AI × DePin: The Synergistic Evolution of Intelligent Infrastructure

Read time: 15 Minutes

Introduction

The Decentralized Physical Infrastructure Network (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 models of physical devices through a decentralized architecture, demonstrating the potential to cause disruptive changes in traditional infrastructure sectors, such as power grids and waste management systems. Traditional infrastructure projects have long been under the centralized control of governments and large corporations, often facing issues like high service costs, inconsistent service quality, and limited innovation. DePIN offers a new solution aimed at achieving decentralized management and control of physical devices through distributed ledger and smart contract technology, thereby enhancing the system's transparency, trustworthiness, and security.

Functionality and Advantages of DePIN

Decentralized Management and Transparency: DePIN achieves decentralized management of physical devices through the distributed ledger and smart contracts of blockchain technology, allowing device owners, users, and relevant stakeholders to verify the device's status and operations through a consensus mechanism. This not only enhances the security and reliability of the devices but also ensures the operational transparency of the system. For instance, in the field of Virtual Power Plant (VPP), DePIN can make the traceability data of power outlets public and transparent, enabling users to clearly understand the production and circulation process of the data.

Risk Dispersion and System Continuity: By distributing physical devices across different geographical locations and multiple participants, DePIN effectively reduces the centralized risks of the system, 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 the continuity and high availability of the system.

Smart Contract Automation: DePIN uses 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 step recorded, allowing anyone to verify the execution of the contract. This mechanism not only improves the efficiency of contract execution but also enhances the transparency and trustworthiness of the system.

Five-layer Architecture Analysis of DePIN

Overview

Although cloud devices typically have highly centralized characteristics, DePIN (Decentralized Physical Infrastructure Network) has successfully simulated the functions of centralized cloud computing through a multi-layered modular technology stack design. Its architecture includes the Application Layer, Governance Layer, Data Layer, Blockchain Layer, and Infrastructure Layer. Each layer plays a key role in the entire system to ensure the network's efficient, secure, and decentralized operation. The following will provide a detailed analysis of these five layers.

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, the underlying technology and infrastructure are transformed into functions that users can directly use, such as IoT applications, distributed storage, decentralized finance (DeFi) services, etc.

Importance:

User Experience: The Application Layer determines the way users interact with the DePIN network, directly affecting user experience and the popularity of the network.

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 transforms the network's technological advantages into actual value, promoting the continuous development of the network and the realization of user benefits.

Governance Layer

Function: The Governance Layer can operate on-chain, off-chain, or in a hybrid mode, responsible for formulating and executing network rules, including protocol upgrades, resource allocation, and conflict resolution. It typically adopts decentralized governance mechanisms, such as DAO (Decentralized Autonomous Organization), to ensure that the decision-making process is transparent, fair, and democratic.

Importance:

Decentralized Decision-making: By decentralizing decision-making power, 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, strengthening users' sense of belonging and promoting the healthy development of the network.

Flexibility and Adaptability: Effective governance mechanisms enable the network to quickly respond to changes in the external environment and technological advancements, maintaining competitiveness.

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 the integrity, availability, and privacy protection of data, while providing efficient data access and processing capabilities.

Importance:

Data Security: By encrypting and decentralizing storage, the Data Layer protects user data from unauthorized access and tampering.

Scalability: Efficient data management mechanisms support network expansion, handling a large number of concurrent data requests, ensuring system performance and stability.

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

Blockchain Layer

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

Importance:

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

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

Smart Contracts: The Blockchain Layer supports automated and decentralized business logic, enhancing the network's functionality and efficiency.

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 equipment, data centers, and power supplies. This layer ensures the high availability, stability, and performance of the network.

Importance:

Reliability: A robust infrastructure ensures the continuous operation of the network, preventing service unavailability due to hardware failures or network interruptions.

Performance Optimization: Efficient infrastructure improves the network's processing speed and response capabilities, enhancing the user experience.

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

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 centralized cloud services or decentralized networks, 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, the management and maintenance of devices often rely on regular inspections and passive repairs, which are not only costly but also prone to undetected equipment failures. By introducing AI, the system can achieve the following optimizations:

• Failure Prediction and Prevention: Machine learning algorithms can predict potential equipment failures by analyzing historical operation data and real-time monitoring data. For example, by analyzing sensor data, AI can detect potential failures in transformers or power generation equipment in the power grid in advance, schedule maintenance in advance, and avoid larger-scale power outages.

• Real-time Monitoring and Automatic Alarming: AI can monitor all devices in the network 24/7 and immediately issue an alarm when anomalies are detected. This includes not only the hardware status of the device but also abnormal changes in its operational performance, such as temperature, pressure, current, and other parameters. For example, in a decentralized water treatment system, AI can monitor water quality parameters in real-time, and once it detects pollution exceeding standards, it immediately notifies maintenance personnel for processing.

• Intelligent Maintenance and Optimization: AI can dynamically adjust maintenance plans based on the device's usage and operating conditions, avoiding over-maintenance and under-maintenance. For example, by analyzing the operation data of wind turbines, AI can determine the optimal maintenance cycle and maintenance measures, improving power generation efficiency and equipment life.

Resource Allocation and Optimization: The application of AI in resource allocation and optimization can significantly improve the efficiency and performance of the DePin network. Traditional resource allocation often relies on manual scheduling and static rules, which are difficult to cope with complex and variable actual situations. AI can dynamically adjust resource allocation strategies through data analysis and optimization algorithms, achieving the following goals:

• Dynamic Load Balancing: In decentralized computing and storage networks, AI can dynamically adjust task allocation and data storage locations according to the load conditions and performance indicators of nodes. For example, in a distributed storage network, AI can store data with higher access frequencies on nodes with better performance, while distributing data with lower access frequencies on nodes with lighter loads, improving the storage efficiency and access speed of the entire network.

• Energy Efficiency Optimization: AI can optimize the production and use of energy by analyzing the energy consumption data and operating modes of devices. For example, in a smart grid, AI can optimize the start-stop strategy of power generation units and the distribution plan of electricity according to users' electricity usage habits and power demands, reducing energy consumption and carbon emissions.

• Resource Utilization Improvement: AI can maximize resource utilization through deep learning and optimization algorithms. For example, in a decentralized logistics network, 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 a decentralized physical infrastructure network (DePin), data is one of the core assets. Various physical devices and sensors in the DePin network continuously generate a large amount of data, including sensor readings, device status information, network traffic data, etc. AI technology shows significant advantages in data collection and processing:

• Efficient Data Collection: Traditional data collection methods may face  problems such as data fragmentation and poor data quality. AI collects high-quality data in real-time at the device level through smart sensors and edge computing, and dynamically adjusts the data collection frequency and scope according to demand.

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

• Real-time Data Processing: The DePin network needs to process and analyze massive amounts of data in real-time to quickly respond to changes in the physical world. AI technology, especially streaming processing and distributed computing frameworks, makes real-time data processing possible.

Intelligent Decision-making and Prediction: Intelligent decision-making and prediction are one of the core areas of AI application in decentralized physical infrastructure networks (DePin). AI technology can achieve intelligent decision-making and precise prediction of complex systems through deep learning, machine learning, and predictive models, improving the system's autonomy and response speed:

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

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

Security

Real-time Monitoring and Anomaly Detection: In a decentralized physical infrastructure network (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, identifying abnormal activities. For example, in a decentralized communication network, AI can monitor the flow of data packets, detect abnormal traffic and malicious attack behaviors. Through machine learning and pattern recognition techniques, the system can quickly identify and isolate infected nodes, preventing the further spread of attacks.

Automated Threat Response: AI can not only detect threats but also take automated response measures. Traditional security systems often rely on human intervention, while AI-driven security systems can take immediate action after detecting threats, reducing response time. For example, in a decentralized energy network, if AI detects abnormal activity in a node, it can automatically disconnect the node, activate backup systems, and ensure the stable operation of the network. In addition, 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 equipment failures and take protective measures in advance. For example, in a smart transportation system, AI can analyze traffic flow and accident data, predict potential high-incidence areas of traffic accidents, and deploy emergency measures in advance to reduce the probability of accidents. Similarly, in a distributed storage network, AI can predict the failure risk of storage nodes and perform maintenance in advance to ensure the security and availability of data.

How DePin Changes AI

Advantages of DePin in AI Applications

1. Resource Sharing and Optimization: DePin allows different entities to share computing resources, storage resources, and data resources. This is particularly important for AI training and inference scenarios that require a large amount of computing resources and data. The decentralized resource sharing mechanism can significantly reduce the operating costs of AI systems and improve resource utilization.

2. Data Privacy and Security: In traditional centralized AI systems, data is often centrally stored on a central server, which poses data leakage and privacy issues. DePin ensures the security and privacy of data through decentralized storage and encryption technology. Data owners can share data with AI models for distributed computing while retaining data ownership.

3. Enhanced Reliability and Availability: Through a decentralized network structure, DePin improves the reliability and availability of AI systems. Even if a node fails, the system can continue to operate. The decentralized infrastructure reduces the risk of single-point failures and enhances the system's resilience and stability.

4. Transparent Incentive Mechanism: The token economy 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, forming a virtuous cycle.

Potential Application Scenarios of DePin in AI

1. Distributed AI Training: AI model training requires a large amount of computing resources. Through DePin, different computing nodes can work together 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 distribute computing tasks to edge devices close to the data source, improving computing efficiency and response speed. For example, smart home devices can use DePin to achieve localized AI inference, enhancing the user experience.

3. Data Marketplace: The performance of AI models depends on a large amount of high-quality data. DePin can establish a decentralized data marketplace, allowing data providers and users to conduct data transactions while ensuring privacy. Through smart contracts, the data transaction process is transparent and trustworthy, ensuring 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, where users can upload images, and the platform processes and returns results through distributed computing nodes. This kind of platform not only improves service reliability but also incentivizes developers to continuously optimize algorithms through token mechanisms.

AI + DePin Projects

In this section, we will explore several AI-related DePin projects, focusing on the decentralized file storage and access platform Filecoin, the decentralized GPU computing power rental platform Io.net, and the decentralized AI model deployment and access platform Bittensor. These three play important roles in data storage access, computing power support training, and model deployment usage in the AI field.

Filecoin

Filecoin is a decentralized storage network that achieves global distributed data storage through blockchain technology and a cryptocurrency economic model. 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 Filecoin tokens (FIL) or earn FIL by providing storage services.

Functions:

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

Market-Driven: The storage market of Filecoin is determined by the relationship between supply and demand. Storage prices and service quality are dynamically adjusted through a free market mechanism, allowing users to choose the optimal storage solution based on their needs.

Verifiable Storage: Filecoin ensures effective storage and backup of data at storage providers through mechanisms such as Proof-of-Spacetime (PoSt) and Proof-of-Replication (PoRep).

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

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

Pain Points Solved:

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

Data Security and Privacy Issues: Decentralized storage and encryption technology ensure the privacy and security of data, reducing the risk of data leaks due to centralized storage.

Data Storage Reliability: The Proof-of-Spacetime and Proof-of-Replication mechanisms provided by Filecoin ensure the integrity and verifiability of data during the storage process, enhancing the reliability of data storage.

Trust Issues with Traditional Storage Platforms: Filecoin achieves storage transparency through blockchain technology, eliminating the monopoly and manipulation of data by third-party organizations, and enhancing users' trust in storage services.

Target Users:

Storage Providers: Connect idle disk space to the platform to respond to users' storage requests and earn tokens. Storage providers need to pledge tokens, and if they cannot provide effective storage proof, they will be punished and lose part of the pledged tokens.

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

Data Storers: Through the market mechanism, submit the price they are willing to pay, match with storers, and then send the data to the storers. Both parties sign a transaction order and submit it to the blockchain.

Data Users: Users submit a unique file identifier and payment price, and file retrievers will find the storage location of the files, respond to storage requests, and provide data.

Token Economic System:

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.

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 contribution to the network consensus.

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

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

Io.net

Io.net is a distributed GPU computing platform that collects and clusters idle computing power to provide market computing power scheduling and temporary supplementation, 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 of task distribution and processing. Io.net expects to provide an effect close to cloud computing platforms through a distributed computing power sharing model while significantly reducing service costs.

Functions:

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

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

Secure Transmission and On-chain Storage: The platform uses end-to-end encryption technology to ensure the security of user data. At the same time, task execution information will be stored on-chain, achieving transparent and permanent storage of logs.

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

Pain Points Solved:

Lack of Computing Power: Due to the rise of large models, the market demand for GPU computing power required during training has increased dramatically. Io.net fills this computing power gap by integrating idle GPU resources from the public.

Privacy and Compliance: Large cloud platform service providers such as AWS and Google Cloud have strict KYC requirements for users, while Io.net avoids compliance issues through a decentralized approach, allowing users more flexibility in choosing to use resources.

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

Target Users:

Computing Power Providers: Connect idle GPUs to the platform for others to use. According to the performance and stability of the provided equipment, you can receive token rewards.

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

Stakers: Stakers support the long-term stable operation of the platform by staking platform tokens and obtain staking rewards from equipment leasing, which helps to improve the ranking of high-quality equipment.

Token Economic System:

Token Usage: All transactions within the platform use the native token $IO to reduce transaction friction in smart contracts. Users and suppliers can pay with USDC or $IO, but a 2% service fee is required when using USDC.

Total Token Supply: The maximum supply of $IO is 800 million, with 500 million issued at launch, and the remaining 300 million is used to reward suppliers and stakers. The token will be gradually released over 20 years, starting with 8% of the total amount in the first year, decreasing by 1.02% per month.

Token Burning: A portion of the platform's revenue will be used to repurchase and burn $IO. The fee sources include a 0.25% booking fee for both parties and a 2% service fee for payments made with USDC.

Token Distribution: Tokens will be distributed 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 market that aims to promote the production and circulation of AI models by allowing different intelligent systems to evaluate and reward each other. Bittensor creates a market that can continuously produce new models and reward contributors for their information value through a distributed architecture. The platform provides researchers and developers with a platform to deploy AI models to earn profits; users can use various AI models and functions through the platform.

Functions:

Distributed Market: Bittensor has established a decentralized AI model market, allowing engineers and small AI systems to directly monetize their work, breaking the monopoly of large companies on AI.

Standardization and Modularization: The network supports multiple modes (such as text, image, voice), allowing different AI models to interact and share knowledge, and can be expanded to more complex multimodal systems.

System Ranking: Each node is ranked based on its contribution to the network, including the node's execution effect on tasks, other nodes' evaluation of its output, and its trust in the network. Nodes with higher rankings will receive more network weight and rewards, incentivizing nodes to continue providing high-quality services in the decentralized market. This ranking mechanism not only ensures the fairness of the system but also improves the overall computational efficiency and model quality of the network.

Pain Points Solved:

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

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

Target Users:

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

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

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

Token Economic System:

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

Token Generation: A block is produced every 12 seconds, generating 1 TAO token, which is allocated according to the performance of the subnet and the nodes within it. The token allocation ratio is: 18% is allocated to subnet owners, and subnet miners and validators each receive 41%. The maximum supply of the token is 21 million.

Challenges and Conclusions for DePin

As an emerging network architecture, DePIN achieves decentralized management of physical infrastructure through the integration of blockchain technology. This innovation not only solves problems faced by traditional infrastructure, such as data privacy, service interruption, and high expansion costs, but also empowers network participants with more control and participation through token incentive mechanisms and self-organizing models. Although DePIN has shown great potential, it still faces some challenges.

1. Scalability: The scalability issue of DePIN stems 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 increase. Especially as DePIN applications connect with the physical world, higher requirements for information transmission are needed. This can lead to extended transaction confirmation times and increased transaction fees, affecting the 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 authorities to monitor fund flows, potentially leading to an increase in illegal fundraising, pyramid schemes, and money laundering activities. Additionally, in terms of tax regulation, due to the anonymity of accounts, governments find it difficult to collect evidence needed for taxation, posing a challenge to the existing tax system.

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 operation model of physical infrastructure.