Entry #6 · Mar 10, 2026
What follows is an analysis of AI site usage data collected from CloudFront logs for rozz.genymotion.com between March 3 and March 10, 2026. ChatGPT-User is a documented OpenAI bot that fetches web pages during response generation. It is distinct from GPTBot, which indexes content for training, and OAI-SearchBot, which builds the retrieval index. ChatGPT-User shows up at response time when a human uses ChatGPT, not the API. It queries the AI Site in real time. Each ChatGPT-User request is stored in our logs with a timestamp, a URI, and an IP hash. The three pieces of information are used to reconstruct multi-turn ChatGPT sessions. A session model is created. Each session has one or more turns. Each turn corresponds to a fetch event. In a turn, ChatGPT-User pulls a set of pages to respond to something. We then analyze page contents to infer what the user was asking about at that turn.
Session data: March 3–10
| Metric | Value | |---|---| | ChatGPT-User visits | 681 | | Reconstructed sessions | 168 | | Content sessions (non-index) | 127 | | Pages fetched | 587 | | Avg pages per content session | 4.6 | | Multi-turn sessions | 38 (30%) | | Unique questions identified | 109 |
The session reconstruction is imperfect. We use heuristics to infer behaviors. The patterns that emerge are coherent enough to be meaningful. In previous weeks you might have noticed the number of ChatGPT-User visits has gone down. That change is due to refining the counting method.
The signal: ChatGPT-User in CloudFront logs
ChatGPT-User is a documented OpenAI bot that fetches web pages during response generation. It is distinct from GPTBot, which indexes content for training, and OAI-SearchBot, which builds the retrieval index. ChatGPT-User shows up at response time when a human uses ChatGPT, not the API. It queries the AI Site in real time.
Because the AI Site is hosted on CloudFront, every ChatGPT-User request is stored in our logs with a timestamp, a URI, and an IP hash. We used those three pieces of information to reconstruct multi-turn ChatGPT sessions. The result is a session model. Each session has one or more turns. Each turn corresponds to a fetch event in which ChatGPT-User pulls a set of pages to respond to something. We then analyze page contents to infer what the user was asking about at that turn.
Finding 1: ChatGPT-User fetches multiple pages per turn
A naive model of AI retrieval assumes one query maps to one fetch: the bot finds the best page, reads it, answers. The log data doesn’t support that. In the turns we can reconstruct, ChatGPT-User on average pulls 4.6 pages in a tight burst before moving on.
Pricing questions generated the most fragmented fetch patterns. Across the week, four near-duplicate Q&A pages covering pricing were fetched a combined 47 times:
| Page | Visits | |---|---| | what-pricing-plans-are-available-for-genymotion | 18 | | what-are-genymotion-s-pricing-options | 15 | | what-are-the-pricing-options-for-genymotion | 7 | | what-are-the-costs-for-using-genymotion-saas | 7 |
The same pattern appeared for macOS compatibility (3 pages, 38 combined visits) and Google Play installation (4+ pages, 30+ combined visits).
One interpretation: ChatGPT-User is verifying and consolidating across sources, not just reading the first relevant result. If that is right, it justifies an important aspect of our AI site design: having multiple facets of a topic covered by distinct Q&A pages, due to variations in the way users ask questions in the chatbot. This isn’t redundant information. It may be exactly what the bot is looking for—providing either complementary information or information validation.
Finding 2: 28% of sessions hit the index
40 of 168 sessions fetched only /index.html and stopped. No Q&A pages, no content pages, no further navigation. The previous index was an infrastructure page that listed API endpoints, content counts, and navigation links. ChatGPT-User doesn’t use JSON APIs: it GETs HTML pages. When it arrived at that index, without enough signal to decide which page to fetch next, the session ends on our end: no further turns or page queries are visible, as if the bot had moved on.
So we adapted our index page to open with a product description and a topic directory with inline descriptive summaries that would give the bot enough context to proceed. We’ll check over the next weeks if this improves bot performance on the AI Site.
Finding 3: 30% of sessions are multi-turn
Nearly 1/3 of sessions involved a second or third fetch cluster that can be attributed to the same session. We found that we could follow a meaningful conversation… that happened in ChatGPT, not in our own chatbot.
| Date | Turns | Pages | Duration | Fetch pattern | |---|---|---|---|---| | Mar 3 | 4 | 11 | 248s | Web emulator → installation → pricing → user guide | | Mar 4 | 3 | 21 | 95s | macOS compatibility → pricing → Play Store setup | | Mar 6 | 4 | 6 | 637s | SaaS templates → template count → index → user guide | | Mar 7 | 2 | 9 | 1,377s | Linux install + KVM errors → ARM transition |
The March 4 session fetched 21 pages across 3 turns in 95 seconds. The March 7 session had a 23-minute gap between turns. We can almost see the user shifting away, trying something, and returning with a follow-up question.
Multi-turn sessions reveal something that citation tracking cannot: the sequential nature of AI-mediated discovery. The fetch patterns show natural progressions: from compatibility to pricing to setup, or troubleshooting to related technical questions. If we get these right, what are the chances that the AI will recommend us?
Get This for Your Site
ROZZ builds this infrastructure automatically. AI site. Q&A pages from your chatbot. Schema.org markup on every page. Session analytics derived from weekly log analysis.
$997/month | 168 sessions reconstructed in one week
Data source
CloudFront access logs for rozz.genymotion.com, March 3–10, 2026. Session reconstruction is based on IP hash grouping and timing heuristics. Bot classification is based on User-Agent strings.
Author
Adrien Schmidt, CEO, ROZZ
Bio: Adrien Schmidt is a serial tech entrepreneur with 10+ years of experience building AI systems including Aristotle (conversational AI analytics) and products for eBay and Cartier. He previously founded Squid Solutions and built AI products like Aristotle, the conversational big data analytics chatbot, and an AR jewelry try-on device for Cartier.
March 10, 2026 | Data period: March 3–10, 2026
rozz@rozz.site
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