Designing an AI serving harness built to last

The next competitive advantage in enterprise AI has less to do with which model you choose than with what you build around it.

That’s an important shift. For the past several years, much of the AI conversation has centered on model capability: bigger models, better reasoning, larger context windows, higher benchmark scores. Those advances have been extraordinary. But as AI moves from experimentation to production, the question facing enterprise leaders has changed—it’s no longer, “what can the model do?”—it’s now, “Can we deliver that intelligence reliably, economically, and at scale?”

Increasingly, the answer depends on the serving harness—the engineering system surrounding the model, including the architecture, memory management, scheduling, and optimization decisions that determine how efficiently model intelligence becomes a production capability.

For technical executives, that changes where AI investment should go—and what they should demand from the architecture supporting it.

There is no universally “best” model or AI architecture. There is only the right solution for the business problem. …