AI

AI capital expenditures are the long-term investments technology companies make to develop artificial intelligence computing capabilities, primarily allocated to chip procurement, data center construction, power acquisition, high-speed network deployment, and storage expansion. As generative AI transitions from model training to enterprise deployment and large-scale inference, investment has shifted from isolated hardware purchases to a comprehensive infrastructure cycle encompassing energy, land, construction, networking, and financing.

TUT (Tutorial) is an AI-powered Web3 education project built to help beginners and other users learn blockchain, cryptocurrency, and decentralized applications through an AI learning assistant called Tutorial Agent. Instead of leaving new users to decode technical jargon on their own, it turns topics such as wallet setup and management, decentralized finance (DeFi), and smart contracts into simpler, interactive lessons.

To trade AEON on Gate, users need a Gate account and sufficient USDT, then enter the AEON/USDT spot market and place either a market order or a limit order. Once the order is filled, the purchased AEON appears in the account balance and can be held, sold, or transferred on-chain when deposits and withdrawals are available on a supported network.

SpaceX reported second-quarter 2026 revenue of $7.81 billion, a 92% year-over-year increase that surpassed the market forecast of $6.93 billion. The loss per share was $0.09, outperforming the anticipated $0.26. This growth was primarily driven by the Starlink connectivity segment and the newly integrated AI business. However, quarterly capital expenditures surged to $18.37 billion—more than twice the period’s revenue—with $15.83 billion allocated to AI infrastructure. The financial results confirm that SpaceX has established a diversified growth framework, while also highlighting that its valuation is increasingly tied to the company’s ability to convert high-intensity investments into sustained cash flow.

The best AI stock prediction and trading tools in 2026 include Danelfin for explainable stock ratings, Tickeron for AI-generated trading signals, TrendSpider for building predictive machine-learning strategies, Trade Ideas for intraday stock scanning, Kavout for AI-assisted screening, Zacks for earnings-revision rankings, and Gate’s Stock Portfolio Bot for automated portfolio rebalancing.

Stablecoins are digital assets anchored to the value of fiat currencies, allowing fast transfers, automatic settlement, and worldwide circulation via blockchain technology. With the growing adoption of stablecoins such as USDC, these assets are transitioning from trading instruments to core financial infrastructure, bridging payments, AI agents, and the broader digital economy.

The AEON tokenomics model is designed around cross-chain payments and AI agent commerce. It connects the AEON token to AEON Pay, cross-chain settlement, and the node network through payment gas, validator staking, protocol governance, fee-funded buybacks, and liquidity incentives. AEON has a fixed total supply of 1,000,000,000 tokens allocated across ecosystem development, the team, the foundation, early investors, marketing, liquidity, and airdrops.

AEON is a crypto payment infrastructure designed for AI agents and real-world commerce. Through its node network, cross-chain settlement protocol, and AEON Pay, it connects users, autonomous agents, wallets, and merchants to support scan-to-pay transactions, multichain payments, agent-to-agent settlements, and AI-driven real-world purchases.

Cerebras is a semiconductor company specializing in artificial intelligence computing infrastructure. Its core technology, Wafer-Scale Computing, leverages the design of an entire wafer as a massive AI accelerator, aiming to break through the limitations of traditional GPU clusters in data transfer, scalability, and energy efficiency. The Cerebras WSE (Wafer-Scale Engine) chip series represents a unique AI computing pathway, setting it apart from NVIDIA GPUs.

Cerebras is a semiconductor company specializing in artificial intelligence computing infrastructure. Its high-speed AI inference strategy leverages the Wafer-Scale Engine (WSE) wafer-level chip architecture, enabling faster execution of large AI models through extensive on-chip computing resources and low-latency data transfer design. While traditional AI training primarily focuses on model size and parameter count, AI inference is more concerned with model response efficiency, computational cost, and real-world application performance.

Wafer-Scale Engine (WSE) is a wafer-level AI computing chip architecture developed by Cerebras. By designing the entire semiconductor wafer as a single ultra-large-scale AI accelerator, it surpasses the limitations of traditional chips that depend on interconnecting multiple processors to scale hashrate. WSE delivers a new computational paradigm for training and inference of large AI models through massive integration of compute cores, high-speed on-chip communication, and an optimized dataflow architecture.

BitMine Immersion (BMNR) is a company specializing in Bitcoin mining infrastructure, immersion cooling technology, and high-performance computing. Its core strategy centers on advancing digital asset infrastructure to next-generation computing platforms by optimizing hash rate efficiency and developing AI data center applications.

The 2U2 token is the native token designed for the 2U2.ai ecosystem. It connects AI Meme creation, platform services, user incentives, and community participation through a token-based economic model.

Meme projects rely heavily on community participation, cultural narratives, and continuous content creation. Unlike traditional applications that grow through product features alone, Meme projects often gain attention through user-generated content, social discussions, and viral distribution.

The growth of Meme communities has created a new demand for faster content creation, stronger community engagement, and more efficient ways to distribute viral narratives. As AI-generated content becomes increasingly common, Web3 projects are exploring new tools that connect creation, social interaction, and user participation.