What is an AI Crawler? How Do AI Crawlers Work?

Web isn’t traversed only by humans—bots also roam around. In addition to search engine bots, AI-powered crawlers have recently started visiting websites to gather content. So, what is an AI crawler, what does it do, and what role does it play in the web ecosystem? Below, we discuss these topics under various headings.

What is an AI Crawler?

An AI crawler is a specialized web crawler used by artificial intelligence systems. These bots systematically browse pages on the internet, collect content, and use this data to train an AI model or feed an AI application. Essentially, their operation is similar to traditional search engine crawlers: they follow links and download pages. However, there are significant differences in their objectives and some technical capabilities. Traditional web crawling is aimed at indexing sites and providing search results. Classic crawlers like Googlebot or Bingbot explore web content to build search indices. AI crawlers, on the other hand, primarily collect content as training data for an AI model or to supply information to an AI chat engine. For example, OpenAI’s GPTBot gathers text data from the web directly to improve language models like ChatGPT. This way, the knowledge base on which ChatGPT is trained can be kept current and extensive. Similarly, Anthropic ClaudeBot processes and collects web data for the Claude conversational AI.

How Do AI Crawlers Differ from Traditional Bots?

AI crawlers differ from traditional web crawler bots (for example, Googlebot, Bingbot) mainly in terms of their objectives, content processing methods, and technical capacities. Traditional bots support the indexing processes of search engines by discovering, processing, and displaying web pages in search results. AI crawlers, however, mostly work to train AI models or provide information to AI-powered search systems. Technically speaking, while traditional crawlers like Googlebot can fully render pages, most AI bots (such as GPTBot, ClaudeBot, etc.) do not execute JavaScript and only process raw HTML data. This means that AI crawlers may not interpret dynamic content generated via client-side rendering. AI crawlers generally download pages to create large datasets for model training. In contrast, traditional bots analyze the freshness, link structure, and user search intent of web pages for indexing. AI crawlers can also perform real-time crawling based on specific queries. For instance, AI-based search engines like PerplexityBot can fetch and process relevant content immediately when a user asks a question. Traditional bots, however, continuously crawl and collect content for indexing. These differences also affect the impact of AI crawlers on SEO and web optimization. AI crawlers process content with more contextual analysis, whereas traditional bots focus on search engine rankings. As a result, a new optimization strategy called GEO (Generative Engine Optimization) has emerged, aiming to improve how the data collected by AI crawlers is used in AI models.

In Short, the Differences Between AI Crawlers and Traditional Bots Are:

Different Objectives:

• Traditional bots work to index web pages.
• AI bots collect data to train AI models or provide information to AI-powered search systems.

Different Content Processing Methods:

• Traditional bots can execute JavaScript.
• AI bots generally only process HTML text and do not render JavaScript.

Different Real-Time Crawling Capabilities:

• AI bots can perform real-time crawling based on user queries (e.g., PerplexityBot).
• Traditional bots perform large-scale crawls at regular intervals.

Different Data Storage and Usage Methods:

• AI bots use the collected content to train language models or enhance AI responses.
• Traditional bots process data to improve page rankings and create search indices.

Different Effects on SEO:

• Traditional bots directly affect the search rankings of websites.
• AI bots enable content to be used as a source in AI responses by analyzing it.

Different Approaches to Link Discovery and Site Navigation:

• Traditional bots use sitemaps to explore the link structure.
• AI bots typically focus directly on fetching the content of pages.

Different Crawling Efficiencies:

• AI bots generally have a higher 404 error rate.
• Traditional bots like Googlebot use optimized URL crawling strategies.

Different Content Prioritization:

• Traditional bots crawl sites systematically according to their crawl budget.
• AI bots may assign different priorities to types of content such as HTML, images, and text.

Different Uses:

• Traditional bots focus on optimizing search engine results.
• AI bots gather data for training AI models, content summarization, and powering AI-supported search engines.

What Does an AI Crawler Do?

AI-powered crawlers have many use cases. Their most fundamental function is to collect data at a large scale, but how that data is used can serve different scenarios.

Main Functions of AI Crawlers Include:

• Collecting Data for Language Model Training
• Supplying Information to Search and Q&A Systems
• Content Analysis and Classification
• Collecting Visual and Multimedia Data
• Content Updating and Model Freshness

Collecting Data for Language Model Training

One of the most important use cases is compiling data from the internet to train massive language models (LLMs). AI crawlers browse hundreds of millions of web pages, storing text and images. For instance, the primary role of GPTBot is to gather up-to-date and diverse text data that generative AI models like ChatGPT can learn from. This data expands the model’s knowledge and improves response quality. Similarly, open-access crawlers like Common Crawl (CCBot) archive a vast portion of the web, creating huge datasets that researchers and AI companies can use.

Supplying Information to Search and Q&A Systems

AI-powered search engines and chatbots need to crawl the web for up-to-date information to answer user queries. While Bing Chat uses Bing’s search index in the background, platforms like Perplexity or You.com use their own bots to crawl the web and provide real-time information. These crawlers find and summarize relevant content for specific queries or directly supply the answer to an AI system. Thus, AI crawlers serve as real-time data aggregators and intermediaries. For example, PerplexityBot reviews websites to feed its AI-supported search results, so when a user searches, the LLM can provide answers based on the freshly crawled information.

Content Analysis and Classification

Beyond merely collecting raw data, AI crawlers can also analyze it. Advanced AI crawling systems can integrate functions such as categorizing page content, extracting meaning from text, performing sentiment analysis, or creating summaries during the crawling process. This way, the collected data is stored in a more organized manner and is ready for use in AI models. For example, the Allen Institute AI2Bot conducts crawls for research purposes, categorizing scientific articles and other content for inclusion in its database. Similarly, some bots collect discussion platform data (e.g., Omgili Bot retrieves forum and comment data).

Collecting Visual and Multimedia Data

AI models are not limited to text; visual content is also becoming increasingly important. Therefore, some AI crawlers focus on gathering images, videos, or audio files. In fact, it has been reported that approximately 35% of the requests made by Anthropic’s ClaudeBot are for visual content. This indicates that AI is also incorporating visual data into its training and that image data is valued for multimodal models. ByteDance’s Bytespider bot, for example, crawls the web for content (likely video metadata or visual information) to feed recommendation algorithms and AI systems for platforms like TikTok.

Content Updating and Model Freshness

AI crawlers can also work to keep an already trained model’s information up-to-date. Large language models are trained on data up to a certain point, but the world is constantly changing. Therefore, companies like OpenAI or Google seek ways to periodically crawl new content to update their models. A crawler such as GPTBot regularly adds new articles, news, and web pages to the training data to ensure the next version is more current. Additionally, a bot like Google-Extended collects data for AI independently of the search index, helping maintain the freshness of models like Google’s Bard/Gemini. In summary, AI crawlers undertake data mining tasks. They fetch text, images, and other content from the web and convert it into a format that AI models can understand. This process is crucial both in the large-scale training of models (for instance, for the next GPT version) and in AI applications that provide instant answers to user queries (e.g., an AI search engine).

The Relationship Between AI Crawlers and Generative Engine Optimization (GEO)

The relationship between Generative Engine Optimization (GEO) and AI crawlers marks a new era in digital content strategies. Unlike traditional SEO, GEO aims to ensure that content is used more prominently and effectively by AI models such as ChatGPT, Claude, or Bard. This strategy enables your site to be integrated into AI training processes, thereby enhancing both brand visibility and the quality of the responses provided to users.

On the other hand, AI crawlers play a critical role in training large language models by collecting billions of data points from across the web. These crawlers ensure that the content on your site is stored and organized correctly, helping to maintain the freshness of AI knowledge. For instance, OpenAI’s GPTBot and Anthropic’s ClaudeBot gather text, images, and other types of content, while bots like Google-Extended can fully render JavaScript content.

To better understand the interaction between these two concepts, consider the following key points:

• Data Collection and Training: AI crawlers continuously gather data from massive archives like Common Crawl and other sources to train language models. With a GEO strategy, if your content is crawled, it is more likely to be referenced in future AI responses.
• Content Accessibility and Structured Data: Ensuring that your content is accessible to bots like GPTBot, ClaudeBot, or Google-Extended—and supporting it with structured data markups (e.g., Schema.org)—helps AI better understand your content.
• Opportunities and Risks:
  • Opportunities:
    • You can enhance your brand’s strength by gaining visibility in AI response engines.
    • Platforms like Bing Chat and Perplexity may display your content as a source in user queries.
  • Risks:
    • If AI crawlers directly serve your site’s content as responses, you may experience a decline in visitor traffic.
    • Some major sites choose to block bots like GPTBot via robots.txt to prevent unauthorized use of their content.

From a technical standpoint, several key points should be considered when creating a GEO-compliant content strategy. Allowing access for the relevant bots in your site’s robots.txt file ensures that your content can be included in AI training processes. Moreover, using SSR or SSG methods to create static content rather than relying on JavaScript-loaded content helps AI bots to view your data more smoothly. Adding alternative text for images and using clear, understandable language contribute to making your content more effective on both platforms.

In conclusion, GEO is a new optimization method that enables content to be used more effectively in both traditional search engines and AI response systems. By optimizing your content with an understanding of how AI crawlers operate, you can prevent traffic loss and take advantage of the benefits of AI-supported responses in the future.

Current List of AI Crawlers

AI-powered web crawlers allow for the collection of content to train and update language models. These crawlers, developed by major tech companies such as OpenAI, Anthropic, Google, Apple, ByteDance, and various other organizations, crawl text, images, and document content on the web, paving the way for users to receive more accurate and up-to-date responses from AI assistants. Typically, these bots adhere to robots.txt directives to collect only the content that websites allow, giving webmasters control over their access. These emerging AI crawler technologies add a new dimension to the digital experience for both content creators and users.

Currently Recognized AI Crawlers:

• OpenAI GPTBot: The primary crawler that collects text-based content from the web for models like ChatGPT and similar systems.
• Anthropic ClaudeBot: Crawls text, documents, and images to supply information for the Claude AI assistant.
• Google Gemini Crawler (Google-Extended): A specialized crawler capable of rendering pages (including JavaScript) for Google’s Gemini models.
• AppleBot (and AppleBot-Extended): A crawler that navigates sites for Siri, Spotlight, and potential future Apple AI projects, featuring a Safari-like rendering system.
• PerplexityBot: Crawls web data for Perplexity.ai’s response engine, directly citing sources in its answers.
• ByteDance ByteSpider: Crawls the web to support content discovery and search features for platforms like TikTok and other ByteDance services.
• Other Active AI Crawlers: These include Amazonbot, YouBot (by You.com), DuckAssistBot (by DuckDuckGo), AI2Bot (Allen Institute), Cohere, and various bots by OpenAI designed for fetching text data, as well as nonprofit Common Crawl (CCBot) and Cloudflare’s identification of well-intentioned AI bots.

Conclusion

AI-powered crawlers are increasingly influential in the web ecosystem. On one hand, AI crawlers act as the unseen workforce behind massive language models, compiling the vast knowledge of the internet; on the other, they are reshaping our search habits and redefining content strategies. Understanding the technical details (such as JavaScript limitations) and knowing the objectives of these bots is critical for both website owners and digital marketers in determining their GEO and SEO strategies. By making your site compatible with both search engines and AI agents, you can maintain your digital visibility and reach users through both channels. Remember, the internet is evolving, and the new consumers of content might be AI. Taking precautions and adapting now is key to staying competitive in the future.