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 rankings to model relevance

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.

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

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.

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. 

Put another way, GEO is the competition to get into the model’s mind.

How to make the mentions: A market map of GEO tools >>

Zach Cohen is a partner on the consumer tech team at Andreessen Horowitz, where he focuses on companies building at the application layer in generative AI.

Seema Amble is a partner at Andreessen Horowitz, where she focuses on SaaS and fintech investments in B2B fintech, payments, CFO tools, and vertical software.

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