The production gap

The production gap is the distance between a working prototype and a system that runs in production: the review, data preparation, integration, permissions, monitoring, and operational ownership that a demo does not need and a real system cannot live without. As AI made prototypes nearly free, the gap widened — code got faster to write, while verifying and operating it did not get faster to do.

Why it matters

A buyer who has seen a convincing prototype has seen the cheapest part of the system. Decisions priced at demo time systematically underestimate the far side of the gap — which is where most of the cost, and most of the failure, actually sits.

Common confusion

The gap is not a maturity problem on the buyer's side. Prototypes stall on the way to production mostly for structural reasons in how the work was sold and staffed — not because the organization "wasn't ready."

Where we use it

The structural argument is Why mid-market AI POCs fail to reach production; the cost consequences run through What a quoting system costs to run.