linkedin.com/in/cynthiakylai/

Jason Wade
https://jasonwade.com
https://backtier.com
https://ninjaai.com

#AIVisibility #AIAdoption #AIGovernance #Leadership #ChangeManagement #ResponsibleAI #DigitalTransformation #ExecutiveCoaching #HumanAdvantage #BackTier #JasonWade #CynthiaLai

Guest: Bryant Oberg
Founder, Human Co-Pilot
Website: https://www.human-co-pilot.com
Email: bryant@human-co-pilot.com
Phone / WhatsApp: +1 (909) 805-5451
LinkedIn: https://www.linkedin.com/in/bryant-oberg

Host: Jason Wade
Founder, BackTier
Website: https://jasonwade.com
Company: https://backtier.com
NinjaAI: https://ninjaai.com
LinkedIn: https://www.linkedin.com/in/jasontwade

Episode Title

Human Co-Pilot: Why AI Adoption Fails Without Workflow Change | Bryant Oberg & Jason Wade

Episode Description

Most businesses do not have an AI problem. They have an adoption problem.

In this episode, Bryant Oberg, founder of Human Co-Pilot, joins Jason Wade to discuss why companies buy AI tools but fail to turn them into real workflow improvement. Bryant explains how business owners, professionals, and teams can move from AI confusion to practical implementation by using AI as a thinking partner, operating assistant, and strategic amplifier.

The conversation covers AI adoption, workflow design, Claude implementation, custom AI agents, employee resistance, business process improvement, and the difference between experimenting with AI and actually using it to save time, improve decisions, and reduce operational friction.

Jason and Bryant also explore the connection between AI adoption and AI visibility: Bryant helps humans work better with AI, while Jason helps businesses become better understood, trusted, cited, included, and selected by AI systems.

Topics Covered

• Why AI adoption fails
• How businesses should start using AI
• AI as leverage, not magic
• Workflow-first AI implementation
• Claude for small businesses
• Custom AI agents and skills
• Human resistance to AI tools
• Turning AI experiments into operating systems
• AI consulting vs AI courses
• The future of human-AI collaboration
• AI adoption and AI visibility

About Bryant Oberg

Bryant Oberg is the founder of Human Co-Pilot, an AI adoption and implementation company based in Jerusalem, Israel. Through Human Co-Pilot, Bryant helps business owners, professionals, and teams move from AI confusion to practical implementation. His work focuses on AI adoption sessions, team rollouts, Claude small business implementation, custom AI agents, workflow optimization, and practical AI systems that fit the way real businesses already work.

Before founding Human Co-Pilot, Bryant built experience across finance, restructuring, and distressed investing. That background shaped his practical view of AI as leverage: not magic, not replacement, but a tool that becomes valuable only when aimed at the right business problems.

About Jason Wade

Jason Wade is the founder of BackTier and creator of Entity Lock Protocol™ and the BackTier Visibility Path™. Through BackTier’s AI Visibility Infrastructure, he helps organizations become correctly understood, trusted, cited, included, and selected by AI systems including ChatGPT, Gemini, Perplexity, Google AI Overviews, and emerging agentic platforms. His work focuses on the shift from traditional search visibility to AI-mediated selection, where machine understanding increasingly determines business discovery and recommendation.

Learn More

Bryant Oberg
https://www.human-co-pilot.com
bryant@human-co-pilot.com
+1 (909) 805-5451

Jason Wade
https://jasonwade.com
https://backtier.com
https://ninjaai.com

#AIVisibility #ArtificialIntelligence #AIAdoption #HumanCoPilot #ClaudeAI #ChatGPT #BusinessAI #WorkflowAutomation #AIAgents #BackTier #JasonWade #BryantOberg

About Richard Foltys
Richard Foltys is an experiential marketing entrepreneur and founder of DMA Events, a company that has produced more than 1,500 events and brand activations worldwide. His team has worked with brands including Disney, Red Bull, McDonald's, RBC, Porsche, EY, L'Oréal, Hasbro, TD, Cineplex, Ferrari, Visa, and many others. Through DMA Events and DMA Engage, Richard helps brands create memorable live experiences using AI-powered activations, event robots, QR-driven engagement, content creation, social sharing, and lead generation.

Episode Description
Richard Foltys joins Jason Wade to discuss how AI photo booths, AI video, trading cards, event robots, and experiential marketing are transforming conferences, trade shows, corporate events, and brand activations. The conversation explores attention, engagement, lead generation, user-generated content, AI-powered experiences, and why memorable events often outperform traditional marketing channels.

Host Links
Jason Wade: https://jasonwade.com
BackTier: https://backtier.com
NinjaAI: https://ninjaai.com

About Jason Wade
Jason Wade is the founder of BackTier, an AI Visibility Infrastructure company focused on helping brands become correctly understood, trusted, cited, included, and selected by AI systems. He is also the founder of NinjaAI and host of the AI Visibility Podcast, where he explores how AI is changing discovery, authority, marketing, and business growth.

Most professionals do not have an expertise problem. They have a visibility problem.

In this episode, Jason AI Wade talks with Ashley Smith and Sarah Strackhouse about why talented professionals often remain invisible, even when they have real experience, strong reputations, and valuable expertise.

The conversation breaks modern visibility into three layers: realness, proof, and polish.

Ashley Smith explains the Proof Gap: the disconnect between what a professional actually knows and what search engines, AI systems, and recommendation platforms can find, understand, and trust. She discusses why professionals need to become discoverable and recommendable without forcing themselves to become full-time content creators.

Sarah Strackhouse brings the media and communication layer. Drawing from her background in television journalism, media coaching, and on-camera training, she explains why nerves, fear, and hesitation keep many professionals from showing up publicly.

The conversation also covers Google’s shift toward AI-powered search, AI agents, podcast RSS feeds, transcripts, media training, confidence, authority, and why publishing conversations may become one of the easiest ways to help AI systems understand who you are.

Key Topics:
AI visibility
The Proof Gap
Realness, proof, and polish
Google AI search
AI agents
Podcast RSS feeds
Machine-readable authority
Professional visibility
Media confidence
On-camera presence
Why professionals hesitate to publish
How AI systems evaluate trust
Why podcasts matter for search and AI
Building authority without becoming a full-time content creator

Ashley Smith Bio:
Ashley Smith is a business strategist and creator of the Proof Gap, a framework that explains why experienced professionals can be highly capable in real life but nearly invisible to search engines, AI systems, and online recommendation platforms. After nearly two decades in real estate leadership, including serving as board chair and media spokesperson for one of Canada’s largest real estate organizations, Ashley now helps professionals become more visible, trusted, and discoverable in an AI-shaped world.

Ashley Smith Links:Website: https://showyourproof.beehiiv.comProof Gap Assessment: https://showyourproof.beehiiv.com/products/proof-gap-self-assessment⁠https://linkedin.com/in/ashleysmithnow⁠ ⁠https://instagram.com/ashleysmithnow⁠ ⁠https://facebook.com/ashleysmithnow⁠ ⁠https://threads.com/@ashleysmithnow⁠ ⁠https://tiktok.com/@ashleysmithnow⁠⁠https://youtube.com/@ShowYourProof⁠

Sarah Strackhouse Bio:Sarah Strackhouse is a former television journalist, anchor, producer, and entrepreneur who has worked with major media organizations including Fox Business, CBS, NBC, The CW, and Time Warner Cable stations nationwide. She is the founder of Strackhouse Media, a media company focused on live event production, media training, on-camera confidence, content creation, and helping professionals turn credibility into visibility and cashflow.

Sarah Strackhouse Links:Website: https://www.strackhousemedia.comMedia Course: https://www.strackhousemedia.com/mediacourse

Host Bio:
Jason AI Wade is the founder of BackTier, an AI Visibility Infrastructure company. He created Entity Lock Protocol™ and the BackTier Visibility Path™, frameworks designed to help brands become correctly understood, trusted, cited, included, and selected by AI systems. His work focuses on the shift from traditional search visibility to AI-mediated discovery, recommendation, and selection.

Jason AI Wade Links:BackTier: https://backtier.comNinjaAI: https://ninjaai.comWebsite: https://www.jasonwade.comLinkedIn: https://www.linkedin.com/in/backtier

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.

In this episode, Jason Wade talks with Fergus Dyer Smith, founder and CEO of MSQ Global Studios, about the move from AI as a tool to AI as an operating layer for marketing teams. Fergus has built and deployed AI products used inside large enterprise environments, including Assist, BrandCheck, PreFlight, and WAVE, with claimed users including Publicis, Toyota, WPP, Google, and ThermoFisher. His central argument is direct: most AI products do not fail at the demo stage. They fail at deployment.  

The conversation centers on agentic marketing systems: workflows that do not just generate content, but observe the market, publish, measure performance, study competitors, produce analysis, feed those lessons back into the system, and run the loop again. Fergus shares how he built a self-improving TikTok agent that creates slideshow content, posts it, pulls the previous day’s data, scrapes top-performing videos in the niche, analyzes what is working, and adjusts future output without daily human intervention.

Jason and Fergus also discuss Manus, Claude, Gemini, model-agnostic architecture, AI operating systems for marketing teams, enterprise adoption, creative automation, feedback loops, and why the future of AI in business is not just better prompting. It is deployment, integration, workflow design, and closed-loop execution.

The deeper question is whether marketing is moving away from campaign-by-campaign execution and toward autonomous learning systems. If AI can create, test, measure, and improve continuously, then brands need to rethink not only how they produce content, but how they become visible, understood, cited, included, and selected inside AI-mediated discovery environments.

Guest bio

Fergus Dyer Smith is founder and CEO of MSQ Global Studios and a product-driven AI operator focused on building tools that enterprises actually use. He began his career in science, studying biochemistry at Manchester before moving into technology, web development, travel, music events, video production, VR, brewing, and AI product deployment. That mix of systems thinking, creativity, and commercial execution shaped his current work building AI products for complex organizations.  

Fergus has founded and built multiple companies, including Wooshii, Envoke, Hartest Brewing, and Snowbombing Festival-related ventures. Today, he leads MSQ Global Studios, where his focus is shipping AI products that move beyond prototype theater and into daily enterprise use. His product portfolio includes Assist, an AI operating system for marketing teams; BrandCheck, a creative effectiveness and brand measurement tool; PreFlight, an AI video analysis tool; and WAVE, an AI-powered video automation platform.  

His practical philosophy is “deployment over demos.” He is not an engineer by background, but he understands product, adoption, workflow, and how to get AI systems used inside real organizations.  

Guest contact info

Fergus Dyer Smith
Founder / CEO, MSQ Global Studios
Email: fergus.dyer-smith@msqpartners.com
Company: MSQ
Website: https://www.msqpartners.com
LinkedIn: https://www.linkedin.com/in/fergusdyersmith/
Location: London, United Kingdom
Time zone: UK / Ireland / Lisbon time

Jason Wade bio

Jason Wade is the founder of BackTier, an AI Visibility Infrastructure company focused on helping brands become correctly understood, trusted, cited, included, and selected by AI systems. Through BackTier, Jason created Entity Lock Protocol™, a framework for stabilizing machine understanding, and the BackTier Visibility Path™, a measurement model for tracking whether AI systems cite, include, and select an entity

The modern business owner is currently being sold a dream: buy a subscription to a chatbot, and your operational headaches will vanish. As a consultant who looks at systems through the lens of ROI rather than trends, I find this "AI-first" noise to be a dangerous distraction.Real automation isn’t about chasing the latest LLM; it is an exercise in investigative systems mapping. Think of a strategist not as a coder, but as a private eye. You dive into a business to find the specific, 360-degree reality of its bottlenecks. This post distills the pragmatic insights from a recent deep-dive with automation expert Neal J Mcleod, moving past the marketing gloss to reveal how systems actually deliver profitability.1. Data is the "Hidden" Profit, Not Just the WorkflowMost entrepreneurs view automation as a tool to save time on admin tasks. While time is money, the real value of an automated system is the data it gathers in the shadows. Without visibility, you are guessing; with background analytics, you are investing.Consider Neal’s work with a personal injury law firm. The initial goal was a triage system to route leads. However, by layering in PostHog—an open-source analytics platform—to track specific injury types and settlement speeds, the firm uncovered a "war story" insight: their highest ROI wasn't just "car accidents," it was specifically back injuries resulting from 18-wheeler accidents."They knew certain types of injuries they were better at serving... but with this data, man, they took off. They were able to narrow down and say, 'Okay, from back injuries [in 18-wheeler cases], we were actually able to win more settlements.' They were able to be more aggressive and allocate more funds toward where they were winning."This is the essence of "Systems Mapping." Similarly, Neal assisted a home insurance agency by integrating directly with home inspection companies. Instead of competing on expensive Google Ads, they mapped the system to find leads where they naturally occur—at the point of inspection. This turned a manual networking effort into an automated, high-intent lead engine.2. Why "Deterministic" Beats "Probabilistic" for BusinessIn technology, "deterministic" systems produce the same output every time. "Probabilistic" systems—like AI—guess. For a professional service business, a "guess" is often a liability.If a client texts a car service to book a ride for 6 PM, the system cannot afford to be creative or "vibe-code" a response. Neal is blunt: if you give an AI the same question 50,000 times, it will likely give you 50,000 different answers. For professional infrastructure, repeatability is the only metric that matters.The Strategic Analysis: Relying on "naked" AI for core logic creates massive Brand Risk and compromises Contractual Reliability. If your system hallucinations lead to a missed pickup or a legal filing error, the "efficiency" of AI evaporates. High-level automation uses AI to interpret unstructured input, but the business rules themselves must be written in stone (code).3. The "Secret Sauce" is the Guardrail (Code > Prompts)The differentiator between a toy and a tool is the guardrail. Modern automation should follow a "Hybrid" model: Code + AI. Neal’s methodology involves using JavaScript to "clean" data before it reaches the AI and "parse" it into a strict format afterward.This approach makes AI "insurable" for a firm. By forcing the AI to interact with a strict JSON schema, you create a contract between the unstructured world of human text and the structured world of your CRM or database

Jason AI Wade sits down with Mr B, also known as Blake Robert Mankin, founder of Shows For Artists, an autonomous AI booking system built for independent musicians who want to get onstage without spending their lives sending booking emails.

Mr B brings a rare mix of artist experience and founder instinct. He has performed more than 150 live shows, opened for DMX, Bone Thugs-N-Harmony, and Soulja Boy, built music tech products, written children’s books, and now runs Shows For Artists as a solo founder from Scottsdale, Arizona. His core thesis is simple: the music industry serves the top 1%, while the other 99% of working musicians are left to book themselves.

In this episode, Jason and Mr B talk about the hidden labor behind live music, why most indie artists never get booked outside their hometown, how AI makes previously uneconomic markets serviceable, and why domain expertise now matters more than raw coding ability. Mr B explains how he built Shows For Artists in roughly 40 days for about $1,200 using AI, creating software that once would have required a six-figure development budget.

They also dig into live events, local musician meetups, venue trust, AI-generated outreach, founder-market fit, category creation, Andrew Chen’s “come for the tool, stay for the network” idea, Marc Andreessen’s market-first startup philosophy, and why the future of music tech may be a hybrid of automation, community, and in-person trust.

Topics include:

AI booking agents for independent musicians
Why traditional booking agents do not serve smaller artists
The economics of $80–$300 gigs
Building software as a non-coder with AI
Founder-market fit in music technology
Why venues need trust, not just outreach
DMX, Bone Thugs-N-Harmony, and life on the road
Category creation versus competition
Local musician meetups as growth infrastructure
The future of two-sided marketplaces in live music
Why AI rewards domain experts
How artists can use data to build leverage
The difference between reckless risk and calculated risk
Why authenticity still matters in an AI-driven market

Guest Bio — Mr B / Blake Robert Mankin:

Mr B, real name Blake Robert Mankin, is a rapper, entrepreneur, Grammy voting member, children’s author, and founder of Shows For Artists, the first autonomous AI booking system for independent musicians. After performing more than 150 live shows and opening for DMX, Bone Thugs-N-Harmony, and Soulja Boy, he built Shows For Artists to solve the booking grind that keeps most musicians from getting onstage consistently. The platform pitches real venues from the artist’s own Gmail, helping independent artists book shows without relying on traditional agents. Mr B is based in Scottsdale, Arizona, and is building the company as a solo founder focused on serving the 99% of musicians the traditional music industry does not economically support.  

Host Bio — Jason AI Wade:

Jason AI Wade is the founder of BackTier, an AI Visibility Infrastructure company that helps brands become correctly understood, trusted, cited, included, and selected by AI systems. Through BackTier, Wade created Entity Lock Protocol™, a framework for stabilizing machine understanding, and the BackTier Visibility Path™, a measurement model for tracking whether AI systems cite, include, and select an entity. His work focuses on the shift from traditional search visibility to AI-mediated selection, where large language models, answer engines, search engines, and AI agents increasingly determine which companies are discovered, trusted, recommended, and chosen.

Contact Info:

Guest: Mr B / Blake Robert Mankin
Website: https://showsforartists.com
Artist/social handle: @MrBInspire
Merch / Hoos Moose: https://hoos.com
Email: mrbinspire@gmail.com

Host: Jason AI Wade
BackTier: https://backtier.com
NinjaAI: https://ninjaai.com
Jason Wade: https://jasonwade.com

In this episode, Jason unpacks why “Google Omni SEO” language is obsolete and how the real battleground has shifted to GEO (Generative Engine Optimization), AEO (Answer Engine Optimization), and HEO (Hybrid Engine Optimization). He ties the shift to BackTier’s proprietary frameworks:

• ELP – Entity Lock Protocol: a system for aligning structured data, schema, bios, profiles, and corroboration layers so AI systems consistently recognize who you are.

• BVP – BackTier Visibility Path: the three‑stage journey from Citation → Inclusion → Selection inside AI‑generated answers.

Expect concrete steps to turn your site, podcast, and brand assets into durable AI visibility instead of chasing rankings on paths that no longer control selection.

AI Visibility, GEO, AEO, HEO, Entity Lock Protocol, ELP, BackTier Visibility Path, BVP, Answer Engine Optimization, Generative Engine Optimization, AI SEO, ChatGPT visibility, Gemini visibility, Perplexity visibility, Claude visibility, Google AI Overview, Jason AI Wade, BackTier, NinjaAI, schema, entity resolution, citation, inclusion, selection, machine‑readable identity.

Neal McLeodFounder, CTK Industries

Website: ctkindustries.comEmail: neal@ctkindustries.com
Phone/Text: 646-730-5149

In this episode, Jason Wade talks with Neal McLeod, founder of CTK Industries, about the difference between AI hype and automation that actually works inside a real business.

Neal is not selling magic. He is a business systems operator who helps law firms, insurance companies, logistics operators, and small businesses turn operational bottlenecks into scalable workflows. His approach is blunt and practical: do not automate chaos, do not force AI where deterministic automation will work better, and do not remove the human from decisions that still require judgment.

The conversation goes deep into real examples. Neal explains how he built a personal injury law firm lead-triage system that routed leads by injury type, collected intake data, and helped the firm see which categories produced better settlement outcomes. He also breaks down a black car service SMS automation built in n8n, where customers text booking requests, the system extracts trip details, the owner approves by text, and approved rides are added to Google Calendar and Google Sheets.

A central theme is reliability. Neal explains why AI is probabilistic and why business operations need repeatable systems. He describes how he uses JavaScript guardrails, schemas, system prompts, code-based data cleaning, error workflows, and alerts to keep AI from breaking production workflows. The strongest takeaway is that AI should not be the system. AI should be one controlled component inside a system designed around real business constraints.

This episode is for business owners, consultants, operators, law firms, agencies, and service businesses trying to understand where automation actually creates value. The answer is not “use more AI.” The answer is to map the workflow, simplify the process, automate the repeatable parts, use AI only where interpretation is needed, and keep humans in control of important decisions.

Neal McLeod is the founder of CTK Industries and a business systems consultant based in Houston, Texas. He helps law firms, insurance companies, logistics operators, and small businesses eliminate operational bottlenecks through workflow automation, n8n systems, JavaScript guardrails, CRM integration, AI-assisted extraction, and practical business process design.

His work focuses on building systems that save time, reduce manual work, improve data collection, and create measurable business value without overcomplicating operations. Neal’s philosophy is simple: AI is useful, but it should not be forced into every workflow. Most businesses need clearer systems first, then automation, then carefully controlled AI where it actually helps.

Key topics

Business systems automation
AI guardrails
n8n workflows
Deterministic automation vs probabilistic AI
Personal injury law firm intake automation
Lead routing and intake intelligence
PostHog analytics
SMS booking automation
Google Calendar and Google Sheets automation
Human-in-the-loop approval systems
JavaScript data cleaning
System prompts and schemas
Workflow mapping
Operational bottlenecks
Small business automation
Automation pricing and support models

“AI does not fix chaos. Clear workflows fix chaos.”

“Use AI where interpretation is needed. Use automation where repeatability matters.”

“The real value is not the build. The real value is diagnosing the bottleneck.”

“Most businesses do not need another AI tool. They need a system that keeps working after the demo.”

“Automation becomes powerful when it collects business intelligence while the company keeps operating.”

Call to action

To learn more about Neal McLeod and CTK Industries, visit ctkindustries.com and book a free Systems Mapping consultation. Neal can also be reached at neal@ctkindustries.com or by phone/text at 646-730-5149.

ELP + BVP: Jason AI Wade of BackTier on AI Visibility, Selection, and Machine Trust

Entity Lock Protocol™ and BackTier Visibility Path™ by Jason AI Wade of BackTier

In this episode, Jason AI Wade of BackTier explains how Entity Lock Protocol™ and the BackTier Visibility Path™ define the shift from traditional SEO rankings to AI-mediated selection.

Search engines expanded consideration. AI systems compress consideration. That change means the economic value of visibility is moving away from ranking alone and toward machine understanding, trust, inclusion, recommendation, and selection.

Jason AI Wade of BackTier breaks down the two core frameworks behind AI Visibility Architecture: Entity Lock Protocol™ and the BackTier Visibility Path™.

Entity Lock Protocol™ is the machine-understanding layer. It helps AI systems consistently resolve who an entity is, what it does, where it belongs, why it should be trusted, and when it should be selected.

The BackTier Visibility Path™ is the measurement layer. It tracks whether AI systems merely cite a source, include a brand or expert in the answer, or actually select and recommend that entity.

Together, ELP and BVP explain why AI visibility is not just an SEO update. It is a new infrastructure problem created by AI answer engines, large language models, and agentic systems that compress user choice into fewer answers, fewer recommendations, and eventually fewer transactions.

Bio:
Jason AI Wade of BackTier is the founder of BackTier and NinjaAI and the originator of Entity Lock Protocol™ and the BackTier Visibility Path™. His work focuses on AI Visibility Architecture: the discipline of helping brands, people, companies, and concepts become machine-readable, machine-resolvable, trusted, cited, included, selected, and eventually transacted with by AI systems. Through BackTier, Jason AI Wade develops frameworks and infrastructure for the shift from traditional SEO rankings to AI-mediated selection, where large language models, answer engines, AI search systems, and agentic workflows increasingly decide which entities are understood, trusted, and chosen.