AI Search Isn’t Just “Google 2.0”

It’s a Whole New Discovery System

For decades, most companies have understood search the same way: people type something into Google, Google shows a list of links ranked mostly by backlinks, keywords, and web authority. If you were #1, you got the clicks and often the sale.

But that world is changing fast.

Recent research from Profound, based on analyzing hundreds of millions of real AI responses, reveals something big: AI search engines are not just copying Google’s ranking system — they’re building their own.

And that has huge implications for how brands must think about visibility today.

The Old World: Rankings and Position 1

In classic search:

• Brands wanted to be the top result in Google

• Clicks and traffic were the currency

• Most SEO effort went into ranking high on page one, especially position #1

That worked because humans scrolled for results.
But AI search doesn’t present a list of links — it gives a single synthesized answer.

In this new ecosystem:

• LLMs pull from many different sources, not just the top result

• Brands ranked 5–10 on Google may still be heavily represented in AI answers

In other words: being Google #1 is no longer the same as being “AI visible.”

AI Search Is Its Own System

Profound found that when large language models (LLMs) respond to queries:

• They don’t just copy Google

• They build their own internal ranking and citation logic

• They evenly distribute attention across many different sources instead of privileging position #1

That means brands need a new kind of optimization — one that’s designed for AI discovery, not just search engines.

So What Matters Now?

Here are the key shifts:

1. Google Indexing Still Matters — But It’s Just Step One

If your content isn’t indexed by Google, many AI search tools still may never see it.
So classic SEO remains a qualifier, but it’s no longer the full game.

2. Structured, Machine-Ready Data Is Essential

AI models are increasingly feeding on structured product data, not just raw HTML pages scraped from the web.
This is similar to how Google Shopping evolved:

• attributes like stock levels, spec fields, and ratings

• machine-readable product information

These help the AI understand and present your products correctly.

3. AI Engines Invent Their Own Queries

In AI search, a single question can produce multiple underlying intents, each needing to be answered accurately.
You’re no longer optimizing for a single keyword — you’re optimizing for clusters of intent that the model derives from a user’s natural language prompt.

Examples:

“best lightweight jacket” can fan out into:

• best for rainy climates

• best for backpacking

• best for travel with kids

AI doesn’t rank pages — it interprets meaning.

Content Must Be Built So AI Can Cite It

AI doesn’t guess — it cites what it uses.

That means:

• your content needs to be structured

• factual

• clearly related to user intent

• and easy for models to reference and pull into answers

This is a big shift from writing just for humans or just for search engines.
Now you must write in a way that both humans and AI understand and trust.

Bottom Line

We are living through a real paradigm shift in how information is discovered.

Traditional SEO and Google rankings are not going away — but they are no longer enough.
AI search engines like ChatGPT, Perplexity, and others are starting to decide what counts as visibility in entirely different ways.

Brands that cling only to old-school ranking tactics will quietly lose ground, while those who learn to optimize for AI-centric visibility will increasingly own the conversation where decisions are made.

We call this new discipline Generative Engine Optimization (GEO) because it’s about optimizing for the engines that generate answers, not just lists.

As AI becomes people’s first stop for discovery and decision-making, GEO becomes mission-critical for visibility, relevance, and long-term business growth.

Inspired by research and insights shared by Profound