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Key Takeaways AI is consuming more power than most businesses realize. A standard enterprise server rack draws roughly 5-10 kilowatts. An AI-optimized rack running GPU clusters can pull 40-100 kilowatts or more.
Energy costs flow downstream, and so do supply chain constraints. For any business that relies on cloud-hosted AI services, these bottlenecks translate directly into pricing pressure and reliability risk.
Businesses that understand this full picture, digital and physical together, will make sharper investment decisions, carry less unmanaged risk and build infrastructure that scales without breaking.
The numbers coming out of Silicon Valley sound almost too large to process. Every time a company deploys a new large language model or scales its AI infrastructure, it’s not just spinning up servers. It’s demanding industrial-scale electricity, water for cooling and physical real estate at a pace the global grid was never designed to handle.
This isn’t a future problem. It’s already reshaping how businesses operate, where they invest and what risks they carry.
AI is consuming more power than most businesses realize
Most executives think of AI as software. That’s the first mistake. Behind every AI-powered workflow is a physical machine running at sustained high-intensity load, often 24 hours a day.
Traditional vs. AI workloads
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