In the algorithmic age, mastery of chips, AI and cloud—not solely territory or armies—determines state power; the U.S.-China tech competition is reshaping global polarity into a techno-structural contest.

Basil Muhsin Muhanna (2026). "Algorithmic Sovereignty and the Reconfiguration of International Power: The U.S.-China Rivalry as a Structural Model." Majallat al-Maʿhad. DOI: https://doi.org/10.61353/ma.0240472

Main Finding

Sovereignty in the twenty‑first century is increasingly embedded in computational infrastructures (semiconductors, AI systems, cloud/digital infrastructures, telecommunications standards). The distribution of algorithmic capabilities — not only territory or conventional military power — now structures international hierarchy. The U.S.–China technological rivalry exemplifies how control of strategic digital value chains becomes a source of systemic power, deterrence, and leverage, producing alternative futures (digital unipolarity, technological bipolarity, distributed multipolarity).

Key Points

• Conceptual shift: the paper traces sovereignty across historical stages (Westphalian territorial, industrial/resource, nuclear deterrence, digital sovereignty) and defines a new stage — algorithmic sovereignty — based on control of computational decision‑making systems.
• Algorithmic Structural Realism: extends structural realism to treat algorithmic capabilities as core determinants of systemic position and power distribution.
• Strategic value chains: focuses on three interlocking domains where algorithmic sovereignty is produced and contested:
  • Advanced semiconductors (fabrication, design, supply chains).
  • Cloud computing and hyperscale digital infrastructure (data centers, HPC/TPU capability).
  • Telecommunications standards (5G/6G, protocols, interoperability).
• Mechanisms of algorithmic deterrence and coercion: export controls, technology sanctions, supply‑chain weaponization, standards‑setting authority, and control over critical infrastructure.
• Scenarios: constructs three variable‑based futures depending on trajectories in semiconductor dominance, AI innovation leadership, standards regulation, and alliance formation:
  • Digital unipolarity (one actor dominates algorithmic stack).
  • Technological bipolarity (two blocs centered on rival capabilities).
  • Distributed digital multipolarity (fragmented, regionally resilient ecosystems).
• Core argument: the ability to deny, degrade, or commandeer digital infrastructures becomes a central balancing mechanism in global politics.

Data & Methods

• Methodological approach: qualitative, analytical, and theoretical extension of structural realism.
• Comparative case study: systematic examination of the U.S.–China technological rivalry across semiconductors, AI/cloud infrastructure, and telecom standards.
• Historical tracing: situates algorithmic sovereignty within the genealogy of sovereignty (Westphalia → industrial → nuclear → digital → algorithmic).
• Futures/scenario analysis: constructs normative/analytic scenarios based on key variables (chip production, AI leadership, standards control, alliances).
• Empirical basis: the paper is primarily conceptual and descriptive; it synthesizes existing literature, policy developments, and observed strategic actions (e.g., export controls, investment patterns) rather than presenting new quantitative datasets.

Implications for AI Economics

• Market structure and rents:
  • Concentration risk: dominance in chips, cloud infra, or standards creates durable economic rents for incumbent firms/nations (higher barriers to entry, sustained market power).
  • Vertical/value‑chain capture: control over upstream inputs (advanced semiconductors) translates into downstream advantage in AI product markets.
• Investment and innovation incentives:
  • Shifts in allocation: public and private capital will increasingly prioritize onshore chip fabs, sovereign cloud capacity, and standards R&D — raising capital intensity of frontier AI.
  • Strategic subsidies and industrial policy: governments may subsidize semiconductor and AI capability to secure algorithmic sovereignty, altering global comparative advantage.
• Trade, fragmentation, and transaction costs:
  • Fragmentation risk: technological bipolarity or multipolarity implies standard and regulatory divergence, increasing compliance and switching costs for multinational firms.
  • Impact of export controls: sanctions and controls alter input prices, delay deployment cycles, and reshape global production networks — relevant for modelling supply elasticity and risk premia.
• Diffusion and inequality:
  • Slower global diffusion of frontier AI if compute and chips are concentrated, potentially increasing productivity divergence across countries and firms.
  • Developing economies may face higher costs to access leading AI capabilities, reinforcing digital dependence.
• Firms’ strategic behaviour:
  • Firms will internalize geopolitical risk in location, supplier choice, and IP strategies (reshoring, diversification, alliances).
  • Platform/cloud providers may become quasi‑geopolitical actors; their pricing and contractual terms will influence national algorithmic capacity.
• Empirical indicators and measurement opportunities for AI economics:
  • Operationalize algorithmic sovereignty using measurable variables: semiconductor fabrication capacity (nm nodes), share of global chip output, cloud market share (regionally), TPU/HPC capacity (exascale FLOPS), frontier AI publications/patents, cross‑border data flow restrictions, incidents of supply‑chain controls.
  • Incorporate geopolitical shocks (export controls, sanctions) into econometric models to estimate impacts on AI adoption, prices of compute, R&D outcomes, and productivity growth.
• Policy and welfare tradeoffs:
  • Protectionist measures to secure algorithmic sovereignty may protect domestic capabilities but raise global inefficiencies; economists should model trade‑offs between security externalities and allocative efficiency.
  • Designing international cooperation on standards and secure supply chains can mitigate fragmentation costs while addressing strategic concerns.
• Research directions:
  • Quantify the relationship between compute availability and national productivity growth.
  • Model how supply‑chain disruptions (e.g., export controls) propagate into AI model development costs and market structure.
  • Study how different scenarios (unipolarity, bipolarity, multipolarity) affect global welfare, inequality, and innovation diffusion.

Limitations noted in the paper: largely theoretical and qualitative; empirical validation requires systematic measurement of the proposed variables (chips, compute, standards power, alliances) and causal testing of their effects on systemic hierarchy.

If you want, I can: - Draft a short set of measurable indicators and datasets to operationalize the paper’s variables for empirical analysis. - Produce an outline for an econometric study testing the impact of export controls on AI compute prices and model development.