Evolution in the AI Field: Accelerated Integration of Web2 and Web3

Recently, observing the development trends in the AI field, I found an interesting evolution logic: 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 fields are accelerating their fusion.

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 IoT terminals. Meanwhile, the realization of AI-AI dialogue marks the transition of AI from individual intelligence to collaborative clusters.

This development trend has triggered new demands: how to ensure data consistency and decision reliability among distributed AI instances when AI carriers are highly decentralized? This reflects a demand logic chain: technological advancement (model lightweighting) leads to changes in deployment methods (distributed carriers), which in turn creates new demands (decentralized verification).

On the other hand, the evolution path of Web3 AI is also very clear. Early projects were mostly focused on MEME attributes, but recently the market has begun to shift towards the systematic construction of more foundational AI infrastructure. Various functional aspects such as computing power, reasoning, data labeling, storage, etc., have seen the emergence of specialized projects.

This reflects a supply logic chain: after the hype around MEME cools down, the demand for infrastructure becomes apparent, prompting the emergence of specialized division of labor, ultimately forming an ecological synergistic effect.

Interestingly, Web2 AI is technically maturing increasingly, but it lacks economic incentives and governance mechanisms; Web3 AI has innovations in its economic model, but its technical implementation is relatively lagging. The integration of the two can perfectly complement each other's strengths.

This integration is giving rise to a new paradigm: an AI combination of "efficient computation" off-chain and "rapid verification" on-chain. In this paradigm, AI is not just a tool but also becomes a participant with economic identity. While resources such as computing power, data, and reasoning are primarily offline, a lightweight verification network is also needed.

This combination maintains the efficiency and flexibility of offline computation while ensuring credibility and transparency through lightweight on-chain verification.

It is worth noting that the rapid development of AI will not distinguish between Web2 and Web3, but human biases may. Therefore, we need to view the development and integration trends in the AI field with an open and forward-looking perspective.