Artificial intelligence investment is entering a more selective phase as companies and investors look beyond early excitement and focus on the data centre infrastructure required to run AI systems.
Recent analysis from Goldman Sachs suggests the market is moving toward what the firm describes as a “flight to quality.” In practice, investors are paying closer attention to companies that own and operate large data centres and computing infrastructure. Firms offering narrow AI tools or experimental software are receiving less attention.
Goldman Sachs expects spending on AI infrastructure to grow rapidly as companies expand computing capacity for model training and deployment. Hyperscale cloud firms are investing tens of billions of dollars each year in new data centres and computing hardware. Networking systems are also expanding to support this growth.
AI demand is reshaping the data centre market
Goldman Sachs Research estimates that AI workloads could account for about 30% of total data centre capacity in the next two years, as demand for computing power grows in cloud services and enterprise applications. The change reflects how AI tasks differ from traditional cloud workloads. Training large models requires thousands of chips running in parallel for extended periods. Inference, the process of generating responses or predictions, also requires steady computing power when services run.
Cloud providers and AI developers are now expanding data centre capacity at a pace not seen during earlier phases of cloud computing. Infrastructure demand extends beyond computing hardware. Energy supply is becoming a central issue in the AI race.
Goldman Sachs Research estimates that global data centre power demand could rise about 175% by 2030 compared with 2023 levels, driven largely by AI workloads. The firm says this increase would be roughly equal to adding the electricity demand of another top-10 power-consuming country to the global grid. Rising power demand is also pushing utilities and governments to consider new investment in energy infrastructure.
Infrastructure limits are shaping AI strategy
The growing need for power and cooling is influencing where new AI data centres are built. Space requirements are also shaping site selection. Large facilities are often located near stable energy sources and high-capacity fibre networks. Some companies are building AI training clusters in remote areas where land and electricity are easier to secure. The location of data centres can also affect environmental impact. Academic research on AI infrastructure shows that cooling systems and geographic location can influence energy use and water consumption as much as hardware efficiency.
The limits are starting to affect how technology firms plan their AI strategies. Building new models or software is only part of the challenge. Companies must also ensure they have the infrastructure needed to run those systems reliably. In many cases, building that infrastructure takes years.
Construction of large data centres involves complex supply chains. Projects often require land acquisition and grid connections. Many also depend on long-term energy agreements. Shortages of electrical equipment and delays in grid expansion can slow new projects. The constraints help explain why investors are paying more attention to companies that already control large data centre networks.
A selective phase of the AI market
During the first wave of generative AI adoption, many companies saw their market value rise simply by associating themselves with AI. That phase is now beginning to change as investors reassess where AI growth will occur.
Investors are examining which companies have the infrastructure and revenue models needed to support long-term deployment. Data centre operators and chip manufacturers sit near the base of that ecosystem. Their services are required regardless of which AI applications gain traction.
During previous waves of computing growth, companies that built the underlying infrastructure often captured stable revenue. Software platforms, in contrast, rose and fell more quickly. A similar dynamic may now be forming in the AI sector.
Infrastructure expansion also raises new questions. Energy demand and grid capacity are becoming central issues for governments and industry planners. Environmental impact is also drawing closer scrutiny.
In the coming years, the AI economy may depend as much on power plants and cooling systems as it does on algorithms and software. That reality is shaping the next stage of the AI race.
(Photo by Lightsaber Collection)
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