Web Scraping Explained: What It Is and How AI Companies Use It

Web scraping is a fundamental data collection technique that involves the automated extraction of content from websites. This process enables organizations to gather large volumes of information for various purposes, from market research to artificial intelligence training.

At its core, scraping is the automated retrieval of online content. This typically occurs on publicly accessible websites, including social media profiles where user data is openly available. However, the practice isn’t limited to public sites—even websites behind paywalls can be subject to scraping operations, though this raises additional legal and ethical considerations.

The significance of web scraping has grown substantially with the rise of artificial intelligence. Most AI companies now rely heavily on Common Crawl, which represents the largest freely available collection of scraped web data. This massive dataset contains more than 9.5 petabytes of information dating back to 2008, serving as a foundational resource for training large language models and other AI systems.

AI developers typically don’t use Common Crawl in its raw form. Instead, they train their models on carefully filtered versions of this data, selecting content that best serves their specific training objectives while attempting to remove problematic material.

The scale of Common Crawl—9.5 petabytes—is difficult to conceptualize but represents an enormous trove of human-generated content spanning over a decade of internet history. This vast collection of text, images, and other data forms the knowledge foundation upon which many modern AI systems are built.

As web scraping continues to play a crucial role in AI development, questions about data ownership, consent, and the ethical use of publicly available information remain at the forefront of discussions in the tech industry.