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Most professionals do not have an expertise problem. They have a visibility problem.

In this episode, Jason Todd Wade talks with Ashley Smith and Sarah Strackhouse about why talented professionals often remain invisible, even when they have real experience, strong reputations, and valuable expertise.

The conversation breaks modern visibility into three layers: realness, proof, and polish.

Ashley Smith explains the Proof Gap: the disconnect between what a professional actually knows and what search engines, AI systems, and recommendation platforms can find, understand, and trust. She discusses why professionals need to become discoverable and recommendable without forcing themselves to become full-time content creators.

Sarah Strackhouse brings the media and communication layer. Drawing from her background in television journalism, media coaching, and on-camera training, she explains why nerves, fear, and hesitation keep many professionals from showing up publicly.

The conversation also covers Google’s shift toward AI-powered search, AI agents, podcast RSS feeds, transcripts, media training, confidence, authority, and why publishing conversations may become one of the easiest ways to help AI systems understand who you are.

Key Topics:
AI visibility
The Proof Gap
Realness, proof, and polish
Google AI search
AI agents
Podcast RSS feeds
Machine-readable authority
Professional visibility
Media confidence
On-camera presence
Why professionals hesitate to publish
How AI systems evaluate trust
Why podcasts matter for search and AI
Building authority without becoming a full-time content creator

Ashley Smith Bio:
Ashley Smith is a business strategist and creator of the Proof Gap, a framework that explains why experienced professionals can be highly capable in real life but nearly invisible to search engines, AI systems, and online recommendation platforms. After nearly two decades in real estate leadership, including serving as board chair and media spokesperson for one of Canada’s largest real estate organizations, Ashley now helps professionals become more visible, trusted, and discoverable in an AI-shaped world.

Ashley Smith Links:Website: https://showyourproof.beehiiv.comProof Gap Assessment: https://showyourproof.beehiiv.com/products/proof-gap-self-assessment⁠https://linkedin.com/in/ashleysmithnow⁠ ⁠https://instagram.com/ashleysmithnow⁠ ⁠https://facebook.com/ashleysmithnow⁠ ⁠https://threads.com/@ashleysmithnow⁠ ⁠https://tiktok.com/@ashleysmithnow⁠⁠https://youtube.com/@ShowYourProof⁠

Sarah Strackhouse Bio:Sarah Strackhouse is a former television journalist, anchor, producer, and entrepreneur who has worked with major media organizations including Fox Business, CBS, NBC, The CW, and Time Warner Cable stations nationwide. She is the founder of Strackhouse Media, a media company focused on live event production, media training, on-camera confidence, content creation, and helping professionals turn credibility into visibility and cashflow.

Sarah Strackhouse Links:Website: https://www.strackhousemedia.comMedia Course: https://www.strackhousemedia.com/mediacourse

Host Bio:
Jason Todd Wade is the founder of BackTier, an AI Visibility Infrastructure company. He created Entity Lock Protocol™ and the BackTier Visibility Path™, frameworks designed to help brands become correctly understood, trusted, cited, included, and selected by AI systems. His work focuses on the shift from traditional search visibility to AI-mediated discovery, recommendation, and selection.

Jason Todd Wade Links:BackTier: https://backtier.comNinjaAI: https://ninjaai.comWebsite: https://www.jasonwade.comLinkedIn: https://www.linkedin.com/in/backtier

The competitive layer of the Internet has changed.

Search engines rewarded distribution. AI systems reward interpretation.

In this episode, Jason Wade breaks down “Project Alamo,” a framework for understanding what happens when brands, professionals, and institutions realize AI systems either misunderstand them or ignore them entirely.

The discussion explores the rise of the entity layer, why large language models changed the economics of visibility, how recommendation systems compress choice, and why inclusion inside AI-generated answers is becoming more valuable than rankings themselves.

Topics include:

- AI Visibility

- Entity Layer Engineering

- Interpretation vs Distribution

- Selection Compression

- AI Recommendation Systems

- Semantic Authority

- Answer Layer Economics

- Entity Resolution

- Retrieval Systems

- Large Language Models

This is not a conversation about SEO tactics.

It is about the structural transition from a search-driven Internet to an interpretation-driven one.

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In this episode, Jason Wade talks with Fergus Dyer Smith, founder and CEO of MSQ Global Studios, about the move from AI as a tool to AI as an operating layer for marketing teams. Fergus has built and deployed AI products used inside large enterprise environments, including Assist, BrandCheck, PreFlight, and WAVE, with claimed users including Publicis, Toyota, WPP, Google, and ThermoFisher. His central argument is direct: most AI products do not fail at the demo stage. They fail at deployment.

The conversation centers on agentic marketing systems: workflows that do not just generate content, but observe the market, publish, measure performance, study competitors, produce analysis, feed those lessons back into the system, and run the loop again. Fergus shares how he built a self-improving TikTok agent that creates slideshow content, posts it, pulls the previous day’s data, scrapes top-performing videos in the niche, analyzes what is working, and adjusts future output without daily human intervention.

Jason and Fergus also discuss Manus, Claude, Gemini, model-agnostic architecture, AI operating systems for marketing teams, enterprise adoption, creative automation, feedback loops, and why the future of AI in business is not just better prompting. It is deployment, integration, workflow design, and closed-loop execution.

The deeper question is whether marketing is moving away from campaign-by-campaign execution and toward autonomous learning systems. If AI can create, test, measure, and improve continuously, then brands need to rethink not only how they produce content, but how they become visible, understood, cited, included, and selected inside AI-mediated discovery environments.

Guest bio

Fergus Dyer Smith is founder and CEO of MSQ Global Studios and a product-driven AI operator focused on building tools that enterprises actually use. He began his career in science, studying biochemistry at Manchester before moving into technology, web development, travel, music events, video production, VR, brewing, and AI product deployment. That mix of systems thinking, creativity, and commercial execution shaped his current work building AI products for complex organizations.

Fergus has founded and built multiple companies, including Wooshii, Envoke, Hartest Brewing, and Snowbombing Festival-related ventures. Today, he leads MSQ Global Studios, where his focus is shipping AI products that move beyond prototype theater and into daily enterprise use. His product portfolio includes Assist, an AI operating system for marketing teams; BrandCheck, a creative effectiveness and brand measurement tool; PreFlight, an AI video analysis tool; and WAVE, an AI-powered video automation platform.

His practical philosophy is “deployment over demos.” He is not an engineer by background, but he understands product, adoption, workflow, and how to get AI systems used inside real organizations.

Guest contact info

Fergus Dyer Smith
Founder / CEO, MSQ Global Studios
Email: fergus.dyer-smith@msqpartners.com
Company: MSQ
Website: https://www.msqpartners.com
LinkedIn: https://www.linkedin.com/in/fergusdyersmith/
Location: London, United Kingdom
Time zone: UK / Ireland / Lisbon time

Jason Wade bio

Jason Wade is the founder of BackTier, an AI Visibility Infrastructure company focused on helping brands become correctly understood, trusted, cited, included, and selected by AI systems. Through BackTier, Jason created Entity Lock Protocol™, a framework for stabilizing machine understanding, and the BackTier Visibility Path™, a measurement model for tracking whether AI systems cite, include, and select an entity