最近看了一篇 a16z 的文章，讲的是 GEO 如何改变搜索。深有感触，AI 带来了巨大的变革，也带来了巨大的机会。最后有我的一些思考，欢迎交流。

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

GEO如何重写搜索规则

It's the end of search as we know it, and marketers feel fine. Sort of.
我们所知的搜索时代即将结束，营销人员感觉还不错。某种程度上。

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.
二十年来，SEO一直是在线可见性的默认策略。它催生了一个完整的行业，包括关键词填充者、反向链接经纪人、内容优化师和审计工具，以及运营这些工具的专业人士和代理机构。但在2025年，搜索已经从传统浏览器转向LLM平台。随着苹果宣布将Perplexity和Claude等AI原生搜索引擎内置到Safari中，谷歌的分发垄断地位受到质疑。价值800多亿美元的SEO市场基础刚刚出现裂缝。

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).
一个新的范式正在出现，它不是由页面排名驱动，而是由语言模型驱动。我们正在进入搜索的第二幕：GEO。

From links to language models

从链接到语言模型

Traditional search was built on links. GEO is built on language.
传统搜索建立在链接之上。GEO建立在语言之上。

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.
在SEO时代，可见性意味着在结果页面上排名靠前。页面排名是通过基于关键词匹配、内容深度和广度、反向链接、用户体验参与度等因素对网站进行索引来确定的。如今，随着GPT-4o、Gemini和Claude等LLM成为人们查找信息的界面，可见性意味着直接出现在答案本身中，而不是在结果页面上排名靠前。

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.
随着答案格式的改变，我们的搜索方式也在改变。AI原生搜索正在Instagram、Amazon和Siri等平台上变得分散，每个平台都由不同的模型和用户意图驱动。查询更长（平均23个词，而不是4个），会话更深入（平均6分钟），响应因上下文和来源而异。与传统搜索不同，LLM能够记忆、推理，并以个性化的多源综合方式响应。这从根本上改变了内容的发现方式以及需要如何优化。

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.
传统SEO奖励精确性和重复性；生成式引擎优先考虑组织良好、易于解析且意义密集（不仅仅是关键词）的内容。像"总结"或项目符号格式这样的短语有助于LLM有效地提取和复制内容。

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.
还值得注意的是，LLM市场在商业模式和激励机制方面与传统搜索市场根本不同。像谷歌这样的经典搜索引擎通过广告将用户流量货币化；用户用他们的数据和注意力付费。相比之下，大多数LLM是付费墙、订阅驱动的服务。这种结构性转变影响了内容的引用方式：模型提供商没有太多动机去展示第三方内容，除非它对用户体验有所增加或强化产品价值。虽然广告市场可能最终会在LLM界面之上出现，但规则、激励和参与者可能看起来与传统搜索非常不同。

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.
与此同时，LLM界面价值的一个新兴信号是出站点击量。例如，ChatGPT已经为数万个不同的域名带来了推荐流量。

From rankings to model relevance

从排名到模型相关性

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.
这不再仅仅关乎点击率，而是关乎引用率：你的品牌或内容在模型生成的答案中被引用或用作来源的频率。在AI生成输出的世界中，GEO意味着优化模型选择引用的内容，而不仅仅是你是否或在哪里出现在传统搜索中。这种转变正在改变我们定义和衡量品牌可见性和表现的方式。

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.
已经有像Profound、Goodie和Daydream这样的新平台使品牌能够分析它们在AI生成响应中的表现，跟踪模型输出中的情感，并了解哪些发布者正在影响模型行为。这些平台通过微调模型以镜像品牌相关的提示语言、战略性地注入顶级SEO关键词，以及大规模运行合成查询来工作。然后将输出组织成可操作的仪表板，帮助营销团队监控可见性、消息一致性和竞争性声音份额。

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.
Canada Goose使用了这样一个工具来了解LLM如何引用该品牌——不仅仅是在保暖或防水等产品特性方面，还有品牌认知本身。关键收获不是用户如何发现Canada Goose，而是模型是否会自发地提及该品牌，这是AI时代无提示认知的指标。

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.
这种监控变得与传统SEO仪表板一样重要。像Ahrefs的Brand Radar这样的工具现在跟踪AI概览中的品牌提及，帮助公司了解它们如何被生成式引擎框定和记住。Semrush也有一个专门的AI工具包，旨在帮助品牌跟踪生成式平台上的感知，优化AI可见性的内容，并快速响应LLM输出中的新兴提及，这表明传统SEO玩家正在适应GEO时代。

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.
我们看到了一种新型品牌策略的出现：不仅考虑公众中的感知，还考虑模型中的感知。你如何被编码到AI层中是新的竞争优势。

Lessons from the SEO era

SEO时代的教训

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.
尽管规模庞大，SEO从未产生垄断性赢家。帮助公司进行SEO和关键词研究的工具，如Semrush、Ahrefs、Moz和Similarweb，都各自成功，但没有一个占据了完整的技术栈（或通过收购增长，如Similarweb）。每个都开拓了一个利基市场：反向链接分析、流量监控、关键词情报或技术审计。

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.
SEO总是分散的。工作分布在代理机构、内部团队和自由职业者之间。数据混乱，排名是推断的，而不是验证的。谷歌掌握着算法密钥，但没有供应商控制过界面。即使在巅峰时期，最大的SEO玩家也是工具提供商。他们没有用户参与度、数据控制或网络效应来成为SEO活动集中的中心。点击流数据——用户在浏览网站时点击链接的记录——可能是了解真实用户行为的最清晰窗口。然而，从历史上看，这些数据一直难以获取，被锁定在ISP、SDK、浏览器扩展和数据经纪人背后。这使得在没有深度基础设施或特权访问的情况下，构建准确、可扩展的洞察几乎不可能。

GEO changes that.
GEO改变了这一点。

How to make the mentions: The emergence of GEO tools

如何获得提及：GEO工具的出现

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.
这不仅仅是工具的转变，这是一个平台机会。最引人注目的GEO公司不会止步于测量。他们将微调自己的模型，从跨垂直领域的数十亿隐式提示中学习。他们将拥有整个循环——洞察、创意输入、反馈、迭代——通过差异化技术，不仅观察LLM行为，还塑造它。他们还将找到捕获点击流数据并结合第一方和第三方数据源的方法。

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.
在GEO中获胜的平台将超越品牌分析，提供行动基础设施：实时生成活动，优化模型记忆，并随着LLM行为的变化每日迭代。这些系统将是可操作的。

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.
这释放了比可见性更广泛的机会。如果GEO是品牌确保在AI响应中被引用的方式，那么它也是管理与AI层本身持续关系的方式。GEO成为与LLM交互的记录系统，允许品牌跟踪生成式平台上的存在、表现和结果。拥有那一层，你就拥有了背后的预算。

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.
这就是垄断潜力：不仅仅是提供洞察，而是成为渠道。如果SEO是一个去中心化的、数据相邻的市场，GEO可以是相反的——中心化的、API驱动的，并直接嵌入到品牌工作流程中。最终，GEO本身可能是最明显的楔子，特别是当我们看到搜索行为的转变时，但最终，它实际上是进入更广泛的效果营销的楔子。驱动GEO的相同品牌指导原则和用户数据理解可以驱动增长营销。这就是大企业的建立方式，因为软件产品能够测试多个渠道，迭代并在它们之间优化。AI使自主营销人员成为可能。

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.
时机很重要。搜索刚刚开始转变，但广告资金移动很快，特别是当存在套利机会时。在2000年代，那是谷歌的Adwords。在2010年代，那是Facebook的定向引擎。现在，在2025年，是LLM和帮助品牌导航其内容如何被这些模型摄取和引用的平台。换句话说，GEO是进入模型思维的竞争。

In a world where AI is the front door to commerce and discovery, the question for marketers is: Will the model remember you?
在一个AI是商业和发现前门的世界中，营销人员面临的问题是：模型会记住你吗？

对 GEO 的思考

GEO 和 SEO 的完整对比

GEO vs SEO：完整对比表格

💡 关键洞察

• • 本质区别：SEO是"被发现"的艺术，GEO是"被记住"的科学。
• • 战略转变：从"排名竞争"到"认知植入"，从"流量思维"到"影响力思维"。
• • 为 AI 创作内容，而不是人类
• • 为 Agent 制作工具，而不是人类

对 GEO 感兴趣的，在公众号后台发送：GEO，即可加群讨论，一切刚刚开始，欢迎探讨