Yet while AI-related demand is a new addition to energy transition plans, in absolute terms it will be a relatively small part of the overall system.\u00a0For context, data centres today consume 415 terawatt hours of electricity \u2014 equivalent to about 1.5 per cent of global demand, says the International Energy Agency.<\/a> While the IEA predicts this will more than double to 945 TWh by 2030, electric vehicles and air conditioning will be bigger contributors to demand growth.<\/p>\n <\/p>\n Meanwhile, AI\u2019s reach is likely to be larger than its energy footprint. The technology is expected to improve the efficiency of almost everything we do. It is true of course that, for climate change purposes, cutting CO\u2082 today is worth more than cutting it tomorrow. But looking at the numbers at stake, if AI facilitated even modest savings on overall electricity use, it would be a net positive for the energy transition.<\/p>\n That looks all the more likely because some pockets of the energy system are staggeringly inefficient. The materials value chain \u2014 which produces 60bn tonnes a year of steel, glass, hydrogen, ammonia, copper and the like \u2014 uses four to five times more energy than the bare minimum required by the chemical reactions involved, according to consultancy Thunder Said Energy<\/a>.<\/p>\n That\u2019s an opportunity for improvement writ large. Finding new materials, catalysts or processes that can produce stuff more efficiently is the sort of \u2018\u2018needle in a haystack\u201d problem that AI is ideally suited to, and is helping to solve in the biotechnology sector.<\/p>\n In the battery space, the race is on to use AI to find better materials, and even chase a breakthrough on solid-state devices which should be smaller, lighter and could be used to store energy for longer-distance transport. Microsoft and a US government laboratory have teamed up to screen tens of millions of new solid-state electrolytes for lithium-based batteries, notes Simon Bennett from the IEA,<\/a> narrowing it down to 23 potentially viable candidates.<\/p>\n There are plenty of others using AI models to sift through databases, too. And that\u2019s just the low-hanging fruit. As \u201csmart\u201d appliances and sensors proliferate, AI should help reduce waste in energy production, transport and lots more besides. Amid a lot of hazy use cases, this one has the potential to do a power of good.<\/p>\n<\/a><\/p>\n