Strategy guide
Many teams say they want to block AI, but the real decision is more nuanced. They may want to limit model training while still appearing when users ask AI search tools about their product, documentation, or public articles.
Training control is about whether content should be collected for model improvement or broad downstream datasets. Publishers, course creators, and paid research sites often review this carefully because the content itself is a core asset.
AI search visibility is about whether public pages can be fetched, summarized, or cited when a user asks for information. Blocking every AI-related crawler may reduce discovery even when the team still wants users to find the site.
| Goal | Possible policy | Trade-off |
|---|---|---|
| Maximize citation and discovery | Allow AI search and user-triggered fetchers | More visibility, less control over retrieval contexts. |
| Protect licensed archives | Block training crawlers or specific archive paths | Better content control, but requires maintenance. |
| Reduce bandwidth from broad crawlers | Block selected high-volume crawlers | Can reduce noise but may miss future useful integrations. |
| Keep normal SEO | Do not block Googlebot/Bingbot accidentally | Requires careful separation of crawler groups. |
The best policy is usually not the longest list. It is the clearest expression of what the site owner wants: which public resources should be discoverable, which resources are licensed or sensitive, and which crawlers are being treated differently because their role is different.