The Huge 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, computational power and model architecture are certainly important, but the real moat lies in the training data. This article will explore the potential of the AI data race and how Web3 DataFi is emerging as a new force in this field.

The Importance of AI Data

With the rapid development of large language models, the industry's focus has gradually shifted from model architecture to computing power, and now it is turning its attention to data. Data is becoming a key factor for AI companies to maintain a competitive advantage.

The training of AI models is divided into two stages: pre-training and fine-tuning. The pre-training stage requires a large amount of text, code, and other information crawled from the web, while the fine-tuning stage requires carefully designed and selected professional datasets. These two types of data constitute the main body of the AI Data track.

High-quality training data is crucial for enhancing the model's capabilities, just like a martial arts manual is to a martial arts expert. Over time, data assets will also possess compounding abilities, becoming increasingly valuable.

Advantages of Web3 DataFi

Compared to traditional centralized 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. The blockchain provides clear incentive and settlement mechanisms.
4. Conducive to building an efficient and open data market

For ordinary users, DataFi is one of the lowest threshold ways to participate in decentralized AI projects. Users can participate by providing data, evaluating models, and other simple tasks to receive corresponding rewards.

Overview of Potential Projects

Multiple Web3 DataFi projects have secured significant funding, including:

• Sahara AI: Decentralized AI Infrastructure and Trading Market
• Yupp: AI Model Feedback Platform
• Vana: Personal Data Monetization Platform
• Chainbase: On-chain data processing service
• Sapien: Human knowledge transformed into AI training data
• Prisma X: Robot Open Coordination Layer
• Masa: Bittensor ecosystem subnet project
• Irys: Programmable Data Storage and Computation
• ORO: A platform for ordinary people to contribute to AI
• Gata: Decentralized Data Layer

Development Challenges and Prospects

The main challenges faced by the current DataFi project include:

1. How to establish user stickiness and platform advantages
2. Ensure data quality and avoid the bad money driving out the good.
3. Enhance project transparency to truly embody the spirit of Web3.
4. Balance between toC user engagement and toB client needs

DataFi represents the long-term symbiotic relationship between human intelligence and machine intelligence. For those curious about the AI era and holding blockchain ideals, participating in DataFi may be a timely choice.