AI’s diffusion is now inseparable from its compute infrastructure. 2025 marks the year when nations and hyperscalers began measuring, monetizing, and governing “AI compute” as a strategic asset, linking sovereign cloud capacity, public-private infrastructure build-outs, and the spread of AI capabilities across sectors and economies.

Summary

Together, these reports show that AI diffusion now depends as much on infrastructure and governance as on algorithms, marking the emergence of compute capacity as a new dimension of economic and national power.

Three complementary 2025 reports (by Google Cloud, Microsoft Research, and the OECD) collectively define how the global AI ecosystem is measured, built, and diffused.

• The Google Cloud State of AI Infrastructure Report reveals that nearly every major enterprise is now deploying or experimenting with generative AI, with 98% adoption rates and a decisive shift toward hybrid cloud architectures for scalability, compliance, and cost efficiency.
• The Microsoft AI Diffusion Report examines how AI capabilities propagate through economies, linking compute access and digital infrastructure to measurable productivity gains, and proposing new diffusion metrics for inclusive AI growth across nations and sectors.
• The OECD Measuring Global Public Compute 2025 report establishes the first standardized method for tracking the geographical distribution of AI compute, mapping where public-cloud regions equipped with advanced accelerators (e.g., H100s, TPUs) are physically located. Its findings reveal a stark global “compute divide,” with the U.S. and China hosting nearly half of all AI-ready cloud regions.

Why This Matters

Policy, not just capital, determines advantage: Governments are recognizing compute availability as a national-security and economic-resilience issue (paralleling energy or semiconductor policy).

• Compute equals capability: The OECD’s new methodology reveals that AI progress is constrained or enabled by the global distribution of compute (especially public-cloud regions equipped with modern accelerators).
• Diffusion defines dominance: Microsoft’s AI Diffusion Report highlights that global productivity and innovation gains hinge on how rapidly AI permeates firms, workforces, and national systems.
• Infrastructure is the new battleground: Google Cloud’s State of AI Infrastructure Report shows nearly every major organization deploying generative AI workloads, driving exponential demand for scalable, secure, hybrid compute.

Summaries of Each Report

OECD Measuring Global Public Compute 2025: Develops a methodology to track the global physical distribution of AI-ready public-cloud infrastructure. It counts cloud regions and accelerator availability across nine providers (AWS, Azure, Google, Alibaba, Tencent, Huawei, OVH, Hetzner, Exoscale). The pilot found major disparities: the U.S. and China host most AI compute; 13 OECD countries qualify as training-relevant. Forms the foundation for the forthcoming OECD.AI Observatory Index.

Google Cloud 2025 State of AI Infrastructure Report: Based on a survey of 513 executives, the report finds 98% of firms using or testing GenAI, 74% preferring hybrid cloud, and 70% struggling with data governance. Cost optimization, robust AI platforms, and edge-based scalability emerge as top priorities. Google positions its “AI Hypercomputer” as the backbone for distributed, secure AI compute.

Microsoft AI Diffusion Report (Oct 2025): Analyzes how AI capabilities diffuse across economies and sectors, emphasizing compute access as a determinant of equitable innovation. Highlights the correlation between infrastructure investment and productivity spillovers, advocating shared metrics across OECD and GPAI members.

Key Points

• OECD Framework: Establishes the first reproducible method for measuring the geographic distribution of public-cloud AI compute, using accelerator availability as a proxy for national capacity.
• Pilot Findings: Only 13 OECD members currently host “training-relevant” compute (e.g., NVIDIA H100/A100). The U.S. and China dominate, with nearly half of global public-cloud regions.
• Infrastructure Transition: Google Cloud’s survey of 500+ tech leaders finds 98% of organizations are experimenting with or deploying generative AI; 74% prefer hybrid architectures for compliance and flexibility.
• Security and Data Governance: 70% of firms cite data integration and governance as key adoption barriers; 62% list security and privacy as their top concern.
• Cost Efficiency: 83% of respondents prioritize cost optimization in AI infrastructure – spurring FinOps frameworks and demand for specialized hardware like TPUs and GPUs.
• Microsoft’s AI Diffusion Model: Tracks how compute access, data availability, and model adoption drive productivity diffusion across economies, informing policy design for inclusive growth.
• Global Compute Inequality: The OECD’s early dataset shows a sharp “compute divide” between economies hosting advanced accelerators and those dependent on remote or foreign access.
• Industrial and Edge Expansion: Gen AI now extends beyond data centers – into manufacturing, healthcare, and retail (through edge computing and distributed cloud deployments).

WSJ: “It’s Not Just Rich Countries: Tech’s Trillion-Dollar Bet on AI Is Everywhere”

A recent WSJ article underscores the global diffusion of AI investment beyond advanced economies, aligning closely with the OECD, Microsoft, and Google Cloud analyses.

The article details how AI infrastructure buildouts are expanding rapidly across emerging markets – from data centers in Southeast Asia and the Middle East to national AI hubs in Africa and Latin America – driven by both hyperscale cloud providers and sovereign digital initiatives. This investment wave, now measured in trillions of dollars globally, reflects an effort to localize compute, talent, and data pipelines that were once concentrated in the U.S. and China.

Relative to the OECD’s compute mapping, the WSJ piece provides the market context behind the same trend, showing how public and private sectors are racing to close the “compute gap.” It reinforces the Google and Microsoft findings that AI diffusion is no longer limited by innovation intent but by infrastructure reach, signaling a new phase in which access to compute and cloud capacity defines a nation’s place in the global AI economy.

What Next?

• Compute becomes a policy metric: OECD’s forthcoming AI Observatory Index will benchmark nations on public-cloud AI compute availability (shaping AI industrial strategies and digital-sovereignty debates).
• Hybridization accelerates: Enterprises are re-architecting toward hybrid and multi-cloud models to balance performance, compliance, and sovereignty.
• Regulation tightens: The EU AI Act and similar frameworks will pressure firms to maintain data-sovereign infrastructure and verifiable data lineage for model training.
• Geoeconomic realignment: Expect alliances around compute sharing, cross-border cloud investment, and state-backed “AI factories” in regions like the Gulf and Europe.
• Diffusion measurement expands: Microsoft and academic partners are extending “AI diffusion indices” into labor markets, productivity data, and compute accessibility metrics.

Recommendations from the Reports

1. Adopt Compute Transparency: Governments should integrate compute-availability data (as defined by OECD) into national AI strategies to detect dependency risks.
2. Incentivize Regional AI Infrastructure: Use public-private partnerships to expand domestic accelerator capacity and mitigate the global “compute divide.”
3. Mandate Secure Data Lineage: Require verifiable provenance and governance standards for all data feeding AI models to ensure compliance and trust.
4. Implement Hybrid Cloud Standards: Encourage architectures balancing on-premise sovereignty with hyperscale efficiency.
5. Support Diffusion Measurement: Fund research that links compute metrics with AI productivity and innovation outcomes.

Additional OODA Loop Resources

More On Our 2025 OODA Loop Research Series: The Macroeconomics of AI in 2025

By combining quantitative economic analysis with real-world business applications, this series aims to provide decision-makers with actionable insights on how AI is shaping the global economy and where the real opportunities and risks lie.

The OODA Loop Macroeconomics of AI in 2025 series of posts is a market-driven, enterprise-focused quantitative series of posts that aims to provide a data-driven perspective on AI’s macroeconomic impact. It seeks to move beyond the hype cycle by grounding discussions in concrete economic metrics and empirical analysis. The series examines global AI development, adoption trends, and economic maturation, analyzing real-world use cases and case studies to assess AI’s contributions to GDP and productivity growth.

A key analytical framework used is Jeffrey Ding’s Tech Diffusion Model, which helps measure AI adoption rates and integration across industries. The series also incorporates extensive research reports and white papers (from organizations like the CSET, Stanford’s HAI, and the NBER, amongst other trusted sources), including research-based insights on AI’s role in workforce automation, augmentation, and replacement metrics. The focus is on understanding AI-driven economic acceleration, growth, diffusion, and productivity shifts while addressing policy imperatives to ensure balanced and sustainable AI integration.

From The Macroeconomics of AI in 2025 Series

The Macroeconomics of AI in 2025: This post explores the uncertain macroeconomic impacts of artificial intelligence (AI). While AI holds the promise of enhancing productivity and spurring economic growth, concerns remain about potential job displacement, intensifying global competition, the rapid acceleration and diffusion of AI technologies, and the possibility of misleading economic indicators. The analysis underscores the need for a nuanced understanding of AI’s multifaceted effects on the global economy.

A New Economic Paradigm: Transformative AI Diffused Across Industry Sectors: This post discusses how transformative AI is reshaping various industries, compelling businesses to reconsider their value creation and delivery methods. It emphasizes the necessity for companies to adapt their business models and value propositions to remain competitive in this evolving economic landscape. The post delves into how AI redefines growth, labor dynamics, innovation processes, value creation, and policy frameworks, signaling a shift towards a new economic paradigm driven by AI integration across sectors.

The Stanford Institute for Human-Centered AI (HAI): Annual Macroeconomic AI Trends and Impacts: Since 2017, The Stanford Institute for Human-Centered AI (HAI) has produced one of the best-in-class annual studies on macro-level trends and impacts of Artificial Intelligence. The HAI AI Index 2024 frames the key shifts over the last year, providing a critical outlook for decision-makers navigating the economic transformation induced by the diffusion of AI across industry sectors.

The Macroeconomic Impact and Security Risks of AI in the Cloud: The rapid expansion of artificial intelligence in cloud environments is reshaping industries, but security vulnerabilities are mounting just as fast. The “State of AI in the Cloud 2025” report by Wiz Research highlights the rapid integration of artificial intelligence (AI) into cloud environments, emphasizing both opportunities and challenges. The report underscores the necessity for organizations to explore innovation coupled with robust security and governance frameworks. As AI becomes an indispensable part of cloud operations, businesses must strike a balance between innovation and protection to navigate this accelerating infrastructure build-out.

Cross-Border Data Flows and the Economic Implications of Data Regulation: Any macroeconomic consideration of the impacts of artificial intelligence must also include the future of data writ large: in this era of exponential disruption and acceleration, the flow of data across borders has become the backbone of global trade and economic activity. Currently, regulatory interventions, driven by concerns over privacy, security, and national interest, are reshaping this landscape. A recent report sheds light on the economic trade-offs of data regulation, offering empirical evidence to inform business decisions.

The Economic Transformation of Generative AI: Key Insights on Productivity and Job Creation: If there is any macroeconomic impact of artificial intelligence we have been looking to frame (as part of this OODA Loop Macroeconomics of AI in 2025 series), it is this: “Fears of large-scale technological unemployment are probably overblown. The history of general-purpose technologies shows that the growth they bring is accompanied by a strong demand for labor. However, this increased demand is often in new occupations. For example, more than 85% of total U.S. employment growth since 1940 has come in entirely new occupations.” In this post: find further valuable insights from MIT Professor Andrew McAfee’s report Generally Faster: The Economic Impact of Generative AI (produced while Andrew was the inaugural Technology & Society Visiting Fellow at Google Research in 2024). Generative AI could enable nearly 80% of U.S. workers to complete at least 10% of their tasks twice as fast without quality loss, creating substantial economic opportunities and reshaping the labor market.

The AI Acceleration of Moore’s Law: Actionable “Long Task” Performance Metrics for Your Business Strategy: AI is evolving faster than expected: researchers at METR have discovered that AI’s ability to autonomously complete tasks has been doubling every 7 months since 2019, mirroring Moore’s Law. If this trend holds, AI could independently execute month-long human-equivalent projects by 2030, transforming automation, workforce dynamics, and business strategy.

Big Tech’s Cloud Dominance: Fueling the AI Arms Race: In the rapidly evolving landscape of artificial intelligence (AI), cloud computing has emerged as the linchpin of Big Tech’s strategy to dominate the AI frontier. As companies like Microsoft, Amazon, and Google pour billions into AI development, their expansive cloud infrastructures are not just supporting this growth – they’re driving it.

Managing AI’s Economic Future: Strategic Automation in a World of Uncertainty: RAND’s roadmap for AI-driven economic policy confronts the high-stakes trade-offs of growth, inequality, and global competition.

Mapping the AI Economy: Task-Level Insights from Millions of Claude Conversations: In a recent seminar at the Stanford Digital Economy Lab, Alex Tamkin of Anthropic presented findings from an analysis of over four million Claude conversations to reveal how AI is currently used in real-world economic tasks. The study identifies where and how AI tools like Claude augment or automate work by mapping AI activity to the U.S. Labor Department’s O*NET job database.

Thriving in a Post-Labor Economy of AI and Automation : Explores how organizations, governments, and individuals can proactively adapt to the accelerating displacement and augmentation of human labor by AI systems. The article frames the post-labor economy as a strategic opportunity rather than a threat (emphasizing the need for new educational paradigms, universal digital infrastructure, and institutional agility to manage the transition).

Notable Voices on The Post Labor Economy: Things will be better, faster, cheaper, and safer: A chorus of techno-optimist perspectives from leading thinkers like Marc Andreessen, Sam Altman, and Balaji Srinivasan, argues that the rise of AI and automation will usher in a future of abundance, efficiency, and safety. These voices suggest that as AI offloads more labor, human creativity and well-being will flourish, not diminish. This divergence underscores a critical tension: between public techno-utopian narratives and the quiet, risk-averse signals emerging from the boardrooms of America’s largest companies.

When AI Becomes a Material Risk Class: What the S&P 500’s AI Disclosures Reveal About Executive Risk Perception: The Autonomy Institute’s new report reveals a sharp rise in AI-related risks disclosed by S&P 500 companies, signaling a pivotal shift in corporate awareness, regulatory exposure, and competitive threat perception. This analysis reveals how generative AI is reshaping corporate risk disclosures (and where systemic threats to the U.S. economy may emerge next).