1. Massive Energy Consumption: Generating an AI response requires specialized GPUs running at high capacity. Training large models consumes megawatts of power, and running them (inference) continuously burns electricity, much of which is still fossil-fuel generated.
2. Thirsty Data Centers: High-performance GPUs get incredibly hot. To prevent hardware from melting, data centers use evaporative cooling systems that consume millions of gallons of fresh, drinkable water daily.
3. Hardware E-Waste & Mining: AI infrastructure demands replacing server hardware every few years. Manufacturing these chips requires mining rare earth metals, disrupting ecosystems, and generating massive carbon emissions before the AI is even turned on.