How to bridge the US energy supply gap to meet the rising demands for computing power in the era of generative AI?

Abstract

The US Government has stated its desire for the US to be the home of the world's most advanced Artificial Intelligence (AI). Arguably, it currently is. However, a limitation looms large on the horizon as the energy demands of advanced AI look set to outstrip both current energy production and transmission capacity. Although algorithmic and hardware efficiency will improve, such progress is unlikely to keep up with the exponential growth in compute power needed in modern AI systems. Furthermore, even with sufficient gains in energy efficiency, overall use is still expected to increase in a contemporary Jevons paradox. All these factors set the US AI ambition, alongside broader electrification, on a crash course with the US government's ambitious clean energy targets. Something will likely have to give. For now, it seems that the dilemma is leading to a de-prioritization of AI compute allocated to safety-related projects alongside a slowing of the pace of transition to renewable energy sources. Worryingly, the dilemma does not appear to be considered a risk of AI, and its resolution does not have clear ownership in the US Government.

Keywords: AI Compute Governance, AI Energy Consumption, Hardware, Energy Supply, Jevons Paradox