Challenges and Opportunities in the Development of Web3 AI

Web2 AI has made breakthrough progress in the field of multimodal models, establishing technical barriers such as high-dimensional embedding spaces, precise attention mechanisms, and deep feature fusion. These breakthroughs have further deepened the technical threshold in the AI industry and have also driven up the stock prices of related companies.

However, Web3 AI faces difficulties in mimicking the Web2 model. The decentralized structure of Web3 makes it challenging to achieve high-dimensional embedding and complex modular systems. In the current low-dimensional space, Web3 AI is unable to perform effective semantic alignment and cannot design sophisticated attention mechanisms. Feature fusion is also limited to simple static concatenation.

Although the barriers in the AI industry are deepening, the opportunities for Web3 AI have not yet truly emerged. Web2 AI is still in the early stages of its dividend period, and the competition among leading companies is driving rapid technological advancement. The right timing for Web3 AI may have to wait until the dividends of Web2 AI wane and significant pain points are left behind.

Before this, Web3 AI should adopt the "rural encirclement of cities" strategy. It should start from marginal scenarios, accumulating experience in lightweight and easily parallelizable tasks, such as LoRA fine-tuning, post-training behavior alignment, and crowdsourced data processing. By selecting sufficiently small application scenarios to continuously iterate products, it can maintain flexibility to adapt to the ever-changing technological landscape and market demands.

The key to the future development of Web3 AI lies in finding the right positioning, leveraging decentralized advantages in suitable scenarios, while maintaining sufficient flexibility and innovation capability to prepare for larger-scale application opportunities in the future.