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 — , and . 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.
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.
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." 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.
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.
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.
Editorial negative-space. My game recommender for these sites: a fast, indexable page with typed comparison fields is what a second-stage research click needs.
| 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 |
The comparison and filtered-browse rows are the actual load-bearing columns of this bet.
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 of the sites. I've moved to custom domains and covers the reasoning.
How does Claude Haiku generate the structured editorial fields?
Each ETL run sends entries through a shared Claude Haiku client that uses system-prompt caching to amortize the cost across batch runs. The prompts are tuned to force specific attribute outputs — avoid-if caveats, audience fit, freshness status — not open-ended descriptions.
What if one site works and two don't?
That's a useful outcome, not a failure. The format that works tells me something specific about the intent type. I'll invest in what works and document what didn't.
Where will you publish the October 2026 verdict?
On this blog, with raw Search Console screenshots. I'll publish regardless of whether the numbers are favorable.
Part of an ongoing 6-month experiment running three AI-curated directory sites. The technical claims here are real; this article was AI-assisted.
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