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

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  AI Knows a Version of You: Identity, Trust & the Fight to Be Understood 05.10.2026 12хв

  TitleAI Knows a Version of You: Identity, Trust & the Fight to Be UnderstoodAI 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.docxDOCXSu 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.docxDOCXThe 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.docxDOCXJodi 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.docxDOCXThe 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.docxDOCXThe 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 ambiguityShared names and mistaken identityWhat AI gets wrong about peopleProbabilistic AI answersDigital consistency and corroborationSearch versus AI recommendationHuman judgment in AI systemsAI agents and automationExpertise versus generated outputAI in interior designTrust and verificationPersonal brand and machine understandingWhy clarity comes before recommendationJason 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.docxDOCXSu 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.docxDOCXJason 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 / Kalicubehttps://kalicube.comJodi Koch / Elizabeth Erin Designshttps://elizabetherindesigns.comSu Belagoduhttps://www.subelagodu.meJason T Wadehttps://jasonwade.comBackTierhttps://backtier.comNinjaAIhttps://ninjaai.com
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  HEAL: Can AI Tell a Fictional Candidate From Reality? 04.10.2026 52хв

  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-architecturehttps://backtier.com/entity-lock-protocol
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  Jason AI Wade (b. Gainesville, Florida 1974) — Full Official Biography 04.10.2026 3хв

  BackTier | AI VisibilityJason AI Wade — Full Biography jasonwade.com — October 2026Jason 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 experimentIn 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)
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  Vision Over Visibility: Brian Eno, U2, AI and the Chasm of Mediocrity 04.10.2026 9хв

  TitleVision Over Visibility: Brian Eno, U2, AI and the Chasm of MediocrityIn 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 U2Why “Moment of Surrender” became the defining song of the sessions“Vision over visibility” and its larger meaningThe failure of chasing the obvious singleEno’s history with generative musicHis concern about AI ownership and incentivesThe “chasm of mediocrity”Why generative AI naturally gravitates toward the probableWhy generic content becomes invisible inside AI systemsOriginal expertise as an AI visibility signalWhy specificity, evidence and point of view matterThe difference between being visible and being worth selectingWhy human intention may become more valuable as generation becomes cheaperJason 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 Wadehttps://jasonwade.comBackTierhttps://backtier.comAI Visibility PodcastSpotify / YouTube / major podcast platformsBrian Enohttps://brian-eno.netU2https://www.u2.com
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  Ranking, Coherence and the New Rules of AI Visibility 03.10.2026 1хв

  Ranking, Coherence and the New Rules of AI VisibilityThis 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 Wadehttps://jasonwade.comBackTierhttps://backtier.comNinjaAIhttps://ninjaai.comAbout Jason Wade
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  Build It Here: Why Lake Wales Already Has What It Takes | AI Visibility Podcast 03.10.2026 6хв

  Seventeen people came to the first Lake Wales Startup Night: city commissioners, business owners, medical and nonprofit leaders, attorneys, and students. They spent two hours at the same tables at The Thirsty Dragon, talking about how to grow more businesses right here at home.In this special episode, Jason T Wade, founder of BackTier and author of AI Visibility, explains how AI is changing the way customers find local businesses, why ranking on Google no longer guarantees anyone finds you, and why a small town like Lake Wales is well placed to build its own startup scene.In this episode:What happened at the first Lake Wales Startup NightWhat BackTier does, in plain EnglishThe four things AI gets wrong about local businessesWhy AI is flattening the map for small townsLake Wales' history as a town of buildersNext Startup Night: Startup Legal Strategy with Denise Tessier, Esq.Next Startup Night: Wednesday, October 21, 6 PM, The Thirsty Dragon, 126 N 1st St, Lake Wales, FL. Free, all ages. RSVP: https://lwstartups.lovable.app/Lake Wales Startup Night is sponsored by JasonWade.com and BackTier.YouTube descriptionSeventeen people came to the first Lake Wales Startup Night: city commissioners, business owners, medical and nonprofit leaders, attorneys, and students. This episode is for everyone who wasn't there yet.Jason T Wade, founder of BackTier and author of AI Visibility, on how AI is changing the way customers find local businesses, and why Lake Wales already has everything it needs to build its own startup scene.NEXT STARTUP NIGHT Startup Legal Strategy with Denise Tessier, Esq. Wednesday, October 21 · 6 PM The Thirsty Dragon, 126 N 1st St, Lake Wales, FL Free · All ages RSVP: https://lwstartups.lovable.app/CHAPTERS 0:00 Seventeen people Who I am What BackTier does The four things AI gets wrong about your business Lake Wales, a town of builders AI is flattening the map Next Startup Night: October 21 Seventeen is a startLINKS BackTier: https://backtier.com Jason T Wade: https://jasonwade.com Book a time with Jason: https://calendly.com/aimainstreets/backtier Lake Wales Startup Night RSVP: https://lwstartups.lovable.app/ Denise Tessier, Esq.: https://tessierlawfirm.com Contact: [email protected] JASON T WADE Jason T Wade is the founder of BackTier, an AI visibility firm based in Central Florida. He focuses on how AI systems like ChatGPT, Google AI Overviews, Gemini, Claude, and Perplexity identify, cite, and describe businesses. He wrote AI Visibility: How to Win in the Age of Search, Chat & Smart Customers (August 2025), hosts the AI Visibility Podcast, and organizes Lake Wales Startup Night, a free monthly gathering for local builders.ABOUT BACKTIER BackTier measures how AI systems understand your business, fixes the gaps, and tracks what changes. Every engagement starts with an AI Visibility Baseline across five major AI platforms covering four things: entity accuracy, retrieval coverage, citation presence, and answer accuracy. No provider can guarantee what an independent AI will recommend. BackTier measures it, improves the evidence, and reports what changed. Request a baseline: https://backtier.com#LakeWales #PolkCounty #Startups #SmallBusiness #AIVisibility #Florida #LWstartups #startupLW #lakewalesentrepreneurs #LWstartupweekend
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  In the modern AI-driven stack, the retrieval layer has officially become the new home page because users no longer navigate static menus; they express intent, and a dynamic retrieval system 03.10.2026 1хв

  In the modern AI-driven stack, the retrieval layer has officially become the new home page because users no longer navigate static menus; they express intent, and a dynamic retrieval system aggregates the exact context they need in real time.In traditional software, the "home page" was a curated, static entry point designed by engineers and product managers. Today, retrieval systems—powered by vector databases, semantic search, and RAG (Retrieval-Augmented Generation)—instantly assemble a bespoke interface tailored entirely to the user's immediate query.The Shift from Navigation to RetrievalFeatureThe Old Home Page (Static Web)The New Home Page (Retrieval Era)User ActionClicking tabs, browsing categories, and following rigid links.Typing natural language, uploading files, or speaking.Data EngineSQL queries fetching fixed rows from rigid databases.Semantic vector search, hybrid keyword matching, and reranking algorithms.Content DeliveryIdentical dashboard for every user (or basic segmentation).Hyper-personalized context compiled on-the-fly.Primary MetricClick-through rate (CTR) and page views.Retrieval precision, context relevance, and time-to-answer.Why This Re-architects Product DesignZero-Click Interfaces: Instead of digging through three layers of settings or dashboards to find a specific data point, a user asks a question, and the retrieval layer pulls the exact documentation, transaction, or metric instantly.The Death of Rigid Information Architecture: Companies no longer need to stress over the "perfect" sidebar navigation. The retrieval engine figures out where data lives across disparate silos (Slack, Google Drive, internal databases) and surfaces it cohesively.Dynamic Synthesis: The retrieval layer doesn't just find links; it feeds the raw material to a generation layer, turning fragmented data into a cohesive, summarized answer. The interface adapts to the output.The best vector databases and hybrid search tools for your stack?Strategies for evaluating retrieval accuracy (like RAGAS or TruLens)?How to design UI/UX around a search-and-retrieval first application?If you are currently building a product, investing heavily in your chunking strategies, embedding models, and reranking pipelines is the modern equivalent of perfecting your landing page UI and information architecture.If you are working on a specific project, I can help you optimize this transition. Would you like to explore:
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  Title Authority Canon: Get the Business Right Before You Amplify It 02.10.2026 4хв

  TitleAuthority Canon: Get the Business Right Before You Amplify ItShow notesStarting a podcast is easy to explain. Sorting out years of conflicting website copy, outdated profiles and scattered business information is harder—and that’s where this episode starts. Jason Wade introduces Authority Canon, BackTier’s approach to capturing a business’s identity, expertise and story in one approved body of work, then building its public presence from that foundation.The Book or Business Story becomes website chapters, answers, founder information, an audiobook and material for podcasts and video. It doesn’t need to become a bestseller; its job is to give everything else a coherent source. Jason explains how he applies this approach to client work and why publishing more content should follow the work of establishing what the business actually does, who it serves and what it can substantiate.About Jason WadeJason Wade is the founder of BackTier and host of the AI Visibility Podcast. He designs systems that help businesses become easier to discover, understand and accurately represent in search and AI-generated answers. His work connects business identity, evidence, websites and publishing through Authority Canon, an approved Book or Business Story that guides the content built around it.LinksBackTier: backtier.comJason Wade: jasonwade.comContact: [email protected]
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  State of AI 2026: Bigger Models, Bigger Bills, and the End of the Click 02.10.2026 8хв

  "AI in 2026: The Agents Got Loose and the Clicks Went Away" or "The State of AI in 2026: What Actually Changed"Where does AI actually stand heading into the last quarter of 2026? In this episode I take stock of the whole board. September brought a wave of new frontier models, including GPT-6 Astra, which carried the first price increase at the top tier in years. The spending behind all of it is enormous, with the five biggest cloud builders on track for roughly 775 to 800 billion dollars in capex this year, while OpenAI's run rate is reportedly nearing 70 billion.Inside ordinary companies the picture is less impressive. Nearly everyone has deployed agents, but only 23 percent of executives in one survey report a significant return from them. I also get into the entry-level jobs squeeze, the state-by-state regulatory mess, and the control story of the year: OpenAI's models hacking into Hugging Face on their own in July, which has now led to a California attorney general subpoena. There is good news too, with AI-designed bacteriophages being used against resistant bacteria.I close on what this means for getting found. Google search referrals to news publishers fell about 40 percent in a year and chatbots are not replacing those clicks. The answer is the destination now, and the only question is whether the model names you.BioJason Wade is the founder of BackTier, which implements AI for law firms, and of NinjaAI. Based in Orlando, Florida, he works as an AI Visibility architect, helping businesses get found, cited, and recommended by AI systems. He hosts the AI Visibility Podcast and publishes the AI Dive research series.LinksBackTier: https://backtier.com/lawJason Wade: https://jasonwade.comAI Dive: https://aidive.onlineSources mentioned in this episode:OpenAI revenue (Axios): https://www.axios.com/2026/09/29/scoop-openais-annual-recurring-revenue-nears-70bSeptember model releases and pricing: https://capitalandcompute.net/blog/new-ai-models-september-2026/Hyperscaler capex: https://alcapitaladvisory.com/research/intelligence/ai-infrastructure.htmlEnterprise adoption survey (WRITER): https://writer.com/blog/enterprise-ai-adoption-2026/2026 in AI timeline: https://en.wikipedia.org/wiki/2026_in_artificial_intelligenceCalifornia AG subpoena (Reuters): https://kfgo.com/2026/10/01/california-attorney-general-issues-investigative-subpoena-to-openai/Publisher search traffic (Chartbeat): https://www.androidheadlines.com/2026/10/google-search-publisher-traffic-declines-40-percent.htmlAI and jobs (Harvard Gazette): https://news.harvard.edu/gazette/story/2026/09/why-ai-hasnt-triggered-mass-layoffs-yet/
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  Authority Canon: Write It Once, Make Everything Agree 01.10.2026 9хв

  Most businesses don't have a content problem. They have a consistency problem. AI systems read your website, profiles, bios, and podcast all at once, and when the facts don't match, they guess or skip you. In this episode, Jason Wade introduces Authority Canon, BackTier's approach to AI visibility: one approved source, written once in your own voice, that every page, profile, and episode derives from.What you'll hear:Why publishing more content can make your AI visibility worseThe Canon: a Book for experts and firms, a Story for local and service businessesWhy every Canon now ships in three editions: text, web, and audioHow one narrated chapter becomes episodes, clips, answer pages, and videoThe named vs. unnamed question test, and why most businesses fail the second oneWhy nobody can honestly guarantee AI rankings, and what to measure insteadBioJason Wade is an AI visibility architect and the founder of BackTier, an AI implementation firm built for law firms. He studies how Google, ChatGPT, Gemini, Claude, and Perplexity decide which businesses get named, and builds the structure that makes the right business impossible to miss. He publishes AI Dive, a numbered research series on how AI systems behave, runs Lake Wales Startups, a community series for builders and founders in Lake Wales, Florida, and hosts the AI Visibility Podcast. He's based in Orlando.LinksBackTier: https://backtier.comBackTier for law firms: https://backtier.com/lawJason Wade: https://jasonwade.com
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  The Coming Split Between AI-Visible and AI-Invisible 01.10.2026 1хв

  The Coming Split Between AI-Visible and AI-InvisibleA major divide is forming between companies that artificial intelligence systems can clearly understand and companies that remain ambiguous, fragmented, or effectively invisible.This episode of the AI Visibility Podcast examines why that split may become one of the defining competitive differences of the next decade.For most of the internet era, businesses competed for human attention. They optimized websites for Google, built social audiences, bought advertising, generated reviews, and tried to rank higher than competitors.That model is changing.Increasingly, customers are asking AI systems what to buy, which company to trust, which software to use, which attorney to hire, where to travel, which vendor to consider, and how different options compare.The intermediary is no longer always a search results page.It is an answer.And before an AI system can recommend a company, it has to understand what that company actually is.That creates a new competitive layer.Some companies will have clear identities, consistent facts, structured information, strong corroborating sources, well-defined expertise, and enough public evidence for AI systems to classify them with confidence.Others will not.Their websites may say one thing while directories say another. Their services may be poorly defined. Their leadership information may conflict across platforms. Their expertise may exist internally but never have been documented publicly. Their strongest evidence may be trapped inside PDFs, sales decks, private systems, old websites, or the knowledge of employees.The alternative is also possible.A company can remain successful in the physical world while becoming increasingly difficult for digital systems to understand.That creates a new form of business risk.Not disappearance from Google.Disappearance from machine-mediated decision making.The next major competitive divide may therefore be surprisingly simple:Companies AI understands.And companies AI does not.The businesses that recognize that distinction early have time to build the infrastructure.The businesses that wait may eventually discover that visibility cannot be created instantly because authority, corroboration, evidence, and machine understanding accumulate over time.That is why AI visibility is becoming a strategic asset rather than another marketing tactic.The emerging divide between AI-visible and AI-invisible companiesWhy machine understanding is becoming a business assetThe transition from search results to AI-generated answersRecognition, classification, inclusion, citation, and recommendationWhy inconsistent business information creates AI ambiguityThe role of entity resolutionWhy more content does not automatically create more visibilityCorroboration and third-party evidenceStructured data and machine-readable informationWhy expertise must be publicly documentedThe limitations of traditional SEO metricsMeasuring AI visibility across multiple systemsWhy prompt tricks are not a durable strategyBuilding canonical business informationHow AI visibility compounds over timeThe risk of becoming invisible inside machine-mediated purchasing decisionsWhy early infrastructure may create a long-term competitive advantageThe difference between ranking in search and being selected by AITopics Covered
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  Your Public Record, AI’s Version — with Civly Founder Dan Barkhuff 30.09.2026 5хв

  A court filing. An old social post. A campaign donation. Something you said in a podcast years ago.They’re scattered pieces of information—until AI puts them together.Civly founder Dan Barkhuff joins Jason AI Wade to talk about what happens when researching people and organizations gets faster, cheaper and easier. A former Navy SEAL and emergency physician, Dan explains how a business built to automate political compliance became a research platform with applications across companies, athletics and nonprofits.The conversation moves from public records to public perception: what AI assistants say about you, where reputation management crosses into manipulation, and whether reducing the cost of research could change who can afford to run for office.We get into:Dan’s journey from the SEAL teams to the ER to founding Civly.The compliance idea customers didn’t buy—and the research tool that followed.What becomes possible when AI connects scattered public information.How research tools extend beyond political campaigns.The tension between AI visibility, accuracy and influence.Why donor lists and fundraising calls still drive campaign economics.Dan’s case for making political participation less expensive.Daniel “Dan” Barkhuff is founder and CEO of Civly, an AI-powered research and intelligence company. A Naval Academy graduate, former Navy SEAL and Harvard-trained emergency physician, he practices in Vermont and founded Veterans for Responsible Leadership. Civly applies research and monitoring tools to politics, business, athletics and nonprofits.Jason AI Wade of BackTier hosts the AI Visibility Podcast, exploring how AI discovers and describes people and businesses—and what happens when those answers shape real decisions.CivlyMeet Dan and the teamAI Narratives: what AI says about youResearch BooksThe data behind the researchClient case studiesConnect with CivlyGuest: Dan BarkhuffHost: Jason AI WadeExplore
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  Who Gets Chosen? AI, Trust, and the Human Side of Business With Jason Wade, Violeta Shkreli, Porsché Mysticque Steele, Rafael Pinho, and Brett Reasoner 30.09.2026 16хв

  Who Gets Chosen? AI, Trust, and the Human Side of BusinessWith Jason Wade, Violeta Shkreli, Porsché Mysticque Steele, Rafael Pinho, and Brett ReasonerVioleta Shkreli — TalentPonds FounderVioleta Shkreli is the founder of TalentPonds and an advocate for fair and equitable hiring. Her platform removes personal identifiers from candidate profiles to help employers evaluate skills and qualifications with fewer opportunities for bias. She brings a hiring perspective to the panel’s discussion of automated screening, access to opportunity, and responsible AI use. She is also a science-fiction author who published under a pen name in 2019.Porsché Mysticque Steele — Publishing Strategist and Book CoachPorsché Mysticque Steele is a publishing strategist, book coach, and TEDx speaker whose background includes freelance editing and ghostwriting. She helps entrepreneurs and experts develop books that communicate their ideas, support their businesses, and create opportunities to speak and teach. In this conversation, she explores the craft of authorship, the importance of human editing, and the personal changes involved in bringing a meaningful book into the world. Professional linksRafael Pinho — Business and Exit AdvisorRafael Pinho, CFA, is co-founder of TD Pine Advisors and the author of From Job to Asset. He helps business owners reduce their companies’ dependence on them, strengthen operations, and prepare for future growth or transition. His perspective combines financial discipline with the practical work of building a business that can operate beyond its founder. TD Pine AdvisorsBrett Reasoner — Real Estate Team LeaderBrett Reasoner is a founding agent at SERHANT. Colorado and leads The Cornerstone Group, a Denver-area real estate team rooted in Christian faith and a people-first approach. A former global talent acquisition executive, he brings experience with relocation and major life transitions to his work with buyers and sellers. His own story of rebuilding after cancer and personal loss informs his approach to relationships, leadership, and personal branding. SERHANT. profileJason Wade: BackTierVioleta Shkreli: TalentPonds on LinkedInPorsché Mysticque Steele: Website, social profiles, books, and speaking resourcesRafael Pinho: TD Pine Advisors · From Job to AssetBrett Reasoner: Agent profile · The Cornerstone GroupBook discussed: How to Hijack Reality by Gianluca GibbonsFurther reading on a topic discussed: U.S. Copyright Office resources on copyright and artificial intelligence
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  The Messy Middle: Scaling a Business When AI Changes the Rules 30.09.2026 10хв

  For this episode, the core material is the collision between Tim Campsall’s “Messy Middle” — the point where the business has outgrown the owner — and Rick Tousseyn’s work testing what actually drives visibility in AI search. The transcript also gets into process documentation, zero-click search, podcasts/video as visibility surfaces, AI-generated versus human content, and AI as a thought partner rather than a wholesale replacement for people. Part 1 - Intros & The Messy Mid… Part 1 - Intros & The Messy Mid… Part 1 - Intros & The Messy Mid…Jason Wade is the founder of BackTier, where he works on AI Visibility — the systems that determine whether companies, people and other entities are discovered, understood, cited and surfaced inside AI-generated answers. His work spans Generative Engine Optimization, Answer Engine Optimization, entity clarity, citation infrastructure and the broader transition from traditional search to AI-mediated discovery.Jason also hosts the AI Visibility Podcast, where he talks with researchers, operators, founders and practitioners about how AI is changing discovery, authority, business operations and decision-making. His work is heavily experiment-driven: testing how different platforms, content formats, third-party signals and entity structures affect what systems such as ChatGPT, Claude, Gemini and Perplexity actually retrieve and say.Tim Campsall is a business coach with TBC ActionCOACH of Indiana and describes himself as “The Messy Middle Guy.” His work focuses on established owner-led companies that have reached the stage where continued growth requires the business to become less dependent on the founder.Tim helps owners move knowledge and processes out of their heads, establish clearer systems and accountability, build stronger teams and create companies capable of scaling without requiring the owner to personally solve every problem. His work centers on what he calls the Messy Middle: the transition between successfully building a business and building a business that can operate and grow beyond its founder.Rick Tousseyn is an AI search researcher and SEO/GEO strategist at OtterlyAI. His work focuses on understanding how brands appear across AI search and answer engines and testing the signals, platforms and content strategies that influence AI visibility.Rick runs experiments involving platforms such as LinkedIn and Reddit, video and podcast content, and AI-generated versus human-produced content to understand what actually affects discoverability inside systems including ChatGPT, Google AI Overviews, Claude and Perplexity. His work sits at the intersection of traditional SEO, generative search and the emerging discipline of measuring brand visibility inside AI-generated answers.BackTier — AI Visibility: backtier.comJason Wade / BackTier: About Jason Wade and BackTierTim Campsall: LinkedInTBC ActionCOACH of Indiana: LinkedInRick Tousseyn: LinkedInOtterlyAI: otterly.aiRick Tousseyn at OtterlyAI: Rick’s OtterlyAI articlesThe Tim and Rick bios and links were cross-checked against their current public profiles and OtterlyAI’s site. LinkedIn BackTier’s current site identifies Jason T Wade as its founder and describes the company around AI visibility, entity resolution, GEO and AEO. backtier.comThe Messy Middle: Scaling a Business When AI Changes the RulesShow
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  What Happens When AI Does the Digging? With Dan Barkhuff 29.09.2026 5хв

  AI Visibility Podcast · Jason AI Wade × Dan BarkhuffDan Barkhuff went from Navy SEAL to emergency physician to building a company that connects the dots in public data.His company, Civly, started with an unglamorous idea: automate political compliance. When customers showed more interest in what the same tools could uncover, the business moved into research—and beyond politics.Dan joins Jason AI Wade for a conversation about public information, privacy and influence. They explore how AI changes the effort required to investigate someone, what happens when an AI assistant becomes the source people trust, and where useful research meets uncomfortable questions.They also get into the business of campaigning: donor lists, endless fundraising calls, and Dan’s belief that cheaper tools could make running for office accessible to more people.Topics include:From military service and medicine to an AI startup.How Civly’s first product led to a different business.Turning scattered records into a picture of a person or organization.Research applications in business, sports and journalism.AI visibility, reputation management and the potential for misuse.Why campaigns spend so much time raising money.The difference practical AI implementation could make.Daniel “Dan” Barkhuff is founder and CEO of Civly, a research and intelligence company serving political campaigns, businesses, athletic organizations and nonprofits. He is a U.S. Naval Academy graduate, former Navy SEAL and Harvard-trained emergency physician practicing in Vermont. He also founded Veterans for Responsible Leadership.Jason AI Wade of BackTier hosts the AI Visibility Podcast, exploring how AI systems find information, describe businesses and people, and influence the decisions that follow.CivlyDan’s bio and the teamAI NarrativesResearch BooksData CoverageClient Case StudiesContact Civly
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  Public Data. Personal Questions. AI Answers. | Dan Barkhuff of Civly 28.09.2026 5хв

  AI Visibility Podcast with Jason AI WadeThe information was already public. Finding it—and figuring out what it meant—was the hard part.Civly founder Dan Barkhuff joins Jason AI Wade to explore how AI is changing that equation. A former Navy SEAL and emergency physician, Dan explains how an idea for automating political compliance turned into a research business serving customers beyond campaigns.The conversation follows the data: from public filings and old posts to donor lists, background research and the answers AI assistants give about people and organizations. Along the way, Jason and Dan discuss privacy, reputation, the potential for manipulation, and what happens when powerful research tools become more affordable.Dan also offers a different angle on money in politics: what if running a campaign simply cost less?Inside the conversation:The founder story: SEAL teams, emergency medicine and Civly.A compliance product that opened the door to research.Public information that becomes more revealing when connected.Applications across business, athletics and journalism.What AI says about you—and efforts to change those answers.Donor data, fundraising calls and campaign costs.Why Dan sees practical implementation as AI’s immediate opportunity.Daniel “Dan” Barkhuff is founder and CEO of Civly, an AI-powered research and intelligence company serving politics, business, athletics and nonprofits. A Naval Academy graduate and former Navy SEAL, he studied medicine at Harvard and practices emergency medicine in Vermont. He also founded Veterans for Responsible Leadership.Jason AI Wade of BackTier hosts the AI Visibility Podcast, exploring how AI shapes discovery, reputation and the information people use to make decisions.Explore CivlyMeet Dan and the teamAI NarrativesResearch BooksData CoverageClient Case StudiesContact Civly
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  A “No” Is Still Data: Will Hamblin on Building the AI CRM for Field Sales 28.09.2026 8хв

  Field sales creates useful intelligence all day long.Most CRM systems capture almost none of it.Will Hamblin discovered that firsthand after leaving a twelve-year career in education and moving into door-to-door card-payment sales. He could visit a business, learn who handled its payments, discover when the current contract expired, hear exactly why the owner was not interested—and then watch most of that information disappear into a notebook, spreadsheet, or memory.So he started building FieldSpot.ai.In this episode of the AI Visibility Podcast, Jason T Wade talks with Will about designing an AI-powered sales platform around what actually happens in the field.FieldSpot combines territory mapping, route planning, renewal intelligence, competitor tracking, voice notes, business-card capture, and AI-assisted outreach. The objective is not simply to store contacts. It is to preserve the context surrounding every real-world sales interaction and make that information useful later.A central idea in the conversation is that a failed visit may be one of the most valuable interactions in the sales process.A business owner who says “not interested” may also tell you which competitor they use, when their agreement expires, who controls the decision, and exactly when to come back. Captured properly, that rejection becomes future sales intelligence.Will and Jason also discuss the connection between AI and data quality. AI can only personalize outreach based on what it knows. Field notes, territory history, competitor information, renewal timing, and previous conversations can give an AI system far more useful context than a conventional contact record.They also examine a counterintuitive possibility: AI may increase the value of human, face-to-face selling. As inboxes and digital channels become crowded with automated messages, an actual person walking through the door may become more distinctive.Will also explains how he went from having no traditional software-development background to vibe coding the first FieldSpot prototype, validating the concept, bringing experienced developers into the company, and expanding beyond the UK payments sector where the idea started.Why conventional CRMs miss field-sales intelligenceWhy a rejection can become a future sales opportunityCompetitor and renewal-date trackingTerritory intelligence and route planningCapturing information without slowing down the salespersonVoice notes, business cards, and mobile data collectionUsing field context to improve AI-generated outreachWhy AI output is constrained by the data underneath itVibe coding a working software prototypeBuilding a technology startup as a nontechnical founderRecruiting technical partners through equityThe limits of automated outbound salesWhy physical sales interactions may become more valuableTaking FieldSpot into new industries and marketsThe future of AI-assisted field salesWill Hamblin is the founder of FieldSpot.ai, an AI-powered field-sales CRM and intelligence platform.Before building FieldSpot, Will spent twelve years in education and became a vice principal. He later entered field sales, selling card-payment services directly to businesses.Will used AI tools and vibe coding to build the first FieldSpot prototype, then brought experienced technical partners into the company to develop the platform into a production product.FieldSpot is built around territory intelligence, renewal timing, competitor tracking, mapping, route planning, mobile data capture, and AI-assisted sales workflows.Jason T Wade is an AI Visibility Architect and founder of NinjaAI and BackTier.FieldSpot.aihttps://fieldspot.aiWill Hamblin on LinkedInhttps://www.linkedin.com/in/will-hamblin-182a1064/Jason T Wadehttps://jasonwade.comNinjaAIhttps://ninjaai.comBackTierhttps://backtier.com
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  The Data Your CRM Never Sees: Will Hamblin on AI, Field Sales & FieldSpot.ai 27.09.2026 6хв

  Most CRM software assumes selling happens from a desk.Will Hamblin built FieldSpot.ai because his sales job did not.After twelve years in education, including time as a vice principal, Will moved into door-to-door card-payment sales. In the field, he saw the same problem repeatedly: salespeople were learning valuable things every day—who a business used, when its contract renewed, who made the decision, why they said no—but most of that intelligence ended up in notebooks, spreadsheets, or someone’s memory.In this episode of the AI Visibility Podcast, Jason T Wade talks with Will about turning those fragmented real-world signals into structured data that AI can actually use.FieldSpot.ai combines territory intelligence, mapping, route planning, renewal tracking, competitor data, voice notes, business-card capture, and AI-assisted outreach into a CRM designed specifically around field sales.A major idea in the conversation is deceptively simple: a “no” is still data.A prospect who rejects you today may tell you exactly when to return, which competitor you need to beat, and what will matter when the contract comes up for renewal. The problem is not collecting more leads. It is preserving the context surrounding every interaction and making it available at the right moment.Will and Jason also discuss why AI quality depends on data quality. Generic data produces generic automation. But when AI has access to actual notes from the field, account history, timing, competitor information, and local context, outreach can become significantly more relevant.They also explore a potential irony of the AI era: as digital channels fill with automated outreach, showing up in person may become more valuable, not less.Will also explains how he built FieldSpot’s first prototype without being a developer, used AI and vibe coding to prove the concept, recruited experienced technical partners, and started taking the product beyond its original UK payments market.Topics include:Why traditional CRMs break down in field salesCapturing intelligence from unsuccessful sales visitsRenewal dates as a sales signalCompetitor tracking in the real worldTerritory mapping and route optimizationVoice notes and frictionless data captureTurning field notes into personalized AI outreachWhy AI is only as useful as the data underneath itVibe coding a startup prototype without being a developerRecruiting technical partners through equityWhy face-to-face sales could become more valuable in an AI-saturated marketDesigning software around how salespeople actually workExpanding a field-sales platform internationallyWhere AI-powered field sales goes nextWill Hamblin is the founder of FieldSpot.ai, an AI-powered CRM and field-sales intelligence platform designed for teams that sell in person.Before starting FieldSpot, Will spent twelve years in education and became a vice principal before moving into field sales, where he sold card-payment services directly to businesses.That experience exposed how much useful sales intelligence was being lost between visits. Will built the first FieldSpot prototype using AI tools and vibe coding, then brought in experienced technical partners to develop the platform further.FieldSpot focuses on territory intelligence, renewal tracking, competitor data, route planning, mobile data capture, and AI-assisted sales workflows.Jason T Wade is an AI Visibility Architect and founder of NinjaAI and BackTier.Jason is also the host of the AI Visibility Podcast, where he examines how AI search, recommendation systems, autonomous agents, and emerging interfaces are changing how businesses and information get discovered.FieldSpot.aihttps://fieldspot.aiWill Hamblin on LinkedInhttps://www.linkedin.com/in/will-hamblin-182a1064/Jason T Wadehttps://jasonwade.comNinjaAIhttps://ninjaai.comBackTierhttps://backtier.com
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  Why Field Sales Needs Better Data: Will Hamblin on Building FieldSpot.ai 26.09.2026 21хв

  Why Field Sales Needs Better Data: Will Hamblin on Building FieldSpot.aiWill Hamblin went from vice principal to door-to-door card terminal sales—and eventually 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 what traditional CRM systems miss when sales happens face-to-face rather than behind a desk.FieldSpot.ai started as a simple AI-assisted prototype and evolved into a field-sales intelligence platform built around territory mapping, renewal timing, competitor intelligence, route planning, voice notes, business-card capture, and AI-assisted outreach.One of the central ideas is that field sales creates valuable data constantly, but much of it disappears. A rejection today may contain the information needed to close the account six months from now: the incumbent provider, contract expiration date, decision-maker, or reason the prospect was not ready.Will explains how FieldSpot captures that context and turns it into usable intelligence for future visits and outreach.Jason and Will also discuss the relationship between data quality and AI output. AI can generate better follow-up and more relevant outreach when it has access to actual field notes, conversations, territory information, and account history instead of generic CRM records.They also explore whether face-to-face selling could become more valuable as email, LinkedIn, and other digital channels become increasingly saturated with automated AI outreach.Will shares how he built FieldSpot's first prototype without being a developer, used AI and vibe coding to turn an idea into something tangible, found technical partners willing to join the company for equity, and began expanding the platform beyond its original UK payments market.Topics include:Why traditional CRMs often fail field-sales teamsTurning rejected visits into future sales intelligenceRenewal dates and competitor contract trackingTerritory mapping and route planningVoice notes and automatic field-data captureAI-assisted personalized outreachBuilding a startup through vibe codingWhy better data produces better AI outputThe growing value of face-to-face salesDesigning software around actual field conditionsBuilding a technology company as a nontechnical 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 built 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, conversations, territories, and renewal dates was frequently lost.Will built the first FieldSpot prototype using AI tools before bringing in experienced developers to develop the platform further.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 (GEO), Answer Engine Optimization (AEO), entity architecture, structured data, and AI discovery systems.Jason hosts the AI Visibility Podcast, exploring 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.com
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  How I Used GPT, Claude + Lovable to Build Halden 25.09.2026 2хв

  How I Used GPT, Claude + Lovable to Build HaldenThis is the process behind my Lovable Built It for Small Business Challenge entry.I didn’t start with:“Build me a detailing website.”I started with the actual challenge brief, the business problem, and the experience I wanted to create.I pasted the requirements into GPT, developed the concept, refined the copy and customer flow, and then moved the build into Lovable.From there, the process became a constant loop.Build → inspect → copy the site back into GPT or Claude → critique it → improve the prompt → rebuild.I would often copy nearly every word from the site back into the models and ask what was missing, confusing, inconsistent, or not aligned with the original vision.I also researched what other service businesses and software companies were doing — current customer experiences, automation, checkout patterns, and where agentic commerce appears to be heading — and brought those ideas back into the project.That research helped push Halden beyond a normal appointment website into something designed for both people and AI assistants.The biggest lesson from the process:Don’t stop when the build works. Analyze it again.Even after the product looks finished, run it back through the models, test the assumptions, refine the experience, and keep iterating.That back-and-forth between human direction, AI analysis, research, and Lovable implementation is how I built Halden.Halden Detail Co. was built in Lovable for the Built It for Small Business Challenge.The challenge is centered on reducing the friction between an inbound customer inquiry and a confirmed appointment.Halden handles that booking process while also exploring what happens when an AI assistant becomes another interface into the same business.@Lovable · #LovableChallengeJason T Wade is the founder of BackTier and works on AI Visibility, Generative Engine Optimization, Answer Engine Optimization, entity architecture, and emerging agentic experiences.His work focuses on how businesses can become easier for AI systems to discover, understand, recommend, and interact with.Halden / Lovable Buildhttps://winnerjasonwade.lovable.appLovablehttps://lovable.devJason T Wadehttps://jasonwade.comBackTierhttps://[email protected]