AI

Gate, a leading global crypto asset trading platform, has officially launched GateAI, a new AI-powered market intelligence assistant. The product is positioned as the industry's first AI investment support tool built with fact-driven principles at its core, aiming to help users interpret market movements more efficiently in an increasingly information-dense environment. GateAI has been fully integrated into Gate App version 8.2.0 and above, and can be accessed via the floating icon on the homepage, the sidebar shortcuts, as well as key usage scenarios, including homepage token search, spot candlestick charts, and the community feed.

The author provides a comprehensive analysis of diverse investment approaches, spanning miHoYo, insurance capital, and manufacturing family offices. He frames the IPO as the beginning of the next competitive phase, not the ultimate goal. This perspective highlights the core nature of the industry: commercialization is still unclear, while R&D spending remains relentless.

The Agent Economy is a new kind of digital economic model emerging alongside advances in artificial intelligence. At its core is the shift of AI agents from support tools into economic actors with the ability to act autonomously and create value. This means participants in the digital world are no longer limited to humans, but instead form a hybrid economy where humans and machines participate and collaborate together. Within this framework, AI agents can execute tasks independently, take part in decision-making, and operate continuously, even completing cycles of value creation and reinvestment without human intervention.

Audiera is an Agent-native social rhythm platform built on BNB Chain. Its core design integrates AI agents directly into the economic system, allowing them to function not just as tools, but as digital participants that can co-create, interact, and share value alongside humans. By combining rhythm-based gameplay (GameFi), AI-generated music, and blockchain asset mechanisms, Audiera unifies entertainment, content creation, and on-chain economics into a single system where both users and AI agents can continuously collaborate and generate value.

The evolution from Audition to Audiera represents a paradigm shift from Web2 rhythm games to a GameFi platform model. The core transformation lies in upgrading traditional gameplay and social interaction into an AI- and blockchain-powered interactive economic system, where players are no longer just content consumers but participants in creation, competition, and value distribution. By introducing Dance-to-Earn (D2E) mechanics and AI-driven content generation, rhythm games are redefined as a digital ecosystem with built-in rewards and economic cycles.

This article offers a comprehensive, actionable framework that spans contextual design, memory and state management, agent architecture selection, proactive issue mitigation, challenging the dashboard paradigm, and a thorough long-term cost analysis of proprietary agent development versus general AI SaaS adoption.

Audiera’s agent-native design is a platform architecture where AI agents are treated as primary participants rather than auxiliary tools. These agents are assigned identity, behavioral capabilities, and economic roles, allowing them to execute tasks, interact with users, and earn rewards. This approach creates a hybrid economic system where both humans and AI agents collaborate and generate value within the same ecosystem.

This article starts by examining Meta and Manus, along with the public listings of Zhipu and MiniMax, to break down the criteria for AI products transitioning from interesting demos to meaningful deployment—shifting from specialized applications to general-purpose platforms.

Anthropic’s latest report delivers a startling message: AI is rapidly transforming the landscape for advanced educational tasks. High school assignments are processed nine times faster, and college-level work is completed at twelve times the usual rate. Human–AI collaborative sessions now last as long as nineteen hours, highlighting an emerging “deskilling” crisis—where meaningful intellectual engagement is replaced by repetitive administrative tasks.

Leading Silicon Valley investors Jason Calacanis, Chamath, David Friedberg, and David Sacks have unveiled their definitive forecasts for 2026, addressing topics such as artificial intelligence, IPO cycles, prediction markets, critical metals, China–US relations, and global political and economic developments. This article distills 11 key insights, offering an in-depth analysis of the sectors and asset classes most worthy of full commitment in the year ahead.

The article addresses key challenges in the generative AI era—including data ownership, web crawler compensation, and vibe application synchronization—and anticipates a future where users gain control over their AI companions and develop sustained, accompanying relationships.

SaaS: Doomsday or a New Golden Age? As AI agents start autonomously writing code, software companies are left with only one true moat: proprietary data. In early 2026, feature updates from Anthropic and OpenAI sparked a sharp decline in SaaS stocks. Yet, robust earnings from Bloomberg, Salesforce, and ServiceNow provided a surprising counterpoint. This article explores the logic behind the “factor scarcity shift”: In the era of AI agents, tool-building capabilities have become abundant, while authentic business context and historical transaction data have turned into extremely scarce fuel. Lightweight software lacking data access will be effortlessly displaced by agents, while legacy giants with deep “contextual knowledge graphs” are moving from selling software to charging “rent” for powering AI engines.

Drawing from NVIDIA CEO Jensen Huang's recent in-depth interview, this article provides a systematic analysis of the AI token economy, pricing mechanisms for computing power, the emergence of inference computing, and the growing trend of AI industrialization. It delves into how AI is transforming production functions and redefining the global computing power industry.

This article draws on highlights from the Bloomberg Originals podcast, focusing on the investment cycle where Nvidia invests up to $100 billion in OpenAI, OpenAI leases computing power from Oracle, and Oracle purchases Nvidia chips. Through this lens, it offers a clear analysis of the profitability challenges facing the industry during the current AI boom.

How does AI power NFTs? The key lies in breaking through the traditional NFT framework that focuses only on ownership verification and scarcity, and instead embedding intelligence directly into the asset structure itself. The original purpose of Non-Fungible Tokens was to solve the problem of digital ownership and uniqueness. Through blockchain technology, artworks, music, and virtual items gained verifiable ownership for the first time. However, this design also defined a structural limitation. Once minted, an NFT's content and functionality are permanently fixed, causing most NFTs to resemble on-chain certificates rather than assets that can be actively used or continuously evolve. As a result, their value depends heavily on narratives and market sentiment.