Arweave + AO Computer + AI empower the implementation of the Web3 value internet

Currently, 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. 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:

• Why the current Web3 needs to integrate with decentralized AI to realize the implementation of the value internet.

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.

• The intrinsic relationship between decentralized storage and computing platforms, decentralized models, and DAI-Agent: the combination of the three can open up the closed loop of economic activities of Web3 data assets, thereby achieving a true value internet.

I. Introduction to Key Components and Relationships

1. DAI-Agent

One of the core features of Web3 is users' control over their own data. DAI-Agent can help users centrally manage and aggregate data, effectively addressing 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. It is necessary to explore the relationship between AI-Agent and DID in detail to lay the groundwork for subsequent discussions. In the Web3.0 environment, DID and DAI-Agent are highly complementary and compatible:

• a. Data Integration and High-Quality Input:

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.

• b. Identity Authentication and Authorization Foundation:

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.

• c. Enhanced Trust and Reduced Interaction Costs:

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 and the system and simplifying complex operations. Moreover, with the emergence of federated learning and privacy computing technologies, in the future, DAI-Agent will be able to achieve cross-platform and cross-domain data collaboration and intelligent decision-making without exposing original data, providing users with more accurate and personalized services.

2. Decentralized Model

The model can largely be seen as the "brain" of the AI-Agent, serving as the core component for achieving intelligence. In the future, a large number of AI-Agents will emerge and play roles in various industries, and these specialized fields (such as healthcare, education, finance, etc.) will require corresponding AI models for support. General AGI can meet users' basic needs, but for each specialized field, a large number of specialized AI-Agents will still be needed to work collaboratively, necessitating a wide variety of models. Due to the advantages of decentralized models over centralized models, such as being permissionless and verifiable, they will undoubtedly be favored by DAI-Agent in the future: the permissionless feature allows anyone to participate in model development without relying on the approval of centralized institutions, thus promoting technological openness; at the same time, the permissionless feature allows DAI-Agent to flexibly schedule various models, significantly enhancing its intelligent attributes. In addition to the aforementioned advantages, in the future, federated learning and cross-domain collaboration mechanisms will also become key technologies driving the development of decentralized models, protecting data privacy while ensuring the efficiency and security of model training. Especially in high-sensitivity fields such as finance and healthcare, the training process and data sources of models must undergo multiple verifications to ensure the overall trustworthiness and robustness of the system.

3. Decentralized Storage and Computing Platforms Based on Blockchain Technology

To achieve data rights confirmation in Web3, it is essential to build decentralized storage and computing platforms to establish a verifiable data consensus infrastructure that supports large-scale data exchange. Specifically, the overall solution of Arweave and AO builds a data consensus infrastructure on both storage and computing ends, achieving the following goals:

• Reducing data storage costs while ensuring data security and immutability;
• Promoting large-scale data exchange, providing a solid foundation for the hosting and operation of decentralized AI ecosystems;
• Simplifying the data integration process through a unified data storage layer, reducing the complexity of integration caused by data fragmentation;
• At the same time, this platform also provides the necessary data support for building DID systems in Web3, enhancing the management and application of digital identities.

The above three points complement each other:

• DAI-Agent, combined with a token incentive mechanism, encourages users to contribute data and actively interact with Web3, thereby generating more data;
• The generation of large amounts of data drives the development of decentralized storage and computing platforms, as these platforms can not only reduce data storage costs but also promote data rights confirmation;
• Decentralized models need to be hosted on decentralized platforms, which can reduce storage and computing costs while ensuring the verifiability and censorship resistance of the models, thus enhancing model security and trustworthiness, further promoting model development.

In addition, decentralized model training requires massive amounts of high-quality data, and the emergence of large-scale high-quality data will significantly improve model quality; the improvement in model quality will make DAI-Agent increasingly intelligent, further stimulating user interactions and generating more data; and the continuous enrichment of data will further promote the improvement of storage and computing platforms, forming a positive cycle, interlocking and perpetuating, ultimately constituting a complete data asset economic ecosystem. This ecosystem creates a closed loop for the circulation of data assets, which is key to forming a true value internet ecosystem. As shown in the figure:

Based on the above logical analysis, we can see that DAI-Agent is just a key link in the entire ecosystem, and its development is largely constrained by the support of the other two parts (i.e., decentralized storage/computing platforms and decentralized models). Therefore, when investing in such projects, it is essential to pay attention to whether the project has the capability to build a complete data asset economic ecosystem or whether it has established relatively stable cooperative relationships with the other two aspects. If one only invests in a single directional project, the risks will significantly increase. Additionally, the currently popular DAI-Agent protocols such as ELIZA, VIRTUAL, and APC, while supporting diversified models, some of which allow centralized model providers like OpenAI to connect, may meet users' diverse needs, but if the proportion of centralized models is too high, it will restrict the long-term development of the protocol due to the lack of permissionless characteristics.

II. Here I want to focus on: Arweave Permanent Storage + AO Super Parallel Computing Overall Solution

1. Parallel Processing Capability

Unlike networks like Ethereum, whose base layer and various Rollups typically run as a single process, AO supports an arbitrary number of processes running in parallel while ensuring the completeness of computational verifiability. Additionally, these networks need to operate in a globally synchronized state, while the processes in AO maintain independent states. This independence allows each process to handle more interactions, greatly enhancing computational scalability, especially suitable for applications requiring high performance and reliability. In the future, as a large number of DAI-Agents continuously execute tasks on-chain, the demands for system scalability will become increasingly stringent, and AO's super parallel processing capability perfectly meets this need.

2. Capability to Store and Run Large Models and Other Various Models

In the AO network, the current memory limit for a single node is 16 GB, while the protocol-level memory expansion limit can reach 18 EB, which is sufficient to run most models in the current AI field (such as the unquantized version of Llama3, the Falcon series, and various other models). Considering that the parameters of GPT-4 have exceeded 1.76 trillion and GPT-5 is expected to surpass 50 trillion parameters, the scale of models will continue to grow in the future. AO has very strong scalability; it can expand computational units simply by physically increasing memory or graphics cards to meet the operational needs of large models.

Arweave employs a unique blockweave technology that allows new blocks to connect with multiple old blocks, thus possessing strong scalability and theoretically capable of storing various models and large-scale data. At the same time, through WeaveDrive technology, applications can conveniently access data on Arweave as if accessing local disks, providing possibilities for building various applications. Various applications can access the permanently stored data on Arweave, and AO+Arweave has built a data rights confirmation infrastructure from both computing and storage aspects, laying the foundation for large-scale data asset exchange, which is highly attractive to developers intending to develop applications on the AO platform. Meanwhile, various application scenarios provide diverse landing scenarios for various models and DAI-Agents, thereby promoting the development of the AI ecosystem.

3. Data is One of the Three Key Elements of the AI Ecosystem—Most Data in the AO + Arweave Ecosystem is High-Quality Data and Has a Unified Data Storage Layer

Large-scale and high-quality data is crucial for model training. High-quality data typically possesses characteristics such as accuracy, consistency, validity, completeness, timeliness, and uniqueness. In the AO+Arweave ecosystem, the circulating data mostly meets these characteristics. For detailed technical implementation details, please read my previous article "Arweave Permanent Storage + AO Super Parallel Computer: Building Data Consensus Infrastructure." It is particularly important to emphasize the advantages of Arweave's permanent storage: due to its permanent storage attribute, the stored data is often more critical; the longer the data is stored, the more its value can be reflected, as this not only facilitates preservation and traceability but also aids in data rights confirmation. Large-scale high-quality data is extremely important for AI training, and Arweave, as a unified data storage layer, has the ability to integrate data from various projects. In contrast, Ethereum, Solana, and others face greater challenges in data integration due to the lack of a unified storage layer. These characteristics of Arweave play a key role in data collection, integration, and integrity assurance, which is crucial for building DIDs within Web3: a unified data storage layer is far more convenient than cross-platform data integration. Furthermore, the integration of AO and Arweave ensures that all agent interaction data can be permanently stored, providing strong support for establishing accountability mechanisms and DID and reputation systems. For example, the current RedStone project is leveraging Arweave to build DIDs and establish accountability mechanisms, thereby providing infrastructure support for the development of AI-Agents.

4. AO + Arweave Endows AI with High Verifiability

Verifiability is crucial for the development of AI, ensuring that the predictions and outputs of AI models possess transparency, tamper-resistance, and independent verifiability, providing higher credibility and security for AI, thus enabling its widespread application in high-trust fields such as finance, healthcare, law, and autonomous driving. At the same time, verifiability also allows developers to share and collaborate on models with more confidence, without worrying about malicious tampering. AO+Arweave adopts the SCP storage method, allowing all data and models within AO to be holographically stored on Arweave, where anyone can verify the data sources, model operation processes, and output results; at the same time, the encrypted signatures provided by the computing units further ensure the authenticity and integrity of the computational results. With the continuous improvement of zero-knowledge proof technology and distributed verification mechanisms, in the future, it will be possible not only to verify model outputs in real-time but also to trace and audit the entire process of model training data, parameter updates, etc., thereby forming a comprehensive and multi-layered trust system. Additionally, the verifiable confidential computing (vcc) initiated jointly by AO and PADO utilizes ZKFHE (zero-knowledge fully homomorphic encryption) technology, which ensures the privacy of data and models while guaranteeing their verifiability and computability. Such mechanisms significantly reduce the risks of data sharing while providing intellectual property protection for model providers, encouraging more high-quality models to be open and shared. Combined with a token incentive mechanism, this trust system is expected to further stimulate users to actively contribute data, promoting the entire AI ecosystem to develop to a higher level.

The foundational components and interrelationships of the AO+Arweave ecosystem are shown in the figure:

In summary, the AO+Arweave ecosystem provides an excellent operating environment for decentralized AI: it not only possesses outstanding scalability and hosting capabilities, suitable for supporting decentralized AI ecosystems, but also has significant advantages in large-scale high-quality data storage and exchange, parallel computing, and verifiability. These factors collectively make the AO+Arweave ecosystem an ideal platform for the development of decentralized AI, and through the above arguments, it is clear that decentralized AI plays a crucial role among the three essential elements required for the implementation of the Web3 value internet ecosystem. Thus, AO+Arweave+AI is expected to significantly promote the realization of Web3.