New Competitive Dynamics in the Era of AI-Driven Value Creation Revealed by the U.S.–China AI RaceWhat Is Next-Generation AI Industrial Competitiveness?

From Building AI to Creating Value with AI

Rapid advances in frontier AI are changing the nature of competition. The question is no longer simply who can build the most capable models. Increasingly, success depends on how quickly and effectively organizations deploy AI across business and society and convert it into economic and social value. The focus of competition is shifting from AI development to AI-enabled value creation.

Understanding the Full Value Creation Chain

AI industrial competitiveness cannot be measured through isolated metrics such as investment, patents, GPUs, or model performance. This paper presents a four-layer framework consisting of Capital & Computing Infrastructure, AI Intelligence, AI Industrialization, and Economic Value Creation. These layers collectively explain how AI resources are transformed into business and societal value.

Countries and companies may achieve competitive advantage through different pathways. What matters is the ability to connect these four layers and accelerate the conversion of AI capabilities into meaningful outcomes. The effectiveness and speed of value creation increasingly define next-generation competitiveness.

AI Infrastructure Is Moving Beyond Scale

Data centers, AI computing hardware, and reliable power supply remain the foundation of AI competitiveness. However, competitive advantage is increasingly determined not by the quantity of infrastructure alone, but by the ability to extract greater effective AI processing capacity from limited computing and energy resources. The next phase of AI infrastructure competition emphasizes quality and efficiency.

AI Models Are Competing on Utility, Cost, and Efficiency

AI model competition is no longer driven solely by intelligence rankings. Organizations must optimize multiple variables, including performance, cost, processing efficiency, context length, and output speed. The strategic question is evolving from “Open versus Closed” toward building the right portfolio of models for different business needs, customers, and markets.

From AI Assistants to Digital Labor

AI intelligence generates value only when deployed within organizations and workflows. Leading companies are increasingly treating AI agents as digital labor with defined roles, permissions, governance structures, and KPIs. This requires redesigning workflows and management practices around collaboration between humans and AI.

From Digital AI to Physical AI

AI industrialization is extending beyond digital environments into the physical world. Advances in sensors, AI chips, generative AI, world models, and robotics are creating new opportunities for AI systems capable of perception, reasoning, and action. Japan and Europe possess distinctive strengths in manufacturing, quality, safety, industrial ecosystems, and customer bases that may create future advantages in Physical AI deployment.

Measuring What AI Actually Delivers

Evaluating AI success requires more than tracking usage or efficiency gains. Organizations must understand how AI contributes to cost reduction, revenue growth, customer value, risk management, and operational capacity. While U.S. and Chinese organizations emphasize different metrics, both approaches focus on maximizing the business value generated by AI.

Expanding Value Across the Bottom and Top Line

Efficiency gains create value only when redirected into productive activities and business outcomes. Future AI leaders will be those capable of translating AI-driven productivity into new revenue opportunities, stronger customer engagement, and sustainable competitive advantage.