Core Insights - The article discusses the decline of the internet advertising model and explores the potential of combining stablecoins with AI agents to address content monetization challenges [1][2][6]. Group 1: Advertising Model Decline - The internet's reliance on advertising as a default business model has led to negative consequences, including the commodification of user attention and the proliferation of low-quality content [2][6]. - The traditional advertising-supported internet is facing a bleak future, particularly for text-based websites, as the quality of ad-supported content has deteriorated [6][11]. - The shift towards subscription models is seen as a potential solution, emphasizing the need for clear value delivery to subscribers rather than relying on ad revenue [5][9]. Group 2: Microsoft and the Agentic Web - Microsoft’s vision for an "open Agentic Web" aims to enable AI agents to act on behalf of users, requiring protocols for reliable and interoperable connections [12][13]. - The introduction of NLWeb as a natural language interface for websites is intended to enhance the accessibility of services for AI agents [13][14]. - The success of the Agentic Web concept hinges on the integration of native payment systems, which are currently lacking in the proposed framework [17][23]. Group 3: Stablecoins and Microtransactions - Recent developments in stablecoin legislation in the U.S. signify a potential breakthrough for the crypto industry, which could facilitate microtransactions essential for the new content economy [18][19]. - Stablecoins are positioned to solve existing challenges in microtransactions by reducing fees and enabling seamless transfers, making them suitable for AI-driven content monetization [19][20]. - The article suggests that a new content market could emerge, balancing the interests of content creators, AI services, and users, although the path to this future remains uncertain [21][24].

This article has been reviewed according to Science X's editorial process and policies . Editors have highlighted the following attributes while ensuring the content's credibility: Credit: CC0 Public Domain Microsoft sees artificial intelligence transforming the internet as fundamentally as mobile phones have over the past two decades. But the technology's limitations could curb Microsoft's grand vision. Generative AI—which creates content based on a user's request—burst into the zeitgeist in late 2022 w ...

Core Viewpoint - The article discusses the launch of NLWeb, an open-source project by Microsoft aimed at simplifying the development of natural language interfaces for websites, allowing users to interact with any website using natural language [4][5]. Group 1: NLWeb Overview - NLWeb is designed to transform any website into an intelligent application platform with AI capabilities, enabling natural language interactions [5]. - Microsoft CEO Satya Nadella describes NLWeb as a foundational layer for an AI-driven web, similar to how HTML revolutionized document sharing [5][6]. - The project supports the MCP protocol, allowing both humans and AI agents to use the same natural language API [5]. Group 2: Functionality and Benefits - NLWeb allows for easy integration with various AI models and requires minimal coding, making it cost-effective and user-friendly compared to traditional search methods [6][8]. - It can create conversational interfaces, such as chatbots for retailers or recipe recommendation systems for cooking websites, enhancing user engagement [6][7]. - The system leverages structured data formats like Schema.org and RSS, combined with LLM-driven tools, to provide a richer user experience [10]. Group 3: Technical Aspects - NLWeb operates independently of specific technologies, supporting all major operating systems and allowing developers to choose components that best fit their needs [10]. - The goal is to empower web publishers to create intelligent, natural language experiences on their sites, facilitating interaction and discovery by AI agents [10].

Core Viewpoint - Microsoft Build 2025 conference highlighted the company's strategic focus on AI and open-source technologies, showcasing significant advancements in developer tools and AI integration across its platforms [2][4][5]. Group 1: AI and Developer Tools - Microsoft emphasized AI as a crucial strategic direction, with significant updates to its developer tools, including Visual Studio and GitHub Copilot, which now has over 15 million users [6][10]. - The introduction of a new Coding Agent allows Copilot to evolve from a conversational assistant to a collaborative development partner, enabling developers to assign tasks directly to it [11][13]. - The Coding Agent can autonomously manage tasks such as opening pull requests and analyzing code, enhancing the development workflow [14][15]. Group 2: Microsoft 365 and Customization - Microsoft 365 platform received a comprehensive upgrade, introducing Microsoft 365 Copilot Tuning, which allows enterprises to customize AI agents based on their specific data and workflows [24][26]. - This customization aims to create tailored AI solutions that learn from company-specific communication styles and industry knowledge, streamlining the deployment process [27]. Group 3: AI Infrastructure and Performance - Microsoft is focusing on optimizing AI performance, efficiency, and cost across its infrastructure, with Azure becoming the first cloud platform to deploy NVIDIA's GB200 Grace Blackwell chips [59][62]. - The company is enhancing its AI capabilities by integrating various data services, allowing for more efficient data management and AI application development [55][56]. Group 4: Scientific Research and Discovery - Microsoft introduced the Microsoft Discovery platform, designed to revolutionize scientific research by providing AI-driven assistants that can conduct deep reasoning and hypothesis generation [65][66]. - This platform aims to significantly accelerate the discovery process in fields like materials science and pharmaceuticals, demonstrating the potential of AI in transforming traditional research methodologies [66].