Fast Fashion Code: The Hidden Environmental Cost of AI-Generated Garbage
We are entering an era where large language models (LLMs) are being invoked to write everything from college essays to production code. Every product pitch, every enterprise roadmap, every corporate innovation day seems to center on how “AI can write code for you.” But almost no one is asking the deeper question:
What is the environmental cost of generating all this garbage code?
The Illusion of Free Code
LLMs feel magical. You type a sentence and seconds later, code appears. Need a Python script? Done. A CRUD app? Easy. A microservice boilerplate with logging and API hooks? Instant. But this illusion of frictionless productivity hides the staggering material cost behind the curtain.
Running LLMs at scale requires:
- Enormous GPU farms
- High-density data center cooling
- Constant power draw (often from carbon-heavy grids)
- Frequent re-training and fine-tuning, which uses hundreds of megawatt-hours per model iteration
And what are we producing with that energy?
Most of the time: low-quality, redundant, hallucinated garbage.
Welcome to Cognitive Fast Fashion
The current moment in AI mirrors what happened to the clothing industry two decades ago. Fast fashion made it easy to pump out trendy outfits at rock-bottom prices — and in the process, flooded the world with disposable clothing, exploitative labor practices, and textile waste.
Now we’re doing the same thing with code and content.
| Fast Fashion | Generative AI |
|---|---|
| Cheap, fast garments | Cheap, fast code |
| High environmental cost | High energy + compute cost |
| Short-lived utility | Breaks on deployment, gets discarded |
| Displaces craftspeople | Displaces engineers, writers |
| Floods the ecosystem | Floods repos, blogs, feeds |
We’re not building intelligent systems. We’re burning electricity to produce noise.
It’s Not Even Good Code
As someone who writes real-world code — software that needs to be performance-aware, readable, and maintainable — I can tell you that LLM-generated code rarely meets the bar. The second your problem involves clever data structures, memory optimizations, or non-trivial control flow, the AI falls apart.
The worst part? LLMs sound confident. They’ll produce broken logic and wrap it in perfect syntax and helpful comments. It’s like shipping cargo containers of shiny tools made out of plastic. They look great until you try to use them.
When we let AI generate bad code over and over, we don’t just waste time — we waste power, we waste silicon, we waste ecological capital.
The Cognitive Minimalist Ethic
If you care about the environment, it’s time to think differently about AI.
- Don’t ask an LLM to hallucinate code you don’t understand.
- Don’t spin up thousands of tokens on speculative prompts just because it feels fast.
- Don’t treat compute like it’s free. It’s not. It’s heat. It’s carbon. It’s material entropy.
We need a new ethic — call it:
Slow Code
Build less. Build better. Don’t waste kilowatts on junk.
Final Thought
The problem with AI-generated code isn’t just quality — it’s quantity without care. The world doesn’t need another barely working Python script. It needs resilient systems. It needs engineers. It needs stewardship.
We are burning the future to generate scaffolding we’ll throw away.
Let’s stop pretending that’s progress.