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

In the field of AI, data is gradually becoming the most important competitive advantage. As the development of model architectures and computing power stabilizes, high-quality training data will be a key factor for AI companies to maintain their leading position.

The success of Scale AI fully demonstrates this point. The company focuses on providing a large amount of accurate labeled data for AI models, serving several AI giants. Scale AI not only offers existing data mining but also looks ahead to long-term data generation business, providing high-quality data for AI model training through its expert team.

AI model training 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 internet, while the fine-tuning stage requires a targeted selection of datasets. These two types of data constitute the main body of the AI Data track. As model capabilities improve, refined and specialized training data will become increasingly important.

In this context, the Web3 DataFi sector shows great potential. Compared to traditional data companies, Web3 DataFi has multiple advantages:

1. Ensure data sovereignty, security, and privacy through smart contracts
2. Attract the most suitable workforce globally using a distributed architecture
3. Blockchain provides clear incentives and settlement methods.
4. Helps to build 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 get involved and earn rewards by completing simple tasks such as providing data and evaluating models.

Several Web3 DataFi projects have already received significant funding, such as Sahara AI, Yupp, and Vana. These projects cover multiple aspects including data collection, annotation, and evaluation. Although the current barriers to entry for these projects are generally low, there is potential for them to develop platform advantages in the future by accumulating users and ecosystem stickiness.

For these projects, the current key is how to attract and retain quality users while ensuring data quality. In addition, increasing transparency and accelerating the decentralization process are also important directions for the future. Ultimately, the success of DataFi also requires recognition from both ordinary users and enterprise clients.

DataFi represents a new type of interactive relationship between human intelligence and machine intelligence. In the uncertainty of the AI era, participating in DataFi may be a wise choice in line with the trend.