AI Becomes Core Global Infrastructure
Artificial intelligence is no longer confined to research labs or niche applications. It is rapidly becoming foundational infrastructure for economies, governments, and daily life. As adoption accelerates, questions around access, coordination, and long-term sustainability are moving to the forefront of policy debates.
The report frames 2026 as a turning point where AI shifts from experimental growth to structured, system-wide integration across sectors.
Global Governance Moves Up the Agenda
International cooperation on AI governance is emerging as a critical priority. Policymakers increasingly view fragmented rules as a risk to economic stability and public trust. Calls for shared standards now extend beyond ethics into trade, security, and development policy.
The report emphasizes that coordinated governance is essential to ensure AI delivers broadly shared benefits rather than deepening global inequality.
Computing Power Scales at Record Speed
The expansion of intelligent computing capacity remains a central driver of AI progress. Advances in chip design and large-scale infrastructure are enabling unprecedented processing power. GPU clusters containing tens of thousands of units have become standard for training advanced models.
National projects aimed at redistributing and optimizing computing resources are also reshaping regional competitiveness in the AI race.
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AI Applications Enter Industry Mainstream
AI deployment is shifting from general-purpose tools toward specialized, industry-specific agents. Manufacturing, logistics, healthcare, and public administration are increasingly adopting tailored AI systems designed to solve concrete problems.
Governments are playing a growing role by promoting large-scale deployment of industrial AI agents to boost productivity and operational efficiency.
Multi-Modal Interaction Redefines AI Capabilities
AI systems are evolving into intelligent partners capable of processing text, images, audio, video, and three-dimensional data simultaneously. Improvements in model efficiency have significantly reduced deployment costs, accelerating adoption.
This shift enables more natural interaction between humans and machines, supporting complex use cases across education, design, and enterprise workflows.
Embodied Intelligence Moves Toward Scale
The convergence of AI with physical systems is driving rapid progress in robotics. Machines are increasingly able to learn from real-world interaction, adapt to changing environments, and collaborate with humans.
Applications are expanding from pilot projects into factories, warehouses, healthcare facilities, and service industries, signaling a transition toward mass deployment.
Energy and Safety Challenges Intensify
The rapid growth of AI infrastructure is placing new strain on energy systems. Rising electricity demand from data centers has elevated concerns about environmental impact and resource availability. As a result, efficiency-focused architectures and clean energy integration are becoming strategic priorities.
At the same time, safety and security challenges are escalating. Governance frameworks and technical safeguards are advancing to address risks related to ethics, privacy, and adversarial use. Balancing innovation with control will remain one of the defining challenges of AI development in 2026 and beyond.
