The Report Took 20 Minutes - The GPU Ran for 3 Weeks

This story was originally published on HackerNoon at: https://hackernoon.com/the-report-took-20-minutes-the-gpu-ran-for-3-weeks. AI is making leadership reports faster than ever. Nobody told the infrastructure it was supposed to stop running after the meeting ended. Here is what it costs Check more stories related to finance at: https://hackernoon.com/c/finance. You can also check exclusive content about #finops, #cloud-ai-cost-optimization, #finops-cloud-optimization, #cloudwaste, #resource-management, #leadership, #enterprise-ai, #productivity, and more. This story was written by: @prakshal-doshi. Learn more about this writer by checking @prakshal-doshi's about page, and for more stories, please visit hackernoon.com. Leadership teams are using AI tools to spin up reports, dashboards, and data pipelines in hours instead of weeks. The productivity gain is real. The problem nobody is tracking: the infrastructure those reports run on almost never gets torn down. GPU instances, query clusters, and data processing jobs stay alive long after the deck has been presented. 78% of organizations estimate 21 to 50% of their cloud spend is wasted, with AI workloads being a primary driver. GPU utilization in production AI systems sits below 50% on average. A company with a 100-GPU cluster running at 60% utilization throws away approximately $1.4M annually. The fix isn't slowing down the reports. It's treating every AI-generated resource as a provisioning event that requires an expiry date.