As AI summaries compress traditional organic space, referral traffic to publishers is being squeezed, creating a world where search is more used than ever, but the search interface is also retaining users for longer, where it has historically acted like a switchboard.

The outlook from publishers is pessimistic, with traffic expected to halve in the next three years.

In contrast to this reality, Google recently reported that search queries have reached an all-time high, suggesting a golden age for digital visibility.

AI has given Search superpowers, and, as a result, people are searching on Google more than ever before. Last quarter, we saw an all-time high in Search queries.

Google has also announced updates to try and send traffic back to websites, whether this is a PR move to try and fend off antitrust cases remains to be seen.

What this all highlights is that despite opinions that SEO and search engines have been superseded by GEO/AEO and LLMs, this could not be further from the truth. Optimizing for search engines remains relevant, and technical SEO is the foundation for AI search.

LLMs Predict & Ground

Large language models are probabilistic text-generation engines, not databases or reasoning engines. They do not retrieve stored facts; they calculate the statistical likelihood of word sequences.

To make those answers current and grounded, retrieval-augmented generation (RAG) fetches documents from a search index and feeds them to the model before it writes its response. A really good explanation of this was posted to YouTube in December 2024 by Jess Peck, “Oh my god, ChatGPT is not a search engine.“

For an AI search engine to answer a query using RAG, it relies on a high-quality data pipeline. It needs an organized, easily navigable, and authoritative data source that is only possible through the semantic HTML, logical site hierarchy, and clean indexing that SEO professionals provide.

Who builds, structures, and maintains that data source? The SEO community. We are the ones labeling the data, cleaning the clutter, and ensuring machines can actually read what humans write.

Without the foundational architecture of SEO (semantic HTML, logical site hierarchy, and clean indexing pathing), AI search engines are left with inefficient paths and website structures. SEOs are not bystander-victims of the AI revolution.

Modern SEO now encompasses both the legacy work of maintaining site health and specific AI-readiness strategies, optimizing for RAG extraction, and strengthening brand entity signals across the knowledge graph.

By structuring data so that machines can interpret context, SEO professionals provide the exact signals that AI search engines use to verify facts and attribute sources. Technical SEO ensures that the “information gain” of a page is accessible to the models that need to cite it. If you want an AI to recommend your product, your digital footprint must be support that.

Optimization does not disappear in the age of AI; it becomes the baseline for trust.

Can You Optimize For LLMs Without An SEO Program?

The smartest brands are not abandoning SEO for AI search; they are aggressively using SEO to fuel their AI readiness.

They understand that AI search does not replace the need for traditional SEO and information retrieval practices; it highlights the need for SEO and information systems thinking even more.

It’s easy to dismiss SEO as being a relic, but the reality is SEO has built the product LLMs now productize and charge for.

Jamie Indigo summarized it perfectly on LinkedIn:

… We should be clear-eyed about what happened – and intentional about what we build next.

SEO runs the engine room that powers the ship. With AI shifting the digital landscape, the engine room is the absolute best place to be prepared and ready for the future. As we look toward an era of GEO and AEO, we must ask: Can you be a GEO/AEO and optimize for LLMs if you don’t have that grounding in SEO knowledge and expertise?

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