The AI Divide
AI’s benefits are concentrated among a few large technology companies and nations, amplifying inequalities in ways that threaten lasting technological stratification.
2.5 years
expected time to materialize
What does this trend encompass?
The gap is growing between those with access to advanced AI capabilities and those without. AI’s transformative benefits are unevenly distributed, concentrated among a few large technology companies and regions. This is amplifying existing inequalities in digital capabilities and cyber resilience between large and small organizations and developed and emerging economies. This growing AI divide challenges leaders to implement targeted interventions that promote technology equitable outcomes and prevent lasting technological stratification.
Why is it important?
The concentrated nature of AI capacity — in data, compute, and expertise — could amplify global inequality and create systemic vulnerabilities, as under-resourced organizations become weak points in interconnected systems. The AI divide poses threats to economic stability, social cohesion and security by entrenching technological dependence and uneven competitiveness. International cooperation, technology transfer, and equitable access strategies are essential to ensure that AI-driven progress strengthens resilience rather than deepening divides.
How can stakeholders prepare?
As highlighted by DET survey respondents, preparing for the materialization of this trend at the country level depends on the following key drivers:
Digital Infrastructure: addressing the AI divide starts with improving affordability and expanding the reach of digital connectivity to currently underserved populations across regions. Everyone, everywhere should have access to digital connectivity at low cost and latency.
Digital Innovation: scaling open-source AI models and interoperable frameworks will support different economies and innovators to customize solutions efficiently, fostering a more balanced global AI landscape.
Digital Capabilities: beyond access to AI, people need the knowledge and know-how to deploy and maximize its use. Universal access to AI literacy and digital skills are at the core of addressing the growing AI divide between and within countries.
Industry Digital Transformation: how industries integrate AI will shape how widely its benefits are shared. Adapting AI to economic, social, or organizational contexts, sharing knowledge and best practices from early adopters, and maintaining fair competition can help narrow the AI divide.
Digital for Work and Training: AI adoption is transforming skill requirements, creating new demands for work-based learning pathways and micro-credentials tied to AI-related tasks to broaden participation and ensure an equitable distribution of skills.
Impacts on the horizon
Prospective turning points that could catalyze this trend into rapid, widespread materialization
Shared national compute
shared national compute expands access as programs such as the India AI Mission, EU’s EuroHPC AI Factories, and US National Artificial Intelligence Research Resource provide subsidized access to GPU compute, data, and models for startups, SMEs, and researchers.
AI capacity expands in Latin America
with Google investing in Chile’s trans-Pacific undersea cable and Microsoft’s three-year 14.7 billion Reais investment in Brazil, which includes AI upskilling for 5 million people, combining AI human capital, and cloud computing capacity with a low-latency data gateway to Asia-Pacific
The African Union’s Continental AI Strategy
completes its 2025-30 cycle, which is projected to deliver shared data infrastructure, vertical AI integration, and upskilling programs, decreasing the compute, data, and talent gaps that drive the AI divide
Recommendations
Private sector
Co-develop AI access compacts with governments and development partners
to provide affordable computing power, sector-specific tools, and training for SMEs.
Design products with portability and open APIs
to lower switching costs and empower smaller competitors to innovate, creating competitive advantage by enabling ecosystem resilience rather than vendor lock-in.
Form cross-industry consortia
to share open-source models, co-develop local-language datasets, and establish shared infrastructure for AI experimentation and deployment.
Publish transparent metrics, verified by independent audits
to confirm that these resources are reaching underserved communities.
Collaborate with development banks to create financing models for AI adoption in emerging markets
sharing both risks and rewards. Co-develop flexible training programs that build local skills and keep pace with technological advancements.
Public sector
Establish nationally pooled and environmentally sustainable compute infrastructure
with dedicated credits for startups, researchers, and public services. Co-fund with industry and development partners to reach SMEs and underserved regions.
Mandate open standards, API portability, and rights-preserving data-sharing frameworks
enabling local-language datasets and safe model fine-tuning.
Strategically leverage public procurement for major technology contracts
to include vendor co-investment in local talent development and partnerships with domestic SMEs. Turn government spending into a catalyst for ecosystem growth.
Invest in role-specific AI literacy programs
for educators, civil servants, and entrepreneurs, ensuring market-relevant skills reach all levels of government through portable micro-credentials, apprenticeships, and sector-specific training.
IGOs, IOs, and others
Convene multilateral coalitions to develop a globally recognized AI readiness index
measuring national capabilities. Focus on the needs of low-resource countries, small businesses, and public services to guide and incentivize investment rather than punish underperformance.
measuring national capabilities. Focus on the needs of low-resource countries, small businesses, and public services to guide and incentivize investment rather than punish underperformance.
pairing advanced institutions with emerging-market universities to co-create open-source models through global networks, providing neutral guidance for privacy-preserving data sharing in diverse regulatory contexts.
Champion global affordability standards
while developing frameworks for data sharing and accountability that balance openness with safety protections for vulnerable populations.
Promote inclusive AI governance
by supporting cross-regional policy dialogues and capacity-building to ensure equitable access and locally relevant AI deployment.
Read the Digital Economy Trends 2026 report
Explore the full insights and analysis of the 2026 research.