Export controls as tech diplomacy

When the U.S. government floats a proposal requiring federal approval for every AI chip sold overseas, the instinct is to read it as tech policy. Restrict exports, protect innovation, maintain a lead. But that framing misses the bigger picture. AI chip export controls are not really about chips. They are about leverage, and they function more like diplomacy than regulation. In March 2026, reports emerged that the Trump administration was drafting rules requiring Commerce Department authorization for overseas purchases of AI chips from Nvidia and AMD, potentially tying chip access to foreign investment in U.S. data centers. As analyst Patrick Moorhead put it, this is "industrial policy dressed up as security." Washington is using chip access to extract concessions from allies, signal strength to adversaries, and reshape the global AI infrastructure map. The question worth asking is not whether export controls work. It is who they work for, and who gets left behind.

Chips are the new oil

For most of the 20th century, oil was the resource that shaped alliances, triggered conflicts, and determined which nations could industrialize. Today, advanced semiconductors play an eerily similar role. Nvidia holds roughly 80% of the global market for AI training accelerators. TSMC, a single Taiwanese company, manufactures virtually all of them. Access to these chips determines who can train frontier AI models, build AI infrastructure, and compete in the industries that AI is beginning to reshape. This concentration is not an accident. It is the result of decades of specialization across a supply chain so complex that no single country controls every step, but a small number of chokepoints give outsized power to those who do. The U.S. controls chip design (Nvidia, AMD) and critical EDA tools. The Netherlands controls advanced lithography (ASML). Taiwan controls manufacturing (TSMC). When you can restrict access at any of these chokepoints, you hold a form of leverage that transcends traditional trade policy. The Biden administration recognized this in October 2022, when it imposed sweeping export controls targeting China's access to advanced AI chips and chipmaking equipment. The Trump administration has since shifted the approach, moving from blanket restrictions toward conditional access, effectively turning chip exports into a bargaining tool for broader strategic goals.

Follow the supply chain

To understand who gets affected by export controls, trace the supply chain from silicon to software. It starts with TSMC, which fabricates the physical chips at its fabs in Taiwan and, increasingly, at its new facility in Phoenix, Arizona. These chips are designed by Nvidia (and to a lesser extent AMD), which dominates the market for AI training and inference hardware. The chips then flow to cloud providers, the hyperscalers like Microsoft Azure, Google Cloud, and Amazon Web Services, which buy GPUs in quantities of hundreds of thousands to build out their AI infrastructure. Those cloud providers sell compute to startups and enterprises, who use it to train and deploy AI models. Finally, end users interact with the applications those models power. At each step down this chain, the entity has less purchasing power and fewer alternatives. When export controls create uncertainty about chip supply, the effects are not distributed evenly. They concentrate at the bottom.

Second-order effects: who actually gets squeezed

When AI chips become scarce or access becomes conditional, hyperscalers are the last to feel it. Microsoft, Google, and Amazon have multi-year procurement agreements, dedicated fab capacity, and the capital to stockpile inventory. They also have the engineering talent to design custom silicon (Google's TPUs, Amazon's Trainium chips) as a hedge against GPU shortages. The entities that get squeezed are further down the chain. Mid-sized cloud providers in allied nations face uncertainty about whether they can import enough GPUs to remain competitive. Startups that depend on cloud compute see their costs rise and their capacity constrained. Emerging economies trying to build domestic AI capabilities find the door closing before they even walk through it. This is the structural irony of export controls framed as competition policy: they disproportionately protect the already-powerful. The hyperscalers get preferential access. The incumbents consolidate their positions. And the entities that might have disrupted the market, the smaller players in allied and neutral countries, are the ones who bear the cost of supply uncertainty. The March 2026 proposals make this dynamic explicit. Draft rules reportedly require foreign nations to invest in U.S. AI data centers as a condition for purchasing more than 200,000 chips. This is not a technology restriction. It is a toll road, and only the wealthiest travelers can afford the fare.

The rare earth playbook

This is not the first time a critical resource has been weaponized. The history of rare earth export controls offers a useful, if cautionary, parallel. China controls roughly 60% of global rare earth production and 90% of refining capacity. In 2010, China banned rare earth exports to Japan over a territorial dispute, causing prices to spike by up to 500%. The shock prompted a global scramble to diversify supply chains. The U.S. revived its Mountain Pass mine. Australia expanded production. The WTO eventually ruled China's export quotas illegal in 2014. But the story did not end there. China simply shifted tactics, moving from controlling output volumes to controlling which firms could operate and, more recently, imposing licensing requirements on rare earth exports. In April 2025, China introduced export controls on seven rare earth elements. By October 2025, those controls expanded to cover products made with Chinese-sourced materials, even those manufactured outside China. The lesson is instructive. Export controls on critical resources tend to produce three outcomes: short-term disruption, medium-term diversification efforts, and long-term adaptation by the targeted party. China's response to being denied advanced chipmaking tools follows the same pattern. In the short term, Chinese firms have resorted to large-scale chip smuggling and access to U.S. cloud services. In the medium term, the government has poured billions into domestic semiconductor production. In the long term, firms like DeepSeek have innovated around hardware constraints, producing models that rival U.S. counterparts despite limited chip access. As Chris Miller, author of Chip War, notes, export controls have made China a "marginal producer of AI chips," but they have not prevented Chinese labs from producing highly competitive models. The controls have, however, severely limited China's ability to provide AI infrastructure outside its borders, which may be their most strategically significant effect.

The open-source countercurrent

Here is where the story gets interesting for builders. If access to frontier-scale compute becomes politicized and uncertain, the rational response is to build models that need less of it. And that is exactly what is happening. The open-source AI community has been steadily pushing the frontier of what smaller, more efficient models can achieve. DeepSeek's success is the most visible example, but the trend is broader. Techniques like mixture-of-experts architectures, quantization, distillation, and efficient fine-tuning are making it possible to run capable models on commodity hardware. The tighter the chip supply, the stronger the incentive to optimize for efficiency rather than scale. This creates a fascinating tension. Export controls are designed to preserve the advantage of those with the most compute. But by making compute scarce, they accelerate the push toward models that reduce the importance of compute. If the most capable open-weight models can run on hardware that is not subject to export restrictions, the strategic value of controlling chip access diminishes over time. This does not mean export controls are pointless. Training frontier models still requires enormous compute, and the gap between what can be trained and what can be run on commodity hardware remains significant. But the direction of travel favors efficiency, and export controls may be inadvertently accelerating that trend.

Singapore in the crossfire

For a small, trade-dependent nation like Singapore, chip diplomacy carries particular stakes. Singapore occupies an unusual position in the semiconductor supply chain. One in every ten chips worldwide is produced there. The semiconductor industry contributes nearly 6% of GDP and has attracted over S$18 billion in manufacturing and R&D investments in recent years alone. TSMC affiliate Vanguard International Semiconductor is building a facility there. GlobalFoundries has a $4 billion manufacturing plant. Applied Materials is constructing a new $450 million factory. Singapore has also committed to making semiconductors and AI a key focus of its $37 billion five-year R&D masterplan for 2026 to 2030. The country's National AI Strategy 2.0 positions it as a trusted AI hub in a multipolar world. But trusted by whom? In a world where chip access is conditional on geopolitical alignment, Singapore's traditional posture of strategic neutrality, maintaining strong relationships with both the U.S. and China, becomes harder to sustain. If the U.S. requires allies to invest in American data centers as a condition for chip access, and if China retaliates with rare earth restrictions on nations that comply, Singapore faces a classic small-state dilemma: how to remain useful to both sides without being captured by either. The practical risk is not that Singapore loses its semiconductor industry. It is that the rules governing chip flows become so politicized that planning becomes impossible. For a country whose economic model depends on being a reliable node in global supply chains, unpredictability is the real threat.

Incumbent advantage is real

Zoom out, and the pattern becomes clear. Export controls, whatever their stated rationale, tend to entrench incumbents. The hyperscalers get guaranteed access. The largest chip designers get government backing to build domestic manufacturing. The wealthiest nations get to set the conditions under which others can participate in the AI economy. Meanwhile, the startups, the smaller nations, and the open-source community, the entities most likely to produce disruptive innovation, are the ones navigating the tightest constraints. This is not a critique of export controls as a concept. There are legitimate security reasons to restrict the flow of advanced technology to adversaries. But when controls are designed primarily as leverage, the question of who benefits and who pays deserves honest scrutiny. The risk is that in the name of winning an AI competition, we build a system where only a handful of players are allowed to compete.

Practical takeaways

For builders and startups: Bet on efficiency. The models that thrive under supply constraints are the ones optimized for inference on accessible hardware. Invest in techniques that reduce compute requirements, not just ones that scale with more GPUs. For policymakers in small nations: Diversify your supply chain relationships. Invest in domestic semiconductor capabilities where feasible, and build coalitions with other mid-sized nations facing similar pressures. Singapore's approach of attracting fab investment from multiple sources is a reasonable hedge. For anyone watching this space: Pay less attention to the headline restrictions and more to the conditions attached to chip access. The real policy is in the fine print, the investment requirements, the security guarantees, the infrastructure commitments that determine who gets to build with AI and who does not. The age of chips as a neutral commodity is over. What comes next depends on whether we treat semiconductors as tools for broad-based innovation or as instruments of strategic control.

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