How AI Mode and AI Overviews Work: What Google Patents Reveal and Why SEO Strategy Needs a Radical Shift

Two years ago, I was already anticipating the wave that would fundamentally change Search: I clearly stated that Retrieval Augmented Generation is the future of search. Today, AI Overviews and the new AI Mode are already having real effects on organic traffic – that future, honestly, has already started.

Unfortunately, sources that explain in depth how this "AI search architecture" actually works are rare. From analyzing various documentation and patents, plus independent research, I've pulled out what's really relevant for you. For a complete breakdown of AI Mode, I highly recommend this reference analysis from ipullrank.com. Here, I'll succinctly synthesize the connections between AI Mode and AI Overviews, the strategic directions, and the essential steps to "survive" the new waves of generative search.

The Future of Search is Probabilistic. The Past Was Deterministic.

At its core, what's changed is the shift from clear, deterministic rules (the classic "10 blue links") to a probabilistic, AI-driven model. Before, your content would rank exactly as you optimized it, with perfectly controllable SEO levers. Now, Google "decides" how to group, remix, and reformulate everything.

In classic SEO, you had clear levers: title, H tags, structure, internal links, authority. Now, with generative search, the "machine" processes everything through an invisible AI chain – from two identical queries, you can get results that are 80% different. Certainty and control disappear.

What's changed radically? The AI "remembers" not just what you write, but also user history, behaviors, and location. Whether your content is included in a response no longer depends solely on exact query matching, but on semantic coverage, structure, and granularity.

AI Mode and AI Overviews Seen Through the Lens of Google Patents

Google hasn't innovated randomly, but based on extremely sophisticated patents that define every logical stage of these new systems:

1. Search with stateful chat: The backbone of AI Mode.
2. Generative summaries for search results: The tech foundation for AI Overviews.
3. Text Ranking with Pairwise Ranking Prompting: Ranking and reasoning at the fragment level, not just whole documents.
4. User Embedding Models for Personalization: Behavioral embedding models, essential for personalization.
5. Prompt-based query generation for diverse retrieval: "Query fan-out" algorithms.
6. Instruction Fine-Tuning Using Reasoning Steps: Explains the reasoning fine-tuning of Google’s LLMs.

For more context, it's worth reading Google AI Overview: Adapt to the Evolving Search about the conceptual infrastructure of AI Overviews, and Your Comprehensive Guide to Preparing for Google's SGE for strategic validation.

AI Mode – The Pipeline That Changes the SEO Rules

AI Mode takes personalization and diversification to a new level – it works with behavioral embeddings, fan-out queries (derived versions you wouldn't even think of), and prioritizes reasoning and passages, not just document-level matching.

The pipeline, in short:

• Receives the query and context (history, user profile, location).
• Rapidly generates a group of "synthetic queries," anticipating alternative relevant questions.
• Extracts key passages from documents that cover this new universe of queries.
• Classifies the question type (informational, transactional, comparative…).
• Selects the specialized LLM for that type.
• Runs reasoning chains to refine the answer, using user-centric embeddings.
• The final answer is structured, referenced, and displayed—often with zero clicks, but huge impact on awareness and perceived brand influence.

AI Overviews – What Remains and What’s Fundamentally Different

Unlike AI Mode, here answer generation and validation happen strictly in parallel with source retrieval (fan-out and semantic expansion). Thus, defining relevance is no longer a direct query-document relationship, but one between the query and countless variants, plus passages selected for verifiability.

How does Google's Search Generative Experience (SGE) work? shows that AI Overviews now occupy about 55% of the organic space, but sources and order come from a much more fluid mix of selection and combination at the passage or entity level (not the full article). Very often, FAQs, tables, or short summaries are prioritized by LLMs for verification.

What’s Already Showing Up in Data and the Real Impact on Visibility

The fact that SGE/AI Overviews change the rules of visibility and predictability is clear, and can be seen from a few key observations:

• Initially, AI Overviews would only pop up for complex queries, but the covered area is growing rapidly (see Surgegraph).
• Many “classic” SEO metrics no longer reflect reality. Being mentioned in an AI Overview is now the maximum visibility—your brand is in the headline, clicks become optional.

The paradigm shift towards “share of citation” or “influence” was underlined by Conductor and others; there are already brands receiving more mentions than actual clicks, as AI-recommended sources. This change is redefining visibility and authority in B2B, where structure and accessibility become essential.

What “Optimization” Looks Like in the AI-Powered Search Era

The summary? Betting on keywords or page ranking matters less and less. What matters most is how you segment and structure your content at the fragment level, strengthen your topic clusters, and “push” entities, examples, or verifiable citations into every content piece.

3 Essential Directions to Follow:

• Minimize pure keyword targeting. Anticipate and cover indirect, related, lateral questions – “fan-out” strategies.
• Invest in real, organic entities, tables, and FAQs (not artificial ones).
• Build your content as a sum of verifiable chunks, with citations and references—not just a continuous narrative.

Implications for Brand and SEO Strategy – The New Paradigm

This new reality, validated by patents like Generative summaries for search results, shifts the focus to E-E-A-T proven not at the site or author level, but at the grassroot, every citable fragment. Fast, high-quality coverage at a niche level brings much better results than massive, generic presence – in fact, cases analyzed by Surgegraph show niche authority sites being cited ahead of general giants.

Additionally, "confidence" scoring systems increase volatility: sometimes there won’t be any AI Overview if there aren’t enough credible sources (see detailed examples in the Varn analysis).

What’s Next? From Classic SEO to Relevance Engineering

AI’s impact is global, and conversational UIs are becoming the new entry point for information discovery. It’s time to stop clinging strictly to “SEO” and approach the transformation as its own strategic discipline—just as social media marketing quickly became a separate pillar from content and search (see Conductor’s perspective on new metrics and results redefinition).

Content needs to be rethought with a few clear principles:

• Make it easy for reasoning AI to “digest” and process—not just optimized for annoyingly repeated keywords.
• Make it both citable and fragmented, so it covers as many real query intents as possible.
• Proactively monitor for AI “hallucination” risks or mistaken citations, which, honestly, can shake a brand’s reputation (see Google AI Overview: Adapt to the Evolving Search).

AI Overviews and AI Mode are revolutionizing not just the algorithms, but our entire view of performance: it’s not just about traffic and rankings anymore, but about your level of citation, influence, structure, and how well your content adapts.

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Conclusion? The future of search belongs to those who master relevance at the granular level, not just page optimization. This is the chance to create a new discipline: visibility and influence in the AI era, where “share of citation” and adapting for algorithmic reasoning separates top performers from the rest.