The Convergence Trend in the AI Field: The Intersection of Web2 and Web3

Recently, observing the dynamics of the AI field, I found an interesting development trend: Web2 AI is developing towards a distributed direction, while Web3 AI is moving from the proof-of-concept stage to the practical stage. These two areas are accelerating their integration.

The development trends of Web2 AI indicate that AI models are becoming lighter and more convenient. The popularity of local intelligence and offline AI models means that the carriers of AI are no longer limited to large cloud service centers, but can be deployed on mobile phones, edge devices, and even Internet of Things terminals. At the same time, the emergence of AI-AI dialogue technology marks a shift from individual intelligence to collaborative clusters.

This change has raised new questions: how to ensure data consistency and decision credibility among distributed AI instances when the AI carrier is highly decentralized? This reflects a demand logic chain: technological advancements lead to changes in deployment methods, which in turn create new decentralized verification needs.

At the same time, the development path of Web3 AI is also changing. The market's focus has shifted from mere speculation to deeper AI infrastructure construction. Various projects are beginning to specialize in areas such as computing power, inference, data labeling, and storage. For example, some projects focus on decentralized computing power aggregation, some are building decentralized inference networks, and others are making efforts in areas like federated learning, edge computing, and distributed data incentives.

This reflects a supply logic: after the hype cools down, the demand for infrastructure emerges, leading to the specialization of labor, and ultimately forming an ecological synergy.

Interestingly, the demand shortcomings of Web2 AI exactly match the supply advantages of Web3 AI. Web2 AI technology is mature but lacks economic incentives and governance mechanisms, while Web3 AI has innovations in economic models but is relatively behind in technical implementation. The integration of the two can achieve complementary advantages.

This integration is giving rise to a new AI paradigm: combining efficient off-chain computation with rapid on-chain verification. In this paradigm, AI is not only a tool but also a participant with economic identity. Resources such as computing power, data, and reasoning are primarily off-chain, but a lightweight on-chain verification network is simultaneously needed.

This combination maintains the efficiency and flexibility of off-chain computation while ensuring credibility and transparency through on-chain verification. With the rapid development of AI, the boundaries between Web2 and Web3 are becoming increasingly blurred, and the integration of the two will bring more innovation and opportunities to the AI field.