A recent report from Gartner warns that power limitations, not chip supply, may decisively shape and potentially halt future AI data center expansion by 2030. According to the research, surging demand for compute-intensive AI workloads is driving unprecedented data center power growth, making power security the new battleground in the global AI race.
The Power Bottleneck
Gartner estimates that while current data center power needs are capped at 132 GW, they could reach 290 GW by 2030. The study also suggests AI data center power requirements will grow by 26% in 2026 alone, a 13% increase over an earlier forecast that capped growth at 500 TWh. AI data centers currently account for 31% of total data center power consumption, but are projected to exceed conventional server power needs by 2027.
“Surging demand for compute-intensive AI workloads is driving unprecedented data center power growth, while AI capacity is now constrained by power availability, making data center power security the new battleground for scaling and protecting margins in the global AI race,” said Linglan Wang, Director Analyst at Gartner.
The current estimate even makes the most extreme case painted by electric infrastructure provider Schneider Electric look tame, according to the report.
Projected Growth and Grid Strain
Goldman Sachs estimates that as much as $720 billion in grid spending may be needed by the end of the decade to account for the added load from AI data centers. The projection that sees current power needs (565 TWh) more than double (1200 TWh) by 2030 is a very possible scenario, given that every major industry player intends to increase spending on AI infrastructure.
| Metric | Current | Projected 2030 |
|---|---|---|
| Data center power capacity | 132 GW | 290 GW |
| AI data center power share | 31% | Exceed conventional by 2027 |
| Total power consumption | 565 TWh | 1,200 TWh |
| Required grid investment | — | $720 billion (Goldman Sachs) |
Industry Response
Nvidia CEO Jensen Huang has already begun to single out power efficiency as the reason its chips are superior to the competition. In a recent interview with Bloomberg, Huang said that data centers and enterprise consumers alike would want the highest number of "tokens per watt" to eke out maximum value in a power-constrained future.
The industry's focus may shift from raw compute to delivering both power and efficiency over time. Scaling power generation or upgrading grids is arguably a more complex and time-consuming endeavor than AI data center buildout itself.
Implications for Enterprise Technology Leaders
For CTOs and technology procurement leaders, this means future AI infrastructure planning must account for power availability as a critical constraint. Data center power costs and location decisions become strategic. Enterprises relying on cloud AI services may face capacity limitations or price increases as providers prioritize margins. The race for AI advantage is now as much about securing energy as about acquiring chips.
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