1. Computational and Resource Bottlenecks Under Traditional Frameworks

Traditional blockchain technology, represented by Bitcoin and Ethereum, has achieved significant success in decentralization, transparency, and security, driving the development of cryptographic technologies and applications. However, due to the 'Blockchain Impossibility Triangle' dilemma (Figure 1-1), there are obvious bottlenecks in computational performance and resource utilization, hindering technological innovation and application development, presenting challenges for the crypto industry.

Figure 1-1. The Blockchain Impossibility Triangle

First, let us analyze the three elements in the 'Blockchain Impossibility Triangle':

• Security: Security essentially reflects consensus requirements, specifically in ensuring the consistency, integrity, immutability, traceability, and verifiability of block data. Meeting these characteristics allows blockchain to construct a 'trustless' strong trust security mechanism. Therefore, the security of consensus is the primary demand of blockchain and the cornerstone of its development.

• Decentralization: Decentralization means that there is no single control point in the system; power and control are distributed among multiple nodes, which can enhance the system's fault tolerance, censorship resistance, and security, preventing single points of failure and malicious manipulation. While distributed systems are not necessarily decentralized systems (for example, a distributed system controlled by a single entity is not decentralized), decentralized systems are certainly distributed systems.

• Scalability: In the context of the 'Blockchain Impossibility Triangle,' scalability refers to the capability of a distributed system to expand its computational performance. For digital systems, everything is computation, and different applications have varying computational performance requirements. Broadly speaking, scalability refers to the system's ability to handle the growing volume of data, transactions, and users, which encompasses not only TPS (transactions per second) but also storage capacity, network bandwidth, and the number of nodes. High scalability is necessary to support large-scale applications and user growth. The scalability of distributed systems directly affects the innovation and scalability of decentralized applications (DApps) built on them.

Among the three elements mentioned above, blockchain emphasizes decentralization, reinforcing verification and consensus security, while computational performance is relatively weak. This gives rise to the blockchain impossibility triangle dilemma: when the demands for decentralization and consensus security are met, the scalability of computation will be constrained, as seen in Bitcoin. This means that under such a system framework, the distributed system of blockchain struggles to support application innovations with high computational performance or cannot meet the scaling needs of applications, such as AI big data models, graphic rendering, on-chain gaming, and large-scale social interactions.

The above mainly analyzes the computational performance expansion difficulties brought by the blockchain impossibility triangle. What is the root cause of this issue? Next, we will explore the interrelationships among the elements within blocks from the process of block formation.

In blockchain technology, a 'block' refers to a dataset formed by packaging a series of verified transaction data within a specific time interval. This concept includes the following key elements and their interrelationships:

• Consensus (Data): Verified transaction data with state consistency, i.e., consensus data formed within blocks.

• Block space: Refers to the storage space for transaction data. These transactions are encapsulated within blocks, and the number of transactions that can be stored is limited by the block size (set by the system or constrained by the total Gas fees of that block), meaning that on-chain storage space is a limited resource, which in turn affects application scalability.

• Computational performance: The number of packaged transactions divided by block time gives the transactions per second (TPS) = number of transactions in the block / block time. Computational performance is related to the consensus process and storage space.

From the above analysis, it is clear that the three elements of consensus, storage space, and computational performance within a block are interrelated, forming a constrained relationship. While pursuing consistent consensus, blockchain not only constrains the scalability of the storage space of individual blocks but also limits the scalability of computational performance. This is the root cause of the blockchain impossibility triangle problem.

Further analysis shows that during the process of block formation, blockchain systems construct three types of global, system-level resources: data (consensus) resources, storage resources, and computational resources. However, the impossibility triangle problem limits the function and scalability of these three resources, forming resource bottlenecks and making it difficult to fully unleash their potential. If a method exists to break this constraint, would it bring a resource-driven new development landscape for blockchain?

This is precisely the core issue of this article, aiming to find answers. Research shows that from the SCP paradigm, the super-parallel computing model Actor, to the SSI distributed system architecture, a complete technological chain has formed in the engineering practice of AO + Arweave, breaking the blockchain impossibility triangle dilemma, fully unleashing the resource potential of blockchain and distributed systems, and providing empowerment in practice, thus opening up a new development path for value creation and large-scale applications in Web3.

2. SCP: Breaking Through Bottlenecks in Computational Performance and Resource Expansion

2.1. Breaking the Blockchain Impossibility Triangle Based on SCP

AO (Super Parallel Computing Network) is built on Arweave, realizing the engineering application of the Storage-based Consensus Paradigm (SCP). As shown in the figure below:

Figure 2-1. Modular System Architecture of AO + Arweave Based on SCP

Based on the core concept of SCP, the AO + Arweave system architecture achieves effective separation between on-chain storage (consensus) and off-chain computation:

• Storage layer: The storage resources provided by Arweave are responsible for the permanent storage of data, and blockchain technology ensures the traceability and immutability of on-chain data, achieving data consistency and high availability, reflecting the concept of 'storage equals consensus.'

• Computational layer: Computational tasks are migrated off-chain, decoupled from the storage (consensus) layer. This design allows computational performance to be unaffected by on-chain consensus directly, enabling infinite scalability through the addition of off-chain computational nodes, significantly improving processing efficiency and system flexibility.

• Comprehensive effect: Arweave's storage public chain maintains the decentralization of the system and the consensus security of data, while AO off-chain ensures the infinite scalability of computational performance. This structure ensures that the entire AO + Arweave system meets the demands for decentralization, consensus security, and computational performance scalability, effectively addressing the challenges of the blockchain impossibility triangle.

2.2. Constructing Three Types of Global System-Level Resources

The features based on SCP play an important role in the practical application of the system; they make storage, computation, and data (consensus) both interconnected and independently operable system elements, forming global system-level resources, as shown in Figure 2-2:

Figure 2-2. Global system-level resources in the AO network

• Storage space resources: As a storage public chain, Arweave's storage space expansion is not limited by block size or total Gas fees, but is entirely determined by storage demand, achieving true unlimited scalability. This not only meets the system's need for flexible storage space but also enriches the diversity of on-chain data types, providing more possibilities for the innovation of on-chain native applications.

• Computational resources: The AO computing network consists of MU, SU, and CU; here we will first discuss CU, with a detailed analysis of each network unit's roles and interrelations to follow. CU is the unit responsible for computation, capable of horizontal scaling to form CU clusters. These clusters compete for computational rights, supporting different processes to run in different CUs in parallel. This scalability and parallelism design enable AO to provide infinite computational node resources, supporting high-performance parallel computing.

• Data (consensus) resources: On Arweave, any type and size of data can be permanently stored in the form of 'atomic assets,' such as NFTs, documents, images, audio and video, web pages, games, legal contracts, program code, etc. This data forms a tamper-proof massive database, providing a foundation for data monetization and circulation. Meanwhile, AO does not reach consensus on the state of the computation itself but focuses on ensuring that interaction logs are written to Arweave, ensuring the persistent availability and integrity of the data, and ensuring the consistency and verifiability of computational output results. Any type of data can be referenced without permission or trust, enabling new value creation.

• Security resources: In the operation of AO, a security resource supported by the protocol token $AO is also constructed, but this is not directly related to SCP; it involves the operation and security mechanisms of the AO network communication units, which will be analyzed in detail in Section 3, 'Customizable Security and Security Resources.'

2.3. Trusted Computer Based on Storage Consensus

Utilizing the aforementioned system-level resources and distributed characteristics, AO is built on the Arweave storage public chain, forming a cloud computing network. Similar to traditional Web2 cloud computing, AO theoretically possesses unlimited computational and storage resource capabilities to support massive data resources. However, AO's uniqueness lies in its establishment of a decentralized, globally consistent consensus trusted computing platform based on the storage consensus paradigm.

• Firstly, Arweave provides global users with a permissionless, permanent storage service, establishing a consensus data foundation that does not rely on trust.

• Secondly, AO stores the source code of various applications on the Arweave chain; this code can be downloaded and run locally; its input comes from trusted data on-chain, ensuring the consistency and predictability of output results under fixed inputs and execution logic.

• Finally, any client can perform consistency verification because, under the same input parameters and execution logic, the computational output results must be consistent, thereby ensuring trustworthiness.

It can be seen that, since the source program, inputs, and outputs are all deterministic, AO has built a trusted computing system based on storage consensus.

The storage consensus paradigm differs from typical node consensus systems; in the storage consensus paradigm, computation, verification, and consensus are all conducted off-chain, with the final consensus data submitted on-chain for storage, forming the system's availability layer, consensus layer, and settlement layer. In other words, with the support of SCP, computing performance is no longer constrained by consensus and can expand infinitely off-chain. This mechanism provides feasibility for AO network to build a high-parallel and distributed architecture that supports high-performance computing.

So, how has AO evolved into a decentralized world computer with distributed deployment and high parallel operation? This is primarily due to the Actor model, network communication units, and the distributed architecture realized by SSI.

3. Super Parallel: Actor Model and Network Communication Units

3.1. Defining the Basic Framework for Parallel Computing with the Actor Model

The name of the AO network derives from 'Actor Oriented,' indicating that it is a super-parallel computing network. This name originates from its core application of the Actor model, which establishes the foundational structure for parallel computation within the system.

In the Actor model, 'actor' is the basic unit of parallel computation, consisting of three core elements: State, Behavior, and Mailbox. The interaction between these three elements constitutes the core concept of the Actor model, as shown in Figure 3-1:

Figure 3-1. Actor Model Schematic Diagram (Image source: Reference Material 5)

This model defines the system's core components and interaction rules; an actor can be seen as an independent, concurrently active entity that can receive, process, and send messages while dynamically creating new actors. The model has the following characteristics:

• Asynchronous communication: Multiple actors send uniformly formatted messages via point-to-point communication, and the sending and processing of messages occur asynchronously, making this communication method naturally suitable for interactions among nodes in distributed systems.

• Parallel execution: Each actor is independent, with no shared state, thus there is no concern that the state of other actors will affect its own; each actor can independently handle its tasks, achieving true parallel operations.

• Distributed deployment: Actors can be deployed and scheduled to run on different CPUs, nodes, or even different time slices without affecting the final results.

• Scalability: Due to its distributed nature and loosely coupled design, the Actor model can flexibly scale horizontally by increasing nodes and dynamically balancing loads.

In summary, the Actor model optimizes parallel and concurrent issues with its elegant handling mechanisms, making it particularly suited for building distributed systems and high-concurrency applications. The AO network adopts the Actor model as the architectural basis for parallel computing, thus achieving efficient asynchronous communication, parallel operation, distributed deployment, and excellent scalability.

3.2. Efficient Parallel Computing Implementation of Communication Network Units

The Actor model provides a framework for parallel computing, while the AO network communication units embody the concrete practice of this model. These network units include message units (MU), scheduling units (SU), and computing units (CU), each of which is an independent 'actor' that collaborates and synchronizes through uniformly formatted messages (ANS-104). Figure 3-2 illustrates the basic functions and message interaction processes of these network units.

Figure 3-2. Working principle of AO network communication units (Image source: AO White Paper)

In the AO network, launching an application triggers the initiation of one or more processes, and the system configures resources such as memory, virtual machines, and communication network units for each process. Interaction between processes is entirely completed through messages. First, messages from users or other processes are sent to MU, which then forwards the messages to SU for sorting. The sorted messages and their results are permanently stored on Arweave, and state calculations are performed by a CU from the CU cluster competing for computational rights, meaning processes can run on any computational node, demonstrating typical characteristics of decentralized parallel computing. After the computation is complete, CU returns the results to SU in the form of signed credentials to ensure the accuracy and verifiability of the computation results, which are finally uploaded to Arweave by SU. The complete dataset formed by each process—including initial state, processing steps, and final results—will be permanently stored on Arweave, serving as consensus data for others to retrieve, verify, and utilize.

Figure 3-3. Communication process between units in Token transfer (Image source: AO White Paper)

Figure 3-3 illustrates the specific application scenario of the AO network processing Token transfer requests, clearly depicting the composition and communication process of various modular network units, as well as the distributed storage mechanism formed through interaction with Arweave.

The AO system comprehensively utilizes computing resources (distributed CU clusters), storage resources (distributed Arweave nodes), and data resources (long-term available data stored in Arweave), laying the foundation for AO to become a global computing platform. Based on the Actor model construction, AO's computing network not only possesses features of asynchronous communication, parallel execution, and distributed deployment but also has excellent scalability, making it a truly decentralized, distributed, and parallel operational computing network.

3.3. Customizable Security and Security Resources

In the previous section, we explored the composition and working principles of AO network communication units. In this section, we will delve into the network's security, which is closely linked to the native token $AO of the AO protocol. This analysis will echo the 'Security Resource' content in Section 2.2, focusing on the customizable security and security resources in the AO network.

The network communication units composed of MU, SU, and CU are the core components of the AO computing network, constructing the operating mechanism of the decentralized world computer, forming three types of system-level resources: computing, storage, and data. This serves as the foundation for the technical model and resource model in the AO network. Based on the technical and resource models, the AO system creates a demand-driven customizable security mechanism. This is an economic model constructed based on the protocol's native token $AO, where economic games provide security assurances, thereby establishing a security market within AO.

To facilitate understanding, we will simplify the security mechanisms in AO from the user's perspective into several core elements and their interrelationships: customizable requirements, security/economic resources, security mechanisms, and security competition markets.

Figure 3-4. The relationship between various elements in the AO network security mechanism

Figure 3-4 describes the interrelationships among various elements in the AO network security mechanism:

• Customizable requirements: As a super-parallel computing platform, each node in AO independently and in parallel runs various processes, handling different types of data. These different data transaction scenarios have varying demands concerning system latency, cost, and efficiency, which requires AO's security model to be flexible enough to customize security strategies according to needs. Users can specify the particular security level required for each message, thus promoting the customization and effective allocation of security resources.

• Security/Economic resources: $AO is the protocol's native token, serving as a circulating public value unit and economic resource, underpinning the economic game mechanism of all security mechanisms in the AO network.

• Security mechanisms: In various processes of AO, including nodes like MU, SU, and CU, $AO needs to be staked to participate in security mechanisms. By staking economic value, the system manages funds, executing penalties according to rules to prevent malicious behaviors. For example, if MU signs an invalid message or CU provides an invalid signature proof, the system will reduce its staked assets.

• Security competition market: Since security is purchased per message, different messages correspond to different staking requirements, thus creating a dynamic competitive market. The price of security is determined by market supply and demand rather than fixed network rules. This market competition mechanism promotes the effective pricing and allocation of security resources, providing customized security.

In summary, the decentralized peer-to-peer market structure of the AO network essentially allows nodes to independently set the fees for their message-passing services, adapting to the varying security level needs of different data transactions, reflecting the system's efficiency in responding to specific security demands. This flexibility enables it to dynamically adapt to changes in market demand and supply, fostering competition and enhancing response efficiency, thus achieving efficient market balance.

$AO's liquidity serves as a tool for economic games, establishing a comprehensive and real-time token valuation framework while simultaneously establishing the security mechanism, providing a solid foundation for effective valuation of the token. A well-structured $AO token economic model, with comprehensive valuation frameworks and metrics, will undoubtedly further enhance the security of the AO network.

4. SSI: A Distributed System Architecture for Unified Experience

In previous discussions, we have elaborated on the basic framework provided by the Actor model for parallel computation in the AO network, and how the network communication units composed of MU, SU, and CU specifically implement this model. These communication units are deployed across different heterogeneous nodes in the distributed network, allowing process execution to be unrestricted by specific physical locations, and enabling seamless user interactions through the network. All of this together forms a unified computing environment, realizing a single system image (SSI), which is the foundation for the AO network to support countless processes. This section will explore the definition of SSI and its specific role in AO.

Single System Image (SSI) is a core concept in distributed computing, which integrates physically separated heterogeneous computing resources into a unified resource pool through virtualization technology. This integration not only enhances the abstraction level of the system but also greatly optimizes user experience. Under the influence of SSI, although the system may consist of multiple servers, distributed databases, or multiple networks, from the user's perspective, it feels like operating a single computer.

Typically, the SSI structure includes user layer, unified interface, resource management layer, computing nodes, and storage layer, as shown in the schematic diagram Figure 4-1.

Figure 4-1. Single System Image SSI Structure Schematic Diagram

Users interact with the SSI system at the user layer through clients or web frontends. The unified interface is responsible for receiving user requests and distributing these requests to the resource management layer. The resource management layer schedules distributed computing nodes and storage resources to perform parallel computing tasks or read/write operations on data.

SSI provides a feasible solution for the current coexistence of multiple chains in public chains. For example, the Ethereum ecosystem, due to its rapid development, faces congestion, inefficiency, and high cost issues, while Layer2, as the main solution to these scalability problems, introduces new challenges. Each Layer2 chain, while redundantly building infrastructure, also leads to liquidity dispersion and cross-chain asset risks, increasing the complexity and participation thresholds for users switching between chains, severely impacting user experience and the scalability of applications.

Public chains like Solana and Polkadot have already recognized these issues and made adjustments based on their original architectures. However, AO adopted the distributed architecture of SSI from the outset, demonstrating foresight and insight.

Utilizing the Actor model, AO's network communication units are hosted on heterogeneous node sets within a distributed network, which may be spread across various regions globally, including various types and functions of servers. The AO computing network based on the Actor model is a decentralized distributed network requiring a unified architecture for integration to provide consistent availability and user experience.

When a user initiates an AO process through the frontend, the system configures various resources needed to handle tasks such as message passing, transaction sorting, and state computation. For users, the underlying complex distributed architecture is abstracted, so even a large cluster of nodes appears as a single computer. This is because the AO system uses SSI to integrate the complex components of distributed systems, achieving a unified computing environment. In other words, through the SSI architecture, AO integrates multiple distributed computing nodes into a single resource, providing users with a transparent, efficient, scalable, and unified computing platform.

5. Resource-Driven Value Creation and Application Innovation

In summary, through the combination of SCP, Actor, and SSI, AO constructs an innovative architecture that provides three scalable system-level resources for computing, storage, and data (consensus), as well as a security resource supported by $AO. Resources, as core production factors, play a key role in promoting technological progress, stimulating application innovation, and improving economic efficiency. By clarifying the resource elements in the AO + Arweave system, we can optimize resource planning and management, drive technological and application innovation with resources, accelerate value creation in Web3, and promote the growth of the crypto economy.

Here, we provide a summarizing organization:

1. Infrastructure-driven Value Creation

• Decentralized World Computer: AO integrates scalable computing, storage, and data resources, providing a unified decentralized computing platform for all applications, with verifiable and trust-minimized characteristics. Applications only need to focus on business innovation, avoiding the repetition of building wheels, making AO a public infrastructure for application innovation.

• On-chain shared data resource library: Arweave can permanently store almost all types of data, becoming an 'Alexandria Library' that never disappears. Whether financial data or non-financial data, its immutable and verifiable characteristics make it a public good that can provide consensus value, supporting combinatorial innovation.

• Customizable security facilities: AO can provide customized security mechanisms for customers and applications based on different data types and values, achieving a balance between security, cost, and efficiency.

• Bridge between Web2 and Web3: AO operates off-chain, seamlessly integrating with on-chain and off-chain systems, becoming the connecting bridge between Web2 and Web3. Any Web2 application can initiate processes in AO through APIs and messaging mechanisms, invoking network units in AO to perform computations while customizing its security mechanisms.

2. Technological and Application Innovation

As of now, blockchain development, primarily led by public chains such as Bitcoin, Ethereum, and Solana, still leans towards the financial domain, such as asset issuance, trading, collateral lending, and derivatives, leading many to mistakenly believe that the role of blockchain is limited to these areas.

However, the innovative architecture of AO + Arweave adds new feasibility for technological innovation and application development in blockchain. In addition to supporting the financial innovations typical of most public chains, AO, as a universal world computer, supports all types of data and corresponding application innovations, especially those driven by non-financial data.

• Loading AI Models: The AO + Arweave architecture provides unlimited computing, storage, and data resources, supported by three key technologies: WASM64, WeaveDrive, and Llama.cpp large language model inference engine, allowing AO to run various open-source large language models directly in smart contracts, such as the Llama 3 and GPT-2 models, enabling smart contracts to directly process complex data and make concurrent decisions, such as the on-chain autonomous virtual world Llama Land powered by AI-driven Llama 3 model.

• Create Agent and AgentFi: Based on AI model reasoning capabilities, and the ability of the AO process to respond to implicit messages based on time, awaken itself and execute actions, as well as the ability to 'subscribe' to a process by paying fees to MU, thus triggering calculations at an appropriate frequency, AO supports Agents and AgentFi that can meet complex business logic, predefined needs, and diversified autonomous strategies.

• Copyright management and creator marketplace (ContentFi): Arweave stores various types of data in the form of atomic assets, making data easy to identify and verify ownership, allowing it to be monetized as a new form of digital asset through circulation and trading in the market, establishing clear benefit distribution and collaboration models to support copyright management and creator marketplaces.

• Next-generation Internet framework Permaweb: Unlike the traditional three-layer structure of application layer, service layer, and storage layer in Web2, Permaweb achieves permanent storage of all content by replacing the storage layer with Arweave's permanent storage solution, and storing it in Arweave in the form of atomic assets. Based on SCP, it builds various applications supporting AO's super-parallel computation at the application layer, creating a new generation of online, decentralized Internet framework. This framework integrates with Web2, providing a similar experience, but there are significant differences; Permaweb is not a 'walled garden.' It offers developers, operators, and users a fair and open environment: users own and control their own data; data can flow freely between different applications; developers and operators can utilize data to conduct business without special permission within established rules, thus promoting mutual benefit and win-win among all parties.

The above are several typical application innovation directions supported by AO. Of course, AO can support more data types and a wider range of application innovations. Although the AO ecosystem has developed for a short time and its technological and application innovations still need time to be tested, we are more inclined to evaluate the significance and value of these innovations from the perspective of the overall development stage of the Web3 industry and the characteristics of Web2 systems.

Currently, the Web3 industry is exploring feasible paths for large-scale adoption, with many blockchains striving for this goal, such as the integration of TON with Telegram, guiding real Web2 users to real Web3 applications, aiming for large-scale conversion of value from traffic to liquidity; CKB becoming an L2 for Bitcoin, is constructing a lightning network based on CKB, intending to bring high-frequency, small-amount, large-scale peer-to-peer payments.

From an industry development perspective, AO + Arweave redefines the implementation framework for decentralized computers, bringing system flexibility, security, and economic efficiency through innovative architecture, establishing scalable system-level resources, sustainably unleashing resource potential, driving technological and application innovation, realizing value creation and transfer, and promoting the integration of Web3 and Web2, providing a feasible path for the large-scale adoption of Web3.

References

1. Arweave: A protocol that sustainably preserves information permanently.

2. AO Protocol: Decentralized, Permissionless Supercomputer:

https://x.com/kylewmi/status/1802131298724811108

3. The Storage-based Computing Paradigm Realized by Arweave:

https://news.ever.vision/a-storage-based-computation-paradigm-enabled-by-arweave-de799ae8c424

4. Technical Details of AO Super Parallel Computer:

https://www.chaincatcher.com/article/2121544

5. Interpreting SCP: A Trustless Infrastructure Paradigm Breaking Away from Rollup Patterns:

https://mp.weixin.qq.com/s/BPRAsby78G2a835pX1l3iw

6. In-depth Analysis of the Actor Model (I): Introduction to Actors and Their Application in the Gaming Industry:

https://blog.csdn.net/weixin_44505163/article/details/121191182

7. Arweave Permanent Storage + AO Super Parallel Computer: Building a Data Consensus Infrastructure:

https://www.chaincatcher.com/article/2141924