Knowledge Graphs, Live Podcasting and Positioning Yourself as Google’s Untrained Sales Force - Jason Barnard On The Poduty and the News

Podcast News with Jason Barnard Kalicube® January 31 2026

Video by: Jeff Revilla. Host: Jeff Revilla. Guest: Jason Barnard, Founder and CEO of Kalicube®. February 2, 2026

TL;DR: AI assistive engines like Google Gemini, ChatGPT, and Perplexity are effectively your untrained sales force. Currently, they may be selling for your competition because they lack the necessary data to recommend you. Jason Barnard, CEO of Kalicube®, explains that the path to dominating the AI conversation is through the strategic building of Knowledge Graphs and maintaining extreme consistency across your digital footprint.

Key Strategies Discussed:

• Google as an Untrained Sales Force: Shift your mindset from “SEO” to “Sales Training.” AI models are constantly talking to your audience; your job is to feed them the facts, authority signals, and corroboration they need to pitch you instead of a competitor.
• The Power of Knowledge Graphs: A Knowledge Graph is a machine-readable encyclopedia used by AI to fact-check information. While Wikipedia has 6 million articles, Google’s Knowledge Graph has over 54 billion - a scale 10,000 times larger.
• Consistency vs. Authenticity: While being authentic is important for humans, consistency is the mandatory requirement for machines. Inconsistency confuses the “child-like” logic of AI. Consistent, scheduled updates (like a weekly live podcast) “train” the engines to expect and look for your new data.
• Leveraging Offline Moments: Every offline achievement - speaking at a conference, meeting an industry leader, or winning an award - must be logged online. Without a digital record, these authority-building moments essentially do not exist to the AI.
• The “Adjacent” Strategy: Use high-authority “adjacent” events (like South by Southwest or National Puzzle Day) to piggyback into the news cycle. Associating your niche brand with larger cultural moments creates the digital breadcrumbs AI needs to establish your relevance.
• Niche Dominance First: Don’t try to be famous to everyone immediately. Dominate a small pond - a specific industry in a specific location - and then expand your “circle of trust” outward once the machine recognizes you as the definitive local/niche authority.

The Urgent Mandate:
As AI becomes more personalized and conversation-driven, the machines move from providing a list of links to offering a single “perfect” recommendation. If you are not part of the AI’s internal Knowledge Graph, you are invisible to the users who trust these machines as their primary recommenders. You have a window to claim your digital real estate before the “Who’s Who” of AI becomes too crowded to enter.

The session explored how AI Assistive Engines are transforming from passive tools into active participants in the buying journey - effectively acting as an untrained sales force for every brand.

Speaking on the podcast hosted by Jeff Revilla, Jason Barnard explained that AI systems such as Google, ChatGPT, and other assistants are already interacting with potential customers daily. The challenge is that, without proper input, these systems may be recommending competitors instead.

From SEO to Sales Training

A central theme of the discussion was the need to rethink SEO as a form of sales training for machines.

Rather than focusing solely on rankings and traffic, brands must ensure that AI systems have the information they need to confidently:

• Understand the brand
• Trust its expertise
• Recommend it as the best option

When AI lacks clear, structured data, it defaults to safer or more established alternatives - often competitors with stronger digital footprints.

The Role of Knowledge Graphs

The session highlighted the importance of knowledge graphs as the foundation of machine understanding.

These systems act as large-scale, machine-readable encyclopedias that store and validate information about people, companies, and topics. AI Assistive Engines rely on this structured data to confirm facts and relationships before making recommendations.

Establishing a clear presence within these systems significantly increases the likelihood that a brand will be understood and trusted.

Consistency Over Interpretation

A key distinction discussed was the difference between human and machine expectations.

While human audiences value authenticity and nuance, AI systems prioritise consistency. Inconsistent messaging across platforms creates confusion, reducing the algorithm’s confidence in the data.

Maintaining a consistent narrative - across websites, profiles, and content - helps AI systems build a stable and reliable understanding of the brand.

Using Content to Train AI Systems

The discussion also emphasised the role of regular content in shaping AI perception.

Consistent publishing, such as recurring podcasts or updates, signals reliability and provides fresh data points for algorithms to process. Over time, this creates a pattern that AI systems recognise and incorporate into their understanding.

This ongoing input effectively “trains” AI systems to expect, recognise, and prioritise the brand.

Turning Offline Authority Into Digital Signals

Another important point was the need to translate offline achievements into online data.

Events such as speaking engagements, partnerships, or awards contribute to authority - but only if they are documented digitally. Without this online presence, these signals remain invisible to AI systems.

Capturing and publishing these moments ensures they contribute to the overall digital footprint.

Leveraging Adjacent Opportunities

The session introduced the idea of using adjacent opportunities to strengthen visibility and relevance.

By connecting a brand to broader industry events or trending topics, it becomes easier for AI systems to contextualise and associate the brand with recognised moments. This creates additional entry points for algorithms to understand and validate its relevance.

Building Authority Through Niche Focus

The discussion reinforced the importance of starting with niche dominance.

Rather than attempting to appeal to a broad audience immediately, brands should focus on becoming the clear authority within a specific area. Once established, this authority can expand outward, strengthening recognition and trust.

This approach provides clarity for AI systems, making it easier for them to confidently recommend the brand within that niche.

Preparing for AI-Driven Recommendations

The session concluded by highlighting the shift toward AI systems providing a single, confident recommendation rather than multiple options.

As users increasingly rely on these systems, brands must ensure they are included within the AI’s internal understanding. Without this presence, they risk being excluded entirely from consideration.

By building a structured, consistent, and well-documented digital footprint, brands position themselves to be recognised, trusted, and ultimately recommended in an AI-driven environment.