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Most people think the AI boom is a software story.
It’s not.
It’s an energy story.
Every time someone runs a large AI model, generates images, trains systems, or processes massive datasets, it consumes a significant amount of electricity. And this demand isn’t growing slowly. It’s accelerating.
Data centers are already among the largest consumers of power globally, and AI is pushing that demand into a completely different range. Training large models can require megawatts of continuous power, and inference at scale adds another constant load on top. This isn’t a one-time spike. It’s ongoing demand that compounds as adoption grows.
The issue is that the electrical grid was never designed for this.
Most of today’s infrastructure was built decades ago, optimized for predictable, steady demand. Residential use, industrial cycles, and normal business activity. What we’re seeing now is something very different. Multiple high-load systems coming online at the same time, data centers, EV charging, electrified industry, all stacking demand on top of each other.
To put it into perspective, a single large data center can consume as much power as a small city. Now imagine dozens of those being built and expanded at once, all trying to secure stable energy supply.
That’s where the cracks start to show.
We’re already seeing early signs of strain. Utilities are struggling to keep up with interconnection requests. Some regions are delaying new data center projects because they simply don’t have the capacity. Others are warning about future reliability issues if demand continues at this pace.
And this is before AI reaches full-scale adoption.
Now layer in everything else happening at the same time. EV adoption continues to grow, which increases grid load. Industrial electrification is accelerating. At the same time, energy markets are becoming more volatile, with oil already moving from roughly $58 to $76–80, and the potential for further spikes if geopolitical tensions continue.
All of this points to one thing.
Electricity demand is no longer stable. It’s becoming unpredictable and harder to manage.
That’s why the conversation is starting to shift from just “how do we generate more power” to “how do we manage it better.” Coordination, optimization, and localized energy systems are becoming more relevant as the grid gets more complex.
Because if demand keeps rising faster than infrastructure can adapt, the bottleneck won’t be compute.
It will be power.