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What AI Crawlers Actually Do to a Small Blog: 9 Days of Logs

A small Home Assistant blog received 18,209 AI crawler requests in nine days, accounting for 5.2% of total traffic. The majority came from ChatGPT-User (6,687 requests), which performs live fetches for answer engines rather than training crawlers. The blog's developer found that many AI crawlers do not execute JavaScript, causing structured data injected client-side to be invisible, and built an open-source tool to audit this gap.

read3 min views1 publishedJun 28, 2026

I run a small Home Assistant / self-hosting blog. On a normal day a few dozen humans show up. So when I finally grepped my nginx logs for AI crawlers, the number made me stop: in nine days, AI bots hit the site 18,209 times. On a blog this size, the machines reading me now outnumber the people.

Here's the full breakdown, the things that surprised me, and a few points most "should I block AI bots?" threads get wrong.

Of 348,667 total requests, 18,209 (5.2%) came from AI/LLM user-agents:

Bot Requests What it actually is
ChatGPT-User
6,687 OpenAI — live fetch when someone asks ChatGPT about a page
Bytespider 3,369 ByteDance / TikTok crawler
meta-externalagent 3,274 Meta AI
Amazonbot 1,923 Amazon
OAI-SearchBot 1,211 OpenAI search index
ClaudeBot 850 Anthropic — training / index crawler
PerplexityBot 319 Perplexity
DuckAssistBot 225 DuckDuckGo AI
GPTBot 172 OpenAI training crawler
CCBot 86 Common Crawl (feeds many models)
YouBot 68 You.com

The biggest source by far — ChatGPT-User, 6,687 requests — isn't crawling to train a model. It's a live fetch: someone asked ChatGPT a question, ChatGPT decided my page was relevant, and pulled it in real time to answer them. Same story with Perplexity-User

and the other assistant-side fetchers.

That flips the "should I block it?" math. ChatGPT-User isn't scraping you — it's a real person, through their assistant, reading your page right now. Block it and you don't stop any training; you just stop showing up in answers and lose the visit. (I can see the other end of it in my analytics: real sessions arriving from claude.ai

and gemini.google.com

.)

So the mental model "AI bot = scraper to block" is wrong for a big chunk of the traffic. There are training crawlers (GPTBot, ClaudeBot, CCBot) and there are live answer-engine fetchers (ChatGPT-User, Perplexity-User, DuckAssistBot). Treating them the same is the mistake.

I checked who actually requests /robots.txt

:

The practical upshot: a blanket Disallow

is obeyed by the well-behaved training crawlers and ignored by the user-fetchers — because robots.txt was never meant for them. If your goal is "don't feed training, but keep appearing in answers," the default already does roughly the right thing — but only for the bots that honour it.

This is the one that actually cost me, and the reason I ended up building a little tool around it.

Almost every AI crawler fetches your raw server HTML and does not execute JavaScript. So if your framework injects JSON-LD structured data on the client, or your streaming-SSR setup flushes meta tags into the <body>

instead of the initial <head>

, those signals are invisible to the crawler — even though Google renders them and every SEO browser extension tells you you're fine.

I only found six pages on my own site doing exactly that, because I built a crawler that deliberately parses the JS-less view and diffs it against the hydrated DOM. Googlebot renders; GPTBot and ClaudeBot mostly don't. If you care about being represented correctly in AI answers, your structured data and metadata have to live in the server HTML, not get painted on after hydration.

For a blog with a few dozen human readers a day, AI crawlers are now the single largest non-search audience hitting the server. They aren't going away — so it's worth knowing which ones are reading you, and making sure they can actually see what you publish. The JS-less-view crawler I used is open-source ( seo-geo-audit) — it flags exactly this gap, plus the usual SEO checks, in plain Node with no paid dependencies.

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