NVIDIA
The Juggernaut of Silicon
NVIDIA is the undisputed leader of the AI boom. Its GPUs have become the standard engines for training and deploying large models. In Q2 FY2026, sales reached $46.7 billion and gross margins held above 70 percent. The data center division alone generates close to nine-tenths of total revenue. On technology, sales, and profitability, NVIDIA is a juggernaut with very few chinks in the armor.1
Every major player in the AI stack depends on NVIDIA’s hardware. Microsoft, Amazon, Google, and Oracle build their cloud services on its GPUs. OpenAI, Anthropic, Meta, and xAI rely on them for model training. Even as rivals introduce custom accelerators, their scale remains far smaller than NVIDIA’s installed base. Large cloud service providers alone account for half of data center revenue. The company has extended its reach through full-system offerings such as the GB200 NVL72, a liquid-cooled rack that operates as “one giant GPU,” and through software layers like CUDA and domain-specific SDKs that lock developers into its ecosystem. 2
NVIDIA’s weaknesses are tied to its dependencies. Like AMD and Apple, it relies on TSMC for its most advanced GPUs. The Blackwell line is produced using TSMC’s CoWoS-L packaging technology, which connects GPUs to high-bandwidth memory. This makes NVIDIA’s output dependent on TSMC’s ability to scale capacity and maintain yields. Analysts expect NVIDIA to contribute more than 20 percent of TSMC’s revenue in 2025, matching Apple’s share. If TSMC falters, NVIDIA has no substitute 3
Another pressure point comes from regulation. U.S. export controls have already forced NVIDIA to redesign products for the Chinese market, culminating in the H20 line that was itself restricted in April 2025. The company booked a $4.5 billion charge in Q1 FY2026 tied to excess H20 inventory and purchase obligations. China’s State Administration for Market Regulation has also ruled that NVIDIA violated antitrust law in its Mellanox acquisition, signaling that Beijing is willing to put the company under scrutiny. These steps have not derailed NVIDIA’s momentum, but they illustrate how quickly policy changes can affect both demand and product planning 4
The second weak point is customer concentration. In Q2 FY2026, two customers accounted for 39 percent of revenue, up from about 25 percent a year earlier. NVIDIA does not disclose their names, but reporting points to large cloud providers and integrators. Such concentration creates the potential for volatility if a major buyer reduces orders or develops alternative solutions. Demand is strong for now, but the reliance on a small group of customers is a real vulnerability 5
NVIDIA is nevertheless the most secure link in the AI supply chain. Its products dominate the market, its financials are unmatched, and its customers cannot scale without it. Yet its strength rests on narrow foundations. The company depends on TSMC for manufacturing, it faces unpredictable regulatory constraints, and it leans on a handful of hyperscalers for revenue. NVIDIA remains a juggernaut, but its position is not entirely self-contained. 6
(NVIDIA Q2 FY26 press release; CFO commentary).
(GTC 2024 Blackwell materials; NVIDIA annual review FY2025; CFO Colette Kress earnings commentary).
(TrendForce; DigiTimes Asia reporting; Bernstein estimates).
(U.S. BIS filings; NVIDIA Q1 FY26 press release; SAMR ruling September 2025).
(NVIDIA Q2 FY26 10-Q; TrendForce).
(NVIDIA filings; TrendForce; Reuters)
Disclaimer
This analysis is based on public disclosures, regulatory filings, and reported statements as of September 2025. Numbers cited reflect company announcements and may include forward-looking projections that companies themselves have disclosed.
I employ a proprietary diagnostic engine I developed to analyze business fundamentals and identify systemic risks across complex supply chains. Various LLMs assist in research, source verification, and drafting. The analysis, conclusions, and any errors are my own.
This is not investment advice. Corrections welcome.
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