After observing various trends in the AI field over the past month, I found an interesting evolution logic: web2AI is moving from centralization to distribution, while web3AI is transitioning from proof of concept to practicality. The two are accelerating their integration.

1) First, let's look at the development dynamics of web2AI. Apple's local intelligence and the popularization of various offline AI models reflect that AI models are becoming lighter and more convenient. This tells us that the carriers of AI are no longer limited to large cloud service centers, but can be deployed on smartphones, edge devices, and even IoT terminals.

Moreover, Claude and Gemini achieve AI-AI dialogue through MCP, marking an innovation that signifies AI is transitioning from unitary intelligence to collaborative clusters.

The question arises: as the carriers of AI become highly distributed, how can we ensure data consistency and decision credibility among these decentralized AI instances?

Here lies a layer of demand logic: technological advancement (model lightweight) → changes in deployment methods (distributed carriers) → emergence of new demands (decentralized verification).

2) Now, let's look at the evolution path of web3AI. Most early AI Agent projects were primarily based on MEME attributes, but recently, the market has shifted from pure hype of launchpads to systematic construction of underlying architecture for AI layer1 infrastructure.

Projects are beginning to engage in specialized division of labor in various functional areas such as computing power, inference, data labeling, and storage. For instance, we previously analyzed @ionet focusing on decentralized computing power aggregation, Bittensor building a decentralized inference network, @flock_io making strides in federated learning and edge computing, @SaharaLabsAI focusing on distributed data incentives, and @Mira_Network reducing AI hallucinations through decentralized consensus mechanisms, etc.;

Here, a gradually clear supply logic emerges: cooling of MEME speculation (bubble clearing) → emergence of infrastructure demands (driven by necessity) → emergence of specialized division of labor (efficiency optimization) → ecological synergy effects (network value).

You see, the "shortcomings" of web2AI's demands are gradually approaching the "strengths" that web3AI can provide. The evolution paths of web2AI and web3AI are gradually intersecting.

Web2AI is becoming increasingly mature technologically, but lacks economic incentives and governance mechanisms; web3AI has innovations in economic models but lags behind web2 in technical implementation. Their integration can complement each other's advantages.

In fact, the integration of the two is giving rise to a new paradigm of AI that combines "efficient computing" off-chain and "rapid verification" on-chain.

In this paradigm, AI is no longer just a tool, but an economic identity participant; resources such as computing power, data, and inference will be focused off-chain, but a lightweight verification network will also be needed.

This combination is quite clever: it maintains the efficiency and flexibility of off-chain computing while ensuring credibility and transparency through lightweight on-chain verification.

Note: To this day, some still regard web3AI as a pseudo-proposition, but if one carefully feels and possesses a certain foresight, one will understand that, with the rapid development of AI, there has never been a distinction between web2 and web3, but human biases will be.