##Unraveling the Chaos: Tracing Google’s Battle Against the Surge of AI-Generated Content
Gone are the days when generating content for Google’s digital platform was as simple as penning down informative articles. The advent of mass-produced, AI generated content has made it difficult for Google to successfully detect spam. Additionally, discerning high-quality content from low-quality works has become complex for the tech giant. Fortunately, advancements are being made to enhance its capability to identify inferior AI-generated content, a fact that has resulted in an influx of AI-created, subpar content flooding the internet.
For anyone outside the SEO circle, AI-generated content gets its spotlight in Google’s search results and has been doing so for the past year. Initially, Google viewed this content as spam and violative of its guidelines. Over time, it shifted its focus to evaluating the quality of content rather than its production method. Conversant marketers took advantage of this change, resulting in a significant rise of low-quality AI content online.
Perhaps the biggest dilemma that Google faces is to monitor and control the ‘visible web,’ the part of the internet that web crawlers choose for indexing and display in search results. Google tries to maintain an index of around 400 billion documents, but trillions of documents are discovered during the crawling process. This means that Google only indexes about 4% of these discovered documents. The pressure is high on Google to protect users from spam by eliminating undesirable content as much as possible.
Google’s complexity in distinguishing between low and high-quality content has sparked debates among SEO professionals and website managers, with numerous examples of inferior content outranking superior ones. Google’s ongoing fight against spam has inspired advancements such as RankBrain and SpamBrain, both of which aim to enhance the quality of search results.
Even so, AI-generated content continues to overrun the internet. To survive this chaos, Google embarked on an ambitious project involving LinkedIn’s ‘collaborative’ AI articles. These articles invited readers to collaborate with the writers, giving birth to high-quality user-generated content. The strategy was a success, leading to an increase in traffic.
The question that remains is whether Google’s reliance on machine learning systems like BERT and MUM– which were lauded for their potential to change Google’s future reliance on user data– could be the solution to closing the gaping hole caused by AI spam. While the future remains uncertain, it’s hoped that Google’s improved systems will cause a severe drop in the generation and promotion of AI spam, thereby allowing quality content to rule the web once again.
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