New Trends in the AI Industry: From Cloud to Localization

Recently, the AI industry has shown an interesting development trend: moving from the mainstream direction that originally emphasized large-scale computing power and massive models, it has gradually evolved into a new path that favors local small models and edge computing.

This trend is reflected in multiple fields. For example, a certain tech giant's intelligent system has covered 500 million devices, another software giant has launched a dedicated small model with 330 million parameters for its latest operating system, and a well-known AI research institution is developing robotic technology capable of operating "offline."

Cloud AI and local AI have significant differences in their competitive focuses. Cloud AI primarily competes on parameter scale and training data volume, with financial strength becoming a core competitive advantage. In contrast, local AI places more emphasis on engineering optimization and scenario adaptation, having advantages in privacy protection, reliability, and practicality. This is especially important, as the hallucination problem of general models can severely affect their application in specific fields.

This transition brings new opportunities for Web3 AI projects. In the past "generalization" competition, traditional tech giants dominated with their advantages in resources, technology, and user base, making it difficult for Web3 projects to compete. However, under the new landscape of localized models and edge computing, the advantages of blockchain technology are beginning to emerge.

When AI models run on user devices, how can we ensure the authenticity of the output results? How can we achieve model collaboration while protecting privacy? These questions happen to fall within the expertise of blockchain technology.

There have already been some innovative projects in the industry addressing these issues. For example, a certain data communication protocol aims to solve the problems of data monopoly and opacity in centralized AI platforms. Additionally, there are projects that collect real human data through EEG devices to build an "artificial verification layer," which has already generated considerable revenue. These attempts are all making efforts to address the credibility issues of local AI.

Overall, decentralized collaboration can only turn from a concept into a real demand when AI truly "sinks" into every device. For Web3 AI projects, rather than competing in an already crowded generalized track, it may be a more promising direction to focus on providing infrastructure support for the localized AI wave.