Why Our Code Shouldn’t Cost the Earth
Image Credit:FreePixel
AI models are impressive. They write code, generate images, debug faster than most interns, and are the perfect teammates. We must talk about the environmental cost of all this intelligence. It is not being discussed enough.
Prompting a large language model, training a neural net, or pushing heavy compute jobs to the cloud always has a silent byproduct: energy consumption and, by extension, carbon emissions. Training GPT-3 used the same amount of electricity as 120 U.S. homes use in a year. This is not just a tech stat; it's a climate story.
And yet, we rarely build with that in mind.
Why?
It is easy to think code is clean. It's invisible. It's in the cloud. It doesn't smell like smoke or pump out fumes.
But "cloud" is just someone else's very real server farm. Sustainable coding isn't just a nice-to-have anymore—it's a responsibility.
This isn’t about guilt, it’s about awareness and action. There are smart, simple changes we can make as developers:
- Choose green cloud providers
- Optimise code and model sizes
- Batch compute jobs and reduce redundancy
- Measure emissions with tools like CodeCarbon
- Push for green defaults in our teams and open-source tools
We have a direct say in how efficient our tech becomes and whether it scales sustainably. If you use AI tools every day, integrate them in production or build your models, the question isn't just "What can this do?"
It's "What does it cost?"
It is clear that small shifts in our development choices, when multiplied across teams and platforms, can create a real impact.
We must build smarter and cleaner.
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