Summary: Artificial Intelligence isn’t just reshaping industries—it’s rewriting the global electricity bill. According to newly compiled data, AI systems—especially large language models like ChatGPT—may soon pull more energy than many countries. This isn’t a distant future problem. It’s happening now, and fast. Major tech firms are scrambling to balance growth with climate responsibility, but behind the curtain, their own numbers quietly show that AI ambitions are colliding with sustainability targets. Where does that leave the rest of us?
AI’s Energy Appetite: Outpacing Expectations
Let’s start with the facts, not the fiction. Research published in Joule by Alex de Vries-Gao—a data-focused economist and founder of Digiconomist—shows that AI already burns through up to 20% of the world’s total data center electricity. That figure may have doubled before 2023 ended. Let that marinate for a second. In just months, AI could consume nearly half of all global data center power, excluding bitcoin mining.
And this isn’t some small startup-led spike. De Vries-Gao compares current AI spending power to bitcoin’s rise—and finds bitcoin to be the minor leagues. “The money that bitcoin miners had to get to where they are today is peanuts compared to what Google, Microsoft, and others are pouring into AI,” he says. And unlike bitcoin, which had years to ramp up, AI is scaling like it’s shot out of a cannon.
Climate Goals Meet Corporate Reality
These demands aren’t just theory—they’re hitting sustainability reports in black and white. Google’s own emissions have grown nearly 48% since 2019. Their 2024 report even signals concern: meeting climate goals while expanding AI usage could be harder than projected. Why? Because more AI means more computational power—and that means more energy use.
Strategically, this is a visibility problem. Most tech giants don’t disclose exact energy costs of their AI systems. That opacity makes it hard to know how deep the rabbit hole really goes. But based on public financials, component production figures, and market trends, independent researchers can still put together a sharp sketch of what’s coming.
The Hardware Bottleneck is the Canary in the Coal Mine
To get a more accurate picture, de Vries-Gao didn’t just estimate AI’s outputs—he traced inputs at the manufacturing level. Most efficient AI chips today come from a single source: Taiwan Semiconductor Manufacturing Company (TSMC). Using analysts’ projections and technical data, he tracked how much AI-ready equipment TSMC could deliver.
His conclusion? If production runs flat, AI usage alone will consume about 82 terawatt-hours in 2024. That’s the same as all of Switzerland’s yearly electricity. But industry analysts aren’t predicting flat growth—they expect chip production for AI to double. If that happens, AI could eat up nearly half of the global data center power pool within months. Nothing else in modern tech is moving this fast, not even cloud infrastructure during its boom cycle.
Energy Inflation Without Accountability
Thus far, the pattern is clear: rapid acceleration without transparency. Tech companies are building faster than they’re disclosing. Electricity is being drawn in surges, yet public understanding is trickling in by comparison. Sasha Luccioni from Hugging Face—a respected researcher on AI’s climate impact—warns that too many projections, even strong ones like de Vries-Gao’s, rely heavily on assumed growth and indirect supplier data.
Here’s the question: If those running the infrastructure won’t reveal detailed consumption data, how can society have meaningful conversations about scaling responsibly? What’s stopping major players from publishing full power usage of their AI systems? Is it strategy, fear of backlash, or simply the lack of external pressure?
Big Tech is Building a Paradox
Every company with a net-zero mission is now running against their own ambitions as they invest more into AI. They’re simultaneously promoting sustainability while feeding a machine that contradicts that aim. And consumers? They’re left believing something sustainable is being built when the math tells another story.
You might ask: Is energy-hungry AI worth it, especially when results from models like ChatGPT still require heavy human verification? The demand from users is real, but how many know—or care—about the grid strain it takes to ask those questions?
The IEA Prediction: The Curve is Steep
The International Energy Agency confirms the trajectory. In 2024, global data centers sucked up about 1.5% of all energy—around 415 terawatt-hours, nearly what Saudi Arabia consumes. By 2030, the number could sail past 900 TWh. That’s more than double, and AI is the main driver.
Put bluntly, AI’s energy use is growing four times faster than world power demand overall. Read that again: four. times. faster. And yet we don’t have one public standard for full AI energy disclosure from any of the major vendors. That’s the kind of gap that becomes a fault line waiting to crack.
What Happens If We Do Nothing?
Here’s the hard truth: ignoring this doesn’t make it go away. It only makes the eventual reckoning sharper. The goals of progress and sustainability won’t naturally align unless alignment becomes profit-driven—or government-enforced. Until energy disclosure becomes a default, not an exception, researchers like de Vries-Gao will continue guessing while emissions continue rising.
For investors, it creates risk. For utilities, it throws planning into chaos. For regulators, it’s a blind spot they can’t afford to keep ignoring. And for the public, it confirms a suspicion many already feel but can’t easily prove: that growth doesn’t come free. Not anymore.
So Where Do We Go from Here?
The next steps are not just technical—they’re political and ethical. Do you believe Google, Microsoft, Meta, and Amazon should be required to disclose exact TWh of their AI models annually? If not, what standard are we using to measure their climate promises?
And if AI’s power surge really is “a bigger threat,” as de Vries-Gao says, will cities start pushing back? Will users accept slower AI services if it means cutting emissions? Who gets to decide if those trade-offs are worth it?
Asking the right question matters. How much energy are we willing to trade for synthetic text prediction? And who pays the bill when climate goals fall short?
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Featured Image courtesy of Unsplash and Karsten Würth (ZKWgoRUYuMk)
