Global AI Data Competition: The Rise of DataFi and the Advantages of Web3

In the context of the booming AI industry, data is gradually becoming a key pillar for AI models to maintain their competitive advantage. As model capabilities continue to improve, the impact of high-quality, specialized training data on model performance is becoming increasingly important. In this trend, the AI data service industry is ushering in new development opportunities.

The Road to Success of Scale AI

Scale AI, a representative company in the field of AI data services, has early positioned itself in this sector with its keen insights. The company focuses on providing high-quality labeled data services for AI models and is currently valued at $29 billion, serving several AI giants and the United States military. The success of Scale AI fully reflects the important role of data in the AI industry.

Two Stages of AI Model Training

The training of AI models is usually divided into two stages: pre-training and fine-tuning.

1. The pre-training stage requires a large amount of internet text, code, and other data to help the model acquire basic language skills.

2. The fine-tuning stage requires a carefully designed and curated dataset to specifically cultivate the model's particular capabilities.

These two types of data constitute the main business content of the AI data service industry. With the development of the industry, more refined and specialized training data will become the key factor determining model performance.

Advantages of Web3 in the AI Data Field

Compared to traditional centralized data service models, Web3 has many advantages in the field of AI data.

1. Smart contracts ensure data ownership, security, and privacy.

2. Distributed architecture can attract high-quality labor from around the world.

3. Blockchain provides transparent incentive and settlement mechanisms.

4. Conducive to building an efficient and open data trading market.

These advantages make Web3 an ideal platform for AI data services, and DataFi has emerged accordingly. For ordinary users, DataFi is an excellent opportunity to participate in the AI revolution, with low barriers to entry and diverse forms.

Potential Projects in the DataFi Track

Currently, multiple DataFi projects have received substantial funding, demonstrating the capital market's optimism towards this field. Some representative projects include:

• Sahara AI: Build a decentralized AI infrastructure and trading market
• Yupp: AI Model Feedback Platform
• Vana: Personal Data Monetization Platform
• Chainbase: On-chain data service provider
• Sapien: A platform that converts human knowledge into AI training data.
• Prisma X: Robot Open Coordination Layer
• Masa: Bittensor ecosystem subnet project
• Irys: Programmable Data Storage and Computing Platform
• ORO: A platform for ordinary people to contribute to AI.
• Gata: Decentralized Data Layer

These projects each have their own characteristics and cover different subfields of DataFi.

Challenges Facing DataFi Projects

Despite the broad prospects, DataFi projects still face some challenges:

1. How to ensure data quality while attracting users.
2. Improve project transparency and achieve true decentralization.
3. Balance the demand on both the toC and toB sides to create a positive ecosystem.

In the future, DataFi is expected to become an important link for the positive interaction between human intelligence and machine intelligence. For blockchain idealists and participants in the AI era, DataFi is undoubtedly an emerging field worth paying attention to.