New Trends in the AI Industry: The Rise of Localized Small Models and Edge Computing

Recently, observing the development of the artificial intelligence industry, a significant trend is forming: the mainstream direction, which previously focused on large-scale computing power concentration and large models, is gradually evolving into a new path leaning towards local small models and Edge Computing.

This trend is reflected in multiple fields. For example, a technology giant has launched an intelligent system that has covered 500 million devices; another tech company has developed a dedicated small model with 330 million parameters for its operating system; and research institutions are exploring the offline operational capabilities of robots. These are all clear signals of localized AI development.

Cloud AI and local AI have significant differences in their competitive focuses. Cloud AI primarily relies on a large parameter scale and massive training data, with financial strength becoming a core competitive advantage. In contrast, local AI places more emphasis on engineering optimization and scenario adaptation, offering advantages in protecting user privacy and enhancing system reliability and practicality. This is especially important because general models often face issues with insufficient accuracy when applied in specific domains.

This transformation brings new opportunities for Web3 AI projects. In the past, traditional tech giants dominated the competition with their advantages in resources, technology, and user base, pursuing generalized capabilities. However, in the new fields of localized models and Edge Computing, blockchain technology may find more room to operate.

When AI models run on user devices, how can we ensure the authenticity of the output results? How can we achieve collaboration between models while protecting privacy? These are precisely the problems that blockchain technology excels at solving.

Some innovative projects have emerged in the industry to address these challenges. For example, some companies have developed data communication protocols aimed at solving the issues of data monopoly and decision-making opacity in centralized AI platforms. Other projects collect real human data through brainwave devices to build an "artificial verification layer," and have already achieved considerable revenue. These efforts are all exploring ways to enhance the credibility of local AI.

In summary, decentralized collaboration can only shift from concept to actual demand when AI technology truly penetrates every terminal device. For Web3 AI projects, rather than struggling to compete in the fiercely competitive general AI field, it may be more promising to thoughtfully consider how to provide the necessary infrastructure support for the localized AI wave, which could be a more promising direction for development.