Demystifying AEO or GEO

Introduction

Google doesn’t usually hand out its playbook for free. But this time, it kind of did. Through public Google Cloud Discovery Engine documentation, Google exposed how AI Overviews, AI Mode, and future AI-powered search features actually work under the hood. We’re talking real technical details: ranking signals, retrieval logic, chunk sizes, and even how content gets cited by AI systems.

For businesses, publishers, and SEOs, this is a big deal. AI is replacing the first click, and in many cases, the entire search journey. If your content isn’t structured for AI retrieval, it won’t matter how good it is—AI simply won’t see or cite it. Slightly scary? Yeah. But also a massive opportunity if you know what to optimize for.

Key Takeaways

• Google AI Ranking Signals define what content gets cited in AI Overviews and AI Mode
• 500-Token Content Chunks are the core unit AI systems retrieve and quote
• Structured Data directly affects recall, ranking, and AI output
• 4-Stage AI Search Pipeline powers both traditional search and AI search experiences
• Semantic & Chunk-Level Optimization now matters more than raw keywords

The 7 Ranking Signals Google AI Uses

Google confirmed that its AI systems rely on seven concrete ranking signals. These aren’t theories or SEO guesses—these are the real inputs that decide what content gets surfaced and cited.

1. Base Ranking – Core relevance from Google’s traditional algorithm
2. Gecko Score – Semantic similarity using vector embeddings
3. Jetstream – Advanced context understanding (contrast, nuance, negation)
4. BM25 – Classic keyword matching (yes, keywords still matter)
5. PCTR – Predicted click-through rate using a three-tier popularity model
6. Freshness – Time-based decay and recency scoring
7. Boost/Bury Rules – Manual or business-logic adjustments for trust and safety

Together, these signals determine whether your content is ignored, ranked, or directly cited by AI systems like Gemini, ChatGPT, and Perplexity.

The Critical Technical Detail: 500-Token Content Chunks

Here’s the detail that changes everything: Google AI retrieves content in ~500-token chunks, roughly 375 words.

Each chunk must stand on its own. AI systems don’t “read” your whole article—they extract usable blocks. To be retrievable, each block needs:

• Clean heading hierarchy (H2s and H3s)
• Direct answers in 2–3 sentences
• Clear formatting (lists, tables, spacing)
• Factual claims and data points
• Question-based headings that match real queries

If your article is one giant wall of text, AI literally can’t extract it. That content becomes invisible, no matter how insightful it is.

What Structured Data Actually Does in AI Search

Structured data isn’t just for rich snippets anymore. Google confirmed that schema markup impacts three separate AI functions:

Important detail:
A schema field can influence ranking without being visible, or be visible without influencing ranking. That’s why FAQ schema, product schema, and entity markup are critical in AI Mode.

The 4-Stage AI Search Pipeline Explained

Traditional Search, AI Overviews, and AI Mode all use the same pipeline, just configured differently.

1. Prepare – Query understanding, synonym mapping, NLU, autocomplete
2. Retrieve – Chunking, layout parsing, schema extraction, embeddings
3. Signal – Apply the seven ranking signals
4. Serve – Gemini 2.5 Flash generates answers, applies grounding and safety rules

If your content fails at any stage, it doesn’t make it to the final answer.

How the Reddit vs Perplexity Lawsuit Connects the Dots

The Reddit lawsuit against Perplexity revealed something critical:
AI search engines scrape Google’s top results as their discovery layer.

When you combine that with Google’s Discovery Engine docs, the full system becomes clear:

• Discovery Layer – AI platforms pull from Google’s top results
• Retrieval Layer – Content is chunked into 500-token blocks
• Ranking Layer – The 7 AI signals decide citation priority
• Output Layer – AI generates answers using only retrievable content

This explains why modern SEO must optimize for Google rankings and AI extraction at the same time.

The 3 Layers Every AI-Optimized Content Strategy Needs

Layer 1: Semantic Similarity (Gecko)

Your content must closely match user intent, not just keywords. Vector similarity determines whether AI even considers your page for retrieval.

Layer 2: Cross-Attention Relevance (Jetstream)

Jetstream rewards clarity and contrast, including:

• Clear definitions and value props
• “X vs Y” comparisons
• Specific use cases
• Exclusionary language (“without X”)
• Direct, fluff-free answers

This is where most content fails, honestly.

Layer 3: Chunk-Level Clarity

Every 500-token block should include:

• Question-based headings
• 2–3 sentence direct answers
• TL;DR summaries
• Lists or comparison tables
• Clean HTML structure

This is the exact format AI systems quote.

Why This Architecture Changes SEO Forever

Google didn’t hide its AI search system—it exposed it. We now know:

• The ranking signals
• The chunk sizes
• The semantic models (Gecko & Jetstream)
• The role of structured data
• The full AI answer generation flow

In short: AI is the new front page of search. If your content isn’t built for AI retrieval, it won’t matter how high you rank.

If AI replaces the first click, your content must replace the first impression.

Conclusion

Google’s accidental reveal gave us something rare in SEO: clarity. We now understand how AI search systems retrieve, rank, and cite content—from semantic embeddings to chunk-level extraction. This isn’t about gaming algorithms; it’s about structuring content so AI can actually understand and trust it.

The brands that win the next era of search won’t just “rank.” They’ll be the sources AI systems quote, summarize, and recommend. If you adapt now—by optimizing for semantic similarity, structured chunks, and retrievability—you’re not chasing trends. You’re aligning with how search actually works today (and tomorrow).

FAQs

What are Google AI Overviews and AI Mode?
Google AI Overviews and AI Mode are AI-powered search experiences that generate summarized answers using retrieved web content instead of traditional blue links.

Does keyword SEO still matter for AI search?
Yes. BM25 keyword matching is still one of the seven ranking signals, but it works alongside semantic similarity and contextual relevance.

Why is the 500-token chunk size important?
AI systems retrieve and cite content in 500-token blocks. If key information isn’t self-contained within a chunk, it won’t be used.

Is structured data required for AI visibility?
While not mandatory, structured data strongly improves recall, ranking, and retrievability, making it far more likely your content is cited.

Can small sites compete in AI search?
Absolutely. Clear structure, strong semantic matching, and well-optimized chunks can outperform bigger sites with messy or unstructured content.