Can AI be used for war in 2025?

Can AI Be Used for War?

AI is revolutionizing warfare in an era where drones swarm the skies and algorithms decide targets faster than any human, turning science fiction into battlefield reality. With the global military AI market exploding to over $35 billion by 2035, nations are leveraging this technology to gain edges in conflicts, from Ukraine’s autonomous drones evading jams to Israel’s AI-driven targeting systems that process thousands of threats in seconds.

But this power comes with profound risks—could AI spark an uncontrollable arms race, or will it minimize casualties and usher in a new age of precision warfare?

Quick Answer: Yes, AI Is Already Transforming Warfare—Here’s a Snapshot

Yes, AI can and is being used for war, enhancing everything from intelligence gathering to autonomous strikes while sparking ethical debates. At its core, AI processes vast data to make decisions faster, more accurately, and with less human intervention, but it raises concerns over accountability, biases, and escalation. For a quick overview, here’s a mini-summary table of key applications and implications:

Aspect	Description	Examples	Potential Impact
Targeting & Surveillance	AI analyzes imagery and data to identify threats.	Drones in Ukraine are using AI for terrain navigation.	This technology not only minimizes human error but also poses a risk of civilian casualties if it is influenced.
Logistics & Planning	Optimizes supply chains and predicts outcomes.	U.S. DoD’s AI for battle management.	It speeds up operations and has the potential to save billions through increased efficiency.
It detects and counters digital attacks in real time.	Systems that select and engage targets independently.	Israel’s Iron Dome is capable of intercepting rockets.	This increases lethality and raises ethical concerns regarding the use of “killer robots.”
Cyber Defense	It detects and counters digital attacks in real time.	AI tools monitor networks during ongoing conflicts.	While it improves security, it remains susceptible to adversarial AI hacks.

This table distills the essentials—AI boosts efficiency but demands careful oversight to avoid disasters.

Context & Market Snapshot: The Current Landscape of AI in Warfare

As of December 2025, AI’s integration into military operations is accelerating amid geopolitical tensions, with the Russia-Ukraine conflict serving as a live testing ground for innovations like AI-guided drones, resulting in 70–80% of casualties. The global AI in the military market, valued at $14.3 billion in 2024, is forecasted to reach $29 billion by 2030, growing at a CAGR of 12.5%, driven by advancements in machine learning and autonomy.

Similarly, AI and analytics in defense hit $10.42 billion in 2024 and are projected to grow at a 13.4% CAGR through 2034, fueled by the need for real-time decision-making in complex environments.

Key trends include the rise of autonomous systems, with over 80,000 surveillance drones and 2,000 attack drones expected to be procured globally by 2029. Nations like the U.S., China, and Russia are investing heavily: China plans $150 billion in AI by 2030, Russia $181 million from 2021 to 2023, and the U.S. $4.6 billion annually.

Reports from institutions like the Hudson Institute highlight AI’s role in cyberattacks and deepfakes, as seen in Ukraine, where Russia used AI for propaganda. The U.S. Department of Defense (DoD) emphasizes AI for “decision advantage,” with experiments showing boosted battle management speed.

Growth stats underscore urgency: The AI in the aerospace and defense market is set to expand from $27.95 billion in 2025 to $65.43 billion by 2034 at a 9.9% CAGR. Non-state actors, like Hezbollah and Hamas, are also adopting AI drones, democratizing warfare and complicating global security. Credible sources, including the UN’s Group of Governmental Experts (GGE) 2023 report, call for human control over AI weapons, reflecting ongoing regulatory gaps.

Deep Analysis: Why AI in Warfare Works Now—and Its Leverage, Challenges, & Moats

AI’s surge in warfare stems from exponential data growth and computing power, enabling systems to process millions of hours of battlefield footage—over 2 million in Ukraine alone—to train models on tactics and targeting. This “why now” moment is driven by affordable hardware, like $25 AI additions to drones, boosting strike accuracy from 30–50% to 80%, and the need for speed in hyper-fast conflicts where decisions happen in milliseconds.

Leverage opportunities abound: AI creates economic moats for tech-savvy nations, with the U.S. leading in AI integration for overmatch, potentially acting faster and more lethally. In logistics, AI optimizes resupply, saving time and costs—e.g., predicting insurgencies during events to preempt attacks.

Challenges include ethical dilemmas, such as biases leading to civilian harm, and vulnerabilities to low-tech counters like jamming or “Operation Spiderweb,” which damaged $7 billion in Russian assets using basic tactics.

For clarity, here’s a table analyzing key leverage points vs. challenges:

Leverage Opportunity	Description	Challenges	Economic Moat Example
Speed & Precision	AI accelerates targeting, reducing casualties.	Biases in data cause errors.	U.S. AI for deconflicting airspace.
Scalability	Swarms of cheap drones overwhelm defenses.	The proliferation of these drones is a significant threat to terrorist groups.	China has made an investment of $150 billion in AI technology.
Intelligence	Analyzes patterns for predictions.	“Black box” decisions lack transparency.	Ukraine has implemented AI for refugee routes.
Autonomy	This system reduces the risk of harm to humans.	Escalations can lead to a loss of control.	Israel’s Lavender system is processing 37,000 targets.

This analysis shows AI’s double-edged nature: a moat. FAI has the potential to benefit innovators, but it also poses a risk if it remains unregulated.

blogs.icrc.org

Algorithms of war: The use of artificial intelligence in decisions…

Practical Playbook: Step-by-Step Methods for Integrating AI in Military Contexts

While direct “how-to” for warfare is sensitive, this playbook focuses on high-level strategies for defense professionals, policymakers, and ethicists to understand and ethically deploy AI. Expected results: We anticipate enhanced operations within 6-12 months, with potential savings in the billions for large militaries. There are no direct earnings from this initiative, but consider the defense budgets; for instance, the U.S. Department of Defense allocates $4.6 billion annually.

Method 1: Implementing AI for Surveillance & Targeting

1. Assess Needs: Identify gaps in current systems, e.g., manual drone targeting.
2. Select Tools: Use platforms like Palantir AIP for data integration.
3. Train Models: Feed with verified datasets (e.g., 2M+ hours of footage).
4. Test Ethically: Simulate scenarios with human oversight.
5. Deploy & Monitor: Roll out in phases, expecting a 50-80% accuracy boost in 3 months.
   • Tools: Open-source like TensorFlow; templates: DoD AI directives.
   • Time: 6 months; Potential: Reduce response time by 70%.

Method 2: AI for Logistics & Prediction

1. Data Aggregation: Collect from sensors and satellites.
2. Model Building: Use machine learning for supply forecasts.
3. Integration: Link with existing ERP systems.
4. Validation: Backtest against past conflicts.
5. Iterate: Update models quarterly.
   • Tools: C3 AI for defense; exact numbers: Predict attacks with 80% accuracy.
   • Time: 9 months; Potential: Cut logistics costs by 20–30%.

Method 3: Developing Autonomous Systems

1. Define Autonomy Levels: Start with human-in-the-loop.
2. Prototype: Build drones with AI navigation.
3. Regulatory Compliance: Align with UN GGE guidelines.
4. Field Test: In controlled environments.
5. Scale: Deploy swarms for overmatch.
   • Tools: FortiAI for GPS-denied ops; time: 12 months; potential: 2x lethality.

Use bullets for sub-steps and tables for comparisons where needed.

Top Tools & Resources: Authoritative Platforms for AI in Defense

Here’s a curated list of top tools, with pros/cons, The list also includes pricing information (where available) and links. The information is presented in a comparison table for easy reference:

Tool/Platform	Description	Pros	Cons	Pricing	Link
Palantir AIP	AI is utilized for data activation in the defense sector.	Secure LLMs, real-time insights.	Complex setup.	Custom (enterprise).	Palantir Defense
C3 AI Defense	ML for battlefield decisions.	Intuitive model management.	High integration cost.	Subscription-based.	C3 AI
FortifAi	AI is designed to provide precision in environments where access is restricted.	Its features include object detection and autonomy.	Limited to specific uses.	N/A (startup).	StartUs Insights
DoD AI Tools	Unmanned training, tactical control.	Government-backed reliability.	Bureaucratic access.	Free for authorized personnel.	DoD AI
Rocket.Chat AI	Secure comms with AI defense apps.	Cybersecurity focus.	The focus is on cybersecurity, not on traditional warfare.	Open-source options.	Rocket.Chat

These are up-to-date as of 2025, focusing on ethical, secure options.

Case Studies: Real-World Examples of AI in War Successes

Case Study 1: Ukraine’s AI Drones in the Russia-Ukraine Conflict

Ukraine has deployed AI-equipped drones for autonomous targeting, using fiber optics to counter jamming, achieving 80% strike accuracy. Results: Over 70% of casualties were from drones; civilian groups added AI for $25/unit. Verifiable: Reports from the Army War College. Table of results:

Metric	Pre-AI	Post-AI
Accuracy	30-50%	80%
Casualties Caused	Low	70-80%
Cost Savings	N/A	Millions via cheap mods

Case Study 2: Israel’s Lavender System in Gaza

Dubbed the “first AI war,” Lavender processed 37,000 targets using AI, integrating with systems like Iron Dome. Results: Rapid threat neutralization, but ethical scrutiny over civilian risks. Source: Georgetown Journal. Numbers: It has successfully intercepted thousands of rockets.

Case Study 3: U.S. Air Force AI Experiments

DoD’s AI boosted battle management speed, using human-machine teaming. Results: Faster decisions, accuracy gains. Verifiable: DoD reports.

army.mil

Magic Bullets: The Future of Artificial Intelligence in Weapons …

Risks, Mistakes & Mitigations: Navigating AI’s Dark Side in Warfare

Common pitfalls include biases in training data causing misidentifications, “black box” opacity leading to unexplained decisions, and escalation from autonomous systems. Mistakes occurred when Israel’s system flagged civilians, and Mitigations for this issue include using diverse datasets and conducting regular audits.

• Pitfall 1: Bias & Hallucinations—AI can misinterpret data, so it is important to validate findings through human reviews.
• Pitfall 2: Loss of Control—”Killer robots” act independently; mitigate with human-in-loop mandates.
• Pitfall 3: Proliferation—Technology reaches terrorists, necessitating countermeasures through international treaties.
• Pitfall 4: Cyber Vulnerabilities—Hacks disrupt AI; use encrypted, redundant systems.
• Pitfall 5: Ethical Dehumanization—Reduces empathy; enforces IHL compliance.

Sources: Harvard Medical School, ICRC.

Alternatives & Scenarios: Best, Likely, & Worst-Case Futures for AI in War

Best-Case: AI minimizes casualties through precision, with global regulations ensuring ethical use—e.g., the UN bans fully autonomous weapons, leading to peaceful deterrence.

Likely: Hybrid human-AI warfare persists, with escalations in proxy conflicts like Ukraine and market growth to $35B by 2031, but patchy regulations cause incidents.

In the worst-case scenario, an uncontrolled AI arms race leads to “hyperwar,” in which algorithms escalate conflicts on their own, putting the world at risk of a disaster like a nuclear war.

thebulletin.org

AI and the future of warfare: The troubling evidence from the US …

Actionable Checklist: 20 Steps to Engage with AI in Warfare Ethically

1. Research current AI military trends via DoD reports.
2. Assess ethical frameworks like the UN GGE guidelines.
3. Identify key stakeholders (policymakers, tech firms).
4. Evaluate data sources for biases.
5. Prototype AI models with simulations.
6. Implement human oversight protocols.
7. Test for vulnerabilities like jamming.
8. Integrate with existing defense systems.
9. Monitor for compliance with IHL.
10. Train personnel on AI tools.
11. Collaborate internationally for regulations.
12. Audit systems quarterly.
13. Prepare mitigation for failures.
14. Document all decisions for accountability.
15. Engage ethicists in development.
16. Scale responsibly post-testing.
17. Please update models with new data.
18. Advocate for bans on lethal autonomy.
19. Measure impacts on casualties/efficiency.
20. Review and iterate based on real-world feedback.

Start today for safer integration.

FAQ Section

1. Is AI already used in active wars?

Yes, AI is already being used in active wars in Ukraine and Gaza for targeting and drones.

2. What are the ethical concerns with AI in war?

There is a loss of human control, the presence of biases, and the risk of dehumanization.

3. Can AI reduce war casualties?

Yes, it can potentially enhance precision, but there is a risk of errors.

4. What regulations exist for military AI?

There are currently limited regulations for military AI, with ongoing discussions at the UN and no binding treaties in place.

5. How does AI affect cyber warfare?

Artificial Intelligence (AI) improves detection capabilities but also creates new vulnerabilities.

6. Will AI make wars more frequent?

Possibly, AI could increase the frequency of wars by lowering barriers, but it also has the potential to deter through strength.

7. What tools are best for military AI?

The most effective tools for military AI are Palantir and C3 AI, specifically designed for secure applications.

About the Author

Dr. Alex Rivera, PhD in AI Ethics, with 15 years of experience in defense technology consulting for institutions such as the DoD and UN, has authored reports on AI proliferation, cited in publications from Harvard and Georgetown. Peer-reviewed papers on the ethical deployment of AI have verified his expertise. This article draws from primary sources, like DoD datasets and UN reports, for trustworthiness.

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