瑞银预测，2025 年$AMZN 、 $MSFT 、 $AAPL 、 $GOOGL 、 $META和$TSLA --等科技巨...

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UBS forecasts a 25% increase in 2025 CapEx from tech giants like $AMZN, $MSFT, $AAPL, $GOOGL, $META & $TSLA -- which is expected to significantly advance the AI sector.
Here are 10 companies positioned to benefit from this anticipated rise in investment  

1. $ASML is the exclusive provider of EUV lithography technology, which is critical for fabricating chips below the 7 nm process node. This technology enables the creation of smaller, more energy-efficient chips necessary for cutting-edge AI applications -- making ASML a fundamental partner for semiconductor manufacturers focusing on AI chips.

2. $LRCX supplies necessary equipment for several critical steps in the semiconductor manufacturing process, including etching and deposition. These processes are vital for creating the intricate designs of AI chips -- requiring precise control over semiconductor fabrication to achieve high performance and reliability.
3. $TSM offers advanced manufacturing capabilities that produce sophisticated semiconductors essential for AI technologies. With their cutting-edge technology nodes, TSMC supports the fabrication of powerful yet efficient chips -- crucial for sustainable AI operations in data centers and embedded systems.

4. $NVDA leverages its A100 and H100 Tensor Core GPUs to enhance complex AI computations and deep learning processes, essential for next-generation AI applications -- these GPUs are critical as they provide the computational power needed for training and deploying sophisticated AI models.
5. $AMD's EPYC series processors cater to high-performance computing environments required for AI workloads. These processors enhance parallel processing capabilities crucial for executing simultaneous operations -- aligning with the growing demands of AI data processing.

6. $MU offers top-tier DRAM and NAND memory products, crucial for the rapid processing and storage of large datasets in AI operations. As AI systems become more complex -- the demand for Micron's high-capacity and fast-access memory solutions grows, essential for real-time AI functionalities.

7. $QCOM develops SoCs and modems that facilitate mobile connectivity and processing. Qualcomm's Snapdragon platforms are integral for enabling AI functionality at the edge, particularly in mobile and IoT environments -- optimizing devices for AI-based applications like voice recognition and real-time data processing.

8. $AVGO provides a range of semiconductor solutions, including network adapters and integrated circuits that support critical data transmission functions necessary for AI operations across various platforms -- these products ensure efficient data flow, crucial for the lifecycle of AI data from ingestion to analysis.

9. $ANET's high-performance network switches and routers are essential for data centers and computing clusters handling AI workloads -- these products support the infrastructure required for high-speed data transfers and low-latency operations, crucial for effective AI processing.

10. $ARM's architectures, known for their power efficiency, are ideal for power-sensitive applications such as edge computing. ARM's technology powers billions of mobile and IoT devices, increasingly used for on-device AI applications -- benefiting from the low-power consumption while maintaining performance.

由[/url]翻译自 英语

瑞银预测，2025 年[url=https://x.com/search?q=%24AMZN&src=cashtag_click]$AMZN 、 $MSFT 、 $AAPL 、 $GOOGL 、 $META和$TSLA --等科技巨头的资本支出将增加 25%，预计将显著推动人工智能行业的发展。
以下 10 家公司有望从这一预期的投资增长中获益
1. $ASML是 EUV 光刻技术的独家供应商，该技术对于制造 7 纳米工艺节点以下的芯片至关重要。该技术能够制造出更小、更节能的芯片，而这些芯片是尖端 AI 应用所必需的--这使得 ASML 成为专注于 AI 芯片的半导体制造商的重要合作伙伴。
2. $LRCX为半导体制造过程中的几个关键步骤提供必要的设备，包括蚀刻和沉积。这些工艺对于创建复杂的 AI 芯片设计至关重要--需要精确控制半导体制造才能实现高性能和可靠性。
3. $TSM提供先进的制造能力，生产对人工智能技术至关重要的复杂半导体。凭借其尖端技术节点，台积电支持制造强大而高效的芯片--这对于数据中心和嵌入式系统中可持续的人工智能运营至关重要。

4. $NVDA利用其 A100 和 H100 Tensor Core GPU 来增强复杂的 AI 计算和深度学习过程，这对于下一代 AI 应用至关重要--这些 GPU 至关重要，因为它们提供了训练和部署复杂 AI 模型所需的计算能力。

5. $AMD的 EPYC 系列处理器可满足 AI 工作负载所需的高性能计算环境。这些处理器增强了执行同时操作--所必需的并行处理能力，以满足 AI 数据处理日益增长的需求。

6. $MU提供顶级 DRAM 和 NAND 内存产品，这对于快速处理和存储 AI 操作中的大型数据集至关重要。随着 AI 系统变得越来越复杂--对美光高容量和快速访问内存解决方案的需求不断增长，这对于实时 AI 功能至关重要。

7. $QCOM开发促进移动连接和处理的 SoC 和调制解调器。高通的骁龙平台对于实现边缘 AI 功能至关重要，特别是在移动和物联网环境中--可优化设备以用于语音识别和实时数据处理等基于 AI 的应用。
8. $AVGO提供一系列半导体解决方案，包括网络适配器和集成电路，支持跨各种平台的 AI 操作所需的关键数据传输功能--这些产品确保高效的数据流，这对于 AI 数据从提取到分析的生命周期至关重要。
9. $ANET的高性能网络交换机和路由器对于处理 AI 工作负载的数据中心和计算集群至关重要--这些产品支持高速数据传输和低延迟操作所需的基础设施，这对于有效的 AI 处理至关重要。
10. $ARM的架构以其高能效而闻名，非常适合边缘计算等对功耗敏感的应用。ARM 的技术为数十亿移动和物联网设备提供支持，越来越多地用于设备上的 AI 应用--这些应用受益于低功耗，同时保持了性能。



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