After observing various trends in the AI field over the past month, I discovered an interesting evolution logic: web2AI is moving from centralization to distributed, and web3AI is progressing from proof of concept to practicality. The two are accelerating their convergence.
1) First, looking at web2AI's development trends, Apple's local intelligence and the popularity of offline AI models reflect that AI models are becoming lighter and more convenient. This tells us that AI carriers are no longer limited to large cloud service centers, but can be deployed on mobile phones, edge devices, and even IoT terminals.
Moreover, Claude and Gemini achieve AI-AI dialogue through MCP, an innovation that marks AI's transformation from single intelligence to cluster collaboration.
The question is, when AI carriers become highly distributed, how can we ensure data consistency and decision credibility among these dispersed AI instances?
There is a demand logic here: technological progress (model lightweight) → deployment method change (distributed carrier) → new demand generation (decentralized verification).
2) Looking at web3AI's evolution path, early AI Agent projects were mainly MEME-driven, but recently, the market has shifted from pure launchpad speculation to systematic construction of underlying AI layer1 infrastructure.
Projects are beginning to specialize in different functional layers such as computing power, reasoning, data annotation, and storage. For example, @ionet focuses on decentralized computing power aggregation, Bittensor builds a decentralized reasoning network, @flock_io focuses on federated learning and edge computing, @SaharaLabsAI works on distributed data incentives, @Mira_Network reduces AI hallucinations through distributed consensus mechanisms, and so on.
Here's another gradually clarifying supply logic: MEME speculation cooling down (bubble clearing) → infrastructure demand emerging (rigid demand driving) → professional division of labor appearing (efficiency optimization) → ecosystem collaborative effect (network value).
You see, the "shortcomings" of web2AI's demands are gradually approaching the "strengths" that web3AI can supply. The evolution paths of web2AI and web3AI are progressively converging.
Web2AI is becoming more technically mature but lacks economic incentives and governance mechanisms; web3AI has innovative economic models but lags behind web2 in technical implementation. Their fusion can perfectly complement each other's advantages.
In fact, their fusion is giving birth to a new AI paradigm combining off-chain "efficient computing" with on-chain "rapid verification".
In this paradigm, AI is no longer just a tool, but a participant with an economic identity; computing power, data, reasoning, and other resource centers will be offline, but still require a lightweight verification network.
This combination is clever: it maintains the efficiency and flexibility of offline computing while ensuring credibility and transparency through lightweight on-chain verification.
Note: Some people still consider web3AI a false proposition, but if you carefully observe and maintain some forward-looking insight, you'll know that with AI's rapid development speed, it never distinguishes between web2 and web3 - only human bias does.
