Powering Intelligence: The Energy Implications of the AI Boom

The Race to Power AI Sustainably

Supplying electricity to AI infrastructure presents enormous challenges and opportunities. The IEA notes that while renewables like solar and wind will provide about half of the new demand, natural gas and nuclear will also play critical roles. By 2035, renewables are expected to supply an additional 450 TWh for data centres, with natural gas and nuclear each contributing around 175 TWh.

Technology firms are becoming major players in energy procurement. Companies like Google and Microsoft have set ambitious targets for “hourly matching,” ensuring that every hour of electricity consumed is matched by local clean energy generation. While these goals are technically feasible, they require complex portfolios of solar, wind, battery storage, and even emerging technologies like small modular nuclear reactors and next-gen geothermal.

Yet infrastructure remains a bottleneck. Grid connection delays, aging transformers, and long permitting processes mean that 20% of planned data centre projects could be at risk of delay. In regions like Ireland and Virginia, data centres already consume over 20% of the total electricity supply, creating local strain even as national systems cope.

AI’s Role in Optimizing the Energy Sector

If AI is a source of rising electricity demand, it’s also a tool for efficiency. AI is already being deployed to optimize oil and gas exploration, predict maintenance needs, detect grid faults, and forecast renewable energy generation. These applications could unlock up to 175 GW of transmission capacity without building a single new power line.

In manufacturing, AI improves production efficiency and reduces waste. In the building sector, AI-enhanced climate control systems could save 300 TWh of electricity annually—equivalent to the combined output of Australia and New Zealand. In transportation, AI-driven traffic management and vehicle routing could cut energy consumption by as much as 120 million cars’ worth of fuel.

AI also shows promise in accelerating energy innovation. From battery chemistry discovery to carbon capture materials and catalyst design for synthetic fuels, AI can cut years from traditional R&D cycles. However, the report warns that energy start-ups with AI components currently receive only 2% of total clean tech investment—a gap ripe for correction.

Sustainability, Security, and the Unknowns

Despite its energy appetite, AI’s net impact on climate change could be positive. While emissions from data centres may rise from 180 Mt CO₂ today to 300 Mt by 2035 (or 500 Mt in the most intensive scenario), the potential for emissions savings from AI-led efficiencies is even greater. If widely applied, AI tools could reduce energy-related emissions by about 5% globally by 2035. Still, AI is no silver bullet—it must be coupled with strong policy and industrial coordination.

AI also introduces new dimensions to energy security. AI tools can detect cyber threats and physical attacks on infrastructure faster than traditional methods. But the energy and hardware supply chains enabling AI—especially for rare minerals like gallium—are heavily concentrated, with China producing 99% of global gallium supply. By 2030, data centres could demand more than 10% of current gallium output.

Emerging economies may face barriers like poor grid reliability, but they also stand to benefit. AI could enable these nations to “leapfrog” legacy infrastructure with digital energy systems, smart grids, and distributed renewables. However, without targeted support, these regions risk being sidelined in the AI transition.

Conclusion

Artificial intelligence and energy are no longer separate worlds. Their interdependence is deepening, and the implications are profound. From reshaping electricity grids and global trade flows to enabling cleaner, smarter energy systems, AI is now a central force in the energy transition.

But the road ahead is uncertain. Will hardware and software continue their rapid efficiency gains? Can energy systems scale up fast enough to meet AI’s demand without sacrificing affordability or decarbonization goals? Will global collaboration outpace protectionism and supply chain vulnerabilities?

One thing is clear: preparing for the AI-powered future means reimagining the energy systems that will sustain it. Governments, tech giants, utilities, and investors must come together now—not later—to ensure the intelligence revolution doesn’t outpace the power that fuels it.

One example of how AI is already transforming energy management in practice is NZero’s AI-driven Energy Management System. NZero’s platform provides real-time, 24/7 carbon emissions tracking—down to the hourly level—and enables enterprises, municipalities, and building operators to reduce their Scope 1, 2, and 3 emissions more effectively. By using machine learning and automated analytics, NZero helps users not only monitor but optimize energy use and emissions, driving both sustainability performance and operational savings. With increasing pressure to decarbonize and disclose climate-related impacts, AI solutions like NZero’s are becoming essential infrastructure for a net-zero future.