As AI abstracts suppress the traditional living environment, referral traffic to publishers is denser, creating a world where search is used more than ever, but the search interface also retains users for a long time, where they used to act as a switchboard.
Advertisers’ outlook is pessimistic, with traffic expected to halve over the next three years.
Contrary to this fact, Google recently reported that search queries have reached the top, which suggests a good time for digital visibility.
AI has given a huge boost to Search, and, as a result, people are searching on Google like never before. Last quarter, we saw an increase in search queries.
Google has also announced updates to try to send traffic back to websites, though whether this is a PR move to try to fend off antitrust lawsuits remains to be seen.
What all this highlights is that despite the notion that SEO and search engines have been replaced by GEOs/AEOs and LLMs, this could not continue in reality. Search engine optimization is always active, and technical SEO is the foundation of AI search.
LLMs Predict & Ground
The main language models are text generation engines, not databases or logic engines. They do not find the facts preserved; they calculate the statistical probability of a sequence of words.
To make those answers current and grounded, retrieval-augmented generation (RAG) fetches documents from the search index and feeds them to the model before it writes its answer. A really good explanation of this was posted on 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 requires a structured, easily navigable, and authoritative data source that can only be achieved through semantic HTML, logical sitemaps, and clean indexing provided by SEO experts.
Who creates, builds, and maintains that data source? The SEO community. We’re the ones who label the data, clean up the clutter, and make sure machines can read what people write.
Without the basic structure of SEO (semantic HTML, logical domain structure, and a clean indexing method), AI search engines are left with ineffective methods and website structures. SEOs are not victims of the AI revolution.
Modern SEO now includes both legacy work to maintain site health and specific strategies for AI readiness, preparing for RAG releases, and strengthening business signals across the information graph.
By organizing data so that machines can interpret the context, SEO experts provide specific signals that AI search engines use to verify facts and sources of attribution. Technical SEO ensures that the “informational advantage” of the page is accessible to the models who need to mention it. If you want AI to recommend your product, your digital footprint should support that.
Development does not stop in the age of AI; it becomes the basis of trust.
Can You Prepare for an LLM Without an SEO Program?
The smartest brands aren’t abandoning SEO for AI search; they are aggressively using SEO to advance their AI readiness.
They understand that AI search does not replace the need for traditional SEO and information sourcing practices; highlights the need for more thoughtful SEO and information systems.
It’s easy to dismiss SEO as an afterthought, but the truth is that SEO built the product LLMs now produce and charge for.
Jamie Indigo summed it up well on LinkedIn:
… We have to have a clear eye on what happened – and be serious about what we build next.
The SEO runs the engine room that powers the ship. As AI changes the digital landscape, the engine room is the best place to prepare and prepare for the future. As we head into the GEO and AEO era, we have to ask: Can you be a GEO/AEO and prepare for LLMs if you don’t have that foundation in SEO knowledge and expertise?
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