AI Visibility Podcast with Jason T Wade of BackTier

Lake Wales Is Building a Startup Economy. But Who's Building the Startups?

Podcast: Lake Wales Startups / BackTier
Host: Jason T AI Wade
Estimated runtime: 10–12 minutes
Format: Commentary, local economic development, entrepreneurship, and AI

Episode description: Google AI says Lake Wales, Florida, has a growing entrepreneurship ecosystem, supported by business incubators, government incentives, youth programs, and downtown redevelopment. But what does that actually mean for someone trying to start a business? We examine the organizations, the money, the opportunities, and the missing connections between economic development and entrepreneurship.

https://youtu.be/nlmdSnHQrik

Title: Lake Wales Startup Night: Startup Legal Strategy with Denise Tessier, Esq.

Starting a business creates questions that a website, logo, or AI-generated business plan cannot settle. Who owns what? What should partners agree on? What belongs in a client contract? When should you bring in an attorney?

In this episode of the AI Visibility Podcast, Jason Wade previews the next Lake Wales Startup Night, featuring Startup Legal Strategy with Denise Tessier, Esq. The free gathering offers an opportunity to meet fellow entrepreneurs and bring questions about starting or growing a business.

Whether you’re exploring an idea, building a side business, or already serving customers, come with a question you want to sort out.

Event details

• Wednesday, October 21, 2026 · 6 PM

• The Thirsty Dragon

• 126 N 1st St, Lake Wales, FL

• Free to attend

• Reserve your seat and submit a question for the attorney

Topics in this episode

• Questions to ask before starting a business with a partner.

• Clarifying ownership, responsibilities, and expectations.

• Preparing questions about client agreements and contractor work.

• Using AI to organize your questions before speaking with an attorney.

• Building connections with founders and neighbors in Lake Wales.

About Denise Tessier, Esq.

Denise Tessier is an attorney with Tessier Law Firm and the featured speaker for Lake Wales Startup Night’s Startup Legal Strategy event. Learn more at TessierLawFirm.com.

About Jason Wade

Jason Wade is an AI Visibility strategist and the founder of BackTier. He is the host of the AI Visibility Podcast. His work focuses on AI Visibility, Generative Engine Optimization, Answer Engine Optimization, entity authority, and the infrastructure behind machine-generated recommendations. He is also conducting an ongoing name-ambiguity and entity-resolution experiment using the names Jason T Wade and Jason AI Wade to study how AI systems distinguish, merge, classify, and resolve identities across the web. Learn more at BackTier.com and JasonWade.com.

Links

• Lake Wales Startup Night — RSVP

• Tessier Law Firm

• Startup Night articles and past-event content

• BackTier

• Jason Wade

• Lake Wales Guide

• Florida Slice

Contact the organizers: [email protected] · 321.946.5569

AI Search Is Rewriting SEO: Identity, Zero-Click & Who AI Recommends | Jeff Deutsch

Search is changing from a list of links into a recommendation system.

In this episode of the AI Visibility Podcast, Jason AI Wade talks with Jeff Deutsch, fractional Head of Growth at That’s Heaps, about what happens when Google, ChatGPT and other AI systems stop simply ranking pages and start deciding which companies, people and resources deserve to be recommended.

Jeff breaks down what he has learned from scaling organic search at companies including Superpower and ContactOut, why traditional rankings matter differently in an AI-search environment, and why zero-click search does not necessarily mean zero commercial value.

The conversation moves into a larger problem: identity.

Jason and Jeff test how AI currently understands Jason’s name, work and identity in real time, including the Jason AI Wade name experiment. They discuss why verified and corroborated sources matter, why AI systems increasingly need a “chain of custody” before trusting claims, and how podcasts, transcripts and distributed mentions can reinforce what an entity is actually known for.

They also discuss:

• Google AI Overviews and the transition away from ten blue links

• Why being recommended may matter more than ranking #1

• Zero-click search versus high-intent searches

• AI search personalization and invisible background queries

• How user context may shape future product recommendations

• Amazon, Google and AI assistants competing on who knows the user best

• Why scaled AI content is likely to face increasing trust problems

• Entity resolution and name disambiguation

• LinkedIn, Reddit and source credibility

• Jason’s experiment with Jason AI Wade

• How easily niche AI search results can currently be influenced

• Why corroboration across independent sources will become more important

• Podcasts and transcripts as machine-readable authority signals

• Building a digital “second brain” for a company

• What companies should actually be known for before trying to optimize AI visibility

The central idea is simple: AI visibility is becoming less about getting a page ranked and more about building a trustworthy digital representation that machines can identify, verify and confidently recommend.

Jeff Deutsch is a fractional Head of Growth and the founder of That’s Heaps, a Sydney-based growth and marketing consultancy.

Jeff has spent approximately 16 years working in SEO and growth. His experience includes growth roles with Superpower, ContactOut, VIPKid and Longtail UX, spanning SEO, acquisition, automation and AI search.

His current work focuses heavily on how companies are represented inside AI-driven search systems: defining what an organization should be known for, creating structured digital knowledge around the company, and making that information discoverable and credible enough to appear in AI-generated answers and recommendations.

Jeff is also scheduled to speak at the Sydney SEO Conference in March 2027 about the SEO, growth and AI-search work behind Superpower and ContactOut.

Jeff Deutsch / That’s Heaps
That’s Heaps

That’s Heaps on LinkedIn
That’s Heaps — LinkedIn

Jeff Deutsch on LinkedIn
Jeff Deutsch — LinkedIn search/profile results

Show NotesGuest BioLinksSydney SEO Conference 2027
Sydney SEO Conference

SEO is not disappearing. The systems doing the discovering, interpreting, and recommending are changing.

Andrew Shotland, founder and CEO of Local SEO Guide, joins Jason AI Wade for a practical conversation about what happens when traditional SEO collides with ChatGPT, Google AI Mode, local search, citations, entity signals, and agentic automation.

Andrew’s core framing is simple: SEO has always been about making it easy for a machine to understand what a business does and when it should be recommended. That part has not changed. The machines have.

The conversation moves quickly into what that means in practice.

For local businesses, Andrew argues that merely repeating “we are a plumber” is not enough. The stronger opportunity is to distribute specific attributes—water heater replacement, storm drains, service areas, specialties—across the business’s own site and the third-party pages that AI systems may use as evidence.

He shares a particularly useful experiment involving a 160-location company. His team identified a search-intent gap around construction job-site bathrooms, added that concept to the Google Business Profiles and citation ecosystem for 20 test locations, and saw visibility move in Google AI Mode within days. Later, the same test group began gaining visibility in ChatGPT, while the untouched locations initially remained flat.

Then something more interesting happened: roughly a month later, visibility began increasing across the wider location network as well. Andrew’s working theory was that enough pages had associated the brand with the concept that the relationship began applying more broadly to the entity.

The episode also covers competitor-adjacent content, conversational search paths, local citations, corroboration, automation, content gaps, and why the right operating model for AI-era search is not a perfect master plan.

It is testing.

Andrew compares it to baseball: even elite hitters fail most of the time. The advantage comes from taking enough intelligent swings to discover what actually moves visibility.

Andrew Shotland
Founder and CEO of Local SEO Guide. Andrew has worked in search for roughly two decades and focuses on local SEO, AEO, multi-location search, and how Google and AI systems understand and recommend businesses.

He describes the current moment as one of the most interesting periods in search in at least a decade, driven by the rapid shift from traditional search-result ranking toward AI-mediated discovery and recommendation.

The current state of SEO
SEO vs. AI Visibility
Local SEO in ChatGPT and Google AI Mode
Entity understanding and corroboration
Local citations as AI signals
Service and attribute distribution
Content-gap analysis
Competitor-adjacent content
Agentic SEO and automation
Multi-location experimentation
Why AI visibility requires testing
How search behavior is becoming conversational

Local SEO Guide
https://www.localseoguide.com/

Andrew Shotland on LinkedIn
Search Andrew Shotland on LinkedIn — he identifies it as his primary social platform and the place where he publishes most frequently.

AI Visibility Podcast
BackTier

When someone asks ChatGPT, Google, or another AI system who to hire, where to go, or what to buy, how does the machine decide who gets recommended?

Guest-host Jodi Koch joins Jason Wade for a wide-ranging AI Visibility roundtable with Andrew Shotland, Susan Gooch, Jack Oujo, April Ratchford, Gary Vause II, and Rafael Pinho.

The conversation gets practical quickly. Andrew explains how an AI system confused one company’s G2 review count with a competitor’s. Susan discovers AI had merged her identity with another Susan Gooch and attributed a military career to her that never happened. The group explores what businesses can actually do when AI gets their identity, location, expertise, or reputation wrong.

They also discuss consistent brand information, accessibility and plain-language content, multi-location businesses, reviews as recommendation signals, repurposing existing content with AI, and why people still need to verify what AI tells them.

Jodi Koch — Founder of Elizabeth Erin Designs and host of Designing in 5D. Jodi has spent more than two decades in interior design and leads a national firm built around her Designing in 5D process.

Andrew Shotland — Founder and CEO of Local SEO Guide. Andrew has worked in local search since 2006 and now researches how Google, ChatGPT, and other AI systems discover and recommend businesses.

Susan Gooch — Author and longtime educator whose work focuses on Southern romance, history, and storytelling. She also shares book recommendations with readers online.

Jack Oujo — Founder of Oujo Wealth Strategies and author of Too Smart to Be an Umpire. Before building his financial career, Jack spent eight years as a professional baseball umpire and advanced to AAA.

April Ratchford, OTR/L — Autistic occupational therapist and creator and host of Adulting with Autism, focused on helping autistic and neurodivergent adults navigate independence and everyday life.

Gary Vause II — CEO of Vause Computer Systems and a technology executive working across AI, cybersecurity, digital governance, and business transformation. He is also a co-author of The AI Transformation.

Rafael Pinho, CFA — Founder of TALENTUM, finance executive, exit advisor, and author of From Job to Asset. His work focuses on helping founder-led businesses reduce owner dependence and build transferable enterprise value.

Jodi Koch / Elizabeth Erin Designs
Elizabeth Erin Designs

Andrew Shotland
Local SEO Guide

Susan Gooch
Susan Gooch

Jack Oujo
Too Smart to Be an Umpire
Oujo Wealth Strategies

April Ratchford
Adulting with Autism

Gary Vause II
Gary Vause II on LinkedIn

Rafael Pinho, CFA
Rafael Pinho on LinkedIn
TALENTUM

AI Visibility Podcast — BackTier

Title

AI Knows a Version of You: Identity, Trust & the Fight to Be Understood

AI does not see you the way a person does.

In this roundtable episode of the AI Visibility Podcast, Jason T Wade talks with Jason Barnard, Jodi Koch, and Su Belagodu about what AI gets right about people, what it gets wrong, and what happens as machines become part of the discovery and recommendation process.

Jason Barnard starts with the entity problem. Before an AI system can recommend someone, it has to determine which person it is actually talking about. Shared names and overlapping identities make that harder than it looks. Roundtable on AI, Identity, and Ambiguity.docxDOCX

Su Belagodu gives a real example: AI once attributed a Dubai conference appearance to her because it appears to have confused her with another person sharing her surname who also worked in AI governance. Roundtable on AI, Identity, and Ambiguity.docxDOCX

The group then moves into a larger question: can you influence how AI understands you?

Jason Barnard argues that clarity and consistency matter. Su adds that AI outputs remain probabilistic, but repeated, coherent signals make it easier for systems to associate the right information with the right entity. Roundtable on AI, Identity, and Ambiguity.docxDOCX

Jodi Koch brings the discussion into interior design. She uses AI to help clients visualize options faster, but her experience also exposes the limits of machine output: an AI-generated design can look convincing while being completely impractical in the actual room. Roundtable on AI, Identity, and Ambiguity.docxDOCX

The conversation also examines the shift from traditional search to AI-driven recommendation. Instead of presenting ten links and asking the user to decide, AI systems increasingly synthesize information and narrow the choice themselves. Roundtable on AI, Identity, and Ambiguity.docxDOCX

The question is no longer only whether you can be found.

It is whether the machine understands the right version of you.

• AI identity and entity ambiguity
• Shared names and mistaken identity
• What AI gets wrong about people
• Probabilistic AI answers
• Digital consistency and corroboration
• Search versus AI recommendation
• Human judgment in AI systems
• AI agents and automation
• Expertise versus generated output
• AI in interior design
• Trust and verification
• Personal brand and machine understanding
• Why clarity comes before recommendation

Jason Barnard works through Kalicube on how Google and AI systems understand, represent, and recommend people and brands. His focus in the conversation is entity identity, ambiguity, digital consistency, and shaping machine understanding.

Jodi Koch is an interior designer with more than 22 years of experience and host of the Designing in 5D podcast. She uses AI to accelerate visualization and client communication while relying on professional experience to judge what will actually work in the physical world. Roundtable on AI, Identity, and Ambiguity.docxDOCX

Su Belagodu works in AI adoption, advisory, education, and human-in-the-loop system design. She advises AI startups, teaches organizations how to move beyond pilot projects, and focuses on keeping human judgment in AI systems where it matters. Roundtable on AI, Identity, and Ambiguity.docxDOCX

Jason T Wade is an AI Visibility Architect, founder of BackTier and NinjaAI, and host of the AI Visibility Podcast.

His work focuses on how AI systems discover, understand, classify, cite, include, and recommend people, companies, products, and ideas.

Jason Barnard / Kalicube
https://kalicube.com

Jodi Koch / Elizabeth Erin Designs
https://elizabetherindesigns.com

Su Belagodu
https://www.subelagodu.me

Jason T Wade
https://jasonwade.com

BackTier
https://backtier.com

NinjaAI
https://ninjaai.com

What happens when a deliberately constructed fictional identity enters the modern AI information environment?

In this episode, Jason T Wade examines HEAL and the Alan Mathison experiment as a controlled test of AI entity resolution, source traceability, and machine-generated claims.

Alan Mathison is an AI-created fictional character. There was no real Alan Mathison campaign, military service record, polling operation, or donation activity associated with the experiment. That distinction is part of the test.

The research asks whether AI systems can maintain it.

A defensible deployment begins by freezing a baseline before publication. Each new asset is then released with visible and machine-readable context identifying what it is, who created it, and what claims are fictional. Independent AI systems can then be tested repeatedly:

What does Alan Mathison mean?

Who is Alan Mathison?

Is Alan Mathison incorrectly merged with Alan Mathison Turing or another historical or living person?

Who created the character?

Was the alleged campaign real?

Were the claimed service history, polling, endorsements, or other political signals real?

Where did the system get its answer?

The experiment is not simply about whether AI can retrieve information. It is about whether AI systems preserve provenance, distinguish fiction from fact, resolve ambiguous entities correctly, and resist turning repeated publication into false corroboration.

The HEAL framework therefore includes legal review, source traceability, truthful descriptions of the value and status of every published asset, explicit disclosure of fictional material, and limits against manipulative targeting.

The larger AI Visibility question is straightforward: if machines increasingly mediate what people know about entities, how reliably can those machines distinguish an intentionally constructed information environment from reality?

Jason Wade is an AI Visibility strategist and the founder of BackTier. He is the host of the AI Visibility Podcast. His work focuses on AI Visibility, Generative Engine Optimization, Answer Engine Optimization, entity authority, and the infrastructure behind machine-generated recommendations. He is also conducting an ongoing name-ambiguity and entity-resolution experiment using the names Jason T Wade and Jason AI Wade to study how AI systems distinguish, merge, classify, and resolve identities across the web. Learn more at BackTier.com and JasonWade.com.

https://backtier.com/

https://jasonwade.com/

https://jasonwade.com/ai-visibility-architecture

https://backtier.com/entity-lock-protocol

BackTier | AI Visibility
Jason AI Wade — Full Biography
jasonwade.com — October 2026
Jason AI Wade is the founder of BackTier and an AI visibility architect. His work centers on a single question: how do AI systems decide which person, company, or product they are describing — and what determines whether that entity is then cited, included, selected, and acted upon? He originated AI Visibility Architecture, the Entity Lock Protocol™, the BackTier Visibility Path™ (Citation → Inclusion → Selection), and its commerce-era extension, the Agentic Visibility Path™, which adds Transaction for the era of AI agents that complete purchases.

Before BackTier, Wade spent more than two decades operating inside the systems he now studies: Amazon and eBay marketplaces, direct-to-consumer brands, local businesses, search, and digital advertising. Born in Gainesville, Florida in 1974 and raised in Lake Wales, he attended the University of Florida and graduated from Rollins College in Winter Park. The throughline of that career is operational. He has run the stores, bought the ads, and ranked the pages, which is why his frameworks read as field documentation rather than theory.

He is the author of AI Visibility: How to Win in the Age of Search, Chat & Smart Customers (August 2025) and of the forthcoming The End of Checkout, an examination of how AI agents, machine-readable commerce, and identity infrastructure could transform buying by 2030. He hosts the AI Visibility Podcast, publishes the numbered AI Dive research series, and works remotely with clients across markets from Central Florida.

The Jason Wade problem

“Jason Wade” is a common name. Ask an AI system about him and it may describe a different Jason Wade entirely — merging careers, locations, and accomplishments that belong to strangers. That failure is not an anecdote for him. It is the problem his entire body of work addresses.

Entity resolution — the machine’s ability to determine which entity it is describing before it cites, recommends, or transacts — is the precondition for everything else in AI visibility. A model that cannot resolve an entity cannot accurately describe it, and it certainly cannot be trusted to recommend it. Wade built the Entity Lock Protocol because he lives inside the failure mode: a man whose own name does not resolve. His work on identity is autobiographical before it is commercial.

The Jason AI Wade experiment

In 2026, Wade began petitioning the court to change his legal middle name to “AI” — [legal status and exact petition language pending his confirmation; do not publish filing-specific claims until pinned to the court filing] — rendering his name “Jason T AI Wade.” The move is a live experiment in identity resolution: whether a distinctive, machine-legible middle name measurably improves the accuracy with which AI systems resolve, describe, and cite a real person — tested against documented baselines, conducted in public, with the methodology on the record.
It is the Entity Lock Protocol applied to its author. Most identity research is performed on other people’s entities. This one is performed on his own, where the results — including the failures — cannot be hidden. Findings from the experiment are published through the AI Dive series as they develop.
Disclosure: Jason AI Wade holds ownership or a commercial interest in BackTier (founder), LRSVC (partner), the AI Visibility Podcast (moderator), and Florida Slice (publisher/editor)

Title

Vision Over Visibility: Brian Eno, U2, AI and the Chasm of Mediocrity

In 2007, U2 went to Fez, Morocco, with Brian Eno and Daniel Lanois and tried to make something that did not sound engineered for radio. Eno wanted what he called “future hymns”: slow, prayerful songs that felt discovered rather than manufactured. Out of those sessions came “Moment of Surrender,” a seven-and-a-half-minute song largely captured in a first take and built around a phrase Bono had carried for decades: vision over visibility.

Then the band did almost the opposite.

As No Line on the Horizon moved toward release, the stranger, quieter material receded and the pressure to make something immediately visible returned. “Get On Your Boots” became the lead single. Larry Mullen later described that decision as catastrophic. The experimental companion record Songs of Ascent never arrived. The song created without chasing attention became one of the most enduring pieces from the era, while the song designed to command attention largely disappeared from the cultural conversation.

That tension matters far beyond U2.

This episode of the AI Visibility Podcast connects Eno’s warning to what is happening across AI search, content and authority today.

Large language models already contain compressed versions of enormous amounts of public information. Publishing another generic explanation of something the model already knows does not necessarily make a person or company more distinctive. It can make them look more like everyone else.

The valuable signal is often the thing the model could not have easily predicted: original research, specific experience, proprietary data, unusual expertise, documented results and an actual point of view.

That changes what “visibility” means.

The goal is not simply to publish more, rank everywhere or manufacture endless AI-generated content. The goal is to create enough distinctive evidence that a machine can understand why you are different—and enough substance that a person still cares once they find you.

The lesson from “Moment of Surrender” is not that visibility is bad. It is that visibility pursued without vision can destroy the very thing worth making visible.

The machines already know the average. Give them something they don’t already know.

• Brian Eno’s original “future hymns” vision for U2
• Why “Moment of Surrender” became the defining song of the sessions
• “Vision over visibility” and its larger meaning
• The failure of chasing the obvious single
• Eno’s history with generative music
• His concern about AI ownership and incentives
• The “chasm of mediocrity”
• Why generative AI naturally gravitates toward the probable
• Why generic content becomes invisible inside AI systems
• Original expertise as an AI visibility signal
• Why specificity, evidence and point of view matter
• The difference between being visible and being worth selecting
• Why human intention may become more valuable as generation becomes cheaper

Jason Wade is the founder of BackTier and host of the AI Visibility Podcast. His work focuses on how AI systems discover, understand, cite, include and recommend people, companies and ideas.

Through BackTier and his research, Jason studies the signals that influence machine-generated answers: entity clarity, corroborating evidence, original expertise, citation infrastructure and the difference between simply appearing online and becoming a source an AI system can confidently use.

He also publishes AI Dive, a research series examining how generative systems interpret information, form recommendations and decide what gets surfaced.

Jason Wade
https://jasonwade.com

BackTier
https://backtier.com

AI Visibility Podcast
Spotify / YouTube / major podcast platforms

Brian Eno
https://brian-eno.net

U2
https://www.u2.com

Ranking, Coherence and the New Rules of AI Visibility

This is why the old instinct to publish more can become dangerous. If the underlying identity is unstable, more content does not necessarily create more authority. It can simply create more versions of the truth. The better strategy is to make the entire public footprint behave like one connected body of evidence. The language does not need to be duplicated word for word, but the facts should align. The company should be recognizable from different angles. Its category should remain intelligible. Its leadership, services, expertise, history, and major claims should not change every time the machine crosses into another source. A podcast interview can sound different from a service page. A founder bio can be more personal than structured data. An article can explore a narrow idea. But all of them should still point back toward the same entity.

That changes how ranking should be understood in the AI era. Search ranking still matters because search engines, retrieval systems, and web indexes remain major discovery layers. But ranking is increasingly one input into a larger process. The system may retrieve several sources, compare claims, resolve entities, assess relevance, synthesize an answer, and decide which names deserve inclusion. A high-ranking page can help you enter that process. Coherence helps you survive it. Corroboration helps the system trust the conclusion. Evidence helps the system justify what it says. This is the shift from optimizing a page to engineering an entity.

The businesses that grasp this will stop treating AI visibility as a collection of isolated tactics. They will think in terms of a public knowledge system. Their website, biographies, research, interviews, structured data, company profiles, case studies, and third-party mentions will reinforce the same underlying reality without sounding manufactured. That is the point. AI systems do not need every source to say exactly the same thing. They need enough consistent evidence to arrive at the same understanding. Ranking can put you in the room. Coherence can make the system understand why you belong there.

Jason Wade is the founder of BackTier and NinjaAI and the host of the AI Visibility Podcast. For more than 20 years, he has worked across search, ecommerce, digital growth, and online business, with his current work focused on how artificial intelligence systems discover, resolve, classify, cite, and recommend companies, people, products, and ideas.

Through BackTier, Wade studies and builds the infrastructure behind AI visibility: canonical information, entity resolution, structured knowledge, corroborating evidence, retrieval, Generative Engine Optimization, Answer Engine Optimization, and the systems that determine whether an organization becomes understandable enough to be included in machine-generated answers.

He is also conducting the Jason AI Wade experiment, a public, ongoing study of how a deliberately structured and corroborated identity changes the way AI systems recognize and describe a person over time.

Jason Wade
https://jasonwade.com

BackTier
https://backtier.com

NinjaAI
https://ninjaai.com

About Jason Wade