Nvidia's Geopolitical Paradox: How Export Controls Fuel Offshore Demand and Reshape the AI Supply Chain

The U.S. government's escalating export controls on advanced AI chips for China have created a paradoxical outcome: while designed to curb Beijing's access to cutting-edge technology, these policies are inadvertently fueling demand for Nvidia's products through offshore training centers. This unintended consequence highlights the fragility of geopolitical strategies in a globalized tech ecosystem and raises critical questions about the long-term resilience of supply chains in the AI arms race.

The Dual Ban and Its Unintended Consequences

Since 2018, U.S. export controls have sought to limit China's access to advanced semiconductors, with Nvidia at the center of the storm. By 2025, the Biden administration had reduced its market share in China from 95% to 0% through a combination of direct bans and the 2025 global AI Diffusion Rule, which restricts third-party access to U.S. chips. Simultaneously, China's own ban on foreign AI chips in state-funded data centers-targeting firms like Alibaba and ByteDance-forced a pivot to domestic alternatives such as Huawei's Ascend.

Yet these measures have not eliminated demand for Nvidia's chips. Instead, they've driven Chinese tech firms to adopt a dual strategy: offshore training centers in Southeast Asia and aggressive stockpiling of pre-ban inventory. According to a report by , companies like Alibaba and ByteDance are now training AI models in foreign data centers to bypass U.S. restrictions. This circumvention has created a "shadow market" for Nvidia's chips, where demand persists despite-or perhaps because of-geopolitical friction.

Offshore Training Centers: A Strategic Workaround

The rise of offshore AI training centers represents a sophisticated response to export controls. Chinese firms are leveraging jurisdictions with less stringent regulations to access Nvidia's A100 and H100 chips, which remain critical for training large language models (LLMs). For instance, DeepSeek-a Chinese AI startup-stockpiled Nvidia hardware before 2025 restrictions took full effect and continues to train models domestically while collaborating with Huawei to develop next-generation chips. This hybrid approach allows firms to hedge against supply chain disruptions while maintaining access to U.S. technology.

Meanwhile, the Chinese government's push for self-sufficiency has not fully materialized. Domestic chips like Huawei's Ascend, while capable of training advanced models, lag in performance and energy efficiency compared to their U.S. counterparts. As a result, Chinese firms are paying a premium to access offshore Nvidia infrastructure, with some analysts suggesting these costs are subsidized by state-backed infrastructure deals. This dynamic underscores a key vulnerability in China's AI strategy: its reliance on foreign technology persists even as it invests heavily in domestic alternatives.

Investment Implications: Winners and Losers in the New Order

For Nvidia, the offshore demand paradox presents both opportunities and risks. On one hand, the company's chips remain indispensable for training the most advanced AI models, ensuring continued revenue from Chinese firms willing to pay a premium to access them. According to an analysis, this demand is driving significant growth in the company's global footprint. On the other hand, the U.S. government's 15% fee on China sales-imposed under the Trump administration-reduces profit margins and creates regulatory uncertainty. Additionally, the rise of Chinese chipmakers like Huawei and Cambricon threatens long-term market share, particularly as Beijing subsidizes domestic production. As reported by International Banker, this policy shift is accelerating China's domestic chip development.

Chinese startups like DeepSeek are navigating a complex landscape. By stockpiling Nvidia chips and partnering with Huawei, they're positioning themselves to weather supply chain disruptions while advancing their own R&D capabilities. DeepSeek's recent release of the R1 model-reportedly trained at lower costs using efficiency-focused techniques-has already triggered market volatility, signaling its potential to disrupt global AI markets. For investors, this duality (reliance on U.S. tech + push for self-sufficiency) creates both short-term opportunities and long-term risks.

Huawei, meanwhile, stands to benefit from the geopolitical tailwinds. As China's leading chipmaker, it is gaining traction in state-funded projects and partnerships with startups like DeepSeek. As noted in a recent report, Huawei is emerging as a key player in China's AI ecosystem. However, its ability to compete globally hinges on overcoming technical gaps in chip performance and energy efficiency. The company's success will also depend on its capacity to scale production and secure international customers, a challenge given the U.S.-led export control regime.

The Broader Geopolitical and Economic Risks

The offshore training phenomenon highlights a critical flaw in U.S. export control policies: they incentivize adversaries to find workarounds rather than deter access. By driving demand into unregulated markets, these policies risk creating a fragmented global AI ecosystem where compliance is circumvented through jurisdictional arbitrage. For investors, this fragmentation increases operational complexity and regulatory risk, particularly for firms operating in both U.S. and Chinese markets.

Moreover, the U.S. strategy may accelerate China's self-sufficiency in chip design. While domestic chips currently lag, the pressure to innovate is forcing Chinese firms to invest heavily in R&D. As noted by Brookings Institution, overly aggressive bans can backfire by creating a "market pull" for domestic alternatives. This dynamic could erode U.S. leadership in AI over the next decade, particularly if Chinese firms achieve parity in chip performance.

Conclusion: Navigating the New AI Supply Chain

The interplay between U.S. export controls and Chinese AI strategies has created a volatile but lucrative market for Nvidia and its competitors. For investors, the key takeaway is that geopolitical risks are increasingly intertwined with supply chain resilience. Nvidia's dominance in AI chips remains unchallenged for now, but its long-term prospects depend on its ability to adapt to a world where demand is driven by circumvention rather than compliance.

Chinese startups and chipmakers, meanwhile, represent a high-risk, high-reward segment. While their reliance on offshore Nvidia infrastructure is a short-term crutch, their investments in domestic R&D could pay off in the long run. Investors must weigh the immediate benefits of geopolitical-driven demand against the structural risks of a fragmented global AI market.

In this new era of tech nationalism, the winners will be those who can navigate the paradox: leveraging geopolitical tensions to fuel innovation while mitigating the risks of a fractured global supply chain.