"LLM SEO" is searched 880 times a month in the US, usually by founders trying to work out whether the SEO they've been doing still counts. Short version: it does, and there's a bit more to add. This is a plain comparison of optimizing for search engines versus optimizing for language models, without the breathless framing that treats one as dead and the other as magic.
In short: traditional SEO optimizes to rank a clickable link in search results. LLM SEO optimizes to be the source a language model cites when it answers a question. They share most of the same foundation, helpful content, clear structure, expertise and trust, because the models learn from and retrieve the same web Google indexes. What's new is that being extractable and widely referenced now matters as much as ranking. You need both, and doing traditional SEO well gets you most of the way to LLM SEO for free.
What each one optimizes for
Traditional SEO has a clear target: rank in the top results for a query so a human clicks through. Success is a position and the traffic that comes with it.
LLM SEO has a different target: be the source a model draws on when it composes an answer in ChatGPT, Gemini, Perplexity or Google's AI Overviews. Success is being cited or mentioned, and the influence that carries even when nobody clicks a link.
What overlaps (most of it)
The part the hype skips: the two run on the same foundation. Google has confirmed there's no special AI markup and no separate AI index, so the helpful, well-structured, trustworthy pages that rank are the same pages that feed AI answers. That means the core work is shared:
- Useful content that answers real questions.
- A crawlable, fast, well-structured site.
- Topical authority built by covering a niche deeply.
- Trust: demonstrated expertise and mentions from other reputable sites.
Do those, and you're optimizing for both at once.
What's new for LLMs
A few things change when the consumer is a model, not a person:
- Extractability matters more. Models lift clean, self-contained passages, so burying your answer in paragraph six costs you a citation even if the page ranks.
- Being referenced elsewhere counts double. A Semrush study of AI answers found they lean heavily on widely-referenced sources, so mentions across the web feed citations directly.
- Original data outperforms opinion. Models favor sources with primary evidence, so original data gets cited more than a well-argued take.
- One answer, not ten links. There's often a single response, so the prize is being the source it's built from, not one of ten options.
Side by side
| Traditional SEO | LLM SEO | |
|---|---|---|
| Goal | Rank a clickable link | Be a cited source |
| Consumer | A human searcher | A language model |
| Success metric | Position and clicks | Citations and mentions |
| Foundation | Helpful, structured, trusted content | The same foundation |
| Extra emphasis | Keywords, links, on-page | Extractability, third-party mentions, original data |
| Where it shows | Search results | AI answers and AI Overviews |
What a founder should do about it
Don't split your effort into two programs. Run one content program built on the shared foundation, then add the LLM-specific touches: answer questions early and cleanly, structure pages for extraction, publish original data, and earn mentions. That single approach earns rankings and citations together. The fuller AI-side playbook is in generative engine optimization for B2B SaaS, and the on-page craft is in answer engine optimization.
