What are the areas where artificial intelligence and Web3 are integrated?

The application areas of the integration of artificial intelligence and Web3 are rapidly expanding, mainly covering the following directions (as of June 2025):

1. Decentralized Infrastructure
Computing Power Network
Build an open computing power market by aggregating idle GPU resources globally, supporting a reduction of 63%-80% in AI training/inference costs. Representative projects include Render Network (GPU rendering), Gensyn (distributed training), and Akash (cloud computing).

Storage and Privacy Protection
Decentralized storage networks (Filecoin, Arweave) combine cryptographic technology to return data sovereignty to users, while ensuring privacy and security through technologies such as zero-knowledge proofs (ZK).

2. Data and Model Innovation
Data Trading Market
A compliance data sharing network based on Web3 data rights protocols (such as Ocean Protocol and Grass) is built to incentivize users to contribute behavioral data and lower the threshold for AI training.

Trustworthy AI Model
Using zero-knowledge proofs (ZK) and fully homomorphic encryption (FHE) technologies to verify the model inference process, addressing the black box problem. For example, Modulus Labs' zkML solution reduces the misdiagnosis rate in medical diagnostics by 42%.

3. Financial and Economic Systems
Smart Contract Optimization
AI-assisted design of dynamic clause contracts, real-time monitoring of market anomalies and automatic strategy adjustments to enhance the security of DeFi protocols. The People RWA system optimizes the security of token contracts through AI.

Decentralized Finance (DeFi)
AI-driven risk assessment, investment strategy generation, and liquidity mining optimization provide personalized financial services for small and medium investors, increasing annualized returns by 27%.

4. Content and Experience Upgrade
AIGC and NFT
AI-generated content (text/images/videos) combined with NFTs allows for instant copyright upon creation. The Story Protocol supports modular content authorization, reducing copyright disputes by 89%.

Metaverse and Games
Blockchain games use AI to generate NPC behavior patterns, optimizing the immersive experience. The Matr1x game introduces AI-driven dynamic level design, resulting in a 3.2 times increase in user payment rates.

5. Industry and Public Services
Supply Chain Management
AI predicts market demand and optimizes logistics paths, achieving full-process traceability through blockchain. A coffee cooperative in Kenya has compressed the sales cycle from 45 days to 72 hours via the Web3 network.

Digital Identity and Governance
Decentralized identity systems (such as Worldcoin) combine biometric technology to construct a fraud-proof on-chain identity system. DAO governance introduces AI agents (AEA) to enhance decision-making transparency.

6. Emerging Integration Scenarios
Healthcare
Under the federated learning framework, medical institutions share encrypted medical data to train AI models while protecting patient privacy. Phala Network's Trusted Execution Environment (TEE) ensures data processing security.

Green Computing
The mobile zero-energy mining technology for Web3 networks, combined with AI to optimize energy distribution, reduces carbon emissions by 99.7% compared to traditional blockchains.

Technical Challenges and Trends
Core contradiction: There is a conflict between decentralized data storage and the demand for centralized AI training, which needs to be balanced through technologies such as federated learning.
Future direction: Cross-chain AI collaboration protocols (such as Openpond) will promote multi-chain scenario applications, with the relevant market size expected to exceed 30 billion USD by 2026.
In the above fields, the infrastructure layer (computing power/storage) and the application layer (finance/content) currently have the highest investment enthusiasm, and projects in the technology validation period should pay attention to the activity indicators of the developer ecosystem.