As generative AI becomes part of daily life, we’ve entered an era of extraordinary capability and extraordinary consumption. What once felt theoretical is now measurable down to the smallest units. Recent studies from Mistral AI and Google suggest that the footprint of a single interaction is nearly negligible — fractions of a watt, a few drops of water, and emissions on par with streaming video for less than a minute.

On their own, these numbers feel trivial, but this is where scale changes everything. Multiply that consumption by billions of prompts every day, across thousands of data centers worldwide, and the aggregate burden is no longer measured in drops. It’s measured in billions of cubic meters of water and gigawatt-hours of electricity. Industry forecasts warn that by 2027, global AI demand could account for up to 6.6 billion cubic meters of water annually.

That’s the point: scale makes the small, big. The difference between a manageable footprint and an unsustainable one won’t be decided by decimal points—it will be decided by how we manage scale. That’s why our industry needs to act now.

Scaling Smarter in What Already Exists

Every rack already deployed in a data center represents an opportunity to change the slope of that curve. With efficient processors, workload tuning, and smarter orchestration, we can deliver far more performance per rack without proportional increases in water and power use. Scale may be inevitable, but the rate at which its impacts grow is not.

Building for Growth Without Accelerating Strain

There will be new data centers, and they will be essential. However, by optimizing the ones we already have, we reduce how quickly we need to add capacity. This ensures new builds become strategic expansions rather than urgent stopgaps. The scale of future generation projects can be greatly reduced when we get more from what’s already in place.

Scale With Stewardship

The per-prompt numbers matter because they give us visibility, but the real measure of responsibility is how we handle the exponential scaling of those numbers. Efficiency at the rack and system level—processors, cooling, orchestration—is how we bend the curve of AI’s footprint while still enabling innovation. At scale, even small efficiency improvements cascade into meaningful reductions in water and energy demand.

The Bigger Picture

Scale is what defines AI’s environmental footprint. The industry’s challenge is not to minimize the decimal—it’s to flatten the curve. At Ampere®, our mission is to help customers maximize performance per rack and scale efficiently, so growth in AI innovation doesn’t mean unchecked growth in environmental strain.

AI will scale. The choice we have is whether its impacts scale just as fast. With smarter infrastructure, they don’t have to.