Search Is Getting Smarter Than You Think
Lesson 1, Module 2 of The AI SEO Playbook for LLMs & AI
In Module 1, we got straight to the point — a practical checklist to help you get your SEO basics right in the age of AI. If you haven’t seen it yet, I recommend giving it a look (it’s all meat, no fluff).
This module takes a step back. Before you can fully optimize for modern search, it helps to understand how Google and AI tools read your content now. Spoiler: it’s not just about keywords anymore.
Think of this as the “why” behind the changes you're seeing in search — so you can adjust with confidence, not confusion.
Let’s dive in.
Google’s AI doesn’t care about your keywords. It cares about meaning. And that’s why SEO, as we knew it, is broken.
I learned a great deal while working with my first consulting client. As I sat and completed the usual big agency-style deliverable, I realized one thing: there was no single keyword or even a very specific keyword that would fit this business. There were many close-match keywords, but nothing that says “yes, this is the one!”
I used SemRush. Nothing. Then I searched Reddit and found 30 real people asking the same question. The demand was real, just invisible to the tools.
The Reddit Problem (and What It Reveals)
The problem with using Reddit as a research tool is that there is no real way to quantify it. There was no way for me to say this keyword gets X amount of searches per month, and therefore it’s worth pursuing. The game has changed in so many ways:
Google has better data on semantic search, and so AI overviews output content based on this data.
There is no LLM data. No clue about how users are searching for information.
No keywords data from Reddit, which happens to be a fast-growing search engine in its regard.
In short, SEO has a major data problem!
But this is going to be a difficult problem to solve. Why? To understand that, you need to understand how AI-powered search works.
How AI Understands Content
AI-powered search is built on terms that sound more like sci-fi than strategy: NLP, ML, BERT, vector embeddings...
What in the Ben & Jerry’s does any of that mean — and how do you optimize for it?
Let’s break it down without the tech-speak.
1. Google Doesn’t Need Exact Keywords Anymore
Thanks to models like BERT and RankBrain, Google now focuses on meaning and intent, not just the words someone types.
For example, a person might search:
“Can I buy this medication for my dad if I’m not there?”
They never say “prescription,” “pharmacy,” or “HIPAA.” Still, Google understands the question and returns answers that cover all those topics, because the AI knows what they meant.
What this means for your business:
You can rank even if you don’t use the exact phrase.
Helpful, plain-language content that answers real questions performs best.
Pages often rank for long, specific searches they never directly targeted.
This is why Google’s new AI Overviews highlight real answers, not just keyword matches. It’s reading between the lines — and your content should too.
2. From Keywords to Concepts: How Vector AI Works
Google doesn’t just look at words anymore. It looks at relationships between ideas.
This is done through something called vector embeddings — a fancy way of saying that AI maps out meaning. If two phrases mean the same thing, Google treats them as neighbors, even if the words don’t match.
Think:
“Buy sneakers online.”
“Order running shoes”
Different words. Same intent. Same result.
It’s like walking into a well-organized store. Sneakers are grouped, even if one pair says “runners” and another says “athletic shoes.” The grouping makes sense because the purpose is the same.
Google’s doing that with search — organizing content by meaning instead of just matching the words.
And other companies are jumping on board…
Microsoft just rolled out multi-vector field support and semantic scoring in Azure AI Search. Translation: search systems are now being built from the ground up to group content by meaning, not just match keywords.
This kind of “vector-native” search means the shift we’re talking about isn’t coming — it’s already here.
If your content still relies on old-school keyword tactics, it’s going to feel invisible next to sites that are structured around clear, helpful answers built for intent.
3. So, How Do These Terms Fit Together?
Here’s a no-nonsense breakdown:
NLP (Natural Language Processing) = Google’s goal: to understand language like humans do
ML (Machine Learning) = The engine: it trains on patterns in how people search and click
Transformers (like BERT) = The brain: reads full sentences and figures out what’s being asked
Vector Embeddings = The map: shows how different phrases with the same meaning are connected
The Real SEO Blindspot: Data
The way SEOs have traditionally approached keywords is broken. We need to shift our focus to concepts, not just individual terms, recognizing that pages rank based on meaning, not just matches. In many ways, we’re just going back to writing for humans.
We’ve gone from keyword science to intent art.
But here’s the catch: none of that works if we don’t fix SEO’s biggest blind spot—the data itself.
A recent Lumar study found that vector-based models consistently outperformed traditional keyword tools in predicting which pages rank. In other words, semantic relevance is now a measurable advantage. Search engines aren’t just indexing words—they’re organizing ideas.
And yet… most SEO tools still force us to think in keywords, not meaning.
That’s why I stopped trying to duct-tape insights together from spreadsheets, keyword gaps, and Reddit threads—and built a custom GPT instead. It models how AI thinks: through user intent, topic clusters, and semantically aligned content.
It doesn’t chase search trends—it mirrors how LLMs and vector-based search systems process content.
(You can check it out here if you’re curious.)
Found this helpful?
Know others who are confused by how Google search works these days? Share this post with them — it might clear up a few things.
No jargon. Just plain answers.