Artificial intelligence has gone from quietly shaping what we see online to landing squarely in our laps. What began as algorithms recommending our next TikTok or Netflix binge has evolved into generative AI — allowing us to create everything from poetry to images, and even hyper-realistic videos. In just over a year, tools like ChatGPT, Gemini, and CoPilot have exploded in popularity and proliferated at a breakneck pace, with AI now running directly on smartphones, laptops, and even fridges.

The financial implication of this new technology could spiral into the trillions, based on the recent eye-watering investment projects we’ve seen under the Trump regime. But, there’s also a hidden cost to AI - one measured in electricity, water, and raw materials. Every AI-powered query, slimy deepfake, and prosaic Google search consumes massive amounts of energy. That energy is pulled from power grids, cooled with vast amounts of water, and run on computing hardware built from mined metals. And, as AI becomes more advanced, those demands are only growing.

This surge in AI adoption coincides with a critical moment in the global effort to reduce carbon emissions and combat climate change. The environmental impact of AI has sparked urgent questions: How significant is the ecological footprint of these technologies, and can their environmental costs be accurately quantified as they continue to accelerate?. AI gobbles up huge amounts of energy because it relies on powerful computers to process vast amounts of data.

These so-called “servers” are housed in massive data centres, where high-tech processors (or GPUs) work together to carry out complex calculations. The more sophisticated the AI system, especially when creating things like text or images, the more energy it needs to operate, and the more chilled water it needs to offset the crippling heat it emits. In addition, computing chips like GPUs rely on semiconductors, typically made of silicon, layered with metals such as aluminium, copper, lithium, and cobalt. The entire supply chain of these chips adds to the resource requirements of the AI industry, from the fabrication plants where they are manufactured to their transportation.

In the race to develop the leading AI systems, tech giants are pouring billions into the construction of data centres and the procurement of high-powered chips. Meta recently announced plans to invest $65bn (£52.7bn) in AI infrastructure this year alone. The company formerly known as Facebook is building a massive data centre in Louisiana that Mark Zuckerberg claims would be large enough to cover a significant part of Manhattan.

Then there’s the Stargate Project, a US infrastructure gambit worth $500bn overseen by AI, data and networking titans OpenAI, Oracle, and Japan’s SoftBank – with the blessing of President Donald Trump. But, how much energy does AI require exactly? Researchers, policymakers, and industry leaders are beginning to dig up answers, with some early figures shedding light on AI's stark environmental toll.

At the start of last year, the International Energy Agency (IEA) released its global energy outlook for the next two years. For the first time, the report included projections for electricity consumption tied to data centres, cryptocurrency, and artificial intelligence. According to the IEA, these sectors collectively accounted for nearly two per cent of global energy demand in 2022. The agency also warned this demand could double by 2026, making it roughly comparable to the total electricity consumption of Japan.

To explain the mercurial rise of AI, it has been compared to the most valuable commodities throughout history, such as oil and gold. In 2019, a new metaphor emerged when researchers compared the energy burden of AI to the automotive industry. The team from the University of Massachusetts, Amherst, conducted a life-cycle assessment for training several common large AI models. According to AI and sustainability expert Noman Bashir of the Massachusetts Institute of Technology (MIT), this constant cycle of upgrades means AI is also a profligate consumer of energy.

Alongside scientific research and forecasts, recent disclosures from tech giants provide an eye-opening glimpse into the escalating energy demands of AI. Microsoft’s annual environmental report claims its global water usage shot up more than a third between 2021 to 2022, coming in at a total of almost 1.7 billion gallons. This compares to the far more modest 14 per cent increase in water usage Microsoft reported between 2020 and 2021.

Google is also dealing with spiralling water usage. The search giant used about 20 per cent more water than it did last year as per its most recent environmental report. This equates to roughly 5.6 billion gallons, or the equivalent of what it takes to irrigate 37 golf courses annually, as per Google’s own estimates. Notably, Google’s greenhouse gas emissions have also risen 48 per cent since 2019, according to the firm’s latest environmental report, driven by the increasing power demands of AI.