The production of machine intelligence has come to rely almost entirely on a system of benchmarking, where machine learning models are trained to perform well on narrowly defined supervised problems. While this system works well for pushing the performance on these specific problems, the mechanism is weak in situations where the introduction of markets would enable it to excel. For example, intelligence is increasingly becoming untethered from specific objectives and becoming a commodity that is expensively mined from data, monetarily valuable, transferable, and generally useful. Measuring its production with supervised objectives does not directly reward the commodity itself and causes the field to converge toward narrow specialists. Moreover, these objectives (often measured in uni-dimensional metrics like accuracy) do not have the resolution to reward niche or legacy systems, thus what is not currently state of the art is lost. Ultimately, the proliferation of diverse intelligence systems is limited by the need to train large monolithic models to succeed in a winner-take-all competition. Standalone engineers cannot directly monetize their work and what results is centralization where a small set of large corporations control access to the best artificial intelligence.