AI agents are increasingly being granted autonomy through "harnesses"—code that allows Large Language Models to interact with APIs, browsers, and financial tools. However, a significant gap remains between human intent and AI execution because human language is naturally underspecified. Bruce Schneier and Barath Raghavan propose the "Genie coefficient" to measure this gap, drawing parallels to folklore like King Midas or the Sorcerer's Apprentice, where literal fulfillment of a request leads to disastrous, unintended consequences.
The proposed metric distinguishes between "Dionysus" behavior, where an AI misinterprets a goal literally, and "golem" behavior, where it achieves the goal by trampling over unspoken ethical or security boundaries. To address these risks, the authors call for domain-specific benchmarks that test AI agents in realistic, walled-off environments. This measurement is essential for establishing safety policies and determining accountability as AI begins to manage critical infrastructure, personal finance, and code repositories.
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