AI Visibility by Jason Todd Wade, Founder of BackTier

What is AI GEO - Best Explainer AI GEO usually means Generative Engine Optimization: the practice of making your brand and content more likely to be accurately selected, cited, and recommended in AI-generated answers—not merely ranked as a blue link in traditional search.Think of SEO as optimizing to rank on a results page. GEO optimizes to become part of the answer when someone asks ChatGPT, Perplexity, Gemini, Claude, Copilot, or Google’s AI search experiences a question. Google itself describes GEO and AEO as industry terms for optimizing content for AI search experiences, while emphasizing the same fundamentals: helpful, reliable, crawlable content built for users.developers.googleA person asks:“What is the best AI visibility agency for B2B SaaS companies?”A search engine might return ten links.A generative engine may instead write a synthesized answer:“Consider Agency A for technical SEO, Agency B for enterprise content, and BackTier for AI visibility architecture and entity-led optimization…”GEO is the work that increases the chance that:Your company is mentioned in that answerThe description of your company is correctYour site or research is citedYour expertise is used to shape the responseYour product is included in relevant comparisons and recommendationsThat distinction matters because AI systems commonly retrieve multiple sources and synthesize them into an answer rather than simply returning a ranked page.The simplest explanation

For forty years, if you wanted to ask a question of your data, you wrote a query. SQL, JOIN statements, indexes — a whole discipline built around structured retrieval. But in 2026, something strange is happening: people are just pasting their data into a context window and asking in plain English.Here's why. A SQL database is built for exact match. It's brilliant at "show me every order over $500 in March." It's terrible at "show me the orders that feel like they were placed by someone about to churn." That second question used to require a data scientist, a feature pipeline, and three weeks. Now it requires a prompt.Context windows have gone from 4,000 tokens to over a million. That means an LLM can hold an entire mid-sized dataset — or a well-indexed slice of a large one — directly in working memory, and reason over it the way a human analyst would, not the way a query planner would. It doesn't need a schema. It infers structure. It doesn't need you to know the exact column name. It understands "revenue" means the same thing as "total_sales."This isn't a full replacement — let's be honest about that. SQL still wins on scale, on transactional integrity, on anything where you need a guaranteed, auditable answer to a precise question. Nobody wants an LLM approximating your bank balance.But for exploratory work — the messy middle where most business questions actually live — the context window is winning. Retrieval-augmented systems now sit on top of traditional databases, pulling relevant rows into context and letting the model do the reasoning SQL was never designed for: nuance, inference, synthesis across tables that were never meant to talk to each other.The real shift isn't technical, it's organizational. Query writing used to be a specialized skill gating who could ask questions of the data. Now the gate is gone. Which means the bottleneck moves — from "who can write the query" to "who can ask the right question." And that's a much more interesting problem to have.If you're building data infrastructure in 2026, the question isn't SQL versus LLM. It's where the line between them should sit. Get that line right, and you get the best of both — precision where it matters, reasoning where it counts.

Delete Claude.md ? How to and why.

The AI Visibility Gap

Off Website AI, SEO, GEO, AEO and Digital Authority / Marketing in Florida, etc.

AI search is changing how people discover and evaluate brands. In this episode, Jason Todd Wade explores why traditional rankings alone no longer define visibility—and why organizations need to understand how AI systems describe, cite, and recommend them.Jason breaks down the shift from page-level SEO to entity-based visibility, including the role of structured identity, corroborating evidence, machine-readable proof, and authority signals across the web. The discussion covers what brand monitoring should look like across generative search and AI answer engines, why a business may rank in Google but remain absent from AI responses, and how organizations can build a more reliable presence in the systems shaping modern discovery.Jason Todd Wade is the founder of BackTier and an AI visibility strategist working at the intersection of entity resolution, generative search, structured data, SEO, GEO, AEO, and agentic commerce. He helps organizations structure their identity, authority, and proof so AI systems can discover, understand, cite, and recommend them. His work includes the Entity Lock Protocol and AI Visibility Architecture, with a particular focus on high-trust industries such as legal services.jasonwadeGuest bioSuggested episode title

Google AI Overviews are changing the objective of SEO. Ranking pages is no longer enough: brands need to be eligible for retrieval, accurately resolved as entities, supported by verifiable evidence, and credible enough to be incorporated into synthesized answers.In this conversation, AI Visibility architect Jason Todd Wade explains how businesses can move beyond keyword-only SEO and engineer the signals that influence how AI systems discover, interpret, cite, and recommend them. The discussion covers entity authority, structured knowledge, corroborating sources, semantic consistency, content evidence, and monitoring for identity drift across the web.The central shift is simple: traditional SEO seeks position; AI visibility seeks selection. A brand may rank highly in conventional Google results yet remain absent from an AI-generated recommendation if its identity, claims, and proof are fragmented or insufficiently supported.Why a high Google ranking does not automatically produce inclusion in AI Overviews or conversational AI answers.The difference between keyword relevance, entity resolution, source authority, and recommendation eligibility.How to define a canonical entity: what a company is, whom it serves, what it offers, where it operates, and which claims it can prove.Why structured data helps machines interpret facts but cannot substitute for independent corroboration and high-quality source evidence.How earned media, expert authorship, original research, case studies, and reliable third-party references reinforce authority.How to map buyer prompts to the entity–relationship–claim evidence needed for a defensible AI answer.Why brands should monitor citations, factual inconsistencies, entity confusion, and “drift” over time.How GEO, AEO, AI Overview optimization, and technical SEO fit into one visibility architecture.A concise operating model for the discussion:For example, a local law firm pursuing visibility for “best personal injury lawyer in Orlando” should not merely publish a keyword-targeted page. It needs consistent firm, attorney, service-area, credential, and review information; precise structured data; substantive attorney-led evidence; and credible third-party validation that helps systems verify the firm’s relevance and authority.Wade describes this broader approach as AI Visibility Architecture: creating an infrastructure through which systems can accurately discover, interpret, prioritize, cite, and select an entity.Suggested talking pointsPractical frameworkCanonical Entity→Machine-Readable Facts→Independent Evidence→Retrieval Coverage→Citation / RecommendationCanonical Entity→Machine-Readable Facts→Independent Evidence→Retrieval Coverage→Citation / RecommendationSuggested metadataAssetCopyMeta titleWinning Google AI Overviews: Entity Authority & AI VisibilityMeta descriptionJason Todd Wade explains how entity authority, structured evidence, and AI visibility architecture can help brands earn citations and recommendations in Google AI Overviews.URL slug/winning-google-ai-overviews-entity-authority-jason-todd-wadePrimary keywordsGoogle AI Overviews, entity authority, AI visibility, AI SEO, GEO, AEO, entity SEOYouTube thumbnail textWin AI OverviewsSocial hookGoogle rankings are no longer the finish line. The new question: is your brand structured, verified, and authoritative enough for AI systems to select?Follow-upsBuild an entity-relationship audit of your brand’s digital footprint — map how AI systems perceive your authority vs. your competitorsComputer​Create an AI Visibility roadmap — turn your existing content into machine-readable ontology structures for Gemini, ChatGPT, and PerplexityComputer​Jason Todd Wade BackTier SEO methodology entity authorityHow to optimize for Google AI Overviews entity SEOMeasuring AI visibility and brand citations in ChatGPT

AI is changing discovery—but agentic systems will change decisions.In this episode, Jason Todd Wade explains the shift from optimizing for pages and rankings to engineering visibility for the entities AI systems retrieve, interpret, trust, cite, and ultimately recommend. The next buyer journey will not always begin with a person searching, comparing tabs, and filling out a form. Increasingly, AI agents will research options, evaluate claims, filter vendors, and shape the shortlist before a human ever arrives.Jason breaks down what businesses need to establish now: a coherent entity identity, corroborated authority, machine-readable proof, and content architecture that makes the organization understandable across AI-mediated search and recommendation environments.Topics coveredWhy traditional SEO visibility alone is no longer enoughThe difference between a search journey and an agentic buyer journeyHow AI systems resolve, classify, and evaluate organizationsEntity resolution, structured data, corroboration, and proofWhat it means to be selected—not merely mentioned—by AIPractical priorities for brands preparing for agentic commerceJason’s work through BackTier focuses on AI visibility, entity resolution, generative search, and agentic commerce—helping organizations become discoverable, understandable, citable, and recommendable by AI systems.jasonwade+1Jason Todd Wade is the founder of BackTier and host of the AI Visibility Podcast. He builds AI visibility systems at the intersection of SEO, GEO, AEO, entity engineering, structured data, content architecture, and machine-readable proof. His work helps organizations structure their identity and authority so AI systems can discover, understand, cite, and recommend them.jasonwade+1Website: jasonwade.comEmail: [email protected] with Jason: BackTier AI visibility, entity-resolution, research, speaking, and agentic-commerce engagements.Guest bioContact

https://www.backtier.comBackTier | AI Visibility, SEO, and the Future of SearchChad Burmeister saw GPT before almost anyone was saying the letters out loud. He was working with a San Francisco company that kept mentioning a technology he heard as "RG3," and by the time he figured out they meant GPT, he had already watched it research faster and write better email than the reps he was training. That led to a book in 2019, a podcast that has now run more than five years and three hundred guests, and a decade of building outbound systems that most of the market is only now catching up to.This conversation is about the other half of that story: the part AI does not get. Chad crossed the word "artificial" out of his own show artwork and replaced it with "augmented," and he has since trademarked the phrase "humanity per hour" — a way of asking how much of your working hour is genuinely human value and how much is something a machine should have handled. His argument is not that automation fails. It is that companies who automate the human layer watch their conversion rates collapse and then quietly hire the callers back.Along the way: the LinkedIn outreach pattern that produced 350 replies from 580 connection requests, why he never leads with the ask, the AI agent that read six years of his inbox and built him a spreadsheet he didn't ask for, the sales floor experiment where one rep made 1,500 dials and booked 33 meetings in a single day, and the callback where remembering a driveway full of snow ninety days later opened the deal. Plus surveillance versus coaching, Flock cameras, and why the most useful question Chad asks every guest is simply what they're looking at next.TimestampsTime Segment00:00 Two podcast hosts, one mic — Chad's show at 5 years and 300+ guests00:45 The "RG3" story: hearing about GPT before ChatGPT made it public01:40 How he stays ahead — asking every guest what's hot; the operator running 52 agents for $20 a month02:40 The quadrant: repetitive, unwanted, high-value work is where AI belongs03:30 Turning AI loose on six years of inbox — and the guest-pitch spreadsheet it built unprompted04:40 LinkedIn as the highest-yield channel: LinkedIn Helper to GrowthX, 580 requests, ~350 replies06:00 Give, give, ask — why the uppercut never lands on the first message07:20 Career turn: Informatica, the Salesforce acquisition, and two months of a very green lawn08:15 The new role: capturing advisor conversations so one advisor can serve 1,000 clients, not 15009:00 Where the human stays — crossing out "artificial," writing in "augmented"10:00 "Humanity per hour," and the rep who only sells 30% of the day11:20 Relationship memory: SalesCard.ai, birthday prompts, and the CRM that should already do this13:20 The New Jersey callback — 14 inches of snow, 90 days later, perfect timing14:20 Hanging up on SDRs, and the trademark scammers who "are" the USPTO16:20 AI role-play so reps stop practicing on live customers17:00 The floor listen: six minutes, three objections, a million-dollar meeting18:40 Surveillance or coaching? Clari, Flock cameras, and teams that ask to be recorded20:50 Why 10X is an arbitrary number — the 10-cents-a-dial experiment, 1,500 dials, 33 meetings22:50 Where to find Chad: The AI for Sales Podcast, the new book, LinkedInChad Burmeister is the host of The AI for Sales Podcast, now past five years and 300 episodes, and the author of the AI for Sales book series. He has led sales and business development at Cisco-WebEx, RingCentral, ON24, ConnectAndSell, and Informatica, and founded ScaleX.ai and BDR.ai.His operating background runs through Cisco-WebEx, Riverbed, ON24, RingCentral, ConnectAndSell, and most recently Informatica, acquired by Salesforce. He founded ScaleX.ai and BDR.ai, was a Forbes NEXT 1000 honoree, and helped found the OutBound conference.

Starting a podcast or YouTube channel can feel overwhelming: What should you talk about? How do you write a script? What equipment do you need? How do you edit, title, describe, publish, and promote each episode?First-Time Podcasting & YouTubing with AI makes the process approachable.Hosted by Jason Todd Wade, the show follows the real-world journey of using AI as a creative partner—not a replacement for your point of view. Episodes cover topic selection, audience research, episode planning, scripting, recording, audio and video workflow, thumbnails, titles, descriptions, clips, distribution, and content repurposing.You will also hear honest lessons from building in public: what works, what does not, what takes too long, and how to move from “I should start” to publishing your first episode.Whether you are a business owner, aspiring creator, musician, consultant, parent, student, or someone with a story worth sharing, this is a practical place to begin.Episode titleI’m Starting a Podcast and YouTube Channel with AI—Here’s WhyEpisode descriptionWelcome to First-Time Podcasting & YouTubing with AI.In this first episode, Jason Todd Wade shares why he is starting this show, what he wants to learn in public, and how AI will support the process from idea to published episode.This is not a show about pushing a button and letting AI create everything. It is about using AI to reduce friction while keeping your personality, experience, opinions, and voice at the center.In this episode:Why so many people want to create but never publishThe difference between using AI as a tool and outsourcing your identityHow AI can help with topics, outlines, scripts, editing, titles, descriptions, and clipsWhat “good enough to publish” looks like for a first-time creatorWhat to expect as this podcast and YouTube journey developsIf you have been thinking about starting a podcast, launching a YouTube channel, or sharing your expertise online, start here.Personal site and creator hub: jasonwade.comAI visibility and business work: BackTierContact Jason / BackTier: BackTier contact

Ontology is the semantic layer that makes AI visibility repeatable: it defines the entities your brand cares about, their attributes, and the relationships AI systems should be able to infer. In AI search, that shifts the work from “rank this keyword” toward “be the trusted, retrievable source for this entity–relationship–claim.” [advancedwebranking](https://www.advancedwebranking.com/blog/seo-ontology-ai-search-geo-aivo-rag)## Why it mattersLLMs and AI search products synthesize answers around concepts, not merely matching strings. A domain ontology supplies a controlled model of:- **Entities:** Brand, product, service, people, locations, methods, industries, problems.- **Types:** “AI visibility audit” is a type of “consulting service”; “citation share” is a type of “visibility metric.”- **Properties:** Audience, price model, geography served, outcome, evidence, date updated.- **Relationships:** *BackTier provides AI visibility audits*, *an audit evaluates citation presence*, *citation presence contributes to AI share of voice*.- **Constraints and identity:** Canonical names, aliases, identifiers, and which claims are valid for which entities.This is especially important where terms are ambiguous. An ontology lets a system distinguish the *thing* “AI Visibility Architecture” from a generic phrase, and connect it consistently to related concepts such as GEO, AEO, entity resolution, retrieval, citations, and conversion. Ontologies are formal models of concepts, properties, and permitted relationships—the mechanism behind moving from text strings to understood entities. [advancedwebranking](https://www.advancedwebranking.com/blog/seo-ontology-ai-search-geo-aivo-rag)## Ontology vs. taxonomy| Layer | Purpose | Example for AI visibility ||---|---|---|| Ontology | Defines meaning and valid relationships | `AIVisibilityAudit` **evaluates** `CitationCoverage` || Taxonomy | Organizes content/navigation hierarchically | Services → Audits → AI Visibility Audit || Knowledge graph | Stores actual entity instances and facts | BackTier → provides → AI Visibility Audit || Schema markup | Publishes selected machine-readable facts on a page | `Organization`, `Service`, `Article`, `Person` JSON-LD |A taxonomy is useful for site architecture; an ontology is the reasoning model beneath it. Your taxonomy should reflect ontology logic rather than inventing disconnected category labels. [iloveseo](https://www.iloveseo.net/what-framework-to-use-for-increasing-visibility-in-ai-search/)## AI visibility operating modelFor a company like BackTier, build the ontology around four linked layers:1. **Market/problem layer** Define buyer problems: weak AI citations, entity ambiguity, fragmented brand facts, missing source authority, poor answer coverage.2. **Capability layer** Define the solutions: entity reconciliation, AI visibility audits, knowledge-graph strategy, structured-data implementation, content evidence architecture, prompt/citation monitoring.3. **Proof layer** Associate each capability with evidence: methodology pages, original research, client outcomes, expert authors, cited sources, case studies, datasets, and dated updates.4. **Query/answer layer** Map prompts to the entities, relationships, and evidence required to produce a defensibly recommendable answer.A simple graph pattern:\[\text{Buyer Problem} \rightarrow \text{Required Capability} \rightarrow \text{Service} \rightarrow \text{Evidence Asset} \rightarrow \text{AI Citation / Mention}\]For example:> “How can an enterprise improve visibility in AI answers?” > → `AI Search Visibility` > → `Entity Consistency`, `Evidence Coverage`, `Retrieval Readiness` > → BackTier’s service entities > → method documentation, expert content, structured facts, and independently corroborated proof.

Most companies still think of AI as a faster intern — write this email, summarize this call. That's not agentic automation. Agentic automation is when you architect a system that can go find a prospect, qualify them, personalize outreach, handle the reply, book the meeting, and hand off a warm lead — with a human only stepping in at the moments that actually require judgment.Here's how that pipeline is actually built. It starts with a research agent — it pulls firmographic and intent data, cross-references it against your ideal customer profile, and scores fit before a single message goes out. That score feeds a second agent, the outreach agent, which doesn't send templated blasts — it drafts messages grounded in specific, verifiable facts about that account: a recent funding round, a job posting that signals a pain point, a competitor's stumble.The critical piece most people get wrong is the handoff layer. When a prospect replies with something ambiguous — a soft no, a "maybe next quarter," a technical question — that's exactly where a brittle automation breaks. A well-architected system routes that reply to a reasoning agent that classifies intent and either responds appropriately or escalates to a human, with full context attached. No dropped threads, no generic follow-up that makes it obvious a bot missed the nuance.Conversion is where most builders stop too early. They automate the top of funnel and leave the close manual. But the same architecture — score, personalize, route, escalate — applies to nurture sequences, objection handling, even proposal generation. The agents don't need to be smarter than your best rep. They need to know precisely when they're out of their depth and hand off cleanly.The businesses winning with this right now aren't running one giant do-everything agent. They're running a chain of small, specialized agents, each with a narrow job and a clear escalation path. That's the architecture that scales — not because it's more impressive, but because it's debuggable. When something breaks, you know exactly which link in the chain failed, and you fix that link, not the whole system.

The default assumption for years was that AI training belonged in one place — a hyperscaler's data center, tightly coupled GPUs, centralized control. That assumption is being tested by a genuinely different architecture: decentralized training clusters, where compute is pooled across geographically distributed nodes rather than concentrated in one facility.Here's why this is gaining real traction rather than staying a research curiosity. Centralized cloud compute has a structural bottleneck: demand for frontier-scale training capacity has outstripped the physical build-out of new data centers, which means the biggest players are often compute-constrained regardless of budget, simply because you can't build a data center and get it online overnight. Decentralized approaches route around that bottleneck by aggregating spare, distributed capacity — underused GPUs sitting idle across many smaller facilities — into an effective cluster that can rival centralized ones for specific workloads.The technical breakthrough enabling this is in the coordination layer, not the hardware. Training a model across geographically distributed nodes used to be crippled by network latency between nodes — the constant synchronization large models require just couldn't tolerate the delay of nodes being far apart. Newer training approaches reduce how often nodes need to communicate, and tolerate the latency that does occur, well enough that distributed training is now genuinely competitive on cost and, for many workloads, on speed too.The economic case is compelling on its own terms. Idle GPU capacity sitting in smaller facilities is dramatically cheaper to access than reserved capacity at a hyperscaler operating near full utilization. For organizations training large models but not at the very largest frontier scale, decentralized clusters can offer meaningfully lower cost per training run, without the multi-year commitments centralized cloud contracts often require.The honest caveat: this isn't yet the obvious choice for every workload. The most latency-sensitive, tightly-coupled frontier training runs still favor centralized infrastructure. But for a large and growing set of mid-scale training workloads, decentralized clusters are no longer the scrappy alternative. They're becoming the more efficient default — and the gap is narrowing every quarter as the coordination technology improves.Jason Todd Wade is a Florida-based technology strategist, author, and entrepreneur working at the intersection of artificial intelligence, search, identity, and commerce. As founder of BackTier, he develops AI Visibility systems that help people, companies, and products become correctly understood, trusted, cited, and selected by artificial intelligence.Jason is the creator of AI Visibility Architecture and related frameworks, including Entity Lock Protocol™, BackTier Visibility Path™, and Agentic Visibility Path™. His perspective is informed by more than two decades of building and operating businesses across ecommerce, marketplaces, digital advertising, search, and publishing.He also serves as founder and general partner of LRSVC, an early-stage venture firm focused on AI-native companies; publishes the analytical series AI Dive; and hosts the AI Visibility Podcast. His forthcoming book, The End of Checkout, examines how AI agents, machine-readable commerce, and emerging payment systems are reshaping the way products are discovered, selected, and purchased.

There is more content being generated right now than at any point in human history, and an increasing share of it is written by models that can produce a competent paragraph on any subject in seconds. In that flood, you'd expect storytelling — the slow, specific, human craft of narrative — to be the first casualty. It's turning out to be the opposite.Here's why. Generated content, even very good generated content, tends to converge toward the statistically likely — the average of everything similar that's been written before. That makes it fast and competent and, over enough volume, genuinely forgettable. Narrative storytelling resists that convergence, because a real story is built from specific, non-average details: this particular failure, at this particular moment, told by someone who actually lived it. That specificity is exactly what statistical averaging smooths away.Readers and viewers are getting better, often without realizing it, at sensing that smoothness. Not because they can articulate "this feels AI-generated" — most people can't — but because content that never surprises you, never contradicts itself in a human way, never carries the small irrelevant detail that only a real experience produces, starts to feel hollow after enough exposure. That's the tell, even when nobody can name it.This is where the human advantage actually lives — not in craft mechanics like sentence construction, which models have gotten genuinely good at, but in the raw material of lived, specific, contradictory experience that a story is built from. A founder telling the real story of the year the company almost died has access to a texture no model can generate from a prompt, because that texture requires having actually been there.The strategic implication for anyone creating content right now: don't compete with generated content on volume or speed — that's a fight you structurally can't win. Compete on the thing generated content cannot manufacture, which is a specific, true story only you have access to. In a flood of average content, the non-average story isn't just surviving. It's becoming the scarcest, most valuable thing in the room.Jason Todd Wade is an AI Visibility architect, technology strategist, and founder of BackTier. His work focuses on helping organizations structure their identity, authority, and evidence so artificial intelligence systems can accurately discover, interpret, cite, and recommend them.Drawing on more than two decades of experience across ecommerce, marketplaces, search, advertising, and publishing, Jason created AI Visibility Architecture, Entity Lock Protocol™, BackTier Visibility Path™, and Agentic Visibility Path™. He is also the founder and general partner of LRSVC, publisher of AI Dive, host of the AI Visibility Podcast, and author of The End of Checkout.

Claude for Law Firms - Jason Todd Wade of BackTier.com

Ai Makes Starting a Podcast Easy

What is AI AEO?

Jason Wade (BackTier) sits down with Joe Casabona and Sarah Bean (Book Launchers) for a wide-ranging conversation on how AI is reshaping writing, content systems, book marketing, and digital legacy.They dig into the explosion of AI-generated books and content, the difference between using AI for grunt work versus outsourcing thinking, and why consistency still beats perfection. Sarah shares how Book Launchers approaches discoverability in an oversaturated market and introduces the Author Launch Kit. Joe explains his philosophy of keeping AI out of the first draft and using it for systems, proofreading, and automation so solopreneurs can stay consistent without burning out.The conversation turns personal and thoughtful on digital legacy — voice cloning, AI recreations of loved ones, the ethics of talking to the dead via language models, and why preserving real archives, stories, and books still matters more than synthetic versions. They also touch on YouTube/podcast algorithm signals, cold opens, and how all of that data ultimately trains the same machines we’re trying to be visible inside.Key themes: authenticity over volume, intent before tools, systems that support consistency, and the difference between a living legacy and a facsimile.---**Host Bio (Jason Wade)**Jason Todd Wade is the founder of BackTier. He works at the intersection of AI visibility, entity resolution, generative search, and agentic systems. His work focuses on how artificial intelligence discovers, interprets, cites, includes, and selects people, companies, and ideas — frameworks published as AI Visibility Architecture, Entity Lock Protocol™, BackTier Visibility Path™, and Agentic Visibility Path™.He helps brands and individuals become correctly understood and selected by AI systems rather than remaining invisible or misclassified.Website: [jasonwade.com](https://www.jasonwade.com/) BackTier: [backtier.com](https://www.backtier.com/)---**Guest Links****Joe Casabona** Helps solopreneurs build reliable systems (with AI handling tasks, not the thinking) so they can take time off without everything falling apart. Host of *Streamlined Solopreneur*. - Website: [casabona.org](https://casabona.org/) - Streamlined Solopreneur / resources: [streamlined.fm](https://streamlined.fm)**Sarah Bean** Marketing Manager at Book Launchers, a full-service self-publishing company that has worked with 800+ nonfiction authors. Focuses on marketing, partnerships, and discoverability in the age of AI. - Book Launchers: [booklaunchers.com](https://booklaunchers.com/) - Author Launch Kit (AI-powered marketing software for authors): [booklaunchers.com/alk](https://booklaunchers.com/alk/) or [authorlaunchkit.com](https://authorlaunchkit.com) - LinkedIn: [linkedin.com/in/sarahstephens22](https://www.linkedin.com/in/sarahstephens22)

AI SEO for Law Firms - Jason Todd Wade - BackTier

Jason Wade breaks down current frontier models with a practical focus on Anthropic’s Claude Fable 5 — the Mythos-class model that was temporarily restricted by U.S. government export controls shortly after its June 2026 launch and later restored.Key points from the session:- He’s not someone who jumps on every new model release. Most differences are subtle, and models increasingly specialize.- GPT’s auto-routing feels appropriate for a lot of everyday work.- Claude (and specifically Fable 5) requires more intentional use and learning, but delivers when it matters.- Fable 5 performed exceptionally on high-stakes work. He ran a 28-page legal document through it and called the results “unreal.”- Cost is real: he burned through roughly $150 in about two days because Fable 5 usage is not fully included in standard plans and is priced at frontier rates.- Recommendation: use Opus or other lower-tier models for routine work; reserve Fable 5 for the important, complex, or high-accuracy jobs.- Strong at drafting and especially strong at OCR/vision tasks (he cites ~94% performance versus the low-to-mid 80s he sees from GPT in comparable tests). He has also used multi-model systems like Manus that run multiple passes, but still rates Fable higher on the hard stuff.- Fable supports large batch processing (including zip uploads) for volume work — again, at a cost.Overall take: treat Fable 5 as a specialized high-end tool rather than a daily default. Learn the cost structure and route accordingly.**Bio** Jason Todd Wade is the founder of BackTier, an AI Visibility Infrastructure company focused on how artificial intelligence systems discover, interpret, trust, cite, include, recommend, and select people, companies, and brands. He developed AI Visibility Architecture, Entity Lock Protocol™, the BackTier Visibility Path™, and the Agentic Visibility Path™. His work sits at the intersection of entity resolution, generative/answer engine optimization, and agentic systems. He is based in Florida and hosts the AI Visibility Podcast.**Links** - Jason Wade site: https://www.jasonwade.com/ - BackTier: https://backtier.com/ - Claude Fable 5 (Anthropic): https://www.anthropic.com/claude/fable - Fable 5 / Mythos 5 announcement & updates: https://www.anthropic.com/news/claude-fable-5-mythos-5 - Redeployment note (export controls lifted): https://www.anthropic.com/news/redeploying-fable-5 - AI Visibility Podcast / BackTier content: available via jasonwade.com and major podcast platforms

The glory days of unlimited, cheap AI access are over. In this candid episode, Jason Wade breaks down the sudden shift from $20-a-month “do-anything” plans to aggressive usage limits, forced upgrades, and the new reality of paying real money for frontier models.What used to feel laughably inexpensive has turned into a constant game of switching between ChatGPT, Claude, and Grok just to stay productive. Early-adopter windows are closing fast as companies cash in on the demand they created. The message is clear: the name-brand models now cost real money — and the free ride is ending.The $20 era is dead — Heavy users who once ran massive workloads on basic plans are now hitting hard limits and being pushed to $100+ tiers.Usage has exploded — Over the last 12–18 months, AI consumption has grown so dramatically that previous pricing models no longer hold.Providers are cashing out — After attracting early adopters with generous limits, companies are tightening the screws and monetizing the demand they built.Multi-engine survival is the new normal — Users are forced to hop between ChatGPT, Claude, Grok, and others just to avoid hitting daily or monthly ceilings.Free will eventually return — for some — Long-term pressure may push models toward free or heavily subsidized access via Gemini, Copilot, and other distribution channels, but the frontier models will stay paid.[00:00] Opening — The glory days of AI are done. Usage and burn rates have exploded over the past year to year and a half.[00:15] The $20 miracle — Paying $20 to GPT used to unlock insane amounts of work. Fair-use policies were vague and rarely enforced.[00:30] The new reality — Hitting limits and being forced to upgrade to $100 plans. Switching engines becomes the only practical option.[00:45] Claude & Anthropic — Even the alternatives are adding extra charges and usage caps after the base $20–$30 tier.[00:55] Grok as a refuge — Hoping lower overall usage means looser limits. Checking recent Claude spend to gauge the damage.[01:00] Early-adopter trap — Tools that hyped early users are now cashing out. The window is closing.[01:10] Closing advice — Take advantage while you can. Frontier models now cost real money. Break out your wallet.Jason Todd Wade is the Founder of BackTier, focused on AI visibility, entity engineering, and AI Representation Engineering. He works on how AI systems classify, cite, and recommend people and organizations — covering entity resolution, schema markup, Knowledge Graph signals, and the practical infrastructure that determines whether AI actually knows who you are.Jason spends significant time inside the tools he talks about, which is why episodes like this cut through the hype and talk about the real cost of staying productive with frontier models.X / Twitter: @backtier_Brand: BackTier — AI Visibility & Entity EngineeringRelated topics: AI pricing shifts, multi-model workflows, entity consistency under changing tool economics, practical AI productivityConnect: Reach out on X (@backtier_) for conversations about AI tooling, visibility strategy, or the real economics of staying current.Key TakeawaysTimestamped NotesAbout the HostContact & Links

Most companies do not have a lead-generation problem. They have a follow-up, context, and conversion problem.In this episode of the AI Visibility Podcast, Jason Todd Wade is joined by Tom Gersic, founder of YouEx.ai, and Jonathan W. Pritchard, fractional CMO, performer, and AI workflow strategist.Tom explains how AI agents can connect website activity, calendar booking, CRM data, lead research, email follow-up, and personalized outreach into one lead-to-revenue system. He also discusses why warm leads lose value quickly, why five-minute follow-up matters, and how a web agent should function as a concierge rather than another ignored chatbot.Jonathan breaks down his local AI workflow using Claude Code and Obsidian, where notes, client context, frameworks, and institutional knowledge live together as Markdown files. Instead of repeatedly copying information into different AI tools, the AI works inside his existing system.The conversation also explores how AI can prepare prospect research and sales presentations, update CRM records overnight, support distracted or overloaded operators, and improve customer conversations by preserving context.Jonathan shares a broader marketing principle: the website should often be the conversion event, while trust is built through long-form content and human communication. His core point is simple: the company that understands and reflects the customer most accurately usually wins.AI agents for sales and marketingWarm leads versus cold outreachFive-minute lead responseAI-powered calendar bookingWebsite agents and digital conciergesCRM automationPersonalized email outreachProspect research and sales presentationsClaude Code and ObsidianMarkdown as organizational memoryLocal versus cloud-based AIWebsite conversion strategyYouTube and long-form trust buildingEnterprise AI adoptionAI Visibility and revenue operationsThe central takeaway: visibility alone is not enough. The strongest systems connect discovery, context, conversation, follow-up, and conversion. Tom Gersic is the founder of YouEx.ai, an AI-native lead-to-revenue platform designed to help businesses capture, understand, nurture, and convert warm leads.Before launching YouEx.ai, Tom spent 12 years at Salesforce, where he worked on product adoption and enterprise transformation. He later worked with an OpenAI partner supporting major enterprise AI rollouts. His current work focuses on practical B2B AI systems that connect web agents, lead research, CRM activity, calendar booking, and personalized follow-up.Jonathan W. Pritchard is a fractional CMO, communication strategist, performer, and AI workflow educator.After spending 15 years performing around the world, he brought those communication and audience skills into marketing and business strategy. He now helps organizations improve positioning, customer understanding, and conversion while building local AI systems with Claude Code, Obsidian, and Markdown-based knowledge repositories.Jonathan also teaches Obsidian and AI workflows through his online content and describes prompting as a form of directing: “I’ve been prompting people my whole life.”Jason Todd Wade is the Founder of BackTier and host of the AI Visibility Podcast. He helps organizations become understood, trusted, and recommended by AI systems through stronger entity authority, machine trust, and AI Visibility.YouEx.aihttps://[email protected] sitehttps://icanreadminds.comAI and Obsidian systemshttps://getmorewith.aiBackTierhttps://backtier.comLinkedInhttps://linkedin.com/in/backtierAI Visibility Podcasthttps://open.spotify.com/show/2GKjqiFMhh7pO15RXkkG5ETopicsGuest Bio — Tom GersicGuest Bio — Jonathan W. PritchardHost BioContactsTom GersicJonathan W. PritchardJason Todd Wade / BackTier

Using Ai to research and beat competitors w/ intelligence and strategy

Agentic E-Commerce and AI Buying Journeys - BackTier Media by Jason Todd Wade

BE THE DEFAULT ANSWER IN AI ENGINES LIKE CHATGPT, GOOGLE OVERVIEWS, GENINI, CLAUDE, GROK AND DEEPSEEK

Entity Engineering for AI Visibility - Jason Todd Wade of BackTier.com / BackTier

In Part 1, Jason Todd Wade speaks with New York matrimonial attorney Mia Poppe about how AI is changing legal practice from the inside out.Mia explains how she uses AI for idea generation, document comparison, gap analysis, research support, and law-firm marketing—while keeping legal judgment, risk assessment, and strategy firmly in human hands.The conversation covers:Why lawyers have been slow to adopt AIWhere AI is useful—and where it is dangerousUsing AI to compare long settlement agreementsWhy legal expertise still mattersHow AI can improve law-firm efficiencyClient confidence as the real product lawyers sellWhy AI search is changing how clients find attorneysThe shift from traditional SEO to AI VisibilityHow authority, consistency, and lived experience influence AI recommendationsThe central lesson: AI may not replace experienced lawyers, but lawyers who use it intelligently will work faster, communicate better, and become easier for the right clients to find. Jason Wade, Founder BackTier.docxDOCXMia Poppe, Esq. is a New York matrimonial and family-law attorney and the founder of Poppe & Associates. She represents clients in divorce, custody, support, and complex family-law matters. Drawing on both professional and personal experience, Mia brings a direct, strategic, and highly client-focused approach to legal advocacy.Jason Todd Wade is the Founder of BackTier and host of the AI Visibility Podcast. He helps organizations become understood, trusted, and recommended by AI systems through stronger entity authority, machine trust, and AI Visibility.Mia Poppehttps://miapoppe.comBackTierhttps://backtier.comAI Visibility Podcasthttps://open.spotify.com/show/2GKjqiFMhh7pO15RXkkG5E

AI adoption is moving faster than most organizations can govern it.In this episode of the AI Visibility Podcast, Jason Todd Wade speaks with Kate Marshall, Founder of TheGrai, Fractional Chief AI Officer, SHRM AI Instructor, and author of AI at Work.They discuss the growing accountability gap around AI agents, what the Workday litigation could mean for employers, and why non-technical leaders are increasingly being asked to manage systems they did not build and may not fully understand.Kate explains why successful AI adoption requires more than buying tools. Organizations need clear policies, controlled testing, trained employees, approved-tool lists, incident-response plans, and defined human ownership.The conversation also explores:Why many AI pilots remain stuck in experimentationWhere organizations can begin with lower-risk use casesHuman accountability for autonomous systemsChange management and employee fearData quality and organizational readinessCyber-insurance requirements for AI adoptionKill switches, authorization controls, and incident responsePrivacy risks from wearables and always-on recording devicesBalancing innovation against security and complianceThe central question is no longer whether companies will use AI. It is whether they can use it quickly without losing control of risk, accountability, and trust. Jason Wade, Founder BackTier.docxDOCXKate Marshall is the Founder of TheGrai, a Fractional Chief AI Officer, AI adoption strategist, SHRM AI Instructor, and author of AI at Work. After nearly two decades at the SANS Institute, she now helps executives, HR leaders, and teams implement AI through practical training, governance, workforce readiness, and responsible adoption.Jason Todd Wade is the Founder of BackTier and host of the AI Visibility Podcast. He helps organizations become understood, trusted, and recommended by AI systems through stronger entity authority, machine trust, and AI Visibility.Kate Marshallkatemarshall.aiTheGraithegr.aiBackTierbacktier.comAI Visibility PodcastSpotify

Florida Slice Project Update by Jason Todd Wade - BackTier

FAQ's and AI Visibility - Jason Todd Wade, BackTier AI SEO / GEO / Vibe

Most organizations know they need AI—but few can clearly explain what problem they’re trying to solve.In this episode of the BackTier AI Visibility Podcast, Jason Todd Wade sits down with Brian Beck, a senior AI and cybersecurity consultant at Proxurve Solutions, to discuss why successful AI adoption starts long before choosing a model.Brian shares why he calls himself the “concrete guy,” helping organizations build the secure foundation, governance, and roadmap needed before deploying AI. The conversation covers AI readiness, cybersecurity, Microsoft Copilot, Claude, enterprise adoption, organizational change, and why leadership—not IT—is responsible for AI success.Topics include:Why most organizations struggle to define AI strategyBuilding an AI roadmap before implementationCybersecurity as the foundation for AIMicrosoft Copilot, Claude, Gemini, and enterprise AIAI governance and acceptable-use policiesAI readiness versus AI hypeMeasuring ROI from AI investmentsWhy AI is a leadership challenge, not just an IT challengeThe future of enterprise AI adoption“Get out of the me and into the we.”Jason Todd Wade is the Founder of BackTier and creator of the AI Visibility Framework™, helping organizations become understood, trusted, and recommended by AI systems through stronger entity authority, machine trust, and AI Visibility.Brian Beck is a senior AI and cybersecurity consultant at Proxurve Solutions, where he helps organizations prepare for AI through stronger infrastructure, governance, cybersecurity, and strategic planning. His work focuses on helping business leaders build secure, scalable AI initiatives that deliver measurable business outcomes.BackTierhttps://backtier.comJason Todd Wadehttps://jasonwade.comhttps://www.linkedin.com/in/jasontoddwadeBrian Beckhttps://www.linkedin.com/in/brianbeck73Proxurve Solutionshttps://proxurve.comSubscribe to the BackTier AI Visibility Podcast for conversations with AI founders, executives, researchers, and business leaders exploring AI Visibility, enterprise AI, cybersecurity, and the future of AI-powered business.

For the past two years, the AI conversation has been dominated by bigger models, record funding rounds, and breakthrough announcements. This week, the headlines changed.Instead of celebrating the next AI model, financial markets are asking harder questions about infrastructure, capital spending, profitability, and whether the massive investments fueling the AI boom can generate sustainable returns.In this episode, Jason Wade breaks down what’s actually happening behind the headlines—from Nvidia and chipmakers to data centers, global AI competition, and why Wall Street’s concerns represent a new phase of AI rather than the end of it.Most importantly, Jason explains why businesses are focusing on the wrong opportunity. While investors debate AI infrastructure, a much larger commercial shift is emerging: AI systems are becoming the gatekeepers of discovery, recommendations, and purchasing decisions.The next competitive advantage won’t simply be using AI.It will be whether AI chooses you.Why AI headlines suddenly became financial headlinesNvidia’s outsized influence on the AI economyWhat “circular financing” means—and why investors careWhy AI is becoming infrastructure instead of just softwareThe rise of Asia as an AI superpowerHow AI regulation is entering its operational phaseWhy AI Visibility may become one of the most important business categories of the decadeThe shift from optimizing for search engines to optimizing for AI recommendationsJason Todd Wade is the founder of BackTier and NinjaAI and the creator of the AI Visibility framework. He helps organizations understand how large language models evaluate, interpret, and recommend businesses, professionals, brands, and organizations inside AI-generated answers.With more than two decades of experience building digital businesses, Jason focuses on the emerging discipline of AI Visibility—helping organizations improve how they are discovered, trusted, cited, and recommended by AI systems such as ChatGPT, Claude, Gemini, Perplexity, and other large language models.He hosts the AI Visibility Podcast, where he explores the intersection of artificial intelligence, search, knowledge graphs, entity understanding, and the future of machine-mediated discovery.Website: https://backtier.comAI Visibility: https://backtier.comNinjaAI: https://ninjaai.comJason Todd Wade: https://jasonwade.comLinkedIn: https://linkedin.com/in/jasontwadeSubscribe:SpotifyApple PodcastsYouTubeFollow BackTier for research, frameworks, and practical strategies on AI Visibility, AI SEO, entity optimization, and machine-mediated discovery.

Questions and Keywords - AI Visibility by Jason Todd Wade of BackTier.com

AI Visibility PodcastEpisode TitleSmall Models, Big Impact: Why AI Visibility Isn’t Just About GPTFor the past few years, the AI conversation has been obsessed with one thing: bigger models.GPT-4. Claude. Gemini. Massive parameter counts. Bigger context windows. Bigger benchmarks.The assumption has almost always been that bigger equals better.But quietly, another trend has been accelerating beneath the surface.Small models.Today we’re going to talk about why small language models—or SLMs—may become one of the biggest forces shaping AI visibility over the next decade.And more importantly, why almost nobody in SEO, GEO, or AI visibility is talking about what this means.A small language model is exactly what it sounds like.Instead of hundreds of billions—or even trillions—of parameters, these models might contain one billion, three billion, or seven billion parameters.Examples include Microsoft’s Phi family, Meta’s Llama 3.2 1B models, Mistral’s smaller releases, Gemma from Google, and many others.They aren’t trying to compete with GPT-5 at writing novels or solving graduate-level math.They’re designed to be incredibly fast.Cheap.Efficient.And capable of running directly on laptops, smartphones, factory equipment, medical devices, and private enterprise servers.That’s an enormous shift.For years the assumption was simple.Every AI task would be sent to a giant model running in the cloud.Increasingly, that’s not what companies are building.Instead, they’re creating AI systems made up of multiple specialized models.Think of it like a business organization.Not every employee is the CEO.Receptionists answer phones.Accountants handle finances.Lawyers review contracts.Executives make strategic decisions.AI is moving in exactly the same direction.A small model might classify a request.Another determines user intent.A third searches company documentation.Only then does a frontier model generate the final answer.The large model becomes the specialist—not the entire company.This matters because AI visibility doesn’t happen only when ChatGPT writes an answer.It begins much earlier.Imagine you ask an enterprise AI assistant:“I need an employment attorney in Orlando.”Before a large model ever starts writing, several things probably happen.A small model identifies that this is a legal question.Another determines that it’s employment law.Another extracts the geographic location.Another retrieves candidate firms.Only then does the reasoning model compare options and produce recommendations.Your organization has to survive every one of those interpretation steps.If a small model misunderstands your business, the larger model may never even know you exist.This is why I’ve increasingly described AI visibility as an interpretation problem rather than simply a generation problem.Generation gets the attention.Interpretation determines who gets invited into the answer.Every AI system first has to decide what you are before it can recommend you.That’s true whether we’re talking about ChatGPT, Claude, Gemini, Perplexity, enterprise copilots, customer support agents, or autonomous business workflows.Recognition comes before recommendation.Small models may actually make structured information even more valuable.Large frontier models possess enormous amounts of world knowledge.Smaller models don’t.They’re more likely to depend on explicit relationships.Structured metadata.Entity names.Clear descriptions.Schema.Knowledge graphs.Consistent terminology.That means ambiguity becomes even more expensive.If your organization describes itself five different ways across the web, smaller models may struggle to confidently classify what you actually do.Consistency becomes a competitive advantage.This also changes how businesses should think about AI optimization.

7/26 AI News by Jason Todd Wade of BackTier and LRSvc

BackTier.com / JasonWade.com TitleAI Visibility in Under Five Minutes: Why AI Has to Understand You Before It Can Recommend YouWhat is AI Visibility?In this solo episode, Jason Todd Wade explains one of the most misunderstood concepts in AI: before ChatGPT, Gemini, Claude, or Google AI Overviews can recommend your business, they first have to understand it.Jason explores why AI Visibility isn't about gaming algorithms or publishing more content—it's about creating a clear, consistent, and trustworthy digital identity that AI systems can confidently interpret.He also introduces the concept of Entity Lock, why founder stories matter more than marketing slogans, and how organizations can move beyond legacy branding to become the obvious answer when AI is making recommendations.Topics include:What AI Visibility actually meansWhy AI must understand your business before it can recommend itThe difference between branding and entity understandingEntity Lock and building a consistent digital identityWhy founder stories shape organizational trustThe future of search in an AI-first worldHow businesses can prepare for AI-driven discoveryJason Todd Wade is the Founder of BackTier and the creator of the AI Visibility framework. He helps organizations become understood, trusted, and recommended by artificial intelligence through stronger entity authority, machine trust, and structured digital narratives. His work explores how AI systems interpret businesses and why visibility in the AI era requires far more than traditional SEO.BackTierhttps://backtier.comJason Todd Wadehttps://jasonwade.comhttps://www.linkedin.com/in/backtierFollow the AI Visibility PodcastSpotifyApple PodcastsYouTubeIf you enjoyed this episode, subscribe to the AI Visibility Podcast for conversations exploring AI Visibility, machine trust, entity authority, and how organizations can become the businesses AI chooses to recommend.

BackTier.com / JasonWade.com

[email protected]

Most organizations are asking how to adopt AI. Few are asking a more important question:How does AI decide whether to trust your business?In this episode, Jason Todd Wade is joined by Anne Cantera, founder of Elementyl Intelligence, and Nathan Graham, founder of Synthetic Echo, for a wide-ranging discussion on the next phase of AI.The conversation covers practical AI implementation, agentic systems, voice AI, automation, AI development workflows, human-centered design, and why trust—not just adoption—may become the defining competitive advantage of the AI era.Topics include:Human-centered AI implementationAgentic AI and enterprise developmentPractical AI workflows for growing businessesVoice AI and conversational designAI governance and responsible deploymentBuilding AI products with modern coding toolsWhy AI trust may become the next competitive advantageAI Visibility and how organizations become understood, trusted, and recommended by AI systemsWhether you're building AI products, leading digital transformation, or preparing your organization for an AI-first future, this conversation explores where the industry is headed—and what comes next.Jason Todd Wade is the Founder of BackTier and creator of the AI Visibility framework. His work focuses on helping organizations become understood, trusted, and recommended by artificial intelligence through stronger entity authority, machine trust, and AI Visibility.Anne Cantera is the Founder and CEO of Elementyl Intelligence, where she helps organizations safely design, adopt, and deploy human-centered AI. Her expertise spans conversational AI, voice AI, agentic systems, UX, AI strategy, and responsible AI implementation. Anne is also the creator of VoiceofAI.io, a free educational platform dedicated to AI learning and workforce readiness.Nathan Graham is the Founder of Synthetic Echo, an AI consulting and automation company helping small businesses implement practical AI systems that increase capacity without sacrificing the human relationship. He is the author of multiple books on AI workflows and hosts The Synthetic Echo Podcast, where technology, business, and human connection intersect.Jason Todd Wade / BackTierhttps://backtier.comhttps://linkedin.com/in/jasontoddwadeAnne Canterahttps://elementylintelligence.aihttps://voiceofai.iohttps://linkedin.com/in/anne-canteraNathan Grahamhttps://syntheticecho.comhttps://linkedin.com/in/nathan-grahamhttps://syntheticecho.com/podcastIf you enjoyed this episode, subscribe to the AI Visibility Podcast and leave a review. New conversations explore how AI is changing search, trust, recommendation, and the future of digital visibility.

The CIA has moved past the hype phase of AI. It’s now building AI “co-workers” into analyst workflows, testing over 300 AI projects, and even using AI to generate intelligence reports for the first time in its history. Across the U.S. intelligence community, thousands of analysts already rely on a CIA-developed generative AI system, Osiris, to help with search, drafting, and triage at scale.pbs+3This episode uses the CIA’s AI adoption as a lens for AI visibility. We break down how intelligence agencies are using AI for data triage, translation, transcription, and open-source collection, and why that’s directly relevant to any organization that wants to be discovered, interpreted, and cited by AI systems like ChatGPT, Gemini, Claude, Perplexity, and AI Overviews.meritalk+3You’ll hear how BackTier’s AI Visibility Architecture maps to this reality: turning fragmented, unstructured information about a business into clear, machine-readable authority that models can resolve, trust, and recommend. We’ll connect CIA-style data triage and AI “mission partners” to entity resolution, schema, citation engineering, and answer eligibility — the core layers of BackTier’s visibility stack.open.spotify+2Whether you’re running a brand, a city initiative, or a complex organization, this episode helps you see AI not just as a content generator, but as an interpreter and gatekeeper. If AI systems are becoming the new way decisions get informed, then AI visibility is the infrastructure that decides who shows up in those decisions.youtubepodcasts.apple+1Bio (podcast-optimized):Jason Todd Wade is the founder of BackTier, an AI visibility infrastructure firm that makes brands legible to AI systems and answer engines. His work focuses on entity clarity, structured authority, off-page trust signals, and the systems that determine how platforms like ChatGPT, Google Gemini, Perplexity, and Claude interpret and recommend organizations.podcasts.apple+1youtubeThrough BackTier, Jason builds AI Visibility Architecture, Agentic Lead Generation systems, and Rapid Response Narrative frameworks that turn fragmented information into coherent, machine-readable authority. He documents the methodology in the AiVisibility book series and through the AI Visibility Podcast, BackTier Media, and City Prompt.podcasts.apple+2youtubeBased in Central Florida, Jason’s background spans AI visibility, SEO, entity mapping, civic systems, and applied research, with prior work including founding NinjaAI.com, now part of BackTier.backtieryoutubeYou can use this as a standard “Links” block under every episode:Links mentioned:BackTier — AI Visibility Infrastructurehttps://backtier.combacktierBackTier AI Visibility Architecture (services page)https://backtier.com/services/architecturebacktierAbout BackTier and AiVisibilityhttps://backtier.com/about-usbacktierAiVisibility book series(Link to primary sales page you prefer: Amazon / Audible / Spotify)backtierAI Visibility Podcast by Jason Todd WadeApple / Spotify show pages:https://podcasts.apple.com/ie/podcast/ai-visibility-by-jason-todd-wade-founder-of-backtier/id1826332929podcasts.applehttps://open.spotify.com/episode/4JhkNxIwNe7YJwUl6eOAX1open.spotifyBackTier Media and City Prompt (YouTube / video hub)https://www.youtube.com/watch?v=iTJxR2JeZEQ

Vibe coding exploded into the technology conversation by promising something radical: functional websites, dashboards, presentations, and software created largely through natural language.But has the excitement already peaked?Jason Todd Wade examines Lovable, Base44, Claude Code, and the broader shift from traditional software development toward AI-directed creation. He explains why the current slowdown may represent the trough between initial hype and mass adoption—and why Lovable could emerge as the defining consumer platform of the category.Vibe coding is not disappearing. It may be preparing to replace a meaningful portion of conventional software.Jason Todd Wade is an AI Visibility Architect, entrepreneur, and founder of BackTier and NinjaAI. He designs systems that improve how companies, people, and ideas are understood, included, cited, and recommended by artificial intelligence.BackTierNinjaAIJason Todd WadeAI Visibility Podcast

on

Search for “Jason Todd Wade,” and the identity is increasingly clear: founder of BackTier and NinjaAI, host of the AI Visibility Podcast, and creator of AI Visibility frameworks including Entity Lock Protocol™ and the BackTier Visibility Path™.Remove “Todd,” however, and the results become unstable.In this episode, Jason uses his own identity as a live case study in entity resolution. He explains why ranking for an exact name does not mean AI systems truly understand who someone is—and why the real test is whether the same person can be correctly identified through shortened names, companies, expertise, projects, and natural-language questions.Topics include:The difference between visibility and entity resolutionWhy exact-match rankings create false confidenceHow AI systems distinguish people with similar namesThe difference between identity repetition and independent corroborationHow Entity Lock Protocol™ reduces machine ambiguityWhy more content can sometimes create more confusionThe contextual-query test for people and companiesThe BackTier Visibility Path™: Citation → Inclusion → SelectionWhy reliable recognition matters more than ranking for your nameThe Jason Wade Problem is not merely a personal naming issue. It is a model for understanding whether AI systems can consistently recognize any person, company, product, or organization when the exact identifier disappears.Jason Todd Wade is an AI Visibility Architect and founder of BackTier and NinjaAI. He designs systems that help people and organizations become correctly discovered, understood, cited, included, recommended, and selected by AI systems.He is the creator of Entity Lock Protocol™ and the BackTier Visibility Path™—Citation, Inclusion, Selection. His work focuses on entity resolution, machine-readable authority, AI discovery, GEO, AEO, SEO, and recommendation systems.Jason Todd Wade: JasonWade.comBackTier: BackTier.comEntity Lock Protocol: JasonWade.comNinjaAI: NinjaAI.comFlorida Slice: FloridaSlice.comAI Visibility Podcast: BackTier.com/podcastHost BioLinks

How AI Visibility Became the Umbrella Term for SEO, GEO, and AEOJason reflects on the growing argument over SEO, AI SEO, GEO, AEO, and the expanding collection of acronyms used to describe visibility inside AI-generated answers.For BackTier, the broader term became AI Visibility. Jason did not arrive at it through an industry trend or LinkedIn debate. An AI system recommended the term roughly a year earlier because it could classify the work more clearly than he could at the time.That recommendation became the foundation for a book, podcast, frameworks, and a larger body of work around how companies become understood, included, cited, selected, and recommended by AI systems.The irony is that the term has now spread rapidly. LinkedIn is suddenly full of self-described AI visibility experts, even though the discipline itself is still being defined.This episode examines why AI Visibility is a larger category than SEO, GEO, or AEO—and what happens when a useful strategic term becomes the next industry label everyone adopts.Show Notes

The Wrong AI SEO Debate: Why AI Visibility Is a New Optimization DisciplineEpisode NotesThe SEO industry is obsessed with the wrong debate.Is AI visibility just SEO? Is SEO dead? Those questions miss the real shift.In this episode, Jason Wade argues that the conversation isn't about replacing SEO—it's about understanding how entirely new optimization disciplines emerge.Drawing from weeks of research across information retrieval, recommendation systems, knowledge graphs, large language models, academic literature, and documentation from Google, OpenAI, Anthropic, Microsoft, and Perplexity, he introduces a different framework:Optimization disciplines are not defined by the technology they use—they're defined by the objective they optimize.SEO optimizes retrieval.AI Visibility optimizes the probability that an entity is understood, trusted, selected, cited, recommended, and ultimately acted upon by intelligent systems.That distinction changes everything.Rather than arguing over acronyms like GEO, AEO, LLM Optimization, or AI SEO, this episode explores the larger theory of optimization in the age of artificial intelligence and why the next decade may require an entirely new way of thinking about digital visibility.**Topics include:**- Why the current AI SEO debate misses the bigger picture- Retrieval vs. reasoning as optimization objectives- How AI systems actually decide what to cite and recommend- Why multiple AI models produce different answers- The evolution from SEO to AI Visibility- A framework for the next generation of optimization disciplines- Why terminology matters less than explanatory powerIf the future belongs to intelligent systems rather than search engines alone, what exactly should we be optimizing?---**Podcast Bio**Jason Todd Wade is the founder of BackTier and creator of the AI Visibility framework. He researches how artificial intelligence systems discover, interpret, trust, and recommend people, organizations, and ideas across search engines, large language models, and emerging AI platforms. His work focuses on the evolution of optimization from traditional SEO toward the broader challenge of visibility within intelligent systems.

TitleAI Limits Are Here: Why ChatGPT, Perplexity, and AI Browsers Are Starting to Say "No"Episode DescriptionFor the first time, I hit ChatGPT's usage limit—and it got me thinking about where AI is heading.In this episode, I test a new microphone, talk about recording without headphones, compare browser-based AI experiences like Atlas and Perplexity Comet, and discuss why AI companies are tightening usage limits.The reality is simple: inference is expensive. The era of effectively unlimited AI may be coming to an end as providers look for sustainable business models.Topics include:Testing a condenser microphone with phantom powerRecording with speakers instead of headphonesHitting ChatGPT usage limitsAtlas Browser vs. the ChatGPT appPerplexity Comet and usage creditsWhy AI companies are limiting heavy usersThe economics of AI compute and inferenceWhat AI pricing could look like over the next few yearsIf you use AI every day, these changes will affect you.ContactJason WadeFounder, BackTierAI Visibility Architect🌐 https://backtier.com🌐 https://ninjaai.comFollow the AI Visibility Podcast for practical discussions on AI, search, visibility, and where the industry is heading.

AI Is Establishing the Record of Your BusinessEvery business has a public reputation. Increasingly, it also has an AI record.Large language models don't simply search the web—they reconstruct an understanding of your business from thousands of signals spread across websites, news articles, reviews, business profiles, podcasts, videos, public documents, and countless other sources. That machine-readable record increasingly influences whether your company is cited, recommended, trusted, or ignored.In this episode, Jason Todd Wade explains why traditional SEO is no longer enough and why businesses need to understand how AI systems build, verify, and reinforce their understanding of organizations.Topics include:How AI establishes a business's machine-readable identityWhy inconsistent information creates AI confusionThe difference between ranking in search and being recommended by AICitations, corroboration, and entity understandingWhy every business now has an evolving AI recordHow AI Visibility differs from traditional SEOPractical steps businesses can take todayAs AI becomes the first place people ask for recommendations, the question shifts from "Can customers find you?" to "Will AI recommend you?"Jason Todd Wade is the founder of BackTier and NinjaAI.com and the creator of the AI Visibility framework. He helps organizations understand how artificial intelligence systems discover, interpret, verify, and recommend businesses.With more than two decades of experience in digital strategy, search, e-commerce, and AI, Jason focuses on the emerging discipline of AI Visibility—the practice of deliberately shaping how machine intelligence understands an organization's identity, expertise, and authority.His work explores the transition from traditional search rankings to machine-generated recommendations, helping businesses build durable authority across AI systems rather than optimizing for a single search engine.Website: https://backtier.comNinjaAI: https://ninjaai.comLinkedIn: https://www.linkedin.com/in/jasontoddwadeYouTube: https://www.youtube.com/@BackTierApple Podcasts: https://podcasts.apple.com/Spotify: https://spotify.com/Follow for more conversations on AI Visibility, AI SEO, entity authority, and the future of how businesses are discovered in the age of artificial intelligence.Episode DescriptionAbout Jason Todd WadeConnect

TitleMonday Update: Testing Kimi, Building Better AI Results, and Why Small Podcast Audiences Still MatterJason Todd Wade tests a new microphone and delivers a Monday update on the AI tools, models, and publishing systems he has been experimenting with.He discusses the sudden demand surrounding Kimi and its newest model, why Chinese AI systems such as Kimi and DeepSeek can be frustrating but unusually effective for finding overlooked information, and why users should never judge an AI system solely by its first answer.The real skill is iteration. Jason explains how he continually challenges, redirects, and improves AI output rather than expecting a polished result from the first prompt. He also examines how cultural differences influence the way American, Chinese, and European AI models organize and present information.The episode then moves into AI voice and video production. Jason compares ElevenLabs and VEED for voice cloning, long-form audio, podcast clips, subtitles, and YouTube production, including how he uses the platforms to produce narrated content while managing credit costs.Finally, he addresses the reality of starting a podcast. New shows often receive little or no initial traction. Jason argues that creators should build for their business, expertise, and long-term authority rather than chasing inflated audience numbers. Thirty-eight relevant listeners can be more valuable than thousands of irrelevant ones.Even when a podcast does not immediately attract a large human audience, the published content still creates searchable, indexable material that AI systems, search engines, and recommendation platforms can discover over time.Topics discussed:Testing Kimi and its newly released AI modelWhy DeepSeek has become more useful over timeThe strengths and weaknesses of Chinese AI modelsWhy the first AI response is rarely the final answerCultural differences in AI-generated informationElevenLabs versus VEED for voice cloningProducing short-form video podcasts with AIWhy new podcasts receive little initial tractionRelevant audiences versus vanity metricsPodcasting as long-term AI visibility infrastructure

AI can write, edit, design, code, and even sound like you—but it can't be you.In this episode, Jason Todd Wade joins Michele Flamer, host of the Living Out Loud Podcast and author of The Connect Effect, for a conversation about what becomes more valuable as artificial intelligence becomes more capable.Rather than debating whether AI is good or bad, they explore how it changes the way we communicate, build businesses, create content, and develop relationships. From podcast production and AI-assisted writing to customer experience, research, and decision-making, they share practical ways they're using AI every day while discussing the importance of maintaining human judgment, authenticity, and trust.The conversation also examines AI hallucinations, verification, emotional intelligence, "vibe coding," and why disagreement, curiosity, and genuine listening remain essential skills in an increasingly automated world.As AI lowers the cost of creation, the premium shifts to something machines cannot manufacture: meaningful human connection.Why authenticity becomes more valuable as AI improvesHow AI can increase productivity without replacing peopleUsing AI to think more clearly instead of reacting emotionallyPodcast production, editing, and content creation with AIVibe coding and making software development accessibleAI tools for research, design, marketing, and small businessThe importance of verifying AI-generated informationHallucinations, citations, and responsible AI useHuman customer service in an automated worldAI as a tool for reflection, journaling, and personal growthWhy healthy relationships require disagreement and repairBuilding trust and community in the age of artificial intelligenceMichele Flamer is a technology sales executive, relationship strategist, podcast host, and author whose work focuses on communication, customer experience, leadership, and authentic human connection.With experience spanning retail, e-commerce, software, customer feedback, and national sales leadership, she helps organizations better understand the voice of their customers while building stronger relationships internally and externally.She hosts the Living Out Loud Podcast, featuring conversations with entrepreneurs, nonprofit leaders, public figures, and changemakers making a positive impact in their communities. Michele is also the author of the forthcoming book The Connect Effect, which explores how trust, authenticity, and meaningful relationships create lasting success in business and life.Jason Todd Wade is the founder of BackTier and creator of the AI Visibility framework, helping organizations become discoverable, understandable, and recommendable inside AI systems.His work focuses on AI Visibility, Generative Engine Optimization (GEO), Answer Engine Optimization (AEO), entity authority, structured content, digital reputation, and the shift from traditional search to machine-generated discovery.Jason hosts the AI Visibility Podcast, where he interviews founders, technologists, marketers, attorneys, creators, and business leaders exploring how artificial intelligence is reshaping search, marketing, commerce, and decision-making.LinkedIn: Michele FlamerInstagram: @michele_flamerTikTok: Living Out Loud PodcastPodcast: Living Out Loud PodcastBook: The Connect Effect (forthcoming)BackTier: https://backtier.comJason Wade: https://jasonwade.comEmail: [email protected]

Maxwell Ivey, known worldwide as The Blind Blogger, has spent nearly two decades proving that accessibility isn't about compliance and shame — it's about reputation, innovation, and growth. His journey is one of a kind: carnival owner, amusement ride broker, life goals coach, podcast booker, and now accessibility advisor to PodMatch and founder of The Accessibility Advantage.In this episode, Max shares:Why inclusive design makes products, content, and marketing better for everyoneThe most common website mistakes that cost businesses disabled customersHow large the disability market really is — and why happy disabled consumers become unpaid influencersSimple accessibility improvements you can make todayHis personal toolkit for overcoming adversity: inter-dependence, self-determination, determined positivity, and honest storytellingGuest BioMaxwell Ivey is an accessibility expert, author of four books (two award winners), speaker, and host of The Accessibility Advantage podcast. He taught himself HTML in 2007 to launch his first business selling carnival rides online, and became a trailblazer by being openly blind online when most disabled entrepreneurs hid their challenges. He's been published in Consumer Reports, writes the "Barrier Free Bytes" column for PHP Architect, serves on PodMatch's advisory board, and has appeared on hundreds of podcasts — occasionally singing an original song along the way.Contact & LinksWebsite: https://www.theaccessibilityadvantage.com/homeLinkedIn: https://www.linkedin.com/in/maxwellivey/Instagram: https://www.instagram.com/TheBlindBloggerYouTube: https://www.youtube.com/maxwelliveyFacebook: https://www.facebook.com/maxwelliveyX: https://www.x.com/maxwelliveyPinterest: https://www.pinterest.com/maxwellivey