The most expensive mistake in AI isn't a bad model. It's leaving GPUs idle.

I audited a company's AI infrastructure last month. They had 8 A100 GPUs running 24/7. Average utilization: 23%. They were effectively burning $15,000/month on idle compute.

GPU economics 101 for AI teams:

Training is bursty. You need GPUs intensely for days, then not at all for weeks. Use spot instances or preemptible VMs for training. You'll save 60-70% versus on-demand.

Inference is steady but variable. Auto-scale your serving infrastructure. Don't provision for peak if peak is 10x your average.

Development doesn't need A100s. A T4 or even a CPU instance is fine for debugging, data exploration, and prototyping. Only use expensive GPUs for actual training runs.

Model selection matters enormously. Running a 70B model when a quantized 7B would do isn't a technical choice — it's a $2,000/month mistake.

The engineers who understand GPU economics save their companies more money than most engineers generate in revenue. That's a very secure position to be in.

Track your GPU utilization. If it's under 50%, you're overspending. If it's over 90%, you're under-provisioned. The sweet spot is 65-80%.