AI Visibility Podcast with Jason T Wade of BackTier

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

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

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

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