AI and the Hunt for Ballistic Missile Submarines

The research evaluates the impact of AI on the detectability of SSBNs, finding that AI enhancements are most effective in shallow waters (0-200 ft). This creates asymmetric strategic risks: the US enjoys a geographic advantage with direct deep-water access, while Russia and China's reliance on 'bastion' strategies is reinforced by AI's ability to secure shallow perimeters. The UK and France are identified as particularly vulnerable during the transit phase from port to deep ocean. While AI improves the 'Search' and 'Classify' phases of the detection chain, the authors conclude that fundamental physics and the cost asymmetry between seeking and hiding prevent the realization of 'transparent oceans,' though the perception of such a shift could destabilize nuclear strategic stability.

• AI enhancement potential for ASW platforms is highest in shallow waters (0-200 ft) and decreases as depth increases, with a sharp drop-off at extreme depths (>1,000 ft) where physics constraints on acoustic propagation dominate.
• Machine learning is the most consistent AI enhancement across all platforms and depths, primarily benefiting the 'Search' and 'Classify' stages of the detection chain by improving signal-to-noise ratios and automated contact labeling.
• Autonomy is the most depth-sensitive AI capability due to size, weight, and power (SWaP) limitations, though it offers high potential for unmanned underwater vehicles (UUVs) in shallow-water persistence and bastion defense.
• Sensor fusion is most effective for the 'Localize' phase of the detection chain, particularly for platforms with distributed architectures or multiple sensor types, though it is negligible for SWaP-constrained platforms like UUVs.
• AI-enhanced detection reinforces the viability of 'bastion' strategies for Russia and China, as these states can more economically layer AI-driven sensors to protect their SSBNs in semi-enclosed home waters.
• The United Kingdom and France face increased vulnerability because they must transit shallow, constrained waters—where AI detection is most effective—to reach deep-water patrol areas, unlike the US which has direct deep-ocean access.
• While AI does not fundamentally overturn the structural advantage of the 'hider' (SSBN), the mere perception of increased detectability could destabilize nuclear strategic stability by incentivizing more aggressive launch postures, such as launch-on-warning.

APA

Schlegelmilch, B., & Johns, E. (2026). AI and the Hunt for Ballistic Missile Submarines. The Council on Strategic Risks. https://councilonstrategicrisks.org/2026/08/10/ai-and-the-hunt-for-ballistic-missile-submarines/

Chicago

Schlegelmilch, Barret, and Eliana Johns. AI and the Hunt for Ballistic Missile Submarines. The Council on Strategic Risks, 2026. https://councilonstrategicrisks.org/2026/08/10/ai-and-the-hunt-for-ballistic-missile-submarines/.

Wikipedia

{{cite report |last1=Schlegelmilch |first1=Barret |last2=Johns |first2=Eliana |title=AI and the Hunt for Ballistic Missile Submarines |publisher=The Council on Strategic Risks |date=10 August 2026 |url=https://councilonstrategicrisks.org/2026/08/10/ai-and-the-hunt-for-ballistic-missile-submarines/ |access-date=23 October 2026 |via=Climate Insights Directory}}

BibTeX

@techreport{schlegelmilch2026hunt, author = {Schlegelmilch, Barret and Johns, Eliana}, title = {{AI and the Hunt for Ballistic Missile Submarines}}, institution = {The Council on Strategic Risks}, year = {2026}, month = aug, url = {https://councilonstrategicrisks.org/2026/08/10/ai-and-the-hunt-for-ballistic-missile-submarines/}, urldate = {2026-10-23}, note = {Indexed by Climate Insights Directory} }