The triumph of AI tools such as ChatGPT or jasper.ai for content creation is a major challenge for Google. On the one hand, Google has to use its own crawling resources efficiently. This means that Google does not want to crawl all content available online. The same applies to indexing. It makes no sense for Google to include low-quality content in the search index. The more content Google indexes and has to process in the information retrieval process, the more computing power is required.
Google only performs document scoring on the top n romania cell phone number list documents of a thematically relevant document corpus; the rest are not ranked for efficiency reasons. Nevertheless, irrelevant content costs unnecessary storage space, parsing and rendering resources.
EEAT can help Google to evaluate based on entities or domain and author level on a large scale without having to crawl every single piece of content. At this macro level, content can be classified according to the author entity and given more or less crawling budget. Google can also use this method to exclude entire groups of content from indexing.
Google has repeatedly emphasized that quality plays a special role in crawling and indexing (see here and here )
Vector space analyses for relevance and quality determination
Modern search engines are increasingly using vector space analyses to determine relevance and quality. Both individual words and complete phrases and entities can be spanned as vectors in thematic spaces and related to one another.
The Google patent Website Representation Vector to Generate Search Results and Classify Website describes how a search engine can map websites as vectors and evaluate them with quality scores for topics. Only websites whose quality scores reach a certain threshold are considered for ranking for corresponding keywords.
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