Compute economics is the study of how GPU costs, training budgets, and inference pricing shape machine intelligence strategy. It's the financial physics underneath every AI product decision — determining who can build what, at what price point, and whether any of it makes money.

The numbers set the stakes. Training GPT-4 cost an estimated $100M+. An H100 cluster capable of frontier training starts around $500M with 18-month wait times. Running inference at scale — millions of API calls per day — can run $2–5M per month. These aren't engineering footnotes. They're the constraints that determine whether your AI strategy is viable or aspirational.

Three forces define the current landscape. Training costs are concentrating power: only a handful of organizations can afford frontier model development, which is why the foundation model oligopoly exists. Inference costs are falling roughly 10x per year, which keeps expanding the universe of profitable AI features. And GPU supply remains structurally constrained, making compute access itself a competitive moat.

The decision framework this creates is straightforward. If your use case needs frontier intelligence, buy API access — don't try to train your own model. If you're running high-volume inference, watch the cost curve and evaluate smaller distilled models or edge inference to protect margin. Model your AI spend as a variable cost with a deflationary trend, not a fixed line item.

The companies getting this right treat compute economics the way sharp operators treated cloud economics a decade ago. The ones who built a discipline around it captured margin. Everyone else just paid the bill.

Further reading:

• AI and Compute (OpenAI, 2018) — Original analysis showing 300,000x growth in compute since 2012.
• Epoch AI Compute Trends — Authoritative tracking of ML compute trends.
• AI Server Cost Analysis (SemiAnalysis, 2023) — Deep dive into AI infrastructure economics.