The Integration of Web3 and AI: Building a Decentralized Artificial Intelligence Ecosystem

In a recent speech, an industry insider introduced the concept of "sovereign AI". This has sparked our thinking: how can we build an AI system that meets the interests and demands of the cryptocurrency community? The answer may be found in the combination of Web3 and AI.

A renowned blockchain expert once discussed the synergy between AI and cryptographic technology in an article. He pointed out that the decentralized nature of cryptographic technology can balance the centralizing tendency 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 the data required by AI. This synergy runs throughout the entire industrial layout of Web3+AI.

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

1. Computing Power Layer: Assetization of Computing Power

In recent years, the computational power required for training large AI models has grown exponentially, leading to a serious imbalance in the supply and demand of computational power, causing prices of hardware such as GPUs to soar. However, there are also a large number of idle mid-to-low-end computational resources in the market. Through Web3 technology, a distributed computing network can be established to achieve computing power leasing and sharing, thereby reducing the cost of AI computing power.

The power layer subdivision includes:

• Universal Decentralization Computing
• AI training dedicated Decentralization computing power
• AI inference dedicated Decentralization computing power
• 3D Rendering Dedicated Decentralization Computing Power

The core advantage of this decentralized computing model lies in its ability to quickly scale the network size by combining a token incentive mechanism, while also providing cost-effective computing resources to meet mid-to-low-end computing needs.

2. Data Layer: Data Assetization

Data is a key resource for AI development. In traditional models, a large amount of user data is often concentrated in the hands of a few tech giants, making it difficult for ordinary startups to access extensive data resources. Through the Web3+AI approach, it is possible to achieve lower-cost and more transparent data collection, labeling, and distributed storage.

The data layer projects mainly include:

• Data Collection Projects
• Data Trading Projects
• Data Annotation Projects
• Blockchain data source projects
• Decentralization storage projects

These projects face greater challenges when designing token economic models, as data is harder to standardize.

3. Platform Layer: Assetization of Platform Value

Platform projects aim to integrate various resources in the AI industry, aggregating data, computing power, models, AI developers, and resources and roles from blockchain, among others. Some projects focus on building zkML operation platforms that validate the correctness of model inference through cryptographic techniques, addressing the common data and model black box issues present in AI.

There are also some projects dedicated to establishing AI-specific blockchain networks, helping Web3+AI applications to quickly build and develop by providing common components and SDKs. In addition, Agent Network platforms support the construction of AI Agents for various application scenarios.

Platform projects mainly capture platform value through tokens, incentivizing all parties to participate in co-construction, which is particularly beneficial for the development of startup projects.

4. Application Layer: AI Value Assetization

Application layer projects mainly leverage AI to solve specific problems in Web3 applications. This includes two main directions:

1. AI as a participant in Web3: For example, as a player in Web3 games, engaging in arbitrage trading on DEX, or providing analytical forecasting capabilities in prediction markets.

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

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

Outlook

The Web3+AI field is still in its early stages, and there are differing opinions within the industry regarding its development prospects. However, we look forward to the combination of Web3 and AI creating products that are more valuable than centralized AI, breaking free from the labels of "big tech control" and "monopoly," and achieving a more community-driven "co-governance AI" model. By participating more closely in the development and governance of AI, humanity may find a balance in understanding AI, maintaining awe while reducing fear.