Author: a16z
Compiled by: Deep Thinking Circle
Have you ever wondered if the $80 billion SEO industry might be coming to an end? The search rules we've taken for granted for over two decades—keyword rankings, backlinks, page optimization—are being utterly disrupted by a whole new set of game rules. When Apple announced the integration of AI-native search engines like Perplexity and Claude into Safari, Google's long-standing distribution monopoly began to shake. We are witnessing the most significant paradigm shift in search history: from the link-based search era to the generative engine optimization (GEO) era driven by language models.
This is not a gradual improvement but a complete rewrite. Imagine this: in traditional search, success means your webpage appears among the top search results. But in the GEO era, the definition of success has completely changed: is your content directly cited in AI-generated answers? Does your brand hold a significant position in the model's 'memory'? This shift is reshaping the entire digital marketing ecosystem, from content creation strategies to the metrics of brand visibility, everything needs to be reconsidered. What I see is not just a technological update but a fundamental restructuring of business models and competitive landscapes.
The most important change from SEO to GEO is the shift in traffic distribution channels. For someone involved in marketing and growth, channel changes are the most sensitive; each new channel brings a new wave of traffic opportunities. Recently, I shared some latest data and insights from Google AI Overview on Jike, and those interested can check the chart. a16z just published a new article today discussing how the emergence of GEO is changing the rules of traffic and marketing, which I would like to share along with my thoughts and insights.
The leap from the link era to the language model era
Traditional search is built on links, while GEO is built on language understanding. This difference may seem subtle, but it represents two completely different worldviews. In the SEO era, visibility means ranking high on search results pages, which requires optimizing page rankings through keyword matching, content depth and breadth, backlinks, user experience, and other factors. But today, as large language models like GPT-4o, Gemini, and Claude become the primary interfaces for obtaining information, the meaning of visibility has fundamentally changed: you need to appear directly in the answer itself, not just rank high on the results page.
I find that the impact of this shift is far deeper than it appears on the surface. The format change in answers has fundamentally altered search behavior. AI-native search is showing a fragmented trend across platforms, driven by different models and user intents, such as Instagram, Amazon, and Siri. User queries are becoming longer (an average of 23 words compared to only 4 words in traditional search), search sessions are deeper (averaging 6 minutes), and responses vary based on context and source. Unlike traditional search, large language models have the ability to remember, reason, and provide personalized multi-source comprehensive responses. This fundamentally changes how content is discovered and how it needs to be optimized.
More importantly, the large language model market is fundamentally different from traditional search markets in terms of business models and incentive mechanisms. Classic search engines like Google monetize user traffic through ads, with users paying the price with their data and attention. In contrast, most large language models are subscription-driven services behind paywalls. This structural shift affects how content is cited: model providers have little motivation to showcase third-party content unless it adds value to user experience or reinforces product value. While the advertising market may eventually appear on large language model interfaces, its rules, incentive mechanisms, and participants are likely to differ significantly from traditional search.
In this new environment, I've observed an interesting phenomenon: traditional SEO rewards precision and repetition, while generative engines prioritize content that is well-organized, easily parseable, and meaning-dense (not just keyword-dense). Phrases or bullet point formats like 'summary' help large language models effectively extract and replicate content. This difference reveals the fundamental adjustments needed in content optimization strategies: shifting from catering to algorithms to catering to language understanding systems.
One emerging signal that I believe is worth paying attention to is the outbound click-through rates from large language model interfaces. For example, ChatGPT has already been driving referral traffic to tens of thousands of different domains. This suggests that even in an era where AI directly answers questions, high-quality original content still holds its value, but the way this value is realized is fundamentally different from the past. Brands and content creators need to rethink how to create and maintain their value in this new ecosystem.
The shift from ranking to model relevance
The game rules now are no longer just about click-through rates, but about citation 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 the content that models choose to cite, rather than just whether you appear or where you rank in traditional search. This shift is redefining the ways in which brand visibility and performance are measured.
I see new platforms like Profound, Goodie, and Daydream emerging, allowing brands to analyze their performance in AI-generated responses, track sentiment trends in model outputs, and understand which publishers are influencing 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 output is then organized into actionable dashboards to help marketing teams monitor visibility, information consistency, and competitors' share of voice.
Canada Goose uses tools like this to understand how large language models cite the brand—not just product features like warmth or waterproofing, but also brand awareness itself. The key insight lies not in how users discover Canada Goose, but whether the model will spontaneously mention the brand, which is an indicator of unassisted brand awareness in the AI era. This type of monitoring becomes as important as traditional SEO dashboards. Tools like Ahrefs' Brand Radar now track mentions of brands in AI overviews, helping companies understand how they are positioned and remembered by generative engines.
Semrush has also launched a dedicated AI toolkit aimed at helping brands track awareness on generative platforms, optimize AI visibility content, and respond quickly to new mentions arising from large language model outputs. This indicates that traditional SEO participants are adapting to the GEO era. We are witnessing the emergence of a new type of brand strategy: one that considers not only public awareness but also awareness within the model. How you are encoded into the AI layer is a new competitive advantage.
Currently, GEO is still in the experimental phase, much like the early stages of SEO. Each significant model update carries the risk of relearning (or forgetting) how to best interact with these systems. Just as Google's search algorithm updates once led companies to scramble to respond to ranking fluctuations, large language model providers are still adjusting the rules behind how their models cite content. Various ideas are emerging: some GEO strategies are already quite clear (such as being mentioned in source documents cited by large language models), while other hypotheses are more speculative, such as whether models prioritize news content over social media content or how preferences vary with different training sets.
I think this uncertainty is both a challenge and an opportunity. For brands that can adapt and experiment quickly, it is a time to gain a first-mover advantage. But at the same time, investment decisions need to be more cautious because strategies that are effective today may not be applicable tomorrow. This requires marketing teams to cultivate stronger adaptability and a spirit of experimentation rather than relying on fixed best practices.
Lessons learned from the SEO era
Despite the massive scale of the SEO market, it has never produced a monopolistic winner. This phenomenon gives me a lot of inspiration. Tools that help companies with SEO and keyword research, such as Semrush, Ahrefs, Moz, and Similarweb, have all found success in their respective niches, but none have fully occupied the entire tech stack (or grown through acquisitions, like Similarweb). Each company has carved out its own niche: backlink analysis, traffic monitoring, keyword intelligence, or technical audits.
SEO has always been fragmented. Work is distributed among agencies, in-house teams, and freelancers. Data is chaotic, and rankings are inferred rather than verified. Google holds the algorithm key, but no vendor fully controls the entire market. Even at its peak, the biggest SEO players were merely tool providers. They lacked user engagement, data control, or network effects to become the center of SEO activities. Clickstream data—the record of users clicking links while browsing websites—could be argued to be the clearest window into understanding real user behavior. However, historically, this data has been hard to obtain, locked behind ISPs, SDKs, browser extensions, and data brokers. This makes it nearly impossible to build accurate, scalable insights without deep infrastructure or privileged access.
GEO changes everything. The key to this shift is that the workings of large language models are inherently more transparent and predictable. While we cannot fully understand the internal mechanisms of the models, we can understand their behavior patterns through large-scale queries and analyses. This creates opportunities for a new generation of tools and platforms that can provide more precise and actionable insights than those in the SEO era.
Platforms that win in GEO will go beyond brand analytics to provide actionable infrastructure: real-time generation of marketing campaigns, optimization of model memory, daily iteration, and adjustments in response to changes in large language model behavior. These systems will be operational. This unleashes an opportunity broader than visibility. If GEO is the way brands ensure they are cited in AI responses, it is also the way brands manage their ongoing relationship with the AI layer itself. GEO becomes a record-keeping system for interacting with large language models, allowing brands to track their presence, performance, and results on generative platforms. With that level, you hold the budget behind it.
The rise of GEO tools and platform opportunities
This is not just a tool shift; it’s a platform opportunity. I believe the most competitive GEO companies will not be satisfied with mere data measurement capabilities. They will build their model fine-tuning capabilities, learning from billions of implicit prompts across industries. They will have a complete closed loop—insights, creative input, feedback, iteration—using differentiated technology not only to observe large language model behavior but also to actively shape that behavior. More crucially, they will find ways to acquire clickstream data and integrate first-party and third-party data sources.
In my view, this is where the potential for monopoly lies: not just providing insights but becoming the channel itself. If SEO is a decentralized, data-adjacent market, then GEO might be exactly the opposite—centralized, API-driven, and directly embedded in brand workflows. GEO itself may be the most obvious wedge, especially as we see the shift in search behavior, but ultimately, it really cuts into the broader performance marketing realm. The brand guiding principles and understanding of user data that underpin GEO can also drive growth marketing. This is how large enterprises are built, where software products can test multiple channels, iterate, and optimize across them. AI makes autonomous marketers possible.
Timing is crucial. Search is just beginning to transform, but advertising dollars are moving quickly, especially when there are arbitrage opportunities. In the 2000s, it was Google’s AdWords. In the 2010s, it was Facebook’s targeted engine. Now, in 2025, it is large language models and platforms that help brands navigate how their content is consumed and cited by these models. In other words, GEO is the competition to enter model thinking.
One key trend I have observed is that successful GEO platforms are evolving from mere analytical tools into full-stack marketing operating systems. They not only tell brands how they perform in AI responses but also provide tools for creating, optimizing, and distributing content to improve visibility in generative engines. This integrated approach creates stronger customer lock-in and higher lifetime value.
More interestingly, I see some GEO platforms beginning to explore predictive capabilities. By analyzing the behavior patterns of large language models, they can predict which types of content are more likely to be cited in future queries and which topics are about to gain popularity. This forward-looking capability provides brands with a tremendous strategic advantage, allowing them to position themselves favorably ahead of competitors.
I believe the real opportunity lies with platforms that can integrate GEO into a broader marketing tech stack. When GEO tools can seamlessly integrate with CRM systems, content management platforms, social media management tools, and analytics dashboards, it transforms from a standalone optimization tool into the core hub of marketing operations. This integration not only improves efficiency but also creates new data insights and automation possibilities.
The future of marketing: the competition for brand memory in the AI era
In a world where AI becomes the front door of commerce and discovery, marketers face the question: will the model remember you? This question is deeper and more complex than it appears. It concerns not only brand awareness but also the brand's position within the AI system, the context in which it is cited, and its relative importance compared to other brands.
I find that the essence of this competition is distinctly different from traditional marketing. In the SEO era, brands competed for positions on search results pages. In the social media era, brands fought for users' attention and engagement. But in the GEO era, brands compete for their position and assigned weight in the 'memory' of AI models. This is a whole new dimension of competition that requires new strategic thinking.
Even more interesting is that this competition is not only occurring among brands within the same industry but also at a cross-industry level. When users ask for 'the best investment options,' traditional financial brands may need to compete with tech companies, real estate platforms, and even cryptocurrency projects for AI citations. This cross-industry competition blurs traditional industry boundaries and requires brands to rethink their positioning and value propositions.
I believe successful GEO strategies must be built on a deep understanding of how AI systems work. This includes not only technical understanding but also insights into AI training data, update frequency, and bias tendencies. Brands need to understand the characteristics and preferences of different AI models just as they understand Google’s algorithms. For example, some models may lean more towards authoritative content, while others may prioritize novelty or practicality.
In the long run, I believe GEO will give rise to entirely new marketing professions and skill sets. Just as SEO experts have become standard in digital marketing teams over the past two decades, GEO experts will become indispensable roles in future marketing teams. These experts will need to have a deep understanding of AI technologies, data analytical skills, content strategy thinking, and agility to quickly adapt to technological changes.
I also see the far-reaching impact of GEO on content creation. Traditional content marketing focuses on creating content that is valuable to human readers. In the GEO era, content needs to be valuable to both humans and AI systems. This requires content creators to master new skills and understand how to create content that appeals to human readers while being effectively understood and cited by AI systems.
Ultimately, GEO is not just a new marketing strategy; it represents a fundamental shift in how brands interact with consumers. In this new world, a brand's success no longer solely depends on how many consumers they can reach, but on whether they can be chosen and recommended by AI systems at critical moments. This shift demands that brands reevaluate their value propositions, content strategies, and technology investments to remain competitive in an AI-driven future.
I firmly believe that brands that can early understand and master GEO rules will gain significant competitive advantages in the coming years. Those that cling to traditional marketing thinking may find their visibility in the AI era sharply declining. This is not an exaggeration but an inevitable result of technological change. The GEO era has arrived, and the rules of the game have changed. The key question is: Are you ready?