AI-enabled content networks are facing retention issues due to the low quality of content produced through easy creation processes. Early-stage AI 1.0 applications facilitate direct user interactions with AI but lack network effects, resulting in high competition and low user engagement. In contrast, AI 1.5 applications attempt to harness network effects by allowing users to generate and share content with others. However, the current model experiences a retention problem because the abundance of low-quality content produced diminishes user interest.
To address this issue, the focus should shift from making content creation easier to introducing constraints that enhance creative friction. This approach encourages thoughtful content creation among motivated users, resulting in higher-quality outputs that better engage consumers. By fostering a more meaningful user experience, these networks can create the conditions for robust network effects, ultimately improving retention and moving closer to a successful AI 2.0 ecosystem.
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