What Is The AI Divide In Data Centers, Semiconductors, And Sovereignty?

The AI divide represents a strategic inflection point for global business. Those without these resources will fall behind, not just technologically but competitively. Productivity, customer engagement, and decision-making is widening across industries and geographies, creating systemic issues for companies that fail to invest, such as:

  • The infrastructure gap. This gap is a key driver of the AI divide, as advanced AI requires high-performance computing, scalable cloud platforms, and secure data pipelines. Companies with modern infrastructure can deploy AI at scale and innovate faster, while those without it face operational bottlenecks and competitive risk. For business leaders, closing this gap through strategic investment and partnerships is essential to maintain growth and resilience in an AI-driven economy.
  • Economic disparities. Massive investments in sovereign AI infrastructure and hyperscale projects such as Stargate are amplifying economic disparities in AI adoption. For business leaders, this trend underscores the urgency of strategic investment and partnerships to remain competitive in an AI-driven global economy.
  • The productivity gap. The productivity gap is widening as companies deploy increasingly autonomous, agentic AI systems that handle complex workflows with minimal human oversight. For business leaders, this trend makes adopting agentic AI a strategic imperative to maintain competitiveness and shareholder value.

What’s Driving The AI Divide?

The global AI divide is widening as nations and corporations grapple with sovereignty, trade barriers, and resource constraints. Governments are enforcing data sovereignty laws to protect national interests, while tariffs and export controls on advanced chips and AI technologies — especially amid US-China tensions — are reshaping supply chains and driving up costs. At the same time, building competitive AI systems requires massive compute power, vast datasets, specialized talent, and energy infrastructure, creating a steep entry barrier. For business leaders, these dynamics make AI not just a technology issue but a strategic imperative that requires localized compliance, geopolitical risk planning, and partnerships to secure critical resources. We highlight the following trends:

  • Governments are increasingly pursuing sovereign AI strategies. Governments pursue sovereign AI strategies to strengthen domestic competitiveness, encourage local AI development, and prioritize the use of nationally governed data, creating advantages for businesses operating within their borders (see Figure 1). But sovereign AI isn’t universally attainable: Many countries face binding constraints in capital availability, energy capacity, water resources, land, industrial ecosystems, and skilled talent, limiting their ability to build and sustain fully independent AI stacks.
World map highlighting countries with AI sovereignty laws, frameworks, or draft policies, shown in different colors.
Figure 1: Countries With AI Sovereignty Laws (source: IAPP, Global AI Law and Policy Tracker)
  • Tariffs promote the use of local goods and services. The impact of tariffs and reciprocal tariffs on AI isn’t just from semiconductors but also from the raw materials and equipment needed for facilities supporting AI, such as data centers and power plants. Depending on where you plan to build your AI infrastructure and the complexity of your supply chain, tariffs can more than double costs (see Figure 2). Negotiations between nations will hopefully conclude soon and eliminate the uncertainty that enterprises face in where and when to build their AI capabilities.
World map showing average tariff rates by country, with darker blue indicating higher tariffs and lighter blue indicating lower tariffs.
Figure 2: Tariff Rate, Applied, Weighted Mean, All Products By Country (source: Trading Economics)
  • Export controls are being used to exert control over the AI race. Restricted access to AI hardware and models is being employed to maintain a lead in AI development and innovation (see Figure 3). This ensures that specific countries and AI companies preserve their position in pushing state-of-the-art AI. This has created strong competition, however, in the form of more efficient AI models (DeepSeek), hardware (Huawei Ascend), and new semiconductor foundries.
World map highlighting countries subject to U.S. export controls, shown in shaded blue against a gray background.
Figure 3: Countries With US Export Controls (source: US Bureau of Industry and Security, Interactive Commerce Country Chart)
  • The requirements for AI infrastructure are ever increasing. Increasingly powerful AI chips are driving up power and cooling demands required for IT infrastructure and data center facilities (see Figure 4). This prevents AI from being built just anywhere: Many existing enterprise data centers don’t have the additional power from the grid to accommodate state-of-the-art AI systems and aren’t plumbed for water. Furthermore, hyperscalers need a lot of land, energy, and water to handle their multitenant AI services.
World map showing electricity output by country in terawatt-hours, with darker blue indicating higher electricity production.
Figure 4: Electricity Output, Terawatt-Hours (source: International Energy Agency, Electricity 2025)

Research Goals

We’re researching what the AI divide means to enterprise organizations globally. Specifically, we’re assessing:

  • How it impacts organizations that operate globally, regionally, or locally.
  • The benefits and disadvantages.
  • The strategies that can be used to mitigate the negative impacts of the AI divide.

You can look forward to future blogs and reports on how to address these points! Forrester clients: If you have any questions about what the AI divide means for your organization and how you can mitigate its negative impacts, you can request an inquiry or guidance session or reach out to your account team.