The AI data track holds enormous potential, and Web3 DataFi may become a new blue ocean.

In today's increasingly intense global AI competition, data is gradually becoming the key moat for building excellent foundational models. As the gap in model architectures and computing power narrows, high-quality training data will become the core factor determining the competitiveness of AI companies.

The most notable event in the AI circle this month is undoubtedly Meta's massive recruitment of talent, forming a top AI team primarily composed of Chinese researchers. Among them, 28-year-old Alexander Wang is particularly eye-catching. His company, Scale AI, is currently valued at as much as $29 billion and provides data services to many AI giants, including the U.S. military. Scale AI's ability to stand out among many unicorns is attributed to its early insight into the crucial role of data in the AI industry.

If we compare a large model to a person, then the model is the body, computing power is the food, and data is knowledge and information. In the rapid development of large language models, the industry's focus has shifted from model architecture to computing power, and most models now use transformers as the basic framework. Major companies either build their own supercomputing clusters or sign long-term agreements with cloud service providers to meet the basic computing power needs. Against this backdrop, the importance of data is increasingly prominent.

Unlike traditional B2B big data companies, Scale AI focuses on building a solid data foundation for AI models. Its business not only includes mining existing data but also dedicates itself to long-term data generation efforts, assembling an AI training team made up of experts from various fields to provide higher quality training data for models.

Model training is usually divided into two phases: pre-training and fine-tuning. Pre-training is similar to the process of a baby learning to speak, requiring a large amount of text, code, and other information collected from the internet. Fine-tuning is like going to school, with clear rights and wrongs and direction, cultivating the model's specific abilities through carefully designed datasets.

Therefore, AI data can also be divided into two categories: one category is massive data that requires little processing, such as crawler data from social media and code repositories; the other category requires careful design and selection to ensure that it cultivates specific excellent qualities in the model, which requires data cleaning, labeling, and other tasks. These two types of datasets constitute the main body of the AI Data track.

As the capabilities of models continue to improve, various specialized and refined training data will become key variables determining model performance. High-quality datasets are like the ultimate secret manuals of martial arts masters, crucial for enhancing model capabilities. In the long run, AI Data is also a field with a snowball effect; as initial work accumulates, data assets will generate a compounding effect, and their value will become increasingly prominent.

In this context, Web3 DataFi, as an emerging field, has a natural advantage in AI data.

1. Smart contracts ensure data sovereignty, security, and privacy. Users can clearly understand how their data is being used, while sensitive information is protected through technologies such as zero-knowledge proofs.

2. Distributed architecture attracts the most suitable workforce globally. The decentralized nature of blockchain and its transparent incentive mechanisms can attract a global workforce to participate in data contribution, which helps to enhance the diversity of data.

3. Blockchain provides clear advantages in incentives and settlements. Smart contracts can implement a transparent incentive system, avoiding problems that may arise in traditional centralized companies. At the same time, on-chain settlement methods can overcome geographical limitations, enabling more efficient cross-border payments.

4. Conducive to building an efficient and open data market. A decentralized market can facilitate more transparent and efficient connections between data supply and demand, promoting the prosperous development of the ecosystem.

For ordinary users, DataFi is the best entry point to participate in decentralized AI projects. Compared to the high barriers of computing power mining or model development, users can participate simply by completing tasks such as providing data, evaluating models, etc. This offers ordinary people the possibility to seize opportunities in the AI revolution.

Currently, a number of promising projects have emerged in the Web3 DataFi field, such as Sahara AI, Yupp, and Vana. These projects each have their own characteristics in terms of user incentives, data quality management, and more. However, they also face some common challenges, such as how to balance short-term interests with long-term quality and improve transparency.

In the future, the large-scale application of DataFi requires progress in two aspects: first, attracting enough ordinary users to participate, forming a strong force for data collection and generation; second, gaining recognition from mainstream large companies to attract large orders. Some leading projects have already made good progress in these two areas.

Overall, DataFi represents a new type of interaction model between human intelligence and machine intelligence. Through smart contracts, it not only guarantees the returns of human labor but also provides nourishment for the development of machine intelligence. For those who are full of hope for the AI era and uphold the ideals of blockchain, DataFi is undoubtedly a field worth paying attention to and investing in.