NVIDIA AI platform
NVIDIA makes the chips that power almost all AI training and much of AI inference — the hardware that does the actual computation. Why should a business leader care? Because NVIDIA's supply and pricing directly affect how much AI costs to run. When GPU prices go up, AI service costs follow. Understanding this supply chain helps you evaluate whether an AI vendor's pricing is sustainable or likely to change.
Go deeper
Your AI vendor just told you their prices are going up 20% because GPU costs have spiked. You don't need to become a chip expert, but understanding this is like understanding fuel costs when you run a fleet — it's a direct input to your operating expenses. If you're running AI-intensive operations like real-time scheduling optimization or large-scale document processing, your costs are ultimately tied to GPU availability.
The trap most companies fall into is ignoring the infrastructure layer entirely because it feels too technical. Then they're blindsided when their AI vendor raises prices, slows down response times, or can't scale during peak periods. You don't need to buy GPUs — you need to understand that your AI costs have a hardware supply chain behind them, just like your parts costs have a manufacturing supply chain.
Questions to ask
- When our AI vendor raises prices, do we understand whether it's a margin increase or a pass-through of infrastructure costs?
- Are we running any AI workloads that could shift to less GPU-intensive models without meaningful quality loss?
- Does our AI vendor have committed GPU capacity, or are they buying spot compute that could get more expensive or unavailable?