After observing various trends in the pan-AI field over the past month, I found a very interesting evolutionary logic: web2AI has evolved from centralization to distribution, and web3AI has evolved from proof of concept to practicality. The two are merging at an accelerated pace.

1) Let’s first look at the development of web2AI. The popularity of Apple’s local intelligence and various offline AI models reflects that AI models are becoming lighter and more convenient. This tells us that the carrier of AI is no longer limited to large cloud service centers, but can be deployed on mobile phones, edge devices, and even IoT terminals.

Claude and Gemini achieve AI-AI dialogue through MCP. This innovation marks the transformation of AI from single intelligence to cluster collaboration.

The question is, when the carrier of AI becomes highly distributed, how to ensure data consistency and decision credibility between these decentralized AI instances?

There is a layer of demand logic here: technological progress (model lightweight) → change in deployment method (distributed carrier) → emergence of new demands (decentralized verification).

2) Let’s look at the evolution path of web3AI. Early AI Agent projects were mostly based on MEME attributes, but in recent times, the market has begun to shift from pure launchpad hype to the systematic construction of AI layer1 infrastructure with a more underlying architecture.

Projects are beginning to specialize in computing power, reasoning, data annotation, storage and other functional aspects. For example, we have previously analyzed @ionet focusing on decentralized computing power aggregation, Bittensor building a decentralized reasoning network, @flock_io focusing on federated learning and edge computing, @SaharaLabsAI in the direction of distributed data incentives, @Mira_Network reducing AI illusions through a distributed consensus mechanism, etc.

There is also a gradually clear supply logic here: MEME hype cools down (bubble clearing) → infrastructure demand emerges (driven by rigid demand) → specialized division of labor emerges (efficiency optimization) → ecological synergy effect (network value).

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

Web2AI is becoming more and more mature in technology, but lacks economic incentives and governance mechanisms; web3AI has innovations in economic models, but its technical implementation lags behind web2. The integration of the two can complement each other's strengths.

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

Under this paradigm, AI is no longer just a tool, but a participant with an economic identity; the focus of resources such as computing power, data, and reasoning will be offline, but a lightweight verification network is also required.

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

Note: Until now, some people always think that web3AI is a false proposition, but if you feel it carefully and have a certain forward-looking insight, you will know that with the rapid development of AI, it will never distinguish between web2 and web3, but human prejudice will.