AEO 终极指南：如何让 ChatGPT 推荐你的产品 | Ethan Smith（Graphite）
AEO 终极指南：如何让 ChatGPT 推荐你的产品 | Ethan Smith（Graphite）
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精彩预告
Lenny Rachitsky： 现在有个词大家都在听说了——AEO。
Ethan Smith： 答案引擎优化（Answer Engine Optimization），就是如何在大语言模型（LLM）的回答中出现。
Lenny Rachitsky： 在 AEO 上取胜感觉是一件非常重要的事情。
Ethan Smith： 比如要赢得”什么是最好的网站构建工具”这类查询，在 Google 时代，蓝色链接排在第一就算赢。但在 LLM 里不是这样，因为 LLM 是在汇总大量引用，所以你需要尽可能多地被提及。
Lenny Rachitsky： ChatGPT 给我的 Newsletter 带来的流量已经超过了 Twitter。
Ethan Smith： 你明天就可以通过某个引用被提及，然后立刻出现在回答中。你可以有一条 Reddit 帖子，可以有一个 YouTube 视频，也可以在一篇博客中被提及。所以早期公司也能赢，而且能很快取胜。
Lenny Rachitsky： 这些答案引擎给企业带来的线索真的有价值吗？
Ethan Smith： 价值显著更高。Webflow 发现 LLM 流量的转化率是 Google Search 流量的 6 倍。
Lenny Rachitsky： 很多人觉得一切都变了，以前做的都不管用了，必须重新思考一切。
Ethan Smith： 关于 AEO 存在大量错误信息。有新闻文章说 Google Search 会因为新事物的出现而消亡。但 Google 的那块饼并没有变小，只是整个饼变大了。
嘉宾介绍
Lenny Rachitsky： 今天的嘉宾是 Ethan Smith。Ethan 是 Graphite 的 CEO，也是我在所有 SEO 相关问题上的首选专家。SEO 正在经历一场重大转型。过去，人们有任何问题、在找产品或做研究时，都会去 Google。而如今，很多人转向 ChatGPT、Claude、Gemini 和 Perplexity 来获取答案，而且这个趋势只会不断加速。
甚至 Google 自己也在以相当激进的方式改变搜索体验——顶部出现了 AI Overviews，还有新推出的 AI Mode，基本上就是他们自己的 ChatGPT 版本。这意味着 SEO 世界正在经历一场巨变，包括 AEO（Answer Engine Optimization）的兴起。简单来说，AEO 就是针对 ChatGPT 的 SEO，让你的产品出现在人们得到的回答中。
Ethan 一直走在这一新技能和新渠道的最前沿。在这次对话中，他分享了自己关于如何让产品更频繁地出现在人们回答中所学到的一切。Ethan 分享的建议极其务实，价值巨大。请好好吸收，用在你自己的产品上。
接下来，请出 Ethan Smith。
Lenny Rachitsky： Ethan，非常感谢你来参加节目，欢迎来到播客，也欢迎再次回到播客。
Ethan Smith： 很高兴回来。
SEO 的变迁
Lenny Rachitsky： 我们大概两年半前做过一期播客。我认为那是如何赢得 SEO 的权威指南，从那以后很多人一直在引用它。我为当时的内容感到自豪，但情况已经变了。SEO 的世界正在发生变化，所以我很高兴再次和你聊聊，谈谈在这个 AI 正在改变 SEO 运作方式、AEO 和 GEO 崛起的新兴世界中如何取得成功。
先问一个问题：你做 SEO 多久了？在这一领域，有什么变化的重要程度能与之相比吗？
Ethan Smith： 我是在 2007 年开始做 SEO 的，到目前已经 18 年了。实际上，我刚入行时做的是程序化 SEO（Programmatic SEO）和电商 SEO，比如 NexTag、Shopping.com 和 PriceGrabber。那个时代你可以做大规模的自动生成着陆页。
那可能是最大的转变。后来 Google 推出了一系列算法更新，比如 Panda 等等，来阻止你做垃圾内容。所以本质上，SEO 从”做垃圾”变成了”不能做垃圾”。那大概是最大的变化，而这次可能是第二大变化。
我认为这里的关键是，它确实与搜索相关，但本质上是对搜索结果的汇总，而且有新的输入来源。所以它大概是第二大变化。
Lenny Rachitsky： 这很有意思，因为我觉得很多人认为一切都变了，以前做的都不管用了，必须重新思考一切。而你说这其实只是第二大变化，就像当年 Google 的算法更新其实比这更重大？
Ethan Smith： 对。
AEO 与 GEO 的定义
Lenny Rachitsky： 很好。我们来给大家一些背景，先定义一些术语。有个词大家都在听说了。实际上有两个——AEO 和 GEO。它们分别代表什么？是不同的东西吗？具体指的是什么？
Ethan Smith： 我认为它们是一样的。归根结底，一个词的定义就是一群人共同认可的约定，所以我们还得看人们最终怎么定义这些词。我可以给出我的定义：AEO 和 GEO 本质上试图描述的是同一件事——就是如何在 LLM 的回答中出现？
Ethan Smith： 我个人更倾向于答案引擎优化（Answer Engine Optimization），而不是生成引擎优化（Generative Engine Optimization）。因为”生成”可以生成图片、视频等非答案类内容，而”答案”的定义更为狭窄，所以当我们讨论优化 LLM 时，我个人更偏好 AEO 这个说法。相比”生成”，“答案”的定义更加精确。但归根结底，名称和定义最终取决于大家的共识。
Lenny Rachitsky： 好的。答案引擎优化听起来确实更简洁。知道它们其实是同一回事很好。有些人出于某种原因更偏好后一种说法。
有意思的是，最近——我不知道有没有跟你说过——我查了一下我的引荐流量，发现 ChatGPT 给我的 Newsletter 带来的流量已经超过 Twitter 了，这是我完全没想到的。所以这已经在发生了。我很想了解怎样更好地利用这一点，进一步优化它。
Ethan Smith： 你什么时候看到流量激增的？是什么时候开始大幅增长的？
Lenny Rachitsky： 遗憾的是，我用的数据看板不能提供很好的引荐流量分析。你觉得大概是什么时候开始的？
流量增长的时间节点
Ethan Smith： 我们合作的公司大约从一月份开始看到变化。原因有两方面：一是用户采用率提高了；二是回答变得更具可点击性了——出现了地图、购物轮播、可点击的卡片等。所以回答的可点击性增强了，加上用户采用率提升，大约就是一月份左右。
Lenny Rachitsky： 好的。我想回到这个问题——ChatGPT 抓取我的所有内容，给用户答案，然后按一定比例给我导流，这到底是好事吗？但我们先不谈这个。我想先聊聊你能对内容在 ChatGPT 中的出现频率产生多大的影响。
AEO 是否可以被优化
我最近请了 ChatGPT 的负责人 Nick Turley 来做播客。我问他：“你怎么看 AEO、GEO 这些东西？“他说：“不用担心那些，只要写出优质内容就行，系统会找到最好的内容的。“我想你肯定不太同意这个说法。我想你在主动让内容出现在这些答案引擎方面已经看到了实际效果。能谈谈你看到的影响，以及你对这番话的反应吗？
Ethan Smith： 我既同意也不同意。我的看法是：任何东西都可以被优化。你只需要理解底层系统和游戏规则，如果你做到了，就可以优化任何东西——算法可以优化，人也可以优化，什么都可以被优化。
我觉得他大概想表达两层意思：第一，“请不要对我的产品进行垃圾信息轰炸”；第二，“如果你这么做了，我看到了，我会阻止你。” 所以制造垃圾内容不是一种长期、稳健的策略，就像在 Google 上制造垃圾内容也不是长期策略一样。Google 迟早会说：“那些大型购物比价网站生成了一亿个自动搜索页面，我不喜欢，我要把整个类目都干掉。” ChatGPT 也一样，任何东西都可以被优化，但如果你在制造垃圾信息，他们会看到。他们会专门安排团队盯着这件事，然后修改算法来阻止你。
Lenny Rachitsky： 你具体看到了多大的影响？你跟很多公司合作过，我们后面会聊几个案例。能不能先分享一个，让我们有个直观感受——你能把内容在 ChatGPT 等平台中的出现频率提升多少？
Webflow 案例
Ethan Smith： 影响可以非常大。举一个具体的例子——Webflow。我们在为他们做 SEO，优化他们的内容，在答案引擎优化方面也看到了很多成果。
具体做了几件事。第一件就是传统 SEO：针对高搜索量关键词制作着陆页，比如”best no-code website designer”之类的。然后你可以免费获得答案引擎优化的效果——这就是传统 SEO，但对 AEO 同样非常有效。
Lenny Rachitsky： 我正想问，这听起来跟普通 SEO 完全一样。
Ethan Smith： 没错。我的总结是：在 SEO 中有效的方法，在 AEO 中同样有效，但 AEO 还有一些超越 SEO 的额外手段。第二件事——我对 AEO 与 SEO 区别的理解是：头部和长尾的逻辑都不同。
AEO 与 SEO 的关键差异
头部的不同在于：要赢得”什么是最好的网站构建工具”这类问题，即使 Webflow 的 URL 在引用来源中排在第一位，他们也不一定能赢得最终答案——在 Google 上他们能赢，因为蓝色链接排第一就赢了，但在 LLM 中不是这样。因为 LLM 是在综合多个引用来源进行摘要，所以你需要被尽可能多的来源提及。通常当你问”X 的最佳工具是什么”时，排名第一的答案是在引用来源中被提及次数最多的——这跟 Google 非常不同。
所以对于 Webflow，我们帮他们做了 YouTube 视频、Vimeo 视频，在 Reddit 中获得提及，在其他博客、联盟网站中被提及等等。我们尝试了很多方法。效果最好的：第一是纯 SEO；第二是 YouTube 视频；第三是 Reddit 优化。
Lenny Rachitsky： 等等。所以你是说，如果 Webflow 在 ChatGPT 回答”什么是最好的网站构建工具”时排在第一位，但实际上给它带来的流量还不如在摘要中被提及的次数最多来得重要？
Ethan Smith： 是的。这件事之所以有趣，是因为当创业公司来找我做 SEO 咨询时，我的第一反应是：“先别做。把时间花在其他事情上，因为你在早期阶段根本做不起来 SEO。“因为你没有足够的域名权重（Domain Authority），而获得域名权重需要时间，只有积累到一定权重之后才能获得排名。所以在 Google 上做 SEO 通常要到 A 轮、B 轮甚至更晚才开始做，而不是一上来就做，因为早期根本赢不了。
但答案引擎优化不一样，因为你可以明天就被某个引用来源提及，然后立刻出现在回答中。你可以有一个 Reddit 帖子、一个 YouTube 视频，你可以在某个博客中被提及——比如一家全新的 YC 公司发布了产品，大家都在讨论，他们明天就可能出现在回答中。所以早期阶段的公司可以赢，而且赢得很快。任何人都可以通过被尽可能多的引用来源提及来快速获胜。这就是头部逻辑的不同。
长尾的不同在于：聊天场景下的长尾比搜索场景大得多。平均查询词数——我想 Perplexity 跟别人提过——大约是 25 个词，而 Google 大约是 6 个词。长尾要大得多得多，人们会问很多追问。
Lenny Rachitsky： 长尾，本质上就是你输入的提示词，就是你问的问题？
Ethan Smith： 对。意思是说，如果你把人们问的所有问题都映射出来——类似 SEO 中的长尾关键词——如果你去做长尾问题的映射，这个尾巴的规模更大。也就是说，那些非常具体的问题，其数量更多，占比更高，搜索量也更大。
可能还有很多从未被问过的问题，从未被搜索过的问题，因为搜索无法支持大量非常具体、极其细分的内容。而聊天工具恰恰就是为了追问和对话而设计的。
所以现在出现了大量从未被问过或搜索过的问题，而你可以赢得这些流量。我刚入行 SEO 时，做的就是长尾 SEO——为每一个关键词做一个页面，这种方式现在已经不管用了，但现在长尾在聊天场景下又回来了。
如果你知道人们在问的那些非常具体的问题，你同样可以赢得这些流量，而且可能还很早就能赢。我见过一些早期阶段的公司，刚推出某个非常具体的 AI 支付处理 API 之类的产品，就会出现回答中。他们能出现，是因为他们在回答从未被回答过的问题。
Lenny Rachitsky： 这些答案引擎给公司带来的线索真的有价值吗？对 B2B SaaS 来说，这些是高质量线索吗？
Ethan Smith： 价值显著更高。Webflow 的数据显示，来自大语言模型的流量转化率是 Google 搜索流量的 6 倍。
Lenny Rachitsky： 六倍？
Ethan Smith： 六倍，所以质量明显更高。我认为原因可能有几个。可能是因为你在这个过程中已经充分”预热”了——你在进行多轮追问的对话中积累了大量意图。
你可能已经非常精确地锁定了自己想要的东西，所以当你点击前往某个页面时，意向性非常高。所以我们看到的转化率确实高得多。
答案引擎流量的转化优势
Lenny Rachitsky： 哇，这太有意思了，而且完全说得通。人们信任 ChatGPT 给出的答案，而如果你就是那个答案，你就拥有了巨大的优势。那正是人们想要知道的，然后”好的，谢谢，我去看看这个产品。”
这一切都很合理。回到你分享的三个杠杆，基本上就是那些能让你在答案引擎中更多出现的手段——着陆页、YouTube 视频和 Reddit。对吗？
Ethan Smith： 这些是其中一部分。
Lenny Rachitsky： 好的。
站内优化与站外引用
Ethan Smith： 其他方面，我会把它分成站内和站外两类。站内就是传统 SEO，区别在于这个长尾。我还想说另一个区别是大量的追问——你的产品能不能做这个？使用场景是什么、功能、集成、支持的语言？
详细介绍你的产品，非常具体的产品细节，这些放在你自己的网站上。第二类是站外，也就是出现在所有引用来源中。引用来源包括视频、UGC（如 Reddit 和 Quora）、联盟网站。
Dotdash Meredith 到处都在出现——Glamour、Good Housekeeping——在这些媒体中被提及，还有博客。就是这两大类。
Lenny Rachitsky： 这听起来跟 SEO 非常相似——出现在别人的页面上。比如从 Reddit 获得链接一直都很重要。
有意思的是 Reddit 竟然这么重要。你觉得这是怎么回事？
Reddit 的特殊地位与优化策略
Ethan Smith： Reddit 是最有趣的现象之一。它在 LLM 中被大量引用。客户问我的头号问题就是”我们怎么优化 Reddit？“这又回到了 ChatGPT 负责人那个问题——“请不要在我的产品里灌垃圾信息。”
Reddit 是一个真实观点、真实用户的社区，内容由社区自身严格管理，而且社区管理得非常好。增长人员的显而易见策略就是：“让我们制造大量自动垃圾信息，到处刷 Reddit，让我的产品到处出现。”
这就是增长思维，也是拼搏思维，可以理解。那人们在做什么呢？创建几百个假 Reddit 账号，冒充别人。我一个人，搞 100 个 Reddit 账号，自动发帖，然后给自己的评论点赞。
然后攒信任分，然后到处喊我的产品是最好的产品。好在效果不太好，但这就是最显而易见的策略。我们看到有人在尝试，同时也看到这些账号被封、评论被删。所以人们在尝试刷垃圾信息但并不成功，这是一种策略。
另一种策略是：Reddit 的根本目的就是发布有用、高质量、真实的个人评论。在 Webflow，我们有几个人会去评论区说：“我叫什么名字，我在哪里工作，这里有一条有用的信息。“具体策略是：找到你想要出现在其引用来源中的帖子。
表明你的身份，说明你在哪里工作，然后提供一条有用的信息，效果非常好。如果你不是那种”我需要扩展到几百条评论”的增长思维，这听起来很简单。但你实际上并不需要一万条评论，哪怕五条就够了，完全可以应对。
所以 Reddit 策略就是最显而易见的策略——做 Reddit 的真实用户。注册一个账号，表明身份，说明你在哪里工作，给出有用的回答。
Lenny Rachitsky： 之前我们请过 Deel（D-E-E-L）的早期增长负责人来播客。这就是他们最初的获客方式——甚至在 AI 出现之前——就是在 Reddit 上大力回答人们的问题。
就是那种”嘿，我是 Deel 的，能帮你解决这个问题吗？“的方式。很有意思。Reddit 竟然是阻止 ChatGPT 被垃圾信息淹没的屏障。不是 ChatGPT 自己在阻止垃圾信息，而是 Reddit 本身在这方面做得很好。
Ethan Smith： 我认为从某种意义上说，ChatGPT 也在进行监管，因为 ChatGPT 运行搜索，找到引用来源。有一个搜索算法在选择哪些引用来源是有用的。ChatGPT 有专门的团队在调优搜索算法，选择他们信任的信息源。
我相信有一个搜索评估团队在判断”这些引用来源好不好？Reddit 出现了吗？我希望它出现。“所以 ChatGPT 确实有人有意识地在配置算法，使用 Reddit，因为它是可信的。如果不可信，他们就不会用。
Google 也一样。Google 专门配置了搜索算法来排名 Reddit、Twitter 和 Quora，因为他们想要用户生成内容。如果内容不好，他们就会调整算法，不给予排名。所以我认为他们确实在某种意义上进行了监管。
Lenny Rachitsky： 明白了。而这些都属于后训练阶段的、面向搜索的功能，不是模型训练时用的数据，对吗？
RAG 与核心模型的区别
Ethan Smith： 我认为是这样的——有核心模型，然后有 RAG。核心模型是看过了 Common Crawl 上数十亿网页，然后对模型进行再训练。如果你问”加州的首府是什么？“它会预测下一个词，也就是 Sacramento。这基于核心算法——下一个词预测。
然后是 RAG，RAG 基本上就是搜索——检索增强生成（Retrieval-Augmented Generation）。先做搜索，再对搜索结果进行摘要。这是两个不同的东西。所以我刚才描述的大部分内容都是关于 RAG 部分，而不是核心模型部分。要影响核心模型可能极其困难，而且你可能一年后才能看到效果。
而且那可能是一些没人愿意做的、莫名其妙的操作——比如做一百万个页面都写着”X 的最佳产品是某品牌”。我觉得大多数人不会把时间花在这上面。所以我主要关注 RAG 方向，因为那是主要的可控部分。
而且我认为，如果 LLM 在 RAG 的搜索结果中完全没看到你的产品，它大概也不会提到你的产品。所以从优化角度来看，大部分有意思的事情都集中在这里。
Lenny Rachitsky： 明白了。我们刚开始聊这个话题的时候，我甚至没想到这一层，但我觉得这是一个很重要的点需要指出——这跟训练数据完全无关。
这是后训练阶段的事情，是模型上线之后，通过 RAG、网页搜索等方式去获取最新信息的能力。好的，在我们进入具体步骤、讲怎么做 AEO 之前——
在这个领域要取得成功，你认为有哪些两三件重要的事情是大家需要理解的？
AEO 成功的三个关键要素
Ethan Smith： 第一件事是认识到这与搜索有关。它本质上是 LLM 加 RAG——通常是在对一组搜索结果进行摘要。所以 LLM 加 RAG，这是第一点。第二点是话题。在搜索中，一个着陆页会瞄准数百个关键词，这个我们在上一期播客里聊过。
我不再像 2007 年那样只瞄准一个关键词，而是瞄准一千个关键词，每个着陆页需要覆盖这一千个关键词的集合，这就是一个话题。答案引擎优化也是一样的道理。每个页面要瞄准数百、数千、甚至数万个问题。
所以我需要把所有这些问题归类分组，这就引出了内容层面——我怎样才能获得排名？我怎样才能让我的 URL 获得排名？或者说其他 URL 是如何被决定是否排名的？那就是回答所有的问题。你回答的问题越多，效果越好。
在 Google 搜索中，如果我的着陆页是关于网站建设工具的，我的页面回答的子话题和后续问题越多，我就越有可能出现在 Google 搜索结果中。聊天也是一样，你回答的问题越多越好。如果你没有回答某个问题，你大概就不会出现。
如果你回答了别人没回答的后续问题和子话题，你出现的概率就会更高。所以话题是第二点。第三点是问题研究——我怎么知道人们在问哪些问题？这其实挺难的，因为在搜索领域，Google 会通过它的广告 API 直接告诉你。
他们会说”这个关键词的搜索量是多少”。Google 提供了一个基准数据集，而 ChatGPT 并没有给我们这些，至少目前还没有。也许等他们做广告的时候，会给我们更多搜索量的数据，但现在没有基准数据集。那我们怎么知道人们在问什么问题呢？
问题研究的方法
一种方法是把你所有的搜索词转换成问题。比如”网站建设工具”这个词，你可以推断”什么是最好的网站建设工具”大概是一个被问到的问题，其频率大致与该关键词的搜索量成正比，这是一种方式。
但我之前提到过长尾部分更大，而且长尾中有些部分在搜索中根本不存在。那我们怎么知道长尾长什么样？一个可用的策略是——看看人们在你的销售电话、客户支持和 Reddit 上问你的所有问题。
把这些来自其他渠道的问题全部挖掘出来。同样的问题很可能也在聊天中被问到，这是发现问题的另一种方式。最后一点是引用来源优化，也就是站外部分。再说一次，LLM 是在摘要 RAG 的结果。那我们怎样才能让尽可能多的引用来源指向自己？
你可以把引用来源分成不同的组别：我自己的网站、视频（YouTube、Vimeo）、用户生成内容（User-Generated Content）（Quora、Reddit）、一级联盟站如 Dotdash、二级联盟站、博客等等。就是把所有这些不同类型的引用来源拆分开来，针对每个组别制定专门的策略。
Lenny Rachitsky： Dotdash 具体是什么？
Ethan Smith： Dotdash Meredith 是一个大型媒体集团，旗下有 Good Housekeeping、Allrecipes、Investopedia。它大概是有史以来最成功的 SEO 公司。
同时也是被引用最多的之一，大概在 LLM 中也是被引用最多的。
ChatGPT 能否避免过度 SEO 化的命运
Lenny Rachitsky： 哇，这我还真不知道。听你说话的时候我在想——如果你上 Google，不是冒犯您这位 SEO 专家，但你上 Google 现在就是看到一堆没用的东西，就是那种过度 SEO 化的内容。
你觉得 ChatGPT 能避免这种命运吗——避免变成一堆并非用户真正想要的过度 SEO 化内容？
Ethan Smith： 大概能。你说的 SEO 的状况是——大家都在互相改写对方的内容，非专家在互相改写彼此的内容。所以你用一个内容评分工具，它去看 Google 里的所有搜索结果，然后告诉你”这些是其他文章都在说的内容，这些是你还没说的”，然后给你一堆建议让你写得更”典型”。
然后每个人都在改写别人的文章。另一个有意思的发现是，绝大多数着陆页不会产生任何效果。我们做过一项分析，20 个着陆页中只有 1 个能带来大约 85% 的流量。也就是说 20 个中有 19 个几乎不带来任何流量，这意味着如果我想要获得投资回报，就需要在大量页面上各花少量资金。
所以你找一个非专家来说”把别人的文章改写一下”，因为这样比雇一个《纽约时报》的记者来写”最好的薪酬管理软件是什么”这种文章要便宜得多。但如果你能预判哪几个页面会有效、哪几个着陆页会成功，然后把它们写得非常好，你就可以把所有预算集中到那一页上——这正是我们努力在做的。
但目前的现状就是大家在互相改写彼此的内容，Google 还没解决这个问题。这大概是一个非常难的问题。他们最终能解决吗？有可能。ChatGPT 最终能解决吗？也有可能。如果我来解决的话，一个思路是信息增益（Information Gain）——你是不是说了别人没说过的东西？第二个是你的”典型性”。
你是不是典型到让我觉得你就是别人内容的改写版？Google 有 EEAT——经验、专业性、权威性、可信度，不过实际上我没看到它产生什么效果，但它是有可能产生效果的。我可以说”这个人是专家，这个人是持证理财顾问，把他们的排名提高”。
答案引擎优化的实操方案
Ethan Smith： 但我实际上并没有看到这种效果，不过他们可以提高这部分的权重。所以这些都有可能是解决方案，但我确信目前还没有解决、大家还在互相改写彼此文章的原因，大概就是很难构建一个算法来解决这个问题。但他们最终会解决吗？很可能会。
Lenny Rachitsky： 你刚才分享的这套算法或启发式标准真的很有意思，因为它同样适用于判断什么是好内容——比如一篇通讯或者一期播客？信息增益和典型性——你是不是为对话增加了新东西？是不是独特的？我觉得这是一个非常好的策略，适用于生产优秀的通讯、播客以及世界上所有内容。
Ethan Smith： 是的。理想情况下，你是否做了原创研究？你是否拥有某个领域的专业经验？你是否在内容中体现了这些？
Lenny Rachitsky： 这套标准对所有内容都适用，而这恰恰也是你希望算法去寻找的东西，所以这里的对齐是成立的。
(广告段落已跳过)
Lenny Rachitsky： 让我们给大家一个真正可执行的计划，开始着手去做这件事，基本上就是在答案引擎优化（Answer Engine Optimization）上取胜。如果用我的通讯作为例子有帮助的话，我怎么才能更频繁地出现在 ChatGPT 或 Gemini 之类的平台上？或者如果是 B2B SaaS 公司，哪个更容易讲清楚就讲哪个，我们就直接聊聊具体怎么操作。
Ethan Smith： 首先，我会搞清楚我想为哪些问题排名。怎么搞清楚呢？我会拿我的搜索数据，可能还会拿付费搜索数据——比如”我的核心词是什么？竞品的核心词是什么？“所以如果我是 Rippling，我会看 Deel 在所有付费搜索上投的是什么词。然后把这些词转化为问题。实际上你可以直接把这些关键词给 ChatGPT，说”把这些变成问题”，它做得还不错。所以拿竞品的付费搜索数据，或者你自己的，放进 ChatGPT，得到问题列表——这是第一步。
第二步是追踪它们，把这些放进一个 AEO 追踪器、答案追踪器里。第三件事是谁出现在了引用来源里？然后针对不同类型的引用来源分别制定策略。第三个方面是制作你自己的着陆页。出现的是哪类着陆页？是 listicle 吗？是分类页吗？是文章还是工具页？搞清楚哪种页面类型出现得最多，然后制作你自己的同类页面。
怎么让你的页面排上去？回答所有后续问题。用户可能会追问的所有问题是什么？你可以回到搜索数据，在你的 SEO 主题范围内寻找关键词的分组和主题。答案引擎优化（Answer Engine Optimization）主题也是同样的做法。然后是站外部分——针对不同类型的引用来源，各有不同策略。
我想说，根据公司情况，付费让联盟提及你——这个方法如果有预算的话其实很简单。所以如果你想成为”最佳信用卡”，你付钱给 Forbes，然后你就是最佳信用卡了。这是策略一——贵、简单、可控。YouTube、Vimeo 策略其实也挺简单，因为没有社区会在那里说”我不喜欢你的 YouTube 视频”。你做一个 YouTube 视频，想做什么做什么，也许有人看，也许没人看，但你可以做。有意思的是，尤其是对 B2B 来说，YouTube、Vimeo 这类视频网站上，人们拍视频的主题通常是美食、旅行、娱乐、美妆。关于 AI 驱动的支付处理 API 的视频并不多——尽管这个话题本身也挺有意思的——但这是一个非常好的核心词。所以如果你为这些非常具体、客户终身价值高、可能不太光鲜的关键词、问题、主题制作视频，那其实是一个很大的机会。
然后是 Reddit。我之前提到过我们为 Webflow 做的事——就是创建一个 Reddit 账号，说明你是谁、你在哪里工作，然后给出一个有用的回答。这个方法稍微有点棘手，因为社区可能会说”我不喜欢你的回答”。所以你不能保证你的评论一直在那里，但它操作起来确实简单，所以我会把这个也纳入策略组。
实验设计
哦，然后是实验设计。实验设计，看什么有效。SEO 和答案引擎优化（Answer Engine Optimization）都有一个有趣的特点——大部分信息和最佳实践都是不正确的。原因是大家不做分析。有人说了一句话，然后被反复引用，然后就变成了最佳实践，但从来没有人做过分析。所以你把我刚才提到的所有东西都做了之后，做一个实验，看看有没有效果。也许我说的有一半管用，一半不管用。做你自己的实验。大多数最佳实践、大多数博客文章都是不正确的。
那怎么搭建实验呢？拿到你的问题列表，开启追踪，等几周。做你的改动，设置测试组和对照组。对测试组进行干预，做你的改动，看图表是否上升，看对照组是否没有变化——这样你就知道你的特定策略奏效了。所以我绝对建议做实验，不要假设你在网上读到的东西都是正确的。
团队配置
然后你需要一个团队。你的团队是谁？大概率是你的 SEO 团队，或者你的 SEO 代理公司、SEO 顾问。大概率——也希望——他们能做这些事情。不过我认为很难招到人的是站外那部分。大多数 SEO 人员不太擅长制作 YouTube 视频和制定 Reddit 策略，所以你可能需要一个不同的人来做这件事。这可能是一个偏社区和综合营销的人。所以基本上就是你的 SEO 团队——“请现在开始做答案引擎优化（Answer Engine Optimization）“——然后营销社区团队——“请帮我在更多引用来源中出现。”
Lenny Rachitsky： 哇，好的。这太有价值了。谢谢你分享这些。我觉得你这是把很多了不起的建议免费送出去了，谢谢。首先我想，这其中有层次之分——自己能做的有限。所以最终会到这一步：“好吧，我们真的需要帮助了。“这就是像你们这样的团队发挥作用的地方。
让我问几个后续问题。一个是关于追踪器这个概念。这个追踪器是什么，它能追踪你出现的频率吗？比如 Lenny’s Newsletter 在我瞄准的那些问题的回答中出现的频率？
答案追踪
Ethan Smith： 对，所以有答案追踪，类似于关键词追踪。关键词追踪就是，比如搜”best growth podcast”，你把它放进关键词追踪工具里。这类工具有上百个，都差不多，然后你看自己排不排得上。运气好的话你可能排在第一位。而在答案追踪里情况就很不一样了，但两者又有关联。
比如你问同一个问题，每次会得到不同的回答。你提一个问题，每次运行都会产生不同的答案。ChatGPT 基本上是在计算它会给出的所有潜在答案的一个分布。根据你提问的时机，它本质上就像是一个加权随机抽样，所以你会得到不同的答案。
另外还有问题的变体——你可以用不同方式问同一个问题，你可能在其中一种问法里出现，但在另一种问法里不出现。然后还有不同的平台：Perplexity、Gemini、ChatGPT、Meta AI，这些平台给出的答案各不相同。
所以你本质上需要在这些不同维度上创建一个声音份额——就像一个分布一样。我出现的频率是多少？平均排名是多少？这就是答案追踪。那么答案追踪工具从哪里获取？答案追踪本质上是关键词追踪的演进。我们有一个页面列出了 60 种不同的答案追踪工具。
但它归根结底跟关键词追踪一样，大体上都是同一回事。所以从这 60 个里挑一个，我们自己也在做答案追踪，还有 59 个其他选项，大概都不错，大概也都差不多，挑一个就行。我的一般建议是挑一个最便宜的、能满足你需求的。
就像关键词追踪一样，你排第三就是排第三，没有所谓的”高级版”关键词追踪。所以选一个最便宜的、能做你想做的事的关键词追踪工具。答案追踪也一样。然后当我做实验的时候，把你的答案输入进去，追踪它们，看一个随时间变化的图表，看你的平均排名。
你出现的频率是多少？平均排名是多少？然后你做一个改动，希望排名能上升。
声音份额与平台差异
Lenny Rachitsky： 太棒了。我很喜欢”声音份额”这个词。我之前从没听过，但很有道理。就是在 LLM 中出现的百分比。那么，是只有 ChatGPT 吗？Google 现在和 ChatGPT 等价了吗？你怎么建议人们看待 Gemini、Claude、Perplexity 这些其他的？
Ethan Smith： 有意思的是，这些平台之间有相似的基础算法。它们都在使用搜索，都在使用 LLM，而底层的基础算法都是一样的。但结果其实差别很大。我们正在做一项研究，发现 Google 和 Bing 作为搜索引擎其实没那么相似。
我们还发现 ChatGPT 的引用来源和 Google 搜索结果其实也没那么相似。Perplexity 有意思的是它和 Google 的相似度比和 ChatGPT 更高。我们做了一项研究，考察了数千个问题，发现 ChatGPT 和 Google 的引用来源重叠率大约只有 35%，不算高。
Perplexity 大约是 70%。所以它们本质上算法相似，但引用来源和结果差异很大。那么就去看哪些平台流量最大，然后追踪那些平台。你可能不需要全部追踪，但要综合来看。
但你确实需要看你在所有这些平台上的声音份额，即你出现的频率。你需要多次提问，还需要提问的变体，才能真正了解你出现的频率。
是否需要关注 ChatGPT 之外的平台
Lenny Rachitsky： 考虑到 ChatGPT 在不久的将来会达到大约十亿周活跃用户，你还需要担心 Claude、Gemini 和 Perplexity 吗？那些平台上的流量有意义吗？我知道用户不少，但关注其他 LLM 有多重要？
Ethan Smith： 我会这样回答——我相信 AOL 早期是最大的搜索引擎之一，而 Google 当时不是。所以如果我们回到 1999 年左右问：“我们是不是只关注 AOL 搜索和 Yahoo 搜索就行了？真的需要担心 Google 吗？“答案是——我们其实不知道。
现在还太早了，不知道谁会赢。我确实认为 ChatGPT 肯定会很大。Perplexity 或 Claude 或其他平台会不会和它竞争？很可能会。就像搜索领域一样，我认为可能会有多个赢家，你可能需要针对其中几个进行优化。
我不认为你需要针对十个平台优化，但大概会有三个左右胜出，你需要针对它们优化。
Lenny Rachitsky： 好的。顺便说一下，我想澄清一下，我很喜欢 Claude。我大概同等程度地使用 Claude 和 ChatGPT。我不想让人觉得 ChatGPT 是人们唯一使用的产品。
不同类型公司的策略差异
好的。这个策略会根据你是什么类型的公司而改变吗？比如你是一家 B2B SaaS 公司，或者是消费品公司，这七个步骤里有什么会显著不同吗？
Ethan Smith： 以 B2B 为例。首先，被提及的引用来源会非常不同。所以引用来源优化的方式会差别很大。
Lenny Rachitsky： 澄清一下你刚才说的，你说引用来源策略不同是什么意思？
Ethan Smith： 意思是 B2B 出现的引用来源和交易平台出现的引用来源是不同类型的。比如在 B2B 领域，TechRadar 可能会在我问问题时大量出现。我从来没读过 TechRadar，但不知道为什么它老是出现。我相信它挺好的。但 TechRadar 确实在 B2B 领域频繁出现，不知道什么原因。
在电商领域，就不会是那个，而是 Glamour 和 Cosmopolitan。在交易平台领域，会是 Eater、Yelp、TripAdvisor 这类网站。所以出现的引用来源类型是不同的。我之前讲的大部分内容是 B2B 特有的，和电商不同。
对于大多数 B2B 问题，答案是不可点击的。没有东西可以点。所以如果你真的想衡量效果，你不能只看末次触达的引荐流量。你必须通过追踪来看自己是否出现在了答案中。然后你还需要在转化后问用户”你是怎么了解到我们的”，才能真正知道效果。
所以 B2B 更难追踪。另外对于 B2B 来说，你大概在 50 次触达之后才会决定用哪个薪酬管理软件。不会是你搜了一下就突然花了十万美金买薪酬管理软件，这中间有品牌建设的过程。这就是 B2B。电商则不同——电商现在实际上有了更多可点击的卡片，就像你在 Google 里看到的那样。
比如你问”最适合公寓的电视是什么？“会出现真正的可购物卡片。这些可购物卡片展示了多个卖家。这些卖家有富摘要。Schema 很重要，评论数量很重要，所以其实差别很大。你可以通过末次触达的引荐流量来很好地了解你获得了多少转化。
电商、本地服务与早期阶段的策略
Ethan Smith： 对于电商，餐厅、酒店和本地市场也是类似的情况。然后我要说，早期阶段也不一样。我之前提到过，早期阶段我的建议是完全不要做 SEO。但对于答案引擎优化，一定要做 AEO，而且只做引用来源优化和长尾。不要做那些中间层面的 SEO 工作，只要被引用到、回答非常具体的问题就好。
Lenny Rachitsky： 很有意思的是，这里面很大一部分就是在答案里以那个小标签/药丸的形式出现，因为现在想想这是显而易见的事。
Ethan Smith： 这也是唯一能让人从 LLM 访问你网站的方式——就是点击那个标签，“好吧，让我去看看这篇文章。”
Ethan Smith： 是的。但用户实际做的会是打开一个新标签页，输入品牌名，然后去 Google 搜索。
然后他们会点击你的域名，而你会以为那是一次品牌相关的 Google 搜索，其实不是。
或者他们会打开一个新标签页，直接输入你的域名，直接访问你的网站，然后你错误地以为那是直接流量。
内容被抓取是否是好事
Lenny Rachitsky： 回到你一开始提出的那个问题。对于我的 newsletter 来说，它们把所有这些内容都抓走了——我都不知道被抓走了多少——然后给我发送一定比例的流量。
你有没有什么，怎么说呢，就是感觉这事到底是好是坏？如果你在运营我的 newsletter，你会鼓励所有这些平台去抓你的内容吗？然后它们会说，“哦对，如果你想的话可以去 Lenny’s Newsletter 看看”？
Ethan Smith： 会的。而且我会给出跟 Brian Balfour 在你上一期节目里一样的回答，就是：参不参与这个游戏不是你能选择的。不管你愿不愿意，你都在参与这个游戏，所以你不如尽量让自己出现。如果你只是说”不要看我的任何数据”，那你就无法出现，而你的竞争对手会。
现在，你能做的是说”我不想让你用我的数据做训练，所以你可以索引我的网站，但请不要用我的数据训练。“他们对此有不同的 user agent 和不同的爬虫，所以你可以说”我们正在开发一个 Webflow 应用，用来屏蔽训练但保留索引。”
或者你可以直接在 robots.txt 里写”训练爬虫不允许，索引爬虫允许。“如果你对此有顾虑，我建议这样做，而且可能很多人都会这么做。但是说”你完全不能索引我的网站”，这对我说不通。
Lenny Rachitsky： 说得太对了，因为我不知道在我这个具体领域有没有竞争对手，但基本上它们会出现来替代我，然后我就失去所有这些流量。
Ethan Smith： 对。
Lenny Rachitsky： 说得太对了。好的，让我回到你之前分享的那些步骤，看看有没有什么值得再深入聊聊的。所以这基本上就是如何更成功地出现在 LLM 回复中的方法。第一是搞清楚你想在哪些问题上获得排名。
你可以通过查看竞争对手在投什么付费广告之类的方式来做这件事。看看那些关键词，然后几乎是去问 ChatGPT 或 Claude，“把这些词转化成人们会用来搜索这些词的问题。“然后建一个追踪器，看看你目前的表现如何。你出现的频率是多少？
现在有大量的追踪工具，你有一个链接可以查看这些工具。然后你看目前是谁在出现？用户目前被带去哪里？用这些信息来指导你创建着陆页，更好地回答那些问题。而且要非常明确，不仅回答那个主要问题很重要，回答后续问题也很重要。然后还有一些站外的工作。
所以要进入像 Dotdash 这样的联盟平台，YouTube、Reddit、Quora 看起来是核心，然后跑一个实验。所以你看这个追踪器，让我实际上问一下这个，下一步就是组建一个团队。但回到这一步，你怎么设计一个实验，而不是简单的”前后对比”？你怎么做一个有对照组的实验？
AEO 实验设计与可重复性
Ethan Smith： 好的。我的做法是，我会选取 100 个不同的问题，一半进行干预，一半不干预。或者说，我们取 200 个问题。其中 100 个问题，我什么都不做，这就是我的对照组。我们观察到答案本身就有相当大的波动，即使你什么都不做，所以你一定要有对照组。
而且我们还看到人们越来越多地使用 LLM，LLM 的流量在持续增长。所以你绝对需要对照组，尤其是在答案引擎优化中。对照组就是”完全不要碰，保持现状。“这就是对照组。实验组则是”我现在要去 Reddit 帖子里评论，我们来测试这个。”
或者我现在要做一个 YouTube、Vimeo 视频，或者我现在要付钱给 Forbes Advisor 让它说我是最好的信用卡。也许把这些分成几个不同的类别，分别追踪。要有几周的实验前数据，几周的实验后数据，然后和对照组进行对比。
然后那些在对照组没有变化时却上升了的就是有效的，没有上升的就是无效的，然后把有效的复制推广。所以可重复性非常重要。我的背景是学术研究，做了一项无法被重复的实验是很常见的。要让一个东西真正被学术界接受，它必须是可重复的。
意思是多个人做了这项研究，一遍又一遍地重复出了那个结果。尤其是在 SEO 中，某个东西发生变化是很常见的。你以为是这个因素导致的，其实不是，然后你永远都以为那个方法有效。所以可重复性非常重要。
尽量多次重复那项实验，尽量从其他人那里获得实验结果，如果做了 10 次都有效，那它大概是真的有效。这就回到了浪费的问题——SEO 中大部分工作都是浪费的。AEO 中大部分工作也是浪费的，那你怎么知道什么不是浪费？你要做实验，而不是假设你在网上读到的东西是真的。
你要做自己的实验，然后多次重复，持续做那些有效的，不做那些无效的。
AEO 的重要性与渠道规模
Lenny Rachitsky： 在 AEO 上获胜感觉是一件非常重大的事。回到这个想法——人们来到 ChatGPT、Claude、Gemini 寻找答案。
如果你就是那个答案，我觉得这可能决定你公司的成败。感觉这事甚至比 SEO 还重要，只要把这个做好。
Ethan Smith： 我想说的是，如果我要获得尽可能多的转化，这个渠道有多大？这个渠道还没有搜索大。搜索肯定更大，但它现在已经是一个相当可观的渠道了。Webflow 他们 8% 的注册量来自 LLM。
它现在已经成为你的顶级渠道之一了，所以它是很大的。它不是最大的渠道，不是排名第一的渠道。付费可能才是排名第一的渠道，但它绝对是一个相当大的渠道，值得去优化。
Lenny Rachitsky： 而且正如你所说，可能还在持续增长。
Ethan Smith： 是的。
Lenny Rachitsky： 好的。让我把视角拉远一点，问你这个问题。
关于 AI、SEO 和 AEO，你觉得有哪些最令人惊讶或最少被讨论的话题，是我们还没有谈到的？
Ethan Smith： 第一件事是，关于 AI 和答案引擎优化（AEO），存在大量的错误信息，而且程度相当严重。错误信息与正确信息的比例之高，是相当可观的。一个例子是，每隔两年就会有新闻报道说 Google 搜索要死了、正在消亡，因为有新事物出现。
这种事现在正发生在 AI Overviews 和 AEO 身上——Google 在走下坡路，这其实不是事实。在此之前是 TikTok 搜索，说人人都在用 TikTok，Z 世代用 TikTok，永远不会用 SEO。SEO 要完蛋了，所以你真的需要把重心放在 TikTok 搜索上。这并非全错——不是不真实，但它并没有从 Google 那里抢走份额，它只是一个新的界面。
再之前是 Instagram，再之前是 Facebook 和 YouTube。人们确实在 Instagram、TikTok、YouTube 上搜索和发现内容，但它并没有蚕食 Google 搜索。它是在 Google 之上叠加的。这些都是新的渠道，所以 Google 那块饼的面积没变，只是整个饼变大了。
所以关于 Google 在走下坡路的错误信息——Google 并没有在走下坡路。Google 最近发布了一些内容，他们的搜索副总裁明确表示：“我看了我们发给出版商的流量，并没有下降，还略有上升。“所以 Google 搜索在走下坡路这个说法并不成立。
工具定价与渠道增长曲线
大部分相关新闻都在说它在下降，这是第一个令人惊讶的事。第二个令人惊讶的事是工具。我从没见过一个渠道，有这种极其昂贵的工具，本质上做的却是同质化任务。想象一下，如果我说：“我要收你五万美元来做关键词追踪。”
你会说：“当然，这太荒谬了。关键词追踪而已，我一天就能写出来。“没人会这么做。但对于答案引擎来说，它很神秘，人们不太清楚它是怎么运作的。而且增长曲线的斜率又非常陡，所以我看到人们在本质上属于关键词追踪同质化服务的东西上花费大量金钱。
这是第二件事。第三件事是这个渠道的增长曲线。我们一年前做了一场 Reforge AEO 网络研讨会，当时有一波热情，然后就消散了，之后热情非常低。那是在六月，之后人们就不太关心了。他们在理智上觉得有趣，但并不在意，因为没有看到实际影响。
所以基本上从七月到一月几乎没什么人感兴趣，然后突然在一月，热度就飙升了。ChatGPT 发布时人们非常感兴趣，然后对增长从业者来说就没那么有意思了。六月有一个小高峰，然后就变成了这样——这通常不是你看到的新渠道的样子。
所以曲线的斜率异常陡峭，曲线的形状也非常不寻常。最后一点是，很多人确实认为 SEO 和 AEO 是不同的，但它们其实没有不同。我认为部分原因可能是，说这是一个全新渠道、完全不同，听起来很棒。
SEO 与 AEO 的本质关系
“我是专家，我有工具要卖给你，它是独一无二的，其他那些工具都不适用。“但实际上，两者之间有相当大的重叠。区别在于引用来源优化。头部和长尾有所不同，但核心技术相当相似。这些大概就是最令人惊讶的事情。
Lenny Rachitsky： 关于一月份是拐点这件事，你提到是因为引用来源开始更突出地展示了。这是最大的变化吗？
Ethan Smith： 我认为是因为人们使用 LLM 的程度在增加——它确实在持续增长——然后就是可点击性。我现在正在看到、你也在看到，实际点击量出现了大幅增长。
可能之前你几乎得不到点击，即使你的内容出现在回答中也一样。所以回答的可点击性增加了。
尤其是电商、本地和酒店这类内容，因为它们有丰富的模块，你可以点击上面的东西跳转到某个地方，这在以前是没有的。再加上我认为人们就是越来越多地在使用 LLM。
AI 生成内容的研究
Lenny Rachitsky： Ethan，我只想说，我从这次对话中学到了太多东西了，真是太有趣了。我能看出来你有多么热爱这些东西，你在其中钻研得多么深、多么极客。能和一个在所有这些事情上如此深入、知识如此渊博的人交谈真的很愉快，所以谢谢你和我们分享这一切。
我想稍微换个方向。现在有一整个 AI 内容的世界——人们用 AI 生成内容、生成着陆页。就像，“天哪，SEO 再也不用愁了，这些全用 AI 生成。AI 会让这一切变得更容易。”
你们做了一个非常大的研究，探讨这到底是怎么回事、用 AI 生成内容是否是个好主意。你能谈谈你们从中学到了什么，以及人们应该如何思考 AI 在内容生成中的角色吗？
Ethan Smith： 好的。我记得 ChatGPT 发布的时候，Brian Balfour 在 LinkedIn 上发帖问：“大家觉得 ChatGPT 和 AI 会带来什么？“我的第一反应是——垃圾内容，就是大量大量的垃圾内容，尤其是 SEO 垃圾。之后确实出现了一个围绕 AI 生成内容的完整产业，而我从一开始就知道它行不通。
我之所以知道它行不通——当我说 AI 生成内容时，我指的是没有人在环的自动化内容。我认为内容的未来显然是 AI 辅助的。显然，你和我都会使用 AI 来帮助我们写作，所以不是完全不用 AI，但也不是百分之百由 AI 生成。我一开始就知道这行不通。
为什么我知道？因为我在 2007 年就制造过垃圾内容，我知道 Google 对此做了什么、怎么处理的，我也知道完全相同的事情会再次发生。我在 2007 年做的事是，我和所有其他做购物比价的人互相抓取对方的内容、评论，切碎、拼凑、抓取内容，搞了一亿个搜索页面、片段，效果非常好。
然后它就不管用了，然后那些公司全都消失了。我就知道 AI 生成内容会发生一模一样的事情。所以从一开始，我就没有把重心放在 AI 生成内容上。很多人这么做了，但我不知道，也许它确实有效。有大量关于它有效的案例研究。
大规模 AI 内容检测研究
那就来做一项研究吧，做一个分析。我们同时看了 Google 和 ChatGPT——我们取了数千个搜索词和数千个问题，把这些搜索词输入 Google 搜索，把这些问题输入 ChatGPT，然后查看引用来源或 Google 搜索结果。接着我们用 AI 检测器来分析。
我们用的是 Surfer SEO 的 AI 检测器。当我告诉别人这个做法时，他们说：“你检测不了 AI。“于是我们评估了这个 AI 检测器的有效性和准确度。我们的方法是自己生成数千篇 AI 生成的文章，结果发现它的预测性非常好。然后我们又看了真实文章，用了两种方式。
一种是我们自己撰写真实文章，另一种是从 Common Crawl 过去五年的数据中随机抽取了十万个 URL。然后我们查看了 ChatGPT 发布之前的 AI 检测器结果，所以这些内容必然不是由 AI 创作的。结果误报率大约在 8%，基本上这个 AI 检测器是非常准确的。
于是我们把检测器用在内容上。结果发现，Google 搜索结果中大约有 10%到 12%的内容是 AI 生成内容，ChatGPT 也是类似的情况，90%则不是。我们还做了相关性分析，结果完全一致。所以我们基本上做了一项非常严谨的研究，证明纯 AI 内容行不通。AI 辅助、经过人工编辑的内容则很好——我们自己有时也这样做，其他人也在做，这显然是内容创作的未来。这种方式确实有效，也应该有效。但百分之百纯 AI 生成的内容是行不通的。
我们发现的第二件事出乎意料：互联网上的 AI 生成内容已经超过了人类生成的内容。回到之前 Common Crawl 的研究，我们考察了过去五年十万个不同的 URL，然后你会看到一条曲线——AI 生成内容的占比现在已经高于人类创作内容。互联网上 AI 生成的内容比人类创作的还多，这令人不安。
AI 内容如果行得通会怎样
假设 AI 生成内容真的行得通，那所有人都会去做。就像 2007 年的购物比价网站一样——如果我可以抓取内容，为什么还要花钱请人写？直接从你那里抓过来，拼拼凑凑就行了。于是所有人都会这么做，内容从”大部分是 AI 生成的”变成”几乎全部都是 AI 生成的”。如果这条路行得通，接下来发生的事就是：Google 变成了一个索引 ChatGPT 回复的搜索引擎。如果 Google 只是在索引 ChatGPT 的回复，那 Google 就没有存在的理由了——直接去用 ChatGPT 就好。这和 2007 年发生的事情一模一样。
当时 Google 说：“我看到搜索结果里出现了这么多购物比价搜索引擎。我本质上成了一个搜索搜索引擎的搜索引擎。“我应该直接在结果里展示电视产品本身，不应该展示其他垂直搜索引擎。所以我要把它们清除掉，直接展示产品。
ChatGPT 也会面临同样的问题。假设 ChatGPT 在自己的引用来源中排入了自身衍生出来的内容，那你就会得到一个无限循环的衍生链条。我到 ChatGPT 说”生成 10 篇文章”，把这些文章放进引用来源，然后再说”帮我总结这些由衍生内容组成的引用来源”。然后不断做衍生的衍生，你就得到了一个无限循环的衍生链，AI 在自我总结。有一篇关于这个现象的论文叫 Model Collapse。
模型崩溃与衍生内容的无限循环
这里面涉及两个层面：核心算法和检索增强生成（RAG）部分。核心算法方面，有一个团队做了关于模型崩溃的研究，问题是：如果把 AI 的衍生内容喂回模型，用衍生内容来训练核心模型会怎样？结果出现了各种问题——幻觉、系统迅速崩溃。
那我们研究的是另一个问题：如果把衍生内容喂进 RAG 部分会怎样？生成 10 篇衍生内容，放进 RAG，然后让模型总结。再生成 10 篇，总结之前的总结，无限循环的衍生。会发生什么？
群体智慧与单一意见的收敛
这里涉及一个概念叫”群体智慧”。大语言模型（LLM）在总结许多人的意见。比如你问”最好的冰淇淋口味是什么？“答案不是唯一的，有数千种不同的看法。LLM 在总结这成千上万种意见，这就是群体智慧在起作用。群体智慧的基本原理是：如果你取一大群人的平均回答，这个平均值会比群体中任何一个最优秀的个体的回答都更好。所以意见的多样性越高越好，这就是群体智慧的力量。
那么无限循环的衍生会发生什么？你本质上会收敛到一个单一意见。如果你问”最好的冰淇淋口味是什么？“它最终会说”是香草味，只有香草味，没有其他口味的冰淇淋。“这是一个简单的例子，但如果你把衍生的衍生不断喂回模型，你本质上会吞噬掉群体智慧，它会不断收缩，最终对每件事都只剩下一个单一意见——这是非常糟糕的。以上就是百分之百无人工辅助的 AI 内容如果行得通会发生的情况。
Lenny Rachitsky： 我很害怕这样一个世界：所有内容都被 AI 训练，AI 又在 AI 生成的内容上训练并继续生成 AI 内容，到最后没有任何东西可以信任。我很感兴趣的是，这么多激励因素在推动这一切——如果 ChatGPT 觉得这些东西有价值，人们就会这么做，然后整个系统就失控了。好在有某个团队在那里阻止这一切发生。你认为这件事会怎么演变？如果你是他们，未来几年你会怎么做来保持高质量、避免这些扭曲的激励？
Ethan Smith： 我会先识别可能存在哪些扭曲的激励，AI 生成内容就是其中之一。第二点，我认为 LLM 和搜索将会融合。你在 Google 搜索中已经看到了这一点——他们加入了 LLM，推出了 AI Overviews。你在 LLM 方面也看到了——它们正在整合地图和购物轮播组件，正在向搜索的方向收敛。我认为最终会收敛为一个统一的体验，这是第一件事。第二件事是，搞清楚 2007 年的 Ethan 会怎么做来避免制造垃圾内容，然后确保自己不做那些事——比如 AI 生成内容就不做，要做就做好内容。第三件事是，LLM 还有很多其他有趣的功能和应用场景。
LLM 的未来：个性化与自主代理
比如 LLM 可以帮你记住你问过的所有问题。它可以专门为 Lenny 你个人做个性化定制。一个我认为最终会出现的有趣用例是：我说”帮我规划一趟旧金山旅行”，然后所有决策都替你做好了，不需要你任何干预。我有一位很棒的 EA（行政助理）叫 Jen。我说”Jen，我要去迈阿密，你帮我搞定一切”，她就全帮我搞定了。她了解我，知道我的偏好，知道我要海景房，知道我想去有音乐表演的餐厅。她把这些全部搞定，我不需要插手。AI 最终基本上也能做到这一点，因为它会深度理解你——记住关于你的一切，拥有上下文，具备推理能力，然后能够在不需要你干预的情况下做出所有这些决策，这就是自主代理（autonomous agents）。所以我认为，对于像我这样的人来说，这也是一个非常值得优化的有趣方向。
Lenny Rachitsky： 对，我刚才想说的是，想象一下甚至都不用被告知”这是为你选择的”——直接”哦，去看看，订阅最好的 newsletter”。如果你在那边，好的事情自然会发生。真是一个疯狂的世界。还有什么我们没覆盖到的、你觉得对那些想在这条路上迈出第一步、做好答案引擎优化（Answer Engine Optimization）的人有帮助的内容吗？
Ethan Smith： 有，最令人兴奋的话题——帮助中心（help center）优化和客户支持。
Lenny Rachitsky： 好。
Ethan Smith： 我之前提到，人们在聊天中会问后续问题，他们在寻找工具——你们有没有这个功能、这个用例、这个集成？这些问题通常可以在帮助中心里找到答案。通常情况下，你不会对一个 SEO 团队说”我们非常希望你们把重点放在帮助中心上”。
帮助中心优化（续）
Ethan Smith： 但在聊天场景中，用户会问各种问题——你们能不能做这个、能不能满足我的这个用例？帮助中心实际上是一个很好的承载这些内容的地方。所以我认为，怎样优化帮助中心？第一，帮助中心通常放在子域名上。无论什么原因，子域名的效果不如子目录，所以要把它移到子目录下，这是第一点。
第二，确保交叉链接做得好。通常内部链接并没有经过优化，所以要把帮助中心的页面相互链接起来，确保有大量的交叉链接。第三，你可能针对头部需求有帮助中心的内容，但长尾部分你可能完全没有覆盖。举个例子，我当时想追踪我们的销售电话，看看谁参加了会议、情绪如何，并且想把数据放进 Looker 里。所以我问：“哪个会议转录工具可以和 Looker 集成？“答案是没有任何一个可以直接做到，但你可以用 Otter，因为 Otter 有 Zapier 集成。你可以通过 Zap 把会议内容发送到 BigQuery，然后在上面跑 Looker。但帮助中心里并没有关于这个的文章，因为这是一个非常小众的用例，但它并不是零需求。所以在长尾部分，会有一堆你可能没有帮助中心文章的问题。
因此，销售电话中有哪些问题？客户支持中有哪些问题？为这些问题创建页面。我甚至会对社区开放——任何人都可以提问，因为社区会帮你填充长尾内容并回答那些问题。而且再说一次，在很多时候，可能根本没人在讨论这些话题。所以你可以成为唯一的引用来源，然后赢得那部分长尾流量。
Lenny Rachitsky： 有没有什么帮助台系统软件已经在让这件事变得更简单了？还是说你觉得这对 Zendesk 或 Intercom 来说是一个机会？
Ethan Smith： 我觉得可能所有这些工具都能很好地工作。我认为你唯一需要做的就是把交叉链接做好，以及用子目录而非子域名——这一点大多数工具应该都支持。所以我觉得它们都可以直接使用，不需要额外成本。主要你需要做的，再说一遍，是对社区开放，确保覆盖长尾。但所有这些工具应该都没问题。
闪电问答环节
Lenny Rachitsky： 好了，我们进入了非常令人兴奋的闪电问答环节。我有五个问题要问你，Ethan。准备好了吗？
Ethan Smith： 准备好了。
Lenny Rachitsky： 你最常推荐给别人两三本书是什么？
Ethan Smith： 第一本是《情商》（Emotional Intelligence）。人们经常谈论情商这个概念，但它背后确实有实际的研究和心理学基础。我记得相关研究是在八十年代发表的，但有一本非常好的书总结了情商方面的基础研究。在建立人际关系和与人沟通时，理解对方的情绪非常有用。这是第一本。做增长也是一样，因为增长就是让人们使用你的产品。如果你有框架来指导人们如何使用你的东西，你就能成为更有效的增长人员。这就引出了我的第二本书——Cialdini 的《影响力》。Robert Cialdini 写了一系列关于说服的书，同样，里面有如何说服别人注册、购买的框架。他把自己的框架拆解了出来，同样是基于心理学的。我觉得尤其是在增长领域，有各种各样的心理学研究和行为经济学研究可以为测试提供指导。如果你读了《思考，快与慢》、《影响力》和《情商》，你基本上可以把这些框架以各种方式应用到增长上。最后一本是《如何衡量任何事物》（How to Measure Anything）。这本书讲的是如何衡量那些看起来不那么容易衡量的事物。他们举了一个例子：他们想衡量一个乐团指挥有多优秀，他们可以做调查，也可以看每位指挥获得了多少次起立鼓掌。起立鼓掌越多，说明这位指挥越好，你不需要去做问卷调查。增长和商业中的很多东西都不是一目了然就知道怎么衡量的，但任何东西都可以被衡量，这是我的第三本推荐。
Lenny Rachitsky： 最近有没有特别喜欢的一部电影或电视剧？
Ethan Smith： 我其实不太看电视，但我看两类东西。一类是非常激烈的体育内容，所以我特别喜欢 Michael Jordan 的纪录片《最后一舞》（Last Dance）。我喜欢 Lance Armstrong 的纪录片，讲他的好斗和对抗性，我也爱看 UFC。我喜欢极度的攻击性和强度。另一类我喜欢看的是攀岩纪录片。Alex Honnold、Jimmy Chin 做的任何事情我都会看，这与激烈的体育完全相反——是禅意、活在当下、慢工出细活的匠心。但这正是我工作的方式——极端的强度和攻击性，然后是禅意的匠心、保持正念。
Lenny Rachitsky： 我喜欢你这个解释——它说明了为什么人们喜欢和你合作，为什么你擅长这些。一方面是这种竞争心，另一方面是对这些东西如何运作的那种极度钻研。然后我没想到还有禅的元素——在这一切中保持平静。
Ethan Smith： 心流，心流状态。
Lenny Rachitsky： 心流，真是一个有趣的缩影，解释了你为什么这么厉害。
Ethan Smith： 谢谢。
喜爱的产品
Lenny Rachitsky： 好，我继续。你最近发现了一个特别喜欢的产品吗？
Ethan Smith： 这个相机和这个麦克风。我买了一台 Sony 无反相机，具体型号我忘了。但是，配一个广角镜头的无反相机真的会彻底改变你的视频通话体验。然后我还有这个 Shure 麦克风，大概一百八十美元。这极大提升了我的视频通话质量。我喜欢设计东西，而你可以设计你的视频通话，让它变得很棒。你可以在背景里放花，比如这边，一些向日葵。
Lenny Rachitsky： 很漂亮。
Ethan Smith： 所以我最喜欢的产品就是用来视频通话的无反相机和麦克风。
Lenny Rachitsky： 你的背景确实很精致，我之前没提过，但看起来非常美。好，还有两个问题。你在工作或生活中有没有觉得特别有用的人生座右铭？
人生座右铭
Ethan Smith： 有一本叫《异类》的书讲了”一万小时定律”。里面的核心主题是：你不需要是最聪明的人，你需要足够聪明，这是第一。第二是刻意练习——不是单纯地努力，而是有目的、有针对性地去做。第三是大量的练习——没有人能因为天赋异禀就精通某件事。他们之所以精通，是因为花了大量时间练习，而且是刻意地练习。所以我的座右铭本质上是这些的结合：我不一定会因为脑子最大或者最拼命而赢。而是因为我在练习中最有目的性，并且对那份练习倾注最大的强度。
最引以为豪的案例
Lenny Rachitsky： 好的，最后一个问题。我很好奇，你有没有哪个 SEO 甚至 AEO 的成功案例是你最引以为豪的？
就是你总会想起来的那种，“哇，不敢相信我做到了，不敢相信我们产生了那么大的影响”。
Ethan Smith： 我一直很喜欢 MasterClass 的黄油生菜那个例子。因为 MasterClass 最初和我合作的时候，他们的域名权重远远不及 Allrecipes 和 Martha Stewart。我其实一度不确定是否应该接这个项目，因为我觉得可能太难了。
但我还是做了，确实很难，但我们最终在竞争非常激烈的关键词上排到了很前面的位置，远超我的预期。我觉得这可能得益于所有那些具体的、细小的执行细节。黄油生菜是我最喜欢的一个案例，而且我自己也喜欢黄油生菜，所以我可以搜索黄油生菜，然后看到 MasterClass 上的食谱。
Lenny Rachitsky： 太厉害了。我不知道黄油生菜以前有没有在这个播客上被提到过。Ethan，这次访谈太精彩了，完全就是我所期待的样子。
我感觉我们让每个人的 SEO 和 AEO 知识都上了一个台阶。别管 GEO 了。
最后两个问题：如果有人想和你们合作，去哪里找你？听众怎样能帮到你？
联系方式与如何帮助
Ethan Smith： 找到我的方式，第一是 LinkedIn。我在 LinkedIn 上花很多时间，而且我发布原创内容——我们做原创研究。我们有一个完整的研究团队，提出假设并验证这些假设。我之前提到的所有研究，我们都会发布在自己网站上，我也会在 LinkedIn 上发布。
所以在 LinkedIn 上关注我、加我好友、给我发消息都可以。LinkedIn 排第一。第二是我们有一个博客，叫 The 5%，路径是 /5%，意思是 5% 的工作、5% 的着陆页驱动了几乎所有的效果，这就是它的主题。这里只放有用的东西。我们的 5% 博客，你可以订阅邮件和研究报告。那人们怎样才能帮到我呢？
我花时间想了想这个问题，有两种方式可以帮助我。第一种是，目前关于 AEO 什么有效的研究并不多，我很想知道大家在测试什么、结果是什么、什么有效。所以如果有人在做研究并发布结果，发给我，我会非常欢迎尽可能多的分析和研究，这是第一。
第二种是在 LinkedIn 上帮我的帖子和我评论下的留言互动。比如你最近发布了 Brian Balfour 那一期，我写了一条很长的、有深度的评论，获得了大约 25 个赞，还有人回复了那条评论。我一直在其他人的 LinkedIn 帖子下评论，也在写这些长篇的 LinkedIn 帖子。当人们评论时，会提升在 LinkedIn 上的互动率，然后我就能获得大规模传播。所以越多的人留下有深度的评论——不是那种”太棒了”，而是能引发讨论的、有深度的长评论。如果人们在我的帖子上评论，我就能在 LinkedIn 上爆发，说不定有一天能跟你一样大。
Lenny Rachitsky： 我喜欢他这个请求如此具体。我注意到 Bryan Johnson——就是那个研究长寿的人——在 Twitter 上也很擅长做这件事。
他回复推文的方式特别有趣，感觉那是他的一个重要增长渠道。所以我很喜欢你和 Bryan Johnson 有这个共同点。
Ethan Smith： 是的。
Lenny Rachitsky： 另外，顺便告诉大家，你的域名是 graphite.io，对吗？
Ethan Smith： 对。
Lenny Rachitsky： 太棒了。Ethan，非常感谢你跟我们分享了这么多，感谢你的到来。
Ethan Smith： 不客气，很高兴来到这里。
Lenny Rachitsky： 大家再见。非常感谢收听。如果你觉得这期内容有价值，可以在 Apple Podcasts、Spotify 或你喜欢的播客应用上订阅本节目。
另外，也请考虑给我们评分或留下评论，因为这对其他听众发现这个播客真的很有帮助。你可以在 lennyspodcast.com 找到所有往期节目或了解更多关于本节目的信息。下期再见。
术语表
| 原文 | 中文 |
|---|---|
| AEO | 答案引擎优化（Answer Engine Optimization） |
| AI detector | AI 检测器 |
| AI Overviews | AI Overviews |
| AI-generated content | AI 生成内容 |
| Alex Honnold | Alex Honnold |
| Allrecipes | Allrecipes |
| autonomous agents | 自主代理（autonomous agents） |
| BigQuery | BigQuery |
| blue link | 蓝色链接 |
| Brian Balfour | Brian Balfour |
| citation | 引用来源 |
| Common Crawl | Common Crawl |
| Cosmopolitan | Cosmopolitan |
| Deel | Deel |
| Domain Authority | 域名权重（Domain Authority） |
| Dotdash Meredith | Dotdash Meredith |
| EA | EA（Executive Assistant，行政助理） |
| Eater | Eater |
| EEAT | EEAT（Experience, Expertise, Authoritativeness, Trustworthiness） |
| Emotional Intelligence | 《情商》（Emotional Intelligence） |
| false positive rate | 误报率 |
| Forbes Advisor | Forbes Advisor |
| GEO | 生成引擎优化（Generative Engine Optimization） |
| Glamour | Glamour |
| Good Housekeeping | Good Housekeeping |
| graphite.io | graphite.io |
| help center | 帮助中心（help center） |
| How to Measure Anything | 《如何衡量任何事物》（How to Measure Anything） |
| human-in-the-loop | 人在环 |
| Intercom | Intercom |
| Investopedia | Investopedia |
| Jimmy Chin | Jimmy Chin |
| Lance Armstrong | Lance Armstrong |
| landing page | 着陆页 |
| Last Dance | 《最后一舞》（Last Dance） |
| last-touch referral traffic | 末次触达引荐流量 |
| Looker | Looker |
| Martha Stewart | Martha Stewart |
| MasterClass | MasterClass |
| Michael Jordan | Michael Jordan |
| Model Collapse | 模型崩溃（Model Collapse） |
| Nick Turley | Nick Turley |
| Otter | Otter |
| Outliers | 《异类》（Outliers） |
| Perplexity | Perplexity |
| Persuasion | 《影响力》（Persuasion） |
| Programmatic SEO | 程序化 SEO（Programmatic SEO） |
| RAG | 检索增强生成（Retrieval-Augmented Generation） |
| rich snippet | 富摘要 |
| Robert Cialdini | Robert Cialdini |
| Schema | Schema |
| share of voice | 声音份额 |
| shoppable card | 可购物卡片 |
| shopping comparison | 购物比价 |
| Shure | Shure |
| Sony | Sony |
| spam | 垃圾内容 |
| Surfer SEO | Surfer SEO |
| TechRadar | TechRadar |
| Thinking, Fast and Slow | 《思考，快与慢》（Thinking, Fast and Slow） |
| TripAdvisor | TripAdvisor |
| UGC | 用户生成内容（User-Generated Content） |
| user agent | user agent |
| Vimeo | Vimeo |
| Webflow | Webflow |
| wisdom of the crowd | 群体智慧 |
| YC | YC（Y Combinator） |
| Yelp | Yelp |
| Zapier | Zapier |
| Zendesk | Zendesk |
| 信息增益 | 信息增益（Information Gain） |
此文档由 AI 分片翻译（translate_long_document）
The ultimate guide to AEO: How to get ChatGPT to recommend your product | Ethan Smith (Graphite)
Episode Sneak Peek
Lenny Rachitsky: There’s this term everyone’s hearing about, AEO.
Ethan Smith: Answer Engine Optimization is how do I show up in LLMs as an answer?
Meet the Guest
Lenny Rachitsky: It feels like such a big deal to win at AEO.
Ethan Smith: In order to win something like what’s the best website builder? At Google, they would win if their blue link showed up first.
But that’s not the case in the LLM, because the LLM is summarizing many citations, and so you need to get mentioned as many times as possible.
The Evolution of SEO
Lenny Rachitsky: ChatGPT is driving more traffic to my newsletter than Twitter.
Ethan Smith: You can get mentioned by a citation tomorrow and start showing up immediately. You can have a Reddit thread, you can have a YouTube video.
You can be mentioned on a blog. So early-stage companies can win, they can win quickly.
Defining AEO and GEO
Lenny Rachitsky: Are the leads that these answer engines are driving to companies actually valuable?
Timeline for Traffic Growth
Ethan Smith: Significantly more valuable. Webflow saw a 6X conversion rate difference between LLM traffic and Google Search traffic.
Lenny Rachitsky: A lot of people are seeing this as everything is different. Nothing we’ve done before is going to work. We have to rethink everything.
Can You Optimize for AEO
Ethan Smith: There’s significant misinformation on AEO. There’s news articles about how Google Search is going to die because there’s a new thing.
Google’s slice of the pie stays the same. The pie gets bigger.
Lenny Rachitsky: Today my guest is Ethan Smith. Ethan is the CEO of Graphite and my go-to expert for all things SEO. SEO is going through a major transition right now. Everyone used to go to Google anytime they had a question, or were looking for a product or doing research. These days, a lot of people are moving to ChatGPT and Claude, and Gemini and Perplexity to get answers to their questions, and this will only be accelerating over time.
And even Google is changing the search experience in a pretty radical way with AI Overviews at the top, and their newly introduced AI Mode, which is basically their own version of ChatGPT. This means that the world of SEO is going through a big change, including the rise of AEO, which stands for Answer Engine Optimization. Basically, SEO for ChatGPT, getting your product to show up in the answers that people get.
Ethan has been at the forefront of this new skill and channel. And in this conversation, he shares everything that he’s learned about how to get your product to show up more often inside of the answers that people get. The advice that Ethan shares in this conversation is incredibly tactical and worth a lot of money. So please slurp it up and use it for your own products.
If you enjoy this podcast, don’t forget to subscribe and follow it in your favorite podcasting app or YouTube, it helps tremendously. And if you become an annual subscriber of my newsletter, you get a year free of 15 incredible products, including Lovable, Replit, Bolt, n8n, Linear, Superhuman, Descript, Wispr Flow, Gamma, Perplexity, Warp, Granola, Magic Patterns, Raycast, ChatPRD and Mobbin.
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Learn more and start building at Orkes.io/Lenny. That’s O-R-K-E-S.I-O/Lenny. My podcast guest and I love talking about craft and taste, and agency and product market fit. You know what we don’t love talking about? SOC 2. That’s where Vanta comes in. Vanta helps companies of all sizes get compliant fast and stay that way, with industry-leading AI, automation and continuous monitoring.
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According to a recent IDC study, Vanta customers slashed over 1,000 off at Vanta.com/Lenny. Ethan, thank you so much for being here and welcome to the podcast. Welcome back to the podcast.
Webflow Case Study
Ethan Smith: Excited to be back.
Key Differences: AEO vs SEO
Lenny Rachitsky: We did a podcast episode just over two and a half years ago. I think of it as the definitive guide on how to win at SEO. People have been referencing it ever since. I’m really proud of what we did there, but things have changed.
Things are changing in the world of SEO. And so I’m excited to talk to you again about how to be successful in this new-emerging world where AI is changing how SEO works, the rise of AEO and GEO.
Let me start with just this question. How long have you been working on SEO at this point? And has anything come close to being this significant in changing the skill of SEO?
Ethan Smith: Yes. So I got started in SEO in 2007, so it’s been 18 years. Actually, the largest change when I got started in SEO, I got started in programmatic SEO and commerce SEO, like NexTag and Shopping.com and PriceGrabber. And that was when you could do mass, auto-generated landing pages.
And that was probably the biggest shift, which is Google introduced a bunch of algorithms, Panda and similar things, to prevent you from doing spam. So essentially, you went from the SEO being spam to not spam. That was probably the biggest change, and then this is probably the second-biggest change.
I think that the main thing here is it is related to search, but it’s a summarization of search and there’s new inputs. So it’s probably the second-biggest change.
Conversion Benefits of Answer Engines
Lenny Rachitsky: Okay, that is really interesting, because I think a lot of people are seeing this as everything is different. Nothing we’ve done before is going to work.
We have to rethink everything. You’re saying this is actually the second-biggest change, and just like Google’s update back in the day was actually even more significant?
On-Site Optimization and Off-Site Citations
Ethan Smith: Yep.
Lenny Rachitsky: Very cool. Okay, let’s set a little context for folks. Let’s define some terms. There’s this term everyone’s hearing about.
There’s actually two, AEO and GEO. What do they stand for? Are they different? What are they referring to specifically?
Reddit’s Unique Role and Strategy
Ethan Smith: They, I think, are the same. Ultimately, the definition of a word is whatever a group of people agree is the definition of a word. So I think we’ll see what people decide is the definition of the word. I’ll put forward my definition. So AEO and GEO are essentially trying to describe the same thing, which is how do I show up in LLMs as an answer?
And I personally prefer Answer Engine Optimization versus Generative Engine Optimization, because generative, you can generate images and videos and things other than an answer. Whereas answer is more narrowly defined, so my personal preference is we’re talking about optimizing LLMs.
So an answer is more narrow of a definition than generative, but ultimately, it’s whatever we decide is the name and the definition is what it will be.
Lenny Rachitsky: Okay. Yeah, yeah. Answer Engine Optimization sounds a lot cleaner to me if you had to pick one. So it’s good to know they’re the same thing. Some people just prefer the latter one for some reason.
It’s interesting because recently, I don’t know if I told you this, but I was looking at my referral traffic. And I found that ChatGPT is driving more traffic to my newsletter than Twitter, which I did not see coming.
So somehow it’s already happening. I’m excited to learn just how to lean into that potentially and optimize it further.
RAG vs Core Models Explained
Ethan Smith: And when did you see the spike? Did you see when it started growing dramatically?
Lenny Rachitsky: Unfortunately, the dashboard I have doesn’t give me great peripheral traffic optimization. When do you think I probably saw it?
Three Keys to AEO Success
Ethan Smith: Companies that we work with started in January and it started, one, because of more adoption, but two is because the answers became a bit more clickable.
You have maps, you have shopping carousels, you have clickable cards. So I think the clickability of the answer is increased, and then the adoption increased and that was around January.
Lenny Rachitsky: Okay. I want to come back to this question of, “Is this good that ChatGPT is sucking all my content and giving people answers, and then sending me some percentage of that?” But let’s not get into that yet. I want to talk about just what kind of impact you can have on having your stuff show up in ChatGPT.
So I had the head of ChatGPT, Nick Turley, on the podcast recently. I asked him, “What do you think of all this stuff, AEO, GEO?” He’s like, “Don’t worry about any of that. Just write awesome stuff, great quality content. It’ll figure it out. It’ll find the best stuff.” I imagine you very much disagree.
I imagine you have seen real impact getting your stuff proactively into these answer engines. Talk about just the kind of impact you’ve seen and just your reaction to that?
Methods for Question Research
Ethan Smith: Yeah. I agree and disagree, but the way that I think about it is anything can be optimized. You just need to understand the underlying systems and the rules of the game, and if you do that, then you can optimize anything. You can optimize algorithms, you can optimize people, anything could be optimized.
What I think he probably meant by that, he probably meant two things. One is, “Please don’t spam my product.” And two is, “If you do, I will see it and I will stop you from doing that.” So it’s not a long-term, robust strategy to create spam, just like it wasn’t a long-term, robust strategy to create spam on Google.
Eventually, Google was going to say, “Huge shopping comparison sites are making 100 million auto-generated search pages and I don’t like it, and I’m going to get rid of the whole category.” So same thing with ChatGPT, anything can be optimized, but if you’re spamming it, they’ll see that.
And they’ll have a whole team looking at that and then they’ll change your algorithm to prevent you from doing that.
Avoiding Over-SEO in ChatGPT
Lenny Rachitsky: What kind of impact have you seen? You’ve done work with a lot of companies, we’ll talk through a few examples.
Maybe share one to give us context just like how much can you impact this sort of thing where you show up in, say, ChatGPT more often?
Practical AEO Strategies
Ethan Smith: You can affect it a lot. So a specific example with Webflow is we are working with Webflow on their SEO. We’re working on their content, and we’re seeing a lot of wins on the Answer Engine Optimization side.
So the specific things that we’ve done there, one is just traditional SEO. So make landing pages for high-search volume keywords, like best no-code website designer.
And then for free, you’ll get Answer Engine Optimization impact from that. So that’s just traditional SEO, which works very well for AEO.
Lenny Rachitsky: I was just going to say, that sounds exactly the same as regular SEO.
AEO Experiment Design
Ethan Smith: Yeah. So I would say everything that works in SEO works in AEO, but there are additional things beyond SEO that also work in AEO. So second thing, and the way that I think about AEO versus SEO is that the head and the tail are different. So the head is different in that in order to win something like what’s the best website builder?
Even if Webflow’s URL shows up number one on the citations, they’re not going to win the answer because their URL showed up number one, but at Google they would win. If their blue link showed up first they would win, but that’s not the case in the LLM. Because the LLM is summarizing many citations and so you need to get mentioned as many times as possible.
So usually when you ask something like, “What’s the best tool for X?” The first answer will be mentioned the most in the citations, because that’s very different from Google. And so for Webflow, we work with them on YouTube videos, Vimeo videos, getting mentioned in Reddit, getting mentioned in other blogs, affiliates, stuff like that.
So tried a bunch of stuff. Stuff that worked especially well was just straight SEO, number one. Number two is YouTube videos, and then the third is Reddit optimization.
Ideal Team Structure
Lenny Rachitsky: Okay, wow. So you’re saying if you can get to number one, when you ask ChatGPT, “What’s the best website builder?”
And Webflow’s at the top, that doesn’t actually drive them as much traffic as simply being mentioned most often across the summary?
Tracking AI Answers
Ethan Smith: Yes. And part of why that’s interesting is because when startups come to me and ask me for SEO help, my first response is, “Don’t do it at all. Spend your time on something else because you’re not going to be able to grow SEO early on in search.” Because you don’t have enough domain authority and it takes a while to get domain authority, and only once you have domain authority can you rank.
And so for Google, it’s usually something that you do Series A, Series B or later. You don’t do it as soon as you start because can’t win early on. That’s not the case for Answer Engine Optimization, because you can get mentioned by a citation tomorrow and start showing up immediately. You can have a Reddit thread, you can have a YouTube video.
You can be mentioned on a blog, like a brand-new YC company launches, everyone’s talking about them. They could show up in an answer tomorrow as a result of that. So early-stage companies can win, they can win quickly. And they can win quickly and anyone can win quickly, by getting mentioned as many times as possible by the citations. So that’s what’s different about the head.
What’s different about the tail is that the tail is larger in chat than in search. So the average number of words, I think, Perplexity said this to somebody else, said that it was around 25 words, where versus Google words it’s around six words. So the tail is just much, much larger. People are asking lots of follow-up questions.
Share of Voice Across Platforms
Lenny Rachitsky: The tail, the prompt essentially, the question you’re asking?
Ethan Smith: Yes. Meaning that if you map out all of the questions that people ask, kind of like an SEO, long-tail keywords, if you do long-tail questions, the size of the tail is larger. Meaning the amount of questions that are very specific is larger, the share and the volume.
And there’s probably questions that have never been asked before and questions that have never been searched before, because search can’t support lots of really specific, super-specific stuff. Whereas chat is specifically made to ask a bunch of follow-up questions and have a conversation.
And so there’s all these questions that have never been asked or searched for before that are now being asked, and then you can win that. And when I got started in SEO, it was long-tail SEO where you have a page for every single keyword, which doesn’t work anymore, but now the long tail is back in chat.
And if you know all those really specific questions that people are asking, you can also win that, and you can probably also win that early. And I’ve seen examples of early-stage companies who just launched some really specific AI-enabled payment processing API thing, and they will show up. And they’ll show up because they’re answering questions that’s never been answered before.
Looking Beyond ChatGPT
Lenny Rachitsky: Are the leads that these answer engines are driving to companies actually valuable? Are these good-quality leads for B2B SaaS especially?
AEO Strategies by Company Type
Ethan Smith: They are significantly more valuable. So Webflow, we saw a 6X conversion rate difference between LLM traffic and Google Search traffic.
Lenny Rachitsky: Six times?
E-commerce, Local, and Early-Stage Strategies
Ethan Smith: Six times so significantly more qualified. I think that’s probably for a couple of reasons. Probably it’s because you’re so primed because you’re having a conversation with multiple follow-ups, and so there’s so much intent that you’ve built.
And you’ve probably really narrowed in on what you want, so when you’re going somewhere, it’s probably highly qualified. And so we’re seeing that it’s just a much higher conversion rate.
The Value of Getting Crawled
Lenny Rachitsky: Wow, this is so interesting, and it makes sense. People trust ChatGPT to tell them the answer, and if you are the answer, you have so much advantage. Like that is what people want to know, and then, “Okay. Cool, thank you. I’m going to go check this out.”
This all just makes sense. Going back to the three levers you shared, essentially it’s the things that you see work in driving you showing up more in these answer engines, landing pages, YouTube videos and Reddit. Is that right?
AEO Experiment Design and Replicability
Ethan Smith: Those are some of them.
AEO Importance and Channel Scale
Lenny Rachitsky: Okay.
Tool Pricing and Growth Curves
Ethan Smith: The other things, so I would break it up into stuff on your site, onsite and offsite. So onsite would be traditional SEO. The difference would be this long tail. I would also say that the difference is lots of follow-up questions about does your product do this thing? What are the use cases, features, integrations, languages?
Tell me about your product and really specific details about that and that’s on your site. And then the second group would be offsite, which is show up in all the citations. Citations are comprised of video, UGC like Reddit and Quora, affiliates.
Dotdash Meredith is showing up all over the place, Glamour, Good Housekeeping, it’s like getting mentioned there, blogs, so it’s those two groups.
The Core Link Between SEO and AEO
Lenny Rachitsky: And that all sounds very similar to SEO showing up on other people’s pages. Showing links from, say, Reddit is always great.
It’s interesting that Reddit is such a big deal. What’s going on there do you think?
Ethan Smith: Okay, Reddit is one of the most interesting things. It’s hugely cited in LLMs. And it’s probably the number one thing people are asking, customers are asking me is, “How do we optimize for Reddit?” And this goes back to the head of ChatGPT’s question about, “Please don’t spam my product.”
And so Reddit is a community where it’s real opinions from people, authentic, and it’s heavily managed by the community and the community is very good at managing it. And so the obvious strategy for a growth person is, “Let’s make a bunch of automated spam and spam Reddit all over the place and get my product to show up everywhere.”
That’s the growth mindset, which makes sense, the hustle mindset. So what are people looking at? They’re looking at creating hundreds of fake Reddit accounts pretending to be someone that you’re not. I have a single person, I’m going to make 100 Reddit accounts. I’m going to autopost comments and then like my own comments.
And then build a trust score, and then shout, say everywhere that my product is the best product. Fortunately, that doesn’t work very well, but that’s the obvious strategy. And so we’re seeing people trying to do that and then we’re also seeing those accounts get banned, those comments get deleted. And so we’re seeing people trying to spam and being unsuccessful, so that’s one strategy.
The other strategy is the whole purpose of Reddit is to post useful, high-quality, authentic comments from real people. So at Webflow, we have a couple of people at Webflow going to comments and saying, “This is my name, this is where I work, and here’s a useful piece of information.” So the strategy is find a thread that is a part of a citation that you want to show up in.
Say who you are, say where you work, and then give a useful piece of information, and that works really well. And that sounds simple if you’re not in the growth mindset of, “I need to scale this to hundreds of comments.” But you don’t actually need 10,000 comments, even five could be great and that scales perfectly well.
So the Reddit strategy is the obvious strategy, which is just to be an actual user of Reddit. Make an account, say who you are, say where you work, and give a useful answer.
Research on AI-Generated Content
Lenny Rachitsky: We had the early-growth leader from Deel, D-E-E-L, on the podcast a while ago. And this is how they grew initially, before AI even came around, just going big on Reddit and answering people’s questions.
And like, “Hey, happens to be Deel. Can I help you with this problem?” So that’s interesting. It’s so interesting that Reddit is what is keeping ChatGPT from being spammed with stuff. It’s not that ChatGPT is stopping the spam, its Reddit is just really good at that.
Detecting AI Content at Scale
Ethan Smith: I think that in a sense, ChatGPT is policing because ChatGPT is running a search, it’s finding citations. There’s a search algorithm that’s trying to select which citations are useful. There are people at ChatGPT who are tuning their search algorithm to select which sources they trust.
I’m sure that there’s a search evaluation team saying, “Do I like these citations, yes, no? Is Reddit showing up? I want it to show up.” So I think that there are actual people at ChatGPT who are intentionally configuring their algorithm to use Reddit because it’s trusted. And if it wasn’t trusted, they wouldn’t use it.
Same with Google. Google has specifically configured their search algorithm to rank Reddit and Twitter and Quora, because they want user-generated content. And if it wasn’t good content, then they would change the algorithm and they wouldn’t rank it. So I think that they are policing it in a sense.
Collective Wisdom vs AI Convergence
Lenny Rachitsky: Got it. And all of this is post-training, search-oriented features of these models. It’s not data they are trained on, is that right?
Ethan Smith: I would assume that so there’s the core model and then there’s RAG. So the core model is I’m looking at common crawl on billions of web pages, and then I’m retraining the model. And if you ask something like, “What’s the capital of California?” It predicts the next word, which is Sacramento. And that’s based on the core algorithm, which is next-word prediction.
Then there’s RAG and RAG basically means search, retrieval-augmented generation. So I’m going to do a search and then I’m going to summarize the search. There are these two different things. And so most of what I’m describing is about the RAG piece, not the core model piece. To influence the core model is probably extremely hard and maybe you’ll see the impact a year later.
And it’s probably something, some sort of obscure thing that nobody would want to do, like make a million pages that say, “Best product for X is brand.” Which I don’t think most people want to spend their time on. So I’m mostly focused on the RAG side, because that’s the main thing that’s controllable.
And I think also the LLM is probably not going to say your product if it didn’t show up anywhere on the RAG. So I think that’s where most of the interesting stuff is from an optimization perspective.
Future LLMs: Personalization and Agents
Lenny Rachitsky: Cool. Yeah. I didn’t even think about this side of it when we started talking about this, but I think that’s an important thing to note, is just this has nothing to do with the training data.
This is post-training, once the model’s live, what it can do to find recent information using RAG, web search, things like that. Okay. Before we get into how to actually do this step-by-step, how to win at AEO.
What are two or three things that you think are important for people to understand to be successful in this world just broadly?
Help Center Optimization (Continued)
Ethan Smith: First thing is just recognizing that this is related to search. So it’s LLM plus RAG, it’s summarizing a set of search results usually. So LLM plus RAG, number one. Number two is topics. So in search, a landing page is targeting hundreds of keywords, which we talked about on the last podcast.
So I’m not targeting one keyword like I was in 2007, I’m targeting 1,000 keywords, and each landing page needs to target that set of 1,000 keywords, and that’s a topic. Same thing is true for Answer Engine Optimization. Each page is targeting hundreds, thousands, maybe tens of thousands of questions.
And so I want to group all those questions, which then brings us into content, so how would I rank? How would I get my URL to rank? Or how are other URLs being decided whether or not they rank? Then answer all the questions. The more of the questions that I answer, the better.
So in Google Search, if I have a landing page about website builders, the more that my page answers all of the subtopic, follow-up questions, the more likely I am to show up in Google Search. Same with chat, the more you answer all the questions, the better. If you don’t answer a question, then you’re probably not going to show up.
And if you answer a follow-up question and subtopic somebody else is not answering, you’re going to be more likely to show up. So topics, number two. The third is question research, so how do I know which questions people are asking? And that’s actually pretty hard, because in search, Google just tells you what their ads API.
They say, “This is the search volume for this keyword.” There’s a truth set from Google and ChatGPT is not giving us that, at least not yet. Maybe when they do ads, they’ll give us more access to search volume, but there’s no truth set. So how do we know the questions that people are asking?
One way would just be to take all my search terms and change them into questions. So website builder, you can assume that what’s the best website builder is probably a question that’s probably asked proportional to the search volume for that keyword, so that’s one.
But then I mentioned that the tail is larger, and there’s parts of the tail that don’t exist in search. So how do we know what the tail looks like? And one strategy that you can use, is what are all the questions people are asking you on your sales calls, customer support on Reddit?
Mine all those questions that exist somewhere else. Probably those same questions are being asked in chat, so that’s another way to find questions. The last is citation optimization or offsite. So again, the LLM is summarizing RAG. So how do we show up with as many citations as possible?
And you can break up the citations into different groups, my site, video, YouTube, Vimeo, UGC, Quora, Reddit. Tier-one affiliates like Dotdash, tier-two affiliates, blogs. So it’s breaking up all those different citations and having specific strategies for each group.
Rapid-Fire Q&A
Lenny Rachitsky: What is Dotdash exactly?
Ethan Smith: Dotdash Meredith is a large media conglomerate with Good Housekeeping, Allrecipes, Investopedia. It’s probably the most successful SEO company of all time.
And it’s also one of the most cited, probably the most cited in LLMs as well.
My Favorite Products
Lenny Rachitsky: Wow, did not know this. As you talk, I think about if you go to Google, no offense, Mr. SEO, but if you go to Google these days, it’s just like a bunch of unuseful stuff, just like this hyper SEO’ed content.
Do you think ChatGPT will be able to avoid that fate where it’s just a bunch of hyper SEO’ed content that is not what you actually want?
My Life Motto
Ethan Smith: Probably. And what you’re saying with SEO is that everyone’s rewriting each other’s content, nonexperts rewriting each other’s content. So I get a content-scoring tool, which then looks at all the results in Google and it says, “These are all the things that the other articles are saying. And then this is what you haven’t said yet, so here are recommendations for how to be more typical.”
And then everyone rewrites each other’s article. And then one other interesting thing is that the majority of landing pages drive no impact. So we did an analysis where one out of 20 landing pages drive roughly 85% of all your traffic. So 19 out of 20 landing pages drive little to no traffic, which means if I want to get ROI, I need to spend a small amount of money on a large number of pages.
And so then you get a nonexpert to say, “Rewrite this other person’s article,” because that’s cheaper than hiring someone from The New York Times to write your article about what’s the best payroll management software? But if you knew the few things that would work, the few landing pages that would work, and you wrote them really well, then you could push all that money to that one page, which is what we try to do.
But right now it’s people rewriting each other’s content, so Google has not solved that yet. That’s probably a very hard problem to solve. Will they ever solve that? Probably. Will ChatGPT ever solve that? Probably. How I would solve that would be, one concept would be information gain. So did you say something that somebody else didn’t say? Two is how typical are you?
Are you so typical that I think that you’re a rewritten version of somebody else’s content? Potentially, Google has EEAT, expertise, authority, trustworthiness, which actually I don’t see having an effect unfortunately, but it could. And I could say, “Well, this person’s an expert, this person’s a certified financial advisor, rank them higher.”
And I’m actually not seeing that, but they could increase the weight of that. So these are all potential solutions, but I’m sure that the reason why it has not been solved yet and why everyone’s rewriting each other’s articles. It’s probably just hard to build an algorithm to solve that, but will they ever solve that? Probably.
Proudest Case Study
Lenny Rachitsky: This algorithm or heuristic you just shared is so interesting, because it’s helpful for just what is good content, say, with a newsletter or a podcast? Info gain, and is it typical?
Are you adding something new to the conversation and is this unique? I think it’s a really good strategy for just producing great newsletters and podcasts and all the content in the world.
How to Connect and Help
Ethan Smith: Yes. And ideally, did you do original research and do you have some domain expertise? And did you mention that in the content?
Lenny Rachitsky: This is a great heuristic for just content in general, which is exactly what you want these algorithms to be looking for, so the alignment is there.
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Let’s give people an actual, actionable plan to start executing on this and winning essentially at AEO. If it’s helpful to use my newsletter as an example, how would I show up more often on ChatGPT or Gemini or whatever? Or if it’s a B2B SaaS company, whatever’s easiest, let’s just talk about how to actually do this.
Ethan Smith: First, I would figure out which questions I want to rank for. How I would figure out which questions I want to rank for, I would take my search data. I would maybe take my paid search data, like, “What are my money terms? What are my competitors’ money terms?” So if I’m rippling, what is deal.com bidding all their paid search on?
Then I would transform those into questions. And actually you can just give those keywords to ChatGPT and say, “Make these into questions,” and it does a pretty good job. So take your competitors’ paid search data or mine or your own, put it in ChatGPT, get the questions. That’s step one. Step two is then track them, so put them in an AEO tracker, in an answer tracker.
Third thing would be who is showing up as citations? And then have a strategy for each of those different groups of citations. The third would be make your own landing pages. So what are the kinds of landing pages that are appearing? Is it a listicle? Is it a category page? Is it an article, tool page? Figure out what page type that seem to be showing up the most, and then you make your own page for that.
How do you have your page rank? Answer all the follow-up questions. So what are all the follow-up questions that someone might ask? You could go back to your search data and look for groups and themes of your keywords that are in your SEO topic. Same thing for AEO topic. Then on the offsite, so different strategies for each of those groups.
And I would say that depending on the company, paying an affiliate to mention you, that’s pretty easy if you have the money. So if you want to be the best credit card, you pay Forbes and then you’re the best credit card. So that’s strategy one, expensive, easy, controllable. The YouTube, Vimeo strategy is also actually pretty easy because there’s no community saying, “I don’t like your YouTube video.”
You make a YouTube video, you do whatever you want. Maybe people view it, maybe they don’t, but you can make a YouTube video or a Vimeo video. And the interesting thing with this, especially for B2B, is that YouTube, Vimeo, other video sites, the kinds of things people make videos for are food, traveling, fun, beauty.
There’s not that many videos about AI-powered payment processing APIs, as interesting as that is, but it’s a great money turn. So if you make a video for these really specific, high-LTV, maybe nonglamorous keywords, questions, topics, that’s actually a big opportunity. Then Reddit, so I mentioned with Webflow what we did, which is just make a Reddit account, say who you are, say where you work and give a useful answer.
That one is a little bit trickier because the community might say, “I don’t like your answer.” So you can’t guarantee that your comment is there, but it is easy, so I would do that group. Oh, and then experiment design, experiment design and seeing what works. So SEO and AEO are both interesting in that the majority of the information and best practices are not correct.
And the reason why is because people don’t do analysis. Somebody will say something and then it will get repeated, and then it becomes best practice and no one ever did an analysis. So you did all the stuff that I just mentioned. Do an experiment and see if it worked. Maybe half the stuff I said works, maybe half it doesn’t. Do your own experiment.
Most best practices, most blog posts are not correct. So how do you set up an experiment? You get your questions, you turn tracking on, give it a couple of weeks. Make your changes, have a test group, have a control group. Intervene on the test group, make your changes, see if the chart went up, see if the control group did not, and now you know your particular strategy worked.
So I would definitely do experiments and I would not assume that stuff you read online is correct. And then you need a team, so who’s your team? Probably your team is your SEO team, or your SEO agency or your SEO consultant. Probably, hopefully they can do this stuff, and then however, what I think is hard to hire for is the offsite stuff.
So most SEO people are not going to be amazing at creating YouTube videos and Reddit strategy, so you might need a different person for that. That might be a community generalist marketing person. So it would basically be your SEO team, “Please now do Answer Engine Optimization.” And then marketing community team, “Please help me show up in more citations.”
Lenny Rachitsky: Wow, okay. That is incredibly valuable. Thank you for sharing all that. I imagine some of this is you’re just giving away a lot of amazing advice for free here. Thank you. First of all, I imagine there’s a layer, there’s only so far you can go on your own.
And so eventually it’s like, “Okay, we really need help.” And that’s where a team like yours comes in. Let me ask a few questions here to follow up. One is this tracker concept. So what is this tracker, it can track how often you show up? Say Lenny’s Newsletter shows up and answers for the questions that I’m targeting?
Ethan Smith: Yeah, so there’s answer tracking, which is like keyword tracking. So keyword tracking would be best growth podcast, and you put that in a keyword tracking tool. There’s 100 of them, they’re all the same, and you see whether or not what you rank. Maybe you rank, hopefully you rank number one. Now in answers it’s very different, but it’s related.
So if you ask the same question, you will have different answers each time. If you ask a question, there’s different answers per run. And so ChatGPT is basically calculating a distribution of all the potential answers it would give. And depending on when you ask it, it’s basically like a weighted, random sample, and so you’re going to get different answers.
You also have question variants, so you can ask different versions of the same question, and you might show up in one and you might not show up in another. Then there’s different surfaces, there’s Perplexity, there’s Gemini, there’s ChatGPT, there’s Meta AI, and so these surfaces have different answers.
And so you essentially need to create a share of voice across all these different things like a distribution. So how often am I showing up? What’s my average rank? And that’s answer tracking. So then where do you get answer tracking? And answer tracking is essentially an evolution of keyword tracking. So we have a page with 60 different answer tracking tools.
But it’s ultimately just like keyword tracking, it’s all the same thing roughly. And so pick one of the 60, we have answer tracking, we’re building answer tracking. There’s 59 other options, probably all pretty good, probably all pretty similar, but pick one. My general suggestion is pick the cheapest one that does what you need.
Just like keyword tracking, you can only, there’s not a premium version of keyword tracking. You rank number three or you don’t. So pick the keyword tracker that is the cheapest that does what you want. Same with the answer tracking. And so then when I’m doing the experiment, put your answers in, track them, see a chart over time, see your average rank.
How often are you showing up and what’s your average rank? And then you make a change, and then hopefully you go up.
Lenny Rachitsky: Amazing. I love this term voice share. I never heard that before, it makes sense. Like percentage of time you’re showing up in LLMs, is there an LLM, is it just like ChatGPT?
Is Google equivalent now to ChatGPT? How do you recommend people think about, say, Gemini or Claude, or Perplexity and others?
Ethan Smith: So interestingly, there are similar, foundational algorithms across all of these. They’re all using search, they’re all using search, and they’re all using LLMs, which foundational algorithms are all the same. The results are actually pretty different. So we’re doing a study, we’re seeing that Google and Bing are not that similar search engines.
We’re seeing that ChatGPT citations and Google Search results are actually not that similar. Perplexity is interestingly more similar to Google than ChatGPT. We did a study looking at thousands of questions and saw the citation overlap with Google Search results was around 35% for ChatGPT and Google, so not that much.
Perplexity was around 70%, but essentially they’re all similar algorithms, but with very different citations and results. So then look at which surfaces have the most traffic and then track those. You probably don’t need to track all of them, but look across all those.
But you do need to look at your share of voice or the percent of time you show up across all these surfaces. You need to ask the question multiple times, and you need to ask the variance of the question to truly know how frequently you’re showing up.
Lenny Rachitsky: Considering that ChatGPT, they’re going to hit something like a billion weekly active users in the near future, do you need to worry about Claude and Gemini and Perplexity?
Is the traffic there meaningful? I know it is a lot of people, but how important is it to focus on those other LLMs?
Ethan Smith: Well, the way that I would answer that is I believe AOL was one of the largest search engines early on and Google was not. And so we could ask in 1999 or whatever, “Should we just focus on AOL search and Yahoo search? Do we really need to worry about Google?” And the answer is we don’t actually know.
It’s very early, we don’t know who’s going to win. I do think that ChatGPT for sure is going to be large. Will Perplexity or Claude or these others compete with them? Probably. Just like search, I think that there will probably be multiple winners and probably you’ll need to optimize for several.
I don’t think that you’ll need to optimize for 10, but there’ll probably be around three or so that will win that you want to optimize for.
Lenny Rachitsky: Okay. By the way, I want to make it clear, I love Claude. I use Claude and ChatGPT equally, roughly. I didn’t want to make it sound like ChatGPT is the only product people use.
Okay. How does this strategy change depending on the kind of company you are? Say you’re a B2B SaaS company or a consumer product, does anything in these seven steps change significantly?
Ethan Smith: Let’s take B2B, for example. The first thing is that the citations that are being mentioned are going to be quite different. So citation optimization will vary quite a bit.
Lenny Rachitsky: Just to clarify what you just said, what do you mean when you say citation strategy is different?
Ethan Smith: Meaning the citations that show up for B2B versus marketplaces are different kinds of citations. So for B2B, it might be like TechRadar shows up a ton when I ask questions. I’ve never read TechRadar, but for some reason it shows up all the time. I’m sure it’s great. But TechRadar is showing up a ton for B2B for whatever reason.
In commerce, it’s not going to be that, it’s going to be Glamour and Cosmopolitan. For marketplaces, it’ll be Eater and Yelp, TripAdvisor, places like that, so the kinds of citations that show up are different. Most of the stuff that I’ve been talking about is specific to B2B stuff that’s different for commerce.
So for most B2B questions, the answers are not clickable. There’s nothing to click on. And so if you actually want to measure the impact, you cannot just look at last-touch referral traffic. You have to see whether or not you showed up in the answer with tracking. And then you also need to ask the user, “How did you hear about us post-conversion to actually know the impact?”
So it’s harder to track for B2B. Also for B2B, you’re probably deciding which payroll management software to use after 50 touchpoints. With a brand, it’s not going to be you just search for something, you suddenly spent $100,000 on payroll management software. So that’s B2B. Commerce is different, so Commerce actually now has more clickable cards like you would in a Google.
So if you ask, “What’s the best TV for apartments?” There are actual shoppable cards. Those shoppable cards are showing multiple sellers. Those sellers have rich snippets. Schema is important, the number of reviews are important, so it’s actually quite different. You can look at last-touch referral traffic to get a good sense about the number of conversions that you’re getting.
For commerce, similar with restaurants and hotels and local marketplaces, similar there. And then I would say early stage is also different. So I mentioned earlier, early stage my recommendation is don’t do SEO at all. For Answer Engine Optimization, definitely do AEO, and only do citation optimization and long tail. Don’t do any of the mid-SEO stuff, just get cited and answer really specific questions.
Lenny Rachitsky: It’s so interesting that so much of this is just showing up as the little tag/pill in the answer, because it’s obvious now that I think about it.
That’s the only way someone will get to your site from an LLM is just clicking that, “Okay, let me go read this article.”
Ethan Smith: Yes. But what they will do is they will open a new tab, and they will type in the brand name and they’ll go to Google.
And then they’ll click on your domain, and you will think that it was a branded Google Search when it wasn’t.
Or they’ll open up a new tab and they will type in your domain, and they’ll go directly to your domain and you’ll falsely think that it was direct traffic.
Lenny Rachitsky: Coming back to a question you raised at the beginning. So for my newsletter, the fact that they’re sucking up all this content, I don’t even know how much, and sending me some percent of traffic.
Do you have any, I don’t know, just sense of is this good? If you were running my newsletter, would you encourage all these outlets to suck up my stuff? And then be like, “Oh yeah, you could check it out in Lenny’s Newsletter if you want”?
Ethan Smith: Yes. And I would give the same answer that Brian Balfour gave on your previous episode on this, which is that it’s not your choice whether to play the game. You are playing the game whether you want to or not, so you might as well try to show up. If you just say, “Don’t look at any of my data,” then you cannot show up and your competitors will.
Now, what you can do is you can say, “I don’t want you to train on my data, so you can index my site, but please don’t train on my data.” And they have different user agents for that and different bots, so you can just say, “And we’re building a Webflow app to block training but not indexation.”
Or you can just put it in your robots.txt, “This training bot not allowed. Index bot, you are allowed.” So if you’re concerned about that, I would suggest that, and I think probably a lot of people will do that. But saying, “You can’t index my site at all,” that doesn’t make sense to me.
Lenny Rachitsky: Such a good point, because I don’t know if I have competitors in this exact space, but basically they would show up instead and then I lose all that traffic.
Ethan Smith: Yes.
Lenny Rachitsky: Such a good point. Okay. Let me come back to the steps you shared just to see if there’s something here that’s worth diving into a little further. So this is essentially how to be more successful showing up in LLM responses. One is figure out what questions you want to rank for.
And you could do this by looking at what your competitors are advertising and their paid ads and things like that. Just look at the terms, ask almost ChatGPT or Claude, “Turn these into questions people would ask to find these terms.” Then set up a tracker to see just how you’re doing today. How often are you showing up?
There’s a million trackers, you have a link willing to check these out. Then you look at who is showing up today? Where are they being taken today? Use that to inform landing pages that you create to answer those questions better. And you make it very clear that it’s very important not to just answer that main question, but also follow-up questions. Then there’s offsite stuff.
So get into affiliates like Dotdash, YouTube, Reddit, Quora sounds like are the core, and then run an experiment. So you look at this tracker, and let me actually ask this, and the next step is just set up a team. But just to come back to this step, how do you set up an experiment that isn’t just like a before, after? How do you do a control group situation?
Ethan Smith: Yeah. So what I would do is I would take 100 different questions, half of them I will intervene, half of them I won’t. Or let’s say, let’s take 200 questions. So 100 of the questions, I’m not going to do anything, so that’s my control group. And we are seeing a fair amount of variance and answers just without doing anything at all, so you definitely want a control group.
And also we’re seeing people are using LLMs more and LLM traffic is going up. So you definitely need a control group, especially in Answer Engine Optimization. So control group is, “Don’t touch it at all, leave it as it is.” That’s the control group. Test group would be, “I’m going to now comment on Reddit threads, so let’s test that.”
Or I’m now going to make a YouTube, Vimeo video, or I’m now going to pay Forbes advisor to say that I’m the best credit card. Maybe break those up into a few different buckets, track them. Have a couple of weeks before, a couple of weeks after, compare against the control group.
And then the stuff that went up when the control group did not worked, and the stuff that didn’t did not, and then reproduce it. So reproducibility is very important. And my background’s in academic research, and it’s common to do a study that cannot be reproduced. And so for something to truly be accepted with an academian, it needs to be reproducible.
Meaning multiple people have done this study and reproduced that thing over and over again. And especially in SEO, it’s common for something to change. And you think that it was this thing that caused it and it’s actually not, and you just assume forever that that works. So reproducibility is very important.
Try to do that study multiple times, try to get studies from other people, and if it works 10 times, then it probably works. And this comes back to the waste problem, most work is wasted in SEO. Most work is wasted in AEO, so how do you know what’s not wasted? You do an experiment, you don’t assume that what you read online is true.
You do your own experiment, and then you reproduce it multiple times, and keep doing the stuff that works and don’t do the stuff that doesn’t.
Lenny Rachitsky: It feels like such a big deal to win at AEO. Just coming back to this idea that people are coming to ChatGPT, Claude, Gemini looking for an answer.
If you’re that answer, I feel like that could just make or break your company. It feels like even more important than SEO, just getting this right.
Ethan Smith: I would say that where I want to get the most conversions possible, how big is the channel? The channel is not as big as search. The search is definitely larger, but it is a substantial channel now. And Webflow, they get 8% of those signups from LLMs.
It’s now one of your top channels so it’s large. It’s not the largest channel, it’s not the number one channel. Paid is probably the number one channel, but it’s definitely a substantially large channel and one worth optimizing for.
Lenny Rachitsky: And as you said, probably growing over time.
Ethan Smith: Yes.
Lenny Rachitsky: Okay. Let me zoom out a little bit, and let me just ask you this.
What do you think are maybe the most surprising or underdiscussed topics when it comes to AI and SEO and AEO that we haven’t already talked about?
Ethan Smith: The first thing is that there’s significant misinformation on AI and on AEO, and it’s pretty extreme. It’s unusually the percent of misinformation to correct information is pretty substantial. So one example is every two years there’s news articles about how Google Search is going to die or it is dying because there’s a new thing.
So that’s happening right now with AI Overviews and with AEO, Google’s going down, which is not true. Before that it was TikTok search, so everyone is using TikTok now. Gen Z is using TikTok, they’re never going to use SEO. SEO’s going to be dead, and so you really need to focus on TikTok search, which is not false. It’s not untrue, but it’s not taking share away from Google, it’s just a new surface.
And then before that it was Instagram, and then before that it was Facebook and it was YouTube. And people do search and discover on Instagram, TikTok, YouTube, but it doesn’t take away from Google Search. It adds on top of it. These are all new channels, so Google’s slice of the pie stays the same, the pie gets bigger.
And so misinformation about Google going down, Google is not going down. Google published something recently, their VP of search explicitly said, “I looked at the traffic that we’re sending to publishers, and it is not down, it’s up slightly.” So it is not true that Google Search is going down.
And most of the news information about that is saying that it’s going down, so that’s the first surprising thing. The second surprising thing is tooling. And I’ve never seen a channel where these extremely expensive tools that essentially do commodity tasks. So imagine if I said, “I’m going to charge you $50,000 for keyword tracking.”
You would say, “Well, of course, that’s absurd. It’s keyword tracking, I could write this in a day.” No one would do that. But for answer engines, it’s mysterious and people don’t really know how it’s working. Also, the slope of the growth curve is so significant, that I’m seeing people spend huge amounts of money on what are essentially keyword tracking commodities.
So that’s the second thing. The third thing is the growth curve of the channel. And we did a Reforge AEO webinar a year ago, and there was excitement and then it died and there was very little excitement about it. This was in June, and then people didn’t really care. They were intrigued intellectually by it, but they didn’t care because they didn’t see the impact from that.
So there was essentially very little interest between July and January, and then suddenly in January it’s just skyrocketing. So it’s ChatGPT launches, people are very interested, and then it’s not that interesting for growth people. And then there’s this little spike in June, and then it’s like this, which is usually not what you see with a new channel.
So the slope of the curve is unusually steep, and the shape of the curve is also very unusual. The last is that a lot of people do think that SEO and AEO are different and they’re not different. I think probably part of that is because it sounds great to say that there’s this new channel, it’s completely different.
And I’m an expert and I have a tool to sell you, and it’s totally unique and all these other tools are not relevant. In reality, it’s actually there’s quite a bit of overlap. There is the difference of the citation optimization. The head is different and the tail is different, but the core technology is pretty similar. So those are probably the most surprising things.
Lenny Rachitsky: This piece about January being the inflection point, you mentioned that it was because references started showing it more prominently. Is that the big change?
Ethan Smith: I think it’s increase of adoption of LLMs by people, so it’s just actually growing more and then the clickability. And I am seeing, you are seeing now this large increase of actual clicks.
Probably before you got no clicks, even if you showed up an answer, so the clickability of the answer has increased.
Especially for things like commerce and local and hotels, because they have these rich modules where you can click on stuff and go somewhere, which was not true before. That and I think people are just using LLMs more.
Lenny Rachitsky: Ethan, let me just say, I’m learning so much from this conversation, what a fun thing. I could see, it’s just clear how much you love this stuff, and just how nerdy and deep you get into it. And it’s just fun to talk to someone that’s so deep and knowledgeable about all these things, so thank you for sharing all this with us.
I’m going to go in a slightly different direction. There’s this whole world of AI content, people generating content with AI, generating landing pages. Just like, “Oh my God, SEO is never going to just generate all this stuff. AI is going to make all this stuff easier.”
You guys did a really big study on how that works, whether it’s a good idea to generate content with AI. Can you just talk about what you learned from that, and how people should think about AI in generating content?
Ethan Smith: Yes. So I remember when ChatGPT launched and Brian Balfour posted on LinkedIn, “What do you people think that is going to happen from ChatGPT and AI?” And my immediate response is spam, so just lots and lots of spam, especially SEO spam. And then there was a whole industry around AI-generated content, and I knew immediately that it wouldn’t work.
And the reason why I knew it wouldn’t work, and when I say AI-generated content, I mean automated content with no human-in-the-loop. So I think that the future of content is clearly AI-assisted. Clearly, you and I will be using AI to help us write, so it’s not no AI at all, but it’s not 100% generated with AI. I immediately knew that it wouldn’t work.
Why did I know that? I knew that because I created spam in 2007, and I knew what Google did about it and how, and I knew the exact same thing was going to happen. So what I did in 2007 is I and all the other shopping comparison people scraped all each other’s content, reviews, chopped it up, scraped content, 100 million search pages, snippets, and it worked really well.
And then it stopped working, and then all those companies disappeared. I knew that was exactly what’s going to happen with AI-generated content. And so from the beginning, I’ve not focused on AI-generated content. Many people have, but I don’t know, so maybe it does work. There’s lots of case studies about it working.
So let’s do the study, let’s do an analysis. So we took, we looked at both Google and at ChatGPT where we took thousands of searches and thousands of questions, and we put those searches into Google Search. We put those questions into chat and the ChatGPT, and then we looked at the citations or the Google Search results. Then we looked at an AI detector.
So we used Surfer SEO’s AI detector. Now, when I tell people this, they say, “Well, you can’t detect AI.” So then we evaluated the efficacy and the accuracy of the AI detector. So we did that by generating thousands of AI-generated articles and it was very predictive. And then we looked at real articles, we did that two different ways.
One way is we write real articles, and the other is we took a random sample of 100,000 URLs from Common Crawl over the last five years. And then we looked at the AI detector before ChatGPT was launched, so it necessarily was content not created by a human. And then the false positive rate was around 8%, so basically the AI detector is very accurate.
So we took that, then we ran it on the content. So then what we saw was around 10% to 12% of content in Google Search, and then ChatGPT or AI-generated, 90% are not. And we ran a correlation analysis showing the exact same thing. So we essentially did a very rigorous study showing that AI content does not work. AI-assisted content edited is great.
We do that sometimes, other people do that, that is clearly the future of content. So that does work and should work and that’s good, but purely 100% AI-generated does not work. So then the second thing that we did was we found that, this was unexpected, but we found that there’s more AI-generated content on the internet than human-generated content.
So back to the Common Crawl study, we looked at 100,000 different URLs over the past five years. And then you can see this curve where AI-generated is now higher than human-created. So there’s more AI-generated content on the internet than human-generated content, which is disturbing. So then let’s say that AI-generated content did work.
If AI-generated content worked, then everyone would do it. Just like in 2007, shopping comparison sites, if I can scrape my content, why would I pay anyone to write it? I’ll just scrape it from you and I’ll chop it up. So then everyone will do that, and then it will go from most content is AI-generated to almost all of the content is AI-generated.
Then what will happen if that works, is that Google now becomes a search engine for ChatGPT responses. So if Google’s a search engine for ChatGPT responses, there’s no reason for Google to exist. Just go to ChatGPT, which is the exact same thing that happened in 2007.
Google said, “I see all these shopping comparison search engines showing up in my search results. So I’m essentially a search engine for search engines.” I should be showing the TV in my results. I shouldn’t be showing other vertical search engines, so I’m going to get rid of them and I’m just going to go straight to the product.
The same thing will be true for ChatGPT. Now for ChatGPT, let’s say that ChatGPT ranks its own derivatives in its citations, so then you have this infinite loop of derivatives. So I go to ChatGPT, I say, “Generate 10 articles.” I put those articles into the citations and then I say, “Summarize these citations that were derivative.”
And then I keep on doing derivatives of derivatives, and then you have an infinite loop of derivatives, and now AI is summarizing itself. There’s a paper about this called Model Collapse. So again, there’s the core algorithm and then there’s the RAG piece. So the core algorithm, a group did a study showing model collapse, which was what if you feed in AI derivatives into the model and train the core model on the derivatives?
And then what happened was you had all these problems, hallucinations, things break very quickly. Okay. So then we did a study on what if you feed derivatives into the RAG piece? So generate 10 derivatives, put that into RAG, summarize that. And then generate 10 more, and then summarize my summarizations, infinite loop of derivatives. What happens?
And so what happens is there’s a wisdom of the crowd. The LLM is summarizing the opinion of many people. So if you ask a question like, “What’s the best flavor of ice cream?” There’s not one answer, there’s thousands of opinions. So the LLM is summarizing these many, many opinions in this wisdom of the crowd.
And the wisdom of the crowd basically says that, “If you take the average of a large group of people, their average response will be better than the best single individual in the group.” And so it’s better to have more diversity of opinions, wisdom of the crowd. So what happens to the infinite loop of derivatives? You essentially converge on one opinion.
So if you ask, “What’s the best flavor of ice cream?” It will eventually say, “It’s vanilla and it’s only vanilla, and there’s no other flavor of ice cream.” And so that’s a simple example, but if you feed in derivatives of derivatives into the model, you’ll basically take the wisdom of the crowd.
And that will shrink and you’ll have a single opinion on everything, which is really bad. So that’s what happens if AI content, 100% unassisted AI content works.
Lenny Rachitsky: I’m afraid of this world where everything is trained on AI, and AI is trained on AI and generating AI, and just like nothing is trusted. And I love how it’s interesting just how much of these incentives are driving this.
If ChatGPT was finding this valuable, this is what people do and then just goes off the rails. So there’s just some team there that is keeping this from happening. How do you think this evolves?
If you were them, what would you do over the next few years to keep things high quality and not drive these perverse incentives?
Ethan Smith: So I would identify what the perverse incentives might be, and AI-generated content is one of them. The second thing is I think that LLMs and search are going to converge. And so you’re seeing that with Google Search where they’re having LLM, AI Overviews. You’re seeing that with LLMs where they’re incorporating maps and shopping carousels, and it’s converging on search.
I think it’ll converge on a single experience, so that’s the first thing. Figure out what 2007 Ethan would do not to create spam and make sure that he doesn’t do that, like AI-generated content or it’s great content. That’d the second thing. And the third thing is there’s all these other interesting features, use cases that LLMs can be great for.
So LLMs could be great for remembering everything that you’ve ever asked. It could be good for personalizing stuff specifically to Lenny. One interesting use case that I think will eventually come would be, I say, “Plan a trip to San Francisco,” and decisions are made for you without any intervention. I have this wonderful EA named Jen.
And I say, “Jen, I’m going to Miami. Please, just do everything for me,” and she does everything for me. She knows me, she knows my preferences, she knows that I want a ocean view and I want a restaurant with music. She does all of that and I don’t have to intervene. AI can essentially do that eventually, and that would do that because it would deeply understand you.
It would remember everything about you. It would have context, it would have a reasoning, and then it would be able to make all these decisions without your intervention, which would be autonomous agents. So I think that that’s also another very interesting place for someone like me to optimize for as well.
Lenny Rachitsky: Yeah. I was just going to say, just imagine not even being told this is what you’re choosing. Like, “Oh, and go check out, subscribe to the best newsletter out there.” And if you’re out there, the good things will happen.
Wow, what a wild world. Is there anything else that we haven’t covered that you think would be helpful to folks that are trying to get better at this stuff? Try to take the first steps down this road of AEO?
Ethan Smith: Yes, the most exciting topic, which is help center optimization and support.
Lenny Rachitsky: Sweet.
Ethan Smith: So I mentioned that people in chat are asking follow-up questions. They’re looking for tools. Do you have this feature, this use case, this integration? And that frequently can be answered in help centers. Usually, you would not have an SEO team and say, “We really want you guys to focus on the help center.”
But in chat, since there’s all these questions about can you do this thing, can you fulfill my use case? A help center is actually a great place to do that, and so I think how can you optimize the help center? So number one is it’s frequently on a subdomain. For whatever reason, subdomains don’t work as well as subdirectories, so move it to a subdirectory, number one.
Number two is make sure that you’re cross-linking well. So usually you do not have optimized internal links, so link from help center page to help center page, make sure there’s lots of cross-linking. The third is you probably have help center content about the head, but the tail you probably don’t have any help center content for.
So an example of this is I was looking for, I wanted to track our sales calls and look to see who was in the meeting and what the sentiment was. And I wanted to put that into Looker, so I said, “Which meeting transcription tool integrates with Looker?” And the answer is none of them, but you could use Otter because Otter has a Zapier integration.
You could send a Zap of the meeting, put it into BigQuery, and then do Looker on top of that. But there wasn’t a help center article about that because it’s a very obscure use case, but it’s not a zero use case. And so the tail, there’s going to be a bunch of questions in the tail that you may not have help center articles for.
So again, what are the questions in sales calls? What are the questions that you’re seeing in customer support? Having pages for that, I might even open up to the community. Anyone can ask anything because the community will then fill on the tail and then answer those.
And again, in many cases there might be nobody talking about this at all. So you could be the only citation for this, and then win that tail of questions.
Lenny Rachitsky: Are there any help desk, I don’t know, system software that are just making this easier yet? Or do you think that’s an opportunity for, say, Zendesk or Intercom?
Ethan Smith: I think probably all of them should work perfectly well. I think that the only thing you need to do is cross-linking and subdirectory rather than subdomain, which probably most of them do. So I think that they should all work for free.
That the main thing you would want to do would be, again, open it up to the community and make sure that you fill in the tail. But probably all those tools should be good for this.
Lenny Rachitsky: Well, with that, we’ve reached our very exciting lightning round. I’ve got five questions for you, Ethan. Are you ready?
Ethan Smith: I’m ready.
Lenny Rachitsky: What are two or three books that you find yourself recommending most to other people?
Ethan Smith: Number one is Emotional Intelligence, and people talk about the concept of emotional intelligence, but there’s actual research and psychology around that. I believe it was published in the ’80s, but there’s a really good book that summarizes the foundational research around emotional intelligence. And it’s very useful when building relationships and communicating with people to understand their emotions. So that’s the first one. And doing growth because growth is getting people to use your stuff. And so if you have frameworks to inform how people will use your things, then you can be a more effective growth person. Which brings me to my second book, which is Cialdini’s Persuasion book. Robert Cialdini does a bunch of books around persuasion, but again, there’s frameworks for how to persuade somebody to sign up, buy something. And so he breaks down his framework for that, and again, it’s based on psychology. And I think especially in growth, there’s all kinds of psychology research and behavioral economics research to inform tests.
And if you just read Thinking, Fast and Slow, Persuasion, Emotional Intelligence, you can basically take those frameworks and apply it to growth in all kinds of different ways. And then the last is How to Measure Anything. So How to Measure Anything is about measuring things that are not immediately obvious to measure.
They give this example of they wanted to measure how good an orchestra conductor was and they could survey or they could see the number of standing ovations for each orchestra conductor. And the more standing ovations probably means it’s this better one and that you don’t need to survey people.
But much of growth and business is things that are not immediately obvious for how to measure, but anything could be measured, and so that’s my third record.
Lenny Rachitsky: Is there a favorite recent movie or TV show you’ve really enjoyed?
Ethan Smith: I don’t really watch TV, but I watch two different groups of things. I watch really aggressive sports, so I really like Michael Jordan documentary, Last Dance. I like Lance Armstrong documentaries about how aggressive and confrontational he is, and I love watching UFC. I like extreme aggression and intensity. The other group of stuff that I like to watch are climbing documentaries.
So anything that Alex Honnold, Jimmy Chan do, I watch all that, which is the exact opposite of aggressive sports. So it’s zen, being present, slow-and-steady craftsmanship. But this is how I approach my work, which is extreme intensity and aggressiveness, and then the zen craftsmanship, being present.
Lenny Rachitsky: I love how this explains why people love working with you and why you’re good at this, is like this competitiveness and also just the super nerdiness to get really knowledgeable about how this stuff works.
And then I didn’t think about the zen element of it, just lik staying calm throughout it all.
Ethan Smith: Flow, flow state.
Lenny Rachitsky: Flow, what a funny microcosm of why you’re so good at this.
Ethan Smith: Thank you.
Lenny Rachitsky: Okay, I’m going to keep going. Do you have a favorite product you’ve recently discovered that you really love?
Ethan Smith: This camera and this microphone. So I got a Sony mirrorless SLR, I forget which one. But, sorry, getting a mirrorless SLR with a wide-angle lens really transforms your video calls. And then I have this Shure microphone and I think it’s like $180.
This dramatically improves the quality of my video call. And I like to design things and you can design your video calls and you can make them amazing. You can have flowers in the background, over here, some sunflowers.
Lenny Rachitsky: Beautiful.
Ethan Smith: So my favorite products are my SLR camera that I use for video calls and my microphone.
Lenny Rachitsky: Your background is quite exquisite and I didn’t mention that, but it looks beautiful. Okay, two more questions.
Do you have a life motto that you find really useful in work or in life?
Ethan Smith: There’s the Outliers book about 10,000 hours. And the themes there are you don’t have to be the smartest, you have to be sufficiently smart, number one. Number two is focused practice, so it’s not just trying hard, it’s doing it in an intentional, focused way. And the third thing is lots of practice, so no one can master anything because they’re a genius.
They master it because they spent a significant amount of time practicing and they practice in an intentional way. And so my motto is essentially a combination of those things, which is that I’m not going to necessarily win because my brain is the largest brain or that I tried the hardest.
It’s because I’m going to be the most intentional about my practice, and I’m going to be as intense as I possibly can be about that practice.
Lenny Rachitsky: Okay, final question. I’m curious if there’s just like an SEO or even an AEO win, you’re just most proud of?
That you always think about, “Wow, I can’t believe I pulled that off. I can’t believe the impact we had there”?
Ethan Smith: I always liked the example of butter lettuce with MasterClass. Because MasterClass, when I was first working with them, they did not have nearly as much authority as Allrecipes and Martha Stewart. And I actually didn’t know if I should take the project because I thought it might be too hard.
But I did the project and it was hard, but we were able to rank really competitively and way better than I expected. And I think it’s probably because of all these specific, little execution details. But butter lettuce was my favorite one, and I like butter lettuce, so I can search for butter lettuce and I can get a recipe on MasterClass.
Lenny Rachitsky: That’s amazing. I don’t know if butter lettuce has been mentioned on this podcast before. Ethan, this was incredible. This was everything I was hoping it’d be.
I feel like we’ve just leveled up everyone’s knowledge on what the hell is happening with SEO and AEO? Forget about GEO.
Two final questions, where can folks find you if they want to potentially work with you guys? And how can listeners be useful to you?
Ethan Smith: So where you can find me, number one, is on LinkedIn. I spend lots of time on LinkedIn and I publish original, so we do original research. We have a whole research team hypothesizing and evaluating those hypotheses. So we publish, all the studies that I mentioned, we publish on our site and I publish them on LinkedIn.
So follow me on LinkedIn, add me on LinkedIn, send me a message. LinkedIn, number one, and then number two is we have a blog which we call The 5%. So /5%, which stands for 5% of work, 5% of landing pages drive almost all the impact, so that’s the theme. This is only useful stuff. So our blog at 5%, you could subscribe to our email and to our studies. And then how can people be useful to me?
So I spent time thinking about this and there’s two ways people can help me. The first way is that there’s not that much research around what works in AEO, and I would love to know what people are testing and what the results are and what works. So people doing studies and publishing that are sending it to me, I would love as much analysis and research as possible, number one.
Then the second one is to help me on LinkedIn by commenting on my posts and on my comments. So you posted most recently the Brian Balfour episode, for which I wrote a long, thoughtful comment, and then I got about 25 likes and then I got responses to that. And so I’ve been commenting on other people’s LinkedIn posts and I’ve been writing these long LinkedIn posts.
And when people comment, it boosts the engagement within LinkedIn and then I get mass distribution. So the more people and thoughtful comments, so not this is great, but a long, thoughtful comment that stimulates conversation. So if people comment on my posts, then I’m just going to blow up on LinkedIn and I might be as big as you someday.
Lenny Rachitsky: I love how tactical his ask is. It’s something Bryan Johnson I noticed is really good at on Twitter, the longevity guy.
He just replies to tweets in a really funny way and feels like that’s a big growth channel for him. So I love that you have this in common with Bryan Johnson.
Ethan Smith: Yes.
Lenny Rachitsky: Also, just to point people to your domain, graphite.io, is that the right domain?
Ethan Smith: Yep.
Lenny Rachitsky: Amazing. Ethan, thank you so much for sharing so much with us and for being here.
Ethan Smith: Absolutely. It’s good to be here.
Lenny Rachitsky: Bye, everyone. Thank you so much for listening. If you found this valuable, you can subscribe to the show on Apple Podcasts, Spotify, or your favorite podcast app.
Also, please consider giving us a rating or leaving a review, as that really helps other listeners find the podcast. You can find all past episodes or learn more about the show at lennyspodcast.com. See you in the next episode.
Glossary
| English | 中文 |
|---|---|
| AEO | 答案引擎优化（Answer Engine Optimization） |
| AI detector | AI 检测器 |
| AI Overviews | AI Overviews |
| AI-generated content | AI 生成内容 |
| Alex Honnold | Alex Honnold |
| Allrecipes | Allrecipes |
| autonomous agents | 自主代理（autonomous agents） |
| BigQuery | BigQuery |
| blue link | 蓝色链接 |
| Brian Balfour | Brian Balfour |
| citation | 引用来源 |
| Common Crawl | Common Crawl |
| Cosmopolitan | Cosmopolitan |
| Deel | Deel |
| Domain Authority | 域名权重（Domain Authority） |
| Dotdash Meredith | Dotdash Meredith |
| EA | EA（Executive Assistant，行政助理） |
| Eater | Eater |
| EEAT | EEAT（Experience, Expertise, Authoritativeness, Trustworthiness） |
| Emotional Intelligence | 《情商》（Emotional Intelligence） |
| false positive rate | 误报率 |
| Forbes Advisor | Forbes Advisor |
| GEO | 生成引擎优化（Generative Engine Optimization） |
| Glamour | Glamour |
| Good Housekeeping | Good Housekeeping |
| graphite.io | graphite.io |
| help center | 帮助中心（help center） |
| How to Measure Anything | 《如何衡量任何事物》（How to Measure Anything） |
| human-in-the-loop | 人在环 |
| Intercom | Intercom |
| Investopedia | Investopedia |
| Jimmy Chin | Jimmy Chin |
| Lance Armstrong | Lance Armstrong |
| landing page | 着陆页 |
| Last Dance | 《最后一舞》（Last Dance） |
| last-touch referral traffic | 末次触达引荐流量 |
| Looker | Looker |
| Martha Stewart | Martha Stewart |
| MasterClass | MasterClass |
| Michael Jordan | Michael Jordan |
| Model Collapse | 模型崩溃（Model Collapse） |
| Nick Turley | Nick Turley |
| Otter | Otter |
| Outliers | 《异类》（Outliers） |
| Perplexity | Perplexity |
| Persuasion | 《影响力》（Persuasion） |
| Programmatic SEO | 程序化 SEO（Programmatic SEO） |
| RAG | 检索增强生成（Retrieval-Augmented Generation） |
| rich snippet | 富摘要 |
| Robert Cialdini | Robert Cialdini |
| Schema | Schema |
| share of voice | 声音份额 |
| shoppable card | 可购物卡片 |
| shopping comparison | 购物比价 |
| Shure | Shure |
| Sony | Sony |
| spam | 垃圾内容 |
| Surfer SEO | Surfer SEO |
| TechRadar | TechRadar |
| Thinking, Fast and Slow | 《思考，快与慢》（Thinking, Fast and Slow） |
| TripAdvisor | TripAdvisor |
| UGC | 用户生成内容（User-Generated Content） |
| user agent | user agent |
| Vimeo | Vimeo |
| Webflow | Webflow |
| wisdom of the crowd | 群体智慧 |
| YC | YC（Y Combinator） |
| Yelp | Yelp |
| Zapier | Zapier |
| Zendesk | Zendesk |
| 信息增益 | 信息增益（Information Gain） |
Reformatted by reformat_english.py