The global AI boom has triggered an unprecedented investment wave—nearly $7 trillion by 2030—to scale data centers that fuel artificial intelligence, sparking intense competition, uncertainty, and strategic risk across industries.

An April 2025 report from McKinsey quantifies the monumental stakes and capital requirements to meet surging AI compute demands. The insights from the report are critical for:

• Navigating capital allocation under uncertain AI adoption and technological trajectories.
• Shaping industrial policy, energy infrastructure, and technology sovereignty strategies.
• Executives and strategists in cloud computing, semiconductors, and utilities aiming to secure competitive advantages in AI infrastructure; and
• Monitoring dependencies on critical compute and energy assets.

Key Points

• Strategic risks include grid constraints, supply chain bottlenecks, escalating energy demands, and regulatory uncertainties.
• $6.7 trillion in global capital expenditures needed by 2030 to meet projected compute demand, with $5.2 trillion for AI-specific data centers.
• AI workloads driving 70% of future data center demand; inference workloads to dominate by 2030.
• Investment needs split among five archetypes: builders, energizers, technology developers, operators, and AI architects.
• Major capital allocations:
  • 60% to technology developers (chipmakers like NVIDIA, Intel)
  • 25% to energizers (utilities, cooling, power infrastructure)
  • 15% to builders (real estate, construction firms)
• Efficiency gains (e.g., DeepSeek’s 18x training cost reductions) will likely be offset by rising experimentation and training demands (a form of Jevons Paradox).
• Geopolitical drivers fuel government investment in AI infrastructure for security, economic leadership, and technological independence.

For the full report, see: The Cost of Compute: A $7 Trillion Race to Scale Data Centers

What Next?

Expect continued acceleration in:

• AI-driven infrastructure investment races among hyperscalers, nations, and private investors.
• Emergence of alternative compute architectures (e.g., neuromorphic, photonic) seeking to address energy and efficiency bottlenecks.
• Expansion of clean energy investments and regulatory frameworks as power demand for AI strains existing grids.
• Heightened competition for semiconductor supply chain resilience amid export controls and geopolitical tensions.
• The need to balance short-term capacity expansion with long-term technological flexibility.

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 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. This synergy between cloud services and AI is reshaping the technological and economic paradigms.