What actually makes ChatGPT or Google AI recommend one local business instead of another?

Andrew Shotland, founder and CEO of Local SEO Guide, joins Jason AI Wade to break down what his team is testing right now across local SEO, AI visibility, citations, service pages, and multi-location businesses.

Andrew’s core point is simple: the fundamentals of SEO still matter. A machine has to understand what your business does before it can recommend you. The difference now is that AI systems are assembling that understanding from more pieces of evidence and using it inside conversational answers.

That changes how businesses should think about visibility.

It is not enough for a plumber to say, “We’re a plumber.”

If the business wants to be recommended for water heater replacement, emergency service, or storm drains, those specific services need to appear clearly across the company’s website and the third-party sources AI may use to corroborate them.

Andrew describes those external citations almost like additional sides of a die: the more relevant sources that connect a business with the right location, service, and attributes, the more opportunities an AI system has to encounter the same association.

Then he shares a real test.

His team analyzed search data across a 160-location company and found a missing concept around construction job-site bathrooms and toilets. They added that language to the Google Business Profiles and local citation network for 20 locations.

Within days, visibility changed in Google AI Mode.

Later, the test locations began improving in ChatGPT while the untouched locations initially remained flat.

Roughly a month later, visibility began increasing across the wider network as well.

Andrew’s working theory: enough pages had connected the brand with the concept that AI systems began applying that relationship more broadly across the entity.

The conversation also gets into competitor-focused content, local search strategy, agentic SEO, automation, content-gap analysis, and what Andrew calls the “infinite conversational paths” created by AI search.

The practical lesson is not to wait for a perfect GEO playbook.

Test something.

Measure whether visibility changes.

Then apply what works.

Andrew compares the process to baseball: even a great hitter misses most of the time. The advantage comes from taking enough intelligent swings.

Andrew Shotland
Founder and CEO of Local SEO Guide.

Andrew has worked in search for roughly two decades and now focuses heavily on local SEO, AEO, multi-location businesses, and how AI systems understand and recommend companies.

He describes the current period as one of the most interesting moments he has experienced in search.

AI recommendations for local businesses
Local SEO and GEO
Google AI Mode
ChatGPT visibility
Local citations
Entity corroboration
Service-page strategy
Content gaps
Competitor alternatives
Conversational search
Agentic SEO
Multi-location testing
AI visibility experiments

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

Andrew Shotland on LinkedIn
Andrew identifies LinkedIn as his main social platform and where he posts most often.

AI Visibility Podcast
BackTier

Why Consistent Anchor Information Matters in AI Visibility

One of the most overlooked problems in AI visibility is consistency.

Companies tend to think about their website, LinkedIn page, directory listings, biographies, press mentions, podcasts, and third-party profiles as separate marketing assets. AI systems do not necessarily experience them that way. They encounter all of these sources as evidence about the same entity.

That creates a simple but important problem: if the basic facts keep changing depending on where the system looks, confidence in the entity can weaken.

I call these basic facts anchor information.

Anchor information is the relatively stable information that helps a machine determine who or what an entity is. For a company, that includes the company name, founders, location, primary category, core services, major products, important dates, leadership, and the language used to describe what the business actually does.

For a person, it may include name, professional role, company relationships, areas of expertise, location at an appropriate level, published work, and other persistent associations.

None of this sounds particularly sophisticated. That is exactly why companies underestimate it.

The problem begins when the same organization describes itself five different ways across five different sources. One profile says the company is an SEO agency. Another describes it as an AI consultancy. The website calls it an AI visibility company. An older directory calls it a digital marketing agency. The founder's biography uses yet another category.

A human can usually reconcile those descriptions.

A machine has to decide whether they represent complementary facts, historical changes, marketing language, or genuinely different classifications.

That uncertainty becomes more important as AI systems move beyond finding pages and begin comparing entities, selecting sources, recommending companies, and answering questions directly.

The objective is not to make every page identical. That would be unnatural and unnecessary.

It is an information architecture problem.

Before an AI system can cite a business, recommend it, or associate it with a particular area of expertise, the system first has to determine what the entity is and what facts about it can be trusted.

That starts with anchors.

A company should be able to answer a basic question: if an AI system encountered our website, founder biography, LinkedIn profile, podcast appearances, business directories, press coverage, and third-party references, would those sources collectively describe the same organization?

His central thesis is straightforward: before a company can influence how AI describes it, the company first has to make the truth about itself clear enough for machines to understand.

Jason Wade
https://jasonwade.com

BackTier
https://backtier.com

NinjaAI
https://ninjaai.com

The Democratization of AI Agents

When Everyone Can Build a Bot That Actually Does Something

For years, building software that could automate business processes required programmers, expensive integrations, and technical expertise.

Now AI agents are making automation accessible to people who have never written a line of code.

A small business owner can describe a task in plain English and increasingly use AI tools to build an agent that researches information, handles customer inquiries, updates records, or coordinates work across multiple systems.

What once required a development team may now be possible with a subscription and some experimentation.

The next AI revolution isn't just about everyone having access to intelligence. It's about everyone having access to automated labor.

• From Chatbots to Agents: Why answering questions and taking actions are fundamentally different capabilities.

• The No-Code Revolution: How natural-language tools are lowering the technical barriers to building AI agents.

• Bots for Everyone: Why small businesses, independent professionals, and entrepreneurs can now experiment with automation previously reserved for larger organizations.

• The One-Person Company: How agents can help individuals operate workflows that once required multiple employees or contractors.

• Agents Working Together: How specialized agents can delegate tasks, exchange information, and coordinate across business systems.

• The Agent Marketplace: What happens when businesses can buy, customize, or deploy agents instead of developing every automation themselves.

• The Reliability Problem: Why agents can still make mistakes, misunderstand instructions, and fail during multistep tasks.

• The Security Question: What happens when an inexpensive bot has access to customer records, financial systems, or company communications?

• The Agent Economy: How automated software may change service businesses, employment, pricing, and competition.

• The AI Visibility Connection: Why companies may eventually need to serve both human customers and the AI agents acting on their behalf.

The internet democratized publishing.

Cloud software made powerful business applications accessible through subscriptions.

Generative AI made sophisticated writing, coding, and analysis available through conversational interfaces.

AI agents extend that progression from generating information to executing work.

But access doesn't guarantee competence.

An agent that can operate software still needs accurate instructions, appropriate permissions, reliable tools, monitoring, and clear boundaries.

The businesses that benefit most may not be those deploying the greatest number of agents, but those identifying valuable workflows and making automation dependable.

AI is lowering the barrier between having an idea for automation and actually deploying it.

That creates opportunities for small businesses and independent operators, but it also introduces new risks when automated systems act without sufficient oversight.

The competitive advantage will increasingly come from knowing what to automate, how to connect systems, and when human judgment is still required.

The internet gave everyone a voice. AI agents are beginning to give everyone a workforce.

Presented by BackTier | NinjaAI.com

Topics: AI Agents, Democratization of AI, Agentic AI, No-Code Automation, AI Bots, Autonomous Agents, Small Business AI, AI Workforce, Multi-Agent Systems, AI Economy, AI Visibility.

What We'll CoverThe Bigger PictureKey Takeaway

You Didn't Write That Email. Neither Did They. Welcome to AI Business Communication.

You receive a polished business email. It's professional, carefully worded, and perfectly structured. But there's a good chance the person who sent it didn't actually write it.

And there's a reasonable chance you'll use AI to write your response.

In this episode of the AI Visibility Podcast, Jason A.I. Wade examines the increasingly strange reality of business communication in the age of artificial intelligence.

Executives, attorneys, entrepreneurs, and professionals are using AI to draft emails, negotiate language, respond to complaints, and communicate with customers. The result is an expanding volume of correspondence that may be technically accurate and professionally written but reveals surprisingly little about what the sender actually thinks.

What happens when one person's AI writes an email, another person's AI summarizes it, and a third AI generates the response?

Are we communicating more effectively, or simply automating the appearance of communication?

The episode explores the difference between assistance and authorship, the risks of manufactured professionalism, and why accountability still belongs to the person whose name appears at the bottom of the message.

The central question: If neither person wrote the email, and neither person reads the reply, who exactly is having the conversation?

Jason A.I. Wade is an AI Visibility architect based in Orlando, Florida, and founder of BackTier, which implements artificial intelligence for law firms.

He helps businesses get found, cited, and recommended by AI systems through NinjaAI.com, AI SEO, Generative Engine Optimization (GEO), and Answer Engine Optimization (AEO).

Jason hosts the AI Visibility Podcast, examining artificial intelligence, business communication, digital identity, and the changing relationship between human judgment and machine-generated information.

NinjaAI: https://ninjaai.com
BackTier: https://backtier.com
Jason Wade: https://jasonwade.com
Lisa Latte Effect: https://lisalatte.lovable.app

The AI Visibility Podcast is produced by BackTier Media, exploring artificial intelligence, business, technology, and the increasingly blurred boundaries between human expertise and machine-generated communication.

The irony: This episode's show notes were written with artificial intelligence.

Dan McGaw: The $15,000 AI Bill, AI Agents, and Why Marketing Attribution Is Breaking

AI Visibility Podcast | Jason AI Wade with Dan McGaw

AI is no longer just a chatbot. For companies operating at the cutting edge of marketing technology, it's becoming the infrastructure behind everyday business operations.

Dan McGaw, founder of McGaw.io and UTM.io, joins Jason AI Wade from Barcelona for a conversation about what happens when AI agents begin running workflows, marketing attribution becomes less reliable, and businesses must rethink how they use artificial intelligence.

Dan shares what he's learning from implementing AI inside his own companies, including a $15,000 monthly token bill, a major reduction in AI usage through smarter architecture, and the growing difficulty of tracking customers who discover businesses through AI.

One of Dan's most significant observations is that AI chat is becoming the new homepage of a business.

People increasingly interact with AI systems before visiting a website, contacting a company, or making a buying decision.

That creates a new challenge for marketing: businesses need to be discoverable and understandable to AI systems while also finding ways to measure the resulting demand.

Dan explains how ChatGPT, Claude, and Perplexity handle referral information differently, making conventional analytics less dependable for tracking AI-originated traffic.

The conversation connects traditional marketing attribution with the emerging world of AI Visibility, Generative Engine Optimization (GEO), and Answer Engine Optimization (AEO).

Dan describes how dramatically his workflow has changed.

Instead of spending most of his time using AI chat interfaces, he now spends approximately four to five hours a day managing AI agents.

His team uses Claude, Codex, Notion AI, shared skills, and automated workflows to execute tasks that previously required considerably more manual work.

Dan argues that marketing technologists already possess an important advantage in this transition: they understand integrations, data systems, automation, and workflow architecture.

AI makes those capabilities substantially faster to deploy.

Building an AI-powered operation comes with costs.

Dan reveals that his company incurred approximately $15,000 in AI token charges in one month.

Rather than simply paying for more computing capacity, his team redesigned its agent architecture to route different tasks to appropriate models.

Small tasks go to smaller, less expensive models. Complex tasks receive more capable models.

Dan reports that this approach reduced usage by approximately 70%, demonstrating how AI orchestration can improve efficiency without sacrificing the quality required for more demanding work.

Dan offers a warning about using language models to analyze business data.

The distinction between an AI-generated answer and a verified result is becoming increasingly important as businesses automate decisions.

The conversation closes with a look back at Orlando's startup community, StarterStudio, and Dan's earlier work supporting entrepreneurs.

Jason recalls Dan's approach to creating authoritative content: find the best existing resource on a subject and create something substantially better.

Dan agrees that the principle remains relevant.

Even as AI changes how information is discovered and delivered, genuinely differentiated content continues to provide value.

Dan McGaw is a marketing technology entrepreneur, author, and founder of McGaw.io and UTM.io. His background includes leadership roles at Kissmetrics and Code School and extensive experience in marketing analytics, attribution, growth engineering, and business automation.

Based in Barcelona, Dan continues to work on marketing technology, AI-powered operations, and the evolving infrastructure behind customer acquisition.

Connect with Dan

McGaw.io: https://mcgaw.io

UTM.io: https://utm.io

Find Dan McGaw on LinkedIn, TikTok, Instagram, and YouTube.

AI Visibility: Political Day After — 19-to-1, $0.21 Per Vote, and the Power of Visibility

Host: Jason AI Wade | AI Visibility Podcast | BackTier

What happens when a political challenger raises just one-nineteenth of the incumbent's campaign contributions and finishes within 2,282 votes of winning?

In this election-day follow-up, Jason AI Wade discusses his behind-the-scenes involvement in Alethea Pugh's 2026 campaign for Florida House District 48 in Polk County, Florida.

The incumbent, Republican Jon Albert, received 51.71% of the vote, while Democratic challenger Alethea Pugh received 48.29%, according to unofficial election-night results.

The financial disparity was substantial. An October 13 report by The Ledger identified $132,615 in contributions to Albert compared with $6,894 to Pugh—a ratio of approximately 19.2 to 1.

That translates to approximately $3.85 raised per vote for Albert versus $0.21 for Pugh, using those contribution snapshots and unofficial vote totals.

Jason discusses his involvement in campaign research, media outreach planning, messaging, visibility, and the challenge of helping a candidate with limited financial resources and a publicly scrutinized past.

The episode also explores how artificial intelligence, strategic communications, local relationships, and organized outreach might change the economics of political campaigns.

• 19-to-1 fundraising disparity: How two campaigns with dramatically different financial resources finished just 3.42 percentage points apart.

• The $0.21-per-vote calculation: What contribution dollars per vote reveal—and why they are not the same as actual campaign expenditures per vote.

• AI and political visibility: The potential applications of AI-assisted research, media intelligence, candidate discoverability, and communications systems.

• Media outreach and message control: Jason's experience helping organize research, outreach, and campaign communications.

• Reputation and accountability: Addressing documented historical controversies through factual communication rather than evasion.

• Political conditions: How a favorable broader election environment may have influenced the district result.

• Campaign consulting: Lessons Jason intends to apply to future political research and visibility engagements.

Election margin: 2,282 votes, or approximately 3.42 percentage points.

Important distinction: The fundraising numbers represent contributions reported as of October 13, not verified final expenditures. The election results do not establish that any specific consulting activity caused the outcome.

Jason AI Wade is the founder of BackTier, an AI visibility architect, author of AI Visibility: How to Win in the Age of Search, Chat & Smart Customers, and host of the AI Visibility Podcast.

His work focuses on AI Visibility, Generative Engine Optimization (GEO), Answer Engine Optimization (AEO), entity resolution, machine understanding, and strategies that help organizations become more discoverable, credible, and accurately represented across AI-powered systems.

His work also includes research, communications strategy, and AI-assisted organizational systems.

Election and reporting

• Florida Division of Elections — https://electionwatchfl.com/

• The Ledger's October 13 candidate profile — https://www.theledger.com/story/news/politics/elections/2026/10/13/jon-albert-faces-alethea-pugh-florida-house-48-election-polk-county/92169249007/

• OpenSecrets — https://www.opensecrets.org/

Jason AI Wade

• Website: https://JasonWade.com

• BackTier: https://BackTier.com

• Email: email@jasonwade.com

A 19-to-1 fundraising disparity and a 3.42-point election margin create an important case for studying political communications, resource allocation, voter engagement, and campaign economics.

30 Years of the Web: From Googlebot to AI Agents

Who Is Reading Your Website Now—and What Are They Doing With It?

For roughly 30 years, businesses have built websites primarily for two audiences: human visitors and search engine crawlers.

Googlebot visited pages, followed links, indexed content, and helped determine what appeared in search results. Website owners used robots.txt, XML sitemaps, metadata, and SEO to influence how their content was discovered and processed.

The arrangement was relatively straightforward.

But the web is changing.

Today, websites may be accessed by traditional search crawlers, AI training crawlers, AI search retrieval systems, automated browsers, and increasingly autonomous agents operating on behalf of users.

Some are collecting information. Others are retrieving answers. Some are navigating interfaces, filling forms, comparing services, or attempting to complete tasks.

The website is no longer just a destination. It's becoming an information source and an operating environment for machines.

In this episode of POD, we examine how web crawling evolved, what AI crawlers and agents actually do, and why businesses need to rethink how their websites are discovered, understood, and used.

• 30 Years of Web Crawling: From early search engines and Googlebot to modern AI retrieval and autonomous agents.

• How Googlebot Works: Crawling, rendering, indexing, and the relationship between search engines and websites.

• Robots.txt: What the Robots Exclusion Protocol does, what it doesn't do, and why it isn't a security mechanism.

• The AI Crawler Explosion: How AI companies use different crawlers for training, search, retrieval, and other purposes.

• Grok and xAI: How Grok-related systems fit into the changing ecosystem of AI discovery.

• AI Browsers and Superagents: Why automated systems increasingly interact with websites rather than simply reading pages.

• The New Website Builders: How AI-powered development platforms such as Base44 change the speed and accessibility of website creation.

• Agent-Ready Websites: What businesses need to consider when software becomes a website visitor and potential customer representative.

• Who Controls Access? The emerging tension between open discovery, content licensing, crawler permissions, and machine access.

Traditional crawlers primarily discover and collect web content.

AI retrieval systems may access content to answer a specific question.

AI agents go further. Depending on their tools and permissions, they may navigate websites, interact with forms, compare products, schedule services, or initiate transactions.

These systems should not all be treated as interchangeable.

A crawler that gathers content for model training has a different purpose from an agent acting on a customer's explicit instructions.

And a website that allows crawling does not automatically authorize every possible automated action.

For decades, robots.txt has provided a standardized way for website operators to communicate crawling preferences.

But robots.txt is not authentication, access control, or a guarantee that every automated system will comply.

Modern website operators need to distinguish between search indexing, AI training, live retrieval, and interactive agent access.

Blocking everything may reduce unwanted collection but can also interfere with legitimate discovery.

Allowing everything may improve accessibility while increasing exposure to unwanted automated activity.

The right policy depends on the business, the content, and the purpose of the automated visitor.

Businesses increasingly need websites that are:

• Discoverable: Important information can be found by appropriate crawlers and retrieval systems.

• Intelligible: Services, products, identities, and relationships are understandable to machines.

• Accessible: Pages and interactions use reliable, standards-based interfaces.

• Actionable: Authorized agents can complete supported tasks.

AI for Everyone: Building, Creating, and Learning with AI

AI Visibility Podcast | November 3, 2026
Host: Jason AI Wade | Presented by: BackTier

Artificial intelligence is making it easier than ever to build businesses, develop software, create content, and turn ideas into reality. But does easier technology automatically produce better results?

Jason AI Wade welcomes Mike Turner and David Raine for a free-flowing conversation about entrepreneurship, creativity, AI tools, robotics, and the importance of actually building things rather than just talking about them.

Mike Turner — Founder, AlamoTech

Marine Corps veteran, technology entrepreneur, and fractional CTO helping small businesses adopt AI, improve IT infrastructure, and develop practical technology solutions. Mike also hosts I'm Up! He Sees Me, I'm Down!, a podcast about leadership, resilience, and personal growth.

Website: https://alamotechllc.com

David Raine — Software Developer, Robotics Developer, and Author

Software developer and science-fiction author exploring AI, robotics, automation, and digital creativity. David discusses MIM, his experimental learning-oriented AI project, and how emerging technology is changing what individuals can create.

• AI for small businesses: How AI makes sophisticated software and automation accessible to smaller organizations.

• Just build it: Why experimenting, publishing, and learning matter more than perfection.

• AI and creativity: Music generation, writing, video, and whether technology changes the meaning of artistic work.

• Authenticity in the AI era: Voice cloning, synthetic media, and preserving genuine human connections.

• AI meets robotics: The challenges of connecting intelligent software with physical machines.

• The creator economy: Podcasting, audience growth, social media metrics, and creating for the right reasons.

ChatGPT, ElevenLabs, Descript, Riverside, Suno, Google Analytics, AI avatars, and emerging robotics technologies.

AI is expanding what individuals and small businesses can accomplish, but the technology alone doesn't create success. Experimentation, persistence, sound judgment, and the willingness to put real work into the world still matter.

Jason AI Wade is the founder of BackTier, an AI visibility architect, author of AI Visibility: How to Win in the Age of Search, Chat & Smart Customers, and host of the AI Visibility Podcast. His work explores how AI systems discover, understand, cite, and recommend people and businesses.

JasonWade.com: https://jasonwade.com
BackTier: https://backtier.com

AI Visibility Podcast | AI for Everyone — November 2026

Meet the GuestsIn This EpisodeTools DiscussedKey TakeawayAbout the Host