_version_ 1866909798692290560
author Kruus, Nicholas
Thakur, Madhavendra
Khoja, Adam
Nagel, Leonhard
Nicholson, Maximilian
Sharma, Abeer
Hausenloy, Jason
KoTafoya, Alberto
Mukhanova, Aliya
Katila-Miikkulainen, Alli
Chandran, Harish
Zhang, Ivan
Chen, Jessie
Raj, Joel
Nguyen, Jord
Hao, Lai Hsien
Jayasundara, Neja
Sen, Soham
Zhang, Sophie
Tamaklo, Ashley Dora Kokui
Thakur, Bhavya
Close, Henry
Lee, Janghee
Sefton, Nina
Thakur, Raghavendra
Munagala, Shiv
Kim, Yeeun
author_facet Kruus, Nicholas
Thakur, Madhavendra
Khoja, Adam
Nagel, Leonhard
Nicholson, Maximilian
Sharma, Abeer
Hausenloy, Jason
KoTafoya, Alberto
Mukhanova, Aliya
Katila-Miikkulainen, Alli
Chandran, Harish
Zhang, Ivan
Chen, Jessie
Raj, Joel
Nguyen, Jord
Hao, Lai Hsien
Jayasundara, Neja
Sen, Soham
Zhang, Sophie
Tamaklo, Ashley Dora Kokui
Thakur, Bhavya
Close, Henry
Lee, Janghee
Sefton, Nina
Thakur, Raghavendra
Munagala, Shiv
Kim, Yeeun
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2509_17087
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Governing Automated Strategic Intelligence
Kruus, Nicholas
Thakur, Madhavendra
Khoja, Adam
Nagel, Leonhard
Nicholson, Maximilian
Sharma, Abeer
Hausenloy, Jason
KoTafoya, Alberto
Mukhanova, Aliya
Katila-Miikkulainen, Alli
Chandran, Harish
Zhang, Ivan
Chen, Jessie
Raj, Joel
Nguyen, Jord
Hao, Lai Hsien
Jayasundara, Neja
Sen, Soham
Zhang, Sophie
Tamaklo, Ashley Dora Kokui
Thakur, Bhavya
Close, Henry
Lee, Janghee
Sefton, Nina
Thakur, Raghavendra
Munagala, Shiv
Kim, Yeeun
Artificial Intelligence
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.
title Governing Automated Strategic Intelligence
topic Artificial Intelligence
url https://arxiv.org/abs/2509.17087