SEO已死，GEO当立：AI搜索时代的新游戏规则
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多万美元的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 re