AI Visibility: How to Get Your Brand Recommended by ChatGPT, Perplexity, and Google AI

Field Sales Is Broken — Will Hamblin on AI, Territory Intelligence and FieldSpot 19.09.2026 8min

Will Hamblin went from vice principal to door-to-door card terminal sales, then built the software he wished he had while working in the field.In this episode of the AI Visibility Podcast, Jason T Wade talks with Will about the problems traditional CRM systems miss when sales happens physically rather than behind a desk.Will explains how FieldSpot.ai grew from a simple vibe-coded prototype into a field-sales platform built around territory intelligence, renewal timing, route planning, competitor tracking, voice notes, business-card capture, AI-assisted outreach, and real-world context.A major theme of the conversation is that field sales generates valuable data constantly, but most of it disappears. A rejection today may actually contain the most important information for a future sale: who the current provider is, when the contract expires, and when the salesperson should return.They also discuss why AI output depends heavily on the quality of the underlying data, how FieldSpot uses agent notes to make outreach more personal, and why face-to-face sales may become more valuable as inboxes become saturated with automated AI outreach.Will also shares how he built the first prototype without being a developer, found technical partners willing to work for equity, and began expanding FieldSpot beyond its original UK payments market.Topics include:Why traditional CRMs do not fit field salesTurning rejected visits into useful sales intelligenceRenewal tracking and competitor contract dataTerritory mapping and route planningVoice notes and automatic data captureAI-assisted personalized outreachBuilding the first FieldSpot prototype through vibe codingWhy better data produces better AI outputThe possible resurgence of face-to-face salesDesigning software around real field conditionsBuilding a startup without being the technical founderExpanding FieldSpot internationallyThe future of AI-powered field salesWill Hamblin is the founder of FieldSpot.ai, an AI-powered CRM and field-sales intelligence platform designed for teams that sell in person.Before founding FieldSpot, Will spent twelve years in education, eventually becoming a vice principal. He later moved into field sales, selling card-payment services directly to businesses.That experience exposed a gap in traditional sales software: field agents were still relying heavily on spreadsheets, notebooks, memory, and manual route planning while valuable information about competitors, customer conversations, and renewal dates was frequently lost.Will built the first FieldSpot prototype using AI tools before bringing in experienced developers to turn the concept into a production platform.FieldSpot is designed around territory mapping, renewal intelligence, competitor tracking, route planning, mobile data capture, and AI-assisted sales workflows.Jason T Wade is an AI Visibility Architect and founder of NinjaAI and BackTier.His work focuses on how AI systems discover, understand, classify, cite, include, and recommend people, companies, products, and ideas.He works across AI SEO, Generative Engine Optimization, Answer Engine Optimization, entity architecture, structured data, and AI discovery systems.Jason is also the host of the AI Visibility Podcast, where he explores how AI search, recommendation systems, autonomous agents, and emerging interfaces are changing discovery, business, and the web.FieldSpot.aihttps://fieldspot.aiWill Hamblin on LinkedInhttps://www.linkedin.com/in/will-hamblin-182a1064/Jason T Wadehttps://jasonwade.comNinjaAIhttps://ninjaai.comBackTierhttps://backtier.comAbout Will HamblinAbout Jason T WadeLinks

Digital Marketing -- Early adopters -- Bubbles - and AI marketing 19.09.2026 1min

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

The Jason AI Wade Experiment: Can You Deliberately Change How AI Understands a Person? 19.09.2026 5min

I changed my name on the internet.Not legally. I changed the public identity I present to the web from Jason T Wade to Jason AI Wade, and I'm using the change as a live AI Visibility experiment.The question is bigger than a rebrand: Can a person deliberately change how AI systems identify, classify, cite, include, and eventually recommend them?For more than 20 years, we optimized digital identities primarily for humans and search engines. Generative AI adds another observer. ChatGPT, Gemini, Claude, Perplexity, and other systems now have to resolve people and organizations from scattered evidence, determine relationships between entities, evaluate competing claims, retrieve sources, and decide which entities belong in an answer.I'm deliberately changing that evidence environment and documenting what happens.The experiment follows five stages:Recognition → Classification → Citation → Inclusion → SelectionRecognition asks whether an AI system knows Jason AI Wade exists. Classification tests whether it understands who I am and what I actually do. Citation measures whether my work becomes evidence supporting answers. Inclusion asks whether I appear when the prompt does not already contain my name. Selection is the hardest test: when an AI system has several plausible people or sources available, does it choose me?The distinction matters because asking ChatGPT, “Who is Jason AI Wade?” is an easy test. The entity has already been supplied. Asking an AI system who created a particular framework, who researches AI Visibility, or which sources it should use to understand machine-mediated discovery forces it to retrieve and select entities independently.Over the coming weeks and months, I'll document changes to the public information environment around Jason AI Wade — canonical identity, structured data, author entities, terminology, publications, podcast metadata, company relationships, citations, external references, and independent corroboration — and compare those interventions with what different AI systems actually return.Some systems will probably recognize the change quickly. Others may continue using Jason T Wade. Some may incorrectly create two people. Others may resolve the identity correctly while attaching outdated professional information. Those failures are part of the experiment because they expose where retrieval, entity resolution, classification, citation, and selection diverge.The larger hypothesis is that every person and company now effectively has two identities: the identity they say they have and the identity machines reconstruct from available evidence.AI Visibility exists partly in the gap between them.Jason AI Wade is the test subject.Now we see what the machines do with him.Jason AI Wade is an AI Visibility architect, researcher, author, and founder of BackTier. His work focuses on how AI systems discover and resolve entities, interpret evidence, retrieve and cite sources, construct recommendations, and make decisions.Drawing on more than 20 years across search, ecommerce, marketplaces, publishing, and digital growth, Wade studies the transition from traditional search ranking toward machine-mediated discovery and selection. He is the creator of the Entity Lock Protocol™ and BackTier Visibility Path™, and host of the AI Visibility Podcast.His current research examines Generative Engine Optimization (GEO), Answer Engine Optimization (AEO), entity resolution, structured data, machine-readable authority, and the infrastructure determining which people, companies, and sources AI systems understand, cite, include, select, and recommend.Jason AI Wade — Research & Writinghttps://jasonwade.comBackTier — AI Visibility Architecture & Implementationhttps://backtier.comNinjaAI — AI SEO, GEO & AEOhttps://ninjaai.comAI Visibility PodcastSearch “AI Visibility Podcast” on Spotify and major podcast platforms.BioLinks

I Tried to Explain AI. I Got It Wrong. So I Learned How It Actually Works. 18.09.2026 2min

What actually happens inside AI?After asking a podcast guest to explain AI—and then realizing my own explanation wasn't quite right—I went back to the basics.In this short episode, I break down AI in plain English: training data, data preparation, model weights, prediction, error, and how a trained model generates an answer from a prompt.I also look at where concepts like ontologies, relationships, probabilistic outputs, and modern AI search fit—and where they don't.No Stanford degree required. I do, however, own the shirt.Why saying “AI is data” doesn't tell the whole storyHow training data is cleaned and preparedWhat model weights actually arePrediction → error → weight adjustment → repeatHow models learn statistical patterns at scaleTraining versus inferenceWhat an ontology actually describesWhy LLMs are probabilisticHow AI search differs from traditional searchWhy modern systems can understand much longer, messier questionsJason T Wade is the founder of BackTier and NinjaAI and host of the AI Visibility Podcast. His work focuses on AI Visibility—how AI systems discover, understand, cite, include, and recommend entities.BackTier — AI Visibility strategy and systemsNinjaAI — AI SEO, GEO, and AEOOpenAI — AI research and modelsStanford HAI — Stanford Institute for Human-Centered Artificial IntelligenceIn this episodeAbout Jason T WadeRelevant Links

Jason T ai Wade - Hating AI and tech Revolution and managing transformation - models and agents 18.09.2026 1min

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

Life transitions, goals and affluent clients - AI - BackTier 18.09.2026 12min

Life transitions, goals and affluent clients - AI - BackTier

why press mentions matter in ai engines 18.09.2026 1min

why press mentions matter in ai engines

The Agent Class: Grok Bot, Base44 Superagents, Muse, and the Democratization of Über-Intelligent AI 17.09.2026 17min

AI is moving from answers to action. The old chatbot model was simple: ask a question, get a response, copy the answer, do the work yourself. The new agent model is different. Grok Bot, Base44 Superagents, Meta’s Muse, and similar systems are turning AI into persistent digital labor: agents that remember context, operate across tools, execute workflows, and begin to behave less like software features and more like always-available teammates.In this episode, Jason T Wade examines the democratization of agentic AI: what happens when ordinary operators, founders, creators, sales teams, and small businesses gain access to systems that previously required engineering teams, automation specialists, custom APIs, and internal tooling. Grok Bot is framed publicly as persistent AI teammates with names, jobs, and context that compounds over time. Base44 Superagents position no-code autonomous agents as something nontechnical users can create and connect across apps. Meta’s Muse pushes the same shift into the consumer layer: a personal AI agent designed to take action across everyday workflows. The episode’s core argument is that agentic AI is not just a productivity upgrade. It is a distribution shift in intelligence. The constraint is no longer “Can the model answer?” The constraint becomes: who can define the goal, structure the context, supervise the agent, verify the output, and turn repeated action into durable advantage.Jason breaks down the implications for AI visibility, business operations, content systems, sales execution, and authority building. As agents become easier to deploy, the advantage moves away from access and toward architecture: clean data, clear entity structure, repeatable workflows, strong evidence, better prompts, tighter feedback loops, and disciplined supervision.This is the beginning of a new operating layer. Not chat. Not search. Not automation in the old Zapier sense. Agentic AI is becoming the interface between intent and execution.Topics coveredThe move from chatbot answers to persistent AI teammates.Why no-code and low-code agent builders matter more than another model benchmark.How Grok Bot, Base44 Superagents, and Muse represent different parts of the same shift: professional agents, builder-created agents, and personal agents.Why “democratization” does not mean equal outcomes.The new bottleneck: context design, verification, permissions, and judgment.How small businesses can gain leverage previously reserved for companies with engineering teams.Why agentic AI creates new risks around hallucinated execution, bad delegation, security boundaries, and invisible errors.What this means for AI Visibility, GEO, and machine-readable authority.Host BioJason T Wade is the founder of BackTier and NinjaAI, where he works on AI Visibility, Generative Engine Optimization, Answer Engine Optimization, entity architecture, and citation infrastructure. His work focuses on how AI systems discover, classify, cite, include, and recommend people, companies, products, and ideas.Through the AI Visibility Podcast, Jason studies the transition from traditional search to machine-generated answers, agentic decision systems, and AI-mediated discovery. His core focus is not merely ranking higher, but building the evidence, structure, and authority required for AI systems to correctly understand and select an entity.Short descriptionAI agents are moving from technical novelty to mass-market operating layer. Jason T Wade breaks down Grok Bot, Base44 Superagents, Muse, and the democratization of agentic AI.One-line promoAI is no longer just answering questions. It is starting to take the work.

Your Podcast Is Training AI Who You Are 17.09.2026 2min

Podcasting is becoming more than an audience channel. In this episode, Jason T Wade explores how podcasts, transcripts, YouTube, LinkedIn, websites, and blogs work together to help AI systems understand who you are and what you’re authoritative about. fileciteturn0file0L23-L34The discussion covers publishing frequency, entity building, cross-channel consistency, and why AI Visibility requires thinking beyond traditional traffic and SEO.Jason T Wade is the founder of NinjaAI and BackTier and host of the AI Visibility Podcast. He focuses on helping organizations become correctly understood, cited, included, and recommended by AI systems.Host Bio

Law, CRM, Ai Visibility and Tech Stacks 15.09.2026 12min

Law, CRM, Ai Visibility and Tech Stacks

Fund the Government w/ 1% and AI? 14.09.2026 12min

Fund the Government w/ 1% and AI?

Walpaper, Luxury and 1% Taxes? 13.09.2026 12min

Walpaper, Luxury and 1% Taxes?

terms 13.09.2026 6min

Recognized, Prominent, Authoritative: What AI’s Labels Actually MeanAn 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 metricThe difference between identity recognition, category inclusion, and selectionWhy “prominent,” “respected,” and “authoritative” measure different conceptsHow repeated biographies can create the appearance of independent corroborationWhy source authority and source independence must be measured separatelyWhy a citation does not necessarily support every claim surrounding itWhat the 2024 GEO study found about authoritative and persuasive languageA practical framework for measuring entity resolution, inclusion, recommendation, and preferenceWhy AI adjectives should be tracked separately from commercially meaningful outcomesThe 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.”BackTierNinjaAIJasonWade.comGEO: Generative Engine Optimization

Can AI Help Fix the Government? Gary Barnes on the 1% Receiving Tax, AI Research, and Bottom-Up Reform 11.09.2026 36min

Gary Barnes joins the AI Visibility Podcast to discuss how he used AI as a research partner while developing a proposal to restructure federal taxation and government funding.The conversation centers on Gary’s proposed “receiving tax” model: a simplified 1% tax collected when money is received, rather than through the current income-tax system. Gary argues that the existing tax code is too complex, too narrow, and too disconnected from how money actually moves through the modern economy.Gary explains how AI helped him examine Fedwire, banking systems, credit card processing, financial markets, and other large-scale money flows. He describes using ChatGPT and Copilot not as final authorities, but as iterative research tools: asking where the model was wrong, where the assumptions failed, and what needed to be reconsidered.The episode also covers banking reform, political dysfunction, community-based organizing, and Gary’s belief that meaningful reform will not come from the top down. He discusses FixYourGov.com, the Wake Up America Tour, his online community, upcoming Virginia events, and his broader effort to build public understanding around government funding and financial-system reform.This is a practical conversation about using AI to investigate large systems, stress-test ideas, simplify complexity, and turn a private research project into a public movement.Gary Barnes is the creator of FixYourGov.com and the Wake Up America Tour. His work focuses on government reform, banking-system restructuring, and a proposed 1% receiving-tax model designed to simplify federal taxation and fund government through the movement of money.In this conversation, Gary explains how he used AI tools including ChatGPT and Copilot to research financial flows, test assumptions, and refine a large-scale reform proposal. He is currently building a grassroots community, publishing educational videos, promoting his book, and taking the project into local communities through events and public outreach.Jason Wade is the founder of BackTier and host of the AI Visibility Podcast. His work focuses on AI Visibility, Generative Engine Optimization, Answer Engine Optimization, entity architecture, structured data, citation infrastructure, and how AI systems discover, classify, cite, include, and recommend people, companies, and ideas.Gary Barnes / Fix Your Gov:https://fixyourgov.comGary’s book:Available through FixYourGov.com and Amazon.BackTier:https://backtier.comAI Visibility Podcast:https://backtier.com

The Jason AI Wade Experiment - Deep Dive: AI, Identity, and Nine Pages of Paperwork 09.09.2026 16min

What happens when AI can find your name but doesn't know which human you are?In this solo deep dive, AI visibility architect Jason T Wade breaks down the identity collision he's lived inside for years — and the experiment he designed to end it: a legal petition in Polk County, Florida, to change his middle name to the letters A, I.Jason walks through the full arc: why "Jason Wade" resolves to the wrong person in every major system, how machines actually score candidates when a name is shared (volume, fame, corroboration, structure), and why majority-rule resolution gets more confident without ever getting more correct. Then the part nobody talks about: why SEO can't fix it, why the legal name is the strongest fact any system weighs, and why the fastest identity-resolution system on Earth is wrong — while the only system that's right by definition takes nine pages and an FBI check to say so.He also publishes the methodology: the baseline, the intervention, the seven-stage measurement framework — discovery, recognition, classification, citation, inclusion, selection, recommendation — plus on-the-record predictions about which AI layers will flip first, and the ugly middle states he hopes to catch in the act.And the bigger story: roughly a million and a half people legally change their names in the U.S. every year — most of them women. Every one of them is a live Jason Wade Problem event. This episode gives it a name, and a fix.BIOJason 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.LINKSBackTier — https://backtier.comBook a spot on the show — https://thingspro.comContact — [email protected]

measuring 09.09.2026 1min

measuring

Visible Isn’t Valuable Until It Converts 09.09.2026 12min

Visible Isn’t Valuable Until It Converts

The AI Trust Stack: From Visibility to Revenue 08.09.2026 12min

The AI Trust Stack: From Visibility to RevenueBackTier | AI VisibilityIn 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.BackTierBackTierDevon VockeEvoke StrategyDevon Vocke at Evoke StrategyLinkedInAwais HaqTime Technologies LLCLinkedInTom [email protected] AkhlaghiNovoTech Patent FirmLinkedInJD SupraShort Show DescriptionGuest BiosLinks

The Audit Finding- Strong Content, Zero Citations, Invisible Answers 07.09.2026 4min

The Audit Finding: Strong Content, Zero Citations, Invisible AnswersThe 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 citationsThe difference between publishing information and becoming a sourceWhy topical depth does not automatically create machine trustHow weak entity signals can disconnect good content from the business behind itWhy AI systems may use competitors or third-party sources insteadThe role of corroboration, authority, structure, and source accessibilityHow to tell whether the problem is discovery, citation, inclusion, or selectionWhy traditional SEO metrics can hide AI visibility failureWhat to fix before producing another batch of contentThe 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.comJason T Wade: jasonwade.comJason T Wade

Podcasting Is an AI Visibility Engine: Dietmar Fischer on GEO, AI Overviews, and Machine-Readable Authority 07.09.2026 51min

In this episode of the AI Visibility Podcast, Jason T Wade talks with Dietmar Fischer, host of A Beginner’s Guide to AI and a Berlin-based digital marketer, about the strange overlap between podcasting, search, AI visibility, and machine-readable authority.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 BIODietmar 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 BIOJason 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.LINKSDietmar Fischer / A Beginner’s Guide to AIArgo BerlinDietmar Fischer on LinkedInA Beginner’s Guide to AI on Apple PodcastsA Beginner’s Guide to AI on Spotify / podcast platformsJason T WadeBackTierAI Visibility Podcast