AI

A comprehensive analysis of the "2028 Global Intelligence Crisis" report, offering a reasoned evaluation of whether AI could lead to a systemic economic collapse. The discussion also examines humanity’s future path in the context of technological diffusion, changes in employment structure, and the reconfiguration of wealth distribution.

The Gate AI Market Assistant is now seamlessly integrated into the core pages of the Gate App. Leveraging real-time data analysis and risk boundary design, it empowers users to grasp the underlying logic of market volatility and enhances information efficiency in navigating complex market environments.

While the stock market lost $800 billion in value following Anthropic’s breakup, this article offers a counter-perspective by dissecting the so-called AI doom loop—layoffs, weakened consumer spending, and more automation. Through a historical lens of productivity expansion and its effects on GDP, the article contends that commoditizing AI cognition will compress service costs, lower the threshold for entrepreneurship, broaden aggregate demand, and potentially contribute to geopolitical stability.

This article offers a comprehensive analysis of SBF’s comments on "whether AI will adopt cryptocurrencies," exploring the long-term implications of AI and cryptocurrency integration through the lenses of identity architecture, payment infrastructure, and regulatory frameworks.

OpenAI has teamed up with the U.S. Department of Defense to implement AI solutions on classified networks, prompting extensive debate about national security, the limits of technology, and evolving power dynamics. This article examines the institutional implications and long-term trends associated with the integration of AI into military infrastructure.

The crypto market is inundated with information, but the real challenge lies in the ability to make sense of it. As on-chain data, community sentiment, and real-time news converge, traders are confronted not just with information asymmetry, but with cognitive imbalance resulting from information overload. GateAI was designed to tackle this issue—not by adding more signals, but by empowering users to reconstruct their analytical framework.

AINFT is a Web3 platform built within the TRON ecosystem. Its core concept is to natively embed artificial intelligence capabilities into the NFT structure, transforming digital assets from static ownership certificates into intelligent assets that can learn, interact, and continuously evolve. Unlike traditional NFTs, whose content is fixed once minted, AINFT combines AI models with on-chain assets, enabling NFTs to dynamically adapt based on data inputs and user behavior. These NFTs can execute tasks, generate content, and even act on behalf of their holders as digital entities.

A Web3-native AI model aggregation platform integrates multiple AI model capabilities into on-chain architecture and redefines access rights and value distribution through decentralized identity and usage-based payment mechanisms. In the Web2 era, AI services have primarily existed as centralized platforms, where users must register accounts, link payment methods, and obtain model access through subscription plans. While this model accelerated early AI adoption, it has gradually revealed structural limitations, including fragmented models, non-transferable access rights, a severe mismatch between costs and actual usage frequency, and full ownership and control retained by platform providers. These characteristics fundamentally conflict with Web3 principles of sovereignty, composability, and assetization.

The key difference between AINFT and traditional NFTs lies in their underlying design. AINFT embeds AI capabilities directly into the asset structure, creating intelligent NFTs, while traditional NFTs are static digital assets centered on ownership verification. Traditional NFTs (Non-Fungible Tokens) derive their core value from establishing ownership. Through blockchain technology, they create verifiable and tamper-resistant proof of ownership for digital content, fundamentally addressing the question of who owns an asset. Once minted, the content and functionality of these NFTs are typically fixed, with limited ability to respond to changes in environment, data, or time. As a result, they function more like on-chain digital collectibles. In contrast, AINFT does not represent a surface-level enhancement of the NFT format. By embedding AI capabilities into the NFT structure itself, AINFT transforms NFTs from static ownership markers into intelligent assets capable of understanding, responding, and taking acti

a16z crypto, a division of Andreessen Horowitz, points out that the payment model for AI Agents will shift from “tourist-style swipe transactions” to “local B2B credit settlements.” As Agents establish long-term supplier relationships, traditional credit cards struggle to support micropayments and streaming settlements. Programmable payment networks such as stablecoins may become the new infrastructure, creating entrepreneurial opportunities for billing systems, arbitration mechanisms, and credit frameworks.

Gate has officially launched Gate for AI, establishing the industry's first unified AI portal that brings together five core functions: CEX, DEX, wallet, real-time information, and on-chain data. Leveraging a dual-layer architecture with MCP and Skills, Gate grants AI Agents comprehensive access to its core exchange capabilities, empowering them to conduct research, make decisions, execute actions, and monitor processes—all within a single integrated framework.

Gate has launched a groundbreaking AI Quantitative Workbench, seamlessly integrating strategy development, historical backtesting, and live trading within a unified platform powered by natural language interaction. Traders simply articulate their trading concepts, and the system automatically generates quantitative strategies, performs backtesting, and enables one-click deployment to real markets. This innovative approach dramatically reduces the technical requirements for programming and data environments in quantitative trading, empowering more traders to rapidly transform market insights into actionable strategies and advancing the widespread adoption of AI and quantitative trading.

Gate has officially released Gate for AI, seamlessly connecting CEX, DEX, wallets, information services, and on-chain data into a unified interface framework designed for AI Agents, marking a foundational upgrade to exchange infrastructure.

With the Gate No-Code AI Quantitative Trading Workbench, traders can create, backtest, and deploy quantitative trading strategies effortlessly—no programming experience required. Natural language strategy generation simplifies the trading process, boosts execution efficiency, and enables every trader to fulfill their quantitative trading needs with ease.

From Spotify’s music distribution to AI programming tools lowering the barriers for developers, technological “equalization” repeatedly raises the baseline but simultaneously lifts the ceiling. Warp founder Naman Bhansali, drawing from his own entrepreneurial experience, points out: AI won’t eliminate disparities; instead, it amplifies the power-law distribution. What’s truly scarce isn’t execution, but rather aesthetics, insight, and the ability to compound value over the long term.