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Here are 1-3 brief analogies to describe NVIDIA:

• Qualcomm for AI chips
• Microsoft for AI computing
• Caterpillar for AI infrastructure

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• GPUs (Graphics Processing Units): Processors designed to accelerate graphics rendering, parallel computation, and AI workloads across gaming, professional visualization, data centers, and automotive applications.
• CUDA Platform: A parallel computing platform and programming model that enables developers to use NVIDIA GPUs for general-purpose computing.
• NVIDIA AI Enterprise: An end-to-end cloud-native software suite optimized for developing and deploying AI and data science applications.
• NVIDIA Omniverse: A platform for connecting and building 3D tools and applications based on Universal Scene Description (USD) for virtual world simulation and design collaboration.
• NVIDIA Networking: High-performance InfiniBand and Ethernet interconnect solutions, including network adapters, switches, and software, for data centers and supercomputing.
• NVIDIA DRIVE: A comprehensive AI platform that includes hardware and software for autonomous vehicles, robotics, and intelligent transportation systems.
• NVIDIA Jetson: A series of embedded computing boards and modules designed for AI and machine learning applications at the edge, ideal for robotics and IoT devices.

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NVIDIA (symbol: NVDA) sells primarily to other companies (B2B), providing its graphics processing units (GPUs) and related platforms to various industries for integration into their products, services, and infrastructure. These direct customers then deploy NVIDIA technology in their own offerings for their end-users or internal operations.

Its major customers include, but are not limited to:

• Microsoft (MSFT): A leading cloud service provider (Azure) utilizing NVIDIA GPUs extensively for AI and high-performance computing services offered to businesses and developers.
• Amazon (AMZN): Through Amazon Web Services (AWS), it provides a wide range of GPU-accelerated instances powered by NVIDIA technology for cloud computing workloads.
• Google (GOOGL): Its Google Cloud platform deploys NVIDIA GPUs for AI training, inference, and various cloud computing services for its global customer base.
• Meta Platforms (META): A significant buyer of NVIDIA GPUs for its internal AI research and infrastructure, including the training of large language models and other advanced AI applications.
• Oracle (ORCL): Oracle Cloud Infrastructure (OCI) has made substantial investments in NVIDIA's data center GPUs to power its enterprise cloud offerings, particularly for AI and HPC.
• Dell Technologies (DELL): Integrates NVIDIA GPUs into its servers, workstations, and gaming PCs (e.g., Alienware brand) that are sold to businesses and consumers.
• Hewlett Packard Enterprise (HPE): Offers servers and high-performance computing systems featuring NVIDIA GPUs for enterprise and scientific applications globally.
• Super Micro Computer (SMCI): A prominent manufacturer of servers and storage solutions, known for heavily integrating NVIDIA GPUs into its systems for AI and data center applications.
• Lenovo Group (0992.HK): Incorporates NVIDIA GPUs into its personal computers, professional workstations, and data center products, serving both business and individual customers.
• Mercedes-Benz Group AG (MBG.DE): A key automotive partner utilizing NVIDIA's DRIVE platform for software-defined vehicles and advanced autonomous driving capabilities in its production cars.

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• Taiwan Semiconductor Manufacturing Company Limited (TSM)
• SK Hynix Inc. (HKMYY)
• Samsung Electronics Co., Ltd. (SSNLF)
• Micron Technology, Inc. (MU)
• ASE Technology Holding Co., Ltd. (ASX)
• Amkor Technology, Inc. (AMKR)

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Jensen Huang, Founder, President and CEO

Jensen Huang co-founded NVIDIA in 1993 and has served as its President and CEO since its inception. Prior to founding NVIDIA, he worked as a microprocessor designer at Advanced Micro Devices (AMD) and as a director at LSI Logic Corporation.

Colette Kress, EVP and Chief Financial Officer

Colette Kress has been the Executive Vice President and Chief Financial Officer at NVIDIA since 2013. Before joining NVIDIA, she held senior finance positions at Cisco, serving as Senior Vice President and CFO of the Business Technology and Operations Finance organization, and spent 13 years at Microsoft, including a role as CFO of the Server and Tools division. She began her career at Texas Instruments.

Chris A. Malachowsky, Founder & NVIDIA Fellow

Chris A. Malachowsky co-founded NVIDIA in 1993. He has overseen various areas within the company, including IT, operations, and product engineering, and currently focuses on developing NVIDIA's core technologies as an NVIDIA Fellow and senior technology executive. Previously, he held engineering and technical leadership roles at HP and Sun Microsystems.

Jay Puri, EVP, Worldwide Field Operations

Jay Puri is the Executive Vice President of Worldwide Field Operations at NVIDIA, responsible for the company's global sales and field operations. Before joining NVIDIA in 2005, Puri spent over two decades in sales, marketing, and management at Sun Microsystems, where he was a Senior Vice President of its Asia-Pacific Group. He also held roles at Hewlett-Packard, Booz Allen, and Texas Instruments.

Debora Shoquist, EVP, Operations

Debora Shoquist serves as the Executive Vice President of Operations, overseeing NVIDIA's global operations, including supply chain management, logistics, and procurement. She joined the company in 2007 and has been instrumental in scaling NVIDIA's manufacturing and operational efficiency.

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1. Geopolitical and Regulatory Risks

   NVIDIA faces significant challenges due to geopolitical tensions, particularly between the U.S. and China, which impact its ability to operate in key markets. U.S. export controls and restrictions on advanced AI chips limit NVIDIA's sales to China, a crucial market, leading to substantial revenue losses and forcing costly supply chain diversification. China, in turn, has initiated antitrust probes and imposed bans on certain NVIDIA chips, viewing its technology as critical for national security.

2. Intensifying Competition

   The company is experiencing increasing competition in the AI accelerator market. Rival chipmakers such as AMD (with its MI300 series) and Intel (with Gaudi) are actively developing and promoting their own AI chip solutions, which are projected to erode NVIDIA's dominant market share. Furthermore, major hyperscale cloud providers like Google, Amazon, and Microsoft are designing and deploying their own custom AI chips, optimized for cost and specific inference workloads, thereby potentially reducing their reliance on NVIDIA's GPUs.

3. Supply Chain Disruptions and Constraints

   Despite robust demand for its AI chips, NVIDIA has consistently faced difficulties in meeting orders due to persistent supply chain disruptions and manufacturing bottlenecks. Critical advanced packaging processes at its primary supplier, TSMC, have struggled to scale, leading to delayed deliveries for products like the Blackwell series AI chips. These constraints raise concerns about NVIDIA's capacity to adapt and scale production to match the rapidly expanding AI market.

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• Increased competition in the AI accelerator market, particularly from AMD's Instinct MI300X series, which has demonstrated competitive performance in certain AI workloads and is gaining adoption among major hyperscale cloud providers like Microsoft, Meta, and Oracle.
• The development and deployment of custom in-house AI accelerator chips by major hyperscale cloud providers (e.g., Google's TPUs, Amazon's Inferentia and Trainium, Microsoft's Maia 100, and Meta's MTIA). These initiatives aim to reduce their reliance on external vendors like NVIDIA, optimize for their specific software stacks and workloads, and potentially lower costs, thereby reducing NVIDIA's total addressable market within these critical customers over time.

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NVIDIA (NVDA) operates in several significant addressable markets for its main products and services:

• Data Center (AI and Accelerated Computing): This segment, encompassing GPUs like the NVIDIA A and H series, DGX systems, and software platforms such as CUDA and AI Enterprise, is NVIDIA's largest revenue generator. The global data center GPU market was valued at approximately USD 16.94 billion in 2024 and is projected to reach about USD 192.68 billion by 2034, exhibiting a Compound Annual Growth Rate (CAGR) of 27.52% from 2025 to 2034. More broadly, the global data center market size was estimated at USD 242.72 billion in 2024 and is projected to grow to USD 584.86 billion by 2032. Another estimate places the global data center market at USD 319.53 billion in 2024, with a projection to reach USD 987.68 billion by 2035. NVIDIA itself estimates its long-term opportunity in the data center market to be $300 billion.
• Gaming: NVIDIA's GeForce GTX and RTX series GPUs cater to the gaming market. The global gaming GPU market size was estimated at USD 4.89 billion in 2025 and is anticipated to reach USD 20.98 billion by 2030, with a CAGR of 33.84%. Other estimates suggest the global gaming GPU market was valued at USD 78.15 billion in 2024, growing to USD 219.48 billion by 2035, and another at USD 72.3 billion in 2024, projected to reach USD 405.58 billion by 2033. NVIDIA holds a dominant position in the gaming GPU market, with a 92% market share in Q1 2025.
• Automotive (AI and Autonomous Vehicles): NVIDIA's offerings in this sector include the Tegra series and the NVIDIA Drive platform (Drive PX, Drive Thor, Orin), providing AI solutions for autonomous driving. The global automotive artificial intelligence (AI) market is estimated at USD 4.71 billion in 2025 and is predicted to grow to approximately USD 48.59 billion by 2034. Another report values the global AI in automotive market size at USD 4.8 billion in 2024, with an estimated CAGR of 42.8% between 2025 and 2034. North America dominated the automotive AI market in 2024, with the U.S. market size for automotive AI projected to be around USD 7.68 billion by 2034, up from USD 820 million in 2025. NVIDIA is targeting approximately $5 billion in automotive revenue for fiscal year 2026.
• Professional Visualization: This segment focuses on professional workstations, design, and content creation, utilizing Quadro/NVIDIA RTX GPUs. NVIDIA's Professional Visualization segment generated $511 million in revenue in January 2025 and experienced a 10% increase in 2025. The global data visualization tools market was valued at USD 7.77 billion in 2024 and is expected to reach USD 15.57 billion by 2033. Furthermore, the global visualization and 3D rendering software market was valued at US$ 3.38 billion in 2023 and is projected to grow to nearly US$ 14.50 billion by 2030. North America holds a dominant share in the global visualization and 3D rendering software market. NVIDIA has a market share of 24.55% in the 3D rendering market.

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Expected Drivers of Future Revenue Growth for NVIDIA (NVDA)

• Dominance in AI Data Centers and Accelerated Computing: NVIDIA's position as the leading provider of GPUs and full-stack solutions for artificial intelligence and accelerated computing is a primary driver of future revenue. The company is experiencing robust demand for its Hopper architecture and the rapid ramp-up of its new Blackwell platform, which is central to the ongoing "AI race." Industry forecasts suggest that global AI data center demand will surge significantly by 2030, creating a multi-trillion-dollar market opportunity for NVIDIA as enterprises and cloud providers aggressively invest in AI infrastructure for training and inference workloads.
• Expansion in the Automotive and Robotics Markets: NVIDIA's automotive division has shown substantial year-over-year revenue growth, driven by advancements in self-driving technology and AI applications in vehicles. The company's full-stack NVIDIA DRIVE AV software platform is in full production, and initial shipments of the DRIVE AGX Thor system-on-a-chip are underway, leading to increased adoption by major automakers. Furthermore, the emerging market for industrial AI and robotics, coupled with surging investments in physical AI, is expected to contribute significantly to future revenue, with the autonomous machine market presenting a long-term opportunity of $300 billion.
• Robust Software and Platform Ecosystem (including Omniverse): NVIDIA's comprehensive software and platform strategy, including its Omniverse platform, is a key competitive advantage and a significant accelerator of revenue growth. This ecosystem, comprising vast software libraries and development tools, simplifies customer implementations and enables NVIDIA to penetrate multiple vertical markets simultaneously. The company anticipates its AI enterprise full-year revenue to more than double, with annualizing software service and support revenue projected to exceed $2 billion, demonstrating the increasing monetization of its software offerings.
• Continuous Innovation with New Product Cycles: The ongoing introduction of new and more powerful GPU architectures, such as the Blackwell platform and future designs, is critical for maintaining NVIDIA's leadership in AI and high-performance computing. These new product cycles drive upgrades and expand the total addressable market by offering enhanced performance, lower costs, and improved energy efficiency for developing and running complex AI models. The rapid ramp-up of these new architectures ensures NVIDIA stays at the forefront of technological advancements, capturing new demand in evolving markets.
• Growth in Professional Visualization and AI PCs: While not as large as the data center segment, the professional visualization market continues to be a growth area for NVIDIA, with recent revenue increases and the introduction of new RTX PRO Blackwell GPUs. Additionally, the gaming segment, particularly with the integration of AI technologies, is contributing to revenue growth through the adoption of GeForce RTX GPUs for gaming, creative applications, and new AI PCs featuring Microsoft's Copilot+ capabilities. These segments benefit from the broader demand for powerful graphics processing and AI capabilities in end-user devices and professional workstations.

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Share Repurchases

• NVIDIA's board approved an additional $60 billion share repurchase authorization on August 26, 2025, which has no expiration date. This authorization follows previous programs, bringing the total authorized for future repurchases to nearly $75 billion (combining the new $60 billion with approximately $15 billion remaining from prior authorizations).
• In fiscal year 2025, NVIDIA repurchased $34.0 billion of its common stock.
• During the first half of fiscal 2026, the company returned $24.3 billion to shareholders through a combination of share repurchases and cash dividends.

Share Issuance

• NVIDIA's shares outstanding have shown a decline over the past year, with 24.532 billion shares outstanding for the quarter ending July 31, 2025, representing a 1.27% decrease year-over-year, indicating that share repurchases have outweighed shares issued.
• As of January 26, 2025, 274 million shares were eligible for issuance under the 2007 Equity Incentive Plan, and an additional 1.4 billion shares were available for future grants.

Outbound Investments

• NVIDIA has made significant strategic investments, including a $5 billion investment in Intel for unified GPU-CPU infrastructure and a co-acquisition of Aligned Data Centers with Microsoft and BlackRock in October 2025.
• The company pledged a $100 billion commitment to OpenAI, with funds primarily intended for OpenAI to lease NVIDIA chips over future years.
• NVIDIA invested up to $2 billion in xAI's $20 billion capital raise in October 2025, largely to facilitate xAI's procurement of NVIDIA AI processors, and made acquisitions such as Run:ai for $700 million and OctoAI for $250 million in 2024.

Capital Expenditures

• NVIDIA's latest twelve months (TTM) capital expenditures as of July 2025 amounted to approximately $5.012 billion.
• For fiscal year 2025, capital expenditures were $3.24 billion, primarily focused on expanding Blackwell accelerator production and AI infrastructure.
• Future capital expenditures are projected to average $7.891 billion over the next five fiscal years, with an expected $6.514 billion for the upcoming fiscal year, driven by accelerating AI infrastructure buildouts and data center expansion.