Why I’m betting on AI-curated directories when Google AI Overviews answer the same queries

为什么在 Google AI 概览能回答相同查询的情况下，我仍押注于 AI 策划的目录网站

The obvious counterargument to everything I’m building is this: Google already does it. You type “best AI tools for video editing” into Google and an AI Overview surfaces a curated list, synthesized from the same kind of data I maintain, without requiring a click. My three directory sites — Top AI Tools, Find Games Like, and Open Alternative To — are competing with a feature baked into the world’s dominant search engine. 对于我正在构建的一切，最明显的反驳论点是：Google 已经做到了。你在 Google 中输入“最佳视频编辑 AI 工具”，AI 概览（AI Overview）就会呈现一个精选列表，这些内容是从我所维护的同类数据中合成的，甚至无需点击。我的三个目录网站——Top AI Tools、Find Games Like 和 Open Alternative To——正在与全球主流搜索引擎内置的功能竞争。

I launched these sites on 2026-04-23, built on an architecture that runs at about $25/month. Traffic is essentially zero — the sites have been indexed for three weeks and organic crawling takes time. The question I keep returning to isn’t whether Google will eventually index my pages. It’s whether anyone will prefer clicking through to my site over reading the AI Overview box that already answered the same question. 我在 2026 年 4 月 23 日推出了这些网站，其架构运行成本约为每月 25 美元。目前的流量基本为零——网站已被索引三周，而自然爬取需要时间。我不断思考的问题不是 Google 是否最终会索引我的页面，而是是否有人会愿意点击进入我的网站，而不是阅读那个已经回答了相同问题的 AI 概览框。

Here’s my honest, falsifiable position. The bet, stated plainly: By October 2026 — six months post-launch — at least one of the three sites will show organic click trends in Google Search Console indicating real query traffic to specific comparison or filtered-browse pages. I define that as: at least 200 non-homepage organic clicks per month, sustained for two consecutive months, from queries I didn’t directly drive through social or newsletter posts. If that doesn’t happen, I’ll publish the Search Console screenshots and write a post explaining what I got wrong. I’m committing to that here. 这是我诚实且可证伪的立场。简单来说，我的赌注是：到 2026 年 10 月（发布后六个月），这三个网站中至少有一个会在 Google Search Console 中显示自然点击趋势，表明有真实的查询流量进入特定的对比或筛选浏览页面。我的定义是：每月至少有 200 次非首页的自然点击，且持续两个月，且这些查询并非由我通过社交媒体或时事通讯直接引导而来。如果没能实现，我会公布 Search Console 的截图，并写一篇文章解释我错在哪里。我在此做出承诺。

The counterargument I take seriously: AI Overviews have gotten genuinely good at list-and-compare synthesis. If you search “open source alternative to Notion” today, Google often returns a four-item structured list with one-sentence descriptions directly in the Overview box. My Open Alternative To site covers that territory. The AI Overview absorbs the zero-click version of that query. The optimistic response is: “my site appears as a citation source.” The pessimistic response is: “Google consumes your signal and stops sending clicks.” 我认真对待的反驳论点是：AI 概览在列表和对比合成方面已经变得非常出色。如果你今天搜索“Notion 的开源替代品”，Google 通常会在概览框中直接返回一个包含四项内容的结构化列表，并附带单句描述。我的 Open Alternative To 网站涵盖了这一领域。AI 概览吸收了该查询的“零点击”版本。乐观的回应是：“我的网站作为引用来源出现。”悲观的回应则是：“Google 消耗了你的信号，却不再发送点击流量。”

The pessimistic version has supporting evidence — industry-wide CTR on informational queries dropped measurably as AI Overviews expanded through 2025, and the trend hasn’t reversed. I don’t think the pessimistic version is the whole story, but I’m not dismissing it. The most dangerous move is to assume the counterargument is wrong without designing around it. 悲观版本有证据支持——随着 AI 概览在 2025 年的扩展，信息类查询的行业整体点击率（CTR）出现了明显下降，且这一趋势尚未逆转。我不认为悲观版本就是全部事实，但我不会忽视它。最危险的做法是在没有针对性设计的情况下，就假设反驳论点是错误的。

Where AI Overviews have structural blind spots: AI Overviews are strong at synthesizing “what exists.” They’re weaker at three things I’ve deliberately built for. AI 概览的结构性盲点在于：AI 概览擅长合成“存在什么”，但在我刻意构建的三个方面表现较弱。

1. Attribute-based filtering. If someone wants “open source Notion alternatives that work offline and have a mobile app,” AI Overviews give hedged prose answers because they’re synthesizing text, not querying structured fields. My Turso DB has works_offline, has_mobile_app, and last_commit_date as typed columns. Faceted filtering on those fields is something a browseable directory does better than a language model writing a paragraph about the general landscape.

2. 基于属性的筛选。如果有人想要“支持离线工作且有移动应用的 Notion 开源替代品”，AI 概览会给出模棱两可的散文式回答，因为它们是在合成文本，而不是查询结构化字段。我的 Turso 数据库将 works_offline、has_mobile_app 和 last_commit_date 作为类型化列。在这些字段上进行分面筛选，是可浏览目录比语言模型撰写一段关于整体概况的文字做得更好的地方。

3. Editorial negative-space. My game recommender includes “avoid if” caveats — structured fields that answer “who should skip this?” generated by a Claude Haiku prompt that specifically forces a critical answer. AI Overviews don’t have a mechanism to surface structured negatives. They default to positive framing, which means someone with a specific disqualifying requirement gets an unhelpful answer.

4. 编辑负面空间。我的游戏推荐器包含“避免使用如果”的警告——这是通过 Claude Haiku 提示词专门强制生成批判性回答的结构化字段，用以回答“谁应该跳过这个？”。AI 概览没有呈现结构化负面信息的机制。它们默认采用正面框架，这意味着有特定排除需求的用户会得到无用的答案。

5. Freshness on maintenance status. The ETL that populates the AI tools directory pulls GitHub commit activity weekly. A tool that hasn’t been touched in 14 months is marked as low activity. AI Overviews don’t distinguish between a tool actively maintained in 2026 and one that peaked in 2024 — they rely on the recency of web mentions, which can lag by months after a project goes dormant.

6. 维护状态的新鲜度。填充 AI 工具目录的 ETL 每周抓取 GitHub 的提交活动。一个 14 个月未更新的工具会被标记为低活跃度。AI 概览无法区分 2026 年仍在积极维护的工具和 2024 年达到顶峰的工具——它们依赖于网络提及的新鲜度，而这在项目进入休眠状态后可能会滞后数月。

None of these defenses are permanent. Google could build structured attribute filtering into AI Overviews. But they require deliberate pipeline design, not just synthesis, and the gap exists now. 这些防御手段都不是永久的。Google 可以将结构化属性筛选构建到 AI 概览中。但它们需要刻意的管道设计，而不仅仅是合成，且目前这个差距确实存在。

The downstream click thesis: Even if my sites lose the zero-click battle on broad discovery terms, there’s a second query type I’m explicitly targeting: the downstream comparison query. The sequence: someone types “Notion alternatives” into Google, gets an AI Overview naming four tools, then types “Appflowy vs Anytype performance” to compare the two they’re considering. That second query is post-AI-Overview research. It has commercial intent. It wants a verdict, not another list. For that query, a page with structured attribute comparison, a clear verdict, and fast load time competes directly with another AI-style answer — and structured data beats generative prose for “which one wins on attribute X.” 下游点击论点：即使我的网站在广泛的发现类搜索词上输掉了“零点击”之战，我还有第二个明确瞄准的查询类型：下游对比查询。流程是：某人在 Google 输入“Notion 替代品”，得到一个列出四个工具的 AI 概览，然后输入“Appflowy vs Anytype 性能”来对比他们正在考虑的两个工具。第二个查询属于 AI 概览后的研究，具有商业意图。用户想要的是结论，而不是另一个列表。对于该查询，一个包含结构化属性对比、明确结论且加载速度快的页面，可以直接与另一种 AI 式回答竞争——在“哪个在 X 属性上胜出”的问题上，结构化数据胜过生成式散文。

This is partly why I chose static SSG over dynamic AI rendering for these sites: a fast, indexable page with typed comparison fields is what a second-stage research click needs. 这也是我为这些网站选择静态站点生成（SSG）而非动态 AI 渲染的部分原因：一个带有类型化对比字段、快速且可索引的页面，正是第二阶段研究点击所需要的。

Query type	AI Overview strength	Directory strength
Discovery (“best tools for X”)	High — often answers directly	Low for zero-click intent
Comparison (“X vs Y, which wins”)	Medium — hedges, rarely commits	High — structured attrs + verdict
Filtered browse (“offline + mobile app”)	Low — prose, no filters	High — faceted structured data
Freshness (“is X still maintained?”)	Inconsistent — lags commits	High — weekly ETL refresh

查询类型	AI 概览优势	目录网站优势
发现（“X 的最佳工具”）	高——通常直接回答	低（针对零点击意图）
对比（“X vs Y，谁赢”）	中——模棱两可，很少下结论	高——结构化属性 + 结论
筛选浏览（“离线 + 移动应用”）	低——散文，无筛选	高——分面结构化数据
新鲜度（“X 是否仍在维护？”）	不一致——滞后于提交	高——每周 ETL 刷新

The comparison and filtered-browse rows are the actual load-bearing columns of this bet. 对比和筛选浏览这两行，才是这个赌注中真正的核心支撑。

Why the cost structure matters for intellectual honesty: At $25/month, I can run this experiment for a year without needing revenue to justify continuing. I’m not under pressure to interpret ambiguous signals optimistically. Compare that to a project burning $200/month on infrastructure: you’d rationalize flat Search Console data as “still in the sandbox phase” past the point where the data actually says something. The full cost breakdown is genuinely minimal — Vercel Pro at $20, Turso starter at $0, Claude Haiku API in single-digit dollars for monthly ETL runs, GitHub Actions on free minutes. 为什么成本结构对知识诚实很重要：每月 25 美元的成本，我可以运行这个实验一年，而无需通过收入来证明其合理性。我没有压力去乐观地解读模糊的信号。相比之下，如果一个项目每月在基础设施上消耗 200 美元：你可能会在数据已经说明问题的情况下，仍将平淡的 Search Console 数据合理化为“仍处于沙盒阶段”。完整的成本明细确实非常低——Vercel Pro 20 美元，Turso 免费套餐 0 美元，Claude Haiku API 每月 ETL 运行仅需个位数美元，GitHub Actions 使用免费额度。