AO+Arweave: Reshaping the future of decentralized AI infrastructure

Author: Qin Jingchun

In the previous article, we discussed how decentralized AI has become a key component for the implementation of Web3 value Internet, and pointed out that AO + Arweave provides an ideal infrastructure for this ecosystem with its technical advantages such as permanent storage, super-parallel computing, and verifiability. This article will further focus on the technical details of AO + Arweave, reveal its unique advantages in supporting the development of AI through comparative analysis with mainstream decentralized platforms, and explore its complementary relationship with vertical decentralized AI projects.

In recent years, with the rapid development of AI technology and the continuous increase in the demand for large model training, decentralized AI infrastructure has gradually become a hot topic of discussion in the industry. Although traditional centralized computing platforms have been continuously upgraded in computing power, their data monopoly and high storage costs are also increasingly revealing their limitations. On the contrary, decentralized platforms can not only reduce storage costs, but also ensure that data and calculations cannot be tampered with through decentralized verification mechanisms, thus playing an important role in key links such as AI model training, reasoning and verification. In addition, Web3 currently has data fragmentation, inefficient DAO organizations, and poor interoperability among platforms. Therefore, it must be integrated with decentralized AI for further development!

This article will compare and analyze the advantages and disadvantages of each mainstream platform from four dimensions: memory limitations, data storage, parallel computing capabilities, and verifiability, and discuss in detail why the AO+Arweave system has shown obvious competitive advantages in the field of decentralized AI.

1. Comparative analysis of various platforms: Why AO+Arweave is unique

1.1 Memory and computing power requirements

With the continuous expansion of the scale of AI models, memory and computing power have become key indicators for measuring platform capabilities. Taking the running of relatively small models (such as Llama-3-8 B) as an example, it requires at least 12 GB of memory; and models like GPT-4 with more than one trillion parameters have even more amazing requirements for memory and computing resources. During the training process, a large number of matrix operations, backpropagation, and parameter synchronization operations require full use of parallel computing capabilities.

• AO+Arweave:AO can split tasks into multiple subtasks for simultaneous execution through its parallel computing unit (CU) and Actor model, realizing fine-grained parallel scheduling. This architecture not only makes full use of the parallel advantages of hardware such as GPUs during training, but also significantly improves efficiency in key links such as task scheduling, parameter synchronization, and gradient updates.

• ICP:Although ICP's subnet supports a certain degree of parallel computing, it can only achieve coarse-grained parallelism when executed inside a unified container, which makes it difficult to meet the needs of fine-grained task scheduling in large-scale model training, resulting in insufficient overall efficiency.

• Ethereum and Base Chain:Both use a single-threaded execution mode, and their original architectural design was mainly aimed at decentralized applications and smart contracts. They do not have the high parallel computing capabilities required for training, running, and verifying complex AI models.

Computing power demand and market competition

With the popularity of projects such as Deepseek, the threshold for training large models continues to decrease, and more and more small and medium-sized companies may join the competition, resulting in an increasing shortage of computing power resources in the market. In this case, decentralized computing power infrastructure with distributed parallel computing capabilities like AO will become more and more popular. As the infrastructure of decentralized AI, AO+Arweave will become the key support for the landing of Web3 value Internet.

1.2 Data storage and economic efficiency

Data storage is another crucial indicator. Traditional blockchain platforms, such as Ethereum, are usually only used to store key metadata due to the extremely high on-chain storage costs, and large-scale data storage is transferred to off-chain solutions such as IPFS or Filecoin.

• Ethereum platform:Relying on external storage (such as IPFS, Filecoin) to save most of the data, although it can ensure the immutability of the data, the high cost of writing on the chain makes it impossible to store large amounts of data directly on the chain.

• AO+Arweave:Using Arweave's permanent low-cost storage capabilities, long-term archiving and immutability of data are achieved. For large-scale data such as AI model training data, model parameters, and training logs, Arweave can not only ensure data security, but also provide strong support for subsequent model lifecycle management. At the same time, AO can directly call the data stored in Arweave to build a complete data asset economic closed loop, thereby promoting the implementation and application of AI technology in Web3.

• Other platforms (Solana, ICP):Although Solana has optimized state storage through the account model, large-scale data storage still needs to rely on off-chain solutions; while ICP uses built-in container storage and supports dynamic expansion, but long-term data storage requires continuous payment of Cycles, and the overall economics are more complicated.

1.3 The importance of parallel computing capabilities

In the process of training large-scale AI models, parallel processing of compute-intensive tasks is the key to improving efficiency. Splitting a large number of matrix operations into multiple parallel tasks can significantly reduce time costs while making full use of hardware resources such as GPUs.

• AO:AO achieves fine-grained parallel computing through independent computing tasks and message passing coordination mechanisms. Its Actor model supports splitting a single task into millions of sub-processes and efficient communication between multiple nodes. Such an architecture is particularly suitable for large model training and distributed computing scenarios. In theory, it can achieve extremely high TPS (transactions per second). Although it is actually subject to limitations such as I/O, it far exceeds traditional single-threaded platforms.

• Ethereum and Base Chain: Due to the single-threaded EVM execution mode, these two seem to be unable to cope with complex parallel computing requirements and cannot meet the requirements of large AI model training.

• Solana and ICP:Although Solana's Sealevel runtime supports multi-threaded parallelism, the parallel granularity is relatively coarse, and ICP is still mainly single-threaded in a single container, which makes it obvious when processing extremely parallel tasks.

1.4 Verifiability and System Trust

A major advantage of decentralized platforms is that through global consensus and tamper-proof storage mechanisms, the credibility of data and calculation results can be greatly improved.

• Ethereum:Through global consensus verification and zero-knowledge proof (ZKP) ecology, the execution of smart contracts and data storage are ensured to be highly transparent and verifiable, but the corresponding verification cost is relatively high.

• AO+Arweave:AO builds a complete audit chain by holographically storing all computing processes in Arweave and using the "deterministic virtual machine" to ensure the reproduction of results. This architecture not only improves the verifiability of the calculation results, but also enhances the overall trust of the system, providing a strong guarantee for AI model training and reasoning.

2. The complementary relationship between AO+Arweave and vertical decentralized AI projects

In the field of decentralized AI, vertical projects such as Bittensor, Fetch.ai, Eliza and GameFi are actively exploring their respective application scenarios. As an infrastructure platform, AO+Arweave has the advantage of providing efficient distributed computing power, permanent data storage and full-chain audit capabilities, which can provide the necessary basic support for these vertical projects.

2.1 Examples of Technology Complementarity

• Bittensor:

  Bittensor participants need to contribute computing power to train AI models, which places extremely high demands on parallel computing resources and data storage. AO's ultra-parallel computing architecture allows many nodes to perform training tasks simultaneously in the same network, and quickly exchange model parameters and intermediate results through an open message passing mechanism, thereby avoiding the bottleneck caused by the sequential execution of traditional blockchains. This lock-free concurrent architecture not only improves the model update speed, but also significantly improves the overall training throughput.

  At the same time, the permanent storage provided by Arweave provides an ideal storage solution for key data, model weights, and performance evaluation results. The large data sets generated during the training process can be written to Arweave in real time. Due to its data immutability, any newly added node can obtain the latest training data and model snapshots, thereby ensuring that network participants can collaborate on training on a unified data basis. This combination not only simplifies the data distribution process, but also provides a transparent and reliable basis for model version control and result verification, allowing the Bittensor network to obtain computing efficiency close to that of a centralized cluster while maintaining the advantages of decentralization, thereby greatly promoting the performance ceiling of decentralized machine learning.

• Fetch.ai's Autonomous Economic Agents (AEAs):

  In the multi-agent collaborative system Fetch.ai, the combination of AO+Arweave can also show excellent synergy. Fetch.ai has built a decentralized platform that enables autonomous agents (Agents) to collaborate on economic activities on the chain. Such applications need to handle the concurrent operation and data exchange of a large number of agents at the same time, which has extremely high requirements for computing and communication. AO provides Fetch.ai with a high-performance operating environment. Each autonomous agent can be regarded as an independent computing unit in the AO network. Multiple agents can execute complex operations and decision logic in parallel on different nodes without blocking each other. The open messaging mechanism further optimizes the communication between agents: agents can asynchronously exchange information and trigger actions through on-chain message queues, thus avoiding the delay problem caused by the global state update of traditional blockchains. With the support of AO, hundreds of Fetch.ai agents can communicate, compete and cooperate in real time, simulating the rhythm of economic activities close to the real world. At the same time, Arweave's permanent storage capability enables Fetch.ai's data sharing and knowledge retention. Each agent's important data generated or collected during operation (such as market information, interaction logs, protocol agreements, etc.) can be submitted to Arweave for storage, forming a permanent public memory library that other agents or users can retrieve at any time without trusting the reliability of centralized servers. This ensures that the records of collaboration between agents are open and transparent - for example, once the terms of service or transaction quotes issued by an agent are written into Arweave, they become public records recognized by all participants and will not be lost due to node failure or malicious tampering. With AO's high-concurrency computing and Arweave's trusted storage, Fetch.ai's multi-agent system can achieve unprecedented depth of collaboration on the chain.

• Eliza Multi-Agent System:

  Traditional AI chatbots usually rely on the cloud, use powerful computing power to process natural language, and use databases to store long-term conversations or user preferences. With AO's hyper-parallel computing, the on-chain intelligent assistant can distribute task modules (such as language understanding, dialogue generation, and sentiment analysis) to multiple nodes for parallel processing, and can respond quickly even if a large number of users ask questions at the same time. AO's message passing mechanism ensures efficient collaboration among modules: for example, the language understanding module extracts semantics and transmits the results to the response generation module through asynchronous messages, so that the conversation process under the decentralized architecture remains smooth. At the same time, Arweave acts as Eliza's "long-term memory bank": all user interaction records, preferences, and new knowledge learned by the assistant can be encrypted and stored permanently. No matter how long the interval is, the user can call up the previous context when interacting again to achieve personalized and coherent responses. Permanent storage not only avoids memory loss caused by data loss or account migration in centralized services, but also provides historical data support for continuous learning of AI models, making the on-chain AI assistant "smarter with use".

• GameFi Real-time Agent Application:

  In decentralized games (GameFi), the complementary characteristics of AO and Arweave play a key role. Traditional MMOs rely on centralized servers for a large number of concurrent calculations and state storage, which is contrary to the decentralized concept of blockchain. AO proposed to distribute the game logic and physical simulation tasks to the decentralized network for parallel processing: for example, in the on-chain virtual world, scene simulations in different areas, NPC behavior decisions, and player interaction events can be calculated simultaneously by each node, and cross-region information can be exchanged through message passing to jointly construct a complete virtual world. This architecture abandons the bottleneck of a single server, allowing the game to linearly expand computing resources as the number of players increases, maintaining a smooth experience.

  At the same time, Arweave's permanent storage provides reliable status records and asset management for the game: key states (such as map changes, player data) and important events (such as rare item acquisition, plot progress) are regularly solidified as on-chain evidence; metadata and media content of player assets (such as character skins, item NFTs) are also directly stored to ensure permanent ownership and tamper-proofing. Even if the system is upgraded or the nodes are replaced, the historical state saved by Arweave can still be restored to ensure that players' achievements and property are not lost due to technological changes: no player wants these data to disappear suddenly. There have been many similar incidents before, for example: Vitalik Buterin was furious when Blizzard suddenly cancelled the Life Straw skill of the magician in World of Warcraft many years ago. In addition, permanent storage also enables the player community to contribute to the game chronicles, and any important events can be retained on the chain for a long time. With the help of AO's high-intensity parallel computing and Arweave's permanent storage, this decentralized game architecture effectively breaks through the bottlenecks of traditional models in performance and data persistence.

2.2 Ecosystem Integration and Complementary Advantages

AO+Arweave not only provides infrastructure support for vertical AI projects, but is also committed to building an open, diverse, and interconnected decentralized AI ecosystem. Compared with projects that focus on a single field, AO+Arweave has a wider ecological scope and more application scenarios. Its goal is to build a complete value chain covering data, algorithms, models, and computing power. Only in such a huge ecosystem can the potential of Web3 data assets be truly released and a healthy and sustainable decentralized AI economic closed loop be formed.

III. Web3 Value Internet and Permanent Value Storage

The advent of the Web3.0 era marks that data assets will become the most core resource on the Internet. Similar to the Bitcoin network storage of "digital gold", the permanent storage service provided by Arweave enables valuable data assets to be preserved for a long time and cannot be tampered with. At present, the monopoly of user data by Internet giants makes it difficult to reflect the value of personal data. In the Web3 era, users will have data ownership, and data exchange will be effectively realized through token incentive mechanisms.

• Attributes of value storage:

  Arweave has achieved powerful horizontal expansion capabilities through Blockweave, SPoRA and bundling technology, especially in large-scale data storage scenarios. This feature enables Arweave to not only undertake the task of permanent data storage, but also provide solid support for subsequent intellectual property management, data asset transactions and AI model life cycle management.

• Data asset economy:

  Data assets are the core of the Web3 value Internet. In the future, personal data, model parameters, training logs, etc. will become valuable assets, and efficient circulation will be achieved through token incentives, data rights confirmation and other mechanisms. AO+Arweave is an infrastructure built based on this concept, and its goal is to open up the circulation channels of data assets and inject continuous vitality into the Web3 ecosystem.

Fourth, Risks, Challenges and Future Prospects

Although AO+Arweave has shown many advantages in technology, it still faces the following challenges in practice:

1. Complexity of economic model

AO's economic model needs to be deeply integrated with the AR token economic system to ensure low-cost data storage and efficient data transmission. This process involves incentive and punishment mechanisms among multiple nodes (such as MU, SU, CU), and a flexible SIV sub-staking consensus mechanism must be used to balance security, cost and scalability. In the actual implementation process, how to balance the number of nodes with task requirements and avoid idle resources or insufficient income is a problem that the project party needs to seriously consider.

2. Insufficient construction of decentralized model and algorithm market

The current AO+Arweave ecosystem focuses mainly on data storage and computing power support, and has not yet formed a complete decentralized model and algorithm market. Without a stable model provider, the development of AI-Agent in the ecosystem will be restricted. Therefore, it is recommended to support decentralized model market projects through ecological funds to form high competitive barriers and long-term moats.

Despite many challenges, with the gradual arrival of the Web3.0 era, the confirmation and circulation of data assets will promote the reconstruction of the entire Internet value system. As a pioneer in infrastructure, AO+Arweave is expected to play a key role in this change and help build a decentralized AI ecosystem and Web3 value Internet.

Conclusion

Based on a detailed comparative analysis of the four dimensions of memory, data storage, parallel computing, and verifiability, we believe that AO+Arweave has shown obvious advantages in supporting decentralized AI tasks, especially in meeting the needs of large-scale AI model training, reducing storage costs, and improving system trust. At the same time, AO+Arweave not only provides strong infrastructure support for vertical decentralized AI projects, but also has the potential to build a complete AI ecosystem, thereby promoting the closed loop of Web3 data asset economic activities and bringing greater changes.

In the future, with the continuous improvement of economic models, the gradual expansion of ecological scale, and the deepening of cross-domain cooperation, AO+Arweave+AI is expected to become an important pillar of the Web3 value Internet, bringing new changes to data asset rights confirmation, value exchange, and decentralized applications. Although there are still certain risks and challenges in the actual implementation process, it is through continuous trial and error and optimization that technology and ecology will eventually usher in breakthrough progress.