The Potential of AI Data Track and the Rise of Web3 DataFi

In an era where the world is competing to build the best foundational models, while computing power and model architecture are indeed important, the real moat lies in the training data. This article will explore the potential of the AI data track and how Web3 DataFi is emerging as a force in this field.

The Importance of AI Data

Computing power, models, and data are the three pillars of AI models. With the popularity of the transformer architecture and the gradual resolution of computing power issues, the importance of data is becoming increasingly prominent. Model training is divided into two stages: pre-training and fine-tuning, each requiring different types of data:

1. Pre-training stage: Requires a large amount of text, code, and other information crawled from the internet.
2. Fine-tuning phase: A carefully designed and selected dataset is required to cultivate specific capabilities of the model.

These two types of data constitute the main body of the AI data track. With the enhancement of model capabilities, high-quality, specialized training data will become a key factor determining model performance.

Advantages of Web3 DataFi

Compared to traditional data companies, Web3 has a natural advantage in the AI data field:

1. Smart contracts ensure data sovereignty, security, and privacy.
2. Distributed architecture attracts the most suitable workforce globally.
3. Blockchain provides clear incentive and settlement mechanisms.
4. Conducive to building an efficient and open one-stop data market

For ordinary users, DataFi is the best entry point for participating in decentralized AI projects, with low barriers to entry and various ways to participate.

Potential Projects in Web3 DataFi

Multiple DataFi projects have secured substantial funding, demonstrating immense potential:

1. Sahara AI: Building a decentralized AI super infrastructure and trading market
2. Yupp: AI Model Feedback Platform
3. Vana: Transforming personal data into monetizable digital assets
4. Chainbase: Focus on on-chain data
5. Sapien: Transforming human knowledge into high-quality AI training data
6. Prisma X: The Open Coordination Layer for Robots
7. Masa: Bittensor ecosystem's data subnet project
8. Irys: Focused on programmable data storage and computation
9. ORO: Empowering ordinary people to participate in AI contributions
10. Gata: Decentralized Data Layer

Project Development Recommendations

1. Focus on early user incentives and experience optimization
2. Emphasize data quality management and establish long-term healthy cooperative relationships.
3. Enhance project transparency and demonstrate the commitment to decentralization.
4. Dual approach: Attract toC participants to build an ecological closed loop while striving for recognition from toB major clients.

DataFi represents the symbiotic relationship between human intelligence and machine intelligence. For those who are both excited and somewhat anxious about the AI era, participating in the DataFi project is a timely choice.