To The Moon

$NVIDIA(NVDA)$ 's Q1 Fiscal 2026 (ended April 27, 2025) earnings results strongly indicate that enterprise CAPEX spending on AI remains robust, particularly from hyperscale cloud providers and, increasingly, from other enterprises and nations.

Breakdown Of Evidence From Nvidia Latest Earnings Report

Soaring Data Center Revenue: Nvidia's Data Center revenue reached a record $39.1 billion in Q1 FY26, up 73% year-over-year and 10% sequentially. This segment is the primary beneficiary of AI infrastructure spending, and its continued, rapid growth clearly demonstrates strong demand.

"Incredibly Strong Global Demand for AI Infrastructure": CEO Jensen Huang explicitly stated, "Global demand for NVIDIA's AI infrastructure is incredibly strong." He highlighted that "AI inference token generation has surged tenfold in just one year, and as AI agents become mainstream, the demand for AI computing will accelerate." This suggests that the current wave of AI adoption is driving sustained and increasing investment.

Hyperscaler Commitments are "Firm": Nvidia's CFO, Colette Kress, noted that "Our customer commitments are firm." This refers to the large cloud service providers (CSPs) like Microsoft, Google Cloud, AWS, and Oracle, who are investing heavily in AI infrastructure to support their own AI services and offer AI capabilities to their enterprise customers. The Blackwell ramp (Nvidia's next-gen AI platform) is reportedly the fastest in Nvidia's history, with Microsoft being a key early adopter.

AI Factory Buildouts Driving Revenue: Kress also mentioned that "AI factory build outs are driving significant revenue." This refers to the construction and equipping of large-scale data centers specifically designed for AI workloads, indicating substantial capital expenditure.

"Nations are investing in AI infrastructure like they once did for electricity and the internet": Jensen Huang's statement emphasizes the view that AI is becoming foundational infrastructure globally, driving investments not just from tech giants but also from governments and various industries ("sovereign AI," "enterprise AI," and "industrial AI").

Despite China Export Controls: Even with the impact of U.S. export restrictions on sales to China (which resulted in a $4.5 billion charge for H20 inventory and an anticipated $8 billion loss in H20 revenue for Q2), Nvidia's overall revenue and data center segment still showed exceptional growth. This underscores the strength of demand from other regions and customers.

Future Outlook: Nvidia's guidance for Q2 FY26 revenue at $45.0 billion (plus or minus 2%) also suggests continued strong demand, even with the projected $8 billion hit from China. The company is working towards achieving gross margins in the mid-70% range later this year, indicating healthy pricing power driven by demand.

While some reports noted that leading cloud operators like Microsoft and $Alphabet(GOOGL)$ have signaled intentions to moderate their AI hardware expenditures going forward after initial large buildouts, Nvidia's Q1 results and their forward-looking statements contradict any notion of an immediate or significant slowdown in overall AI CAPEX.

Instead, the focus seems to be shifting from initial foundational builds to more targeted, efficient, and diversified AI deployments, with Nvidia actively developing new solutions like NVLink Fusion to cater to this evolving landscape and expand its reach into the broader enterprise market beyond just hyperscalers.

Time To Look At Other Semis Beyond Nvidia

With enterprise CAPEX on AI remaining robust, several other semiconductor companies beyond Nvidia are poised to benefit. The AI ecosystem is complex and requires a variety of specialized components and manufacturing capabilities.

Here are some key areas and companies that would benefit:

1. High-Bandwidth Memory (HBM) Suppliers: AI accelerators, like Nvidia's GPUs, require immense amounts of data to be processed quickly. HBM is a specialized type of RAM that provides significantly higher bandwidth than traditional DRAM, making it essential for AI workloads.

SK Hynix: A leading pioneer and strong player in HBM development and production. They have been a key supplier for Nvidia's current generation of AI GPUs.

Samsung Electronics: Another major player in memory, Samsung is heavily investing in and ramping up its HBM production.

Micron Technology: Micron is also a significant producer of HBM and has seen strong demand for its HBM3E products, particularly those used in Nvidia's H200 GPUs.

2. Advanced Packaging and Foundry Services (The "Pipes" for AI Chips): The complex integration of AI chips, including GPUs and HBM, relies on advanced packaging technologies like 2.5D and 3D stacking. Foundries are also critical for manufacturing these cutting-edge chips.

Taiwan Semiconductor Manufacturing Company (TSMC): As the world's largest independent pure-play semiconductor foundry, TSMC is indispensable. Their CoWoS (Chip-on-Wafer-on-Substrate) advanced packaging technology is crucial for integrating Nvidia's GPUs with HBM. They are expanding their advanced packaging capacity significantly.

Samsung Foundry: Samsung also offers foundry services and advanced packaging, competing with TSMC in the leading-edge nodes required for AI chips.

Intel (Foundry Services): While Intel is still catching up in leading-edge foundry, its Intel Foundry Services (IFS) division is a long-term play, and they are investing heavily in advanced packaging technologies like Foveros and EMIB, which could benefit AI chip manufacturing.

Amkor Technology & ASE Technology Holding (OSATs - Outsourced Semiconductor Assembly and Test): These companies specialize in outsourced packaging and testing. As packaging becomes more complex and critical for AI, OSATs like Amkor and ASE (the world's largest) will see increased demand for their advanced services.

3. Custom AI Silicon/ASIC Designers & IP Providers: While Nvidia dominates with its GPUs, hyperscale cloud providers (like Google with its TPUs, Amazon with Inferentia/Trainium, and Meta with MTIA) and other large enterprises are increasingly designing their own custom AI ASICs (Application-Specific Integrated Circuits) to optimize for their specific workloads and reduce reliance on a single vendor.

$Broadcom(AVGO)$ : Broadcom is a major beneficiary because it designs and supplies custom chips, including those for networking and specialized accelerators, to large hyperscalers. They are often involved in co-developing custom ASICs for AI alongside these tech giants.

Marvell Technology (MRVL): Marvell provides data infrastructure semiconductor solutions, including custom silicon and networking chips that are crucial for connecting AI accelerators within data centers. They recently announced a collaboration with Nvidia on NVLink Fusion technology for custom cloud platform silicon.

$ARM Holdings(ARM)$ : Arm's CPU architecture is becoming increasingly relevant for AI, especially at the edge and for host processors in data centers that manage AI accelerators. Its licensing model means it benefits from a wide array of companies designing their own Arm-based AI chips.

Synopsys & Cadence Design Systems: These companies provide Electronic Design Automation (EDA) software and intellectual property (IP) necessary for designing complex chips, including AI ASICs. As more companies design custom AI silicon, the demand for EDA tools and pre-verified IP blocks will grow.

4. Networking and Interconnect Solutions for AI Data Centers: AI workloads require extremely fast and low-latency communication between GPUs and servers within a data center.

Broadcom (AVGO): Beyond custom silicon, Broadcom is a significant player in high-speed networking chips (Ethernet switches, transceivers) essential for AI infrastructure.

Arista Networks (ANET): While not a semiconductor manufacturer themselves, Arista builds the high-performance networking switches that are critical for connecting AI clusters. They use chips from various semiconductor vendors, indirectly benefiting those suppliers.

Marvell Technology (MRVL): Also active in networking solutions, including optical modules for data centers.

5. Power Management and Analog Chips: AI data centers consume massive amounts of power. Efficient power delivery and management are crucial.

Texas Instruments (TXN): TI provides a wide range of analog and embedded processing chips, including power management ICs that are essential for the efficient operation of AI servers and data centers. They are partnering with Nvidia on 800V high-voltage DC power distribution systems for next-gen AI data centers.

Analog Devices (ADI): Similar to TI, ADI provides high-performance analog, mixed-signal, and DSP integrated circuits that are vital for power management, data conversion, and signal processing in AI systems.

Summary

The AI boom is not just about the "brains" (GPUs/AI accelerators) but also the entire nervous system and supporting infrastructure of an AI factory. This widespread demand creates opportunities across various segments of the semiconductor industry.

Nvidia's Q1 FY26 earnings results definitively demonstrate that enterprise CAPEX spending on AI remains exceptionally strong, driven by widespread adoption across cloud providers, enterprises, and nations.

I think cloud provider as well as chip design company like ARM would continue to benefit from this demand as companies would return back to their CAPEX spending.

Appreciate if you could share your thoughts in the comment section whether you think chip makers would be able to enjoy a rally with stronger demand in AI pushing ahead and with the court blocking the tariffs plan by President Trump.

@TigerStars @Daily_Discussion @Tiger_Earnings @TigerWire appreciate if you could feature this article so that fellow tiger would benefit from my investing and trading thoughts.

Disclaimer: The analysis and result presented does not recommend or suggest any investing in the said stock. This is purely for Analysis.