AI's infrastructural consolidation is creating geocognitive power poles; full-stack sovereignty is unrealistic, so states should build institutional capacity to govern structured dependence rather than chase autonomy. The paper proposes a 'Governed Interdependence' paradigm and a Governance Membrane (with compliance and dependence indices) as a practical framework for managing asymmetric AI infrastructures.

Main Finding

The paper argues that AI is reshaping the global digital order into a cognitive-informational system dominated by a few integrated technological ecosystems — "geocognitive power poles." Under this structural concentration, full-stack AI sovereignty is infeasible for most states. The author proposes the Governed Interdependence (GI) paradigm: digital sovereignty should be reframed as institutional capacity to govern structured participation in globally distributed AI infrastructures rather than as technological autarky. To operationalize GI, the paper presents a reference Governance Membrane (GM) architecture and three instruments (Normative Compliance Model, Infrastructure Status Index, Cognitive Dependence Index), with a sovereign SLM+RAG configuration offered as an example pathway where embedded governance is possible.

Key Points

• Cognitive-informational order: AI systems are becoming embedded cognitive infrastructures that shape how knowledge is generated, interpreted, and operationalized within institutions.
• Geocognitive power poles: Concentration of (1) hyperscale compute, (2) frontier model architectures, and (3) global platform ecosystems around dominant actors (e.g., AWS+Anthropic, Azure+OpenAI, Google+DeepMind, Alibaba+Tongyi, plus European initiatives) creates jurisdiction-like influence over institutional reasoning.
• Dual-layer digital sovereignty:
  • Institutional sovereignty: capacity to regulate, negotiate, and structure participation in global AI infrastructures.
  • Epistemic sovereignty: capacity to govern the data, knowledge bases, norms, and contextual use of externally developed reasoning capabilities. Both layers are interdependent; neither alone secures effective sovereignty.
• Governed Interdependence (GI): A normative and operational shift from pursuing full technological autonomy toward institutionally governed participation that manages dependence.
• Governance Membrane (GM): A reference architecture that mediates between external AI systems and domestic governance, embedding instruments for alignment and diagnostics.
• Instruments:
  • Normative Compliance Model (NCM): alignment/compliance mechanism for externally sourced AI capabilities.
  • Infrastructure Status Index (ISI): diagnostic of domestic infrastructural depth and readiness.
  • Cognitive Dependence Index (CDI): diagnostic measuring epistemic dependence on external reasoning architectures.
• Sovereign SLM+RAG: An illustrative embedded-mode instantiation combining a sovereign model (SLM) with retrieval-augmented generation (RAG) to localize reasoning while leveraging external capabilities.
• Policy context: Builds on emerging policy recognition that full-stack sovereignty is infeasible (e.g., EU CADA preparatory work, UN Global Digital Compact) and complements recent proposals for managed/strategic interdependence.

Data & Methods

• Research approach: Theory-building using Design Science Research (DSR) in the design-theory tradition (Gregor & Jones). The paper is primarily conceptual and prescriptive.
• Evidence base: Extensive literature review across platform capitalism, data colonialism, STS, and recent AI governance/policy reports; illustrative country-case mappings (used for demonstration, not empirical validation).
• Quantitative anchor: Industry projection cited — leading hyperscalers expected to commit approximately $650–700 billion CAPEX in 2026, mostly for AI infrastructure — used to motivate scale-based barriers.
• Outputs: Conceptual constructs (cognitive-informational order, geocognitive power poles), a governance paradigm (GI), a reference architecture (GM), and three operational instruments (NCM, ISI, CDI). The sovereign SLM+RAG is presented as a design-oriented instantiation.

Implications for AI Economics

• Market structure and rents
  • Compute, model-architecture, and platform concentration creates strong economies of scale and scope, reinforcing dominant incumbents and increasing market power. This raises persistent barriers to entry and generates supracompetitive rents tied to control of cognitive infrastructure.
• Pricing and access to key inputs
  • Hyperscale CAPEX concentration implies pricing power over compute and storage inputs. States and firms that cannot internalize these inputs face higher transaction and access costs, influencing comparative advantage and industrial competitiveness.
• Dependency externalities and bargaining asymmetries
  • Epistemic dependence (captured by CDI) creates asymmetric negotiation positions for small and medium-sized states (SMS) in procurement, data-sharing agreements, and regulatory talks. Dependence can translate into lock-in externalities that raise long-term economic vulnerability.
• Innovation diffusion and knowledge accumulation
  • Dominant geocognitive poles both accelerate global knowledge diffusion (through widely used services) and standardize epistemic priors, which can crowd out local model development and domain-specific innovation unless governed. Economies with weak institutional sovereignty risk importing not just tools but embedded norms and economic logics.
• Trade, industrial policy, and strategic investment
  • The infeasibility of full-stack sovereignty shifts policy from subsidizing domestic full-stack replication toward designing institutional bargaining, alliance-building, and targeted investments (e.g., sovereign data stores, RAG-enabled local models) to maximize local spillovers per dollar spent.
• Cost–benefit of domestic infrastructure vs. governed interdependence
  • Given scale economies, the marginal cost of matching frontier capabilities is often prohibitive for SMS. The GI paradigm implies optimizing institutional investments (e.g., ISI-informed prioritization) to obtain critical epistemic control where it matters economically, while accepting governed access for other layers.
• Regulatory economics and market design
  • The Normative Compliance Model and governance membrane create demand for certification, compliance services, and intermediaries that can become new domestic markets (or sources of regulatory rent). Regulators may need to design market interventions (subsidies, standards, interoperability requirements) to reduce dependence externalities and promote contestability.
• Allocation of human capital and specialization
  • High centralization of frontier R&D concentrates specialized human capital and raises migration/brain-drain pressures. SMS should consider policies that combine co-development partnerships and targeted talent retention to capture downstream economic value.
• Fiscal and macro implications
  • Large-scale external dependence may require recurrent fiscal commitments (procurement, subscription/licensing fees, compliance costs). Conversely, targeted sovereign capabilities (e.g., SLM+RAG for critical sectors) can yield asymmetric returns by protecting high-value institutional decision-making and reducing systemic exposure.
• Measurement and policy targeting
  • Operational indices (ISI, CDI) are potentially valuable policy tools: they permit sector-level measurement of infrastructural readiness and epistemic dependence, enabling more efficient allocation of scarce public funds and clearer assessments of economic exposure to geocognitive poles.
• Strategic alliances and multilateral bargaining
  • Economic strategy should emphasize coalition formation (regional clouds, shared sovereign models, standards bodies) to aggregate demand, lower unit costs, and increase bargaining leverage vis-à-vis dominant providers.

Practical takeaway for economists and policymakers: treat AI infrastructure not just as an input market problem (compute, data) but as an epistemic-institutional problem where market power, dependency externalities, and normative alignment jointly determine economic outcomes. Policy instruments should combine targeted infrastructure investments, governance architectures (e.g., GM), index-based diagnostics (ISI/CDI), and alliance-building to maximize domestic economic returns under realistic cost constraints.