The Integration of Web3 and AI: Ushering in a New Era of the Internet

Web3, as a new generation of decentralized internet paradigm, has a natural opportunity for integration with AI technology. Under the traditional centralized architecture, AI computing and data resources face many limitations, such as computing power bottlenecks and privacy risks. However, Web3, based on distributed technology, can inject new momentum into AI development through shared computing power networks and open data markets. At the same time, AI can also empower the Web3 ecosystem, such as optimizing smart contracts and improving anti-cheating mechanisms. Exploring the combination of Web3 and AI is of great significance for building the next generation of internet infrastructure and unleashing the value of data and computing power.

Data-Driven: The Solid Foundation of AI and Web3

Data is the core driving force behind AI development. AI models require a large amount of high-quality data to gain deep understanding and strong reasoning capabilities, and the quality of data directly impacts the accuracy and reliability of the models.

The traditional centralized AI data model has the following problems:

1. The cost of data acquisition is high, making it difficult for small and medium-sized enterprises to bear.
2. Data resources are monopolized by large tech companies, creating data silos.
3. Personal data privacy is at risk of leakage and abuse.

Web3 provides a new decentralized data paradigm to address these pain points:

• The decentralized data collection platform allows users to sell idle network resources, providing real high-quality data for AI training.
• The "Labeling for Earnings" model incentivizes global workers to participate in data labeling, gathering expertise.
• The blockchain data trading platform provides a transparent trading environment for both data suppliers and demanders, promoting data innovation and sharing.

Nevertheless, the acquisition of real-world data still faces issues such as inconsistent quality and processing difficulties. Synthetic data may become a highlight in the future as it can simulate the attributes of real data, serving as an effective complement to improve data utilization efficiency. In fields like autonomous driving, financial trading, and game development, synthetic data has already shown mature application potential.

Privacy Protection: The Application of FHE in Web3

In the data-driven era, privacy protection has become a global focus. The introduction of strict privacy regulations reflects this trend, but also brings challenges: some sensitive data cannot be fully utilized due to privacy risks, which limits the potential of AI models.

Fully Homomorphic Encryption ( FHE ) allows for direct computation on encrypted data, obtaining results consistent with plaintext calculations without the need for decryption. FHE provides solid protection for AI privacy computing, enabling GPUs to perform model training and inference in environments that do not access the original data. This presents a significant advantage for AI companies, allowing them to safely open API services while protecting trade secrets.

FHEML supports the encryption of data and models throughout the entire machine learning lifecycle, ensuring the security of sensitive information and preventing data leaks. FHEML enhances data privacy and provides a secure computing framework for AI applications. It complements ZKML, which proves the correct execution of machine learning, while FHEML focuses on computing with encrypted data to maintain privacy.

Power Revolution: AI Computing in Decentralized Networks

The current complexity of AI system calculations is rapidly increasing, leading to a surge in computing power demand that far exceeds existing supply. For example, training a large language model requires computing power equivalent to 355 years on a single device. This shortage not only limits the advancement of AI technology but also makes it difficult for most researchers and developers to access advanced models.

At the same time, the global GPU utilization is less than 40%, coupled with factors such as the slowdown in processor performance improvement and supply chain issues, making the supply of computing power even more tense. AI practitioners face a dilemma of either purchasing hardware or renting cloud resources, making efficient and flexible computing service solutions urgently needed.

The decentralized AI computing power network aggregates idle GPU resources globally, providing an economically accessible computing power market for AI companies. Demand-side users can publish computing tasks, and smart contracts will assign tasks to miner nodes. Miners execute the tasks and submit results, which are verified for rewards. This solution improves resource utilization efficiency and helps alleviate the computing power bottleneck in fields such as AI.

In addition to the general computing power network, there are dedicated platforms focusing on AI training and inference. The decentralized computing power network provides a fair and transparent market, breaking monopolies, lowering barriers, and improving efficiency. In the Web3 ecosystem, such networks will play a key role in attracting innovative applications and promoting the development of AI technology.

DePIN: Web3 Empowers Edge AI

Edge AI allows computation to occur at the data source, achieving low latency and real-time processing while protecting user privacy. This technology has been applied in key areas such as autonomous driving. In Web3, the more familiar term is DePIN. Web3 emphasizes decentralization and user data sovereignty, and DePIN enhances privacy protection through local processing, reducing the risk of data leakage. The Web3-native token economy can incentivize nodes to provide computing resources, building a sustainable ecosystem.

Currently, DePIN is rapidly developing in a high-performance public blockchain ecosystem, becoming one of the preferred platforms for project deployment. The public chain's high throughput, low fees, and technological innovations provide strong support for DePIN projects. Currently, the market capitalization of DePIN projects on this public chain has exceeded ten billion dollars, with several well-known projects making significant progress.

IMO: New Paradigm for AI Model Release

The initial model issuance of IMO( provides new ideas for the tokenization of AI models. In traditional models, developers find it difficult to obtain continuous revenue from the subsequent use of models, especially when the models are integrated into other products. Additionally, the performance and effectiveness of AI models often lack transparency, which limits market recognition and commercial potential.

IMO provides innovative funding support and value-sharing methods for open-source AI models. Investors can purchase IMO tokens to share in the subsequent profits of the model. A certain protocol uses specific technical standards, combining AI oracles and on-chain machine learning technology to ensure the authenticity of the model and the profit-sharing for token holders.

The IMO model enhances transparency and trust, encourages open-source collaboration, adapts to trends in the cryptocurrency market, and injects momentum into the sustainable development of AI technology. Although it is still in the early stages of experimentation, the innovation and potential value of IMO are worth looking forward to as market acceptance increases and participation expands.

AI Agent: A New Era of Interactive Experience

AI Agents can perceive their environment, think independently, and take action to achieve goals. Supported by large language models, they not only understand natural language but can also plan decisions and execute complex tasks. As virtual assistants, AI Agents learn user preferences through interaction, providing personalized solutions and even autonomously solving problems without explicit instructions.

A certain open AI native application platform provides comprehensive and easy-to-use creation tools, supporting users to configure robot functions, appearance, voice, and connect to external knowledge bases, dedicated to building a fair and open AI content ecosystem. The platform has trained specialized large language models to make role-playing more humanized; its voice cloning technology significantly reduces voice synthesis costs and accelerates personalized interaction with AI products. The customized AI Agent using this platform can be applied in various fields such as video chatting, language learning, and image generation.

The current integration of Web3 and AI focuses more on the infrastructure layer, exploring key issues such as data acquisition, privacy protection, on-chain model hosting, efficient use of decentralized computing power, and validation of large language models. As these infrastructures gradually improve, the integration of Web3 and AI is expected to give rise to a range of innovative business models and services.

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