Manufacturing in the U.S. is seeing its fastest growth in more than four years, driven in large part by rising demand for AI infrastructure. That expansion is arriving alongside severe supply shortages and higher input costs.

Manufacturers look to AI to increase capacity and operate more efficiently. But routing every request through the largest available model creates another capacity problem inside the technology itself: higher costs, greater compute requirements, and an architecture that becomes difficult to scale.

How general-purpose models can create inefficiencies

When companies add AI to a workflow, the default is often the largest model available. Yet many manufacturing tasks are narrow, such as finding a requirement in a technical specification, identifying a maintenance procedure, or calculating a value from a graph. A frontier model may handle each one, but using it for everything is a brute-force approach.

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