The Value of Data in the Age of Artificial Intelligence: The Rise and Prospects of DataFi

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

The Path to Success of Scale AI

Scale AI stands out due to its early insights into the importance of data in the AI industry. As one of the three pillars of AI models, the significance of data is increasingly highlighted. Scale AI not only provides a large amount of accurate labeled data but has also expanded its business into the data generation field and formed an AI trainer team to provide high-quality data for model training.

Data Requirements for Model Training

Model training consists of two stages: pre-training and fine-tuning. The pre-training phase requires a large amount of information such as text and code crawled from the web, while the fine-tuning phase requires carefully processed and targeted datasets. These two types of data make up the main body of the AI Data track. As model capabilities improve, high-quality, specialized training data will become a key competitive factor.

Advantages of Web3 DataFi

Compared to traditional data companies, Web3 DataFi has the following advantages:

1. Smart contracts ensure data sovereignty, security, and privacy
2. Distributed architecture attracts the most suitable workforce globally.
3. Clear blockchain incentive and settlement mechanisms
4. Build an efficient and open one-stop data marketplace

For ordinary users, DataFi is an ideal entry point for participating in decentralized AI projects, without the need for expensive hardware investments or a professional technical background.

Web3 DataFi Potential Projects

Multiple DataFi projects have received considerable funding, including:

• Sahara AI: 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 for converting human knowledge into AI training data.
• Prisma X: Robot Open Coordination Layer
• Masa: The data subnet project of the Bittensor ecosystem
• Irys: A programmable data storage and computing solution
• ORO: A platform for ordinary people to contribute to AI.
• Gata: Decentralized Data Layer

Project Development Thoughts

The challenges currently faced by DataFi projects include:

• Build user and ecological stickiness
• Ensure data quality and prevent bad money from driving out good money.
• Increase transparency to achieve true decentralization
• Balance the needs of toC participants and toB large clients

Conclusion

DataFi represents the long-term symbiotic relationship between human intelligence and machine intelligence. For those filled with uncertainty about the AI era, participating in DataFi projects may be a wise choice to align with the trend.