null
Author: K, Web3Caff Research Fellow
In the development trajectory of artificial intelligence, the past two years have experienced a profound structural shift. Model capabilities continue to break through, inference efficiency keeps improving, and global capital and state machinery are flocking in. However, behind the frenzy and capital-focused wave of centralization, DeAI (Decentralized AI Training and Inference Architecture) is becoming another path to the future, directly addressing two major hidden dangers in today's AI development: blind trust in mechanisms and expansion vulnerabilities.
The prosperity of centralized AI is built on massive physical infrastructure, from supercomputing clusters to closed black boxes for model inference, from packaged SaaS products to internal enterprise API calls. But just as the internet has evolved from closed to open, from Web2 platforms to Web3 protocols, the development of AI also