Data intelligence platform Tracxn has released its latest report, “Who Controls AI Infrastructure: Compute as the Next Frontier,” detailing the hidden layers of power governing the global AI engine.
To unpack this global power dynamic, the report introduces a three-layer diagnostic framework:
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Layer 1 (Territorial Control): The physical location of data centers.
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Layer 2 (Ownership Control): The cloud platform software.
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Layer 3 (Chip Control): The underlying hardware.
According to Tracxn, each subsequent layer is significantly harder to achieve and confers a more foundational level of power over the broader AI stack.
The hardware and cloud imbalance
The data highlights a profound global imbalance at the most critical layers of compute. The United States currently leads global chip development with 101 companies and $10.9billion in equity funding. This represents more than triple the $3billion raised by China’s 40 companies (when excluding state funds). When removing government subsidies from the equation, every other country remains a distant third.
Furthermore, a consistent structural pattern has emerged across regions where chip strength and cloud strength do not move in tandem. For example, India boasts the highest number of cloud companies outside the US but minimal chip capital. Conversely, Israel features the most chip funding per company but has a minimal cloud presence.
The ‘Territorial’ investment trap
The report highlights a major misconception in current national tech strategies: while many countries have successfully established a Territorial Presence (Layer 1) by hosting physical data centers, they largely lack the funded chip and cloud ecosystems required for true technological independence.
Consequently, Tracxn notes that infrastructure investment and actual control are not the same thing. A country can spend billions on physical data centers and remain entirely dependent on the upper layers that determine who truly governs AI at scale.
Despite this reality, nations are currently investing the most capital where they are least behind. Territorial Presence dominates most national policy announcements, yet the real strategic gaps and vulnerabilities remain at Layers 2 and 3.
The roadmap for ‘Calibrated Dependency’
The report concludes that for most governments, attempting to build full-stack AI sovereignty is an unrealistic near-term goal due to the 10-15 year development timelines required for hardware.
Instead, Tracxn outlines “Calibrated Dependency” as the most pragmatic and underused strategy. This approach involves:
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Routing sensitive or highly critical workloads to domestic or alternative providers to maximize national control.
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Keeping standard commercial tasks on global hyperscalers to maintain performance and cost-efficiency.
Ultimately, this allows a nation to successfully balance security and performance, provided they can clearly classify which specific workloads genuinely require sovereign-level control.
