Technology and AI in Modern Warfare: What's Actually Being Used in 2026

Wars have always been the most brutal forcing function for technological change. The internet traces its lineage to ARPANET, a US Defense Department experiment in survivable communications. GPS exists because the US military needed to guide missiles. The microwave oven was accidentally invented by a Raytheon engineer working on radar. Night vision, digital photography, the jet engine — nearly every technology that shapes modern life passed through a military laboratory at some point.

That relationship between warfare and technology has not stopped. In 2026, with active conflicts running across Europe and the Middle East, AI sits at the center of modern military operations in ways that were theoretical just five years ago. At the same time, those conflicts are sending shockwaves through global technology supply chains, the cybersecurity landscape, and the career decisions of developers worldwide.

This is a clear-eyed look at what is actually happening: what AI is doing on the battlefield, what military conflict is doing to the tech industry, and what developers should understand about the world they are building in.

How Modern Wars Disrupt the Tech Industry

Wars do not stay on the battlefield. The effects propagate through global systems in ways that reach every developer and every company building software.

Energy costs hit cloud infrastructure first. Data centers are among the largest electricity consumers on earth. When geopolitical conflicts drive oil and gas prices up — as every major Middle East flare-up has historically done — the operating costs of cloud infrastructure rise. AWS, Azure, and GCP absorb those costs initially, but sustained energy price pressure eventually translates into compute pricing. Developers who build cost-sensitive applications feel this downstream.

Semiconductor supply chains are fragile and politically exposed. The conflict in Ukraine, which disrupted neon gas supplies (critical for chip manufacturing lasers) in 2022, was a sharp reminder of how geographically concentrated critical inputs are. Taiwan Strait tensions remain the industry's single largest supply chain risk — TSMC produces the majority of the world's most advanced chips from a single island. Every major military conflict in or near semiconductor supply regions forces re-evaluation of where to build and how to stockpile.

Undersea cables are vulnerable physical infrastructure. Roughly 95% of international internet traffic travels through undersea fiber cables. These cables pass through geopolitically sensitive chokepoints — the Red Sea, the Persian Gulf, the Taiwan Strait, the Black Sea. Military conflict near these routes creates real risk of disruption, whether from deliberate attack or accidental damage. The 2022 Red Sea cable cuts, linked to conflict activity in the region, were a preview of how fragile global connectivity actually is.

Tech talent gets displaced. Conflict zones lose engineers. The Russian invasion of Ukraine produced a significant diaspora of Ukrainian and Russian tech workers — many of whom relocated to Poland, Germany, Georgia, and the EU, reshaping the Eastern European tech ecosystem. Similar patterns emerge wherever conflict escalates: engineers leave, startups lose key people, and the local tech ecosystem takes years to recover. This is a human cost first, and a structural shift in global talent distribution second.

Export controls and sanctions reshape what you can ship. Governments respond to geopolitical conflict with technology export restrictions. The US chip export controls targeting China — broadened in 2023 and 2024 — changed the business model of every company selling AI compute globally. Sanctions regimes limit which developers can use which cloud services, which APIs are accessible in which countries, and which payment processors work across borders. Developers building global products increasingly need to account for these constraints.

AI on the Battlefield: What's Actually Being Used

The use of artificial intelligence in military operations is no longer theoretical or experimental. It is being used right now, in active conflicts, at scale.

Precision strike guidance. Modern precision munitions use AI-assisted targeting that fuses data from multiple sensors — satellite imagery, radar, infrared, historical strike data — to identify and track targets. This is not autonomous killing; there is a human in the loop for authorization. But AI does the sensing, pattern matching, trajectory calculation, and course correction. The accuracy improvements over GPS-only guidance are significant, and the technology has spread from the US military to allied forces and, through less controlled channels, beyond.

Drone swarms. The Russia-Ukraine war made drone warfare mainstream. Both sides have deployed thousands of cheap first-person-view (FPV) drones as precision strike weapons. The next evolution is AI-coordinated swarms — where dozens or hundreds of drones share sensor data, coordinate approach vectors, and adapt to electronic countermeasures collectively. Ukraine's development of AI-guided drones that can navigate without GPS (to defeat Russian jamming) is one of the clearest examples of AI-driven military innovation under real operational pressure.

Missile defense systems. Israel's Iron Dome, David's Sling, and Arrow systems are among the most publicly well-documented AI-assisted defense platforms. When an incoming projectile is detected, the system must assess threat level, calculate intercept probability, select an interceptor, and execute a launch — in under ten seconds, at scale across hundreds of simultaneous threats. This is operationally impossible without machine learning-based threat classification and trajectory prediction. The system's data improves with every engagement.

OSINT and intelligence analysis. Satellites, drones, and signals intercepts generate vastly more data than human analysts can review. AI systems process satellite imagery to detect changes (new vehicles, excavations, troop concentrations), classify objects, and flag anomalies. Companies like Planet Labs provide commercial satellite imagery; companies like Palantir build the analytical layer. Both have defense contracts. Open-source intelligence (OSINT) — public social media, flight tracking, ship AIS data — is increasingly processed by AI to build operational pictures that would have required enormous human analyst teams a decade ago.

Electronic warfare and GPS spoofing. Modern conflict involves jamming enemy communications and sensors, spoofing GPS signals to cause navigation confusion, and deceiving radar systems. AI is used to generate more adaptive jamming patterns that learn from the adversary's countermeasures in real time. GPS spoofing has been documented extensively in conflict zones — civilian aircraft and ships near active military operations have reported significant positioning errors. The Black Sea and Eastern Mediterranean have been particularly affected.

Logistics and supply chain optimization. Less visible than targeting systems, but arguably more impactful at scale: AI is used by military logistics to predict maintenance needs before equipment fails, route supplies through contested terrain, and model force positioning under uncertainty. The US military has been investing in these applications through programs like JEDI (cloud infrastructure) and various DARPA initiatives for over a decade.

The Cyberwarfare Dimension

Every modern military conflict has a parallel cyber dimension. State-sponsored hacking groups operate continuously — in peacetime as espionage, in wartime as sabotage and disruption. In 2026, with multiple active conflicts and elevated great-power tensions, the cyber threat landscape is at its most complex since the early internet era.

The most significant attacks on record give a sense of what states can actually do:

NotPetya (2017). Attributed to Russian military intelligence (GRU), this destructive malware was initially delivered through Ukrainian accounting software and spread globally within hours. It caused an estimated $10 billion in damage — hitting Maersk (shipping), FedEx, Merck (pharmaceuticals), and dozens of others. It was not targeted at those companies; they were collateral damage. It remains the most costly cyberattack in history.

Stuxnet (2010). Attributed to the US and Israel, Stuxnet was the first publicly confirmed cyberweapon designed to cause physical destruction. It targeted Siemens industrial control systems at Iranian uranium enrichment facilities and destroyed centrifuges by causing them to spin at damaging speeds while reporting normal operation to operators. It demonstrated that software could cause real-world physical damage at a state-level target.

SolarWinds (2020). Russian intelligence compromised SolarWinds' software update pipeline and used it to gain access to approximately 18,000 organizations, including US government agencies (Treasury, State, Homeland Security) and major technology companies. The attack went undetected for months. It is the template for how sophisticated supply chain attacks work.

Volt Typhoon (2023–2025). A Chinese state-sponsored group was found to have pre-positioned itself inside US critical infrastructure — power grids, water systems, communications networks — apparently waiting for a geopolitical crisis moment to activate. This is not espionage; it is preparation for disruption. CISA, the NSA, and allied agencies issued joint advisories about it, which is unusual and signals genuine concern.

What these attacks have in common: they exploit trust relationships (software updates, supply chains), they persist undetected for extended periods, and they cause damage wildly disproportionate to their initial footprint. Developers building infrastructure, platforms, or supply chain software are in the path of these attacks whether they know it or not.

The Ethical Fault Lines Every Developer Should Know

The use of AI in warfare is not an abstract policy question. It is a question about what the technology that developers build every day is being used for.

Autonomous weapons and the kill chain. International humanitarian law requires that lethal force be authorized by a human being with the judgment to distinguish combatants from civilians, and to assess proportionality. As AI systems get faster and more capable, there is operational pressure to shorten or remove the human decision step — because adversaries with more autonomous systems can act faster. The Campaign to Stop Killer Robots, backed by many prominent AI researchers, argues for a binding international treaty. No such treaty exists. The debate is live and unresolved.

The dual-use problem. The same computer vision model that detects tumors in medical scans can identify humans in drone footage. The same large language model that helps you write code can generate targeted phishing campaigns or synthetic propaganda at scale. The same reinforcement learning algorithms that optimize game-playing agents can optimize autonomous weapons. There is no clean separation between civilian AI and military AI. Every general-purpose AI capability is, in principle, dual-use.

Tech company defense contracts — the new employee flashpoint. In 2018, Google employees forced the company to decline renewal of Project Maven, a Pentagon contract to use Google AI for drone footage analysis. Google eventually walked away. Microsoft, Amazon, Palantir, Anduril, and Shield AI stepped into the space. Anthropic — whose AI model was designated a supply chain risk by the US Defense Department, a story covered separately on this blog — occupies an unusually complicated position for an AI safety company. Every major AI lab must decide, explicitly or by default, what relationship it will have with military and intelligence clients. These decisions will increasingly define company culture and employee retention.

Algorithmic targeting and civilian harm. A detailed investigation by +972 Magazine revealed that the Israeli military used an AI system called "Lavender" in Gaza to generate target lists at a speed and scale no human process could match. Critics argued that error rates in such systems were treated as acceptable operational thresholds rather than individual human judgments — that the efficiency of AI targeting changed the moral calculus of who was considered targetable. The military disputed some findings. The underlying question — whether AI-assisted targeting produces more or fewer civilian casualties than unassisted human decision-making, and who is accountable when it goes wrong — has no clean answer and will define international law debates for the next decade.

How Wars Have Built the Technology We Use

Before addressing what to do with all of this, it is worth being honest about the historical pattern. The relationship between military investment and civilian technology is not incidental. It is foundational.

The internet. ARPANET, funded by DARPA in 1969, was designed to maintain communications even if parts of the network were destroyed in a nuclear attack. The packet-switching architecture that makes the internet resilient is a direct product of that military requirement. TCP/IP did not emerge from a university's pure research agenda; it emerged from survival engineering under nuclear threat.

GPS. The Global Positioning System was built and is still operated by the US Department of Defense. It was opened for civilian use in 1983, after a Korean Air flight was shot down when its crew made a navigation error. Today GPS underpins logistics, rideshare, food delivery, financial transaction timestamping, agriculture automation, and construction surveying. Its annual economic contribution to the US alone is estimated at hundreds of billions of dollars.

The microwave oven. Percy Spencer, a Raytheon engineer working on radar magnetrons in 1945, noticed that a chocolate bar in his pocket had melted while standing near active radar equipment. He patented the microwave cooking process. The first commercial microwave oven was six feet tall and cost $5,000. By the 1970s, it was in millions of homes.

Digital photography and imaging sensors. High-resolution imaging systems developed for reconnaissance satellites — specifically the US Corona program in the 1960s — drove foundational advances in sensor technology, data storage, and image processing. Those advances are direct ancestors of the digital cameras and smartphone cameras used by billions of people today.

Drones. Unmanned aerial vehicles were developed primarily for military surveillance and strike missions. The commercial drone industry — DJI's consumer drones, agricultural drones for precision farming, infrastructure inspection drones, Amazon delivery prototypes — is entirely built on technologies that originated in defense programs.

The web. Indirectly: Tim Berners-Lee built the World Wide Web at CERN, a particle physics laboratory that exists because of Cold War scientific competition and decades of US government funding for basic research with dual-use potential.

This pattern will repeat. The AI targeting systems, drone coordination algorithms, hardened satellite communications, and electronic warfare countermeasures being developed and deployed in 2026 will find civilian applications over the next decade. Some of those applications will be beneficial. Some will be troubling. Most will be both.

What This Means for Developers in 2026

Most developers are not building weapons. But the world in which developers work is being shaped by military technology, military conflict, and the geopolitical dynamics of 2026 — in ways that are worth understanding clearly.

Cybersecurity is now a geopolitical exposure, not just a technical one. If you are building anything that touches financial services, energy, water, healthcare, or government — and many developers are, without necessarily thinking of it that way — your application is a potential target for state-sponsored actors. CISA publishes advisories about known threat group techniques and targeted sectors. Reading them is not paranoid; it is baseline professional awareness. Implement MFA everywhere, patch aggressively, audit your dependencies, and understand your blast radius if a key library is compromised.

Defense tech is a growing, well-compensated career path with real ethical stakes. Palantir, Anduril, Shield AI, and dozens of smaller defense-focused startups are competing for software engineers with compensation packages comparable to Big Tech. The US Department of Defense is investing heavily in software — logistics, intelligence, maintenance prediction, HR, and operations. This is a genuine career option that more developers will face. It comes with genuine ethical complexity, and that complexity deserves honest engagement rather than reflexive acceptance or rejection.

AI regulation is being shaped by military use right now. The EU AI Act, US executive orders on AI, and proposed UN frameworks on autonomous weapons are all being written in the context of military AI applications. Export controls on advanced AI chips and models have already changed global compute access. Developers who want to understand where AI regulation is going should watch how governments and international bodies respond to the use of AI in active conflicts.

Supply chain security is a geopolitical problem, not just a software quality problem. The xz backdoor attempt in 2024 — where a sophisticated attacker spent years building trust in an open-source project before inserting a backdoor — showed that the open-source commons is an attack surface for patient, well-resourced adversaries. State-sponsored actors have the motivation and capability to compromise widely-used packages. Dependency auditing, reproducible builds, and software composition analysis are not optional extras.

Your data is worth more to some people than you might realize. Satellite imagery, mapping data, social media posts, shipping manifests, flight tracking — all of this is intelligence. Developers who build products that aggregate or process data from these sources should understand who can request access and under what legal authorities. This is especially relevant for developers working at infrastructure companies, mapping services, telecom providers, and social platforms.

The Uncomfortable Honest Position

Wars are terrible. They kill people, destroy infrastructure, displace populations, and set back human development in ways that take generations to repair. There is nothing romantic about the fact that the internet came from military funding, and the people who paid for that technology were the ones who died in the conflicts that necessitated it.

At the same time, the history is the history. Military investment has driven an extraordinary share of the technology that underlies modern life, and that relationship continues in real time. The AI systems being developed and tested in active conflicts right now are early versions of technology that will shape civilian applications in ways that are not yet visible.

The most useful thing developers can do is understand this clearly — what AI can and cannot do in high-stakes environments, what the failure modes look like at scale, what the ethical trade-offs actually are — and bring that understanding into their work. That is relevant whether you are building a consumer app, contributing to open-source security tooling, deciding which job offer to take, or thinking about what to build next.

The world does not separate neatly into "war technology" and "peaceful technology." It never has. The question is always what you do with that knowledge.