The Integration and Development of AI and Web3: Opportunities and Challenges

In recent years, artificial intelligence ( AI ) and Web3 technology have made significant progress, attracting widespread attention. AI has achieved breakthroughs in areas such as facial recognition and natural language processing, bringing transformation to various industries. Web3, based on blockchain, has realized data sharing and user autonomy through smart contracts and distributed storage. The combination of the two is creating new possibilities.

The Current Development Status of AI+Web3

Web3 empowers AI

1. Decentralized Computing Network

Some projects like Akash and Render are attempting to establish decentralized computing networks through token incentives to provide computing power support for AI. These networks are mainly used for AI inference rather than training large models.

2. Decentralized Algorithm Model Market

Projects like Bittensor are building a decentralized AI algorithm service market, connecting different AI models to provide users with the most suitable services.

3. Decentralized Data Collection

Projects like PublicAI incentivize users to contribute data through tokens, providing a more diverse data source for AI training.

4. Protecting Privacy using Zero-Knowledge Proofs

Projects like BasedAI are exploring the use of zero-knowledge proof technology to conduct machine learning while protecting data privacy.

AI empowers Web3

1. Data Analysis and Prediction

Many Web3 projects integrate AI services to provide users with investment strategies, on-chain analysis, price predictions, and other functionalities.

2. Personalized Service

Platforms like Dune use AI to optimize user experience, such as automatically generating SQL queries. Some content platforms also utilize AI for content summarization.

3. Smart Contract Audit

Projects like 0x0.ai utilize AI technology to analyze smart contracts, identifying potential vulnerabilities and security risks.

Challenges Faced

1. Decentralized computing networks struggle to support large model training.

Due to high requirements for stability, multi-card parallelism, etc., decentralized computing power is currently mainly used for AI inference and is difficult to support large model training.

2. The combination of AI and Web3 is still rough.

Many projects only superficially use AI without achieving deep integration and innovation. Some projects introduce the concept of AI more for marketing considerations.

3. The token economic model needs improvement.

Some projects introduce token economic models more for financing and hype rather than addressing actual needs.

Future Prospects

Despite the challenges, the fusion of AI and Web3 still holds great potential. AI can provide intelligent capabilities for Web3, while Web3 brings decentralization advantages to AI. In the future, we are expected to achieve innovative breakthroughs in data sharing, privacy protection, and computing power supply, building a smarter, more open, and fairer system. The key lies in deeply integrating the advantages of both, rather than simply overlaying them. With technological advancements and deeper exploration, more innovative applications are believed to emerge.