Why scraping isn't enough for AI agents and how DNS-verified profiles could help (github.com)

A new position paper proposes a revolutionary publishing model called "Skin," aimed at optimizing how information is made accessible for AI agents. Traditionally, web content is structured primarily for human navigation, requiring extensive organization by publishers to ensure that users can find and understand the information. However, as AI agents become capable of interpreting semantic information, the paper argues that publishers should directly provide structured semantic data without the need to build user-facing websites. This paradigm shift, which focuses on atomic semantic facts rather than navigation-heavy prose, could streamline the way AI agents retrieve and interpret information, enhancing their responsiveness and accuracy. The significance of this model lies in its potential to reshape content creation and management in the AI/ML landscape. Skin profiles would encompass essential data controlled by verified publishers, allowing AI agents to bypass traditional web interfaces and access up-to-date information in real time. Key features of the proposal include the ability for publishers to maintain their information easily, support for multilingual outputs without the intricacies of translation, and a shared schema for interoperability among various AI models. This approach not only promotes efficiency but also ensures that AI agents utilize data that is contextually rich and directly relevant, setting a new standard for how information is disseminated and consumed in the age of intelligent automation.