开放权重 AI 发布在不对称扩散下的福利模型：延迟究竟卡住了谁

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Abstract:Withholding a dual-use AI model delays only the actors that lack other routes to a comparable capability. If sophisticated adversaries obtain substitutes faster than distributed defenders, restriction can delay defenders more than the adversaries it targets. We compare controlled access, a defender-first window followed by public release, safeguarded open weights, and minimally restricted open weights in a discounted welfare model with actor-specific substitute acquisition. Under exponential acquisition, restriction gives adversaries a positive discounted access advantage exactly when they substitute faster than defenders, and, with equal usefulness, immediate release adds more expected capability at a fixed horizon to the slower-substituting group. Neither result implies that release is preferable, because opportunistic misuse, defensive reach, safeguard friction, and irreversible losses can reverse the ranking. In a linear benchmark, broad release overtakes control above a unique adversary-substitution threshold whenever such a threshold exists, and we derive the probability that selected defenders deploy before both adversary substitution and public release. In a nonlinear implementation, each of the four policies is optimal somewhere in the parameter space. Three nested 2,048-point designs over thirteen inputs show that policy shares depend strongly on the chosen parameter bounds. Release records and cybersecurity reports illustrate the quantities a release review would need to measure and are kept separate from the calibration.

Submission history

From: Daniel Commey [view email]
[v1] Fri, 24 Jul 2026 23:51:06 UTC (86 KB)
[v2] Thu, 8 Oct 2026 16:53:57 UTC (285 KB)