AI

Gate Research: Large language models and AI agent technologies are pushing trading systems into a new stage of development. Quantitative trading, which previously relied heavily on programming skills and complex engineering systems, is gradually evolving into product forms with much lower barriers to entry. Gate has introduced products such as AI Quant Workspace and Gate for AI, which aim to integrate strategy generation, backtesting, and automated execution within a single platform through natural language interaction, no-code quant tools, and unified trading interfaces, allowing more users to participate in strategy trading. As AI technology continues to mature, trading platforms are also evolving from traditional matching tools into AI-driven trading infrastructure.

In 2026, the competitive frontier for enterprise software has shifted from a "feature war" to an "interface reconstruction." This article delves into how AI is reshaping the three core systems of SAP, Salesforce, and ServiceNow: during the implementation phase, AI agents are used to reduce migration risks worth hundreds of millions of dollars; in the usage phase, "Large Action Models (LAMs)" simplify complex interfaces; and in the expansion phase, lightweight applications replace bloated custom development. The ultimate goal of AI is not to replace these "Systems of Record (SoR)," but to rewrite the interaction logic, gradually rendering cumbersome traditional software "invisible" and turning them into underlying databases for AI-driven "Systems of Action (SoA)."

Tether has launched the QVAC Fabric framework, which supports cross-platform LoRA fine-tuning for the BitNet model. With this advancement, large language models can now operate and be trained on mobile devices and conventional hardware. This breakthrough greatly reduces the barriers to AI development and paves the way for new opportunities in decentralized AI.

Tether has introduced the QVAC AI framework, allowing mobile devices to train models with billions of parameters and dramatically reducing the barrier to computational power. This article examines the technical foundations, industry implications, and far-reaching effects on decentralized AI and the computing power market.

YZi Labs has announced a lead investment of $52 million to support RoboForce, a Silicon Valley AI robotics company, in developing its Physical AI technology and TITAN robotics platform. RoboForce specializes in tackling workforce shortages in demanding sectors including energy, manufacturing, and logistics. The company leverages data flywheel strategies and AI models to continuously improve the performance of its robots.

Gate Exchange for AI provides access to centralized exchange trading systems, while Gate DEX for AI connects AI agents to on-chain decentralized finance environments. These two execution paths differ in transaction routing, custody structure, and operational control. Understanding how these execution architectures work helps explain how AI agents interact with centralized and decentralized financial infrastructure in modern crypto ecosystems.

Global investment in AI infrastructure is expected to exceed $700 billion. This article uses the "AI Five-Layer Cake" model (energy, chips, cloud, models, applications) to deeply deconstruct the profit flow patterns in the AI era: revenue flows upward, while capital sinks downward. The article reveals a harsh truth: while model companies like OpenAI are still "burning cash" on billions in computing costs, the underlying layers—Nvidia (chips), TSMC (manufacturing), ASML (equipment), and power suppliers—are reaping huge profits through monopolistic barriers in the physical world. This is an investment guide that teaches you how to switch from a "consumer mindset" to a "supply chain mindset" to identify确定性 opportunities within the AI technology stack.

Audiera combines blockchain infrastructure with artificial intelligence to build a music data network designed for creators, data contributors, and AI developers. The network provides a verifiable and permission-based environment where music data can be shared while maintaining traceable ownership records. Through on-chain recording and token-based incentives, Audiera aims to create a decentralized data infrastructure that allows music datasets used in AI training to maintain clearer provenance tracking and more transparent revenue distribution.

Audiera's AI music data network combines blockchain recording mechanisms with data authorization frameworks to manage how music datasets move through AI training and application environments. By recording the origin of data, the terms of authorization, and the way datasets are used, the network allows music data accessed by AI systems to generate a traceable history while also supporting mechanisms that may distribute value to data contributors.

Audiera is a decentralized network focused on AI training and music data sharing, bringing together music creators, data contributors, AI model developers, and application developers. Leveraging blockchain technology, the Audiera network records the provenance, access, and usage of music data, ensuring traceability throughout the AI training and application development lifecycle.

The x402 protocol is a Web3 API payment standard designed for AI agents that addresses the challenge of paying for API services in automated environments. The protocol builds on the HTTP standard 402 Payment Required status code and combines it with blockchain based payment mechanisms, allowing programs to automatically complete payments and settlement when requesting API services. Through this design, x402 provides new infrastructure for machine to machine service transactions on the internet.

The article explains how immortal agents can accumulate Bitcoin assets over the long term, reshaping traditional human financial behavior that is constrained by the end of life. It further analyzes their far-reaching impact on the fee market, UTXO state, network settlement models, and the broader economic ecosystem.

This article analyzes the breakthroughs in multimodal video generation technology (such as Byte's EX-4D, Google Veo, etc.) and discusses their profound impact on the creator economy and Web3 AI.

DeFAI combines DeFi (Decentralized Finance) with AI (Artificial Intelligence). Using AI technology, DeFAI makes DeFi more user-friendly and accessible. Its intelligent tools and interfaces simplify complex DeFi systems while lowering barriers to entry, which helps attract everyday users to the decentralized finance ecosystem. As innovation progresses, DeFAI is transforming DeFi from complex operations into a smart, convenient experience, marking the beginning of a new era in intelligent finance.

This article explores four cutting-edge AI companion projects: AI Companions, CharacterX, Sleepless AI, and Flame. It provides a detailed analysis of their technical architecture, tokenomics, market potential, and development roadmaps. The goal is to help investors better understand the transformative impact these AI companions could have in the Web3 era.