DeAI: In the Era of AI’s “Unchecked Growth,” Why Web3 is Needed to Govern It

In the trajectory of artificial intelligence development, the past two years have witnessed a profound structural shift. Model capabilities continue to break through, inference efficiency is constantly optimized, with global capital and state machinery flocking to the field. However, behind the frenzy and capital focus on centralized waves, DeAI (Decentralized AI training and inference architecture) is emerging as another path to the future. It directly addresses two major hidden dangers in today’s AI development: blind trust mechanisms and scalability fragility.

The prosperity of centralized AI is built upon massive physical infrastructure, from supercomputing clusters to closed model inference black boxes, from packaged SaaS products to internal enterprise API calls. Yet, much like the internet’s journey from closed to open, from Web2 platforms to Web3 protocols, AI development will inevitably face two fundamental questions: First, how can users verify that model inference results have not been tampered with and possess authenticity? Second, when training and inference cross geographical, device, cultural, and legal boundaries, can centralized architectures still maintain cost and performance advantages?

DeAI networks propose a fundamentally different solution path compared to the centralized paradigm. Centered on the core idea of “Verifiable Compute,” it uses 加密貨幣graphy and consensus mechanisms to ensure that every model execution has a traceable, provable execution path. This not only solves the user’s problem of “blind trust” in models but also provides a universal foundation of trust for cross-border collaboration. Current pioneers like Prime Intellect and Inference Labs have already implemented partially verifiable inference across geographically distributed GPU clusters, opening new possibilities for distributed training and autonomous AI services. [70]

From an economic perspective, the rise of DeAI is also closely related to the shift in the AI industry’s RoG (Return-on-GPU, i.e., the revenue generated per hour of GPU computing power). The design of GPT-4.1 no longer simply pursues large models and brute-force computing power but emphasizes fine-tuning and optimized inference resource allocation. For example, it aims to reuse existing context during generation and reduce unnecessary recomputation, thereby lowering invalid outputs and token consumption, allowing more computing power to be used for truly valuable inference processes. [68] This marks a shift in industry focus from “how many GPUs can be burned” to “how much value can be obtained per hour.” This efficiency-oriented approach precisely provides an excellent breakthrough point for decentralized AI networks.

The high fixed costs and efficiency bottlenecks of centralized GPU clusters in large-scale deployment will struggle to compete with a permissionless, heterogeneous GPU network contributed by global users. If such a network possesses “verifiability,” it can not only compete with centralized infrastructure like AWS and Azure on cost structure but also inherently offers transparency and trustworthiness advantages.

Furthermore, the impact of DeAI extends far beyond the technical layer; it will reshape the ownership and participation structure of AI development. In the current closed training ecosystem dominated by giants like OpenAI and Anthropic, the vast majority of developers can only exist as “model users,” unable to participate in model training profits or inference decisions. In a DeAI network, every contributor—whether a node providing computing power, a user providing data, or an engineer developing Agent applications—can participate in governance and share profits through the protocol. This is not only an innovation in economic mechanisms but also a step forward in the ethics of AI development.

Of course, DeAI is still in its early exploratory stages. It has not yet established performance levels sufficient to replace centralized models, nor has it broken through bottlenecks like network stability and verification efficiency. But the future of AI will not be a single path; it will be multi-track parallel. Centralized platforms will continue to dominate the enterprise market, pursuing extreme productization with RoG optimization. Meanwhile, DeAI networks will grow in edge scenarios and emerging markets, gradually evolving an open model ecosystem with its own vitality. Just as the internet is to information freedom, DeAI is to intelligent autonomy. Its importance lies not only in its technical advantages but also in the possibility it offers of another world—a future where we don’t need to trust specific intermediaries but can still trust intelligence itself.

This content is excerpted from the research report published by Web3Caff Research: “Web3 2025 Annual 40,000-Word Report (Part 2): Facing the Historical Convergence of Finance × Computing × Internet Order, Is a Major Industry Shift Imminent? A Panoramic Analysis of Its Structural Changes, Value Potential, Risk Boundaries, and Future Outlook”

This research report (now available for free reading) was authored by Web3Caff Research analyst K. It systematically outlines the core logic behind the developmental stage changes of Web3 in 2025, focusing on discussing why application exploration and system collaboration are gradually becoming new focal points against the backdrop of continuous evolution in underlying infrastructure and regulatory capabilities. The key points include:

1. Stage Evolution Background: The underlying reasons for the shift in industry focus after the completion of a phase of infrastructure construction.
2. Key Mechanism Changes: The impact of increasingly clear rule frameworks and on-chain mechanisms on system operation methods.
3. Main Application Directions: Exploration paths centered on payment settlement, real-world scenario mapping, and programmable collaboration.
4. Future Development Direction: Exploring the evolution trends of Web3 in 2026 and beyond.

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