Original Author: K, Web3Caff Research Analyst
In the trajectory of artificial intelligence development, the past two years have witnessed a significant structural shift. Model capabilities continue to break through, reasoning efficiency is constantly optimized, and global capital and state machinery are flocking to the field. However, behind this wave of fervor and capital focus on centralization, DeAI (Decentralized AI training and reasoning architecture) is emerging as another path to the future, directly addressing two major hidden dangers in current AI development: blind trust mechanisms and scalability fragility.
The prosperity of centralized AI is built upon massive physical infrastructure, from supercomputing clusters to closed black-box model reasoning, from packaged SaaS products to internal enterprise API calls. But just as the internet evolved from closed to open, from Web2 platforms to Web3 protocols, the development of AI will inevitably face two fundamental questions: First, how can users verify that the results of model reasoning have not been tampered with and are authentic? Second, when training and reasoning 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. It centers on the concept of "Verifiable Compute," using cryptography and consensus mechanisms to ensure that every model run has a traceable, provable execution path. This not only solves the user's problem of "blind trust" in the model but also provides a universal trust foundation for cross-border collaboration. Current pioneers like Prime Intellect and Inference Labs have already implemented partially verifiable reasoning in remote 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 stacking computing power but emphasizes fine-tuning and optimized reasoning resource allocation—for example, reusing existing context during generation and reducing unnecessary recomputation to minimize无效输出 and token consumption, thereby directing more computing power towards truly valuable reasoning processes. [68] This marks a shift in industry focus from "how much GPU can be burned" to "how much value can be obtained per hour." This efficiency-oriented approach 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 users globally. If such a network possesses "verifiability," it can not only compete with the cost structures of centralized infrastructures like AWS and Azure but also inherently offers transparency and trustworthiness.
Furthermore, the impact of DeAI extends far beyond the technical level; 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 the training profits or reasoning decisions of the models. 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 such as network stability and verification efficiency. But the future of AI will not be a single path; it will be multi-track and parallel. Centralized platforms will continue to dominate the enterprise market, pursuing极致 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 brought information freedom, DeAI brings autonomy over intelligence. 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, yet can still trust intelligence itself.
This content is excerpted from the research report "Web3 2025 Annual 40,000-Word Report (Part 2): Facing the Historic Convergence of Finance × Computing × Internet Order, Is a Major Industry Shift About to Begin? A Panoramic Analysis of Its Structural Changes, Value Potential, Risk Boundaries, and Future Prospects" published by Web3Caff Research.
This report (now available for free reading) was written by Web3Caff Research analyst K. It systematically梳理 the core logic behind the developmental changes in Web3 for 2025, focusing on why application exploration and system collaboration are gradually becoming new focal points against the backdrop of evolving underlying infrastructure and regulatory capabilities. Key points include:
- Background of Stage Evolution: The underlying reasons for the shift in industry focus after the completion of a phase of infrastructure construction;
- Key Mechanism Changes: The impact of gradually clarifying rule frameworks and on-chain mechanisms on system operation methods;
- Main Application Directions: Exploration paths围绕 payment settlement, real-world scenario mapping, and programmable collaboration;
- Future Development Directions: Discussing the evolution of Web3 in 2026 and beyond.
