Web2 AI is transitioning from centralization to distribution, while Web3 AI is moving from proof of concept to practicality. The two are achieving complementary advantages through the model of "off-chain efficient computing + on-chain trusted verification."

Written by: Haotian

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

1) First, let's look at the development dynamics of web2AI. The local intelligence from Apple and the proliferation 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 mobile phones, edge devices, and even IoT terminals.

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

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

There is a layer of demand logic here: technological advancement (model lightweighting) → change in deployment methods (distributed carriers) → emergence of new demands (decentralized verification).

2) Now, let's look at the evolution path of web3AI. Early AI Agent projects were mostly characterized by MEME attributes, but recently, the market has shifted from pure launchpad speculation to a more systematic construction of AI layer1 infrastructure at a deeper level.

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

Here, there is a gradually clearer supply logic: cooling of MEME speculation (bubble clearing) → emergence of infrastructure demand (driven by necessity) → appearance of specialized division of labor (efficiency optimization) → ecological synergy effect (network value).

You see, the "shortcomings" of web2AI demand are gradually aligning with the "strengths" that web3AI can provide. The evolution paths of web2AI and web3AI are gradually intersecting.

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

In fact, the fusion 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 a participant with economic identity; resources such as computing power, data, and inference will be focused off-chain, but a lightweight verification network is also needed.

This combination is 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 consider web3AI to be a false proposition, but if one carefully observes and possesses a certain foresight, it becomes clear that the rapid development of AI has never distinguished between web2 and web3, but human biases do.

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