The Fusion of Web3 and Artificial Intelligence: Opportunities and Challenges in the New Era

At the recent World Government Summit, a tech giant proposed the concept of "Sovereign AI". This raises the question: which form of AI can truly align with the interests and needs of the cryptocurrency community? The answer may lie in the combination of Web3 and AI.

The founder of Ethereum discussed the synergy between cryptocurrency and AI in a recent article. He pointed out that the decentralized nature of cryptocurrency can balance the centralized trends of AI; the transparency of blockchain can compensate for the opacity of AI; and blockchain technology is also beneficial for the storage and tracking of data required by AI. This synergy runs throughout the entire industrial ecosystem of Web3+AI.

Currently, most Web3+AI projects focus on leveraging blockchain technology to address the infrastructure construction issues in the AI industry, while a few projects are dedicated to using AI to solve specific problems in Web3 applications. The Web3+AI industry mainly involves four aspects:

1. Computing Layer: Assetization of Computing Power With the explosive growth in demand for AI large model training, the long-term imbalance between supply and demand for computing power has led to a rapid increase in hardware prices and computing power costs. Web3 technology can provide more economical AI computing power solutions by establishing a distributed computing power network that integrates idle mid-to-low-end hardware resources through leasing and sharing. This field includes general decentralized computing power, specialized computing power for AI training and inference, as well as sub-segments like 3D rendering computing power.

2. Data Layer: Data Assetization Data is the core resource for AI development. The combination of Web3 and AI can achieve lower cost and more transparent data collection, labeling, and storage processes, while also allowing ordinary users to benefit from it. This field includes project types such as data collection, trading, labeling, blockchain data source provision, and decentralized storage.

3. Platform Layer: Assetization of Platform Value Platform-based projects aim to integrate various resources in the AI industry, including data, computing power, models, and developer communities. Some projects focus on building trustworthy and transparent machine learning inference platforms that utilize cryptographic technologies such as zero-knowledge proofs to verify the correctness of model execution. There are also projects dedicated to developing blockchain networks specifically for AI or constructing AI agent network platforms.

4. Application Layer: AI Value Assetization Application layer projects mainly explore the specific applications of AI in the Web3 ecosystem. For example, AI can act as a participant in Web3 games, an arbitrage tool for decentralized exchanges, or an analyst for prediction markets. Another important direction is to create decentralized private AI systems, enhancing user trust and acceptance of AI through community governance.

Despite the fact that the Web3+AI field is still in its early stages, its development prospects have sparked widespread discussion within the industry. This integration is expected to create products that are more valuable than traditional centralized AI, breaking free from the labels of "big tech control" and "monopoly," and achieving a more democratized model of AI governance. By engaging more deeply in the development process of AI, humanity may be able to reduce unnecessary fears while maintaining a sense of awe toward AI.