Search as we know it is ending, and a16z says the $80 billion SEO industry needs to adapt or become obsolete. For over two decades, search engine optimization (SEO) dominated how brands gained visibility online. But in 2025, that foundation cracked. With Apple announcing that AI-native search engines like Perplexity and Claude will be built into Safari, Google's grip on search distribution is weakening. Andreessen Horowitz now argues we are entering a new era: Generative Engine Optimization (GEO), where visibility means appearing in AI-generated answers, not ranking high on a results page. What Is Generative Engine Optimization and How Does It Differ From Traditional SEO? Traditional SEO was built on links and keywords. Generative Engine Optimization is built on language. In the SEO era, visibility meant ranking high on a results page through keyword matching, content depth, backlinks, and user engagement signals. Today, large language models (LLMs) like GPT-4o, Gemini, and Claude act as the interface for how people find information. This means visibility now means showing up directly in the answer itself, rather than ranking high on the results page. The format of search has fundamentally changed. AI-native search is becoming fragmented across platforms like Instagram, Amazon, and Siri, each powered by different models and user intents. Queries are now longer, averaging 23 words compared to just 4 words in traditional search. Sessions are deeper, averaging 6 minutes, and responses vary by context and source. Unlike traditional search engines, LLMs remember, reason, and respond with personalized, multi-source synthesis. This fundamentally changes how content is discovered and how it needs to be optimized. How Are Brands Adapting to Measure Success in the GEO Era? New platforms are emerging to help brands navigate this shift. Companies 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. Legacy SEO tool providers are also adapting. Ahrefs' Brand Radar now tracks brand mentions in AI Overviews, helping companies understand how they are framed and remembered by generative engines. Semrush has launched 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 large language model outputs. This monitoring is becoming as important as traditional SEO dashboards. 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 key takeaway was 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. Steps to Optimize Your Content for Generative Engines - Prioritize Well-Organized, Meaning-Dense Content: Traditional SEO rewards precision and repetition; generative engines prioritize content that is well-organized, easy to parse, and dense with meaning rather than just keywords. Phrases like "in summary" or bullet-point formatting help LLMs extract and reproduce content effectively. - Focus on Reference Rates Over Click-Through Rates: 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. ChatGPT, for instance, is already driving referral traffic to tens of thousands of distinct domains. It is no longer just about click-through rates; it is about reference rates, how often your brand or content is cited or used as a source in model-generated answers. - Monitor Your Brand Perception Across Multiple AI Platforms: Use tools like Brand Radar or Semrush's AI toolkit to track how your brand appears across different generative engines. Understanding whether models spontaneously mention your brand is a key indicator of unaided awareness in the AI era, similar to traditional brand awareness metrics. - Understand the Business Model Shift: Unlike classic search engines like Google that monetized user traffic through ads, most LLMs are paywalled, subscription-driven services. This structural shift affects how content is referenced; there is less incentive by model providers to surface third-party content unless it is additive to the user experience or reinforces product value. The shift from SEO to GEO represents a fundamental change in how brands define and measure visibility. In a world where AI models generate answers rather than ranking pages, how you are encoded into the AI layer is the new competitive advantage. Why Is the Business Model Behind AI Search Different From Google? The LLM market is 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 is less incentive by model providers to surface third-party content, unless it is additive to the user experience or reinforces product value. While it is 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. 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. Vercel, a web development platform, reported that ChatGPT now refers 10 percent of new signups, which have also accelerated. Is GEO Just Another Version of SEO, or Something Entirely New? GEO is still in its experimental phase, much like the early days of SEO. With every major model update, companies 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, such as 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. 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. 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. GEO changes that dynamic. This is not just a tooling shift; it is a platform opportunity. The most compelling GEO companies will not stop at measurement. They will fine-tune their own models, learning from billions of implicit prompts across verticals. They will own the loop of insight, creative input, feedback, and iteration with differentiated technology that does not just observe LLM behavior, but shapes it. They will also figure out a way to capture clickstream data and combine first-party and third-party data sources. Platforms that win in GEO will go beyond brand analysis and provide the infrastructure to act.
