Search is changing, again.

In the early days of the web, businesses tried to get listed in Yahoo. Then for a generation, SEO meant Google optimization. Now we’re witnessing another seismic shift as AI-powered systems (both Google’s AI Overview and chat platforms like ChatGPT) transform how information is discovered and presented online.

This shift is already underway. Between December 2024 and February 2025, ChatGPT saw a 33% increase in its user base, jumping from 300 million to 400 million users. Meanwhile, Google’s search market share dropped below 90% for the first time since 2015. The data tells a clear story: AI search is rapidly consuming market share.

According to recent reports, Apple may even be considering replacing Google with AI search features in Safari. This would represent another massive disruption to the search landscape. The potential move by Apple, which controls roughly 20% of U.S. mobile search traffic, would further accelerate the transition toward AI-first search experiences.

Two Paths to AI Visibility: Training vs. Retrieval

There are fundamentally two mechanisms through which content and brands achieve visibility in AI systems:

Training Data Presence: This is the most durable form of AI visibility. When content becomes part of the datasets used to train AI models, that information becomes embedded in the model’s fundamental understanding of the world. The model “knows” about a brand without having to search for it. This is analogous to knowing facts without looking them up.

When an AI confidently states that “Hubspot is a CRM company” or “Toyota makes reliable cars” without checking external sources, those brands have achieved training data presence. This form of visibility is:

• More persistent across model updates
• Less susceptible to algorithm changes
• Harder to achieve but longer-lasting
• Valuable for brand identity and category positioning

Retrieval Citation: This is the more immediate form of visibility that occurs when AI systems pull content from the web in real-time. Content appears as citations, links, or references in AI-generated responses. This is analogous to looking up specific information when needed rather than relying on memory.

When a model cites a webpage in response to “best mattresses 2025,” that site has achieved retrieval citation. This form of visibility is:

• Immediately actionable through optimization
• Susceptible to changes in retrieval algorithms
• Easier to influence in the short term
• Valuable for specific products and timely information

The interplay between these mechanisms creates unique dynamics for different query types. Factual queries like “difference between an espresso machine and a coffee maker” typically draw from training data, while time-sensitive or subjective queries like “current mortgage rates” trigger retrieval processes.

Subjective queries create particularly valuable opportunities. When users ask “What’s the best CRM software,” they’re entering territory where AI systems must synthesize opinions and competing claims. There is no single “right” answer, so the model weighs collective judgments, creating strategic opportunities to influence perceptions through both mechanisms.

The best AI visibility strategy addresses both optimizing current content for immediate citation and building the kind of authoritative presence that eventually becomes embedded in training data.

Traditional SEO Metrics Don’t Always Apply

Amplifying’s extensive analysis of a large number of queries reveals an interesting data point: AI citation behavior simply can’t be explained by conventional SEO metrics like traffic or backlinks. A top Google ranking doesn’t guarantee mentions in AI-generated results. In fact, ChatGPT operates with multiple distinct bot types (unlike Google’s unified Googlebot), including separate bots for real-time user actions (ChatGPT-User), training data collection (GPTBot), and search (OAI-SearchBot). This creates an entirely different visibility landscape with unique crawling patterns and priorities for each function.

Each AI system has its own distinct characteristics and preferences when citing sources:

• Some favor authoritative reference sources and encyclopedic content
• Some prioritize popular user-generated content, especially if the content is considered highly relevant to the user query
• Certain engines strive to maintain domain-agnostic citation patterns, drawing intentionally from a diverse mix of websites
• Several gravitate toward established publications and recognizable media brands

These varied citation behaviors represent the next evolution of search query refinements and vertical-specific optimization strategies, similar to how Google might favor fresh content for news queries while prioritizing established domains for YMYL topics. A one-size-fits-all approach to optimization is unlikely to succeed across all AI platforms.

Example: Which is the Best Guitar?

As shown below, the same query “best acoustic guitar under $500” returns different results in Google, ChatGPT, and Perplexity.

The Competition for Visibility

Despite assertions from some AI search providers that they working to prevent the necessity of AI search optimization, such claims are largely aspirational. All AI systems must source their data from somewhere. Most training and retrieval data ultimately come from the internet, making complete immunity from influence impossible.

The difference lies in query types. For factual recall or even complex reasoning, AI systems can be trained to provide consistent, verifiable answers. But for queries such as “best running shoes,” “most reliable laptop,” and “best tahoe ski resort,” taste and opinion factor heavily.

These queries represent a significant opportunity for brand influence precisely because there is no single “right” answer. Ultimately, many user queries are seeking subjective opinions rather than “factual” answers. When a user asks “what’s the best CRM software?” or “best acoustic guitar under $500”, they’re entering territory where AI systems must synthesize collective judgments and weigh competing claims. This creates a unique opportunity for brands to shape perception by becoming part of the collective “truth” that AI systems draw upon to form answers about subjective matters.

The Rise of AI Search Optimization Tools

As with any paradigm shift, new tools emerge to help businesses navigate uncharted territory. Several companies have launched platforms specifically designed for AI search optimization:

• Amplifying addresses both training data optimization and citation visibility through monitoring across different AI platforms over the long term. The platform tracks mentions, analyzes content patterns favored by different AI engines, and provides geographical analysis for location-specific products and brands.
• Otterly offers a simpler approach where users input their own prompts to track how products and brands appear in AI-generated recommendations. While less comprehensive, this may be sufficient for smaller businesses or those just beginning to explore AI visibility.
• Profound focuses on enterprise-level visibility tracking and brand mentions across AI platforms. Their solution targets larger organizations with complex brand presence requirements across multiple products and markets.
• Scrunch helps businesses understand their current AI search presence and identify optimization opportunities. They specifically highlight discrepancies between official data, third-party content, and AI-generated outputs, enabling companies to quickly close accuracy gaps.

Other emerging players include Bluefish AI, BrandRank, Evertune, and Knowatoa, each with slightly different approaches to the AI visibility challenge.

Technical Optimization for AI Search

AI search optimization requires specific technical approaches that differ from traditional SEO. Key optimizations showing promise include:

• llms.txt files: Similar to robots.txt, this emerging standard aims to signal to AI engines how to interact with your content. It’s still in early stages with limited adoption, though Anthropic appears to have implemented support for it for its own documentation site.
• Meta description optimization: Presenting your entire content in agent-viewable meta descriptions aids AI summarization and could increase citation likelihood.
• Content structure: So far, comparative listicles are heavily represented in AI citations. AI engines prefer concise, confidence-worthy sources structured for easy information extraction.
• Recency signals: AI engines pick up and cite fresh content rapidly, sometimes within days of publication.
• User agent management: Understanding and properly configuring access for AI crawlers is critical. Many website configurations unintentionally block AI retrieval bots while attempting to block training data collection, resulting in missed citation as well as training data visibility opportunities.
• JavaScript limitations: Some AI retrieval bots can’t execute JavaScript, meaning they only see the HTML version of sites. While this limitation is likely temporary, it currently provides a strategic advantage to publishers who serve content in plain HTML. Organizations can use this temporary limitation to their advantage by ensuring critical brand messaging and differentiators are accessible without JavaScript, potentially gaining an edge over competitors with JavaScript-heavy implementations.

The Durability Question

While meta description tweaks and JavaScript workarounds may yield quick visibility, they remain vulnerable to AI engine updates, much like traditional SEO tactics often lose effectiveness after algorithm changes. When this happens, marketers (and optimization tools) must quickly pivot to discover and implement new tactics as the landscape evolves.

A critical strategy for durability is becoming part of the “internet truth” in training data. When AI models consider your content foundational knowledge rather than just a citable source, that presence persists across model updates.

Again, the most prudent approach combines both: optimizing for immediate visibility while building the authority and ubiquity that makes your content part of AI’s core understanding of a topic.

Example: Which is the Best CRM?

The power of durable visibility is evident when asking for “best crm” without web search enabled: ChatGPT 4o consistently recommends HubSpot from its training data, while GPT-o4-mini returns Salesforce, showing how different models develop different “truths” from their training data. These base recommendations persist even when web search is enabled, though sometimes supplemented with additional retrieved options.

The Path Forward

We’re entering an era where being visible to AI platforms will soon be as important as being well ranked by Google. The pattern is clear: user queries are becoming conversational AI-focused.

For a growing number of users, the search journey now begins with a chat prompt rather than a Google query. This shift in user behavior requires corresponding changes in optimization strategies.

The need for AI search optimization is already here.