Integration of AI Technology: The Convergence of Web2 and Web3

Recently, observing the development trends in the field of general AI, an interesting evolutionary logic has been discovered: Web2 AI is transitioning from centralization to distribution, while Web3 AI is moving from the proof-of-concept stage to the practicality stage. These two fields are accelerating their integration.

The latest development trends of Web2 AI show that AI models are becoming lighter and easier to deploy. For example, the popularity of local intelligence and offline AI models means that the application scope of AI is expanding, no longer limited to large cloud service centers but can be deployed on mobile phones, edge devices, and even IoT terminals. At the same time, the dialogue function between AIs marks a shift from individual intelligence to collaborative clusters.

This technological advancement brings new challenges: how to ensure data consistency and decision credibility among decentralized AI instances when AI is deployed in a highly distributed manner? This demand perfectly aligns with the advantages of Web3 AI.

Recent developments in the Web3 AI field have shifted from mere conceptual hype to more systematic infrastructure construction. The market is beginning to focus on the construction of AI layer 1 infrastructure, with various projects specializing in computing power, inference, data labeling, storage, and other aspects. For example, some projects focus on decentralized computing power aggregation, while others build decentralized inference networks, and some are making efforts in directions such as federated learning, edge computing, and distributed data incentives.

This specialized division of labor reflects the development path of the industry from bubble clearing to demand-driven growth, and then to efficiency optimization, ultimately forming an ecological synergy effect.

The integration of Web2 AI and Web3 AI is giving rise to a new paradigm: combining efficient off-chain computation with quick on-chain verification. In this model, AI is not just a tool but also becomes a participant with an economic identity. Although resources such as computing power, data, and reasoning are primarily concentrated off-chain, a lightweight on-chain verification network is equally necessary to ensure credibility and transparency.

This combination maintains the efficiency and flexibility of off-chain computing while ensuring the credibility of the system through on-chain verification. It represents a deep integration of AI technology and blockchain concepts, opening up new possibilities for future AI applications.

It is worth noting that although there are still those who question the concept of Web3 AI, the rapid development of AI is blurring the boundaries between Web2 and Web3. The advancement of technology often surpasses our inherent cognitive frameworks, and the future AI ecosystem is likely to be the result of the complementary advantages of Web2 and Web3 technologies.