Despite speculation that recent large language models (LLMs) are likely to be used maliciously to improve the quality or scale of influence operations, uncertainty persists regarding the economic value that LLMs offer propagandists. This research constructs a model of costs facing propagandists for content generation at scale and analyzes (1) the potential savings that LLMs could offer propagandists, (2) the potential deterrent effect of monitoring controls on API-accessible LLMs, and (3) the optimal strategy for propagandists choosing between multiple private and/or open source LLMs when conducting influence operations. Primary results suggest that LLMs need only produce usable outputs with relatively low reliability (roughly 25%) to offer cost savings to propagandists, that the potential reduction in content generation costs can be quite high (up to 70% for a highly reliable model), and that monitoring capabilities have sharply limited cost imposition effects when alternative open source models are available. In addition, these results suggest that nation-states -- even those conducting many large-scale influence operations per year -- are unlikely to benefit economically from training custom LLMs specifically for use in influence operations.

翻译：尽管近期有推测认为大型语言模型（LLMs）可能被恶意用于提升影响力行动的质量或规模，但LLMs能为宣传者带来何种经济价值仍存不确定性。本研究构建了宣传者大规模内容生成的成本模型，并分析：（1）LLMs可能为宣传者节省的潜在成本；（2）对API可访问LLMs实施监控管控的潜在威慑效应；（3）宣传者在进行影响力行动时，在多个私有和/或开源LLMs之间进行选择的最优策略。主要结果表明：LLMs只需以相对较低的可靠性（约25%）产出可用内容即可为宣传者节省成本；内容生成成本的潜在降幅可能相当显著（高可靠性模型可达70%）；当存在可替代开源模型时，监控能力对成本施加的制约效果极为有限。此外，这些结果还表明，即便是每年开展多起大规模影响力行动的国家，从经济角度而言也不大可能通过专门训练用于影响力行动的自定义LLMs来获益。