AI bots explained: What powers platforms like ChatGPT?

Over the past year, we’ve seen clicks and SEO traffic declining while crawler traffic powered by AI platforms has exploded. As AI-driven search becomes our new norm, understanding these new crawlers isn’t just helpful, it’s necessary. The key to understanding and tracking visibility in AI search is log analysis.

Log analysis has long been a cornerstone of technical SEO, helping SEOs track and understand Googlebot’s behavior and ultimately its impact on indexation and rankings. For AI bots, the method is the same: the same server logs, the same analysis techniques, and often the same tools.

But the story those logs tell is fundamentally different. AI platforms rely on several types of crawlers, each built for a different purpose than traditional search engine crawling. These AI bots work together to gather, interpret, and transform web content into the data pipelines that power modern AI models and interact directly in the grounding process when AI chatbots use web search tools.

AI bot log analysis: Same method, different story

Understanding what you are looking for changes the way you read your logs. When you’re tracking several bots with different behaviors and objectives, you’re no longer looking for the same things. Instead of monitoring crawl efficiency or indexation patterns, you’re now trying to decipher how multiple AI systems interact with your content and what will keep them coming back.

In this article, we’re going to look at the different kinds of AI bots, how they differ, what they do, and what you can actually learn from their activity in your logs.

Three categories of AI bots

Not all AI bots are created equal. While they all visit your website, each type has a distinct purpose. Certain bots are meant to gather data for the next generation of AI models, others are used to build search indexes, and some work in real-time to answer user’s queries.

Understanding which bot is doing what changes how you interpret your log data. AI bots fall into three main families of crawlers, which are used by platforms like ChatGPT. These are the same categories you’ll see in Oncrawl’s AI bots dashboard:

AI training bots

Crawl purpose

AI training bots crawl your website to scrape content that may be used in LLM training. Their activity reflects how AI systems gather data from your site for future model development.

These were the original AI crawlers, conceived when companies needed to collect massive amounts of content to train their models. Most collect data for their own AI platforms, but some, like CCBot from Common Crawl, act as data providers to multiple AI companies.

Crawl behavior

At first glance, AI training bots appear to work like traditional web spiders, following links and crawling entire domains. However, when you take a closer look at the data, you see that their behavior is far less predictable than traditional crawlers like Googlebot.

The main differences when compared to Googlebot:

• They don’t have a crawl budget concept
• They don’t handle JavaScript rendering

Impact on AI search

The impact on AI search is minimal in the long run. Having your content crawled by an AI training bot doesn’t guarantee it will be used to train the LLM. In actuality, most crawled content never makes it into model training due to the extensive data cleaning pipelines. Here’s what happens between crawling and training:

Key takeaways

You cannot correlate AI training bot activity with model knowledge updates, which limits the actionable insights you can draw from this bot category.

However, tracking these bots still provides value for long-term strategic questions:

• Can they successfully crawl your website and access your content?
• What content and resources are they targeting?
• What’s the cost (on your server or resources) of allowing them to crawl your website?

AI search bots

Crawl purpose

AI search bots crawl your website asynchronously for indexing and improving search results. Their activity is closer to classic SEO signals like Googlebot hits.

When AI platforms like Perplexity, and later ChatGPT, introduced search and grounding processes on top of their LLMs, they needed crawlers dedicated to search and indexing, just like Google uses Googlebot.

For platforms building proprietary indexes, like Perplexity or Ibou, these bots function exactly like Googlebot: they crawl the web to build and maintain an index of web pages.

For platforms using third-party search engines in their grounding process, like ChatGPT does, the role is less obvious. According to OpenAI’s official documentation, ChatGPT’s SearchBot (OAI-SearchBot) “is used to link to and surface websites in search results in ChatGPT’s search features.”

Crawl behavior

Unlike AI training bots, AI search bots show clear crawl patterns and take a more strategic approach to crawling websites. They don’t crawl as extensively as Googlebot and it’s still unclear exactly how they explore and discover web pages.

Impact on AI search

These bots are essential to the grounding process and directly impact AI platforms’ ability to surface your website in responses when users activate the search feature.

If you block these bots, your chances of being featured in results drop significantly. However, the grounding process may still access your pages through third-party search engine indexes, even if the AI search bot itself is blocked.

Key takeaways

AI search bots provide similar insights to Googlebot log analysis. You’ll be able to identify:

• How much of your website is crawled (coverage ratio)
• Page types that are crawled most frequently or ignored by these bots
• Pages with the highest crawl frequency (a qualitative signal of importance)
• Issues preventing access to pages:
  • Status codes
  • Server response times

Increasing AI search bot traffic is a positive signal of interest in your content from AI platforms. Conversely, decreasing AI search bot traffic is a warning sign that requires deeper investigation.

AI user bots

Crawl purpose

AI user bots crawl your website in real-time to find the answer to a user prompt. Their activity is a direct signal of visibility in AI interfaces and serves as a proxy for impressions.

AI user bots operate on behalf of users to scrape the content needed at the final stage of the grounding process:

Prompt → Query fan-out → Search results → Content scraping → Response → Click

Crawl behavior

AI user bots don’t crawl in the traditional sense. They fetch a single page on demand without following links, though they do follow redirects.

ChatGPT-User weekly crawl behavior

If you focus on ChatGPT-User, you can spot a week-by-week pattern that follows typical SEO traffic trends, with noticeable drops during weekends.

ChatGPT-User daily crawl behavior

If you zoom into daily patterns, you’ll see traffic drops during nighttime hours, much like traditional SEO traffic.

Impact on AI search

AI user bots are the most valuable bots to track for measuring and understanding AI search. They open the AI black box and help you measure your website’s visibility in the zero-click era.

Visits from AI user bots serve two critical purposes:

• Measure and report your efforts: Track visibility as a KPI over time
• Explore and find insights to improve: Identify what’s working and what isn’t

Key takeaways

AI user bot visits can be used as an AI search visibility metric: a proxy for impressions or prompt volume.

Using bot hits from AI user bots, you’ll gain insights into:

• Which pages AI platforms fetched to answer real user questions
• How many different pages are being accessed and how often
• Which pages are fetched by AI platforms versus those never accessed

You can track this over time, just like you track traffic, and use it as a KPI to measure your AI search optimization results.

Conclusion

Reliable AI search data remains one of the biggest challenges facing SEO professionals today. Log analysis offers a solution, providing the most valuable field data currently available for understanding AI platform behavior.

Tracking AI bots through your logs requires understanding their different purposes because each tells a different story. The most important bots to monitor are the two involved in the search and grounding process: AI user bots and AI search bots. Both provide ways to report results and uncover actionable insights.