AI’s Energy Appetite: Why It Matters and What’s Being Done About It

Sorry about the silence on this blog - it's college application season! In the last post, we discussed what Artificial Intelligence (AI) could do to help with energy efficiency. Over the last few months, it's become clearer to me that AI itself is a huge energy hog - so this post is less actionable and more informational. While I'm at it, Happy Thanksgiving to you all.

Artificial Intelligence is shaping almost everything we do: homework help, language translation, personalized recommendations, smart devices, and even how electricity grids run. But behind every reply and voice command, there’s something most people never think about: AI runs on enormous amounts of energy.

Why AI Uses So Much Energy

Modern AI systems rely on large models trained on huge amounts of data. Training one frontier-level model like Gemini, GPT, or Claude, can require weeks (or months) of nonstop computation on thousands of specialized chips (called accelerators). Research estimates that a single large training run can consume tens of gigawatt-hours of electricity, roughly the same power used by several thousand U.S. homes in a year.

And training is only the beginning. Once these models go “live”, they answer billions of daily queries (called inference). Each prompt may use only a tiny amount of energy, but globally, these interactions add up. After all, queries are much more common than training a large model. Data centers (the buildings full of servers that run AI) already consume about 2% of global electricity, and the International Energy Agency projects that demand from AI and data centers could double by 2026. That’s equivalent to the entire electricity consumption of some industrialized nations.

The Environmental Impact

If the energy powering AI comes from fossil fuels (which as discussed before, it mostly does), it increases carbon emissions. Some data centers also use large volumes of water for cooling, sometimes millions of gallons a day! This creates real tension: AI can help solve environmental problems, but its own footprint may grow sharply if nothing changes.

So What’s Being Done About It?

The good news is that enormous innovation is already underway to make AI more energy-efficient:

1. More Efficient Hardware

Companies like Google, NVIDIA, and AMD are designing chips that perform AI calculations with far less energy per operation. Google’s new TPU generations, for example, focus heavily on energy efficiency.

2. Smarter Model Design

AI engineers are reducing the energy cost by:

• Using Mixture-of-Experts (MoE) models, which activate only small parts of the model at a time.

• Applying algorithmic optimizations that cut redundant computations.

• Developing smaller, specialized models for specific tasks so that not every request uses the largest model.

3. Renewable-Powered Data Centers

Tech companies are aggressively shifting to renewables. Google, Microsoft, Meta, and Amazon have all committed to running data centers on carbon-free or 100% renewable energy, with many sites now powered by wind, solar, geothermal, or hydropower.

4. Re-using Waste Heat

In parts of Europe and the U.S., data centers are exploring ways to use their waste heat to warm nearby buildings—turning an energy problem into a community benefit.

5. Policy and Transparency

Governments and researchers are increasingly calling for energy-use reporting for AI models. This could encourage companies to design greener systems from the start.

The Takeaway

AI is powerful, but power-hungry. As AI grows, so does its energy footprint—but innovation in chips, model architecture, and clean-energy data centers is already helping. With smart design and responsible use, AI can evolve into a tool that supports our environmental goals instead of working against them.