New Trends in the AI Field: The Integration of Web2 and Web3

Recent observations of the developments in the field of artificial intelligence reveal an interesting evolution logic: Web2 AI is transitioning from centralized to distributed, while Web3 AI is moving from the proof-of-concept stage to the practical stage. These two directions are accelerating their integration.

The development dynamics of Web2 AI show 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 realization of AI-AI dialogue marks the transition of AI from individual intelligence to cluster collaboration.

This change has raised new questions: How can we ensure data consistency and decision credibility among distributed AI instances when the 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 generates new demands (decentralized verification).

The evolution path of Web3 AI is also very clear. Early projects were mainly focused on speculation, but recently the market has begun to shift towards the systematic construction of more foundational AI infrastructure. Various functional aspects such as computing power, inference, data labeling, and storage have started to show specialized divisions of labor. For example, some projects focus on decentralized computing power aggregation, building decentralized inference networks, developing federated learning and edge computing, or reducing AI hallucinations through distributed consensus mechanisms.

This reflects a supply logic: after the hype cools down, the demand for infrastructure emerges, specialization appears, and ultimately forms an ecological synergy.

Interestingly, the demand shortfall of Web2 AI is gradually aligning with the advantages offered by Web3 AI. Web2 AI is technically maturing but lacks economic incentives and governance mechanisms; Web3 AI has innovations in economic models but is relatively lagging in technical implementation. The integration of both can achieve complementary advantages.

This integration is giving rise to a new paradigm: a combination model of "efficient computation" off-chain and "rapid verification" on-chain. In this paradigm, AI is no longer just a tool, but a participant with economic identity. The focus of resources such as computing power, data, and reasoning will be off-chain, but a lightweight verification network is also needed.

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

It is worth noting that although some believe that Web3 AI is a false proposition, the rapid development of AI does not distinguish between Web2 and Web3. Only by maintaining an open mindset and forward-looking insights can one truly grasp the direction of AI development.