Definition
AI crawler controls are the mechanisms a site uses to allow or block the automated crawlers that AI companies run to collect web content for training models and for generating live answers. These are separate from the traditional search crawler: a site can be fully open to search indexing while choosing whether to permit AI training crawlers, AI answer/retrieval crawlers, or both.
Control is exercised mainly through robots.txt directives targeting the named AI user agents, and sometimes through server rules or emerging preference signals.
The decision is a trade-off with no universal right answer: blocking AI training crawlers protects content from being absorbed into models without compensation, but blocking answer/retrieval crawlers can also remove the site from AI-generated answers where being cited has visibility value. Sites make different choices, and the norms and tools are still evolving.
In context
For an iGaming affiliate, the AI crawler decision comes down to weighing content protection against answer-visibility. The site's substantive assets — original operator testing, first-hand payout data, a large hand-written glossary, proprietary research — represent real investment, and there is a case for not letting AI training crawlers ingest all of it freely.
At the same time, blocking the answer/retrieval crawlers can mean the site is not cited when an AI answer covers a query it would otherwise appear for, losing brand visibility.
A common pragmatic approach is to distinguish the two where the crawlers and directives allow: permit answer/retrieval crawling so the site can be cited in AI answers, while blocking or limiting bulk training crawlers on the most valuable original content. Whatever the choice, it should be implemented cleanly in robots.txt with the correct named user agents, kept updated as new AI crawlers appear, and monitored (server logs show which AI agents are hitting the site and how much).
It should not conflict with the normal search crawler, which must stay allowed. For affiliate-facing content, the framing is that AI crawler control is a separate decision from search indexing, that it trades content protection against AI-answer visibility, that the practical middle path is often to allow retrieval/citation crawlers while restricting bulk training crawlers, and that the implementation should be precise and actively maintained as the crawler landscape changes.
Worked example
An affiliate updates robots.txt to allow the normal search crawler and the AI answer/retrieval crawlers (so its glossary can be cited in AI answers) while blocking the named bulk AI training crawlers from its original testing data and research sections. It monitors server logs for new AI user agents and updates the rules as the landscape changes.
Related terms
Frequently asked questions
Browse the full iGaming & affiliate glossary — hundreds of EN/RU terms with examples.
← Back to glossary