State Power and the New Economic Frontier: Who Wins, Who Follows, and Who gets Left Behind

Artificial intelligence continues to shape global strategy and economic power in 2026, not as a distant technical frontier, but as a present-day infrastructure question and a governance challenge that every serious nation must now answer. The global conversation has shifted: from “What can AI do?” to “Who controls compute, who sets standards, and who captures value?”

This week’s edition covers:

• Saudi AI infrastructure financing boost

• UAE AI adoption in public sector trends

• UK government AI data policy moves

• Davos perspectives on AI adoption inequalities

Across regions, from the Middle East’s energy-backed cloud build-outs, to Europe’s regulatory posture, to Africa’s emerging talent corridors, governments and institutions are learning that AI leadership is not merely technical. It depends on power grids, data stewardship, semiconductor access, workforce pipelines, and credible governance models. In practical terms: countries are competing on infrastructure and trust, not just algorithms.

The purpose of this edition is to cut through the noise and examine real signals, not hype cycles. We look at how compute capacity, data sovereignty, policy coherence, and ethical deployment are now shaping competitiveness. And we ask a critical question: Who will get to shape the AI future, and who will be left consuming the outcomes designed by others?

From my vantage point; working across policy, enterprise transformation, and smart-city strategy, the throughline is clear: AI is becoming a test of state capacity and leadership maturity. Some nations are positioning to produce, govern, and export. Others risk being locked into dependency. The choices being made in this decade will define economic trajectories well beyond it.

It is the leaders job to make those choices visible,and to equip themselves to respond.

SAUDI INFRASTRUCTURE AT LARGE
Saudi Arabia’s AI Infrastructure Scaling

Saudi Arabia’s AI ecosystem took a strategic leap this week. According to Reuters reporting on 21 January 2026, Saudi AI firm Humain secured up to $1.2 billion in financing with support from the National Infrastructure Fund to expand AI and data centre capacity in the kingdom by megawatts of dedicated power and hundreds of megawatts of compute infrastructure. The financing is part of a broader diversification strategy to reduce oil dependency and anchor technology as an economic pillar, combining capital investment with sovereign direction and global partnership potential. 

This financing drive is not just about hardware. It reflects an emerging pattern: nations are treating AI infrastructure as critical national infrastructure, worthy of long-term planning, regulatory clarity and capital allocation. Build-out ambitions include co-anchoring investment platforms that attract institutional capital, a sign that sovereign strategy now includes private sector coordination rather than top-down edicts. Countries aspiring to “AI producer” status understand that infrastructure and ecosystem scale form the foundation for future competitive advantage. In Saudi Arabia’s case, positioning itself beyond fossil fuels entails enabling local training, inference, and governance capabilities that align with broader economic goals.

Yet success hinges not just on capacity but execution. Financing must be matched by sustainable energy planning, workforce capability, and governance frameworks that ensure ethical and compliant deployment. This underscores a responsible leadership approach where infrastructure expansion is coupled with careful policy and oversight to ensure broad economic value. For regions building their own compute networks, the Saudi example offers a model for blending sovereign ambition with practical execution and international engagement.

Strategic financing to build compute and data centre capacity is a defining marker of AI sovereignty in 2026. Leaders must view compute as economic infrastructure, not just technology overhead, and plan accordingly.

 (Reuters, 21 Jan 2026) 

THE PUBLIC SECTOR
UAE Public Sector Adoption: A Governance Opportunity 

Reporting from Gulf Business highlights that the UAE’s public sector is poised for significant AI adoption in 2026, reflecting deliberate policy and organisational readiness. Across government agencies, AI is being structurally integrated into administrative processes, service delivery, and policy execution,signaling a shift from isolated pilots to institutional scale adoption. 

This shift matters for governance because public sector AI adoption is inherently political and social. Governments cannot simply delegate AI deployment to external vendors; they must manage privacy, accountability, and public trust. The UAE’s trends suggest an approach where leaders invest both in technology and in organisational capability, prioritising frameworks that allow deployment with oversight and public accountability. Public sector adoption often acts as a proof point that shapes private sector confidence and international partnerships.

For countries navigating adoption, the critical lesson is clear: AI success in government requires policy maturity, data management infrastructure, human capital, and governance clarity. Nations that invest in these areas, particularly within public administration, are creating environments where private enterprise and public service can co-evolve, leading to broader economic and social impact. The alternative, piecemeal implementation without strategic governance risks creating stranded investments or public mistrust.

Public sector adoption offers a template for responsible scale deployment, where governance, capability and strategic coherence determine whether AI adds value or creates risk.

 (Gulf Business, 2026) 

PUBLIC DATA & AI
Public Data for AI in the UK: Governance Meets Innovation 

Recent reporting in The Guardian reveals the UK government’s plans to make public institution data AI-ready by allowing AI systems to use datasets from national bodies such as the Met Office and National Archives. The initiative includes ethical, privacy, and copyright consideration at its core and seeks to build data infrastructure that supports both business development and societal insights.  

This development demonstrates that data governance is a central component of AI strategy. Nations with coherent data ecosystems, where legal frameworks, public trust, and structured access align, gain an advantage not just in innovation but in inclusive economic participation. The UK’s approach underscores the need for governments to treat data as a sovereign asset that must be curated, protected, and enabled for public benefit while balancing ethical constraints.

Moreover, leveraging public data for AI offers a competitive edge by enabling enterprises and researchers to build on foundational datasets that others cannot access. By codifying frameworks that maintain privacy and transparency, the government is setting a template for how states can balance open innovation with rights protection, a model that many emerging markets may adapt to local contexts. This emphasis on data readiness aligns directly with global debates on AI governance and the centrality of public value in digital ecosystems.

Data stewardship and governance frameworks that unlock public data resources responsibly can act as strategic enablers of innovation and inclusion.

AI ADOPTION
Davos Perspectives and the Adoption Divide 

At the World Economic Forum in Davos this year, leaders like Microsoft CEO Satya Nadella highlighted that AI’s long-term boom could falter if adoption remains concentrated among wealthy countries and large corporations. His comments reflect an intensifying global adoption divide, where equitable diffusion determines whether AI yields broad economic value or deepens inequality. 

Similarly, a conversation between former UK Prime Minister Rishi Sunak and Nadella urged policymakers to prioritise responsible and inclusive AI development, emphasising that leadership and governance, rather than competitive panic, should guide strategy. This sentiment challenges the simplistic “AI race” narrative and reframes competition as a question of collective capacity building, regulation and cooperation. 

These discussions highlight a key reality: infrastructure and capability alone are insufficient without adoption frameworks that ensure broad participation across industries and communities. Nations that succeed will be those that embed equitable access, workforce readiness, and mission-aligned deployment into their AI roadmaps. The gulf between producers and adopters therefore represents both a challenge and an opportunity: narrowing that divide requires public-private alignment, international collaboration, and governance innovation.

Global leadership in AI depends on inclusive adoption strategies that integrate ethical governance and shared prosperity goals, not just competitive infrastructure build-out.

(FT, Jan 2026) 

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