Web3 and AI Integration: Building a Decentralized AI Ecosystem

At the recent World Government Summit, a leader from the tech industry proposed the concept of "Sovereign AI." This raised the question: what form of AI can truly align with the interests and needs of the Crypto Assets community? The answer may be found in the combination of Web3 and AI.

The founder of Ethereum discussed the synergy between AI and encryption technology in an article: the decentralization of encryption technology can balance the centralization tendency of AI; the transparency of encryption technology can make up for the opacity of AI; and blockchain helps with the storage and tracking of data needed by AI. This synergy runs throughout the entire industrial ecosystem of Web3+AI.

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

1. Computing Power Layer: Assetization of Computing Power

With the explosive growth in demand for AI large model training, the imbalance between supply and demand for computing power has led to skyrocketing hardware prices. Web3 technology can establish a distributed computing power network to integrate idle mid-to-low end hardware resources, providing computing power services through leasing or sharing methods, thereby reducing the costs of AI applications. This field includes general decentralized computing power, computing power specifically for AI training and inference, as well as niche markets like 3D rendering computing power.

2. Data Layer: Data Assetization

Data, as a core resource of AI, is often monopolized by large enterprises under traditional models. The combination of Web3 and AI makes the processes of data collection, labeling, and storage more transparent, efficient, and cost-effective. Through distributed networks and token incentive mechanisms, widespread data collection and high-quality labeling can be achieved. Related projects cover areas such as data collection, trading, labeling, blockchain data source provision, and decentralized storage.

3. Platform Layer: Platform Value Assetization

Platform 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, utilizing cryptographic techniques such as zero-knowledge proofs to verify the correctness of model execution. There are also projects dedicated to developing blockchain networks specifically serving AI or building AI agent network platforms.

4. Application Layer: AI Assetization

At the application level, Web3+AI projects primarily utilize AI technology to solve specific issues within Web3 applications. For example, AI can act as a participant in Web3 games, engaging in arbitrage trading on decentralized exchanges, or providing analytical services in prediction markets. Another important direction is the creation of decentralized private AI systems, enhancing user trust and control over AI through community governance.

Although the Web3+AI field is still in its early stages, there are differing views within the industry regarding its development prospects. However, this combination is expected to create products with greater value than traditional centralized AI, breaking free from the labels of "big tech control" and "monopoly," and achieving a more community-driven AI governance model. By participating more deeply in the development process of AI, humanity may be able to reduce unnecessary fears while maintaining a sense of awe towards AI.