The "Share of Model" Metric: Quantifying Brand Presence in AI Overviews

The Collapse of the "Ten Blue Links" Economy

For the last two decades, the contract between search engines and content creators was simple: we gave them content, and they gave us traffic. In 2026, that contract has fundamentally changed. With the dominance of Google’s AI Overviews (formerly SGE), Perplexity’s answer engine, and the integration of SearchGPT, the user journey no longer guarantees a click.

Gartner predicted that by 2026, traditional search engine volume would drop by 25%, with search marketing losing market share to AI chatbots and other virtual agents. We are now living in that reality. The problem is not that people have stopped searching; it is that they have stopped clicking. They are finding their answers directly in the interface.

For B2B SaaS founders and marketing leaders, this presents a terrifying measurement void. If traffic declines but brand interest remains high, how do you attribute value? If your "organic sessions" plummet while your revenue holds steady, where is the demand coming from?

The answer lies in a new metric: Share of Model.

In this guide, we will dismantle the traditional view of organic traffic and replace it with a framework for the Generative Era. You will learn how to quantify your presence in AI answers, why "citation" is the new "ranking," and how to engineer your content so that LLMs have no choice but to reference you.

What is the "Share of Model" Metric?

Share of Model (SoM) is a quantitative metric that represents the percentage of AI-generated responses for a specific topic or category in which a brand is explicitly cited, recommended, or used as a primary data source.

Unlike Share of Voice (which measures advertising reach) or Share of Search (which measures query volume), Share of Model measures generative visibility. It answers the question: "When a user asks an AI about [Category X], how often is [My Brand] part of the answer?"

This metric is critical because LLMs operate on a "winner-takes-most" dynamic. In a traditional SERP, being in position #4 still garnered visibility. In an AI Overview, if you are not one of the top 2–3 cited sources or the primary entity mentioned in the synthesis, you effectively do not exist. Share of Model is the only accurate way to gauge brand health in a zero-click ecosystem.

Why "Share of Model" Matters in 2026

1. The Zero-Click Reality

Most informational queries—especially top-of-funnel questions like "What is Generative Engine Optimization?" or "Best tools for automated content"—are now resolved instantly by the AI. If your KPI is traffic, you are optimizing for a metric that is naturally decaying. If your KPI is Share of Model, you are optimizing for brand influence.

2. The "Authority Bias" of LLMs

LLMs are designed to reduce hallucination by grounding their answers in authoritative sources. They prefer content that is high in Information Gain—unique stats, fresh data, and distinct perspectives. Brands that achieve a high Share of Model are effectively being stamped as the "source of truth" for their industry. This builds immense trust with the user, even if they don't click immediately.

3. Verification vs. Discovery

Users often use AI to verify decisions. A query might look like: "Compare Steakhouse Agent vs. Jasper for technical SEO content." If your Share of Model is low, the AI might hallucinate features or omit you entirely. If it is high, the AI acts as a third-party validator for your product.

How to Measure Share of Model

Measuring this metric requires a different toolkit than Google Analytics or SEMrush. Since there is no single "console" for ChatGPT or Gemini, we must use a sampling methodology.

Step 1: Define Your "Golden Set" of Queries

Identify the top 50–100 questions your target audience asks. These should range from broad informational queries ("How to scale content creation") to commercial investigations ("Best AEO software for startups").

Step 2: Automated Sampling via Agents

You cannot manually check these queries every day across every AI model. The most sophisticated marketing teams now use script-based agents (or platforms like Steakhouse) to run these queries through APIs for OpenAI, Perplexity, and Google.

Step 3: Scoring the Output

For each query, analyze the AI's response against a rubric:

• Mention: Was the brand named? (Yes/No)
• Citation: Was the brand linked or footnoted? (Yes/No)
• Sentiment: Was the context positive, neutral, or negative?
• Position: Was it the first solution mentioned?

The Formula:

(Total Citations / Total Queries Run) × 100 = Share of Model %

Strategies to Increase Share of Model

Achieving a high Share of Model is not about keyword stuffing. It is about Generative Engine Optimization (GEO). You must structure your content so that machines can easily parse, understand, and extract it.

1. Optimize for "Extractability"

LLMs are lazy readers. They prefer structured data over dense, unstructured prose. To increase your citation frequency, your content must be modular.

How to do it:

• Direct Answer Blocks: Start every section with a bold, 40–60 word definition or summary. This is "catnip" for AI summarizers.
• Markdown Formatting: Use clear H2s, H3s, and bullet points. Platforms like Steakhouse are built on this principle, generating content in pure markdown that maps directly to how LLMs ingest training data.
• Data Tables: LLMs excel at reading HTML tables. If you are comparing products or listing features, use a table. It increases the likelihood of your data being pulled into a comparison matrix in the AI Overview.

2. Increase Information Gain

If your article repeats what is already on the internet, the LLM has no reason to cite you. It will cite the original source or the most popular one (usually Wikipedia or a massive publisher).

How to do it:

• Original Data: Publish proprietary stats. "70% of marketers struggle with AEO" is better than "Many marketers struggle..."
• Coin New Terms: Create frameworks or acronyms unique to your brand. If you coin a term (like "Generative Engine Optimization" or "The Content Stack"), the AI must cite you when defining it.
• Contrarian Views: LLMs are trained to provide balanced views. If everyone says "X is good," and you write a well-reasoned article on "Why X is bad," you become the designated counter-argument citation.

3. Entity-First Architecture

Search engines no longer look for strings of text; they look for Entities (people, places, concepts, brands) and the relationships between them. You need to firmly establish your brand as an entity in the Knowledge Graph.

How to do it:

• Schema Markup: Use robust JSON-LD structured data. Organization, Product, FAQPage, and Article schemas are non-negotiable. They tell the AI explicitly: "This is a software tool, it costs $X, and it does Y."
• About Page Optimization: Your "About" page should be a knowledge base of your brand entity. Clearly state who you are, what you do, and your credentials.
• Consistent NAPs: Ensure your Name, Address, and Product details are identical across Crunchbase, LinkedIn, and your site.

Comparative Analysis: SEO vs. GEO

The shift from SEO to GEO is not just a tactical tweak; it is a strategic overhaul. Below is a breakdown of how the goals and methods differ.

Advanced Strategy: The "Citation Loop"

For advanced teams, the goal is to create a "Citation Loop." This occurs when your content feeds the AI, the AI cites you, users click for verification, and that user behavior signals further authority to the model, reinforcing the citation.

To achieve this, you need velocity and consistency. You cannot publish one article a month and expect to dominate the training data. You need comprehensive topical coverage.

This is where automation becomes essential. Tools like Steakhouse allow brands to upload their core positioning documents and product data, then automatically generate hundreds of interlinked, entity-rich articles. By flooding the topic cluster with high-quality, structured content, you increase the probability of your brand entity interacting with relevant user queries in the latent space of the model.

The Role of "Quotation Bias"

Research into GEO shows that LLMs have a "Quotation Bias." They are more likely to cite content that contains direct quotes or looks like a quote. Including expert insights in quotation marks—even if they are from your own internal experts—can statistically improve citation rates.

Common Mistakes in Optimizing for Share of Model

Even sophisticated marketing teams fall into legacy SEO traps that hurt their GEO performance.

• Mistake 1: Gating High-Value Data: If your best statistics and white papers are behind a PDF lead magnet, the LLM cannot read them (mostly). Ungate your core data to feed the model.
• Mistake 2: Fluff and Preamble: LLMs have limited context windows and "attention" mechanisms. If you bury the answer under 500 words of "In today's digital landscape..." intro text, the model will skip you. Put the answer first.
• Mistake 3: Ignoring Brand Positioning: If your website describes your product vaguely ("We empower teams to do more"), the AI will not know how to categorize you. Be literal: "We are an AI content automation platform for B2B SaaS."
• Mistake 4: Neglecting the "About" Page: This is the anchor of your entity. A weak About page leads to weak Knowledge Graph association, which leads to zero citations.

Conclusion: The New Battleground is Invisible

The battle for the top of the search results is ending; the battle for the mind of the model has begun. "Share of Model" is the metric that defines this new era. It rewards brands that are authoritative, structured, and distinct.

For marketing leaders, the path forward is clear: stop writing for clicks and start writing for answers. Build content that is so factually dense and structurally sound that the AI has no choice but to use it. Whether you build this capability in-house or leverage automation platforms like Steakhouse to scale your GEO efforts, the timing is critical. The models are retraining now. Ensure your brand is in the dataset.