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

What is AI GEO - Best Explainer 17.08.2026 1min

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

Why LLM Context Windows Are Replacing Traditional SQL Database Architectures In 2026 16.08.2026 2min

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. 16.08.2026 6min

Delete Claude.md ? How to and why.

The AI Visibility Gap 16.08.2026 1min

The AI Visibility Gap

Off Website AI, SEO, GEO, AEO and Digital Authority / Marketing in Florida, etc. 15.08.2026 1min

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

AI Brand Monitoring: How to Track What AI Says About Your Business 14.08.2026 1min

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

Winning Google AI Overviews: Jason Todd Wade on SEO, Entity Authority, and AI Visibility 14.08.2026 1min

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 competitorsComputerCreate an AI Visibility roadmap — turn your existing content into machine-readable ontology structures for Gemini, ChatGPT, and PerplexityComputerJason Todd Wade BackTier SEO methodology entity authorityHow to optimize for Google AI Overviews entity SEOMeasuring AI visibility and brand citations in ChatGPT

From AI Search to Agentic Buyer Journeys: Winning Visibility Before the Machine Decides 13.08.2026 1min

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@jasonwade.comWork with Jason: BackTier AI visibility, entity-resolution, research, speaking, and agentic-commerce engagements.Guest bioContact

RECOMMENDED Humanity Per Hour: Chad Burmeister on What AI Still Can't Sell 12.08.2026 25min

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.

First-Time Podcasting & YouTubing with AI - Learn, build, publish, and improve your voice with AI 12.08.2026 2min

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 and AI Visibility 11.08.2026 6min

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.

How To Architect Agentic Workflows For Autonomous B2B Lead Generation And Conversion 11.08.2026 2min

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.

Why Decentralized AI Training Clusters are Outperforming Centralized Enterprise Cloud Computing Power 09.08.2026 2min

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.

The Human Advantage Why Narrative Storytelling Survives the Flood of Generated Content 09.08.2026 2min

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 09.08.2026 6min

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

Ai Makes Starting a Podcast Easy 08.08.2026 8min

Ai Makes Starting a Podcast Easy

What is AI AEO? 08.08.2026 1min

What is AI AEO?

AI Writing, Marketing & Digital Legacy: Authenticity, Systems, and What Survives the Flood 08.08.2026 39min

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 07.08.2026 2min

AI SEO for Law Firms - Jason Todd Wade - BackTier

Frontier Models & Claude Fable 5 Review: The One That Got Held Up (and Why I Burned $150 Using It) 07.08.2026 1min

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