Could data center growth halt by 2030? Report claims power demands may halt AI advances within the next few years | Daily Reports Online
- Gartner study suggests AI data center power requirements will grow by 26% in 2026
- This is a 13% increase over an earlier forecast which capped growth at 500TWh.
- AI data centers currently account for 31% of total data center power consumption, but are projected to exceed conventional server power needs by 2027
The last few years have seen AI chip demand skyrocket, with every major player in the industry investing in infrastructure, training, and inference hardware to build out their own data centers and clouds for compute.
The assumption was that better, faster chips were the key to unlocking both Artificial General Intelligence (AGI) and AI-infused efficiency gains as the world shifts its focus from AI agents to AI operators.
The bottleneck that many saw coming but was arguably downplayed is now back in focus: Power limitations may cap future data center growth globally.
Not a chip problem, but an energy conundrum by 2030?
A recent report by Gartner indicates that AI servers might not have a chip supply problem, but power limitations that could decisively shape future data center expansion, bringing it to a grinding halt by 2030 if not addressed.
Gartner estimates while current datacenter power needs are capped at 132 GW, they could reach 290 GW by 2030, indicating that energy constraints will undoubtedly rule the roost in future AI data center planning.
“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 makes even the most extreme case painted by the electric infrastructure provider, Schneider Electric, look tame.
This is why 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.
Scaling power generation or upgrading grids may arguably be a more complex or time-consuming endeavor than just the AI data center buildout, with Goldman Sachs estimating that as much as $720 billion in grid spending might be needed by the end of the decade to account for the added load that AI data centers will bring to the table.
Whether this plays out exactly as projected by Gartner remains to be seen; however, with every industry player indicating that they intend to increase spending on AI infrastructure, the projection that sees current power needs (565TWh) more than double (1200TWh) by 2030 is a very possible scenario, and the industry’s focus might shift to delivering both power and efficiency versus raw compute over time to account for the change.
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