Much of today’s policy debate over artificial intelligence (AI) focuses on efforts to change the costs of access to AI—to shift relative costs such that AI adoption is steered toward channels that society can monitor and away from channels it cannot.
Policy analysis has concentrated on the supply side of this problem, with such mechanisms as export restrictions, licensing requirements, and controls on computing hardware. But where AI capabilities end up depends on the choices of organizations and individuals about what to adopt and through which channels, and it is on this demand side that cost-shifting instruments operate. This demand side of the problem has received far less systematic analysis.
This report addresses this gap with a framework for judging when cost-shifting steers adoption as intended, when it accomplishes little, and when it backfires and makes proliferation worse. The author details a formal model in which AI adoption is driven by relative costs across access channels, and governance instruments operate by manipulating those relative costs. The central question is: When policy changes the relative cost of AI access, how do users reallocate across governed and ungoverned channels, and under what conditions does that reallocation reduce, fail to affect, or accelerate uncontrolled proliferation of AI?
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