I’ve spent the last few years watching AI SEO go from a niche curiosity to an industry-wide buzzword, and honestly, most of what gets sold as “AI SEO” still misses how LLMs and SEO actually work.
What I’m trying to do—through my work, writing, and community participation—is bridge the gap between old‑school, hard‑earned SEO and this new world of LLM-driven discovery.
From Classic SEO to AI-First Search
I came up in SEO the hard way: technical audits, brutal SERPs, and long campaigns where the only thing that mattered was whether traffic, leads, and revenue went up. That background forces me to treat “AI SEO” as an evolution of search, not a new circus trick.
When I talk about AI and LLMs, it’s always anchored in how Google, Bing, and other systems really retrieve, rank, and assemble answers—not in slide‑deck fantasies.
How I Think About AI/LLM SEO
My view is simple: there is no separate magic “LLM ranking algorithm” you can hack in isolation. AI answers sit on top of retrieval systems that still look a lot like search engines, just with more layers and more context. So instead of chasing shiny objects like LLMS.txt or thin “AI‑optimized” rewrites, I focus on the live retrieval layer—where you can actually move the needle on visibility.
In practice, that means:
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Mapping where AI overviews and answer units appear for your market.
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Understanding which questions LLMs keep collapsing into a single answer.
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Building content that matches real user intent and query patterns, not myth‑based checklists.
What I Try to Do in Public SEO Spaces
In SEO communities, my goal is to cut through the noise:
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Correct bad takes about “DA”, “topical authority”, and magical E‑E‑A‑T levers.
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Help people debug real issues—penalties, cannibalization, broken architecture—instead of selling them on hacks.
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Push the conversation toward user intent, information architecture, and real‑world constraints (budget, engineering time, market size).
I’d rather explain how something actually works, even if it’s messy or unpopular, than pretend there’s a one‑line trick you can paste into a meta tag.
Why My AI SEO Approach Looks Different
When I think about AI SEO, I treat LLM visibility as a retrieval and coverage problem, not a prompt‑engineering novelty act.
A big part of that is query fan‑out: instead of obsessing over one vanity keyword, I care about the entire cluster of “how to”, “best”, “vs”, “alternatives”, onboarding, pricing, and integration questions an LLM pulls together for an answer.
I also lean heavily into entity and multi‑domain presence—because modern systems don’t just look at your main site; they look at where you’re mentioned, cited, and discussed across the web.
Why I Write About This on My Blog
On my own blog, I’m trying to document how SEO and AI search really intersect, without the usual hype.
I don’t treat “AI SEO” as a separate channel; I treat it as SEO that understands how LLMs discover, retrieve, and assemble information in 2026.
If you care about long‑term visibility—in Google, in AI overviews, and in LLM answers—that’s the lens I use for everything I publish and every strategy I design.
