Is there still a long-term game for SEO in AI search?

By SEOteric

Search Engine Land’s Crystal Ortiz argues that SEO remains vital—but the rules are shifting as AI-driven, retrieval-augmented systems change how answers are assembled. Ortiz explains that combining traditional SEO fundamentals with an understanding of how large language models (LLMs) and retrieval-augmented generation (RAG) work gives brands the best chance at long-term visibility.

Why the so-called ‘long-term game’ still matters

Ortiz frames SEO’s future with the Red Queen metaphor: to stay competitive, practices must evolve as AI-driven systems and competitors evolve. As she notes, “With this approach, you can keep what’s always worked — like on-page SEO and backlinks from reputable sources.” That balance—retain proven tactics while adapting to new signals—is the core recommendation.

Key takeaways from the Search Engine Land piece

LLMs rely on retrieval and RAG: AI assistants augment limited model knowledge with external sources, so being present in those sources matters.
Topical authority and query fan-out matter: coverage across related subtopics increases the chance of being cited in AI responses.
Internal linking and semantic structure help LLMs map entity relationships, boosting extractability and relevance.
Technical health remains critical: site speed, schema, and clear content structure improve AI extractability and reduce the risk of being excluded by timeouts.

What the data says—and how that changes priorities

Moz’s analysis of nearly 40,000 queries highlights how AI Mode composes answers from a broader set of sources. As Moz reports, “Only 1 in 10 AI citations match the exact URLs in Google’s top 10 organic results,” and Tom Capper summarized that “AI Mode is branching out to a broader set of queries and topics rather than just the exact one you typed in… It makes these sort of ‘fan-out’ queries and aggregates results.”

That means traditional high rankings still help, but they no longer guarantee citation or inclusion in AI-generated summaries. Brands must therefore expand their citation footprint beyond a single high-ranking page.

Actionable guidance for marketers and SEO teams

1) Build topic clusters, not just pages. Create focused pages that own subtopics and adjacent queries. Use semantic headings and concise paragraphs that an AI can extract at the passage level.

2) Strengthen entity connections with internal links. Map your site’s architecture so internal links reinforce semantic relationships and make embeddings more coherent for vector-based retrieval.

3) Expand off-site presence. Moz’s findings show YouTube, Wikipedia, and UGC platforms play important roles in AI citations. Develop video assets and participate on third-party platforms to widen your citation surface area.

4) Prioritize technical performance. Ensure fast, reliable responses—slow endpoints risk being dropped during AI aggregation. Maintain robust schema markup so parsers can extract structured facts.

5) Track AI visibility separately. Traditional traffic metrics don’t reveal AI citation share. Monitor where your brand appears in generative answers and which formats (video, Q&A, lists) perform best.

Implications for long-term strategy

AI-driven search shifts some emphasis from ranking to being part of the answer space. That’s an important distinction: instead of chasing a single top spot, aim to occupy multiple, tightly focused nodes of the topic graph. Over time, that approach compounds authority and increases the odds of inclusion across AI fan-out queries.

Ortiz’s conclusion is pragmatic: SEO’s fundamentals still matter, but teams must broaden their definition of visibility. “Everything’s changed. Yet everything’s the same,” she writes, underscoring that adaptation doesn’t discard past learning—it extends it.