AO+Arweave: Reshaping the future of Decentralization AI infrastructure

Author: Qin Jingchun

In the previous article, we discussed how decentralized AI has become a key component of the landing of Web3 value internet, and pointed out that AO + Arweave provides an ideal infrastructure for this ecosystem with its technological 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 demand for large-scale model training, decentralized AI infrastructure has gradually become a hot topic of industry discussion. Although traditional centralized computing platforms continue to upgrade their computing power, their data monopoly and high storage costs are increasingly revealing limitations. On the contrary, decentralized platforms can not only reduce storage costs, but also ensure the immutability of data and computation through decentralized validation mechanisms, thus playing an important role in key processes such as AI model training, inference, and validation. In addition, Web3 currently suffers from data fragmentation, low efficiency of DAO organizations, and poor interoperability between platforms, so it must be integrated with decentralized AI to further develop!

This article will start from four dimensions: memory limit, data storage, parallel computing capabilities, and verifiability, compare and analyze the advantages and disadvantages of various mainstream platforms, and discuss in detail why the AO+Arweave system shows significant competitive advantages in the decentralized AI field.

1. Comparison Analysis of Various Platforms: Why AO+Arweave Stands Out

1.1 Memory and Computing Power Requirements

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

• AO+Arweave: AO can split tasks into multiple sub-tasks for simultaneous execution through its Concurrent Computing Units (CU) and Actor model, achieving fine-grained parallel scheduling. This architecture not only fully leverages the parallel advantages of hardware such as GPU during the training process but also significantly improves efficiency in key aspects such as task scheduling, parameter synchronization, and gradient updates.
• ICP: Although ICP’s subnet supports a certain degree of parallel computation, when executed within a unified container, it can only achieve relatively coarse-grained parallelism, making it difficult to meet the demand for fine-grained task scheduling in large-scale model training, resulting in overall inefficiency.
• Ethereum and Base Chain: Both adopt a single-threaded execution mode. Their architectural design is mainly aimed at decentralized applications and smart contracts, lacking the high parallel computing power required for training, running, and verifying complex AI models.

Calculation Power Demand and Market Competition

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

1.2 Data Storage and Economics

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

• Ethereum Platform: Relies on external storage (such as IPFS, Filecoin) to save most of the data, which ensures the immutability of the data. However, the high on-chain writing cost makes it impossible to directly implement large-scale data storage on the chain.
• AO+Arweave: Using Arweave’s permanent and low-cost storage capabilities to achieve long-term archiving and tamper resistance of data. For large-scale data such as AI model training data, model parameters, training logs, etc., Arweave can not only ensure data security but also provide strong support for subsequent model lifecycle management. At the same time, AO can directly access the data stored on Arweave, build a complete data asset economy closed loop, and promote the landing and application of AI technology in Web3.
• Other Platforms (Solana, ICP): Although Solana has optimized state storage through an account model, large-scale data storage still relies on off-chain solutions; while ICP adopts built-in container storage, supports dynamic expansion, but long-term storage requires continuous payment of Cycles, and the overall economics is more complex.

1.3 The Importance of Parallel Computing Capability

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

• AO: AO achieves fine-grained parallel computing through independent computation tasks and message passing coordination mechanism. Its Actor model supports splitting a single task into millions of subprocesses and efficiently communicating between multiple nodes. This architecture is particularly suitable for large-scale model training and distributed computing scenarios, theoretically achieving extremely high TPS (transactions per second), although it is actually limited by I/O and other constraints, far exceeding traditional single-threaded platforms.
• Ethereum and Base Chain: Due to the single-threaded EVM execution mode, both of them appear inadequate when facing complex parallel computing requirements and cannot meet the needs of AI large model training.
• Solana and ICP: Although Solana’s Sealevel runtime supports multi-threaded parallelism, the parallel granularity is coarse, while ICP still primarily relies on single-threading within a single container, leading to significant bottlenecks when handling extreme parallel tasks.

1.4 Verifiability and System Trust

One of the major advantages of decentralized platforms is that they can greatly enhance the credibility of data and computational results through global consensus and immutable storage mechanisms.

• Ethereum: Through global consensus verification and zero-knowledge proof (ZKP) ecosystem, ensure the high transparency and verifiability of smart contract execution and data storage, but the corresponding verification costs are high.
• AO+Arweave: By storing all computing processes holographically on Arweave and using the ‘deterministic virtual machine’ to ensure result reproducibility, AO has built a complete audit chain. This architecture not only enhances the verifiability of computing results but also strengthens the overall trust of the system, providing strong security for AI model training and inference.

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

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

2.1 Technical Complementarity Example

• 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 super-parallel computing architecture allows numerous nodes to simultaneously execute training tasks in the same network and quickly exchange model parameters and intermediate results through an open message passing mechanism, thereby avoiding the bottleneck of traditional blockchain sequential execution. This lock-free concurrent architecture not only improves the speed of model updates, but also significantly increases overall training throughput.

At the same time, Arweave’s permanent storage provides an ideal solution for key data, model weights, and performance evaluation results. Large datasets generated during the training process can be written to Arweave in real-time. Due to its immutability, any new node can access the latest training data and model snapshots, ensuring that network participants collaborate on a unified data basis. This combination simplifies the data distribution process, provides transparent and reliable basis for model version control and result verification, allowing the Bittensor network to maintain decentralized advantages while achieving the computational efficiency of a centralized cluster, greatly pushing the performance limits of decentralized machine learning.

• Autonomous Economic Agents (AEAs) of Fetch.ai:

In the multi-agent collaborative system Fetch.ai, the combination of AO and Arweave can also demonstrate outstanding synergistic effects. Fetch.ai has built a decentralized platform that enables autonomous agents to collaborate and conduct economic activities on the chain. Such applications require simultaneous processing of a large number of agents, concurrent operation, and data exchange, with extremely high computational and communication requirements. AO provides Fetch.ai with a high-performance operating environment, where each autonomous agent can be considered as an independent computing unit in the AO network. Multiple agents can execute complex calculations and decision logic in parallel on different nodes without blocking each other. An open message passing mechanism further optimizes communication between agents: agents can asynchronously exchange information and trigger actions through the on-chain message queue, thus avoiding the delay issues caused by traditional blockchain global state updates. With the support of AO, hundreds or thousands of Fetch.ai agents can communicate, compete, and cooperate in real time, simulating an economic activity rhythm close to the real world.

At the same time, Arweave’s permanent storage empowers Fetch.ai’s data sharing and knowledge preservation. Every agent can submit important data generated or collected during operation (such as market information, interaction logs, protocol agreements, etc.) to Arweave for preservation, forming a permanent public memory bank. Other agents or users can retrieve it at any time without relying on the reliability of centralized servers. This ensures that the records of cooperation among agents are publicly transparent. For example, once a service term or transaction quote written by an agent is stored in Arweave, it becomes a publicly recognized record for all participants and will not be lost due to node failures 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-chain.

• Eliza Multi-Agent System:

Traditional AI chatbots typically rely on the cloud for powerful computing to process natural language and utilize databases to store long-term conversations or user preferences. With AO’s massively parallel computing, the on-chain intelligent assistant can distribute task modules (such as language understanding, dialogue generation, emotion analysis) to multiple nodes for parallel processing, enabling quick responses even when numerous users ask questions simultaneously. AO’s message passing mechanism ensures efficient collaboration among modules: for example, the language understanding module extracts semantics and asynchronously transmits the results to the response generation module, maintaining smooth dialogue flow in a decentralized architecture. Meanwhile, Arweave acts as Eliza’s ‘long-term memory bank’: all user interaction records, preferences, and newly learned knowledge by the assistant can be encrypted and permanently stored, allowing retrieval of previous context no matter how much time has passed, enabling personalized and coherent responses when users interact again. Permanent storage not only prevents memory loss caused by data loss or account migration in centralized services, but also provides historical data support for continuous learning of AI models, enabling on-chain AI assistants to ‘get smarter with use’.

• GameFi real-time proxy application:

In decentralized gaming (GameFi), the complementary properties of AO and Arweave play a key role. Traditional MMOs rely on centralized servers for a large amount of concurrent computation and state storage, which is contradictory to the decentralized concept of blockchain. AO proposes to decentralize game logic and physical simulation tasks for parallel processing on a decentralized network: for example, in the virtual world on the chain, different regions’ scene simulations, NPC behavior decisions, and player interaction events can be calculated simultaneously by various nodes and exchange cross-zone information through message passing to collectively construct a complete virtual world. This architecture eliminates the bottleneck of a single server, allowing the game to linearly expand computational resources as the number of players increases, maintaining a smooth experience.

At the same time, Arweave’s permanent storage provides reliable state records and asset management for games: key states (such as map changes, player data) and important events (such as obtaining rare items, plot progress) are regularly solidified as on-chain evidence; player assets (such as character skins, item NFTs) metadata and media content are also directly stored, ensuring permanent ownership and tamper resistance. Even with system upgrades or node replacements, the historical states saved by Arweave can still be restored, ensuring that player achievements and properties are not lost due to technological changes: no player wants these data to suddenly disappear, as many similar events have occurred in the past, for example: many years ago, Vitalik Buterin was enraged when Blizzard suddenly removed the life drain skill of mages in World of Warcraft. In addition, permanent storage allows player communities to contribute to the game’s chronicle, ensuring that any significant events are preserved on the chain for a long time. With AO’s high-intensity parallel computing and Arweave’s permanent storage, this decentralized game architecture effectively breaks through the performance and data persistence bottlenecks of traditional models.

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2.2 Ecosystem Integration and Complementary Advantages

AO+Arweave not only provides infrastructure support for vertical AI projects, but also is committed to building an open, diverse, and interconnected decentralized AI ecosystem. Compared to projects that focus solely on a specific field, the ecological scope of AO+Arweave is wider and has 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 true potential of Web3 data assets be unleashed, forming a healthy and sustainable decentralized AI economic loop.

Three, Web3 Value Internet and Permanent Value Storage

The advent of the Web 3.0 era marks that data assets will become the most core resource in the Internet. Similar to Bitcoin’s network storage “digital gold”, Arweave provides a permanent storage service that 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, while in the Web3 era, users will have data ownership, and data exchange will be effectively realized through token incentive mechanisms.

• Properties of value storage:

Arweave achieves powerful horizontal scalability through Blockweave, SPoRA, and bundling technologies, especially excelling in large-scale data storage scenarios. This feature enables Arweave to not only take on the task of permanent data storage, but also provide solid support for subsequent knowledge property management, data asset trading, and AI model lifecycle 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 all become valuable assets, and efficient circulation will be achieved through mechanisms such as token incentives and data rights confirmation. AO+Arweave is precisely built on this concept of infrastructure, aiming to open up circulation channels for data assets and inject sustained vitality into the Web3 ecosystem.

Four, Risks and Challenges, and Future Prospects

Despite AO+Arweave showing many advantages in technology, it still faces the following challenges in practice:

1. Complexity of the 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 the incentive and penalty mechanisms between multiple nodes (such as MU, SU, CU), and must balance security, cost, and scalability through the flexible SIV sub-collateral consensus mechanism. In the actual implementation process, how to balance the number of nodes with task requirements, avoid idle resources or insufficient income, is a problem that the project party needs to carefully consider.

2. Insufficient construction of decentralized model and algorithm markets

The current AO+Arweave ecosystem mainly focuses on data storage and computing power support, without forming a complete decentralized model and algorithm market. Without stable model providers, the development of AI-Agent in the ecosystem will be constrained. Therefore, it is recommended to support decentralized model market projects through the ecosystem fund to form high competitive barriers and long-term moats.

Despite facing many challenges, with the gradual arrival of the Web3.0 era, the empowerment and circulation of data assets will drive the restructuring of the entire Internet value system. As a pioneer in infrastructure, AO+Arweave is expected to play a key role in this transformation, helping to build a decentralized AI ecosystem and Web3 value Internet.

Conclusion

Comprehensive comparison and analysis from the four dimensions of integrated memory, data storage, parallel computing, and verifiability lead us to believe that AO+Arweave demonstrates significant advantages in supporting decentralized AI tasks, especially in meeting the demands of large-scale AI model training, reducing storage costs, and enhancing 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 formation of Web3 data asset economic activities and bringing about greater changes.

In the future, with the continuous improvement of the economic model, the gradual expansion of the ecological scale, and the deepening of cross-domain cooperation, AO+Arweave+AI is expected to become an important pillar of the Web3 Internet of Value, bringing new changes to data asset 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 the technology and ecology will eventually usher in breakthroughs.