Governing Automated Strategic Intelligence
Abstract
Military and economic strategic competitiveness between nation-states will increasingly be defined by the capability and cost of their frontier artificial intelligence models. Among the first areas of geopolitical advantage granted by such systems will be in automating military intelligence. Much discussion has been devoted to AI systems enabling new military modalities, such as lethal autonomous weapons, or making strategic decisions. However, the ability of a country of "CIA analysts in a data-center" to synthesize diverse data at scale, and its implications, have been underexplored. Multimodal foundation models appear on track to automate strategic analysis previously done by humans. They will be able to fuse today's abundant satellite imagery, phone-location traces, social media records, and written documents into a single queryable system. We conduct a preliminary uplift study to empirically evaluate these capabilities, then propose a taxonomy of the kinds of ground truth questions these systems will answer, present a high-level model of the determinants of this system's AI capabilities, and provide recommendations for nation-states to remain strategically competitive within the new paradigm of automated intelligence.
Cite
@article{arxiv.2509.17087,
title = {Governing Automated Strategic Intelligence},
author = {Nicholas Kruus and Madhavendra Thakur and Adam Khoja and Leonhard Nagel and Maximilian Nicholson and Abeer Sharma and Jason Hausenloy and Alberto KoTafoya and Aliya Mukhanova and Alli Katila-Miikkulainen and Harish Chandran and Ivan Zhang and Jessie Chen and Joel Raj and Jord Nguyen and Lai Hsien Hao and Neja Jayasundara and Soham Sen and Sophie Zhang and Ashley Dora Kokui Tamaklo and Bhavya Thakur and Henry Close and Janghee Lee and Nina Sefton and Raghavendra Thakur and Shiv Munagala and Yeeun Kim},
journal= {arXiv preprint arXiv:2509.17087},
year = {2025}
}