New Solutions for the looming power gap
On-site generation: Power assets installed at data centre facilities supply electricity directly to the user, eliminating or reducing reliance on the main grid. The key advantages are faster deployment timelines, improved reliability, and reduced exposure to interconnection constraints.
While natural gas-fired generation represents the most scalable on-site solution, hyperscalers (large-scale data centres) are also assessing lower carbon alternatives, including geothermal energy, solar paired with storage, and for the longer term, small, modular nuclear reactors (SMRs), as potential sources of low-carbon baseload power.
Co-located generation: Another option is to develop data centres close to large-scale power plants that are under-utilised or retired. This allows operators to secure a dedicated electricity supply while using established grid interconnections. Amid land scarcity, utilities may welcome the opportunity to monetise valuable existing grid connections with retired coal or nuclear sites.
Broad array of options and technologies
The supply of electricity to meet data centre demand may come from a wide range of sources, each with different characteristics in terms of power supply, costs, emissions, the development process, and lead times. Although today's electricity supply mix remains dominated by fossil fuels, the IEA projects that renewables will supply almost 50 % of the additional demand by 2030, with nuclear power becoming increasingly important after 2030.
The rapid expansion of AI data centre capacity is expected to drive sustained investment across a wide range of power generation and energy infrastructure technologies. As hyperscalers and data centre operators prioritise speed, reliability, and scalability while balancing their decarbonisation commitments, a diverse energy mix is likely to emerge.
While any equity investment carries risk of losses, this energy mix may create opportunities for investors in both traditional and renewable generation technologies and infrastructure solutions, including electrical equipment and storage systems. The primary risks to this theme include a lower-than-expected buildout of AI infrastructure, due to revisions in demand or bottlenecks in supply.