Original Author: K, Researcher at Web3Caff Research

In the trajectory of artificial intelligence development, the past two years have experienced profound structural shifts. Model capabilities have continuously broken through, and reasoning efficiency has been optimized, with global capital and national machinery flocking in. However, behind the fervor and capital-focused centralized wave, DeAI (Decentralized AI Training and Inference Architecture) is emerging as an alternative path to the future, directly addressing two major hidden dangers in today's AI development: blind trust in mechanisms and scalability vulnerabilities.

The prosperity of centralized AI is built on vast physical infrastructure, from supercomputing clusters to closed model inference black boxes, from packaged SaaS products to internal API calls within enterprises. But just as the internet transitioned from closed to open, from Web2 platforms to Web3 protocols, AI development will inevitably face two fundamental questions: First, how can users confirm that the results of model inference have not been tampered with and are authentic? Second, when training and inference cross geographical, device, cultural, and legal boundaries, can centralized architectures still maintain cost and performance advantages?

The DeAI network proposes a solution path that is fundamentally different from the centralized paradigm. It centers on the idea of "Verifiable Compute," ensuring that every model run has a traceable and provable execution path through cryptography and consensus mechanisms. This not only addresses the issue of users' "blind trust" in models but also provides a universal trust foundation for cross-border collaboration. Current pioneers like Prime Intellect and Inference Labs have already achieved partial verifiable inference in remote GPU clusters, opening new possibilities for distributed training and autonomous AI services.

From an economic perspective, the rise of DeAI is also closely related to the transformation of 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 stacked computing power but emphasizes fine-tuning and resource allocation for inference, such as reusing existing context during generation and reducing unnecessary recomputation, thereby lowering ineffective output and token consumption, allowing computing power to be used more for truly valuable inference processes. This marks a shift in the industry's focus from "how many GPUs 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 global users. 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 have advantages of transparency and trustworthiness.

Moreover, 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" and cannot participate in the training profits or inference decisions of the models. In the 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 the early exploratory stage. It has not yet established performance levels sufficient to replace centralized models, nor has it overcome bottlenecks in network stability and verification efficiency. However, the future of AI will not be a single path but a multi-track parallel development. Centralized platforms will continue to dominate the enterprise market, pursuing the ultimate productization of RoG optimization; while the DeAI network will grow in edge scenarios and emerging markets, gradually evolving into an open model ecosystem with its own vitality. Just as the internet represents freedom of information, DeAI represents intelligent autonomy. Its importance lies not only in its technological advantages but also in the possibility it offers for another world, a future where trust in specific intermediaries is unnecessary, yet trust in intelligence itself remains.

This content is excerpted from the research report published by Web3Caff Research: "Web3 2025 Annual 40,000-Word Report (Part Two): Facing the Historical Intersection of Finance × Computing × Internet Order, Is an Industry Shift About to Begin? A Comprehensive Breakdown of Its Structural Changes, Value Potential, Risk Boundaries, and Future Outlook"

This report (now open for free reading) was written by K, a researcher at Web3Caff Research, systematically outlining the core logic of changes in the Web3 development stage by 2025, focusing on why application exploration and system collaboration have gradually become new focal points against the backdrop of continuous evolution in underlying and regulatory capabilities. Key points include:

1. Background of Stage Evolution: The intrinsic reasons for changes in industry focus after infrastructure construction reaches a milestone;
2. Key Mechanism Changes: The gradual clarification of rule frameworks and on-chain mechanisms and their impact on system operation;
3. Main Application Directions: Exploration paths around payment settlement, real-world scenario mapping, and programmable collaboration;
4. Future Development Directions: Discussing the evolution of Web3 in 2026 and beyond.

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