AI

The main projects at the infrastructure layer of the AI+Web3 industry basically take the decentralized computing network as the main narrative, low cost as the main advantage, token incentives as the main way to expand the network, and serving AI+Web3 customers as the main goal.

This article provides a detailed introduction to the cross-chain project Zetachain and how it addresses the issue of full-chain interoperability.

AI x Crypto encompasses multiple subfields, including AI Agents, decentralized computing, data, oracles, ZKML, FHEML, coprocessors, Memes, generative art platforms, and gaming applications.

Masa Network presents an innovative AI pricing model that enables users to safeguard personal privacy while sharing data and earning rewards within the Masa ecosystem. Through the Masa SDK, partners can enhance AI applications for tasks like cryptocurrency market forecasts, NFT management, and political prognostication. Masa advocates for users' rights to benefit economically from AI advancements. By engaging with the Masa ecosystem, individuals can safeguard their privacy, contribute to the data economy, and access real-time datasets for informed market insights, cryptocurrency trend analysis, investment exploration, and precise price predictions. Masa's mainnet launch and token release are scheduled for April 11. Users can both protect their digital footprint and support the data economy by actively participating in the Masa ecosystem.

Bittensor is an ecosystem that combines AI and encryption technology. Its token TAO has risen more than other projects, but it is considered too expensive. The quality of Bittensor ecological projects that have emerged recently is of varying quality, making it difficult for investors to participate. Bittensor proposed a product idea that combines AI and Web3, but the encryption field is not optimistic about this. The future of Bittensor remains to be seen, but its ability to attract liquidity and yield results remains a question.

The core of the AI technology revolution lies in ample computing power, algorithm models, and a vast amount of training data. Currently, high-performance GPU computing power is in short supply and expensive, algorithms tend to be homogenized, and there are issues regarding data compliance and privacy protection for model training data. The decentralized and distributed storage characteristics of blockchain technology can facilitate its integration with AI.

Vitalik has published "The promise and challenges of crypto + AI applications," discussing the ways blockchain and artificial intelligence can be combined and the potential challenges. The article presents four integration methods and introduces representative projects for each direction. There are differences in the core characteristics of AI and blockchain, so it's necessary to balance aspects such as data ownership, transparency, monetization capabilities, and energy costs when combining them. Currently, many AI applications are gaming-related, involving interaction with AI and training characters to better fit individual needs. At the same time, there are projects exploring the use of blockchain features to create better artificial intelligence. Decentralized computing power is also a popular direction but still faces challenges. Overall, the AI track needs to find projects with competitiveness and long-term value.

Delve into the background of the OORT track, project operation logic, and more, to glimpse the project's prospects and the opportunities and challenges for the AI+DePIN track in 2024.

Bittensor, Fetch.ai, and SingularityNET share a common goal: using token incentives to drive the supply of AI resources, whether models, compute, or services, while building open networks that lower barriers to AI access and challenge the dominance of centralized platforms. However, their core differences lie in the technical layers they operate on and how they capture value. Rather than competing within a single track, they address three distinct stages of decentralized AI, model production, task execution, and service distribution.

In Part 1 of our second Agentic AI series post, we examine the current Web2 AI landscape and its key trends, platforms, and technologies.

GM.AI is the most advanced AI solution focused on the Solana ecosystem. It can deeply understand and flexibly operate various applications on Solana, providing users with highly automated on-chain services. By simplifying complex DeFi processes, GM.AI significantly enhances the user experience on the Solana platform, making it more convenient and efficient.

This article provides an in-depth analysis of projects at the intersection of cryptocurrency and artificial intelligence, exploring which projects truly leverage AI and blockchain technology and which are just gimmicks. The article points out that most crypto projects do not require AI, and vice versa, and highlights the potential of decentralized physical infrastructure networks (DePINs), data networks, and GPU networks in the development of AI. At the same time, the article proposes how to identify truly valuable "encryption x AI" projects and looks forward to the future development of decentralized AI.