Russian thinking on AI integration and interaction with nuclear command and control, force structure, and decision-making

Historical Foundations of Russian Military AI Thinking

• Roots in the 1960s Cybernetics Movement: Early Russian work on neural networks and their defense applications intensified in the early $1960s$, driven by military demand. In $1962$, the Scientific Research Institute of Physical Problems was established in Zelenograd under physicist Vitaly Stafeyev, focusing on neural networks for missile defense.

• Viktor Bokarev and 'Cybernetics and Warfare' ($1969$): Published by the Ministry of Defense of the Soviet Union, colonel-engineer Viktor Bokarev's book explored modeling the human psyche. He concluded that artificial reasoning was feasible for the future, though fraught with obstacles, and analyzed the burgeoning interaction between humans and machines in warfare.

• The 2009 Encyclopedia Definition: The 'Encyclopedia of the Strategic Missile Forces' defined "artificial intelligence in military affairs" [искусственный интеллект в военном деле] as a "field of research that develops models, systems, and devices that simulate human intellectual activity (perception of various information and logical reasoning) in warfare."

• Pre-Deep Learning Applications: By $2009$, the Strategic Missile Forces (RVSN) were already utilizing AI for:     * Decision support systems.     * Intellectual systems and weapons (onboard control systems).     * Expert systems.

Institutionalization within the Ministry of Defense

• High-Level Political Endorsement: The current AI renaissance in Russia was triggered by attention from President Vladimir Putin and Defense Minister Sergey Shoigu. Putin famously stated in September $2017$: "the leader in creating artificial intelligence will become the ruler of the world."

• Formal AI Consolidation ($2018$–$2022$):     * $2018$: The MoD held its first conference, 'Artificial Intelligence: Problems and ways to solve them,' and launched ERA, a military techno-park where AI is one of $8$ priority research areas.     * $2021$: The $46 ext{th}$ Central Research Institute ($46 ext{th}$ TsNII) was named the leading research organization for military AI.     * $2022$: A new ministerial department for AI development was created. Defense Minister Shoigu approved a (non-public) 'Concept for the activities of the armed forces of the Russian Federation in the development and application of weapons systems using artificial intelligence technologies.'

Strategic and Theoretical Military Perspectives

• The Determinant of Future Warfare: Vasily Burenok (head of $46 ext{th}$ TsNII) argues that AI integration will predetermine the degree of efficiency in combat. He views AI as a main venue for scientific progress.

• Reducing the 'Fog of War': Andrey Kokoshin (former Deputy Defense Minister) posits that AI should provide high awareness of political-military and tactical situations, thereby reducing the Clausewitzian 'fog of war' even during active cyber warfare.

• Shifting Center of Gravity: The RVSN and the Combined Arms Academy suggest that the 'center of gravity' in future conflict is shifting toward the confrontation between management systems (intelligence, command, and strike systems).

• The 'Arms Race' and Mirror Measures: Russian authors often characterize military AI as an arms race between the U.S. and China. They argue Russia must implement 'mirror measures' to prevent U.S. military dominance achieved through AI.

Auxiliary Functions in Nuclear Force Management

• Logistics Automation: A $2015$ Peter the Great Military Academy proposal for a mathematical model of automated logistics for mobile ICBM units aimed to:     * Reduce the management cycle by a factor of $1.9$.     * Reduce information processing time by an average of $2.8 imes$.     * Reduce decision-making time by $1.3 imes$.     * Doubling the likelihood of order transmission under nuclear or electronic warfare conditions.

• Predictive Maintenance: A $2018$ neural-network-based model was proposed to forecast the residual operating time of components in RVSN missile systems, initially tested on air conditioning equipment to maintain combat readiness for legacy systems.

• Eight Classes of RVSN Tasks identified by Matvienko and Uvarov ($2021$):     1. Communication network management and optimization (finding optimal traffic).     2. Information security of automated systems.     3. Automated design for security systems and telecommunication networks.     4. Decision support for day-to-day and operational combat management.     5. Guarding and defense of stationary and mobile objects (TEL patrol routes) using UAVs and robotics.     6. Biometric authentication and behavioral analysis.     7. Processing sensor signals for weapon reliability using non-linear adaptive extrapolating filters.     8. Input and pattern recognition.

Automated Security and Physical Protection

• Robotic Security Complexes: Colonel General Sergey Karakayev (Commander of RVSN) noted that automated systems (like the Typhoon-M) use radar and optoelectronic reconnaissance to detect sabotage units.

• Advanced Implementations: The Kozelsk Missile Division uses robotic (remote-controlled) firing complexes. Future systems like 'Sarmat' will feature turret launchers using neural networks for human and object recognition.

• 'Dym-2' System: Employed at Yars and Avangard launch sites, this remote-controlled defense complex includes salvo fire and thermal cameras, capable of both human-controlled and autonomous operation.

• The 'Friend or Foe' Problem: A critical area of R&D is the use of neural networks to identify detected objects as 'friend or foe' for robotic systems guarding nuclear facilities.

Early Warning Systems (EWS) and Radar Intelligence

• Threat Assessment Goals: Researchers like Dmitry Stefanovich suggest AI should prioritize assessing attack scales, sources, and possible intentions to develop response scenarios.

• Radar Signal Processing: Andrey Zhuravlev (Lieutenant Colonel) argues humans cannot process modern volumes of radar imagery effectively. He advocates for neural networks to handle high-speed processing for reliable results on airspace situations.

• The 'Human Factor' and Terminator Warning: Yury Anoshko (Director General of Radio Technical and Information Systems) insists that while AI handles math and station diagnosis, decision-making based on EWS data must be human to avoid 'irreversible consequences' from software errors. He explicitly referenced the movie 'Terminator' as a warning against AI in nuclear systems.

• The March $2022$ DPRK Example: Sergey Suchkov (Head of Main Centre for Missile Attack Warning) highlighted that during a target detection in March $2022$, while the tech functioned automatically, personnel performed the final analysis and validated the launch.

• Don-2N Radar: This system in Moscow can track more than $100$ complex ballistic targets automatically, but human operators must still 'press the button' for anti-missile launches.

Command and Control of Nuclear Forces

• Human-in-the-Loop Constraint: There is a strong consensus among Russian military scholars (like Igor Fazletdinov) that no level of AI should exclude humans from the control loop of nuclear complexes.

• Automated vs. Automatic: Colonel General Karakayev distinguishes that the system for delivering combat orders is automated (requiring human input) but not automatic (self-executing). He rejects the idea that advancing AI will lead to the abandonment of human duty shifts.

• Perimeter/Dead Hand System: Stefanovich notes that while a fully automated retaliatory strike is technologically feasible, human control is currently maintained. However, pre-delegation to machines could return to the agenda if strategic stability deteriorates rapidly.

Strategic Stability and Arms Control Perspectives

• Evolution of Factors: Traditional stability factors (since the $1990$ Soviet-U.S. statement) included prompt global strike and cyber. AI was added to this list in the late $2010s$.

• Destabilizing Effects: Vadim Kozyulin ($2018$) argues AI creates a 'blurring' where nuclear weapons may be used for tactical missions and non-nuclear weapons for strategic missions.

• AI-Enabled Swarming: Experts fear U.S. AI-enabled cruise missile 'swarms' could target Russia's nuclear deterrent by flying at extremely low altitudes and performing complex group rearrangements.

• Poseidon (Underwater Autonomy): The nuclear-armed autonomous underwater vehicle (AUV) presented by Putin in $2018$ is seen by some (Tselitskiy) as starting a new underwater arms race. Others (Dvorkin) worry it fuels a nuclear arms race without adding significant deterrent value over the existing triad.

• Universal Deterrence vs. Illusion of Superiority: MoD researchers (Protasov et al.) envision two outcomes:     1. 'Universal' deterrence where AI leads to absolute strategic stability.     2. Higher risk of unintended escalation due to an 'illusory assured superiority.'

• Official Dipomatic Proposals: Valery Gerasimov ($2021$) proposed a 'new security equation' that includes AI, outer space, and cyberspace as new spheres of confrontation for arms control talks.

Policy Recommendations for the P5

1. AI Glossary: Expand the 'P5 glossary of key nuclear terms' to include AI and command/control security terminology.

2. 'Fear-mapping': Jointly brainstorm and dissect worst-case scenarios (e.g., AI-triggered nuclear war) to find mitigation strategies.

3. Non-interference Agreements: Discuss expanding the Cold War-era non-interference with national technical means to include AI systems and cyber-security of nuclear assets.

4. Auxiliary vs. Critical Dependency Analysis: Investigate how AI in non-critical systems (logistics/security) might bleed into or impact critical C2 functions.

5. External Safety Audits: Adopt AI safety practices from other more mature fields to establish transparency and interpretability in military systems.

6. Identifying Stabilizing Uses: Research how AI could potentially increase stability and predictability between nuclear states.

7. Conventional-Nuclear Interaction: Analyze the threat of AI-enabled conventional drones against nuclear forces and C2 infrastructure.

8. Regular Expert Exchanges: Maintain a cadence of meetings that include both diplomats and AI practitioners for substantive technical dialogue.