Can chip efficiency slow AI's energy demand?

In March, Nvidia announced a new microchip designed for AI that is 25 times more energy efficient than its predecessor. Two months later, Google announced one with a 67% efficiency improvement. Today, the rest of the semiconductor industry is hyperfocused on efficiency gains.

Will they save us from ballooning data center energy demands?

In this episode, Shayle talks to Christian Belady, former Microsoft vice president now focusing on data center advanced development. They unpack concerns about this new surge of demand and whether it’s different from the energy scare two decades ago. Back in 1999, researchers predicted that data centers could end up consuming half of U.S. electricity. But instead, demand remained largely flat at about 4% as cutting-edge hyperscale cloud computing displaced inefficient, on-premises servers.

And yet, driven by the AI boom, energy concerns are back. The Electric Power Research Institute predicts that data center loads could consume 9% of U.S. power generation by 2030. Demand is already rising fast, with emissions at both Google and Microsoft up significantly. 

Shayle and Christian examine the factors driving those trends and what we can do about it, covering topics like:

• Whether chip efficiency improvements will lead to energy savings or just more powerful computing
• The upper limits of Moore’s Law
• Energy, labor, and other big constraints on AI growth
• Changing computing architecture to find energy savings
• Enlisting data centers in integrated, or compulsory, demand response
• Using AI to improve chip design 

Recommended resources

• Fierce Electronics: Power-hungry AI chips face a reckoning, as chipmakers promise ‘efficiency’
• Latitude Media: The data center of the future looks like a massive virtual power plant
• Latitude Media: Enchanted Rock is selling utilities on flexible data center connection
• Latitude Media: Energy is now the ‘primary bottleneck’ for AI
• Catalyst: Under the hood of data center power demand

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