THE DATA CENTRE INTERVIEW

future silicon roadmaps design chips with liquid cooling as the primary thermal solution. Direct-to-chip liquid cooling will remain the dominant method at least through 2030, Richard insists, requiring organisations competing in AI to align infrastructure strategies accordingly.

The necessity of adopting liquid cooling for AI brings efficiency benefits. Liquid cooling enables higher efficiency outside the data centre through higher water temperatures, reduced chiller reliance or more efficient heat rejection.

While AI requirements drive the adoption of liquid cooling, efficiency, sustainability and performance benefits accompany the transition.

No pressure: navigating scale and execution for trillion-dollar AI investments Looking at the next 12 to 18 months, Richard points to the scale, speed and capital intensity of infrastructure development. With over a trillion dollars of investment flowing into AI infrastructure, he argues that there is no room for error.

“ Power and cooling decisions made today will determine whether these projects succeed or stall,” says Richard.

Operators must consider proven, end-to-end solutions delivered at global scale rather than evaluating individual technologies. Partner selection becomes equally important for consistent execution across geographies as timelines compress and expectations rise.

“ Operators need to get it right and think beyond individual technologies to proven, end-to-end solutions delivered at global scale. Just as important is choosing partners who can execute consistently, across geographies, as timelines compress and expectations continue to rise,” Richard says.

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