On Sovereign Compute

In the world of compute, a lot has changed in the last twelve months. The U.S. imposed, and then reversed, an export ban on chip design software within a six week span. China retaliated against semiconductor restrictions by freezing rare earth shipments. Over 100 countries signed a declaration committing to AI sovereignty. And governments across the world collectively pledged hundreds of billions of dollars to build domestic chip capacity they don’t yet have. Clearly, the compute landscape is changing fast.

What ties all of this together is a single underlying shift. Compute, the GPU infrastructure that powers artificial intelligence, is no longer just a commercial input. It is now a sovereign commodity. Importantly, this isn’t unprecedented — it follows a pattern that has repeated across many commodities as soon as countries begin viewing them as critical to sovereignty.

Indeed, when a critical resource becomes geopolitically contested, governments move to secure access, and the market structure around that commodity is permanently reshaped. Oil followed this arc in the 1970s, rare earths followed it in the 2010s, and liquefied natural gas followed it over two decades. Compute is following the same pattern right now.

Compute is Now a Sovereign Resource

Regardless of political system, ideology, or geography, governments are converging on the conclusion that compute is a strategic reserve. The policies driving this shift span the semiconductor industry broadly, but the most consequential battleground is advanced AI chips — the GPUs and accelerators that power machine learning, and the memory and packaging infrastructure required to build them.

In the U.S., the CHIPS and Science Act appropriated $52.7 billion to boost domestic semiconductor manufacturing, including $39 billion in direct manufacturing subsidies and a 25% investment tax credit. Simultaneously, export controls on advanced AI chips to China have been tightened across successive rounds since October 2022. The Biden administration proposed the AI Diffusion Rule in January 2025, which the Trump administration rescinded before replacing it with a case-by-case licensing framework in January 2026, allowing limited H200 exports under a 25% fee and security review structure. Enforcement has escalated in parallel — in early 2026, the Bureau of Industry and Security (BIS) reached a $252.5 million settlement with Applied Materials for unauthorized semiconductor equipment transfers to China, while the DOJ announced Operation Gatekeeper, disrupting over $160 million worth of AI chip exports. In support of this, Congress approved a 23% increase in the Bureau of Industry and Security’s enforcement budget for the 2026 fiscal year.

China, in response, is accelerating its drive toward semiconductor self-sufficiency, backed by tens of billions in state-directed investment funds. Researchers at Peking University announced a 2D transistor operating 40% faster than TSMC’s 3nm devices, and DeepSeek demonstrated frontier-level AI capabilities built with constrained compute access. The European Union responded to both the U.S. and China as well, mobilizing roughly €43 billion through its EU Chips Act to double Europe’s share of global chip production by 2030.

The pattern extends well beyond the major global powers — Canada has announced a $2 billion sovereign compute strategy, India is building a national compute pool exceeding 38,000 GPUs, and Japan has committed ¥10 trillion in AI infrastructure investment through 2030. Additionally, Saudi Arabia, the UAE, France, and Germany have each launched sovereign AI infrastructure programs. And, as mentioned, over 100 countries adopted a declaration to pursue AI sovereignty at the AI for Developing Countries Forum in February.

The convergence on sovereign compute is unmistakable.

The Concentration Problem

Governments are increasingly viewing compute as a critical commodity because the AI chip supply chain is concentrated at every major chokepoint to a degree that is, at the very least, historically unusual for any commodity of this importance.

TSMC fabricates roughly 90% of the world’s most advanced logic chips — that is, chips at the sub-5nm nodes that power AI training and inference — while commanding close to a 75% share of the overall foundry market. Nearly all of this capacity sits in Taiwan, a small island roughly 100 miles off the coast of mainland China that both the U.S. and China consider strategically vital.

The risks of concentration are not theoretical. A confidential report commissioned in 2022 by the Semiconductor Industry Association and prepared by McKinsey found that cutting the supply of AI chips from Taiwan would trigger the largest economic crisis since the Great Depression, with U.S. economic output falling roughly 11% and China’s declining 16%. Bloomberg Economics estimated the global cost of conflict in Taiwan at over $10 trillion. Senior U.S. intelligence officials, including the CIA director and the Director of National Intelligence, have delivered classified briefings to the CEOs of Apple, NVIDIA, AMD, and Qualcomm specifically about this risk. And, in public, Treasury Secretary Scott Bessent has called Taiwan’s chip concentration “the single biggest point of single failure” for the world economy.

Importantly, chip fabrication is not the only chokepoint. High Bandwidth Memory (HBM), which has become the main price-setting input for compute (accounting for over 60% of the production cost of NVIDIA’s B200 GPU), is produced by just three companies: Samsung, Micron, and SK Hynix. At least one of these major suppliers has confirmed that its entire 2026 HBM output is already fully priced and volume-locked, even as the HBM market is projected to nearly triple from $35 billion in 2025 to $100 billion by 2028. As such, because supply is concentrated, pre-committed, and repriced at discrete rather than continuous contract boundaries, the conditions for sudden cost shocks in deploying compute at scale are present.

Therefore, a single disruption at any one of these nodes would cascade through the entire global compute supply chain within weeks, if not days. Historically, this level of geographic and supplier concentration for commodities that matter to national security has always triggered sovereign action. Compute will not be an exception.

From Embargo to Exchange

The pattern compute is currently entering — one in which critical commodities become geopolitically contested, governments intervene, and market structures thus transform — is nothing novel. Many historical episodes follow this pattern, but three modern examples illustrate it especially well.

[1] Oil, 1970s-80s. Before 1973, global oil pricing was controlled by a small group of major companies — the so-called “Seven Sisters” — through long-term bilateral contracts. Pricing was opaque and relationship-driven. Then, the 1973 Arab oil embargo began, weaponizing supply and causing prices to quadruple almost overnight. The crisis exposed how vulnerable the global economy was to a commodity controlled by a small number of stakeholders willing to use supply as a political instrument.

Within a decade, the NYMEX WTI crude futures contract launched, fundamentally changing how oil was priced globally. Before futures existed, industry participants argued that oil was too complex and relationship-driven for standardized financial products. But the oil crisis proved otherwise — when price volatility became intolerable, financial infrastructure emerged as a necessity.

[2] Rare Earth Minerals, 2010. China controlled approximately 97% of global rare earth production when, in 2010, it restricted exports following a diplomatic dispute with Japan. As a result, prices of key rare earth elements spiked by a factor of ten or more within months. Geographic concentration of supply in a single jurisdiction had made the entire global supply chain vulnerable to a political decision by one government. This crisis triggered national stockpiling programs worldwide and diversification efforts, including new mining operations in the U.S. and Australia.

The rare earth crisis demonstrated that even commodities that most people have never heard of can become flashpoints when supply is concentrated enough, and that once governments recognize this vulnerability, the response is swift, expensive, and permanent. The parallel to compute is striking.

[3] Liquefied Natural Gas, 2000s-2020s. For decades, LNG was traded exclusively through long-term bilateral contracts — 20-year deals negotiated behind closed doors, with pricing often indexed to oil. There was no transparent spot market and no independent benchmark. As a result, buyers were locked in with minimal flexibility.

Starting in the 2010s, oversupply from U.S. shale gas production and new export terminals, combined with buyer resistance to rigid contract structures, drove the gradual emergence of spot trading and eventually benchmark pricing with the entrance of the Japan Korea Marker (JKM) in Asia and the Title Transfer Facility (TTF) in Europe. Importantly, the catalyst for LNG’s financial evolution was, unlike oil and rare earths, not a single crisis but a structural change — new supply entrants and buyers who demanded flexibility broke the old model. In other words, financial infrastructure came to the LNG market because it had grown too large and too important to operate without transparent price discovery.

The throughline across all three of these historical examples is consistent. Whenever nations begin contesting a critical commodity, supply volatility spikes and governments intervene in the market to secure sovereign access. That intervention compounds price volatility through stockpiling, trade restrictions, and fragmented markets until financial infrastructure becomes a necessity. And while the timeline often varies — oil’s transformation took a decade, LNG’s took two, and rare earths triggered an immediate scramble — the endpoint is always the same: benchmarks, price discovery, and risk-transfer mechanisms emerge because the market cannot function without them.

Compute’s Missing Market

Compute is now well into this arc. Governments are already restricting trade through escalating export controls, stockpiling capacity through national GPU reserves, and subsidizing domestic production through the CHIPS Act, EU Chips Act, and sovereign compute programs spanning dozens of other countries. Moreover, the sources of volatility are only multiplying and becoming increasingly driven by governments rather than free markets. Entire national markets can be opened or closed to specific chip products on the basis of a single government decision.

But unlike oil in the early 1980s or LNG in the 2010s, compute’s financial infrastructure barely exists right now. There is no universally trusted transparent benchmark for the price of a GPU-hour,¹ no liquid hedging market, and no standardized mechanism for managing the price risk that comes from supply disruptions.

Thus, the gap between how geopolitically contested compute has become and how underdeveloped its financial infrastructure remains is historically unusual. In every other commodity that followed this arc, financial tools (i.e. benchmarks, futures, hedging instruments) emerged in direct response to exactly this kind of volatility. If anything, compute is behind schedule.

Implications for Market Participants

For data center operators and AI companies, sovereign compute policies are fragmenting the global market. Compute pricing increasingly varies by jurisdiction, not just by hardware and provider. And when an export control decision can shift overnight, budgeting and procurement become exercises in geopolitical risk management, not just supply chain logistics.

For lenders and investors financing AI infrastructure, geopolitical risk is now embedded in the collateral. A GPU cluster’s economics can change not because of technology cycles but because of a trade policy shift. This is a category of risk that traditional equipment finance and infrastructure lending frameworks were not built to capture.

Lastly, for the market more broadly, the price of compute in one jurisdiction may diverge sharply from another as supply becomes politicized and fragmented across national boundaries. And, in this environment, independent benchmarks and risk-transfer mechanisms become more valuable, not less.

Looking Forward

Compute is following a well-documented historical arc. When nations begin treating a commodity as a matter of sovereignty, price volatility increases, supply becomes less predictable, and the absence of financial infrastructure goes from inconvenient to untenable. Compute is going through this now, but faster, because the capital deployed is larger, the supply chain is more concentrated, and the geopolitical stakes are arguably higher.

Ultimately, the structural forces — everything from export controls to sovereign buildouts — are already in motion and accelerating on every front. As such, the question now is not whether the financial infrastructure for compute will emerge, but whether it will emerge fast enough to keep pace with the geopolitical forces reshaping the market.

Selected Sources:

• “Chip Design Software Makers Win US Reprieve in China Trade Deal” (Bloomberg)

• “How China’s new rare earth export controls work” (Reuters)

• “Statement on Inclusive and Sustainable Artificial Intelligence for People and the Planet” (AI Action Summit)

• “Government Chip Funding Spreads Globally” (Semiconductor Engineering)

• “H.R.4346 - CHIPS and Science Act” (U.S. Congress)

• “U.S. escalates tech battle by cutting China off from AI chips” (CNN)

• “Framework for Artificial Intelligence Diffusion” (Federal Register)

• “Department of Commerce Announces Rescission of Biden-Era Artificial Intelligence Diffusion Rule, Strengthens Chip-Related Export Controls” (Bureau of Industry and Security)

• “BIS Announces $252.5 Million Settlement with Applied Materials over Alleged Unauthorized Reexports to China” (Arnold & Porter)

• “DOJ Announces Shutdown of Major China-Linked AI Tech Smuggling Network Through Operation Gatekeeper” (Arnold & Porter)

• “Overview of FY2026 Appropriations for Commerce, Justice, Science, and Related Agencies” (U.S. Congress)

• “China’s new silicon-free chip beats Intel with 40% more speed and 10% less energy” (Interesting Engineering)

• “China is running multiple AI races” (Brookings Institution)

• “EU Chips Act” (European Union)

• “Canadian Sovereign AI Compute Strategy” (Government of Canada)

• “India’s AI Moment” (Government of India)

• “Japan to roll out $65bn in support for chips, AI” (Nikkei Asia)

• “How Taiwan secured semiconductor supremacy – and why it won’t give it up” (The Guardian)

• “TSMC’s Foundry Market Share Estimated To Reach 75 Percent In 2026...” (WCC Technologies Group)

• “The Looming Taiwan Chip Disaster That Silicon Valley Has Long Ignored” (The New York Times)

• “The $10 Trillion Fight: Modeling a US-China War Over Taiwan” (Bloomberg)

• “NVIDIA’s B200 costs around $6,400 to produce, with memory accounting for half” (Epoch AI)

• “Micron’s Sold Out 2026 HBM And US$200b Bet On AI Demand” (Yahoo Finance)

• “Financial results FQ1 2026” (Micron Technology)

• “Oil Prices and the Global Economy: Is it Different this Time Around?” (Center for Economic Studies and Ifo Institute)

• “Oil Shock of 1973-74” (Federal Reserve History)

• “How WTI Became the Most Important Commodity Contract on the Planet” (CME Group)

• “Strategic Implications of China’s Consolidation of Rare Earth Industries” (Jamestown)

• “How Japan solved its rare earth minerals dependency issue” (World Economic Forum)

• “Securing Supply of Heavy Rare Earths to Japan with Additional Investment to Lynas” (Japan Organization for Metals and Energy Security)

• “LNG 101: Exploring the Curious Case of Oil-Indexed LNG Contracts” (Natural Gas Intelligence)

• “LNG In Transition: From Uncertainty To Uncertainty” (Oxford Institute for Energy Studies)

• “How Currency Manipulation Affects Global Cloud Computing Prices” (Medium Corporation)

Contributors: Ornn, Ben Ali Brown

1. Ornn’s indices are the first attempts at these benchmarks. See them at data.ornnai.com