As Yale associate professor Michael Peters argues in an article<\/a> for the\u00a0IMF, America must rediscover\u00a0its dynamism if it is to maintain its global standing. This is why an explicit part of the US AI plan is to invent and embrace productivity-enhancing AI uses that the world wants to emulate. My company\u2019s own analysis suggests that AI has already raised US labour productivity by 0.1 to 0.9 percentage points and might eventually raise global productivity growth by about a half point annually over the coming decade. <\/p>\n <\/p>\n However, increasingly, the key limit to the US leading the AI arms race is less technological and more the physical limits that AI places on US energy demand. <\/p>\n The International Energy Agency estimates that global data centre electricity demand will more than double<\/a>, from 460 terawatt hours of electricity in 2024 to more than 1,000TWh by 2030 and will reach 1,300TWh by 2035. In the US, it expects data centres will account \u201cfor nearly half of electricity demand growth between now and 2030\u201d. <\/p>\n But it is not just the scale of the energy demand needed for US AI that is the issue. It is also the role hydrocarbons play in fuelling this growth. The IEA expects more than half the electricity powering US data centres to still come from fossil fuels, notably natural gas<\/a>, until after 2030. But even by 2035, the IEA forecasts more than 40 per cent of US AI energy being hydrocarbon-based due, in part, to the Trump administration\u2019s cancellations of support for renewable energy initiatives.<\/p>\n <\/p>\n While China also relies on hydrocarbons (mainly coal) to power its data centres, it is looking to change this mix. Its strategy is focused on developing computing resources close to coastal renewable power sources. The IEA estimates that, unlike the US, China will see the level, as well as the share, of data-centre electricity generation coming from hydrocarbons falling after 2030.<\/p>\n One strategic risk from this US AI reliance on hydrocarbons is relative costs. US average electricity prices have already risen 38 per cent since 2020 with these cost increases often linked to rising AI data centre demands. These cost pressures are only likely to rise. In contrast, the cost curves for renewables continue to come down faster than for fossil fuels, potentially disadvantaging US AI vs Chinese AI in the longer run.<\/p>\n However, the bigger strategic challenge for US hydrocarbon-powered AI may not only be price, but also its physical impact on the economy. Energy, water and food production are intimately linked. The increased US energy needs for AI, when powered by hydrocarbons, will lead to more US water demand and stress than if AI growth were powered by renewables. <\/p>\n Recommended<\/p>\n<\/a><\/p>\n
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