The Integration of Web3 and AI: Building a Decentralized Intelligent Ecosystem

Recently, the rapid development of artificial intelligence (AI) has attracted widespread attention. At the World Government Summit held in Dubai, industry insiders proposed the concept of "sovereign AI." This statement has sparked thoughts on how to build AI systems that align with the interests and demands of the crypto community.

The founder of Ethereum once explored the synergistic effects of AI and cryptographic technology in an article. He pointed out that the decentralization characteristic of cryptographic technology can balance the centralization trend 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 landscape of the integration of Web3 and AI.

Currently, most Web3+AI projects are dedicated to using blockchain technology to address the infrastructure construction issues in the AI industry, while a few projects attempt to use AI to solve specific problems in Web3 applications. The combination of Web3 and AI is mainly reflected in the following four aspects:

1. Computing Power Layer: Assetization of Computing Power

With the exponential growth in computing power demand for training large AI models, there has been a long-term imbalance between supply and demand, leading to a rapid increase in hardware prices and computing power costs. Web3 technology can establish a distributed computing power network by utilizing idle mid-to-low-end hardware resources in a leasing and sharing manner to create a Decentralization computing resource network, thereby reducing AI computing power costs. Such projects include general Decentralization computing power, AI training computing power, AI inference computing power, and 3D rendering computing power among other subfields.

2. Data Layer: Data Assetization

Data is a key resource for the development of AI. The integration of Web3 and AI can make processes such as data collection, labeling, and distributed storage more cost-effective and transparent, benefiting users. Through distributed networks and token incentive mechanisms, high-quality and extensive data can be acquired at low cost through crowdsourcing. Related projects cover multiple directions including data collection, trading, labeling, blockchain data sources, and Decentralization storage.

3. Platform Layer: Assetization of Platform Value

Platform projects are dedicated to integrating various resources in the AI industry, including data, computing power, models, and developer communities. Some projects focus on building zkML operation platforms aimed at improving the credibility and transparency of machine learning inference. There are also projects dedicated to developing public chains or layer two networks specifically for AI, as well as Agent Network platforms. These platforms capture value through token mechanisms, incentivizing all parties to participate in co-building the ecosystem.

4. Application Layer: AI Value Assetization

Application layer projects mainly explore the specific applications of AI in Web3 scenarios. For example, AI can act as a participant in Web3 games, engage in arbitrage trading on DEXs, or provide analytical services in prediction markets. Another important direction is to create scalable decentralized private AI, which increases the transparency and credibility of AI systems through community governance.

Although the Web3+AI field is still in its early stages, there are varying opinions within the industry about its development prospects. However, this trend of integration is worth continuous attention. We hope that the combination of Web3 and AI can create products that are more valuable than centralized AI, breaking free from the labels of "giant control" and "monopoly," and achieving a more community-oriented "co-governance AI" model. Through deeper participation and governance of AI, humanity may develop a more rational understanding of AI, reducing unnecessary "fear" in the midst of "awe."