AI×DePin: Collaborative Evolution of Intelligent Infrastructure

Introduction

Decentralized Physical Infrastructure Networks (DePIN) is a cutting-edge concept that combines blockchain technology with the Internet of Things (IoT), gradually attracting widespread attention both within and outside the industry. DePIN redefines the management and control patterns of physical devices through a decentralized architecture, demonstrating the potential to trigger disruptive changes in traditional infrastructure fields such as power grids and waste management systems. Traditional infrastructure projects have long been subject to centralized control by governments and large enterprises, often facing issues such as high service costs, inconsistent service quality, and limited innovation. DePIN offers a novel solution aimed at achieving decentralized management and control of physical devices through distributed ledger and smart contract technology, thereby enhancing the transparency, credibility, and security of the system.

Functions and Advantages of DePIN

1. Decentralized Management and Transparency: DePIN achieves decentralized management of physical devices through the distributed ledger and smart contract of blockchain technology, allowing device owners, users, and relevant stakeholders to verify the status and operations of devices through consensus mechanisms. This not only improves the security and reliability of devices but also ensures operational transparency of the system. For example, in the field of Virtual Power Plants (VPP), DePIN can publicly and transparently disclose the traceability data of sockets, enabling users to clearly understand the production and circulation process of the data.

2. Risk Diversification and System Continuity: By distributing physical devices across different geographical locations and among multiple participants, DePIN effectively reduces the risks associated with centralization, avoiding the impact of single points of failure on the entire system. Even if one node fails, other nodes can continue to operate and provide services, ensuring system continuity and high availability.

3. Automated Operations with Smart Contracts: DePIN utilizes smart contracts to automate device operations, thereby improving operational efficiency and accuracy. The execution process of smart contracts is fully traceable on the blockchain, with each operation recorded, allowing anyone to verify the execution status of the contract. This mechanism not only enhances the efficiency of contract execution but also increases the transparency and credibility of the system.

Analysis of the Five-Layer Architecture of DePIN

Overview

Although cloud devices typically exhibit highly centralized characteristics, DePIN (Decentralized Physical Infrastructure Network) successfully simulates the functionalities of centralized cloud computing through a multi-layer modular technology stack design. Its architecture includes the application layer, governance layer, data layer, blockchain layer, and infrastructure layer, each playing a critical role in ensuring the efficient, secure, and decentralized operation of the network. The following is a detailed analysis of these five layers.

1. Application Layer

• Function: The application layer is the part of the DePIN ecosystem that directly faces users, responsible for providing various specific applications and services. Through this layer, underlying technologies and infrastructures are transformed into functionalities that users can directly utilize, such as IoT applications, distributed storage, decentralized finance (DeFi) services, and more.

• Importance:

• User Experience: The application layer determines how users interact with the DePIN network, directly affecting user experience and the network's adoption rate.

• Diversity and Innovation: This layer supports a variety of applications, contributing to the diversity and innovative development of the ecosystem, attracting developers and users from different fields to participate.

• Value Realization: The application layer converts the technological advantages of the network into actual value, promoting the continuous development of the network and the realization of user interests.

1. Governance Layer

• Function: The governance layer can operate on-chain, off-chain, or in a hybrid mode, responsible for formulating and enforcing network rules, including protocol upgrades, resource allocation, and conflict resolution. It typically employs decentralized governance mechanisms, such as DAOs (Decentralized Autonomous Organizations), to ensure transparency, fairness, and democracy in the decision-making process.

• Importance:

• Decentralized Decision-Making: By decentralizing decision-making authority, the governance layer reduces the risk of single-point control, enhancing the network's resistance to censorship and stability.

• Community Participation: This layer encourages active participation from community members, enhancing users' sense of belonging and promoting the healthy development of the network.

• Flexibility and Adaptability: An effective governance mechanism enables the network to quickly respond to changes in the external environment and technological advancements, maintaining competitiveness.

1. Data Layer

• Function: The data layer is responsible for managing and storing all data within the network, including transaction data, user information, and smart contracts. It ensures data integrity, availability, and privacy protection while providing efficient data access and processing capabilities.

• Importance:

• Data Security: Through encryption and decentralized storage, the data layer protects user data from unauthorized access and tampering.

• Scalability: An efficient data management mechanism supports network expansion, handling a large number of concurrent data requests, ensuring system performance and stability.

• Data Transparency: Public and transparent data storage increases the trustworthiness of the network, allowing users to verify and audit the authenticity of the data.

1. Blockchain Layer

• Function: The blockchain layer is the core of the DePIN network, responsible for recording all transactions and smart contracts, ensuring data immutability and traceability. This layer provides decentralized consensus mechanisms, such as PoS (Proof of Stake) or PoW (Proof of Work), to guarantee the security and consistency of the network.

• Importance:

• Decentralized Trust: Blockchain technology eliminates reliance on centralized intermediaries, establishing a trust mechanism through distributed ledgers.

• Security: Robust encryption and consensus mechanisms protect the network from attacks and fraud, maintaining system integrity.

• Smart Contracts: The blockchain layer supports automated and decentralized business logic, enhancing the functionality and efficiency of the network.

1. Infrastructure Layer

• Function: The infrastructure layer includes the physical and technical infrastructure that supports the operation of the entire DePIN network, such as servers, network devices, data centers, and energy supplies. This layer ensures the high availability, stability, and performance of the network.

• Importance:

• Reliability: A solid infrastructure guarantees the continuous operation of the network, avoiding service unavailability due to hardware failures or network interruptions.

• Performance Optimization: An efficient infrastructure enhances the processing speed and responsiveness of the network, improving user experience.

• Scalability: A flexible infrastructure design allows the network to expand according to demand, supporting more users and more complex application scenarios.

1. Connection Layer

In some cases, a connection layer is added between the infrastructure layer and the application layer, responsible for handling communication between smart devices and the network. The connection layer can be a centralized cloud service or a decentralized network, supporting various communication protocols such as HTTP(s), WebSocket, MQTT, CoAP, etc., to ensure reliable data transmission.

How AI Changes DePIN

Intelligent Management and Automation

• Device Management and Monitoring: AI technology makes device management and monitoring more intelligent and efficient. In traditional physical infrastructure, device management and maintenance often rely on periodic inspections and passive repairs, which are not only costly but also prone to undetected device failures. By introducing AI, the system can optimize in the following ways:

• Fault Prediction and Prevention: Machine learning algorithms can analyze historical operational data and real-time monitoring data to predict potential device failures. For example, by analyzing sensor data, AI can detect potential faults in transformers or generation equipment in the power grid in advance, allowing for maintenance to be scheduled before larger-scale power outages occur.

• Real-Time Monitoring and Automatic Alerts: AI can monitor all devices in the network 24/7 and immediately issue alerts when anomalies are detected. This includes not only the hardware status of devices but also their operational performance, such as abnormal changes in temperature, pressure, current, and other parameters. For instance, in a decentralized water treatment system, AI can monitor water quality parameters in real-time and immediately notify maintenance personnel if pollutants exceed acceptable levels.

• Intelligent Maintenance and Optimization: AI can dynamically adjust maintenance plans based on device usage and operational status, avoiding both over-maintenance and under-maintenance. For example, by analyzing the operational data of wind turbines, AI can determine the optimal maintenance cycle and measures to improve power generation efficiency and equipment lifespan.

• Resource Allocation and Optimization: The application of AI in resource allocation and optimization can significantly enhance the efficiency and performance of the DePIN network. Traditional resource allocation often relies on manual scheduling and static rules, making it difficult to respond to complex and changing real-world situations. AI can dynamically adjust resource allocation strategies through data analysis and optimization algorithms to achieve the following goals:

• Dynamic Load Balancing: In decentralized computing and storage networks, AI can dynamically adjust task distribution and data storage locations based on the load conditions and performance metrics of nodes. For example, in a distributed storage network, AI can store frequently accessed data on higher-performing nodes while distributing less frequently accessed data across lighter-loaded nodes, improving the overall storage efficiency and access speed of the network.

• Energy Efficiency Optimization: AI can analyze device energy consumption data and operational patterns to optimize energy production and usage. For instance, in smart grids, AI can optimize the start-stop strategies of generators and the distribution of electricity based on users' electricity consumption habits and power demand, reducing energy consumption and carbon emissions.

• Resource Utilization Improvement: AI can maximize resource utilization through deep learning and optimization algorithms. For example, in decentralized logistics networks, AI can dynamically adjust delivery routes and vehicle scheduling plans based on real-time traffic conditions, vehicle locations, and cargo demands, improving delivery efficiency and reducing logistics costs.

Data Analysis and Decision Support

• Data Collection and Processing: In Decentralized Physical Infrastructure Networks (DePIN), data is one of the core assets. Various physical devices and sensors in the DePIN network continuously generate large amounts of data, including sensor readings, device status information, and network traffic data. AI technology demonstrates significant advantages in data collection and processing:

• Efficient Data Collection: Traditional data collection methods may face issues such as data dispersion and low data quality. AI can collect high-quality data in real-time at the device level through smart sensors and edge computing, dynamically adjusting data collection frequency and scope based on demand.

• Data Preprocessing and Cleaning: Raw data often contains noise, redundancy, and missing values. AI technology can enhance data quality through automated data cleaning and preprocessing. For example, using machine learning algorithms to detect and correct anomalous data and fill in missing values, ensuring the accuracy and reliability of subsequent analyses.

• Real-Time Data Processing: The DePIN network requires real-time processing and analysis of massive amounts of data to respond quickly to changes in the physical world. AI technology, particularly stream processing and distributed computing frameworks, makes real-time data processing possible.

• Intelligent Decision-Making and Prediction: In Decentralized Physical Infrastructure Networks (DePIN), intelligent decision-making and prediction are core areas of AI application. AI technology can achieve intelligent decision-making and precise predictions for complex systems through deep learning, machine learning, and predictive models, enhancing the system's autonomy and responsiveness:

• Deep Learning and Predictive Models: Deep learning models can handle complex nonlinear relationships and extract latent patterns from large-scale data. For example, by analyzing operational data and sensor data of devices through deep learning models, the system can identify potential signs of failure and perform preventive maintenance in advance, reducing equipment downtime and improving production efficiency.

• Optimization and Scheduling Algorithms: Optimization and scheduling algorithms are another important aspect of AI achieving intelligent decision-making in the DePIN network. By optimizing resource allocation and scheduling plans, AI can significantly improve system efficiency and reduce operational costs.

Security

• Real-Time Monitoring and Anomaly Detection: In Decentralized Physical Infrastructure Networks (DePIN), security is a crucial factor. AI technology can detect and respond to various potential security threats in a timely manner through real-time monitoring and anomaly detection. Specifically, AI systems can analyze network traffic, device status, and user behavior in real-time to identify abnormal activities. For example, in decentralized communication networks, AI can monitor the flow of data packets, detecting abnormal traffic and malicious attack behaviors. Through machine learning and pattern recognition technologies, the system can quickly identify and isolate infected nodes, preventing further spread of the attack.

• Automated Threat Response: AI can not only detect threats but also automate response measures. Traditional security systems often rely on human intervention, while AI-driven security systems can take immediate action upon threat detection, reducing response time. For instance, in decentralized energy networks, if AI detects abnormal activity at a certain node, it can automatically disconnect that node, activate backup systems, and ensure the stable operation of the network. Additionally, AI can continuously learn and optimize to improve the efficiency and accuracy of threat detection and response.

• Predictive Maintenance and Protection: Through data analysis and predictive models, AI can predict potential security threats and device failures, taking preventive measures in advance. For example, in intelligent transportation systems, AI can analyze traffic flow and accident data to predict areas with a high likelihood of traffic accidents, deploying emergency measures in advance to reduce the probability of accidents. Similarly, in distributed storage networks, AI can predict the risk of storage node failures and perform maintenance in advance to ensure data security and availability.

How DePIN Changes AI

Advantages of DePIN in AI Applications

1. Resource Sharing and Optimization: DePIN allows for the sharing of computing resources, storage resources, and data resources between different entities. This is particularly important in scenarios where AI training and inference require substantial computing resources and data. The decentralized resource-sharing mechanism can significantly reduce the operational costs of AI systems and improve resource utilization.

2. Data Privacy and Security: In traditional centralized AI systems, data is often stored in a central server, raising concerns about data leakage and privacy issues. DePIN ensures data security and privacy through distributed storage and encryption technologies. Data holders can share data with AI models while retaining data ownership, enabling distributed computing.

3. Enhanced Reliability and Availability: By utilizing a decentralized network structure, DePIN improves the reliability and availability of AI systems. Even if a certain node fails, the system can continue to operate. The decentralized infrastructure reduces the risk of single points of failure, enhancing the resilience and stability of the system.

4. Transparent Incentive Mechanism: The token economics in DePIN provides a transparent and fair incentive mechanism for transactions between resource providers and users. Participants can earn token rewards by contributing computing resources, storage resources, or data, creating a virtuous cycle.

Potential Application Scenarios of DePIN in AI

1. Distributed AI Training: AI model training requires substantial computing resources. Through DePIN, different computing nodes can collaborate to form a distributed training network, significantly accelerating training speed. For example, a decentralized GPU network can provide training support for deep learning models.

2. Edge Computing: With the proliferation of IoT devices, edge computing has become an important direction for AI development. DePIN can allocate computing tasks to edge devices located near data sources, improving computing efficiency and response speed. For instance, smart home devices can utilize DePIN to achieve localized AI inference, enhancing user experience.

3. Data Marketplace: The performance of AI models relies on large amounts of high-quality data. DePIN can establish a decentralized data marketplace, allowing data providers and users to trade data while ensuring privacy. Through smart contracts, the data trading process is transparent and trustworthy, guaranteeing the authenticity and integrity of the data.

4. Decentralized AI Service Platform: DePIN can serve as infrastructure to support decentralized AI service platforms. For example, a decentralized AI image recognition service platform allows users to upload images, and the platform processes them through distributed computing nodes and returns results. This type of platform not only enhances service reliability but also incentivizes developers to continuously optimize algorithms through token mechanisms.

AI + DePIN Projects

In this section, we will explore several DePIN projects related to AI, focusing on the decentralized file storage and access platform Filecoin, the decentralized GPU computing rental platform Io.net, and the decentralized AI model deployment and access platform Bittensor. Each of these plays an important role in data storage access, computing power support for training, and model deployment and usage in the AI field.

Filecoin

Filecoin is a decentralized storage network that utilizes blockchain technology and cryptocurrency economic models to achieve distributed data storage globally. Developed by Protocol Labs, Filecoin aims to create an open and public storage market where users can purchase storage space in the network by paying with Filecoin tokens (FIL) or earn FIL by providing storage services.

Functions

1. Decentralized Storage: Filecoin stores data in a decentralized manner, avoiding the centralized drawbacks of traditional cloud storage, such as single points of failure and data censorship risks.

2. Market-Driven: The storage market of Filecoin is determined by supply and demand, with storage prices and service quality dynamically adjusted through free market mechanisms, allowing users to choose the optimal storage solutions based on their needs.

3. Verifiable Storage: Filecoin ensures that data is effectively stored and backed up with mechanisms such as Proof-of-Spacetime (PoSt) and Proof-of-Replication (PoRep).

4. Incentive Mechanism: Through mining and transaction reward mechanisms, Filecoin encourages network participants to provide storage and retrieval services, thereby increasing the network's storage capacity and availability.

5. Scalability: The Filecoin network supports large-scale data storage and rapid access through techniques such as sharding, meeting the future demand for massive data growth.

Pain Points Addressed

1. High Data Storage Costs: Through Filecoin's decentralized storage market, users can flexibly choose storage providers, reducing data storage costs.

2. Data Security and Privacy Issues: Decentralized storage and encryption technologies ensure the confidentiality and security of data, reducing the risk of data leakage associated with centralized storage.

3. Reliability of Data Storage: The time-space proof and replication proof mechanisms provided by Filecoin ensure the integrity and verifiability of data during the storage process, enhancing the reliability of data storage.

4. Trust Issues with Traditional Storage Platforms: Filecoin achieves storage transparency through blockchain technology, eliminating third-party monopolies and manipulation of data, thereby enhancing user trust in storage services.

Target Users

1. Storage Providers: By providing idle disk space to the platform, they respond to users' storage requests and earn tokens. Storage providers need to stake tokens, and if they fail to provide valid storage proofs, they will be penalized and lose part of their staked tokens.

2. File Retrievers: When users need to access files, they retrieve the location of the files to earn tokens. File retrievers do not need to stake tokens.

3. Data Storage Users: Through market mechanisms, they submit the price they are willing to pay, and once matched with a storage provider, they send their data to the provider. Both parties sign a transaction order and submit it to the blockchain.

4. Data Users: Users submit a unique file identifier and payment price, and file retrievers will find the storage location of the file, responding to the storage request and providing data.

Token Economic System

1. Circulation of FIL Tokens: FIL is the native cryptocurrency of the Filecoin network, used to pay for storage fees, reward miners, and conduct transactions within the network. The circulation of FIL tokens maintains the normal operation of the Filecoin network.

2. Rewards for Storage Miners and Retrieval Miners: Storage providers earn FIL tokens by providing storage space and data retrieval services. The rewards for miners are related to the storage space they provide, the frequency of data access, and their contributions to network consensus.

3. Network Fees: Users need to pay FIL tokens to purchase storage and retrieval services, with fees determined by the supply and demand relationship in the storage market, allowing users to freely choose suitable service providers.

4. Token Issuance and Inflation: The total supply of Filecoin is 2 billion, with new FIL tokens gradually issued through mining rewards. As the number of miners increases, the network's inflation rate will gradually decline.

Io.net

Io.net is a distributed GPU computing platform that collects and clusters idle computing power to provide computing power scheduling and temporary supplementation to the market, rather than replacing existing cloud computing resources. The platform allows suppliers to deploy supported hardware for users to rent through simple Docker commands, meeting the needs for task distribution and processing. Io.net aims to provide effects close to cloud computing platforms while significantly reducing service costs through a model of distributed computing power sharing.

Functions

1. Easy Deployment: Suppliers can easily deploy hardware through Docker commands, while users can conveniently rent hardware clusters through the platform to obtain the required computing power.

2. Clustered Computing Power: By clustering idle computing power, the platform acts as a scheduler and temporary supplement for market computing power, improving the overall utilization of computing resources.

3. Secure Transmission and On-Chain Storage: The platform employs end-to-end encryption technology to ensure the security of user data. Additionally, task execution information is stored on-chain, achieving transparency and permanent preservation of logs.

4. Node Health Monitoring: The platform records and publicly discloses the health status of each node, including offline time, network speed, and task execution status, to ensure system stability and reliability.

Pain Points Addressed

1. Insufficient Computing Power: With the rise of large models, the market demand for GPU computing power during training has surged. Io.net fills this gap by integrating idle GPU resources from the public.

2. Privacy and Compliance: Major cloud service providers like AWS and Google Cloud impose strict KYC requirements on users, while Io.net avoids compliance issues through a decentralized approach, allowing users to choose resources more flexibly.

3. High Costs: The service prices of cloud computing platforms are relatively high, while Io.net significantly reduces costs through distributed computing power sharing, achieving service quality close to that of cloud platforms through clustering technology.

Target Users

1. Computing Power Providers: They connect idle GPUs to the platform for others to use. They can earn token rewards based on the performance and stability of the devices they provide.

2. Computing Power Users: They rent GPUs or GPU clusters by consuming tokens for task submission or large model training.

3. Stakers: Stakers support the long-term stable operation of the platform by staking platform tokens, earning staking rewards from device rentals, which helps improve the ranking of quality devices.

Token Economic System

1. Token Usage: All transactions within the platform use the native token $IO to reduce transaction friction in smart contracts. Users and suppliers can use USDC or $IO for payment, but using USDC incurs a 2% service fee.

2. Total Token Supply: The maximum supply of $IO is 800 million, with 500 million issued at launch and the remaining 300 million reserved for rewarding suppliers and stakers. Tokens will be gradually released over 20 years, starting at 8% of the total supply in the first year and decreasing by 1.02% each month.

3. Token Burn: A portion of the platform's revenue will be used to repurchase and burn $IO, with sources of fees including a bilateral 0.25% reservation fee and a 2% service fee charged for payments made in USDC.

4. Token Distribution: Tokens will be allocated to seed round investors, Series A investors, the team, the ecosystem and community, and supplier rewards.

Bittensor (TAO)

Bittensor is a decentralized peer-to-peer AI model marketplace aimed at promoting the production and circulation of AI models by allowing different intelligent systems to evaluate and reward each other. Bittensor creates a marketplace capable of continuously producing new models and rewarding contributors for their information value through a distributed architecture. The platform provides researchers and developers with a platform to deploy AI models for profit, while users can utilize various AI models and functions through the platform.

Functions

1. Distributed Marketplace: Bittensor establishes a decentralized AI model marketplace that allows engineers and small AI systems to directly monetize their work, breaking the monopoly of large companies over AI.

2. Standardization and Modularity: The network supports various modes (such as text, image, voice), allowing different AI models to interact and share knowledge, and can scale to more complex multimodal systems.

3. System Ranking: Each node is ranked based on its contributions to the network, with contribution metrics including the effectiveness of the node's task execution, evaluations of its output by other nodes, and its trustworthiness within the network. Higher-ranked nodes will receive more network weight and rewards, incentivizing nodes to continuously provide high-quality services in the decentralized marketplace. This ranking mechanism ensures fairness in the system and enhances the overall computational efficiency and model quality of the network.

Pain Points Addressed

1. Centralization of Intelligent Production: The current AI ecosystem is concentrated in a few large companies, making it difficult for independent developers to monetize their work. Bittensor provides direct profit opportunities for independent developers and small AI systems through a peer-to-peer decentralized marketplace.

2. Low Utilization of Computing Resources: Traditional AI model training relies on single tasks and cannot fully utilize diverse intelligent systems. Bittensor allows different types of intelligent systems to collaborate, improving the efficiency of computing resource utilization.

Target Users

1. Node Operators: They connect computing power and models to the Bittensor network, earning token rewards by participating in task processing and model training. Node operators can be independent developers, small AI companies, or even individual researchers, enhancing their ranking and earnings in the network by providing high-quality computing resources and models.

2. AI Model Users: Users who need AI computing resources and model services can rent computing power and intelligent models from the Bittensor network by paying tokens. Users can be enterprises, research institutions, or individual developers who utilize high-quality models in the network to complete specific tasks, such as data analysis and model inference.

3. Stakers: Users holding Bittensor tokens can support the long-term stable operation of the network through staking and earn staking rewards. Stakers can benefit from the network's inflation and indirectly influence the overall computational efficiency and reward distribution of the network by enhancing the ranking of the nodes they support.

Token Economic System

1. Token Use: All transactions and incentives within the Bittensor network are conducted using the native token, reducing friction in the transaction process. Users can use tokens to pay for computing resources and model services, while node operators earn tokens by providing services.

2. Token Generation: A block is produced every 12 seconds, generating 1 TAO token, which is distributed based on the performance of subnet and its nodes. The distribution ratio is as follows: 18% goes to subnet owners, while subnet miners and validators each receive 41%. The maximum supply of tokens is 21 million.

Challenges and Conclusion of DePIN

As an emerging network architecture, DePIN achieves decentralized management of physical infrastructure by combining blockchain technology. This innovation not only addresses issues faced by traditional infrastructure, such as data privacy, service interruptions, and high scalability costs, but also empowers network participants with greater control and involvement through token incentive mechanisms and self-organizing models. Despite the strong potential demonstrated by DePIN, it still faces several challenges.

1. Scalability: The scalability issue of DePIN arises from its reliance on the decentralized characteristics of blockchain technology. As the number of users and the scale of the network increase, the transaction volume on the blockchain network will also rise, especially as DePIN applications connect with the physical world, requiring higher information transmission demands. This can lead to longer transaction confirmation times and increased transaction fees, affecting overall network efficiency and user experience.

2. Interoperability: The DePIN ecosystem is built on multiple blockchains, requiring DePIN applications to support homogeneous or heterogeneous state transitions and achieve seamless interoperability with other blockchain networks. However, current interoperability solutions are often limited to specific blockchain ecosystems or come with high cross-chain costs, making it difficult to fully meet the needs of DePIN.

3. Regulatory Compliance: As part of the Web 3.0 ecosystem, DePIN faces multiple regulatory challenges. Its decentralized and anonymous characteristics make it difficult for regulatory agencies to monitor the flow of funds, potentially leading to increased illegal fundraising, pyramid schemes, and money laundering activities. Additionally, regarding tax regulation, the anonymity of accounts makes it challenging for governments to collect the evidence needed for taxation, posing a challenge to existing tax systems.

In the future, the development of DePIN will depend on the resolution of these key issues and is expected to play an important role in a wide range of application scenarios, reshaping the operational model of physical infrastructure.