Export Controls as a Battlefield: The Quiet War Over GPUs and Model Weights
11/22/25
By: The Security Nexus
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🧩 Framing the Problem: When Compute Becomes a Sanctioned Asset
Export controls used to be about crates: missiles, machine tools, uranium. In the AI era, the decisive assets are compute capacity and model weights. Both are slippery.
On paper, U.S.-led controls are elegant. Define “advanced computing items,” cap GPU performance and interconnect bandwidth, extend jurisdiction extraterritorially through the Foreign Direct Product Rule (FDPR), and enlist key allies in the Netherlands, Japan, Germany, and South Korea. In theory, that constrains China’s—and others’—ability to train and deploy frontier models.
In practice, we’re fighting on three intertwined fronts:
• a hyper-concentrated upstream tool chain (EDA, fab equipment, specialty chemicals);
• a shadow logistics layer of shell firms and transshipment hubs routing around rules; and
• a software optimization race that squeezes frontier-ish capability from “compliant” GPUs and leaked model weights.
Empirically and theoretically, the risk is the same one we saw in cyber insurance: overconfidence in a neat control lever that sits on top of a complex adaptive system. Heavy-handed controls can slow an adversary—but they can also accelerate network fragmentation, push allies toward substitution, and encourage more aggressive espionage and model theft (Zhang and Zhu 2023; Woods 2025).
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⚖️ The Clause: How GPU Rules and FDPR Became the New Fine Print
In cyber, war-exclusions evolved from boilerplate into a system-level circuit breaker. In chips, the functional equivalent is the export-control clause: how “advanced computing,” “AI training,” and “military end use” are defined in regulation, license guidance, and FAQs.
Three moves matter:
1. Performance-based definitions.
Controls anchor on FLOPs, interconnect bandwidth, and packaging density. Vendors ship “nerfed” parts just under the thresholds—often with more memory—so they remain licensable while still attractive for large-model work (Gupta, Walker, and Reddie 2024).
2. FDPR and extraterritorial reach.
If U.S. IP or tools touched the design or fab process, Commerce claims a say over the final export, even from foreign fabs like TSMC and Samsung. This converts U.S. technological centrality into legal leverage (Alter 2024; Krige 2024).
3. Emerging attention to model weights.
Policymakers are starting to treat some large model weights as controlled “technology,” especially for dual-use or military applications. That’s conceptually tidy and operationally messy: weights are just files. Once copied, they’re nearly impossible to corral.
Strategic consequence: the more expansive and fast-moving these “clauses” become, the more uncertainty they inject into the exact scenarios that matter most for deterrence and resilience. Firms, labs, and allied governments face a moving target in what’s allowed, what might be revoked, and what will be retroactively scrutinized.
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🏭 The Capacity Stack: Where the Real Chokepoints Live
In cyber insurance, reinsurers sit atop the capital stack. In chips, the upper floors are EDA vendors, toolmakers, and leading-edge fabs.
Network analyses of the semiconductor supply chain show:
• Upstream tools and materials form a sparse, highly centralized network; disruptions here propagate widely.
• The United States ranks first in betweenness centrality, anchoring much of the IP, design software, and key equipment leveraged by Asian and European fabs (Zhang and Zhu 2023).
• German “China chokepoint firms” (Siemens, Merck, SÜSS MicroTec, others) are deeply entangled with U.S. tools and markets, creating techno-dependency on Washington’s rule set (Germann et al. 2024).
That’s the structural power story: if Washington and core allies coordinate, they can still meaningfully constrain access to top-end fabs and design flows.
But like reinsurers hiking attachment points, firms are already hedging:
• qualifying non-U.S. suppliers to reduce FDPR exposure;
• exploring new fabs or R&D centers in more permissive jurisdictions;
• quietly lobbying against rules they see as open-ended or politicized (Germann et al. 2024; Krige 2024).
Push too hard, and you don’t just hurt the target; you give allies a reason to design out U.S. dependence over the medium term.
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🛠️ What the System Does Well (and Where It Struggles)
Micro-level, the machinery works better than it gets credit for.
• Licensing officers can and do differentiate between commodity AI workloads and explicitly military programs.
• Banks, freight forwarders, and OEMs run screening tools on counterparties, entity lists, and routing patterns.
• Firms invest in internal export-control teams that coach engineers and sales staff on “red line” customers and configurations.
This is the export-control equivalent of incident-response panels in cyber insurance: rule-of-law states quietly orchestrating a lot of mundane, effective friction.
Macro-level, the cracks look familiar.
• Shadow logistics. As with sanctioned Russian entities sourcing chips via third countries, shell firms in Dubai, Hong Kong, and Southeast Asia can re-label “civilian” components and route them through porous customs regimes (Naumov and Zhiryaeva 2023).
• Optimization around the rule. Chinese labs showing they can train competitive LLMs on export-compliant GPUs by using mixture-of-experts architectures, aggressive quantization, and sharding frameworks (Gupta, Walker, and Reddie 2024).
• Norm-setting by private actors. Just as insurers started defining “cyber war,” chip vendors and cloud providers are now, in effect, defining what counts as “frontier AI,” “high-risk customers,” and “military end user,” often ahead of formal state doctrine.
Where the system shines is ordinary control of ordinary flows. Where it struggles is fat-tailed, system-shaping events: a breakthrough in domestic tools, a major leak of model weights, or a coordinated sanctions-evasion network baked into the fabric of trade.
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🎯 Deterrence Effects: How Chokepoints and Workarounds Change Behavior
1. Defender side (firms and labs).
When advanced GPUs and certain collocations of tools look politically fragile, rational actors hedge. They:
• design models that fit on lower-end, “safe” GPUs;
• prioritize algorithmic and data-efficiency R&D;
• diversify suppliers geographically and juridically.
You don’t just get slowed adversaries; you get more resilient adversaries who are less dependent on Western tech in the long run.
2. Attacker side (sanctioned states and proxies).
Once it’s clear that hardware access is precarious, the relative payoff of espionage, supply-chain compromise, model theft, and insider recruitment increases. Why fight for boxes when you can steal weights? Why build a fab if you can copy a trained model and scale inference on whatever hardware you have?
3. State side (U.S. and allies vs. China and others).
Escalating controls signal intent and resolve—but also escalate the contest itself. Woods’ modeling suggests that persistent escalation from both sides nudges the system toward a structural break: rival techno-blocs with duplicated supply chains and lower overall efficiency (Woods 2025). That’s a very different world than one where the U.S. quietly dominates a shared network.
Deterrence here is double-edged: you can deter some pathways (e.g., buying H100s off the shelf), while incentivizing riskier, less observable pathways (supply-chain infiltration, model exfiltration, indigenous toolchains).
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🧭 A Better Architecture (Policy & Practice)
1️⃣ Narrow, Impact-Based Compute Controls.
Shift from blunt performance ceilings to narrower, impact-linked controls that tie licensing to concrete risk factors:
• model scale and domain (e.g., strategic military applications, advanced bio);
• customer profile (state ties, defense links, sanctions history);
• intended deployment environment.
Use performance thresholds as a screen, not a complete definition of “dangerous.”
2️⃣ Coalition-First Rulemaking.
Treat Germany, the Netherlands, Japan, South Korea, and Taiwan as co-authors, not just addressees, of new rules. Co-design:
• shared entity lists and due-diligence templates;
• minimum attribution and evidence standards for alleging diversion;
• joint review timelines.
The objective is stable, predictable techno-dependency, not perpetual crisis diplomacy.
3️⃣ AI-Enabled Enforcement Graphs.
Steal a page from the supply-chain-risk literature: build shared, ML-based anomaly-detection systems across customs, banks, and freight forwarders (Mittal and Panchal 2023). Score:
• unusual routing patterns;
• improbable combinations of firms, HS codes, and payment structures;
• sudden spikes in sensitive-category imports from permissive jurisdictions.
Use these scores to target audits, license reviews, and intelligence collection.
4️⃣ Upstream Resilience and Red Teaming.
Invest in the health of the chokepoints you rely on:
• R&D subsidies and workforce programs for EDA, metrology, and lithography;
• export-control red-teaming to map how rules might be gamed, with participation from industry, intel, and academia;
• stress tests on scenarios where a key allied toolmaker defects, is acquired, or faces coercion.
5️⃣ Targeted Governance for Model Weights.
Reserve the harshest treatment for a very small set of high-risk models—those that dramatically lower barriers to catastrophic misuse. For them, explore:
• licensing for training and distribution;
• secure training environments and logging requirements;
• international norms on weight-sharing and watermarking.
Avoid trying to treat all models like missiles. That way lies either failure or a self-inflicted wound on innovation.
References:
Alter, Karen J. 2024. “U.S. Export Controls Across Time: Knowledge, Technology, and China.” American Journal of International Law 118 (1): 1–23.
Germann, Julian, Steve Rolf, Joseph Baines, and Sean Kenji Starrs. 2024. “A Chip War Made in Germany? US Techno-Dependencies, China Chokepoints, and the German Semiconductor Industry.” Politics and Governance 12 (1): Article 8265.
Gupta, Ritwik, Leah Walker, and Andrew W. Reddie. 2024. “Whack-a-Chip: The Futility of Hardware-Centric Export Controls.” Working paper, AI Frontiers Initiative.
Krige, John. 2024. “Debate: Building a U.S. Regulatory Empire in the Chip War with China.” Technology and Culture 65 (4): 1081–1108.
Mittal, Utkarsh, and Dilbagh Panchal. 2023. “AI-Based Evaluation System for Supply Chain Vulnerabilities and Resilience Amidst External Shocks: An Empirical Approach.” Reports in Mechanical Engineering 4 (1): 276–289.
Naumov, Vladimir N., and Elena V. Zhiryaeva. 2023. “Assessing the Impact of Technological Sanctions on Computer Equipment Imports.” RUDN Journal of Economics 31 (2): 350–369.
Woods, Dwayne. 2025. “Escaping a Weaponized Network: China’s Reaction to the United States Escalating Technology Controls.” Asian Review of Political Economy 4 (5).
Zhang, Yongli, and Xianduo Zhu. 2023. “Analysis of the Global Trade Network of the Chip Industry Chain: Does the U.S.–China Tech War Matter?” Heliyon 9 (e17092).
“‘The Quiet War for AI Chips.’” Deep Dive podcast transcript. The Security Nexus, 2025.
