Dependence on Hegemonic Tech: A Threat to Small-State AI Sovereignty

Small states can build AI that genuinely reflects their languages, cultures, and values — but only if they retain control.

This article examines the risks to digital sovereignty posed by overdependence on foreign platforms and argues that open-source, context-specific models are crucial to an ethical and autonomous future of AI.

Ed Zitron’s recent critique (see also an excellent explainer of the whole Cursor thing here) of the disproportionate power wielded by dominant AI providers such as OpenAI and Anthropic highlights a broader, more insidious structural concern — namely, the increasing enmeshment of national digital infrastructures with privately owned, foreign-controlled artificial intelligence systems. For small states and microstates, this dependency is not merely a matter of operational convenience or technological procurement; it strikes at the very heart of digital self-determination, institutional autonomy, and the long-term viability of sovereign governance in a computational age.

As national sectors ranging from healthcare and education to judicial systems and public administration begin to embed AI across their core functions, the technologies facilitating this integration are typically closed-source, opaque, and developed in cultural and economic contexts far removed from those in which they are ultimately deployed. The asymmetry of power between those who create these tools and those who must adopt them manifests not only in practical constraints but also in structural dependencies. These systems carry embedded assumptions, normative frameworks, and economic imperatives that may not align with the values or needs of the communities where they are implemented.

This dynamic is increasingly discussed in terms of AI colonialism — a phenomenon wherein dominant computational models, trained largely on data derived from powerful economies and monolingual information sources, are exported to regions with vastly different cultural, legal, and epistemological foundations. These models often reinforce hegemonic narratives and obscure their biases under the guise of neutrality, diminishing local agency and marginalising knowledge systems that fall outside their representational scope. The result is not only technical misalignment but also ethical erosion — where systems designed without meaningful local input shape decisions with life-altering consequences.

Moreover, Zitron’s recent work highlights a particularly acute and emerging risk: the monopolistic market power of large AI providers not only enables them to dictate the technical architecture and ethical assumptions of generative systems but also to dominate pricing structures in ways that actively destabilise downstream innovation. Companies like OpenAI and Anthropic have begun to employ what can only be described as extractive pricing strategies, with little regard for the economic sustainability of the ecosystems built upon their platforms. Anthropic’s recent decision to raise prices so dramatically for its largest customer — the AI-assisted coding startup Cursor — has reportedly forced the latter to overhaul its entire business model, potentially spelling the end of the company. In this way, foundational AI providers behave less like neutral utilities and more like rent-seeking monopolists: able to shift terms unilaterally, externalise risk, and undercut the viability of smaller players.

The European Union has begun to acknowledge the strategic vulnerabilities introduced by excessive dependence on external AI and cloud providers. Through projects such as EuroStack and Open Euro LLM, an emergent effort is underway to construct a federated, values-aligned digital infrastructure that can safeguard data sovereignty, ensure legal compliance, and promote regional interoperability. Nonetheless, the EU’s persistent reliance on American hyperscalers and platform monopolies illustrates how entrenched these dependencies are — and how difficult it is even for relatively resource-rich entities to reclaim digital agency once it has been ceded.

Yet paradoxically, this is also where small states may hold a strategic advantage — the ability to build and deploy context-specific AI models that are closely aligned with local language, culture, history, and demographic realities.

In small-state and micro-state contexts, the stakes are arguably even higher. Constrained by limited fiscal resources, narrow domestic talent pools, and the absence of scalable technological infrastructure, such polities often have no viable alternative but to integrate tools developed elsewhere. Yet paradoxically, this is also where small states may hold a strategic advantage — the ability to build and deploy context-specific AI models that are closely aligned with local language, culture, history, and demographic realities. Generic foundation models may be powerful, but they are not omniscient. They often lack the cultural specificity and interpretive nuance required to serve communities with distinct epistemologies, minority languages, or sovereign legal frameworks.

By focusing on smaller-scale, targeted models that are fine-tuned for local relevance, small states can create systems that are not only more accurate and accountable but also more ethically attuned to their populations. This is a strength — not a limitation. However, this potential is easily undermined by overreliance on foreign infrastructure, such as proprietary APIs or cloud services hosted by multinational tech firms. When model inference, training, or data storage are tied to external entities, the risks of surveillance, censorship, price manipulation, or service denial become significant. Control over data must also mean control over deployment.

When critical functions of governance — including law enforcement, health diagnostics, and linguistic preservation — become reliant on foreign-developed systems whose inner workings are not open to audit, modification, or even complete understanding, the state’s effective sovereignty is compromised.

This is not merely a technical challenge but a fundamental ethical and political question. When critical functions of governance — including law enforcement, health diagnostics, and linguistic preservation — become reliant on foreign-developed systems whose inner workings are not open to audit, modification, or even complete understanding, the state’s effective sovereignty is compromised. The capacity to legislate, regulate, and shape technological ecosystems is increasingly decoupled from traditional mechanisms of democratic accountability. In this context, sovereignty must be reconceived not solely as a matter of territorial control or statutory power but as the ability to shape and influence the infrastructures that mediate cognition, decision-making, and public life.

Addressing this multifaceted challenge demands a strategic, multi-tiered response. First, states must invest in the development of national and regional digital governance frameworks that assert control over data flows, mandate algorithmic transparency, and prioritise the protection of minority languages and cultural knowledge systems. Second, they should seek alliances — especially among other small and medium-sized nations — to pool resources, share best practices, and co-develop open-source AI solutions that are both economically feasible and politically accountable. Third, educational systems must be realigned to build domestic expertise in AI ethics, policy, and development — not merely as users of foreign technologies but as critical co-authors of their own digital futures.

Small states should champion and invest in open-source solutions that are flexible, adaptable, and aligned with their values , ensuring that their digital future is not dictated by the algorithms of global tech giants but shaped through inclusive, sovereign design. To fail in these efforts is to risk entering into a new form of digital dependency: one in which the instruments of modern statecraft are outsourced to private actors with no stake in the long-term flourishing of the societies they serve. Small states and microstates must therefore treat digital sovereignty as a primary concern , not an ancillary issue to be addressed once other development goals are met, but a foundational precondition for meaningful participation in the emerging geopolitical and technological order.

In the era of AI, sovereignty cannot be understood as a static legal concept or a symbolic marker of independence. It is, instead, a dynamic, contested, and deeply infrastructural phenomenon — one that must be continually renegotiated in the face of rapidly evolving technologies and the global forces that seek to control them.

Suppose you’re interested in learning more about the impact of AI in remote communities. In that case, I invite you to explore my other articles on AI in the Faroe Islands, the Arctic, and similar regions. If you’re intrigued by the potential of AI in remote communities, I encourage you to explore this topic further. You can start by exploring my other articles on AI in the Faroe Islands, the Arctic, and similar regions. You can find them all in this reading list.

🤖 I can also be found at www.mohrolsen.com

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