The Strategic Risk of Relying on a Small Number of US Based AI Providers

US technology companies currently lead in artificial intelligence capability and innovation. Their scale, access to capital, research depth, and speed of execution have driven much of the recent progress in AI systems that are now being adopted across the global economy. For many organisations, these platforms have accelerated experimentation and delivered tangible operational value.

This observation is not controversial, nor is it an argument against the use of US technology.

The question worth examining sits elsewhere. It concerns what happens when capability leadership evolves into structural dependency, and when that dependency forms around systems that are increasingly critical to how organisations operate.

AI has moved beyond experimentation. It now underpins decision-making, customer interaction, operational automation, and commercial insight. In many organisations, it is becoming embedded in processes that are difficult to unwind without disruption. At that point, AI should be treated as core infrastructure, subject to the same level of strategic scrutiny as finance, energy, or communications.

When core infrastructure is owned and controlled by a small number of external providers, systemic risk emerges.

That risk does not stem from technical performance. It arises from asymmetry of control. Ownership determines who sets the terms of access, who defines acceptable use, and whose priorities shape future direction. As reliance deepens, leverage shifts.

These risks are amplified by lock-in. Once AI systems are integrated into workflows and data pipelines, switching becomes costly and time-sensitive. Sudden changes in pricing, usage terms, or compliance requirements can force rushed replatforming decisions. Under pressure, organisations tend to accept suboptimal outcomes simply to maintain continuity.

Policy and trade uncertainty adds another layer that boards can no longer ignore. US foreign policy increasingly affects digital services as well as physical goods. Tariffs or restrictions applied to cloud services, APIs, or AI model access are no longer implausible. If introduced, such measures would have immediate cost and availability implications for organisations that rely heavily on these services.

This is not a question of intent but rather a function of jurisdiction. US technology providers operate under US law and policy, which can shift rapidly in response to geopolitical events, trade negotiations, or national security considerations. Threats to allies, regulatory divergence, and retaliatory measures are now legitimate board-level considerations when evaluating long-term technology dependencies.

Data sovereignty sharpens the exposure further. AI systems derive their value from data: customer information, operational signals, and commercial insight. When that data is processed or hosted under foreign control, it becomes subject to access rules and legal frameworks outside the organisation’s governance. Accountability, however, remains local.

A non-technical analogy of Energy markets offer a useful comparison. Importing energy is not inherently risky. The risk emerges when supply is concentrated, alternatives are limited, and control sits beyond domestic influence. In those conditions, price, availability, and political leverage become strategic vulnerabilities. As we have learned over recent years, sensible energy policy focuses on resilience and optionality, not isolation.

AI now occupies a similar position. It is or is quickly becoming infrastructure. Treating it as such does not require abandoning leading providers, but it does require deliberate choices about dependence, diversification, and control.

For businesses, the implications are practical and immediate. How much of your operating model assumes uninterrupted access to a small number of AI providers? How resilient are your systems to changes in pricing, policy, or compliance? Where does your most sensitive data reside, and under whose authority is it ultimately governed?

For governments and public-sector organisations, the considerations are broader still. Strategic autonomy, regulatory intent, and long-term policy flexibility are all affected by technology choices made today. Dependence without leverage constrains future options, even if the current arrangement appears efficient.

There is no single answer or silver bullet.

Wholesale replacement of US platforms is neither realistic nor desirable. At the same time, assuming long-term geopolitical stability is not a strategy. AI investments must be able to survive policy change, not just favourable conditions.

Resilience comes from optionality. Hybrid, portable, and orchestrated approaches allow organisations to benefit from leading technology while preserving the ability to adapt. Autonomy does not mean isolation. It means maintaining meaningful control over critical systems and data.

In an environment characterised by uncertainty and increasing digital protectionism, control is becoming a competitive advantage. The strategic question for boards is not whether to use AI, or even which provider to choose. It is whether their organisation has consciously de-risked its reliance on any single jurisdiction or vendor.

If AI is now core infrastructure, is your organisation confident that its use of AI could withstand material policy, trade, or regulatory change without forcing rushed and costly decisions?

This perspective reflects work undertaken by Stratagems in supporting organisations to develop AI policies and governance approaches designed to reduce strategic dependency and strengthen long-term resilience.