与「CloudInfrastructure」相关的搜索结果

Scaling the AI agent economy requires enterprise-grade infrastructure. As builds the financial infrastructure for the AI agent era, we’re excited to partner with them to deliver the stability, scalability, and performance needed for tomorrow’s intelligent on-chain ecosystem. Together, we are empowering the future of decentralized tech. 🚀 #AIAgents# #TencentCloud# #CloudInfrastructure#

AMD shares jumped after locking in an expanded partnership with Microsoft to power Azure's next-gen frontier AI inference workloads. THE DEPLOYMENT: Microsoft will utilize AMD's Helios rack scale platform, fusing Instinct GPUs and EPYC CPUs together for heavy enterprise AI scaling. THE TRACTION: The growth profile remains robust, supported by $37.45B in total annual revenue and a spectacular 3-year diluted EPS growth rate of over 134%. THE RATING: Driven by explosive multi-year growth indicators and clear cloud infrastructure wins, the Seeking Alpha Quant model rates AMD as a solid STRONG BUY. Will this deep integration into Azure solidify $AMD as the premier choice for AI inference, or will competition cap market share gains?

Build and scale globally with Tencent Cloud. Access reliable cloud infrastructure, scalable computing, storage, databases, CDN and enterprise support through one streamlined platform. Explore flexible cloud solutions for startups, developers and growing businesses.

We are open-sourcing blcli: an Agentic Infra Stack, battle-tested at 30M+ user scale. It allows coding agents like Codex or Claude Code to help manage your whole cloud infrastructure through code, PRs, dry-runs, and deterministic apply workflows. A solid & serious infra that can support to millions of users. This is a collaboration across multiple teams, the same stack that powers @AlvaApp, @Galxe, @GravityChain, and @ReahPlatform. Check it out here: Docs: blcli: Production stack template: Personal account starter: A common take today is: AI agents are useful for toy apps and prototypes, but not for serious infrastructure. The conclusion is wrong, because the issue is not that agents cannot work on real systems. The issue is that real infrastructure requires a large amount of expert context to get it correct in the first place, and even more context to guide agents through the next 18 months of iteration. Production infrastructure is not just a few Terraform files or Kubernetes YAMLs. It includes: cloud projects IAM boundaries networking VPC / subnet / firewall design Terraform state and backend management Kubernetes clusters cluster add-ons secrets management Git-based deployment workflows observability and telemetry (logs, metrics, traces. All integrated together and ready for your Agents to debug live on your prod env) databases, often self-hosted for cost efficiency and control environment separation: stg / beta / prd operational runbooks rollback paths production failure patterns Most of this knowledge usually lives in senior engineers’ heads, internal docs, shell scripts, Slack threads, old runbooks, and lessons learned from real incidents. If an agent does not have that context, of course it will build toy infrastructure. So the real question is: How do we package production infrastructure expertise into a form that AI agents can read, reason about, modify, and operate safely? That is what blcli does. At its core, blcli is a CLI tool plus a whole package of best practices of Infrastructure as Code. The key design principle is simple: Agents are already very good at reading and modifying code. So we make infrastructure code-first. The generated repo is intentionally self-explanatory. An agent can open the repo and understand what happened, and what's next. Who blcli is for? We built blcli for two types of users. 1. Product teams that need to scale beyond prototypes The first group is teams building real products that need infrastructure capable of growing beyond the prototype stage. These teams want the speed of AI-assisted development, but they cannot afford toy infrastructure. 2. Frontier labs and agent teams building self-improving systems The second group is frontier labs, data companies, and agent teams that need infrastructure not just to run applications, but to train, evaluate, and improve agents. If you are building coding agents, infra agents, or long-horizon autonomous systems, blcli stack is a good agent harness/env. Authors: @SiriJhui @p0pUBhv35I8308 @alvinFu1 @ryan4yin @algoxstonk

Really enjoyed my meeting with Prime Minister @narendramodi about what’s ahead for Amazon in India. We’ve been serving customers, sellers, developers, startups, and enterprises in India for more than a decade and just getting started. Shared that we’re investing $48 billion over the coming five years, including $21+ billion in AI and cloud infrastructure. By 2030, we plan to support 3.8 million jobs, enable $80 billion in ecomm exports, and bring benefits of AI to 15 million small businesses and 4 million government school students. Excited about what’s ahead. Still early days for what we can build.

AI is reshaping the business landscape, but is your data infrastructure ready for it? In this episode of Transform Talks, Christophe Hermant, Head of Cloud Infrastructure, Global Delivery and Operations at Orange Business, shares why the next phase of transformation is already underway, and why data protection remains critical to business resilience. Get the insights here!

China is not only pricing technology. It is pricing political freshness. I recently came across the terms “old tech” and “new tech” in Chinese investment circles. I found that interesting and looked into it. “Old tech” refers to the internet giants of the last cycle: Alibaba, Tencent, Meituan, JD, Baidu, NetEase, Xiaomi and their peers. These companies make up bulk of investable indexes. They have users, cash flow, engineers, cloud infrastructure, payment systems, data and distribution. In most markets, that would make them strategic assets. But in China, they also carry political baggage. They are associated with platform monopolies, regulatory crackdowns, gaming restrictions, weak consumption, brutal e-commerce competition, and Xi’s campaign against private platform power. They dominate market capitalization, but no longer dominate the national imagination. “New tech” refers to sectors now favoured by Beijing: AI, large language models, semiconductors, robotics, advanced manufacturing, domestic chips, embodied intelligence and other technologies tied to “new productive forces.” Many of these companies are smaller, less proven, and barely commercial. Yet they command extraordinary valuations because investors are pricing scarcity, policy support, import substitution and national-security relevance. The valuation gap shows the point. Tencent and Alibaba remain huge — roughly HK$4 trillion(USD 600B) and HK$2 trillion in market value — but trade like mature businesses, generally around 10–20x earnings and low-single-digit sales multiples. By contrast, Cambricon, a chip designer, has traded around RMB1 trillion, at more than 100x sales and over 300x earnings. MiniMax, an AI company, was valued at roughly 80x sales at IPO and now trades at more than 600x sales. Zhipu, another AI company, reportedly moved from roughly 66x sales at IPO to over 1,000x sales. For reference, OpenAI is valued at roughly 36x run-rate sales. Anthropic is valued at roughly 20x run-rate sales. This is not a normal growth premium. It is scarcity, policy blessing, import substitution and national-champion imagination being capitalized into market value. That is the real dichotomy in China today: not old versus new, but commercially proven versus politically favoured. China is not the capital market most outsiders think they are investing in. It operates by a different set of rules. Many international investors understand the numbers, but only half-understand the game.

SpaceX 15-Year Investment @elonmusk My evaluation of a long-term bullish investment for SpaceX based on a 15-year investment horizon. The evaluation assumes SpaceX evolves beyond aerospace into a global infrastructure platform spanning communications, defense, transportation, AI infrastructure, and the emerging space economy. Key Value Drivers - Starlink broadband growth
- Direct-to-cell mobile connectivity
- Government and defense contracts
- Starship and launch-cost reduction
- Orbital infrastructure
- Lunar and Mars logistics
- Future industries enabled by low-cost access to space An extremely important development is the impact of large AI infrastructure contracts on the valuation of SpaceX. A key component of the bullish investment thesis is that the market may be underestimating the value of recurring AI compute revenue when compared with traditional launch and satellite communications businesses. Google AI Infrastructure Contract Recent reports indicate that Google entered into a computing capacity agreement with SpaceX valued at approximately $920 million per month. If maintained over the reported contract period, the agreement represents nearly $30 billion of contracted revenue. Anthropic Contract In addition to Google, reports indicate that Anthropic entered into a compute-capacity agreement valued at approximately $1.25 billion per month. Together, the Google and Anthropic contracts imply annualized revenue of approximately $26 billion. Why This Matters Many investors continue to value SpaceX primarily as a launch company and satellite communications provider. However, these AI-related contracts suggest the emergence of a third major business segment: AI infrastructure and compute services. Potential Strategic Position If SpaceX successfully integrates launch services, Starlink communications, AI infrastructure, and future orbital computing capabilities, the company could evolve into a hybrid of a cloud infrastructure provider, global telecommunications company, defense contractor, and transportation platform. Investment Implications Recurring infrastructure revenue generally receives higher valuation multiples than project-based revenue streams. Consequently, sustained growth in AI infrastructure revenue could materially alter the market’s perception of SpaceX and support valuation frameworks significantly above those used for traditional aerospace companies. Bull Case SpaceX becomes the dominant communications and transportation infrastructure provider for Earth and near-Earth economic activity. Starlink achieves global scale, Starship reaches full operational capability, and multiple new industries emerge around orbital infrastructure. Base Case SpaceX remains the global leader in launch services and satellite communications while generating strong growth from Starlink and defense-related revenue streams. Bear Case Growth continues but at a slower pace due to regulatory, competitive, technical, or capital allocation challenges. Comparison with Historic Winners Historically, Amazon, NVIDIA, Apple, Microsoft, and Tesla created extraordinary shareholder wealth by becoming platform companies rather than remaining confined to their original industries. The bullish SpaceX thesis assumes a similar transition. Key Risks The principal risks include execution risk, regulatory risk, geopolitical factors, competition, slower-than-expected adoption of space-based industries, and the possibility that SpaceX enables large industries without capturing most of the resulting value. Conclusion The bullish investment thesis is that SpaceX is transitioning from a launch and communications company into a foundational infrastructure platform serving communications, AI, defense, and future space-based industries. If successful, this transition could support valuation outcomes substantially above conventional aerospace benchmarks A bet on technological progress, and the emergence of a large space-based economy

Oracle keeps pushing deeper into AI. 👀 From cloud infrastructure to enterprise AI solutions, growing demand continues to keep $ORCL in focus. Trade $ORCL on Bybit TradFi:

Roundhill with a filing for a Neocloud ETF.. which according AI is a "specialized cloud infrastructure provider that focuses almost entirely on GPU-as-a-Service (GPUaaS) to power AI and machine learning workloads"