From OpenAI to Google, tech giants ignite AI investments on the same day! What opportunities lie hidden in this list of 'pick-and-shovel' plays?

In less than two days, the global AI arms race has escalated once again.

From OpenAI and Anthropic to $Microsoft (MSFT.US)$ 、 $SpaceX (SPCX.US)$ And, $Alphabet-C (GOOG.US)$ , the five major overseas tech giants have successively unveiled market-shaking investment plans, pouring massive capital into AI infrastructure development.

This clearly signals that the AI hardware arms race is far from peaking. The industry’s focus is shifting comprehensively from upper-layer model development to an ultimate showdown over foundational hardware—including chips, networking, cooling, and power systems.

Tech giants are pouring hundreds of billions of dollars into AI infrastructure—the new main battleground

This wave of investment spans a full-spectrum layout—from AI chips and data centers to power and energy infrastructure—on a staggering scale:

1. Google: Buoyed by strong Q2 earnings, Google significantly raised its full-year 2026 capital expenditure guidance to $195–205 billion and explicitly stated that spending in 2027 will expand further, underscoring its unwavering commitment to AI infrastructure.

2. OpenAI: Focusing on long-term compute dominance, OpenAI has substantially increased its planned compute spending through 2030 from the original $600 billion to $750 billion and announced a $20 billion investment to build a mega data center in Georgia, USA.

3. Anthropic and AMD: The two parties have entered into a deep strategic partnership: Anthropic plans to purchase up to 2 GW of AMD’s next-generation Instinct MI450 chips starting in the first half of 2027; in return, AMD will invest up to $5 billion in Anthropic.

4. Microsoft: Microsoft is doubling down on its European footprint, committing several billion dollars to support French AI startup Mistral AI in building GPU data centers across Europe and deeply integrating its core models into the Azure and Copilot ecosystems.

5. SpaceX AI: Elon Musk’s SpaceX AI is also expanding its compute footprint, having already surveyed multiple potential sites in Texas and planning to lay the groundwork for at least one large-scale data center.

With massive capital inflows, which companies in the AI supply chain deserve attention?

The tech giants' capital expenditures—reaching hundreds of billions or even trillions of dollars—will ultimately translate into tangible revenue for companies across the AI supply chain. Niu Niu has梳理ed this trend and identified three core segments set to benefit significantly:

1. Chips and Memory (the Heart and Brain of AI)

Computing power is the cornerstone of AI development, which relies heavily on various chips and semiconductor equipment:

Core Computing Chips: The GPU space remains dominated by $NVIDIA (NVDA.US)$ and $Advanced Micro Devices (AMD.US)$ amid surging demand for custom ASIC chips, $Broadcom (AVGO.US)$ and $Marvell Technology (MRVL.US)$ faces significant opportunities. In CPUs, the market is held by giants like $Intel (INTC.US)$ 、 $Qualcomm (QCOM.US)$ and $Arm Holdings (ARM.US)$ and others.

Memory Chips: AI model training has an insatiable demand for high-bandwidth memory, $Micron Technology (MU.US)$ 、 $SK hynix (SKHY.US)$ And, $Samsung Electronics (005930.KR)$ Major memory manufacturers will benefit directly.

Semiconductor Equipment and Testing/Packaging: Expansion of chip production capacity has driven demand for upstream equipment and testing/packaging services, such as $ASML Holding (ASML.US)$ 、 $Applied Materials (AMAT.US)$ 、 $KLA Corp (KLAC.US)$ equipment suppliers, as well as $ASE Technology (ASX.US)$ leading OSAT (outsourced semiconductor assembly and test) companies.

II. Networking and Communication Hardware (the neural network of AI)

The massive data transmission within data centers requires extremely high bandwidth and ultra-low latency, accelerating the upgrade cycle for networking hardware:

Optical Components and Transceivers: These are critical for high-speed data transmission, with companies like $Coherent (COHR.US)$ 、 $Lumentum (LITE.US)$ 、 $ZJ INNOLIGHT (03308.HK)$ playing key roles.

Switches and Networking Equipment: $Arista Networks (ANET.US)$ And, $Cisco (CSCO.US)$ are core suppliers driving upgrades in data center network architectures.

High-Bandwidth Interconnects/PCBs: For example, $TTM Technologies (TTMI.US)$ PCB suppliers such as these provide foundational support for high-density computing hardware.

3. AIDC Data Centers and Power Infrastructure (The Body and Blood of AI)

As newly built data centers grow increasingly large in scale (e.g., OpenAI's $20 billion project and Anthropic's 2GW power demand), infrastructure bottlenecks are becoming more pronounced:

Servers and Cloud Operators: In addition to traditional $Dell Technologies (DELL.US)$ 、 $Hewlett Packard Enterprise (HPE.US)$ 、 $Super Micro Computer (SMCI.US)$ providers, emerging AI cloud service providers (Neoclouds) such as $CoreWeave (CRWV.US)$ 、 $NEBIUS (NBIS.US)$ are rapidly rising.

Liquid Cooling and Thermal Management: High computing power comes with high energy consumption and heat generation, making traditional air cooling insufficient to meet demand, $Vertiv Holdings (VRT.US)$ liquid cooling thermal management companies are entering a golden growth period.

Power Grids and Power Supply Equipment: The ultimate bottleneck for AI is energy. Tech giants' expansion of computing capacity has triggered massive electricity demand, $GE Vernova (GEV.US)$ 、 $Eaton (ETN.US)$ leading power equipment providers will become critical enablers for the continuous operation of AI data centers.

Conclusion

The flurry of announcements from the five major tech giants within just two days is not only a vote of confidence in AI’s future but also a clarion call for a new wave of AI infrastructure investment. From silicon chip design in Silicon Valley to data centers in Texas, and from fiber-optic networks to liquid cooling systems, a sweeping industrial upgrade is underway. For investors, the key to capturing this AI supercycle lies in identifying 'picks and shovels'—companies with strong competitive moats—along the value chain of 'chips & storage → networking & communications → AI data center infrastructure.'

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