Starting with the Potential of AI Data Field

In the current AI competition, training data has become a key competitive advantage. As the development of model architectures and computational power levels off, high-quality training data will become the core factor determining the capabilities of AI models.

AI model training is divided into two stages: pre-training and fine-tuning. Pre-training requires a large amount of text and other information crawled from the internet, allowing the model to master basic language skills. Fine-tuning, on the other hand, requires carefully designed datasets to cultivate specific capabilities in the model. These two types of data constitute the core of the AI data industry.

Web3 has a natural advantage in the AI data field, giving rise to the concept of DataFi. Its advantages include:

1. Smart contracts ensure data sovereignty and privacy
2. Distributed architecture attracts global labor force
3. Clear Blockchain Incentive Mechanism
4. Build an efficient and open data market

For ordinary users, DataFi is the best entry point to participate in decentralized AI. Users can get involved by providing data, evaluating models, and other simple tasks.

Currently, several DataFi projects have received significant funding, such as Sahara AI, Yupp, and Vana. These projects each have their own focus and cover multiple aspects including data collection, annotation, and trading.

However, the current DataFi project still faces some challenges:

1. How to ensure data quality and avoid bad money driving out good money.
2. Enhance project transparency to reflect the spirit of Web3
3. Balancing the attraction of ordinary users and service enterprise customers

DataFi represents a new paradigm that nurtures machine intelligence with human intelligence while ensuring the returns on human labor through smart contracts. For ordinary users who want to participate in the AI revolution, DataFi is undoubtedly a good entry point.