Here’s What I Learned About How AI Search Actually Works

The Quick Version, If You’ve Got 30 Seconds

• Google doesn’t just match your words anymore — it tries to understand what you actually mean.
• Most AI-generated answers use completely different phrasing than what people searched.
• Keyword-stuffing isn’t just outdated now — it can actively hurt you.
• What works: content that genuinely covers a topic well and connects the dots between related ideas.
• The sites that keep showing up in AI answers are the ones with real depth on a subject, not the ones with one perfectly optimized page.

The Rulebook Changed. Most Strategies Didn’t.

Old-school SEO was basically a string-matching game. Find the keyword, drop it into your headline, your intro, a few subheadings, done. Query in, document out.

That game is over.

The shift from matching keywords to understanding context didn’t happen in one update — it’s been building for over a decade. But AI-powered search made it impossible to ignore. The question worth asking isn’t “does this page contain the right keywords?” anymore. It’s something much harder to fake: does this site actually understand the subject it’s writing about?

Every major Google update in the last ten years points the same direction — the Knowledge Graph, BERT’s language understanding, RankBrain’s intent modeling, and now AI Overviews sitting right above the organic results. If you want to still be visible in 2025 and beyond, you have to understand where this is headed.

Okay, But What Does “Context” Even Mean?

People throw this word around like it’s some vague upgrade from keywords. It’s not vague at all — it breaks down into four concrete pieces.

How your content connects things. This is entity relationships — how clearly your page ties together the people, products, concepts, and organizations that belong to a topic. Google’s Knowledge Graph (launched back in 2012) was the first serious attempt at mapping this at scale. It’s not enough to mention “content strategy” and “topical authority” on the same page. You need to show how they relate. A page that name-drops four relevant terms without connecting them scores worse than one that draws the lines explicitly.

What the person actually wants. Someone searching “how to fix WordPress login errors” wants a solution. Someone searching “WordPress login errors causes” wants an explanation. Same topic, completely different intent — informational, navigational, commercial, or transactional. AI systems are scarily good at telling these apart now, and content that clearly nails the dominant intent shows up in AI answers far more often.

Whether you’ve actually built depth. One great article, however well-written, loses to a network of pages that cover a topic from multiple angles. It’s the difference between one really good conference talk and an entire team publishing consistently for two years.

How your pages talk to each other. Internal links with descriptive anchor text tell search engines how your content connects. “Click here” says nothing. “How topical authority affects AI citation rates” says everything.

As SEO strategist Ashley Liddell put it, the path forward in content strategy runs through context and user intent — not keyword volume.

Put those four pieces together and you get what I’d call a domain’s contextual identity — basically, the semantic fingerprint that search engines read when they’re deciding whether to trust your content. Keywords cover a sliver of that. Context covers the whole thing.

A Quick Detour Into the Tech (Worth Knowing)

You don’t need an engineering degree to get why this shift happened, but it helps to know the three moments that caused it.

RankBrain (2015) was Google’s first real machine-learning layer for search. It used embedding vectors to map queries into a “semantic space,” so an unfamiliar phrase could get matched to relevant content even without sharing a single word. This was the first real crack in keyword dependency.

BERT (2019) was the bigger leap. It taught Google to actually parse the grammar and relationships between words in a sentence — not just detect their presence. Google’s own example at the time was the query “do estheticians stand a lot at work.” Without BERT, you’d probably get results about standing desks. With it, Google understood you were asking about a specific job’s physical demands. That’s a level of nuance keyword optimization simply can’t touch.

MUM (2021) pushed even further — processing 75 languages at once, interpreting images alongside text, and handling multi-part questions that used to require several separate searches.

But here’s the piece that I think gets glossed over the most: query fan-out.

When someone types a search, Google doesn’t just look for pages containing those words. It expands the search across alternate phrasings, synonyms, connected entities, and conceptually related content that might never use the original terms at all. This is dramatically amplified inside AI Overviews — the model pulls from multiple aligned sources to synthesize an answer instead of picking one winner.

Practically, this means content that covers a topic from several angles has a massively larger retrieval surface than content built around one exact phrase. This isn’t a philosophical preference for “quality” content. It’s a structural advantage.

Three Ways Keyword-Chasing Is Now Actively Hurting You

This isn’t just about missing an opportunity — some old habits are working against you.

Padding content to look thorough. The old belief that longer = more authoritative led a lot of teams to bulk up articles with loosely related sections. In an AI retrieval world, this backfires. It raises what’s called the “cost of retrieval” — how much interpretive work an AI system has to do to pull a clear answer out of your page. Several enterprise audits have actually seen rankings improve after cutting content and tightening the focus.

Optimizing for exact phrases in a system built to paraphrase. Remember that 84.2% figure? AI systems are designed to reword, not echo. Chasing exact-match keywords means optimizing for the smallest possible slice of outcomes — and clunky, keyword-stuffed phrasing actively signals to AI systems that your content was built for an outdated model.

Thinking one page at a time in a game that’s played at the domain level. Old SEO asked whether a URL ranked for a phrase. AI search asks whether your entire site understands the subject. One perfectly optimized page sitting in a domain with no topical depth is just an isolated data point — it doesn’t build the accumulated signal AI retrieval actually responds to.

Building Real Topical Authority

Publishing constantly doesn’t build authority. Publishing coherently does.

A domain earns topical authority when its content — across many pages, over time — forms a structured, consistent picture of a subject. Not a pile of loosely related articles. An architecture.

Three things signal that to Google and to AI systems: page structure that clearly supports the content, content clusters built around core entities (a pillar page plus supporting subtopic pages), and internal links with genuinely descriptive anchor text instead of “click here.”

When you build this consistently, Google starts associating your domain with a subject at the entity level — not because you repeated a phrase enough times, but because the whole architecture demonstrates real understanding. And that identity compounds. New content published into an already-mature cluster ranks faster and gets cited more reliably than the same content dropped on a domain with no structural history.

Before you publish anything, the question isn’t “which keyword should we target?” It’s “how does this strengthen the domain’s understanding of this subject?”

What Actually Gets Cited: Patterns From 96,504 AI Overviews

Looking at what consistently gets pulled into AI Overviews, a pretty clear pattern shows up.

Domain authority matters even outside the top three. AI search leans toward high-authority domains even when they’re not ranking in the top organic positions — meaning domain trust functions as a prior signal, not just a tiebreaker.

If AI can’t extract it cleanly, it won’t cite it. Dense walls of text are a retrieval obstacle. Content with clear headers, tight paragraphs, and organized lists gets cited far more than identical information buried in unstructured prose. This isn’t about looking pretty — formatting reduces the interpretive work an AI model has to do, and that directly raises your odds of being cited.

E-E-A-T isn’t going anywhere. Experience, expertise, authoritativeness, trustworthiness — these map almost directly onto how AI retrieval filters content now. For anything touching health, finance, or legal territory (what Google calls YMYL — Your Money or Your Life), author credentials and cited research matter even more.

Original data wins. Proprietary research, real case studies, expert commentary with actual names attached — none of this can be easily copied. That 84.2% stat driving this whole piece? It only exists because a specific team ran a specific analysis. That’s the point.

Long-tail, question-based searches are where AI visibility lives. Over 52% of AI Overview results come from queries with four or more words. Just 4.2% come from single-word searches. More than 20% are triggered by actual questions. Content that answers real, specific questions — even in totally different phrasing than someone would search — shows up far more reliably than content chasing broad, competitive terms.

Eight Things I’d Actually Change in Your Workflow

1. Map your entities before you write a word. Know the people, tools, and concepts that belong to your topic. A page that misses central entities scores poorly on coherence no matter how well it targets a phrase.
2. Design the cluster before you commission content. One pillar page, several supporting subtopic pages, planned architecture first — writing comes second.
3. Audit for semantic dilution, not keyword gaps. Ask whether each page’s core ideas are clearly expressed, not whether the word count hits a target.
4. Give every page one dominant intent. Trying to serve informational and transactional intent on the same page usually means serving neither well.
5. Write anchor text like it’s a label, not a nudge. “Learn more” tells search engines nothing. Be specific.
6. Format for extraction, not just readability. Clear section openers, short paragraphs, no “as mentioned above” — AI doesn’t read linearly.
7. Generate original data wherever you can. A small survey or a documented case study creates something AI can’t find anywhere else.
8. Track AI visibility separately from rankings. Standard rank trackers don’t measure AI Overview inclusion — you need something that does, or you’re flying blind in the fastest-growing part of search.

Where This Leaves the “Just Chase Keywords” Crowd

The debate is basically over. Not because context sounds nicer as an idea, but because the actual infrastructure — Knowledge Graph, RankBrain, BERT, MUM, the generative models writing AI Overviews — was built specifically to evaluate meaning and treat phrase repetition as background noise.

Teams still anchoring their strategy in keyword volume aren’t just running an outdated playbook. They’re optimizing for a system that no longer controls most high-value search outcomes.

The real question isn’t “which keyword should we target?” It’s: how do we build something search systems actually recognize as genuinely authoritative?

That’s a slower build than a keyword list. But it compounds — and that’s the whole point.

Sourcing note: statistics referenced throughout come from the Writesonic GEO Tool analysis of 96,504 Google AI Overview results, conducted by engineers Harsh Arya and Jashan Sehgal. Google’s algorithmic timeline (Knowledge Graph 2012, RankBrain 2015, BERT 2019, MUM 2021) is drawn from Google’s own published documentation. E-E-A-T guidance reflects Google’s Search Quality Evaluator Guidelines.