In this chapter, we will explore several DePin projects related to AI, focusing on the decentralized file storage and access platform Filecoin, the decentralized GPU computing power leasing platform Io.net, and the decentralized AI model deployment and access platform Bittensor. These three play important roles in data storage and access, computing power support for training, and model deployment and usage in the field of AI.
Filecoin
Filecoin is a decentralized storage network that utilizes blockchain technology and a cryptocurrency economic model to achieve distributed data storage on a global scale. 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.
Features
Decentralized storage: Filecoin stores data in a decentralized manner, avoiding the centralization drawbacks of traditional cloud storage, such as single point of failure and data censorship risks.
Market-driven: Filecoin's storage market is determined by supply and demand, and 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 the effective storage and backup of data at storage providers through mechanisms such as Proof-of-Spacetime (PoSt) and Proof-of-Replication (PoRep).
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.
Scalability: Filecoin network supports large-scale data storage and fast access through the introduction of technologies such as sharding, meeting the demands of future massive data growth.
Addressing Pain Points
High data storage costs: Through Filecoin's decentralized storage market, users can flexibly choose storage providers, reducing data storage costs.
Data security and privacy issues: Decentralized storage and encryption technology ensure data privacy and security, reducing the risk of data leakage associated with centralized storage.
Data storage reliability: Filecoin's time and space proofs and replication proofs ensure the integrity and verifiability of data during the storage process, enhancing data storage reliability.
Trust issues with traditional storage platforms: Filecoin achieves storage transparency through blockchain technology, eliminating the monopoly and manipulation of data by third-party institutions, enhancing user trust in storage services.
Target Users
Storage providers: Access the platform by providing idle disk space, responding to storage requests from users, and earning tokens. Storage providers need to stake tokens, and failure to provide valid storage proofs may result in penalties and loss of some staked tokens.
File retrievers: Retrieve the location of files to earn tokens when users need to access files. File retrievers do not need to stake tokens.
Data storers: Submit the price they are willing to pay through the market mechanism, match with storage providers, and send data to the storage providers. Both parties sign the transaction order and submit it to the blockchain.
Data users: Users submit a unique file identifier and payment to retrieve the file's location, and file retrievers respond to the storage request and provide data.
Token Economic System
FIL token circulation: FIL is the native cryptocurrency of the Filecoin network, used to pay for storage fees, reward miners, and conduct transactions within the network, maintaining 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. Miner rewards are related to the storage space provided, data access frequency, and contribution to network consensus.
Network fees: Users need to pay FIL tokens to purchase storage and retrieval services, and the fees are determined by the supply and demand relationship in the storage market, allowing users to 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-based 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 to meet the needs of task distribution and processing. Io.net aims to provide an effect close to that of cloud computing platforms through the mode of distributed computing power sharing, significantly reducing service costs.
Features
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 serves as a market for computing power scheduling and temporary supplementation, improving the overall utilization of computing resources.
Secure transmission and on-chain storage: The platform uses end-to-end encryption technology to ensure the security of user data. Additionally, task execution information is stored on the chain, achieving transparent and permanent log storage.
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 the stability and reliability of the system.
Addressing Pain Points
Insufficient computing power: With the rise of large models, there is a sharp increase in the market demand for GPU computing power for training. Io.net fills this computing power gap by integrating idle GPU resources from the public.
Privacy and compliance: Major cloud platform service providers such as AWS and Google Cloud have strict KYC requirements for users, while Io.net circumvents compliance issues in a decentralized manner, allowing users to flexibly choose to use resources.
High costs: Service prices on cloud computing platforms are relatively high, while Io.net significantly reduces costs through distributed computing power sharing and achieves service quality close to that of cloud platforms through clustering technology.
Target Users
Computing power providers: 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.
Computing power users: Rent GPUs or GPU clusters by consuming tokens for task submission or training large models.
Stakers: Stakers support the long-term stable operation of the platform by staking platform tokens and earn staking rewards from device leasing, which helps improve the ranking of excellent devices.
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 use USDC or $IO for payment, but using USDC incurs a 2% service fee.
Total token supply: The maximum supply of $IO is 800 million, with 500 million issued at launch, and the remaining 300 million used 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% monthly.
Token burning: A portion of the platform's income will be used to repurchase and burn $IO, with funding sources including a 0.25% bilateral reservation fee and a 2% service fee for payments made using USDC.
Token allocation: Tokens will be allocated to seed round investors, Series A investors, the team, ecosystem and community, and supplier rewards.
Bittensor (TAO)
Bittensor is a decentralized peer-to-peer AI model market designed to promote the production and circulation of AI models by allowing different intelligent systems to evaluate and reward each other. Through a distributed architecture, Bittensor has created a market that continuously produces new models and rewards contributors for the value of their information. The platform provides researchers and developers with a platform to deploy AI models to earn income, while users can access various AI models and functionalities through the platform.
Features
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, and voice), allowing different AI models to interact and share knowledge, and can be extended to more complex multimodal systems.
System Ranking: Each node is ranked based on its contribution to the network, including the node's performance in task execution, evaluations from other nodes on its output, and the trust it gains in the network. Nodes with higher rankings will receive more network weight and rewards, incentivizing nodes to continuously provide high-quality services in the decentralized market. This ranking mechanism ensures fairness and improves the overall computing efficiency and model quality of the network.
Addressing Pain Points
Centralization of AI production: The current AI ecosystem is dominated by a few large companies, making it difficult for independent developers to monetize. Bittensor provides a direct opportunity for independent developers and small AI systems to profit through a peer-to-peer decentralized market.
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
Node Operators: Connect computing power and models to the Bittensor network, earn token rewards by participating in task processing and model training. Node operators can be independent developers, small AI companies, or individual researchers, and can improve their rankings and earnings in the network by providing high-quality computing resources and models.
AI Model Users: Users in need of AI computing resources and model services can rent computing power and intelligent 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 through staking and earn staking rewards. Stakers not only benefit from network inflation but also indirectly affect the overall computing efficiency and reward distribution by staking to improve the ranking of supported nodes.
Token Economic System
Token Usage: 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.
Token Generation: One TAO token is generated every 12 seconds, distributed based on the performance of the subnet and its nodes. The token allocation ratio is 18% to subnet owners, and 41% each to subnet miners and validators. The maximum token supply is 21 million.
Challenges and Conclusion for DePin
As an emerging network architecture, DePin has decentralized management of physical infrastructure by combining blockchain technology. This innovation not only addresses the data privacy, service interruption, and high scalability costs faced by traditional infrastructure but also empowers network participants with more control and involvement through token incentive mechanisms and self-organizing models. Despite demonstrating strong potential, DePin still faces some challenges.
Scalability: DePin's scalability issues stem from its reliance on the decentralized nature of blockchain technology. As the number of users and network scale increases, the transaction volume on the blockchain network also increases, especially with DePin applications connected to the physical world, requiring higher information transmission. This can lead to longer transaction confirmation times, increased transaction fees, and ultimately affect the overall efficiency and user experience of the network.
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.
Regulatory Compliance: As part of the Web 3.0 ecosystem, DePin faces multiple regulatory challenges. Its decentralized and anonymous nature makes 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, the anonymity of accounts makes it challenging for governments to collect the evidence needed for tax collection, posing a challenge to the existing tax system.
In the future, the development of DePin will depend on addressing these key issues and is expected to play an important role in a wide range of application scenarios, reshaping the operation mode of physical infrastructure.
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