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Exploring Entity Optimization and AI with Jason Barnard
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محتوای ارائه شده توسط [email protected] (Jeremy Rivera) and Jeremy Rivera. تمام محتوای پادکست شامل قسمت‌ها، گرافیک‌ها و توضیحات پادکست مستقیماً توسط [email protected] (Jeremy Rivera) and Jeremy Rivera یا شریک پلتفرم پادکست آن‌ها آپلود و ارائه می‌شوند. اگر فکر می‌کنید شخصی بدون اجازه شما از اثر دارای حق نسخه‌برداری شما استفاده می‌کند، می‌توانید روندی که در اینجا شرح داده شده است را دنبال کنید.https://fa.player.fm/legal
Unscripted SEO Show Notes: Entity Optimization with Jason Barnard
Episode Overview
Jeremy Rivera interviews Jason Barnard of KaliCube about entity optimization, AI assistive engines, and the future of search. This conversation explores practical strategies for controlling your digital footprint and optimizing for modern search systems.
Explore the COMPLETE Episode, and read the deep dive on SEO Arcade for further topical exploration in the same field of entities, Ai and SEO.
Guest Bio: Jason Barnard
Jason Barnard is the founder of KaliCube and a pioneer in entity optimization. He started in 1998 with a children's website that grew to one billion page views in 2007, competing with BBC and PBS. In 2012, he successfully changed Google's perception of him from "cartoon blue dog" to "respected entrepreneur and digital marketer" - becoming one of the first to master entity transformation.
Related Episodes You'll Love
️ Similar Deep Dives: If you enjoyed this conversation about entities and AI, check out our interview with Mark Williams Cook on technical SEO innovations where we explored how machine learning is reshaping technical optimization strategies.
️ Foundational Concepts: For more on search evolution and brand building, don't miss our conversation with Rand Fishkin on the future of SEO. Rand's perspective on sustainable organic growth strategies pairs perfectly with Jason's entity optimization methodology.
Key Takeaways
- The Algorithmic Trinity: All modern AI systems (Google, ChatGPT, Perplexity) are built on three core technologies - LLM chatbots, knowledge graphs, and search results - all fed from the same data source: the web. Controlling your digital footprint impacts all three simultaneously.
- Entity Optimization Timeline: Search results update within a week, knowledge graphs take about three months to reflect changes, while LLM training data requires up to a year. Understanding these timelines helps set realistic expectations for entity optimization campaigns.
- Industry-Specific Authority Matters: Authority isn't universal - IMDB dominates for movies, Crunchbase for business, legal directories for law. The key is identifying which platforms algorithms trust within your specific industry rather than chasing generic high-authority domains.
- The Claim-Frame-Prove Method: Establishing expertise requires making a claim, framing it within existing knowledge structures, then getting others to corroborate it. This creates "truth" through repetition and consensus, which AI systems then recognize and amplify.
Best Quotes from the Interview
"Which part of the web do you control? Your own digital footprint." - Jason Barnard
"Truth becomes reality due to people repeating it. What you realize is that people sometimes just repeat what they've heard and they don't actually have an opinion." - Jason Barnard
"If you can organize your data source to be logical (because machines are logical), to be meaningful and valuable, and make sure you're connecting out to the proof that what you're saying is true... then you're onto a winning mindset." - Jason Barnard
"Understandability is the foundation. If you don't have that, you're not even in the game." - Jason Barnard
The Evolution from Webmaster to Entity Optimizer
Jeremy Rivera makes the case for bringing back the "webmaster" title, arguing it better describes modern SEO work: integrating signals across email, organic search, entity creation, and website connections to change perception and drive traffic.
Jason's Journey: Starting in 2012, Jason realized it wasn't just his website but his entire digital footprint that needed optimization. While traditional SEO focused purely on websites, he was already optimizing across Facebook, review sites, articles, and social media profiles.
The breakthrough came in 2015 with Hummingbird and Google's shift from strings to things, leading to the creation of KaliCube.
Brand Search and Google's Gaslighting
The conversation reveals how Google representatives like John Mueller and Gary Illyes function as "public relations people" who carefully word statements to avoid giving away ranking factors.
Key insight: When they say "clicks don't change rankings directly," the word "directly" is their escape clause.
Jason's interviews with Bing team leads (Fabrice Canal, Nathan Chalmers, Frederick D'Bou) revealed more transparent information since "they have nothing to lose." This led to early insights about the whole page algorithm, later confirmed by Google as the Magic Mixer.
The Algorithmic Trinity Framework
Core Concept: All modern search systems use three interconnected technologies:
- LLM chatbots for conversation
- Search results for current/niche information
- Knowledge graphs for fact-checking
Critical insight: All three feed from the same data source - the web. Since you control your digital footprint, you can influence all three systems simultaneously.
Timeline expectations:
- Search results: 1 week
- Knowledge graphs: 3 months
- LLM training: 1 year
Industry Authority and the Medic Update
Discussion of the medic update reveals how "distance from seed sites" became a crucial ranking factor. Chiropractors with superior content couldn't compete with WebMD's direct connections to authoritative medical institutions.
Jason's data identified four knowledge sources:
- Google Knowledge Verticals
- Wikipedia and highly trusted sources
- Second generation (one step from seed)
- Further removed sources
Around 2020-2021, Google began expanding beyond its rigid seed sources, allowing Jason's website to become the authority for his own TV series characters.
The Subjective Nature of Truth
The Claim-Frame-Prove System:
- Claim: Make an assertion about your expertise
- Frame: Connect it to existing knowledge structures
- Prove: Get others to corroborate through repetition
Real example: Jason claims expertise in answer engine optimization, frames it as the precursor to AI assistive engine optimization, then gets validation through interviews and articles.
Jeremy's insight: Creating multiple corroborative sources (show notes, articles, guest posts) transforms subjective claims into "truth" that AI systems recognize.
Unlinked Citations and Modern SEO
Rand Fishkin's concept of unlinked citations from 2013 proves prescient in the AI era. Jason's early investment in mentions without links across SEMrush, Search Engine Watch, and TrustPilot now pays dividends.
Scale perspective: Wikipedia has 6 million articles while Google's Knowledge Graph contains 54 billion entities - 10,000 times larger.
Entity vs. Parameter: In knowledge graphs, you're an entity. In LLMs, you're a parameter. Both can be reinforced through consistent digital footprint management.
Practical Implementation Guide
The Entity Home (About Page)
Essential elements:
- Who you are
- What you do
- Who you serve
- Why you're important in your industry
Technical requirements:
- Links to all corroborative sources
- Social media profiles consistency
- Infinite cycle of self-corroboration
Warning: Google calls this the "point of reconciliation." If you don't own it, they'll assign LinkedIn or Instagram as your entity home - that's rented space you don't control.
Real-World Examples
- Michael McDougald: Struggled with Right Thing agency visibility until adding a proper About page
- Save Fry Oil: Confused entity signals showing "how to save fry oil" instead of the brand until proper entity optimization
Query Fan Out vs. Cascading Queries
Mike King's query fan out concept aligns with Jason's "cascading queries" - both describe how modern systems break down complex queries into component parts.
Micro-AEO approach: Optimize for each cascading query to increase chances of inclusion in LLM outputs.
Warning about reverse engineering: Sites that programmatically answered "people also ask" questions got hit by the HCU update. The solution isn't copying existing answers but adding new perspectives and information gain.
The KaliCube Process
Three-pillar framework:
- Understandability - Does the machine understand who you are, what you do, who you serve?
- Credibility - Does it believe you're the most credible solution in market?
- Deliverability - Does it have content to deliver you to your target audience subset?
Free resources: Download guides at KaliCube.com/guides
Future-Facing Insights
Information Gain Imperative: With LLMs potentially training on their own output, humans must focus on creating genuinely new perspectives and data. A conversation with a 30-year plumbing expert offers more value than another "how to fix a leaky faucet" article.
Continuous Evolution: Every time you provide new information to algorithms, they store it and don't need it again. This creates pressure to keep moving knowledge forward - a positive challenge for perpetual learners.
Essential Resources & Links
Episode Topics:
- Entity-based SEO strategies
- AI optimization techniques
- Digital authority building
- Knowledge graph optimization
- Modern link building approaches
- Future-proofing your SEO strategy
- Technical SEO innovations
- Brand building for search
Industry References:
- Google Knowledge Panels Guide
- Click Behavior as Ranking Factor
- EEAT Guidelines
- Perplexity AI Search Guide
Guest Information:
- Jason Barnard - KaliCube
- Free Process Guide - KaliCube.com/guides
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