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Naman Rathi

Student of Computer ... • 1d

The true reality and Hidden Cost of AI We celebrate AI’s breakthroughs — from GPT coding to art generation — but ignore its hidden costs. Training a single large model can use millions of kWh, with data centers relying on fossil fuels and causing major CO₂ emissions. Cooling systems also strain local water supplies, and the push for faster GPUs adds to e-waste. By 2030, AI’s energy use could match that of small countries. Key issues: • Energy: Training GPT-4 ≈ powering thousands of homes for a year • Water: Cooling drains resources • Waste: Obsolete hardware piling up • Inequality: Only big players can afford compute Questions: Should AI’s carbon footprint be transparent? Can smaller models compete? What’s the role of governments or startups in tackling this? Curious to hear if anyone here is working on green AI, model compression, or alternative compute strategies.

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