The Illusion of AI Sovereignty: US Dominance Exposes a Global ‘Control Gap’
A new report from entity[“company”,”Tracxn”,”data intelligence platform”] is challenging conventional narratives around AI sovereignty, revealing that true control over artificial intelligence infrastructure remains concentrated in a handful of countries—led overwhelmingly by the United States.
Titled “Who Controls AI Infrastructure: Compute as the Next Frontier,” the report outlines a structural imbalance in how nations build and control the foundational layers of AI, exposing what it describes as a global “control gap.”
A Three-Layer Framework of AI Power
To better understand the distribution of influence, Tracxn introduces a three-layer diagnostic model:
- Layer 1: Territorial Control – Physical location of data centers
- Layer 2: Ownership Control – Cloud platforms and software ecosystems
- Layer 3: Chip Control – Semiconductor hardware powering AI systems
Each successive layer represents a deeper level of control, with hardware (chips) forming the most critical—and most difficult to develop—foundation of the AI stack.
Hardware and Cloud: A Deepening Imbalance
The report highlights a stark global disparity at the most strategic layers of compute.
The entity[“country”,”United States”,”country”] leads decisively in semiconductor innovation, with 101 chip companies raising approximately $10.9 billion in equity funding. By comparison, entity[“country”,”China”,”country”] has 40 companies raising around $3 billion (excluding state-backed funding), while other nations trail significantly behind.
Beyond funding gaps, the report identifies a structural disconnect between cloud and chip capabilities across regions:
- entity[“country”,”India”,”country”] has a strong cloud ecosystem but limited chip investment
- entity[“country”,”Israel”,”country”] demonstrates high chip funding per company but minimal cloud infrastructure
This divergence suggests that few countries possess balanced capabilities across both critical layers.
The ‘Territorial’ Investment Trap
A key finding is that many national AI strategies are misaligned. Governments are heavily investing in data centers—achieving Layer 1 (Territorial Control)—while lacking meaningful capabilities in cloud platforms and semiconductor development.
This creates what Tracxn describes as an “illusion of sovereignty.” While countries may host physical infrastructure domestically, control over AI systems often resides elsewhere—within foreign-owned cloud platforms or chip technologies.
In effect, infrastructure investment does not equate to operational control.
Rethinking Sovereignty: The Case for ‘Calibrated Dependency’
Given the immense cost and long timelines—often 10 to 15 years—required to build full-stack AI capabilities, the report argues that true sovereignty is an unrealistic short-term goal for most nations.
Instead, Tracxn proposes a more pragmatic strategy: “Calibrated Dependency.”
This model advocates for a selective approach:
- Sensitive workloads (e.g., national security, critical infrastructure) should be routed through domestic or trusted providers
- Standard commercial workloads can continue to run on global hyperscalers to ensure efficiency and performance
By clearly distinguishing between critical and non-critical use cases, governments can balance sovereignty concerns with economic practicality.
A Shift from Control to Strategy
The report ultimately reframes the AI sovereignty debate. Rather than striving for complete independence—a costly and often unattainable goal—countries must focus on strategic control over key functions within the AI stack.
As global competition intensifies, the ability to intelligently manage dependencies may prove more valuable than attempting to eliminate them altogether.
In this emerging landscape, the real measure of power is not where data centers are built—but who controls the layers that make AI systems run.
