A Cost Analysis of Generative Language Models and Influence Operations
This study addresses the problem of assessing the cost-effectiveness and risks of LLMs for malicious influence operations, providing insights for policymakers and security experts, though it is incremental in building on existing speculation about LLM misuse.
This research analyzed the economic value of large language models (LLMs) for propagandists in influence operations, finding that LLMs can offer cost savings of up to 70% even with low reliability (25% usable outputs), and that monitoring controls have limited deterrent effects when open-source alternatives exist.
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.