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The potential of the AI data track and the rise of Web3 DataFi In an era where the global competition is to build the best foundational models, while computing power and model architecture are certainly important, the true moat is the training data. This article will explore the potential of the AI data track and how Web3 DataFi is emerging as a new force in this field. The Importance of AI Data Computing power, models, and data are the three pillars of AI models. With the popularization of the transformer architecture and the gradual resolution of computing power issues, the importance of data is becoming increasingly prominent. Model training is divided into two stages: pre-training and fine-tuning, each requiring different types of data. 1. Pre-training stage: Requires a large amount of information such as text and code crawled from the internet. 2. Fine-tuning stage: A dataset that requires meticulous design and selection to cultivate specific capabilities of the model. These two types of data constitute the main body of the AI data track. Following

The rankings for author influence and article popularity in March 2025 have been announced, with five authors including Web3 Xiao Lu and Shi Si Jun making the influence list. Popular articles include "The Momentum of XRP, the Potential of Ripple," reflecting the current market trends and technological dynamics.

AI+Web3: Towers and Squares TL;DR 1. AI concept Web3 projects are becoming attractive targets for capital in both primary and secondary markets. 2. The opportunities for Web3 in the AI industry are manifested in: using distributed incentives to coordinate potential supply in the long tail ------ across data, storage, and computation; at the same time, establishing an open-source model and a decentralized market for AI Agents. 3. The main application of AI in the Web3 industry is on-chain finance (crypto payments, trading, data analysis) and assisting development. 4. The utility of AI+Web3 is reflected in the complementarity of the two: Web3 is expected to counteract AI centralization, while AI is expected to help Web3 break out of its circle. Introduction In the past two years, the development of AI has been like pressing the fast forward button, this wave initiated by

The article discusses the importance of the AI data track and the rise of Web3 DataFi, emphasizing the key role of data in AI model training. The decentralization characteristic of Web3 brings advantages such as data sovereignty, security, and privacy to DataFi, attracting ordinary users to participate. Multiple DataFi projects have received funding and face challenges such as user accumulation and data quality management, representing the symbiotic relationship between human and machine intelligence.

AI Layer 1 Project Depth Analysis: Breaking Through Traditional Architecture to Build a Decentralized Artificial Intelligence Ecosystem With the rapid development of artificial intelligence technology, traditional blockchain architectures have struggled to meet the high-performance computing and complex data processing demands of AI applications. As a result, a number of Layer 1 blockchain platforms optimized specifically for AI have emerged, showcasing diverse characteristics in terms of technical architecture, application scenarios, and business models. This article provides a deep analysis of the five leading AI Layer 1 projects: Bittensor, Vana, Kite AI, Nillion, and Sahara AI. Bittensor: Decentralized AI Network Infrastructure Bittensor, as a pioneer in the blockchain AI field, is committed to building an open decentralized artificial intelligence collaboration network. It aims to break the centralized barriers in traditional AI development.