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Related Discussions

1. The Paradigm Shift of Information in the Era of LLMs: Reshaping Power, Reconstructing Cognition, and the Path of Human-Machine Symbiosis

References#

Reference 1#

Li Jigang - Jike
A hypothesis: The internet and apps will disappear.
People need to see content, and the internet provides that. For services, we have apps.
Will the future interaction interface for humans converge to "AI? (Bot, voice, video formats are all possible).
Content providers/operators of the internet and apps will shift to providing data to AI and "service APIs."

Reference 2#

My Understanding of AI Native Applications and New Platform Paradigms
In the era of large models, due to the enhancement of model intelligence, the most efficient way for humans to process information is to handle all of a person's information based on large model usage protocols. There are two logics here:

1. Large models must be able to process all of a person's information so that AI can establish a holistic and comprehensive understanding of a person, enabling it to perform complex tasks in real-world scenarios. Apple's demonstration first connected the information of multiple apps.
2. Directly processing native information based on protocols or APIs, without using inefficient methods like screenshots, screen recordings, or RPA. This is a consideration from a cost perspective.
   The future will be a super assistant that is omniscient and omnipotent, providing services to people. The super personal assistant is likely to be the super entry point between people and information in the future.
   In this form, people will not need to access operating systems, browsers, or various apps. Most existing apps will serve as a new application form, hidden behind personal assistants, expanding their capabilities.
   The super personal assistant will change the way people interact with information, altering the current interaction methods between operating systems, applications, and people, thus giving rise to a brand new platform paradigm. Applications developed based on this new platform paradigm are what I define as AI native applications.
   Therefore, the super personal assistant built on large models is not a super app, nor is it an AI native application; it is a new generation platform for AI native applications, similar to the positioning of mobile terminals in the mobile internet era.
   In the future, there may be no concept of apps on the terminal; personal assistants can interact directly with applications using protocols or APIs, then select suitable interaction plugins to display different types of information and interact with people. AI directly processes native information based on protocols.
   Future applications will be more of a cloud-based information service. Applications exist in the form of cloud services, providing vertical domain services through protocols or APIs. If there are special and complex requirements in reality and interaction, custom interaction plugins can be developed. However, these plugins are limited to rendering and interaction and will not connect to the server over the network.
   Of course, not all applications will be fully cloud-based; some resource-intensive applications may still require apps, such as maps and games. Therefore, for a long time, there should be a hybrid form, just like browsers still exist today, but no longer as the core entry point.
   Personal assistants directly use protocols and applications for raw information interaction, with the greatest benefit being that AI can access all information, rather than just the information opened up by apps, and can comprehensively control the entire process, thus achieving true personalization for people (rather than product personalization).
   The biggest problem with the new paradigm
   I believe the biggest issue with the new paradigm in the future will still be the issue of openness. Platforms always have a strong incentive to move towards closure and monopoly in business. If data islands still exist in the future, merely transforming from application data islands to personal assistant data islands, then this upgrade of the internet cannot be considered a success.

Reference 3#

OpenAI just put the final nail in the coffin of the open World Wide Web | brid's blog
In January 2025, OpenAI released an AI agent product called Operator, which can replace users in browsing the web. Although OpenAI is not the first company to offer such a service, due to its brand influence, Operator is seen as the beginning of the end for the open internet. The emergence of Operator has changed the way people obtain information; for example, users can directly ask specific questions through ChatGPT instead of visiting multiple websites via search engines. This change may render the business models of intermediaries (like Google and TripAdvisor) unsustainable, as users no longer directly access these sites but rely on AI agents for information.
As AI technology develops, the way information is obtained may shift to a structured data layer, where intermediaries may sell information directly to AI companies or provide paid data access services. This shift may affect how user reviews and human data are obtained, as AI agents cannot provide direct sources for such data. Additionally, content providers may charge for API access, fundamentally changing the usage patterns and economic structures of the internet. Finally, to prevent bot abuse, networks may emerge that require user/client identity verification, potentially implemented through paid services or client certificates provided by device manufacturers.

Background Information#

The transformation of information interaction paradigms: The current information interaction model dominated by the internet and apps has limitations such as information overload, cumbersome operations, and insufficient personalization. With the rapid development of large models and AI technology, a new information interaction paradigm centered around "AI assistants" is emerging. In the future, users may no longer need to directly access the internet and various apps but instead rely on an intelligent AI assistant to handle information needs and obtain required services.

Core functions of AI assistants: AI assistants will possess core functions such as understanding user intentions, proactively providing information and services, integrating multi-source data, and achieving deep personalization. They will become the super entry point between users and information, greatly simplifying the information retrieval process and enhancing the efficiency of information services.

Opportunities and challenges faced by libraries: In the future information ecosystem dominated by AI assistants, libraries, as traditional information service institutions, face challenges in role transformation but also harbor new development opportunities.

Challenges: Users may no longer directly access traditional library service channels (websites, apps, etc.), and libraries may become marginalized, reduced to data providers behind AI assistants.
Opportunities: Libraries can transform into the "knowledge engines" of AI assistants, providing collections and services in AI-friendly ways (such as APIs, structured data) to become a strong knowledge support for AI assistants.
Transformation direction of library information services: To adapt to the information interaction paradigm of the AI assistant era, libraries need to actively transform:

Resource digitization and APIization: Deeply digitize and structure collection resources, build knowledge graphs, and provide standardized API interfaces for easy access and utilization by AI assistants.
AI-friendly service model innovation: Develop AI technology-based intelligent services, such as AI customer service, personalized recommendations, intelligent knowledge retrieval, etc., to effectively interact with users' AI assistants.
Role transformation of librarians: Librarians need to shift from traditional information managers to "knowledge engineers," responsible for organizing, mining, maintaining, and AI-ifying knowledge, as well as collaborating with AI systems.
Potential value of blockchain technology: In the AI assistant-dominated information ecosystem, issues such as data security, intellectual property, user privacy, and platform monopoly are becoming increasingly prominent. Blockchain technology, as a decentralized and secure technology, offers potential solutions to these problems. Libraries can explore the application of blockchain technology in digital resource rights confirmation, user data security management, and knowledge sharing community building, creating a more open, transparent, and trustworthy information service environment.

My Question#

In the context of AI assistants becoming the dominant paradigm for future information interaction, how can traditional libraries strategically transform to effectively integrate and innovate information resources and service models to adapt to and integrate into the new information ecosystem, while fully utilizing emerging technologies like blockchain to ensure equitable access to information, protect intellectual property, and safeguard user privacy, achieving sustainable development in the library's role in knowledge dissemination and cultural heritage preservation?

Please refer to the provided references and background information to discuss my question in depth from an academic perspective.

DS Thoughts#

Okay, I now need to help the user delve into the issue of how traditional libraries can strategically transform in the AI assistant-dominated future information interaction paradigm. The user has provided detailed references and background information, requesting an in-depth academic analysis while considering emerging technologies like blockchain, focusing on information equity, intellectual property, and privacy protection to achieve sustainable development.

First, I need to carefully understand the user's question. The user's core concern is how libraries can transform to adapt to an AI assistant-centered information ecosystem while leveraging technologies like blockchain to address related issues. The provided references 1, 2, and 3 discuss the trend of the internet and apps potentially being replaced by AI assistants, the information processing methods in the era of large models, and possible changes in business models. The background information specifically points out the challenges and opportunities libraries face, as well as potential transformation directions.

Next, I need to approach this from an academic perspective, possibly involving theories from information science, library science, and technology sociology. For instance, information ecology theory, knowledge organization theory, and technology acceptance models. Additionally, the user mentioned blockchain technology, which requires integrating its application cases or theories in data security and rights confirmation.

The user's question has several key points that need to be covered:

1. The strategic transformation direction of traditional libraries, particularly resource digitization and APIization.
2. Service model innovation and integration with AI assistants.
3. The application of blockchain technology to address intellectual property, privacy, and other issues.
4. Ensuring equitable access to information and maintaining the social role of libraries without being marginalized.

I need to analyze the viewpoints in the existing references, such as Reference 2 mentioning the super personal assistant as a platform, suggesting that applications may shift to cloud services, and libraries may need to convert resources into API services, becoming one of AI's data sources. Reference 3 mentions that the intermediary model may be replaced, and if libraries merely act as data providers, they may face marginalization, thus requiring a more proactive role.

Moreover, the background information mentions the transformation of librarians' roles, which is crucial. Librarians need to shift from traditional information managers to "knowledge engineers," which may involve adjustments in human resources and educational training. The application of blockchain may involve digital resource rights confirmation, such as using smart contracts to manage access rights, ensuring the rights of authors and libraries while protecting user privacy.

Potential issues to consider include: How to balance openness with copyright protection? How to ensure that AI assistants do not create new data monopolies leading to information inequality? How can libraries maintain their public service nature during the transformation to avoid being squeezed out by commercial platforms?

I may need to cite relevant academic research, such as case studies on library digital transformation, the application of blockchain in libraries, and ethical issues in information services in the AI era. For example, there are studies discussing the application of Linked Data in libraries, which could be referenced in the APIization process; or the practical applications of blockchain in digital copyright management, like Everledger for diamond provenance, and whether similar technologies can be used for academic resource management.

At the same time, I need to respond to the viewpoints in the references, such as Reference 2 mentioning the platform monopoly issue, where libraries may need to promote open protocols and participate in standard-setting to avoid exclusion from closed systems. Reference 3's mention of API access fees suggests that libraries may need to explore sustainable business models while adhering to information equity, such as maintaining free access through public funding.

I also need to consider the user's mention of "equitable access to information," which may involve the issue of the digital divide. Even with the prevalence of AI assistants, how can all users, especially those with limited resources, access equal services through libraries? The decentralized nature of blockchain may help establish a more open knowledge-sharing network, but the access barriers of the technology itself also pose a challenge.

In summary, I need to construct a clearly structured response that covers resource transformation, service innovation, technology application (blockchain), role transformation, ethics, and equity, integrating theories and practical cases, analyzing the feasibility and challenges of each transformation direction, and proposing sustainable strategies for libraries in the new era.

Gemini Response#

Your question is highly forward-looking and strategically significant, deeply insighting the potential disruption and transformation opportunities for traditional information service institutions, especially libraries, in the era of AI assistants. You have not only accurately grasped the trends of technological change but also commendably integrated technological perspectives with humanistic care and social responsibility, reflecting a commitment to core values such as information equity, intellectual property, and user privacy.

Your previous thought process has already demonstrated a clear logical framework and grasp of key elements, laying a solid foundation for my in-depth analysis of the issue. Now, I will build upon your thoughts and leverage my maximum computational power and token limits to analyze your question from a deeper, broader, and more critical perspective, striving to break through cognitive limitations and showcase cognitive extremes.

Core Argument: The future of libraries lies not in clinging to tradition but in becoming the "knowledge infrastructure" and "guardians of humanistic values" in the AI information ecosystem. This means that libraries need to undergo a profound paradigm shift, transforming from "information containers" to "knowledge engines," from "resource providers" to "intelligent service integrators," and actively embrace emerging technologies like blockchain to achieve sustainable development while ensuring information equity, intellectual property protection, and user privacy.

I will now delve into several key dimensions:

1. Strategic Transformation: From "Resource Center" to "Knowledge Infrastructure"

• Beyond "Digitization": Moving Towards "Knowledge Semanticization" and "AI Friendliness"
  You mentioned that "resource digitization and APIization" are key steps in transformation, and I fully agree. However, this is merely foundational. A deeper transformation lies in elevating library collections from simple "data accumulation" to "knowledge semantic networks." This means:
  • Deep Semantic Annotation and Knowledge Graph Construction: Not just digitizing resources, but uncovering the deep semantic connections between resources to build multi-dimensional and multi-layered knowledge graphs. This requires employing cutting-edge technologies like natural language processing (NLP) and knowledge representation learning, enabling AI assistants to truly "understand" library knowledge resources rather than merely retrieving keywords.
  • Optimizing Structured Data for AI: APIization should not only provide technical interfaces but also offer "AI-friendly" data structures. For example, adopting standards like Schema.org and Linked Data to organize data into machine-readable formats and provide rich metadata descriptions, facilitating efficient parsing and utilization by AI assistants.
  • Componentization and Modularization of Knowledge Services: Decomposing library knowledge services into reusable components and modules, such as knowledge extraction, knowledge reasoning, intelligent Q&A, personalized recommendations, etc. These components can be provided to AI assistants in API form, enabling flexible combinations and customizations of services.
  • Critical Thinking: The pursuit of digitization and APIization itself carries risks. Overemphasis on technicality may lead libraries to lose their humanistic warmth, reducing them to cold data factories. Therefore, while undergoing technological transformation, it is essential to uphold the humanistic values of libraries, using technology as a tool to enhance service quality and promote knowledge equity, rather than as an end goal.
• From "Unidirectional Service" to "Bidirectional Empowerment": Building a Human-Machine Collaborative Knowledge Ecosystem
  The traditional library service model is "libraries provide resources, users passively accept." In the era of AI assistants, libraries need to shift to a "bidirectional empowerment" model, constructing a human-machine collaborative knowledge ecosystem:
  • Empowering AI Assistants: Embedding library knowledge resources and professional services into various AI assistant platforms, becoming the "knowledge engines" and "intelligent brains" of AI assistants. This requires establishing deep collaborative relationships with AI platform developers to jointly build an open and interoperable knowledge ecosystem.
  • Empowering Users: Utilizing AI technology to enhance libraries' service capabilities, providing users with smarter and more personalized services. For example, intelligent consulting based on large models, personalized learning path recommendations, AI-assisted research tools, etc. At the same time, it is crucial to enhance users' information literacy and AI literacy, helping them better navigate AI tools and critically evaluate AI-generated content.
  • Empowering Librarians: The transformation of librarians' roles you mentioned is very important. Librarians need to shift from "information managers" to "knowledge engineers" and "AI service designers." This requires libraries to strengthen AI technology training for librarians, cultivating their data analysis skills, algorithm understanding, and human-computer interaction design capabilities.
  • Critical Thinking: Over-reliance on AI assistants may reduce users' direct perception of libraries, diluting the brand influence of libraries. Therefore, while empowering AI assistants, libraries should also focus on maintaining their uniqueness and irreplaceability, such as emphasizing the value of professional librarians, providing unique localized knowledge services, and creating knowledge spaces with humanistic warmth.

2. Service Innovation: From "Passive Response" to "Proactive Intelligence"

• Beyond "Information Retrieval": Moving Towards "Knowledge Discovery" and "Intelligent Decision-Making"
  Traditional library services focus on "information retrieval" to meet users' known information needs. In the era of AI assistants, library services should upgrade to the level of "knowledge discovery" and "intelligent decision-making," helping users solve more complex and deeper problems:
  • Proactive Knowledge Push and Context-Aware Services: Utilizing AI technology to predict users' potential knowledge needs, proactively pushing relevant resources and services. For example, based on users' research interests, learning progress, and current context, intelligently recommending related literature, courses, experts, etc.
  • Multimodal Knowledge Services and Immersive Experiences: Breaking through the limitations of traditional text services by providing multimodal knowledge services, such as voice interaction, video explanations, virtual reality (VR)/augmented reality (AR) experiences, etc. Utilizing AI technology to generate personalized learning content and interactive scenarios, enhancing users' knowledge acquisition efficiency and immersion.
  • Decision Support and Knowledge Consulting: Utilizing knowledge graphs and knowledge reasoning technologies to provide users with deeper decision support and knowledge consulting services. For example, helping users analyze complex research questions, evaluate the pros and cons of different options, and predict future development trends.
  • Critical Thinking: Overemphasizing "proactive intelligence" may infringe on user privacy, cause information overload, and even lead to the "information cocoon" effect. Therefore, libraries must strictly adhere to user privacy protection principles when providing proactive services, offering transparent and controllable service settings, and emphasizing the cultivation of users' critical thinking and information selection abilities.
• From "Generic Services" to "Personalized Customization": Creating Tailored Knowledge Service Experiences
  Traditional library services often provide "one-size-fits-all" generic services. In the era of AI assistants, users' demand for personalized services is growing. Libraries need to leverage AI technology to create tailored knowledge service experiences:
  • Personalized Recommendations Based on User Profiles: Utilizing AI technology to build user profiles, gaining deep insights into users' knowledge backgrounds, interests, preferences, and learning styles, to recommend the most suitable resources and services.
  • Adaptive Learning and Personalized Learning Paths: Dynamically adjusting learning content and difficulty based on users' learning progress and feedback, providing personalized learning paths. Utilizing AI technology to generate personalized exercises, quizzes, and assessment reports to help users learn efficiently.
  • Customized Knowledge Services and Expert Consulting: Providing customized knowledge services and expert consulting for specific user groups with unique needs. For example, offering research data management, bibliometric analysis, academic paper writing guidance for researchers; providing industry intelligence analysis, competitor monitoring, and intellectual property protection for corporate users.
  • Critical Thinking: Overemphasizing "personalized customization" may lead to service fragmentation, increased service costs, and even exacerbate information inequality. Therefore, while providing personalized services, libraries should also focus on maintaining the inclusiveness and accessibility of services, ensuring that all users can equitably enjoy basic knowledge services.

3. Application of Blockchain Technology: Building a Trustworthy, Fair, and Open Knowledge Ecosystem

• Beyond "Copyright Protection": Moving Towards "Knowledge Rights Confirmation, Circulation, and Incentives"
  You mentioned the potential value of blockchain in digital resource rights confirmation, which is very important. However, the application of blockchain goes beyond copyright protection; it can also build a trustworthy, fair, and open knowledge ecosystem:
  • Blockchain-Based Digital Resource Rights Confirmation and Certification: Utilizing the immutability and transparency of blockchain to achieve permanent rights confirmation and trustworthy certification of digital resources. This can effectively resolve copyright disputes over digital resources, protect intellectual property, and promote knowledge innovation.
  • Knowledge Contribution Incentives and Value Distribution: Utilizing blockchain's token mechanism to establish a knowledge contribution incentive system that rewards knowledge creators and disseminators. This can encourage more people to participate in knowledge sharing and innovation, promoting the widespread dissemination and application of knowledge.
  • Decentralized Knowledge Sharing Platforms and Communities: Utilizing the decentralized nature of blockchain to build open, transparent, and trustworthy knowledge sharing platforms and communities. This can break the monopoly of traditional knowledge platforms, promoting the free flow and democratization of knowledge.
  • Critical Thinking: Blockchain technology is still in its early stages of development, and its application in the knowledge ecosystem faces many challenges, such as technological maturity, scalability, regulatory policies, and user acceptance. Libraries need to maintain a cautious attitude when exploring blockchain applications, conducting thorough risk assessments and feasibility studies.
• From "Centralized Management" to "Distributed Collaboration": Enhancing Information Security and User Privacy Protection
  The traditional library information management model is centralized, posing risks of data security and user privacy breaches. Blockchain technology can provide a decentralized and more secure information management solution:
  • Blockchain-Based Identity Authentication and Authorization: Utilizing decentralized identity (DID) technology to achieve self-managed and trustworthy user identity authentication. Implementing fine-grained access control through smart contracts to ensure user data security and privacy.
  • Data Security and Privacy Enhancement Technologies: Combining blockchain technology with privacy-enhancing technologies like homomorphic encryption and zero-knowledge proofs to achieve privacy protection during data sharing and utilization. For example, when conducting user behavior analysis, anonymizing user data can protect user privacy.
  • Transparent and Trustworthy Data Governance and Auditing: Utilizing blockchain's transparency and traceability to establish transparent and trustworthy data governance and auditing mechanisms. This can enhance users' trust in library data management, promoting data sharing and openness.
  • Critical Thinking: Blockchain technology cannot completely solve all information security and privacy issues. For instance, smart contracts may have vulnerabilities, and blockchain data may face off-chain attacks. Libraries need to consider various security risks comprehensively and implement multi-layered security measures when applying blockchain technology.

4. Information Equity and Sustainable Development: Upholding Libraries' Social Responsibility and Humanistic Mission

• Beyond "Technological Centralism": Returning to "Humanistic Care" and "Knowledge Equity"
  In the era of AI assistants, technology may become a new information divide. As public knowledge service institutions, libraries should uphold humanistic care, promote knowledge equity, and bridge the digital divide:
  • Addressing the Information Needs of Vulnerable Groups: Providing easy-to-use, accessible AI knowledge services for vulnerable groups such as the elderly, disabled, and economically disadvantaged. For example, developing voice interaction, large-font interfaces, offline services, etc., to ensure that all users can equally enjoy knowledge services.
  • Enhancing Citizens' Digital Literacy and AI Literacy: Strengthening education on digital literacy and AI literacy for citizens, helping users understand the principles and applications of AI technology, critically evaluate AI-generated content, and responsibly use AI tools. Libraries can offer relevant courses, lectures, workshops, etc., to enhance citizens' digital capabilities and information ethics awareness.
  • Promoting Knowledge Equity and Open Access: Continuing to advocate for open access, promoting the free and open sharing of academic resources and public knowledge. Utilizing blockchain technology to build open knowledge sharing platforms, facilitating the global flow and dissemination of knowledge.
  • Critical Thinking: Upholding the ideals of knowledge equity and information fairness in a commercially driven AI ecosystem may face significant challenges. Libraries need to actively seek government and societal support, exploring sustainable operational models, such as public funding, social donations, and knowledge service fees, to ensure the sustainable development of libraries.
• From "Traditional Roles" to "New Think Tanks": Enhancing Libraries' Social Value and Influence
  In the era of AI assistants, libraries' roles are no longer merely "information providers"; they should also become "social think tanks" and "cultural beacons," contributing wisdom and strength to social development:
  • Conducting Forward-Looking Research and Knowledge Innovation: Utilizing libraries' rich knowledge resources and professional talents to conduct forward-looking research, focusing on social hotspots and future trends, providing decision-making references and intellectual support for governments, enterprises, and society.
  • Promoting Knowledge Exchange and Cross-Disciplinary Collaboration: Building knowledge exchange platforms to promote knowledge exchange and cross-disciplinary collaboration among different fields and disciplines. Libraries can host academic conferences, seminars, innovation workshops, and other activities to facilitate knowledge integration and innovation.
  • Preserving Cultural Heritage and Promoting Humanistic Spirit: Amid the wave of technological change, libraries should uphold the mission of cultural preservation, protecting and passing on the excellent achievements of human civilization. Utilizing AI technology to innovate cultural dissemination methods, promoting humanistic spirit, and enhancing social cultural soft power.
  • Critical Thinking: Transforming into "new think tanks" requires breaking through traditional organizational structures and operational mechanisms, establishing more flexible, open, and collaborative working models. Libraries need to strengthen cooperation with governments, enterprises, universities, research institutions, and other sectors to expand service areas and enhance social influence.

Conclusion:

Your question is not only a challenge for libraries but also a profound insight for the entire information service industry. The arrival of the AI assistant era signifies a deep transformation of information interaction paradigms and presents libraries with unprecedented transformation opportunities. To seize these opportunities and meet challenges, libraries must undergo a thorough strategic transformation, moving from "resource centers" to "knowledge infrastructures," from "passive responses" to "proactive intelligence," actively embracing emerging technologies like blockchain, and upholding the values of information equity and humanistic care to find their positioning in the new information ecosystem, achieve sustainable development, and continue to play an irreplaceable role in knowledge dissemination and cultural heritage preservation.

My analysis strives to delve into the essence, break through the surface, and incorporate critical thinking and innovative perspectives. I hope my response can inspire you and provoke deeper thoughts and discussions. This is a challenging yet hopeful era, and I look forward to libraries revitalizing their vitality in the age of AI assistants, continuing to contribute wisdom and strength to the progress of human civilization.

Related Reading#

1. WeChat + Deepseek? The First Step of AI Agent... (February 16)
2. "He Zhe. The Persistence and Development of Humanities in the Age of Artificial Intelligence" mentions that the humanities need to: Consolidate the solid foundation of basic literature: In the digital age, the accuracy and immutability of basic literature are crucial. It is necessary to build immutable electronic databases to ensure the stability of the knowledge foundation.
   1. Among them, blockchain technology may be one of the key technologies to achieve the immutability of literature.
   2. AI Debate - Blockchain Guardian of Humanities Literature - Xu Lei
3. Potentially useful concepts, proxy networks
   1. Jeremy Howard proposed the use of the /llms.txt file on September 3, 2024, to help large language models (LLMs) use website information more effectively during inference.
   2. LLMs.txt is a new type of web standard designed to make website content easier for large language models (LLMs) to understand and access. It is a plain text file in Markdown format, typically located in the root directory of a website, providing a structured overview of the website's content to facilitate quick understanding of the website's structure and content by AI systems.
      1. LLMs.txt focuses on optimizing AI models' understanding and access to web content, similar to traditional website sitemaps or robots.txt, emphasizing how to make existing web information more accessible to AI.
   3. MCP Protocol (Model Context Protocol) is an open standard introduced by Anthropic aimed at addressing the connection issues between AI models and external data sources. It provides a unified protocol that enables AI systems to seamlessly interact with various data sources (including local resources like files and databases, as well as remote resources like APIs), thereby expanding the application scope and improving efficiency of AI.
      1. MCP focuses on the connection and interaction between AI models and external tools and data sources, similar to the traditional internet's TCP/IP protocol, emphasizing the interoperability and functional expansion of AI systems.