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Generative AI is set to颠覆传统的90-9-1内容创作模式,大幅增加用户生成的内容量,加速迈入“内容过剩”时代,挑战现有的在线互动和信息消费格局。
The internet has long been governed by the 90-9-1 rule, where only 1% of users actively create content, 9% contribute occasionally, and a staggering 90% simply observe. This pattern, while seemingly modest, was sufficient to fuel what Michael Schaefer termed the "content shock" phenomenon in January 2014. He predicted that by 2020, the volume of content produced would far outstrip our capacity to consume it. However, Schaefer did not account for the exponential impact of generative AI, which is poised to escalate this content shock to unprecedented levels.
The advent of generative AI is transforming the landscape of user-generated content (UGC) in ways that were once unimaginable. With the ability to create content at an unprecedented speed and scale, individuals are becoming prolific creators almost overnight. This shift has significant implications for both consumers and businesses, as it alters how we consume information and how brands engage with their audiences.
Traditionally, the 90-9-1 rule dictated that a small fraction of users were responsible for the bulk of content creation. This dynamic was sufficient to drive significant online engagement and community building. However, generative AI is disrupting this equilibrium by enabling anyone with access to these tools to become a content creator. The speed at which AI can generate content-what once took hours of human effort can now be produced in seconds-is revolutionizing the digital landscape.
For instance, consider the rapid evolution of AI-generated images. In just one year, the quality and complexity of AI-generated visuals have improved dramatically. This was highlighted by designer Etienne Mineur at the Kikk Festival in Namur, where he showcased the progression of AI-generated images over a 12-month period (captured by Benoit Zante).
The transformation is not limited to visual content; it extends to all forms of user-generated content. Comments, a cornerstone of UGC, serve as a perfect illustration. These initial steps in online self-expression often evolve into more substantial creative endeavors. Brands are increasingly leveraging comment sections for their marketing strategies, with platforms rapidly evolving to support these new use cases.

For example, Instagram is experimenting with integrated polls in comments, allowing users to engage more interactively with content. Similarly, YouTube is testing options that enable Shorts creators to create response videos to comments on other people’s channels. These features are designed to foster greater engagement and interaction, further blurring the lines between content creators and consumers.
While the potential of AI-driven UGC is immense, it also presents significant challenges. The sheer volume of content can overwhelm users, leading to information overload and decreased attention spans. Moreover, the quality and authenticity of AI-generated content can be questionable, raising concerns about misinformation and the erosion of trust in digital media.
For businesses, the rise of AI-driven UGC offers a unique opportunity to engage with audiences in more dynamic and personalized ways. By leveraging these tools, brands can create content that resonates more deeply with their target audience, fostering stronger connections and driving greater engagement. However, it is crucial for companies to navigate this new landscape thoughtfully, ensuring that the content they produce is both valuable and trustworthy.
The 90-9-1 rule is being fundamentally altered by generative AI, ushering in a new era of user-generated content. As the lines between creators and consumers continue to blur, businesses must adapt to this evolving landscape to remain relevant and effective in their digital strategies. The opportunities are vast, but so too are the challenges. Navigating this new world will require a balanced approach that leverages the power of AI while maintaining a focus on quality and authenticity.
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↗ https://maried.substack.com/p/sifting-through-the-noise?utm_source=tldrai
About the author
Marcus began tracking AI's market implications in 2016, noticing AI-related patent filings accelerating ahead of earnings upgrades before most of the sell-side had caught on. A former fixed-income quantitative analyst, he spent two decades building models that priced risk across emerging markets before pivoting to cover the economic impact of AI full-time. His writing translates opaque technical developments into clear risk/reward terms — and he's rarely diplomatic about the gap between AI valuations and underlying fundamentals. He believes most market participants still underestimate AI's long-run deflationary effect on knowledge work.
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3 November 2023
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