GPUs (Graphics Processing Units) were originally designed for video games, but their ability to perform thousands of calculations simultaneously made them ideal for AI. CUDA is Nvidia's software platform that lets programmers harness this power. Think of it as a specialized language that translates AI code into instructions the GPU can execute. Over 20 years, Nvidia has built an ecosystem of tools, libraries, and trained developers around CUDA — making it extremely difficult for competitors to attract users away, even with superior hardware.
Nvidia controls over 80% of AI GPU market — some estimates reach 90–95%. In a single quarter, its data center division generated $51.2 billion in revenue.
$130B
FY2025 Revenue
4M+
CUDA Developers
88%
Revenue from data center
The moat isn't the hardware — it's CUDA. Over 20 years, Nvidia built an ecosystem with millions of developers and thousands of optimized applications. Switching away means rewriting code, retraining teams, and accepting tradeoffs. Meanwhile, before any chip reaches Nvidia's designers, it must pass through another US chokepoint: EDA software from Synopsys and Cadence, which together control roughly 70% of the tools required to design modern semiconductors.
AI Accelerator Market (2025)
🇺🇸Nvidia (US)84%
🇺🇸AMD (US)8%
🇺🇸Google TPU4%
Huawei Ascend + Others4%
China's Huawei has been building its Ascend AI chip line with its own CANN ecosystem, representing a parallel effort to reduce dependency on Western suppliers.
AI Chip Design Capability
🇺🇸 US & Allies🇨🇳 China
Key Relationships
Synopsys / Cadence (EDA)
US duopoly controls ~70% of chip design software. No chip can be designed without EDA tools.
Nvidia → TSMC → SK Hynix
The tightest three-way dependency in the chain.
Nvidia ↔ Hyperscalers
Biggest customers and most determined competitors.
Huawei Ascend (China)
Building a parallel ecosystem. CANN growing but far behind CUDA’s 20-year head start.