A Critical Look at NVIDIA’s Business Model
(These Reason Street research notes are not meant as investment advice; they are for busy founders and ecosystem leads to make sense of where we are in the hype of AI and make better decisions).
NVIDIA is the central player in AI hardware. Its GPUs power the most advanced AI systems, from ChatGPT to enterprise AI infrastructure. With over 90 percent market share in AI chips, NVIDIA is often seen as the backbone of U.S. dominance in artificial intelligence.
But is that dominance sustainable?
While NVIDIA’s position is strong today, its business model rests on shifting ground. Geopolitical risks, market power shifts, and the evolution of AI itself may challenge both NVIDIA’s dominance and the idea that U.S. leadership in AI is guaranteed. Meanwhile, other regions—including the EU, China, and emerging players in the Global South—are actively working to reduce reliance on U.S.-controlled AI infrastructure and develop their own AI strategies.
NVIDIA’s AI Power: Built on Three Pillars
- Hardware Leadership: The A100 and H100 GPUs are the gold standard for AI compute, relied upon by every major AI company.
- Software Lock-In: CUDA, NVIDIA’s proprietary AI framework, creates high switching costs for developers, reinforcing its ecosystem.
- Deep Cloud and Enterprise Integration: NVIDIA hardware underpins AI workloads in AWS, Google Cloud, and Microsoft Azure.
These advantages have solidified NVIDIA’s control over the AI supply chain. But they may not be enough to guarantee long-term U.S. dominance.
The Fragility of NVIDIA’s AI Power
- Geopolitical Risk and the China Factor
- U.S. restrictions on high-end NVIDIA AI chip exports to China cut off a major revenue stream, with China accounting for roughly a quarter of NVIDIA’s data center sales.
- These restrictions create strong incentives for China to accelerate its own AI chip development, with Huawei and other firms making rapid progress.
- NVIDIA does not manufacture its own chips. Its reliance on Taiwan’s TSMC makes its supply chain vulnerable to geopolitical instability.
- The Shift to Custom AI Chips
- Large AI players are actively working to reduce their dependence on NVIDIA.
- Google has TPUs, Amazon has Inferentia and Trainium, Tesla is developing Dojo, and Microsoft and Meta are working on in-house AI accelerators.
- OpenAI, historically one of NVIDIA’s largest customers, is reportedly considering its own chip development.
- If cloud providers and AI leaders succeed in moving away from NVIDIA hardware, its dominance in AI compute could erode.
- Erosion of the Software Lock-In Advantage
- CUDA has kept developers locked into NVIDIA’s ecosystem, but alternative AI frameworks are emerging.
- Open-source AI stacks like Triton, MLIR, and AMD’s ROCm are improving, making it easier for developers to switch.
- If open AI frameworks and RISC-V-based AI hardware gain traction, NVIDIA’s control over the AI software ecosystem could weaken.
- The AI Market Will Shift from Model Training to Inference
- Training large AI models requires NVIDIA’s most advanced GPUs, but inference—the process of running AI models—does not necessarily require high-cost, high-performance chips.
- Over time, demand may shift from NVIDIA’s expensive GPUs to lower-cost, specialized AI hardware optimized for inference.
- This transition could open the door for challengers such as AMD, Intel, and custom AI chip startups.
What Happens If Other Regions Want to Counter U.S. AI Dominance?
While the U.S. currently leads in AI infrastructure, other countries and regions are actively working to build independent AI ecosystems that reduce reliance on NVIDIA and U.S.-controlled AI systems.
- The European Union is pushing for AI sovereignty with initiatives such as the European Chips Act and funding for alternative AI models and compute resources. The EU’s AI regulations could also shape alternative AI business models that differ from U.S.-driven, VC-backed commercialization strategies.
- China is rapidly developing its own AI chips (Huawei Ascend, Alibaba Pingtouge, and Baidu Kunlun) to replace NVIDIA hardware, particularly after U.S. export restrictions. China’s AI infrastructure is being designed for domestic use cases, bypassing reliance on Western cloud providers.
- India and Southeast Asia are investing in AI infrastructure, leveraging lower-cost AI model development for regional applications such as healthcare, fintech, and agriculture. These markets could favor open-source AI models and lower-cost alternatives to NVIDIA chips.
- The Global South is focused on AI models and infrastructure that serve local contexts, rather than relying on large Western AI companies. Africa’s AI initiatives, for example, are focused on energy-efficient AI, local language processing, and digital infrastructure that operates outside the major U.S. cloud providers.
If these regions succeed in developing alternative AI compute ecosystems, NVIDIA’s central role in global AI may weaken, shifting power from a U.S.-dominated AI stack to a more multipolar AI future.
Does U.S. AI Dominance Depend on NVIDIA?
NVIDIA’s position reinforces U.S. AI leadership for now, but long-term dominance will depend on factors beyond a single company’s success. Key questions include:
- Who controls AI infrastructure? Cloud providers such as AWS, Google, and Microsoft ultimately decide whether NVIDIA remains at the core of AI compute.
- Who owns the semiconductor supply chain? If the U.S. cannot reduce its reliance on Taiwan for advanced chip manufacturing, AI hardware leadership will remain fragile.
- Who leads in open AI hardware and software ecosystems? The rise of open-source AI models and alternative AI chips could shift power away from NVIDIA.
- Who shapes AI policy and governance? Technical superiority alone will not determine AI leadership. Regulation, ethics, and governance structures will play a defining role.
- How will other regions build AI sovereignty? If Europe, China, and emerging economies develop their own AI chips, cloud compute, and policy frameworks, the U.S. may no longer define the future of AI alone.
The Next Five Years: Uncertain AI Power Structures
NVIDIA has positioned itself as the leader in AI hardware, but its long-term dominance is not guaranteed. The rise of custom AI chips, shifts in cloud provider strategies, geopolitical dynamics, and the evolution of AI workloads could all challenge its control over the AI ecosystem.
If the U.S. wants to maintain AI leadership, it must look beyond NVIDIA and strengthen other parts of the AI value chain, from semiconductor manufacturing to AI governance.
At the same time, other regions are actively investing in AI sovereignty, ensuring that AI leadership is no longer dictated by a single country or company.
Will NVIDIA’s AI monopoly hold, or are we entering an era of more distributed AI power? The next phase of AI development will determine whether one company, or one country, can truly dominate.