How Sergey Lucktinov Is Rewriting Semantic SEO for the Age of AI

In this week’s episode of the Niche Pursuits podcast, Sergey Lucktinov and I discuss how Semantic SEO is evolving in response to the rise of AI-powered search engines and large language models (LLMs). We dive into how AI’s content retrieval and ranking systems differ from traditional SEO, and what website owners need to change in their approach to content creation, site structure, and technical performance to stay ahead.

While the interview explores some deeply technical ideas, the core takeaway is this: the SEO game is shifting, and content creators must adapt or fall behind.

Watch the Full Episode

From Traditional SEO to Semantic SEO: The Evolution

Sergey has been in SEO for over 15 years, first working in-house, then at agencies, and finally running his own affiliate sites. For most of his career, he never had major issues with Google updates until a couple of years ago, when one hit him hard.

That prompted a deeper dive into Semantic SEO, particularly the approach developed by Koray Tuğberk Gübür, which emphasizes building topical authority and structuring websites semantically rather than relying heavily on backlinks.

Semantic SEO relies on:

• Macro semantics: How your website is structured across categories and topics.
• Micro semantics: How individual pages are structured and written.
• Topical authority: Covering a subject comprehensively to build trust with search engines.
• Topical maps: Organizing content into macro (broad), seed (mid-level), and node (specific) pages.

After studying AI infrastructure, Sergey realized that nearly 90% of Koray’s system mirrors AI engineering principles.

How LLMs Retrieve and Rank Content

The biggest shift Sergey highlights is how LLMs like ChatGPT retrieve information differently from Google’s traditional search engine.

• Search engines are deterministic; they return the same results for the same query under the same conditions.
• LLMs use probabilities. They extract information from various sources and synthesize an answer using the “cheapest” and clearest content available.

This change in methodology means your content must now serve two masters: the algorithmic consistency of search engines and the probabilistic logic of LLMs. What LLMs look for when retrieving content:

• Clarity and confidence in language.
• A tight, well-defined structure that mirrors their internal knowledge systems.
• Semantic relevance, including related entities and topics.
• Speed of delivery, since slow websites are immediately disqualified from retrieval.

According to Sergey, even a five-second delay can disqualify your page from being considered in the retrieval process.

What is Semantic Retrieval Optimization (SRO)?

Sergey introduces his concept of Semantic Retrieval Optimization (SRO), an evolution of Semantic SEO tailored specifically to how LLMs process and extract content.

SRO is about shaping your content and website structure to match how AI systems retrieve, evaluate, and assemble answers. Key components of SRO:

Website Structure

• Macro pages cover broad topics and link to seed pages.
• Seed pages cover narrower topics and link to node pages.
• Node pages address long-tail queries and link back up the chain.

Strict Hierarchical Linking

• Macro → Seed → Node.
• Node → Seed → Macro.
• Never cross levels arbitrarily.

Content Clarity

• Each page and section must focus on a single idea.
• Use H2s and H3s as discrete “chunks” of information.

Micro Semantics: Writing for LLMs

Once your site structure is dialed in, your content must follow suit. This is where micro semantics comes into play.

What makes content “high-quality” for LLMs:

• Use of semantic triples: Simple, clear sentence structures like “X is Y” that help AI understand relationships.
• Concise, focused paragraphs: LLMs process content in chunks, and every chunk must cover a single topic.
• Factual accuracy: LLMs will penalize content that doesn’t align with their pre-training or contradicts known facts.
• Entity-rich writing: Pages should mention closely related topics (e.g., “Paris” when discussing “Eiffel Tower”).

According to Sergey, an ideal article is readable, accurate, and built to be "cheap" for an LLM to process. That balance is the key to ranking in AI-driven environments.

The Role of Technical SEO in an AI-Driven World

Speed is everything. While structure and semantics are important, Sergey underscores that technical SEO is still foundational. He estimates that fixing speed, structure, and micro semantics covers 80% to 90% of what SRO requires.

Technical factors to prioritize:

• Page speed: If your content doesn’t load fast enough, it won’t be considered for retrieval.
• Site structure: Clear navigation helps LLMs understand content relationships.
• Clean code and schema: AI systems value well-marked, structured content.

Why speed matters:

• LLMs may pull 200+ results for a query.
• Sites are immediately disqualified if they’re too slow.
• The first layer of filtering is entirely based on speed.

Sergey describes the AI retrieval process as a multi-step filtering system:

• LLMs generate potential queries based on user input.
• They pull top results from search engines.
• Only the fastest, clearest, and most trustworthy content makes it into the final answer.

Injecting New Information: The Right Way

LLMs use pre-trained data for general knowledge, but for recent or niche info, they rely on web content. So, injecting new insights is a great way to stand out if you do it right.

Strategies for including new content:

• Support claims with logic or referenced case studies.
• Avoid wild, unsupported statements.
• Mention research institutions or studies when citing data, even without outbound links.

LLMs don’t necessarily follow links, but they do evaluate context and perceived authority.

Keywords Are Dead: Long Live Meaning

One of Sergey’s most actionable recommendations is to move away from keyword-first thinking and start optimizing for meaning and intent.

How to shift your mindset:

• Start with your customer’s journey, not keywords.
• Identify pain points and map content to their questions.
• Think about what a customer needs at each stage and write content that solves that.

Keyword volume isn’t as relevant anymore. AI cares about the depth and clarity of your answers, not whether your phrase gets 1,000 searches a month.

Tools and Tactics: What You Can Use

Sergey is building a custom SaaS suite to support this methodology, but in the meantime, he uses:

• Custom GPTs trained on semantic SEO principles.
• Surfer SEO for baseline optimization (though not LLM-focused).
• Manual content audits for pruning redundant or conflicting pages.

He notes that while entity graph tools exist, many are difficult to use and don't offer actionable insights.

Final Thoughts

Semantic SEO has evolved beyond search engine rankings. With the rise of LLMs, Sergey Lucktinov’s Semantic Retrieval Optimization offers a way to future-proof your content strategy. His data-backed insights, 90% alignment between AI systems and semantic SEO, speed-based disqualification, and entity misalignment penalties, highlight just how different this new era of optimization has become.

Here’s how to stay competitive:

• Structure your site to reflect macro, seed, and node logic.
• Write for clarity, consistency, and semantic accuracy.
• Prioritize page speed and factual precision.
• Focus on meaning over keywords.
• Train writers to use semantic triples and microsemantic tactics.

The future of content isn’t just SEO-friendly. It’s AI-ready.

Links & Resources

• Learn more about Sergey Lucktinov
• What is Semantic Vector?
• Check out Sergey's book: Semantic SEO, SRO & AI

Related Posts

The Miss O and Friends Success Story: How a 16-Year-Old Created a $15 Million Business

Legendary Marketer Review + [All You Need To Know]

The 12 Best Wedding Website Builders To Help Share Your Special Day

1,023 Window Cleaning Business Slogan Ideas for Crystal-Clear Success

1,001 Catchy & Creative Pizzeria Name Ideas To Help Unleash Your Business Potential