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

Recently, a hot topic in the AI field is the concept of "sovereign AI." This has prompted people to think about how to build an AI system that can meet the needs of the crypto community while balancing Decentralization and intelligence. One possible answer is to achieve this through the Web3+AI approach.

Ethereum founder Vitalik Buterin explored the synergy between cryptographic technology and AI 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 through the entire Web3+AI industry ecosystem.

Currently, most Web3+AI projects are focused on 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 Web3+AI industrial ecosystem mainly involves the following four aspects:

1. Computing Power Layer: Assetization of Computing Power

In recent years, the computational power required for training AI large models has grown exponentially, leading to an imbalance in the supply and demand for computational power and skyrocketing prices for hardware such as GPUs. At the same time, there is a large amount of idle mid-to-low-end computational resources in the market. Web3 technology can establish a distributed computing network to enable the rental and sharing of computational power, thereby meeting the diverse needs of AI applications while significantly reducing costs.

The power layer segmentation includes:

• General Decentralization Computing Power
• AI training dedicated Decentralization computing power
• AI inference dedicated Decentralization computing power
• 3D rendering dedicated Decentralization computing power

The main advantage of Web3+AI's computing power assetization lies in the ability to rapidly expand network scale through token incentives, providing cost-effective computing resources to meet medium and low-end computing power demands.

2. Data Layer: Data Assetization

Data is the key resource for the development of AI. In traditional models, a large amount of user data is concentrated in the hands of a few giant companies, making it difficult for ordinary startups to access extensive data resources. The integration of Web3 and AI can make processes such as data collection, labeling, and distributed storage more cost-effective and transparent, allowing users to benefit from it.

The data layer projects mainly include:

• Data Collection Projects
• Data Trading Projects
• Data Annotation Projects
• Blockchain Data Source Projects
• Decentralization storage projects

Such projects face significant challenges in designing token economic models, as data is harder to standardize than computing power.

3. Platform Layer: Platform Value Assetization

Platform projects aim to integrate various resources from the AI industry, including data, computing power, models, AI developers, and blockchain. They provide convenient solutions for various needs, such as building a zkML operational platform to enhance the credibility and transparency of machine learning inference.

Some projects focus on developing blockchain layers specifically for AI, helping Web3+AI applications to quickly build and grow by providing general components and SDKs. There are also projects dedicated to building Agent Network platforms that provide AI Agent construction services for various scenarios.

These types of projects mainly capture platform value through tokens, incentivizing all parties to participate in building the ecosystem together.

4. Application Layer: AI Value Assetization

Application layer projects mainly explore how to use AI to solve specific problems in Web3 applications. Vitalik Buterin proposed two meaningful directions:

1. AI as a Web3 participant: For example, in Web3 games, AI can quickly understand the rules and efficiently complete tasks as a player; in DEX, AI has been widely used in arbitrage trading; in prediction markets, AI agents can analyze and predict using large amounts of data and knowledge.

2. Create a scalable Decentralization private AI: Address user concerns about the AI black box, biases, and potential deceptive behaviors by empowering the community with distributed governance over the AI.

Currently, there are no highly influential projects in the Web3+AI application layer, but the potential is enormous.

Conclusion

The integration of Web3 and AI is still in its early stages, and there are differing opinions within the industry regarding its development prospects. We hope that this integration can create products that are more valuable than centralized AI, breaking free from the labels of "big tech control" and "monopoly" and achieving "co-governance of AI" in a more community-oriented way. By participating and governing more deeply, humanity may develop greater awe for AI and reduce fear.