rently, the field of decentralized AI agents (DAI-Agent) is receiving significant attention, with numerous articles introducing the characteristics of related projects, the problems they solve, and their future potential. Although these articles help investors understand the projects to some extent, most lack in-depth analysis and fail to explore the fundamental characteristics of AI and the current state of Web3. Therefore, it is difficult to clarify the role of decentralized AI in the practice of the value internet in Web3, whether it optimizes Web3 or serves as a key component. Without clarifying the intrinsic logic between decentralized AI and the value internet economy of Web3, it is impossible to deeply understand the role of decentralized AI or grasp how its core components address the issues present in Web3. This not only makes it difficult for us to accurately choose high-potential investment directions, but even if we select the right track and project, we may struggle to persist due to market sentiment fluctuations. Therefore, I plan to conduct an in-depth analysis of the current basic state of Web3 and the fundamental characteristics of AI, exploring how the integration of the two can realize the implementation of the value internet, and how Arweave and AO can assist this process through AI. Due to the richness of the content, I will elaborate in two articles:
The emergence of decentralized AI agents (DAI-Agent) has sparked widespread interest within the crypto community, with numerous articles highlighting the unique characteristics of related projects, the problems they aim to solve, and their potential for future growth. While these articles offer valuable insights, many lack in-depth analysis and fail to fully explore the fundamental characteristics of AI and the current state of Web3. As a result, it becomes difficult to clarify the precise role of decentralized AI in the practice of the value internet in Web3, whether it serves to optimize Web3 or functions as a key component. Without establishing the intrinsic logic between decentralized AI and the value internet economy of Web3, it is impossible to deeply understand the role of decentralized AI or grasp how its core components address the issues present in Web3. For example, what problems do the decentralized model and DAI-Agent each solve, and what is the intrinsic logic between them and Web3? Without understanding these intrinsic logics, it is challenging to assess the potential value of this field. This not only makes it difficult for us to accurately choose high-potential investment directions, but even if we select the right track and project, we may struggle to persist due to market sentiment fluctuations. Therefore, I plan to conduct an in-depth analysis of the current basic state of Web3 and the fundamental characteristics of AI, exploring how the integration of the two can realize the implementation of the value internet, and how Arweave and AO can assist this process through AI. Due to the richness of the content, I will elaborate in two articles:
Currently, many public chain projects focus primarily on optimizing and expanding underlying infrastructure, such as ETH and various L2s, Solana, and other blockchains. However, I believe that if we only pursue the expansion of blockchain without integrating AI, it will be difficult to advance the implementation of the value internet in Web3. Currently, in addition to limited scalability, Web3 also faces data fragmentation issues, where users' personal data is scattered across different chains and DApps, leading to management difficulties, high interaction costs, and complex operations, severely limiting users' active contribution of data. Furthermore, the decentralized nature leads to low management and collaboration efficiency. These issues greatly restrict the development of Web3. AI, with its ability to learn, infer, and make decisions autonomously, can serve as an intelligent assistant for users, significantly enhancing efficiency. The integration of the two will greatly improve user experience, lower entry barriers, and promote the development of Web3.
Crucial to Web3 is users' control over their own data, a feature that DAI-Agent can help to achieve by centrally managing and aggregating data for users. This effectively addresses the pain point of data being scattered across various platforms, while also acting as an intelligent assistant to reduce operational difficulty and enhance interaction efficiency with Web3. For example, DAI-Agent can assist users in managing their DID lifecycle, including creating, updating, and revoking DIDs, thereby simplifying data management and usage experience. To lay the groundwork for subsequent discussions, it is necessary to explore the relationship between AI-Agent and DID in detail. In the Web3.0 environment, DID and DAI-Agent are highly complementary and compatible:
AI-Agent can integrate data across platforms (such as social, medical, and professional data), effectively breaking down information silos; its intelligent algorithms can filter, clean, and format data based on the needs of DID (such as assessing the credibility of various data sources, removing duplicate or low-value data, and organizing data according to DID data model specifications), ensuring the creation of high-quality DIDs. At the same time, using differential privacy, homomorphic encryption, and the latest multi-party secure computation (MPC) technologies, data analysis can be completed without disclosing the original data (for example, when aggregating sensitive medical data, it can meet health information needs while ensuring personal privacy). Additionally, as cross-chain interoperability protocols (such as Polkadot, Cosmos, etc.) continue to mature, DAI-Agent is expected to achieve seamless connections between more data sources, further enhancing the efficiency and accuracy of data integration. The decentralized architecture not only avoids the risks of single points of failure and data being controlled by a single entity but also enables automated data aggregation and real-time updates through smart contracts, providing strong support for building a trustworthy and dynamic digital identity system.
In a decentralized environment, the digital identity system provides the necessary identity authentication and authorization mechanisms for DAI-Agent, allowing the AI-Agent to prove its legitimate identity and authority when securely interacting with other agents. This process relies not only on technical means but can also be governed and regulated by community participation through decentralized autonomous organization (DAO) mechanisms, further enhancing the system's transparency and security.
With the help of the DID system, the identity and behavior of DAI-Agent are more transparent and verifiable, thereby establishing trust and promoting collaboration among other agents; at the same time, AI-Agent effectively alleviates the low efficiency issues caused by decentralization by reducing the interaction costs between users
