Artificial intelligence is already baked into how businesses work day to day, whether it’s changing how hospitals run or the way banks handle transactions. It’s speeding things up, opening the door to fresh ideas, and pushing industries into areas they haven’t really explored before.

But behind all that progress? The real heavy lifters are data centers. They’re not flashy, and they don’t make a lot of headlines, but they’re absolutely holding everything together. The catch is, they burn through a serious amount of electricity. And as AI gets smarter and more widely used, the power these places need is going up fast. In a lot of areas, the energy grids just weren’t built to handle that kind of demand.

Let’s take a moment to look at how AI is putting new pressure on power systems, and where there’s room to actually fix the issue and even turn it into an opportunity.

The Energy Appetite of AI Data Centers

Running AI systems takes a whole lot of power (like, a lot). These things need some seriously heavy-duty computing to work, and all that hardware doesn’t come cheap on the energy side. Right now, data centers are already using about 1% of the world’s electricity, according to the International Energy Agency.

But that’s just an average. In places like the U.S., China, and across the EU, that number jumps up to somewhere between 2% and 4%. And in certain hotspots? It’s way more than that. Northern Virginia, for example, where there’s a massive cluster of data centers, sees these facilities using over 10% of the region’s total electricity. Over in Ireland, it’s passed 20%.

To put that in perspective, a single large-scale data center can pull more than 100 megawatts. That’s roughly the same amount of power as 350,000 to 400,000 electric vehicles would use in a year. And considering there were around 17 million EVs sold globally in 2024, that comparison really helps show just how massive AI’s energy footprint is getting.

And yeah, as the models keep getting bigger and more advanced, those numbers aren’t going down anytime soon.

What AI Means for Power Grids

This wild surge in demand is pushing the capacity of grids hard, especially in places with dense clusters of data centers. Take Northern Virginia, (data center alley) utilities like Dominion Energy have publicly warned that unless the infrastructure gets a serious upgrade, meeting future energy needs could become a real struggle. Some estimates suggest that by 2030, new data centers alone could drive a 50% jump in electricity demand in Virginia.

And it’s not just a local issue. Goldman Sachs (https://www.goldmansachs.com/insights/articles/ai-to-drive-165-increase-in-data-center-power-demand-by-2030) expects global demand from data centers to grow by 50% by 2027 and a staggering 165% by 2030 compared to 2023. Meanwhile, the RAND Corporation (https://www.rand.org/pubs/research_reports/RRA3572-1.html) projects that by 2025, we’ll need another 10 gigawatts of power capacity worldwide just to keep AI data centers running. (That’s more electricity than the entire state of Utah can produce.)

Meeting the AI Power Demand

Trying to keep up with all this growth on the infrastructure side? It’s turning into a real headache. One of the biggest hurdles that we see is that building out new power transmission lines takes forever….usually we’re talking years, not months. Between jumping through permitting hoops and the actual construction, the whole thing usually drags out for five to ten years. And that’s putting the brakes on a lot of expansion plans.

Developers working on data centers are feeling the crunch. In 2023, vacancy rates in key hubs like Northern Virginia dipped under 1%, which makes finding space for new builds even harder. On top of that, delays around permits and getting connected to the grid are only adding to the mess. It’s gotten to the point where some companies are seriously thinking about setting up their own on-site power just to stay on track.

And then there’s the renewable energy piece. Long-term, solar and wind are absolutely the way forward, but they’re not exactly consistent. Since they rely on the weather, you can’t always count on them to keep energy flowing 24/7. That makes things tricky when AI systems need a steady stream of power. Some regions are considering keeping fossil fuel backups in the mix to ensure reliable operation, which has ruffled the green-energy camp’s feathers a bit.

Where Solutions Are Taking Shape

Even with all the power AI is starting to chew through, there’s actually a bit of a silver lining, tech is getting smarter, and innovation’s making some real progress. For one thing, the chips that power AI have gotten way more efficient over the years. According to the International Energy Agency, (https://www.iea.org/commentaries/what-the-data-centre-and-ai-boom-could-mean-for-the-energy-sector) energy use per computation has dropped by 99% since 2008. Plus, energy efficiency is doubling roughly every two and a half years.

Data centers are doing their part, too. They’re bringing in new tools like liquid cooling systems and better server setups to cut down on power use. At the same time, renewables are coming online fast solar and wind are picking up serious momentum. The IEA is projecting 237 gigawatts of new solar power and another 78 gigawatts from wind by 2030.

On a bigger scale, power grids themselves are slowly getting smarter—adding new transmission lines and better systems to juggle supply and demand. Goldman Sachs (https://www.goldmansachs.com/insights/articles/ai-to-drive-165-increase-in-data-center-power-demand-by-2030) says Europe alone might pour close to €850 billion into solar, wind, and related infrastructure over the next decade. Meanwhile, the U.S. is pushing through its own projects, both federally and at the state level, trying to speed things up.

A big part of what’s working right now is collaboration. Data center builders, utility companies, and government agencies are starting to get more in sync. McKinsey (https://www.mckinsey.com/industries/private-capital/our-insights/how-data-centers-and-the-energy-sector-can-sate-ais-hunger-for-power) suggests that this kind of coordination is super important if we want to avoid burning out the grid while still making room for AI to grow. Things like quicker permitting and energy incentives could actually make a big difference here.

Investment and Business Opportunities

All this demand for energy isn’t problematic… it’s actually turning into a huge business play for the investors taking note. According to McKinsey, (https://www.mckinsey.com/industries/technology-media-and-telecommunications/our-insights/ai-power-expanding-data-center-capacity-to-meet-growing-demand) scaling up the infrastructure needed to handle AI could unlock over a trillion dollars in potential investment. And honestly, we’re already seeing big moves. Just take the $7 billion deal between Blackstone and Digital Realty to build AI-focused data centers, and that’s barely scratching the surface of what I believe is coming.

Companies making energy-smart chips and advanced server gear are already seeing the payoff. Same goes for folks in the cooling equipment and alternative energy providers – everyone trying to help data centers run more efficiently is getting pulled into a lot of opportunity for innovation and growth.

For utilities, the spike in demand could be a real boost for revenue. But here’s the tricky part…they’ll have to find a way to scale up without sending customer bills through the roof. Whether they can pull that off might end up being the thing that separates the winners from the ones playing catch-up in this fast-moving space.

Power, Progress, & What Comes Next

AI is pushing industries to move quicker and think on a bigger scale, but keeping it all powered up doesn’t come cheap. Data centers are right at the center of this shift, and the way their energy needs keep climbing is forcing some pretty serious talks about how our power grids are going to keep up.

Still, it’s not all downside. Every significant advancement in industry that encounters problems will lead to improvements that find applications in other use cases. This pressure is also creating space for better ideas, smarter technology, tighter collaborations, and smart bets from investors who see where things are heading.

The thing is, building out AI infrastructure isn’t just a technology problem, it’s starting to shape everything from job markets to energy prices. What we do now is basically going to decide whether AI keeps this kind of hockey stick growth or runs into major energy roadblocks.