## Inside the AI-Led Resource Race

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**Canonical URL:** [Inside the AI-Led Resource Race](https://www.imf.org/en/publications/fandd/issues/2025/12/inside-the-ai-led-resource-race-thijs-van-de-graaf)

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## Bibliographic details
- Authors: THIJS VAN DE GRAAF
- Published: November 17, 2025

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### Overview
- Material demands—for energy, chips, and minerals—will determine which countries and companies dominate AI and data.
- AI’s infrastructure includes servers, cooling systems, chips, and mined minerals; this physical backbone is rapidly expanding with data centers multiplying in number and size.
- The contest in algorithms is increasingly a contest for energy, land, water, semiconductors, and minerals, shaping who can harness the AI revolution.

### Energy and data-center growth
- Data centers already use about 1.5 percent of global electricity supply.
- The International Energy Agency (IEA) expects data center demand to more than double by 2030, with AI responsible for much of the increase.
- AI accounts for less than a tenth of added power demand this decade globally, but national impacts can be large:
  - In the US and Japan, data centers could account for nearly half of new demand by 2030.
  - In Ireland, data centers already use more than a fifth of the country’s electricity.
- Hyperscale centers have power needs in the tens of megawatts; new projects are moving to gigawatt scale, including a planned 5-gigawatt campus in Abu Dhabi that spans 10 square miles.
- Localized strains:
  - In northern Virginia, data centers consume about one-quarter of the state’s power, affecting utilities’ ability to serve other customers.
  - Ireland’s grid operator froze new projects in 2022 except those that could generate their own power.
  - Singapore halted approvals in 2019 and now allows facilities only under strict efficiency rules.

### Big Tech and power markets
- Major technology companies are among the world’s biggest corporate buyers of renewable energy.
  - Microsoft, Amazon, and Google have each signed multibillion-dollar power purchase agreements.
- Firms are experimenting with on-site generation and advanced technologies:
  - Microsoft has explored nuclear, including small modular reactors and possible acquisitions of mothballed plants.
  - Google is backing advanced geothermal.
  - Amazon is testing hydrogen for backup power.
- Geographic concentration: the US is home to more than 40 percent of the world’s data centers.
- Implications:
  - Big Tech’s capital could accelerate clean-energy innovation or cement dependence on fossil fuels.
  - Europe has seen AI boost renewables, while US demand still leans heavily on natural gas.

### Efficiency, software, and semiconductor constraints
- AI can both increase electricity demand and help manage it (balancing grids, forecasting renewables, optimizing energy use, piping waste heat into district heating).
- Efficiency gains in hardware and software:
  - New chips such as Nvidia’s Blackwell processors and Google’s tensor processing units (TPUs) aim for more operations per watt.
  - China’s DeepSeek, released in January 2025, was trained at a fraction of the cost and energy of comparable models from OpenAI and Google.
- Jevons paradox: greater efficiency may spark increased use and higher aggregate demand.
- Semiconductors are a strategic choke point:
  - Training state-of-the-art models requires thousands of specialized chips, most designed by Nvidia and manufactured almost exclusively in Taiwan Province of China by the Taiwan Semiconductor Manufacturing Company (TSMC).
  - Geopolitical responses include US export controls on advanced chips and subsidies for domestic fabrication; China is building domestic capabilities; Europe, Japan, and India are investing in their industries.

### Mineral footprint and supply risks
- Fabrication and data centers require substantial minerals and water:
  - Components include gallium, germanium, silicon, rare earths, copper; a single hyperscale campus can contain nearly as much copper as a midsize mine produces in a year.
- Projected 2030 mineral demand from data centers (IEA estimates cited):
  - More than half a million metric tons of copper each year.
  - 75,000 tons of silicon each year.
  - Data centers’ share of global demand could reach 2 percent for copper and silicon.
  - For gallium, data centers could account for more than a tenth of total demand.
- Concentration and export controls:
  - China controls 80–90 percent of global refining of silicon, gallium, and rare earths.
  - In 2023 it restricted exports of gallium and germanium; since late 2024 new curbs followed on tungsten, tellurium, bismuth, indium, and molybdenum.
- Responses by importing regions include recycling programs and alliances with resource-rich countries in Africa and Latin America.
- Price and supply implications: prices for many of these metals have spiked.

### Land, water, and siting considerations
- Hyperscale data centers locate where cheap power, abundant water, and fast fiber-optic links converge. Land is rarely the limiting factor.
- Water constraints:
  - Cooling demands millions of gallons a day.
  - Two-thirds of new US centers since 2022 have been built in water-stressed regions.
  - Local disputes have arisen (Arizona, Spain, Singapore) over competing claims on scarce water supplies.
  - Most of AI’s water footprint is indirect: power plants supplying data centers consume far more water than the centers themselves.
- Latency and climate considerations influence siting:
  - Ireland’s cluster reflects its role as a transatlantic cable hub.
  - Abu Dhabi’s planned 5-gigawatt campus was chosen partly to minimize delays with Asia and Europe.
  - Colder countries (Norway, Iceland) offer climate advantages via reduced cooling needs.

### Policy challenges and implications
- Planning uncertainty:
  - Forecasts of data center demand for 2030 diverge widely; the highest published estimate is nearly seven times the lowest.
  - Governments must expand electricity systems quickly but face risks of overbuilding and locking in fossil fuels.
- Transparency gap:
  - Little public reporting exists from the industry on data center use of electricity, water, or minerals.
  - Greater disclosure would help regulators, utilities, and communities plan.
- Sustainability and equity:
  - Expanding grids and supply chains without safeguards risks repeating boom-and-bust cycles of past commodity races.
  - Benefits could be tilted toward the rich world if developing economies remain suppliers of raw materials and face higher implied costs for energy and capital.
- Potential outcomes:
  - Managed well: the AI boom could accelerate clean energy and foster more resilient supply chains.
  - Managed poorly: the AI boom risks locking in new emissions and deepening resource dependence.

### Key findings and policy recommendations
- Key factual findings:
  - Data centers use about 1.5 percent of global electricity.
  - The largest hyperscale projects have power needs in the tens of megawatts; new projects are moving to gigawatt scale (example: a planned 5-gigawatt campus covering 10 square miles).
  - The US hosts more than 40 percent of the world’s data centers.
  - By 2030, data centers could consume more than half a million metric tons of copper and 75,000 tons of silicon each year; data centers’ share of global demand could reach 2 percent.
  - China controls 80–90 percent of global refining for several critical materials.
  - Two-thirds of new US centers since 2022 have been built in water-stressed regions.
- Policy recommendations implied by the analysis:
  - Treat power plants, grids, water, and minerals as integral parts of digital/AI policy.
  - Expand and modernize electricity systems rapidly while avoiding lock-in of fossil-fuel infrastructure.
  - Increase industry transparency on electricity, water, and mineral usage to aid planning and regulatory oversight.
  - Implement environmental and social safeguards across energy and mineral supply chains to avoid boom-and-bust dynamics.
  - Promote equitable outcomes so developing economies do not become solely raw-material suppliers while bearing higher costs.

*Thijs van de Graaf, associate professor of international politics at Ghent University and energy fellow at the Brussels Institute for Geopolitics; December 2025.*

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_Source: https://www.imf.org/en/publications/fandd/issues/2025/12/inside-the-ai-led-resource-race-thijs-van-de-graaf_