Google has just rolled out AI Mode, and I’m sitting here with mixed feelings. On the surface, it’s impressive. Queries that used to trip search engines—long, messy, conversational ones—are now handled with clarity. The system doesn’t just point to links; it tries to answer you directly, changing your searches into back-and-forth conversations.

But for SEO, the situation is different. What has guided visibility for decades doesn’t fully apply when your content is pulled apart, rephrased, and sometimes hidden inside an AI-generated response. You can do everything right—schema, crawlability, authority—and still not know how or if your work surfaces. That uncertainty is the real shift. And while many still  shrug it off as “just SEO,” ignoring what’s happening risks leaving the field unprepared. Let’s check out how AI mode works and what the hype is all about.

What is Google’s AI Mode?

AI Mode is Google’s new button sitting right inside the search bar, and at first glance, it feels like a quiet update. But click it, and the experience changes. Instead of scrolling through a page of blue links, you get a direct response stitched together from multiple sources. The answers are longer, more conversational, and in many cases, surprisingly good at handling messy queries that would have confused traditional search.

Think about asking, “Which plants can handle a shaded, clay-heavy corner that kids keep running through?” Classic search would spit out scattered blog posts and forum chatter. AI Mode, on the other hand, parses the details, pulls from different references, and hands back something that feels coherent. Sources are still shown, but often tucked away, and not every response leads straight to a click.

For commercial searches—like comparing skincare serums or shopping for cars—the feature can surface products or options without pointing traffic directly to websites. Right now, Google hasn’t tied AI Mode tightly into buying behavior, but demonstrations at Google I/O hinted that’s coming. Ads, product integrations, even purchase actions may soon flow into this experience.

So while AI Mode feels experimental today, it signals a change in how search works at its core. Instead of Google simply listing what’s out there, it’s acting more like an editor, deciding what to show, what to merge, and how to phrase the final answer.

How AI Mode Works

The easiest way to think about AI Mode is that Google isn’t answering you directly—it’s breaking your query into many smaller ones, then stitching together the results. This behind-the-scenes process is called “query fan-out.” You type a question, and instead of running a single search, the system runs a whole batch in the background. Each one tackles a slightly different angle of your request, and then the model pulls them back together into a single response.

Here’s where things get interesting. Unlike Perplexity or other AI search tools that show you the hidden searches, Google keeps its sub-queries invisible. As marketers, that means we don’t get to see which paths it took. One run might check product comparisons, another might check pricing, and another might gather reviews. But all of that is hidden behind the curtain.

That’s why people experimenting with Gemini noticed a trick: by expanding its “thinking,” you can sometimes see those background searches. While it’s not an exact mirror of AI Mode, it’s close enough to give hints. For SEO, that’s a signal. If Google is breaking queries into subtopics, our content has to live across those edges—answering not just the main question but all the related ones that might spin out from it. So the short version: AI Mode isn’t just search plus AI. It’s a layered process of breaking, fetching, and re-building, and the output you see is only the surface.

What is AI SEO Anyway?

The phrase “AI SEO” gets tossed around a lot, often as if it’s just a rebrand of what we’ve been doing for two decades. But that view misses the point. Traditional SEO was built on a retrieval paradigm: make your content crawlable, indexable, and authoritative enough to rank. If you did those things well, your page appeared in results, more or less intact.

AI Mode breaks that chain. It doesn’t surface full pages the way search engines used to. Instead, it slices content down to passages, rewrites pieces of it, and decides whether or not to cite you at all. What matters is not only that your content is available—it has to be written in a way that models can grab, reason about, and fit into a stitched-together answer.

That means “AI SEO” isn’t about stuffing in more keywords or chasing the same old ranking reports. It’s about engineering relevance at a deeper level: structuring passages, embedding clear facts, and supplying context that an AI system can reuse inside its logic. The work is less about being found directly by users and more about being useful to the machine that intermediates the conversation.

Is SEO Optimized for AI Mode?

If we’re being honest, no—SEO isn’t prepared for AI Mode. The discipline is still built around sparse retrieval models like TF-IDF and BM25, the foundations of keyword-driven ranking. AI Mode doesn’t think in those terms. It operates on dense retrieval, where every query and passage is converted into embeddings and compared in vector space. That’s a different game entirely, and our current tools aren’t measuring for it.

Today, there’s still overlap. Pages that rank well in classic SERPs often appear in AI Mode responses. But as the system leans harder into personalization and memory, that overlap shrinks. Two people can type the same query and see different outputs, shaped by their history and profile. Traditional rank tracking assumes a universal result set, which doesn’t exist in AI Mode.

Then comes the click issue. AI Mode often answers the query directly on the page. Your content may inform that answer, but users don’t need to click through to your site. From a business perspective, that means impressions are happening invisibly—you’re contributing without the proof of traffic. Reports show fewer visits even if your work is central to what users see.

So, no, SEO as it stands isn’t optimized for AI Mode. Classic tasks like crawling, indexing, and authority signals still matter, but they’re table stakes. What’s missing are skills in passage engineering, measuring semantic alignment, and tracking citations in places where clicks never happen. Until those gaps close, the industry remains behind.

The Future of Search is Multimodal

One of the biggest shifts with AI Mode is that search is no longer confined to text. The system can process language, video, audio, and images all within the same retrieval pipeline. That means an answer to your query could just as easily pull from a podcast transcript, a product demo video, or a chart as from a blog post. For Google, it doesn’t matter what format the content started in—the question is whether it fills a gap in the response,  even if it’s a chart from an AI image generator.

That raises a new problem for SEO. Historically, you could map your strategy to text queries and optimize pages accordingly. Now, the field has widened. A podcast quote might replace what your article could have said. A diagram might substitute for your explanation. Even more challenging, Google can remix media formats—turning a chart into text or text into a bullet list—without pointing back to you.

The bigger takeaway is that success in AI Mode isn’t just about ranking web pages anymore. It’s about building content ecosystems across multiple formats so that, however the system chooses to answer, your material is in the pool. That could mean transcribed videos, structured podcasts, or visual data that machines can parse.

Multimodal retrieval is where search is heading, and it means SEO has to stop thinking of content as “pages” and start thinking of it as assets that can be reused, recomposed, and cited in ways we don’t fully control.

What are Google Engineers Saying and Predicting?

The engineers behind AI Mode aren’t shy about where this is going. At Google I/O, during follow-up panels, and even in private discussions with SEOs, their message was consistent: AI Mode is not a temporary experiment. It’s the new direction for search, and many of the features that feel novel today will merge into the default experience sooner than most expect.

So what exactly are they saying—and what should we take from it? Here’s what:

Google Wants to Reduce “Delphic Costs”

One phrase that came up in conversations is “Delphic costs,” a reference to the cognitive load users face when they have to piece together answers from multiple queries. In the past, you’d search several times, gather links, and assemble your own conclusion. Google wants AI Mode to handle that assembly work. The vision is clear: fewer searches per session, but richer answers in a single interaction.

Traffic Isn’t Their Priority

While the SEO community worries about clicks and visibility, engineers frame traffic almost as a side effect. Their goal is to meet information needs inside the results page. That means your content might inform the answer but not always earn the visit. This shift explains why reporting feels unreliable—citations may appear, yet traffic drops. From Google’s standpoint, that’s not a failure; it’s progress.

Personalization is Deepening

The team has also been candid about personalization. AI Mode doesn’t just adapt to the query—it adapts to the user. Embeddings built from search history, location data, and interactions across Gmail, Maps, and YouTube influence what surfaces. Two people in the same city can type the same query and get different answers. Logged-out rank tracking won’t capture that, which is why engineers quietly hint that SEO reporting will need a new foundation.

Ads and Commerce are Entering the Scene

Another theme is commerce. At I/O and later at Google Marketing Live, demos showed how AI Mode could recommend products or even complete transactions. Engineers confirmed that experiments are underway with PMax campaigns and Shopping Graph data feeding into AI Mode. For brands, that means AI-driven placements will eventually matter as much as, if not more than, traditional ads in classic SERPs.

Where This Leaves SEO

When pressed on what role SEO has in this future, engineers didn’t give a tidy answer. They repeated familiar advice—technical health, content quality, clear signals—but stopped short of offering new playbooks. That ambiguity isn’t accidental. It reflects the shift: SEO is still part of the mix, but the ground rules are changing. Google is optimizing for reasoning and user context, not for click delivery.

AI Mode Uses a Layered and Contextual Architecture

AI Mode isn’t a single query-to-answer pipeline. It’s a layered system where context is remembered, queries are multiplied, passages are retrieved, and then the final response is stitched together. Each layer matters, because it changes how information is selected and rewritten. Here’s what you need to know to understand how AI mode works:

Context Layer

AI Mode doesn’t treat your searches in isolation. It keeps a running state that reflects recent queries, device settings, location data, and even signals from other Google apps. This state frames how your next question is interpreted. If you ask about “Seattle to Las Vegas range” after looking at electric SUVs, AI Mode doesn’t restart—it continues the same thread.

For SEO, that means two users can type the same words and trigger different outputs, because their backgrounds shape the way Google interprets intent. Context is no longer optional; it’s baked into the retrieval from the very beginning.

Query Expansion Layer

Once context is set, Google fans the query outward. The system generates multiple hidden searches, each addressing a slice of the original intent—pricing, reviews, feature comparisons, and related topics. You don’t see them, but they’re happening under the hood every time.

This is where Gemini experiments have given marketers a glimpse: by expanding its reasoning traces, you can sometimes peek at those sub-queries. They reveal just how granular the system gets, breaking one broad question into half a dozen smaller investigative runs.

Retrieval Layer

From there, AI Mode doesn’t rank entire pages the way traditional search did. It evaluates at the passage level. A single paragraph buried halfway down an article might be treated as more relevant than the headline or opening lines.

That’s a huge shift for SEO. It means surface-level optimization—titles, headings, meta tags—only goes so far. The passages inside your content need to stand on their own, clear enough that Google can lift them and drop them into an answer without needing the rest of the page for context.

Synthesis Layer

The final step is synthesis. The retrieved passages are rewritten, merged, and sometimes re-ordered to form what looks like a seamless answer. Some citations appear, often pushed to the side. Others vanish entirely. To the user, it feels like a single voice, but in reality it’s fragments stitched from multiple sources.

This rewriting is where most SEO frustration lives. You can inform the answer without being mentioned, or your brand may show up as one citation among many, with no guarantee of clicks. From Google’s perspective, the goal isn’t fairness to publishers—it’s delivering an answer that reads as coherent and trustworthy.

Why This Matters

Understanding these layers changes how we think about visibility. Classic SEO assumed your page was the unit of competition. In AI Mode, the unit is a passage. It’s not enough to just rank—you need to craft content that survives being split apart, reinterpreted, and reassembled into something that might only nod in your direction.

AI Mode and Multi-Stage LLM Processing and Synthesis

When people hear “AI Mode,” they often picture a single model spitting out an answer. The reality is more layered. Google doesn’t rely on one pass through a large language model—it runs multiple stages of processing before you see a result.

The first stage is interpretation. The model reformulates your query, cleaning it up and aligning it with known intent patterns. From there, it moves to expansion: creating the sub-queries that will be used to pull passages from the web and other sources. That’s the fan-out step we touched on earlier.

Once the passages come back, another stage kicks in. Instead of throwing them straight into an answer, the system performs what engineers call “synthesis.” The model weighs the passages, checks for conflicts, and decides which pieces to keep. It then rewrites them into a single voice. Think of it as an editorial desk where different writers hand in drafts, and the editor picks and polishes what makes sense.

There’s often a reasoning stage on top of that, where the model explains its chain of thought internally. You don’t see that reasoning, but it guides how evidence is ranked and phrased. Only after those checks does the text get surfaced as the AI Mode response.

For SEO, the takeaway is that visibility doesn’t depend on one retrieval—it’s a multi-stage pipeline where each layer decides whether your content survives into the final draft.

Dense Retrieval and Passage-Level Semantics in AI Mode

Here’s where things get technical, but stick with me—it matters for SEO more than most people realize. Classic Google search was built on sparse retrieval. Think keyword matching. You typed “best CRM for startups,” Google looked for pages with those exact terms, then ranked them by signals like backlinks and authority.

AI Mode doesn’t think like that. It works in dense retrieval. Instead of just matching words, it converts your query into a numerical vector—essentially a mathematical fingerprint of meaning. Then it hunts for passages across the web with fingerprints that live closest to yours in vector space.

That’s why keywords alone no longer guarantee visibility. The system is reading for meaning, not just matches. A page with the phrase “affordable CRM” could lose to another that never mentions the word “affordable” but talks deeply about pricing tiers, discounts, and contract terms.

And here’s the kicker: it’s not whole pages competing anymore. It’s passages. Google doesn’t need your 2,000-word blog post—it may just grab one 80-word section buried in the middle.

So what does that mean for content?

• Write in self-contained passages. Each block should stand on its own with clear context.
• Cover semantic neighbors. Don’t just repeat keywords—address related concepts the model might link to.
• Think smaller units. Paragraphs are the currency now, not just pages.

AI Mode: Ambient Memory and Adaptive Interfaces

Google isn’t just building a smarter search box. It’s building a search system that remembers. Engineers call this “stateful chat,” and it’s powered by what the patents describe as ambient memory.

Stateful Chat and Ambient Memory

Classic Google was forgetful. Each search was a reset. AI Mode carries memory forward. That memory isn’t just your last query; it’s a compressed representation of your ongoing behavior—topics you’ve searched, links you’ve clicked, even what Google knows from Gmail, Maps, or YouTube. These aren’t stored as strings of text but as embeddings: dense vectors that capture your long-term patterns. Think of them as mathematical fingerprints of your intent.

Two people can type the same words and get very different answers, not because the query is unclear but because the system “knows” them differently. That’s not science fiction. That’s what’s running under the hood now.

User Embeddings in Action

Here’s how those user embeddings shape AI Mode’s pipeline:

• Query interpretation – intent is classified differently depending on your history.
• Synthetic query generation – the fan-out prioritizes sub-queries closer to your profile.
• Passage retrieval – ranking shifts based on affinity with your past behavior.
• Response synthesis – the format itself (list, chart, video, carousel) adapts to your preferences.

Adaptive Interfaces

The interface adapts too. Last year’s Bespoke UI demo hinted at what’s now visible in AI Mode: sometimes answers arrive as bullet points, other times as flowing prose, or even with carousels and charts. It isn’t random. A downstream model predicts which format will satisfy the query fastest, blending retrieval with presentation.

Relevance Engineering

This rewrites the rules. You aren’t just optimizing for keywords or even full pages anymore. You’re engineering passages that can survive in a probabilistic system where visibility depends on both semantic alignment and profile alignment. Logged-out rank tracking? Useless. Every answer is filtered through a user embedding.

If you want to compete, the playbook shifts to:

• Influence search patterns through branding beyond search.
• Write passages that are semantically rich and structured for LLM re-use.
• Anticipate the hidden fan-out queries your content must answer.
• Optimize for clarity at the triple level (entity, attribute, relationship).
• Test through curated profiles rather than generic rank tracking.

AI Mode Query Fan-Out

AI Mode doesn’t just “answer” your query. It expands on it. Google calls this query fan-out, and it’s the engine driving how AI Mode retrieves content. Instead of treating your query as a single search, it multiplies it into a constellation of synthetic sub-queries—each probing a different angle of your intent.

This is why AI Mode feels sharper than classic search: it’s not answering one question. It’s answering the one you asked plus the dozen you didn’t articulate but implied.

How Query Fan-Out Works

At the core is a prompted expansion stage. An LLM like Gemini is tasked with rewriting your query across different dimensions:

• Intent diversity – comparative, decision-making, or exploratory rewrites
• Lexical variation – synonyms, paraphrases, phrasing tweaks
• Entity rewrites – swapping or narrowing brands, features, or product classes

The patents (“Systems and methods for prompt-based query generation for diverse retrieval”) show that this isn’t random hallucination. It’s structured prompting with reasoning built in—what engineers sometimes call a “prompt-based chain of thought.”

Types of Synthetic Queries

The fan-out process generates different query classes:

• Related queries – topical neighbors (“best hybrid SUVs” from “best electric SUV”)
• Implicit queries – inferred needs (“EVs with longest range”)
• Comparative queries – decision prompts (“Tesla Model X vs Rivian R1S”)
• Recent queries – contextual from your session (“EV rebates in NY” → “best electric SUV”)
• Personalized queries – drawn from embeddings (“EVs with 3rd row seating near me”)
• Reformulations – pure rewrites (“which electric SUV is the best”)
• Entity expansions – narrower or broader swaps (“Volkswagen ID.4 reviews”)

Each synthetic query gets routed into Google’s dense retrieval system, pulling candidate passages that might otherwise be invisible if you only tracked the head term.

Filtering and Diversification

Google doesn’t just take all the fan-out queries. The system filters for coverage, ensuring:

• Multiple intent categories (transactional, informational, hedonic)
• Different content types (charts, reviews, tutorials, definitions)
• No overfitting to the same semantic “zone”

This makes AI Mode’s final synthesis more comprehensive—because it’s literally pulling from a customized corpus built around your expanded query space.

Why SEOs Misread Early AI Overviews

When AI Overviews first appeared, SEOs thought Google was “pulling from deep in the SERPs.” More likely? It wasn’t reaching down—it was reaching across. Fan-out queries were grabbing content ranked highly for entirely different queries that looked unrelated to the one typed.

That changes the game: tracking a single keyword ranking means very little. You might rank #1 for “best car insurance” but still lose to someone ranked #4 for a fan-out query like “GEICO vs Progressive for new parents.”

Prompt-Based Chain of Thought

Google’s patents give us a rare look under the hood: reasoning chains aren’t just a parlor trick. They are a designed system for breaking a query into steps, checking those steps, and then knitting them back together into an answer. That’s what “prompt-based chain of thought” really means.

Instead of treating queries as a one-and-done request, AI Mode walks through intermediate reasoning. It pauses to think. It decomposes intent. Then it validates the logic of its own output.

How Reasoning Chains Work

• In-band steps – generated inside the LLM stream (like classic chain-of-thought prompting).
• Out-of-band steps – created elsewhere in the pipeline, then injected back into synthesis.
• Hybrid chains – crossing both tracks: guiding retrieval, re-ranking, and even filtering citations.

This matters because Google isn’t answering with “just one pull.” It’s constructing scaffolding. If your content doesn’t fit into one of those scaffolds, you won’t even be in the running.

You should picture it this way: AI Mode doesn’t ask “which page ranks #1?” but “which passage helps me fill reasoning step #3 in this logic chain?” That’s the big difference.

Reasoning Across the Pipeline

If you’ve been following Google’s AI Mode closely, you already know it’s not just about retrieval. What sets it apart is reasoning. Google isn’t simply pulling in documents and remixing them; it’s actively thinking across steps. The “Instruction Fine-Tuning Machine-Learned Models Using Intermediate Reasoning Steps” patent gives us a sense of how this works. It describes reasoning chains—structured sequences of inferences that connect a query to a final response.

Think of them as scaffolding. A query like “best SUV for long commutes” doesn’t just trigger a document search. It creates a chain:

1. Identify attributes that matter (range, comfort, charging access).
2. Expand into comparison queries to see what cars fit those.
3. Pull candidate passages that mention both EV range and comfort features.
4. Filter by user embeddings (what does this user usually click?).
5. Assemble into an answer that doesn’t just list SUVs, but explains why they fit.

Here’s where it gets interesting: reasoning chains can be in-band (generated directly in the LLM’s output), out-of-band (pre-structured and injected into the process), or hybrid (a mix of both). That means Google’s systems aren’t always improvising. Sometimes, they’re working against a fixed logic map designed to filter what qualifies as an answer.

And reasoning isn’t a single checkpoint—it’s applied at nearly every stage of the pipeline:

• Query classification: Hypotheses about what you really meant.
• Fan-out: Expansion into synthetic queries based on reasoning goals.
• Corpus retrieval: Filtering by what types of content should satisfy each reasoning step.
• Task routing: Assigning different LLMs to sub-tasks like extraction, summarization, or synthesis.
• Final synthesis: Using reasoning as the blueprint for how the answer is constructed.
• Citation: Picking the passages that best fulfill individual reasoning steps—not necessarily the highest-ranking pages.

The SEO angle? You’re no longer writing for a static query-response system. You’re writing for a machine that needs passages to survive multiple reasoning layers. That’s a higher bar than anything we’ve faced before.

Structuring Content to Pass Reasoning Filters

Here’s the painful truth: being “good content” isn’t enough. AI Mode doesn’t evaluate your site as a whole. It tests your passages against checkpoints in the reasoning chain. If you fail at one, you’re dropped.

So, how do you survive? You can think about content engineering across four pillars.

Fit the Reasoning Target

Each passage has to make sense in isolation. The system doesn’t always read your page top-to-bottom. It plucks chunks. If your comparison between two SUVs is buried in a 1,500-word wall of text, you’re invisible.

Write in a way that spells out tradeoffs and choices:

• “The Tesla Model Y offers longer range, while the Ford Mustang Mach-E provides faster charging.”

That’s reasoning-compatible content: complete, contextual, and extractable.

Be Fan-Out Compatible

Fan-out queries are entity-driven. If Google generates “best EV SUV under 60K with third-row seating” and your page only says “family-friendly EVs” without naming the Kia EV9 or Rivian R1S, you’ll miss the cut.

You should seed your content with entity names, model numbers, specs, and contextual phrases that map directly into the Knowledge Graph. Without those hooks, fan-out expansion skips right past you.

Be Citation-Worthy

Google rewards clarity. The LLM wants facts it can grab, verify, and attribute. That means:

• Quantitative claims
• Named sources
• Semantic triples (subject-predicate-object statements)

Compare these two lines:

✘ “The Model Y is popular with families.”
✔ “The Model Y seats seven, offers 330 miles of range, and was the top-selling EV globally in 2023 (Statista).”

Only one of those is citation-ready.

Be Composition-Friendly

AI Mode doesn’t rewrite your prose—it stitches passages. That means your structure matters as much as your language. Lists, answer-first paragraphs, and modular sections are your friend. If your writing can be spliced into different answer shapes—charts, bullets, lists—you increase your chances of reuse.

This isn’t “writing an article.” This is engineering passages for machine recomposition. And if that feels alien, that’s because it is. SEO just became content architecture.

Matrixed Ranking Strategies for AI Mode

Classic rank tracking is dead here. You’re not competing for “a position.” You’re competing in a matrix of synthetic queries, passage checks, and embedding scores. Winning means surfacing across as many reasoning steps as possible.

How do you operate in that environment? Let’s break it down.

Step 1: Pull Fan-Out Rankings

Feed Qforia’s synthetic queries into a keyword tracker. Map which ones you currently appear for, and which competitors own.

Step 2: Generate Passage Embeddings

Take your pages, split them into passages, and vectorize them. You’re trying to replicate Google’s passage indexing.

Step 3: Compare to Citations

Grab the passages that AI Mode actually cites. Vectorize those too. Now compare cosine similarity. Where are you aligned? Where are you miles off?

Step 4: Patch the Gaps

Low similarity means your content isn’t “thinking in the right language.” Fix that with:

• Numbers and claims
• Entity mentions
• Comparative phrasing
• Cleaner semantic chunking

Step 5: Treat Clusters as Matrices

Sometimes one mega-page can handle all subqueries. Other times you’ll need multiple interlinked pages. Let the data decide. Topical clusters are no longer editorial preference—they’re AI Mode-driven matrices. You’re no longer optimizing one page for one keyword. You’re building a web of passages that can intersect reasoning chains in as many places as possible.

Rethinking Mode Explained: How AI Mode Works

Let me be blunt: AI Mode is not search as you knew it. It’s not a performance channel. It’s a probabilistic visibility layer. That means before you dive in, you need to ask: do you even want to compete here?

Why? Because three shifts change the game.

1. Traffic Isn’t the Outcome: Click-through rates are collapsing. Your reward might be a brand mention or citation, not a visit. If you’re chasing conversions, AI Mode will frustrate you.
2. Content Becomes Engineering: You’re not writing blog posts—you’re producing machine-consumable passages. That requires new workflows, new editorial standards, and a mindset closer to data science than copywriting.
3. SEO Becomes Relevance Strategy: You can’t just optimize for keywords anymore. You’ll need to engineer alignment with embeddings, reasoning checkpoints, and personalization layers. That’s a whole new discipline.

So here’s the real question:

• If you’re playing for direct-response ROI, maybe skip AI Mode. Stick to classic SERPs.
• But if your goal is presence, trust, and visibility—if you want your brand baked into machine reasoning—you’ll need to engineer for it.

There’s no middle ground. AI Mode is not “another SERP feature.” It’s a different channel. You either play its game, or you don’t.

The New SEO Software Requirements for AI Surfaces

If you want to survive in AI Mode, you’re going to need tools that understand embeddings, simulate fan-out, track reasoning chains, and model personalization. That doesn’t exist yet at scale. Which means two things:

1. You’re forced to hack it together with custom code, clickstream subscriptions, and browser automation.
2. You should start demanding more from your software providers.

AI Search Measurement in Google Search Console

Let’s start with Google’s own tool. Search Console should be our window into how we show up—but it’s not. The reports are capped, the data feels sanitized, and entire surfaces (AI Overviews and AI Mode) are invisible. Right now, any AI Mode traffic you do get shows up as Direct because of a noreferrer tag. That means your most important search channel is essentially unmeasurable.

What we need:

• Dedicated AI surface reporting (AI Overview, AI Mode, etc.).
• Citation heatmaps: which passages from your site were cited.
• Frequency counts: how often you appear in generative answers.

What you can do today: you can’t measure it natively. But you can scrape generative answers at scale with tools like Profound, or roll your own browser automation and string-matching scripts.

What you should do tomorrow: fill out the GSC feedback form and demand AI visibility data. Google will drag its feet unless enough people push.

Logged-in Rank Tracking Based on Behavioral Personas

Traditional rank tracking is dead weight in AI Mode. Results aren’t universal anymore—they’re synthesized and personalized. What you see as “rank #3” might not even appear for someone else.

What we need:

• Rank modeling that simulates user personas.
• Data tied to intent classes, fan-out expansions, and personalization layers.

What you can do today: build test personas. Spin up logged-in Google accounts, simulate behavior, and run your queries through AI Mode with Operator or a headless browser. Then compare the outputs. 

Vector Embeddings for the Web

Forget keyword density. Forget TF-IDF. Modern Google retrieval runs on embeddings. Queries, documents, passages, even users all live in multidimensional vector space. If you don’t know where your content sits in that space, you don’t know why you are—or aren’t—being retrieved.

What we need:

• An embeddings explorer that reveals site-level, author-level, and passage-level embeddings.
• Scoring for retrievability: how well your passages align with fan-out queries.
• Pruning guidance: what content is too far off-topic to matter.

What you can do today: Screaming Frog can generate embeddings, and you can also hack together custom JS functions.

Clickstream Data

AI Mode traffic is practically invisible in GSC. And many generative results don’t drive clicks at all. That leaves you flying blind unless you have external behavioral data.

What we need:

• Clickstream integration in SEO tools.
• Mapping between organic search and AI surfaces.
• Re-ranking models tied to actual user clicks.
  
  

What you can do today: link clickstream data manually using Similarweb or Datos.

What is QFORIA and How It Works

If you’ve been following the AI Mode patent trail, you’ve probably seen the acronym QFORIA floating around. It is actually one of the most important building blocks for how Google decides what to retrieve, how to reason, and which answers to surface.

QFORIA breaks down search into a structured reasoning pipeline—where each query is expanded, tested, reranked, and synthesized against multiple reasoning layers. Instead of a single ranking system, QFORIA orchestrates the entire process of fan-out queries, passage retrieval, and model-based synthesis.

What QFORIA Means

QFORIA stands for:

• Query – the original user input (typed, spoken, or multimodal).
• Fan-Out – the expansion of that query into multiple subqueries (explicit, implicit, comparative, personalized).
• Reasoning – LLM-based synthesis steps that interpret, chain, and recombine passages.
• Inference – embedding alignment, contextual personalization, and selection of “candidate” answers.
• Aggregation – merging and reranking of passages, citations, and outputs into a final coherent response.

It is a stacked workflow that governs AI Mode from the moment you ask a question to the second it spits out a multimodal response.

How QFORIA Shapes Retrieval

Here’s where it gets interesting: QFORIA doesn’t just expand queries. It uses embeddings, click models, and contextual signals to decide which fan-out queries matter more than others.

• If you search “best running shoes for flat feet”, QFORIA might generate:
  
  
  • Related: “stability shoes for runners”
  • Comparative: “Nike vs Brooks stability running shoes”
  • Implicit: “shoes to prevent pronation injuries”
  • Personalized: “best running shoes available near NYC”

Each of these gets scored for relevance and retrievability in vector space. The top-ranked subqueries drive which passages are pulled into the candidate set.

So, you’re not competing for one keyword—you’re competing for a web of hidden QFORIA subqueries that your content may or may not match.

QFORIA and Reasoning Layers

Where QFORIA really shines is in the reasoning phase. Once the candidate passages are retrieved, they’re not simply stacked and ranked like old-school SERPs. Instead:

• Passages are grouped by intent and context.
• LLMs chain reasoning across them (think: “first, identify stability shoe features; then compare across brands; then synthesize a recommendation”).
• Conflicts or ambiguities are resolved through additional fan-out queries.

The end result? A fluid, synthesized answer that feels conversational but is actually the product of dozens of invisible reasoning steps.

What This Means for SEO

QFORIA changes the optimization game in three big ways:

• You’re optimizing for a query constellation, not a keyword. If your content doesn’t map to the hidden fan-out space, you won’t even make it into the candidate set.
• Passage-level engineering is non-negotiable. Entire pages don’t matter as much as whether a specific passage of your content is retrievable, dense, and reasoning-friendly.
• Relevance is probabilistic, not deterministic. Your inclusion depends on how well your content aligns with multiple hidden subqueries, not just the head term.

The Takeaway on QFORIA

QFORIA is Google’s new “engine under the hood” for AI Mode. It’s not just about ranking—it’s about orchestration: expanding, reasoning, and aggregating across multiple dimensions of a query.

For SEOs, that means the real challenge isn’t “how do I rank?” but “how do I engineer content that survives QFORIA’s multi-layer reasoning and still comes out cited?”

It’s tough, but it also means visibility isn’t locked down to one keyword. If your passages are semantically rich, aligned with intent types, and retrievable across fan-out queries, you stand a fighting chance.

Rethinking Search Strategically for the AI Mode Environment

AI Mode isn’t a feature. It’s a structural reset. Search has shifted from a familiar list of links into a self-contained ecosystem—conversational, multimodal, and memory-driven. The old SEO playbook, built on explicit queries, predictable rankings, and click-based attribution, doesn’t translate here.

Think about how people use ChatGPT or Perplexity: quick, trust-heavy interactions where the answer shows up fully synthesized. No clicks. No blue links. Just “the answer.” AI Mode is headed down the same path, only with more layers—personalization, embeddings, reasoning. That means being visible here is less about where you rank and more about whether you’re encoded into the model’s memory as a source worth citing.

But let’s get real: the first decision is whether you even want to play this game. Some organizations will stick with classic search, content marketing, or ads. AI Mode may not be worth the fight if your channel mix is already strong elsewhere. But if you do want to compete, your strategy has to evolve. And fast. The shift cuts across three major domains: channel reclassification, capability transformation, and data infrastructure. Let’s break them down.

Reclassify Search as an AI Visibility Channel

Traditional organic search has always been split: part performance, part branding. Roughly 70/30. But in AI Mode, that ratio flips. You’re not optimizing for traffic anymore—you’re optimizing for presence.

Think of it like PR. Your “placement” is whether the AI cites you, references you, or aligns with you in its reasoning process. And the KPIs shift accordingly:

• Share of voice in AI responses (how often you appear vs competitors).
• Sentiment and citation prominence (are you the main source or a buried reference?).
• Attribution modeling that considers influence, not just clicks.

If you’re still reporting SEO performance on “rankings” and “organic sessions,” you’re measuring the wrong thing. Leaders need to reframe SEO budgets as investments in visibility within AI cognition.

Build Relevance as an Organizational Capability

Winning in AI Mode isn’t about who can publish the most blog posts or build the biggest link graph. It’s about engineering relevance across vector space. That requires new muscles:

• Semantic Architecture – Organize knowledge so it’s machine-readable, recombinable, and persistent. You’re not just writing articles—you’re creating atomic units of information that the AI can stitch into reasoning chains.
• Content Portfolio Governance – Treat content like assets in a portfolio. Some grow in relevance, others decay. You need to prune, diversify, and optimize based on semantic coverage, not just keyword traffic.
• Model-Aware Editorial Strategy – Create for two audiences: humans and machines. That means writing passages that an LLM wants to cite because they’re precise, factual, and embedding-aligned.

Forward-thinking orgs will pull together SEO, NLP, content, UX, PR, and data science into a single function: Relevance Engineering. It’s no longer enough for SEOs to operate in a silo—AI Mode visibility is too entangled across teams.

Operationalize Intelligence in a Post-Click World

Here’s the hardest pill to swallow: the click is dying as a measurement signal. You can no longer say, “We ranked #1 and got X visits.” AI Mode breaks the chain between impression, click, and attribution. So, how do you measure?

You need an intelligence stack that reflects where you stand in the model’s reasoning process:

• Simulation Infrastructure – Build internal LLM pipelines (with RAG, LlamaIndex, LangChain, etc.) to simulate how your brand shows up in AI responses.
• Citation Intelligence – Track where and why your brand is cited across generative surfaces—even if users never click through.
• Content Intelligence – Manage passage-level embeddings, knowledge graph presence, and reasoning coverage across both classic and AI search.

That means dashboards that don’t just show “organic traffic,” but “where do we exist in the model’s latent space?” Because that’s where trust lives now.

From Performance to Participation

Search strategy is shifting from transactions to participation. You’re no longer asking, “How do we rank?” You’re asking, “How are we represented in AI cognition?”

This is the birth of a new function: Relevance Strategy. It sits alongside Relevance Engineering and guides how your brand participates in algorithmic ecosystems. Think of it as orchestration—aligning technical SEO, content, PR, and product to ensure your brand’s voice carries into machine reasoning. The organizations that thrive will treat visibility as a strategic asset, not a campaign outcome.

But Not Everyone Will Come Along

Let’s be blunt: not every brand will make this leap. Some will retreat to classic SERPs. Others will ignore AI Mode until it eats away their visibility. And a few will double down, building machine-readable knowledge systems that make them unmissable in AI cognition.

This is where the field splits. SEO as “rank chasing” is ending. SEO as “relevance engineering” is beginning. Those who adapt will define the future of visibility. Those who don’t? They’ll fade quietly into obscurity.

Conclusion

Search is no longer a simple exchange between keyword and blue link. AI Mode has turned it into a layered reasoning system, where content lives or dies by how well it can be parsed, reassembled, and cited by machines.

If you’re still optimizing for old rules, you’re already behind. You need to think less like a keyword hunter and more like a relevance engineer. That means engineering passages, anticipating synthetic queries, and preparing content for reasoning pipelines that don’t look anything like the SERPs we grew up on.

You can keep waiting for Google to spell it out, or you can build for what’s already happening. The future of search isn’t clicks—it’s presence inside machine cognition. The only real question left is whether you’re ready to compete for that space. So—are you coming with us?