81.8% of My ‘AI Assistant’ Traffic Was Fake. the Googlebot Number Was Worse

I launched CitationIQ.com recently. Over the last two weeks, my logs claimed 33 AI assistants visited, a little better than two a day.

Of 799 requests carrying its name, only 107 were real, though we all know scammers love to spoof Googlebot. And some of those fake AI visits, while wearing ChatGPT's name, asked my server to hand over its secrets file.

The Thing Nobody Checks

When a bot fetches your page, it announces a name. ChatGPT User. Claude User. Googlebot. CCBot, or whoever they say they are. Your server writes that name into the log, your analytics counts it, and you draw conclusions from it. The name. The measurement question is whether this signal changes a decision, not whether it adds another number to a dashboard. Useful reporting connects visibility, engagement, and business outcomes without pretending every AI influenced journey will produce a clean click path.

The reporting question is whether this signal changes a decision. If it only creates another number in a dashboard, it adds noise. If it helps separate profile activity, website visits, calls, bookings, and direction requests, it can make local performance easier to understand.

The Demand Gap

Start with the demand signal, the requests that come not from a scheduled crawl but from an assistant fetching my page live during a real user's session. That is what these agent names mark: a fetch triggered in real time by someone using. The strategic issue is whether automated visitors can understand, trust, and complete the same journey a human visitor can. Agent readiness is partly technical, but it is also about clear tasks, accessible flows, and reliable evidence.

The risk is usually hidden in the execution layer. A page can look fine to a human and still fail for an automated visitor if the form, call to action, rendering path, or confirmation step is not accessible enough for the agent to complete the task.

The Bigger Number, Which Is Not News

Of 799 requests carrying the Googlebot name, only 107 came from a verified Google address. The other 692, roughly 87%, were not Google. This is not a discovery. Googlebot has been the most impersonated name on the web for the better part. The practical read is that brand signals need to be consistent enough for both people and AI systems to form a stable view of the company, its expertise, and its trust signals.

Two Different Games

First, a clarification, because the numbers are about to get bigger. Everything so far counted demand: Live fetches an assistant makes during a real conversation, the agents whose names end in -User. What follows is a separate population,. The strategic issue is whether automated visitors can understand, trust, and complete the same journey a human visitor can. Agent readiness is partly technical, but it is also about clear tasks, accessible flows, and reliable evidence.

The One I Had To Chase: CCBot

Which brings me to what might be the most consequential training crawler of all, and the best illustration of why that unverifiable column exists. Common Crawl, fetched by CCBot, produces the open dataset that sits underneath a large share. The measurement question is whether this signal changes a decision, not whether it adds another number to a dashboard. Useful reporting connects visibility, engagement, and business outcomes without pretending every AI influenced journey will produce a clean click path.

The One I Could Not Measure: Gemini

There is one major player I could not measure at all, and the reason is the point. Gemini. OpenAI, Anthropic, and Perplexity each expose distinct, verifiable signals. You can separate their training crawler from their retrieval crawler. The measurement question is whether this signal changes a decision, not whether it adds another number to a dashboard. Useful reporting connects visibility, engagement, and business outcomes without pretending every AI influenced journey will produce a clean click path.

The useful check is whether this improves the system behind search performance, not only the words on the page. Internal links, crawlable content, clear entities, current evidence, and a sensible page structure all help the recommendation become easier to trust.

2 Honest Asterisks

Perplexity is murkier than a clean pass or fail. Its crawler failed my IP check on 24 of 36 requests, but Perplexity has been documented fetching from addresses outside its own published ranges, so some failures may be impersonators, and. The strategic issue is whether automated visitors can understand, trust, and complete the same journey a human visitor can. Agent readiness is partly technical, but it is also about clear tasks, accessible flows, and reliable evidence.

The practical value is in connecting the idea to an observable signal. That means deciding what should be checked, what would prove the issue is real, and where the team should make the smallest useful improvement first.

Go Make Your Own Baseline

Do not take my numbers; take the method. My data is thin because my site is new, and yours probably is not. If you have any real traffic, you are sitting on a far better dataset than mine, in your own access logs, right now, and you can. The search implication is whether the section improves the evidence around the page, not simply whether it adds more wording. Clear entities, crawlable structure, internal links, and useful context are what make the topic easier to evaluate.

What You Are Measuring, And What You Are Not

Think about what even a verified number does, and does not, tell you. A confirmed crawl tells you a real bot took your content. It does not tell you what happened next: whether your page ended up in the answer a person saw, whether you. The strategic issue is whether automated visitors can understand, trust, and complete the same journey a human visitor can. Agent readiness is partly technical, but it is also about clear tasks, accessible flows, and reliable evidence.

The Thing Nobody Checks in practice

Introduction I launched CitationIQ.com recently. Over the last two weeks, my logs claimed 33 AI assistants visited, a little better than two a day. That number is a lie. The real number? Six. Googlebot looked worse. Of 799 requests. The practical read is that brand signals need to be consistent enough for both people and AI systems to form a stable view of the company, its expertise, and its trust signals. The same pattern also shows up in AI Agents Read Your Site & It’s Breaking, where the practical question is how the signal becomes visible.

What the visibility signal actually changes

What the visibility signal actually changes: 81.8% of My ‘AI Assistant’ Traffic Was Fake. the Googlebot Number Was Worse: the Practical Angle should be treated as a visibility signal, not a standalone headline. Introduction I launched CitationIQ.com recently. Over the last two weeks, my logs claimed 33 AI assistants visited, a little better than two a day. That number is a lie. The real number? Six. Googlebot looked worse. Of 799 requests carrying its name, only 107. This connects with Questions That Reveal Your Real Search Performance when the same signal needs a clearer operating decision. A useful companion note is Is Google Fixing B2B Marketing?, because it looks at a nearby part of the same system.

What the visibility signal actually changes: the practical question is whether the page, brand evidence, and surrounding content make the answer easier to trust. If that support is weak, search systems can still understand the topic but fail to connect it confidently to the brand.

What the visibility signal actually changes: that is why the response should begin with an audit of the evidence already on the site before creating a new asset. The fastest improvement is often a clearer page, a better internal link, or a stronger explanation of why the brand belongs in the answer.