ManufacturingAI’s infrastructure is starting to strain under its own weight. Models keep growing, but the returns are shrinking. Each new generation costs more to train, deploy, and feed with fresh data.
A recent MIT study found that even frontier models may soon hit diminishing returns, as smaller systems trained more efficiently begin to close the gap. “In the next five to ten years, things are very likely to start narrowing,” said Neil Thompson, one of the study’s authors.
The economics back that up. McKinsey estimates that AI-ready data centers will require about $5.2 trillion in new investment by 2030, and Deloitte reports that two-thirds of executives already see funding gaps as their biggest constraint.
