As artificial intelligence becomes more embedded in our daily lives—from personalized recommendations on Netflix to near-instant search results on Google—the infrastructure powering it is undergoing a dramatic evolution. Behind every AI-powered feature lies a sprawling network of data centers, and the demand these centers place on our energy systems is growing at a staggering rate.
A Looming Power Surge
Projections suggest that AI data centers may demand up to 100 gigawatts (GW) of new power by 2030. To put that in perspective, that’s nearly the entire electricity consumption of California. This surge is driven by two fundamental types of AI operations: training and inference.
“There are two types of data centers fueling AI: one for training models and one for performing predictions using those models, also known as inference,” the report states. “Both are highly energy-consuming due to the use of GPUs and cooling systems.”
Training: The Energy-Hungry First Step
The training phase of AI involves feeding vast datasets through neural networks to ‘teach’ the system how to understand patterns, language, or visual input. This process can take weeks or even longer, depending on the size and complexity of the model.
“The training process for AI models is energy-intensive because it requires a large amount of data and computing power,” the transcript notes. “This is what enables companies like Google to provide quick and accurate search results.”
Inference: Everyday AI, Still Not Cheap
Once an AI model is trained, it’s deployed to perform inference—essentially, applying what it has learned. Inference powers countless features in apps, websites, and devices around the world.
“The inference step, on the other hand, is less energy-intensive,” the report adds. “It involves looking up pre-computed information, and AI is already being used in various aspects of daily life, such as Netflix’s recommendation system.”
What It Means for the Grid—and the Planet
While training uses the bulk of compute and power, the scale of inference—potentially billions of interactions per day—still poses serious energy challenges. And as AI adoption expands, so too does its impact on our power grids and emissions profile.
“The impact of AI data centers on the grid and emissions is still being figured out,” the transcript admits. “But it is certain that AI will continue to increase and become more pervasive in everyday life.”
Building Smart, Building Clean
With the inevitability of AI’s expansion, energy planners and tech leaders alike are facing a critical challenge: how to build and operate this next wave of data centers in a sustainable way.
“The goal is to plan and build AI data centers in a reliable and clean manner,” the transcript concludes, “considering the significant amount of energy they consume and the potential effects on the environment.”
Final Thoughts
AI may be the brain of modern digital life, but energy is its heartbeat. As demand skyrockets, the industry must move quickly to align innovation with infrastructure, and progress with sustainability. The way we power the intelligence of tomorrow will shape more than just technology—it will shape the future of the planet.
