AI Industry Trends: Shift from Cloud to Localization

Recently, the development of the AI industry has shown an interesting trend: from the previous mainstream focus on large-scale computing power and large models, a new route has gradually emerged that emphasizes local small models and edge computing.

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

There are significant differences in the competitive focus between cloud AI and local AI. Cloud AI mainly competes in terms of parameter scale and training data volume, with financial strength being key; while local AI places greater emphasis on engineering optimization and scenario adaptation, having advantages in privacy protection, reliability, and practicality. This is particularly important as the hallucination problem of general models can severely impact their application in specific domains.

This change brings new opportunities for Web3 AI. In the past, when the industry focused on "generalization" capabilities, traditional tech giants held an absolute advantage, making it difficult for Web3 projects to compete. However, in the fields 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 are exactly the areas of expertise of blockchain technology.

Some emerging projects related to the industry have already appeared. For example, a certain company has launched a data communication protocol aimed at addressing the issues of data monopoly and opacity in centralized AI platforms. Another project has collected real human data through EEG devices to build an "artificial verification layer," which has already achieved considerable revenue. These projects are all attempting to solve the "credibility" issue of local AI.

In general, decentralized collaboration can only transform from a conceptual idea into a real need when AI truly "sinks" into every device. For Web3 AI projects, rather than continuing to compete in the generalized track, it is better to seriously consider how to provide infrastructure support for the localized AI wave.