AI Search Engines Overview

Understanding the landscape of AI-powered search is essential for effective AEO. There are fundamental differences between traditional search, pure LLMs, and AI search hybrids—and each requires a different optimization approach.

The Fundamental Difference

There are two types of Generative AI experiences when it comes to search and answer experiences:

1. LLMs with Internal Knowledge Base

Pure LLMs like Claude, Llama, and Grok rely on their internal knowledge derived from extensive training on vast datasets. However, their knowledge is limited to information available up to their most recent training period (knowledge cutoff) and does not extend beyond that point.

Characteristics:

Answers based solely on training data
Knowledge limited to training cutoff date
No real-time information access
Responses generated from learned patterns

2. AI Search (Hybrid Systems)

AI search engines like Google AI Overviews, Perplexity, and ChatGPT Search use a mix of LLM capabilities plus web search to gather up-to-date information from the internet. They optimize search queries into prompts using a technique called RAG (Retrieval-Augmented Generation).

Characteristics:

Combines LLM intelligence with real-time web search
Access to current information
Provides citation links to sources
Faster to optimize (changes reflected sooner)

Comparison: AI Search vs LLMs vs Traditional Search

Aspect	AI Searches	Pure LLMs	Traditional Search
Examples	Google AIO, Perplexity, ChatGPT Search	Claude, Llama, Grok	Google, Bing
Knowledge Based On	Search results + Internal knowledge	Internal knowledge only	Indexed pages
Generation Method	LLM interprets prompts + search results	Prompts processed from training	Search engine shows result pages
Results Format	Text response with citation links	Text response only	Blue links to websites
Optimization Speed	Medium - source links help faster	Slow - needs retraining	Fast - quick to react
Response Speed	Fast	Medium	Fast

Why AI Search is More Relevant for AEO

AI search engines are more important for optimization because:

Real user behavior — People actually use these platforms to make decisions
Location-based results — Personalized based on user context
Faster optimization cycles — Changes in your content can affect results sooner
Citation opportunities — Your content can be directly linked

The AI Search Process

Here's how AI search engines work:

Query interpretation — The AI/LLM processes the user's prompt
Search execution — A search query is sent to a search engine
Source selection — The AI chooses which results to process
Content scraping — The AI reads the content from selected links
Response generation — Based on scraped content + internal knowledge

Key insight: The AI can still decide to give a different representation than the content it processed—that's what makes it different from traditional search, which just finds a match and shows links.

Current Market Landscape

Traffic Distribution

Platform	Monthly Visits	Market Share vs Google
Google.com	~76 billion	100% (baseline)
ChatGPT.com	~4 billion	~5%
Perplexity.AI	~110 million	~0.15%

Growth Trends

Platform	Growth Rate
Google	Flat (0%)
ChatGPT	5-15% month-over-month
Perplexity	17-24% month-over-month

ChatGPT is now among the top 10 websites globally—the first serious alternative to Google in 25 years.

User Behavior is Changing

Query Length Differences

Query Type	Average Length
Traditional search	2-4 words
LLM prompts	~13 words

Instead of searching "downhill mountain bike," users now ask: "What downhill mountain bike would you recommend for a 40-year-old, well-trained cyclist?"