What was the methodology?
Thirty days of access logs from the ChinaGEO site network, filtered by verified AI user agents: OpenAI’s trio (GPTBot for training, OAI-SearchBot for search indexing, ChatGPT-User for live fetches), PerplexityBot, ClaudeBot, and Googlebot’s AI-relevant fetch patterns tracked separately from classic crawling. We counted unique URLs fetched, refetch intervals, status codes, and content-type targets, then cross-referenced fetch timing against our publishing calendar. One site network, one month — directionally useful, not industry statistics. Run the same analysis on your own logs before believing any of it; the tooling guide is in AI crawler logs.
What patterns showed up?
1. Search crawlers and live fetchers behave like different species
OAI-SearchBot crawled methodically — sitemap-shaped, steady cadence, deep into archives. ChatGPT-User fetches were bursty and human-shaped: a question happens somewhere, a fetch follows, and the targets cluster on pages that directly answer real questions. The practical read: indexing gets you into the candidate pool, but answer-first pages are what live fetchers actually visit.
2. PerplexityBot rewards freshness visibly
Consistent with Perplexity’s real-time, sentence-level citation model, its fetches concentrated on recently updated URLs. Pages touched within days saw repeat visits; stale sections faded from the fetch pattern. If Perplexity matters in your category, your update calendar is a crawl-budget policy whether you meant it to be or not.
3. Training crawlers are broad but shallow in consequence
GPTBot and ClaudeBot ranged widely, including sections we would never optimize. Their presence in logs tells you almost nothing about near-term visibility — training crawls feed future models, not today’s answers. The mistake we see teams make is treating a GPTBot spike as a visibility signal; it is not one, in either direction.
4. Errors cluster where nobody looks
The most useful finding was boring: a meaningful share of AI fetch attempts hit URLs returning redirects or error states — legacy paths, parameter variants, a misconfigured trailing-slash rule. Classic SEO tooling had deprioritized these because Googlebot handled them gracefully; live fetchers with latency budgets did not. A one-hour cleanup of redirect chains was the highest-value fix the month produced.
5. Publishing events echo for days
New and substantially updated pages attracted fetch interest across agents within hours (helped by IndexNow), with a visible second wave several days later. The echo pattern suggests staged re-verification rather than a single pass — another argument for keeping dates consistent, because a page that contradicts itself across meta, schema and visible dates invites exactly the re-checks that can drop it.
What should you do with your own logs?
- Segment AI user agents into a standing report — weekly is enough.
- Track status codes per agent; fix redirect chains and errors on answer-bearing paths first.
- Overlay the publishing calendar and learn your own fetch-echo timing.
- Correlate fetch patterns with your monthly question-set scores; movement in one often precedes movement in the other.
One caution on tooling: user-agent strings are trivially spoofed, so where an operator publishes verification guidance — official IP ranges or reverse-DNS rules — verify declared AI bots before drawing any conclusion from their behavior.
What can we not conclude?
Plenty. One month and one network cannot support claims about crawl-budget algorithms, engine ranking internals, or universal fetch frequencies — and we make none. The patterns above are hypotheses with a methodology attached. The broader point stands regardless: AI crawlers are already a measurable distribution layer in your own logs, the segmentation costs an afternoon, and most organizations have never once looked. That asymmetry — cheap to observe, expensive to ignore — is the actual finding. For the measurement layer that pairs with log analysis, see the measurement standard.