# A Cost Analysis of Generative Language Models and Influence Operations

arXiv: https://arxiv.org/abs/2308.03740 · HTML: https://mlsanity.com/paper/2308.03740/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 70 references that are in the map.
- First posted on arXiv 7 Aug 2023.

## Builds on (most cited first, 14 of 14)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Ethical and social risks of harm from Language Models](https://mlsanity.com/paper/2112.04359/) (2021)
- [A Watermark for Large Language Models](https://mlsanity.com/paper/2301.10226/) (2023)
- [Release Strategies and the Social Impacts of Language Models](https://mlsanity.com/paper/1908.09203/) (2019)
- [GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models](https://mlsanity.com/paper/2303.10130/) (2023)
- [Understanding the Capabilities, Limitations, and Societal Impact of Large Language Models](https://mlsanity.com/paper/2102.02503/) (2021)
- [Generative Language Models and Automated Influence Operations: Emerging Threats and Potential Mitigations](https://mlsanity.com/paper/2301.04246/) (2023)
- [Spinning Language Models: Risks of Propaganda-As-A-Service and Countermeasures](https://mlsanity.com/paper/2112.05224/) (2021)
- [The Gradient of Generative AI Release: Methods and Considerations](https://mlsanity.com/paper/2302.04844/) (2023)
- [Structured access: an emerging paradigm for safe AI deployment](https://mlsanity.com/paper/2201.05159/) (2022)
- [AI-assisted Code Authoring at Scale: Fine-tuning, deploying, and mixed methods evaluation](https://mlsanity.com/paper/2305.12050/) (2023)
- [Will AI Make Cyber Swords or Shields: A few mathematical models of technological progress](https://mlsanity.com/paper/2207.13825/) (2022)
- [Machine Learning Model Attribution Challenge](https://mlsanity.com/paper/2302.06716/) (2023)

## Built on by (most cited first, 17 of 17)

- [On the Societal Impact of Open Foundation Models](https://mlsanity.com/paper/2403.07918/) (2024)
- [When Large Language Models Meet Vector Databases: A Survey](https://mlsanity.com/paper/2402.01763/) (2024)
- [The Model Openness Framework: Promoting Completeness and Openness for Reproducibility, Transparency, and Usability in Artificial Intelligence](https://mlsanity.com/paper/2403.13784/) (2024)
- [The AI Community Building the Future? A Quantitative Analysis of Development Activity on Hugging Face Hub](https://mlsanity.com/paper/2405.13058/) (2024)
- [On the Limitations of Compute Thresholds as a Governance Strategy](https://mlsanity.com/paper/2407.05694/) (2024)
- [Evaluating the Efficacy of Open-Source LLMs in Enterprise-Specific RAG Systems: A Comparative Study of Performance and Scalability](https://mlsanity.com/paper/2406.11424/) (2024)
- [How will advanced AI systems impact democracy?](https://mlsanity.com/paper/2409.06729/) (2024)
- [MP2D: An Automated Topic Shift Dialogue Generation Framework Leveraging Knowledge Graphs](https://mlsanity.com/paper/2403.05814/) (2024)
- [LLM Detectors Still Fall Short of Real World: Case of LLM-Generated Short News-Like Posts](https://mlsanity.com/paper/2409.03291/) (2024)
- [PrecLLM: A Privacy-Preserving Framework for Efficient Clinical Annotation Extraction from Unstructured EHRs using Small-Scale LLMs](https://mlsanity.com/paper/2412.02868/) (2024)
- [PRISM: A Design Framework for Open-Source Foundation Model Safety](https://mlsanity.com/paper/2406.10415/) (2024)
- [Error Diffusion: Post Training Quantization with Block-Scaled Number Formats for Neural Networks](https://mlsanity.com/paper/2410.11203/) (2024)
- [Pruning General Large Language Models into Customized Expert Models](https://mlsanity.com/paper/2506.02561/) (2025)
- [Science Checker Reloaded: A Bidirectional Paradigm for Transparency and Logical Reasoning](https://mlsanity.com/paper/2402.13897/) (2024)
- [Hybrid Student-Teacher Large Language Model Refinement for Cancer Toxicity Symptom Extraction](https://mlsanity.com/paper/2408.04775/) (2024)
- [Gradually Compacting Large Language Models for Reasoning Like a Boiling Frog](https://mlsanity.com/paper/2602.04919/) (2026)
- [Position: Preventing AI-Generated CSAM Necessitates New Approaches to AI Safety](https://mlsanity.com/paper/2607.05407/) (2026)

## Abstract (arXiv)

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.