How Generative Engine Optimization (GEO) Rewrites the Rules of Search

It’s the end of search as we know it, and marketers feel fine. Sort of.

For over two decades, SEO was the default playbook for visibility online. It spawned an entire industry of keyword stuffers, backlink brokers, content optimizers, and auditing tools, along with the professionals and agencies to operate them. But in 2025, search has been shifting away from traditional browsers toward LLM platforms. With Apple’s announcement that AI-native search engines like Perplexity and Claude will be built into Safari, Google’s distribution chokehold is in question. The foundation of the $80 billion+ SEO market just cracked.

A new paradigm is emerging, one driven not by page rank, but by language models. We’re entering Act II of search: Generative Engine Optimization (GEO).

From links to language models

Traditional search was built on links. GEO is built on language.

In the SEO era, visibility meant ranking high on a results page. Page ranks were determined by indexing sites based on keyword matching, content depth and breadth, backlinks, user experience engagement, and more. Today, with LLMs like GPT-4o, Gemini, and Claude acting as the interface for how people find information, visibility means showing up directly in the answer itself, rather than ranking high on the results page.

As the format of the answers changes, so does the way we search. AI-native search is becoming fragmented across platforms like Instagram, Amazon, and Siri, each powered by different models and user intents. Queries are longer (23 words, on average, vs. 4), sessions are deeper (averaging 6 minutes), and responses vary by context and source. Unlike traditional search, LLMs remember, reason, and respond with personalized, multi-source synthesis. This fundamentally changes how content is discovered and how it needs to be optimized.

Traditional SEO rewards precision and repetition; generative engines prioritize content that is well-organized, easy to parse, and dense with meaning (not just keywords). Phrases like “in summary” or bullet-point formatting help LLMs extract and reproduce content effectively.

It’s also worth noting that the LLM market is also fundamentally different from the traditional search market in terms of business model and incentives. Classic search engines like Google monetized user traffic through ads; users paid with their data and attention. In contrast, most LLMs are paywalled, subscription-driven services. This structural shift affects how content is referenced: there’s less of an incentive by model providers to surface third-party content, unless it’s additive to the user experience or reinforces product value. While it’s possible that an ad market may eventually emerge on top of LLM interfaces, the rules, incentives, and participants would likely look very different than traditional search. 

In the meantime, one emerging signal of the value in LLM interfaces is the volume of outbound clicks. ChatGPT, for instance, is already driving referral traffic to tens of thousands of distinct domains. 

From rankings to model relevance

It’s no longer just about click-through rates, it’s about reference rates: how often your brand or content is cited or used as a source in model-generated answers. In a world of AI-generated outputs, GEO means optimizing for what the model chooses to reference, not just whether or where you appear in traditional search. That shift is revamping how we define and measure brand visibility and performance.

Already, new platforms like Profound, Goodie, and Daydream enable brands to analyze how they appear in AI-generated responses, track sentiment across model outputs, and understand which publishers are shaping model behavior. These platforms work by fine-tuning models to mirror brand-relevant prompt language, strategically injecting top SEO keywords, and running synthetic queries at scale. The outputs are then organized into actionable dashboards that help marketing teams monitor visibility, messaging consistency, and competitive share of voice.

Canada Goose used one such tool to gain insight into how LLMs referenced the brand — not just in terms of product features like warmth or waterproofing, but brand recognition itself. The takeaways were less about how users discovered Canada Goose, but whether the model spontaneously mentioned the brand at all, an indicator of unaided awareness in the AI era.

This kind of monitoring is becoming as important as traditional SEO dashboards. Tools like Ahrefs’ Brand Radar now track brand mentions in AI Overviews, helping companies understand how they’re framed and remembered by generative engines. Semrush also has a dedicated AI toolkit designed to help brands track perception across generative platforms, optimize content for AI visibility, and respond quickly to emerging mentions in LLM outputs, a sign that legacy SEO players are adapting to the GEO era.

We’re seeing the emergence of a new kind of brand strategy: one that accounts not just for perception in the public, but perception in the model. How you’re encoded into the AI layer is the new competitive advantage.

ChatGPT now refers 10% of new @vercel signups, which have also accelerated https://t.co/LzatDz8n8u

— Guillermo Rauch (@rauchg) April 9, 2025

Of course, GEO is still in its experimental phase, much like the early days of SEO. With every major model update, we risk relearning (or unlearning) how to best interact with these systems. Just as Google’s search algorithm updates once caused companies to scramble to counter fluctuating rankings, LLM providers are still tuning the rules behind what their models cite. Multiple schools of thought are emerging: some GEO tactics are fairly well understood (e.g., being mentioned in source documents LLMs cite), while other assumptions are more speculative, such as whether models prioritize journalistic content over social media, or how preferences shift with different training sets.

Lessons from the SEO era

Despite its scale, SEO never produced a monopolistic winner. Tools that helped companies with SEO and keyword research, like Semrush, Ahrefs, Moz, and Similarweb, were successful in their own right, but none captured the full stack (or grew via acquisition, like Similarweb). Each carved out a niche: backlink analysis, traffic monitoring, keyword intelligence, or technical audits.

SEO was always fragmented. The work was distributed across agencies, internal teams, and freelance operators. The data was messy and rankings were inferred, not verified. Google held the algorithmic keys, but no vendor ever controlled the interface. Even at its peak, the biggest SEO players were tooling providers. They didn’t have the user engagement, data control, or network effects to become hubs where SEO activity is concentrated. Clickstream data — records of the links users click as they navigate websites — is arguably the clearest window into real user behavior. Historically, though, this data has been prohibitively hard to access, locked behind ISPs, SDKS, browser extensions, and data brokers. This made building accurate, scalable insights nearly impossible without deep infrastructure or privileged access.

GEO changes that.

How to make the mentions: The emergence of GEO tools

This isn’t just a tooling shift, it’s a platform opportunity. The most compelling GEO companies won’t stop at measurement. They’ll fine-tune their own models, learning from billions of implicit prompts across verticals. They’ll own the loop — insight, creative input, feedback, iteration — with differentiated technology that doesn’t just observe LLM behavior, but shapes it. They’ll also figure out a way to capture clickstream data and combine first- and third-party data sources.

Platforms that win in GEO will go beyond brand analysis and provide the infrastructure to act: generating campaigns in real time, optimizing for model memory, and iterating daily, as LLM behavior shifts. These systems will be operational.

That unlocks a much broader opportunity than visibility. If GEO is how a brand ensures it’s referenced in AI responses, it’s also how it manages its ongoing relationship with the AI layer itself. GEO becomes the system of record for interacting with LLMs, allowing brands to track presence, performance, and outcomes across generative platforms. Own that layer, and you own the budget behind it.

That’s the monopolistic potential: not just serving insights, but becoming the channel. If SEO was a decentralized, data-adjacent market, GEO can be the inverse — centralized, API-driven, and embedded directly into brand workflows. Ultimately, GEO by itself is perhaps the most obvious wedge, especially as we see a shift in search behavior, but ultimately, it’s really a wedge into performance marketing, more broadly. The same brand guidelines and understanding of user data that power GEO can power growth marketing. This is how a big business gets built, as a software product is able to test multiple channels, iterate, and optimize across them. AI enables an autonomous marketer.

Timing matters. Search is just beginning to shift, but ad dollars move fast, especially when there’s arbitrage. In the 2000s, that was Google’s Adwords. In the 2010s, it was Facebook’s targeting engine. Now, in 2025, it’s LLMs and the platforms that help brands navigate how their content is ingested and referenced by those models. Put another way, GEO is the competition to get into the model’s mind.

In a world where AI is the front door to commerce and discovery, the question for marketers is: Will the model remember you?