We’re Using So Much AI That Computing Firepower Is Running Out

Published
Score
11

Why it matters

AI companies are rationing compute resources as demand for high-performance GPUs and data center capacity has outpaced supply. OpenAI scrapped its Sora video application in April to redirect resources toward coding and enterprise tools, with CFO Sarah Friar acknowledging difficult trade-offs. The company's API token usage surged from 6 billion per minute in October to 15 billion by March. Anthropic has imposed peak-hour usage limits on Claude since mid-February and lost clients including Retool. GitHub implemented Copilot limits on April 10. The constraint stems from explosive demand for agentic AI—systems performing multi-step tasks like coding and workflow automation—that accelerated sharply in early 2026.

The infrastructure gap will persist through 2027. GPU and memory shortages remain acute, while new data centers require four or more years to build amid power grid delays. The International Energy Agency and consulting firms forecast U.S. data center power demand will reach 100 gigawatts by 2030, a 165 percent increase, requiring $500 billion annually in capital expenditure. Pricing pressures and service restrictions have intensified since January, with major rationing announcements from Anthropic in March and OpenAI in April.

A Wall Street Journal investigation published April 12 exposed real-time service disruptions affecting enterprise customers as adoption accelerates. The compute shortage now poses a material risk to user growth and competition if pricing rises while infrastructure expansion lags. Attorneys advising AI vendors, cloud providers, or enterprise customers should monitor service level agreements, capacity commitments, and potential liability exposure as rationing becomes standard practice rather than temporary constraint.

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