The year 2025 marked a turning point in modern warfare. For the first time, directed-energy weapons, digital twin platforms, and autonomous drone swarms left the laboratory and entered real-world combat and defence systems. From the USS Preble’s laser shoot-downs to India’s digital-backbone military networks and Ukraine’s AI-driven drone swarms, the battlefield is no longer defined by steel and gunpowder, but by algorithms, photons, and data.
AMIT DUA
Associate Professor, Computer Science and Information Systems Department, Birla Institute of Technology and Science, Pilani.
a 5 mins read.
In March 2025, the USS Preble made headlines when its HELIOS laser system intercepted 23 drones during a Red Sea patrol. This achieved results once thought impossible by defence officials. For the first time, a ship protected itself with targeted bursts of electrical energy, eliminating threats without firing a single missile or relying on costly ammunition. This shift signalled not just technical progress but the beginning of a larger change in how militaries protect themselves.
What set 2025 apart was the move from experimental tests to real deployments of breakthrough systems. Laser weapons like HELIOS, digital twins (virtual replicas of physical equipment), and AI-controlled drone swarms moved out of labs and into active service across the world. This transition was seen not only in the United States and Israel, but also in India, where new digital twin platforms, indigenous high-energy lasers, and autonomous swarming tactics are now being integrated into military operations.
Yet these systems are not perfect. Each brings unique trade-offs, operational limits, and unforeseen challenges. Let us understand their work in detail.
DIRECTED ENERGY WEAPONS
Directed energy weapons (DEWs), particularly laser beam technology, have emerged as transformative tools in modern defence because they use concentrated electromagnetic energy to disable or destroy targets almost instantly.
Unlike traditional weapons that rely on physical projectiles or explosives, laser weapons deliver a tightly focused beam of light. They travel at the speed of light to a precise point on a target to cause damage through heat or structural failure. The journey from lab experiments to operational deployment has been driven by substantial advances in several key technological areas.
First, the core mechanism behind laser weapons is their ability to convert electrical energy into coherent light within a small and directed beam — similar to the beam-forming used in modern 5G technology. Modern high-energy lasers (HELs) can produce power outputs sufficient to melt or disable small aerial targets like drones and missiles. The effectiveness of these weapons depends significantly on how much energy can be delivered to the target’s surface in a short period. This requires efficient beam generation and focusing.
Laser weapons fuse radar, infrared, and optical sensors to detect and track fast aerial threats, while battle-management software selects laser or missile response based on cost and priority.
Solid-state laser technology allows lasers to be more energy-efficient and reliable compared with earlier gas or chemical lasers. Traditional solid-state laser operation leaves a large amount of waste heat. Advances in thermal management (closed-loop liquid cooling systems) help maintain continuous firing without damaging sensitive components.
How do you compensate for atmospheric disturbances such as turbulence, heat waves, and moisture? Adaptive optics measure wavefront errors in real time and adjust deformable mirrors. This keeps the beam tightly focused on its target with less distortion and scatter over longer distances (in ranges of several kilometres). Laser weapon systems combine radar, infrared sensors, and electro-optical cameras for precision tracking and targeting. They detect, classify, and track fast-moving aerial threats. This sensor fusion allows the laser to maintain lock-on despite environmental factors and target manoeuvres. Active battle-management software further helps by deciding when to engage with lasers versus traditional interceptors based on cost and threat priority.
What made these technological advances possible now is decades of progress in multiple fields. The miniaturisation of high-power laser diodes, novel cooling solutions, fast and precise sensors, advanced algorithms for adaptive optics, and progress in real-time systems engineering made this complex integration a reality. These advancements overcame earlier limitations where lasers were too bulky, inefficient, or vulnerable to atmospheric and thermal constraints.
DIGITAL TWINS
Another technology that proved its power in 2025 was the digital twin.
Digital twin technology became the key tool in 2025 for maintaining and operating complex military fleets and industrial systems. By creating virtual replicas of physical assets, defence agencies and industries monitor and predict the performance of equipment in real time. The technology was integrated successfully into major aircraft, ships, and power plants, allowing complex systems to operate under demanding conditions while maintaining performance.
Digital twins rely on constant sensor data—temperature, pressure, vibration, and more—streamed in real time, enabling high-fidelity simulation and prediction through physics models and machine-learning analytics.
Digital twin technology creates a virtual replica of a physical system. This digital model mimics the entire system — its behaviour, status, and dynamics — in sync with its real-world counterpart in real time. It enables engineers to optimise the physical asset from afar. The core of digital twin technology involves three key components: data acquisition, simulation models, and real-time analytics.
As expected, the foundation of a digital twin lies in collecting data continuously from sensors embedded in the physical system. These sensors gather diverse types of data — temperature, pressure, vibration, movement, fluid flow, and more. Advances in sensor technology, wireless communication, and data streaming allow live data feeds directly into the digital twin. Stream analytics plays a crucial role in modelling and simulation. The high-fidelity simulation model is built using physics-based algorithms, machine learning, and data-driven techniques. It uses machine learning algorithms, including stream analytics tools like Kafka and Flink, to predict in real time.
The continuous data flow reflects the current state of the physical system with high accuracy, visualised through dashboards, 3D renderings, and analytical reports.
In 2025, digital twin technology was integrated into broader operational management platforms, linking fleet-wide assets and systems into a unified digital backbone. This networked approach allowed for holistic management and coordinated responses across naval fleets, aircraft squadrons, and manufacturing plants.
The cheaper availability of increased computational power allowed this complex real-time simulation to become commercially usable.
AUTONOMOUS DRONE SWARMS
We have seen major advancements in technology that have helped in defence (lasers) and maintenance/performance (digital twins). But 2025 also witnessed a technology primarily used for offence. The Ukraine war showcased advancements in autonomous drone swarms, which shook the world regarding technology’s future role. It changed how militaries think about tactical air operations. Ukrainian forces used AI-powered systems to coordinate groups of drones to strike targets, avoid defences, and adapt with minimal human input. These swarms managed complex tasks.
We have seen increasing use of drones since 2022, but for the first time, we saw splitting attack roles, sharing sensor data, and rerouting in response to electronic jamming. The demonstration of autonomous teamwork under real combat conditions made it possible for one operator to command multiple drones at once.
Autonomous drone swarm technology represents a major leap in both artificial intelligence and distributed robotics. At its heart, it combines decentralised coordination, real-time sensor fusion, adaptive response algorithms, robust communication systems, and user-friendly human–machine interfaces. These advancements allowed swarms to operate effectively even in contested and unpredictable combat environments.
A key challenge in swarming is task allocation under weak connectivity. Ukrainian drones use auction-based algorithms to “bid” for targets, then switch to local rules if links fail.
Decentralised autonomy was seen at scale for the first time in 2025. Each drone in a swarm carries a copy of the mission plan, target priorities, and rules of engagement. Instead of relying on a continuous link with a ground controller or central hub, the drones communicate with one another when possible — and are still capable of operating independently if signals are jammed or lost. Coordination algorithms allow drones to make tactical decisions alone, guided by preset instructions and locally sensed information. This approach is called “graceful degradation”, meaning the system continues functioning even if communication links fail.
One key challenge in swarming is deciding which drone does what when the connection is weak or intermittent. Many Ukrainian systems used auction-based algorithms: each drone “bids” for a target based on its location, fuel level, sensors, and payload. Targets are assigned based on these bids. If drones are cut off from the network, they fall back to local rules, avoiding overlap and maximising coverage.
Most small tactical drones previously had basic cameras and sensors, unsuitable for reliable target identification. Developers addressed this by investing in machine-learning models trained on real-world drone footage to improve recognition of vehicles, personnel, and equipment in varied lighting or weather. Diffusion-based machine-learning algorithms now allow drones to “see” clearly through haze, wind, rain, and other extreme conditions.
Autonomous swarms do not remove human oversight. Instead, they enable a small team — often just one operator — to supervise ten or more drones. Interfaces with visual dashboards, quick status drilldowns, and simplified command controls make this possible without overwhelming the operator. The operator designates the zone and objectives; the system executes and coordinates autonomously.
Advanced communication resilience, adaptive response, and learning capabilities further enabled drones to overcome operational constraints unseen before.
2025 showed that when strong engineering meets agile, trust-driven deployment, breakthroughs happen fast — the real task now is making such successes repeatable.
LESSONS FROM 2025
The deployments of 2025 demonstrated that technology alone does not guarantee battlefield success. Human trust in new systems is the critical benchmark. Operators must learn when and how to rely on complex, often imperfect tools to meet military or commercial goals. That trust is built only through live experience and transparent system behaviour during failures.
In 2025, we saw a major shift: countries moved towards adaptation rather than rigid doctrine. The lessons from Israel’s Iron Beam, the USS Preble’s HELIOS, India’s digital twins, and Ukrainian drone swarms highlight the same point — tactical advantage now depends on rapid feedback and iterative learning, not perfect technology.
2025 proved that rapid progress is possible when engineering fundamentals align with agile and trust-centred operational integration. The challenge ahead is to make those rare successes repeatable.
Looking forward, the question is whether defence institutions can absorb these lessons deliberately. Start-ups and defence were separate until recently. India recognised this in 2025, investing heavily in start-ups developing defence technology. The armed forces are actively integrating them. As survival now depends on technological edge, 2025 offered a stark reminder: even when wars are fought on physical battlefields, they will be won through technology.
(Dr Amit Dua is an Associate Professor at BITS, Pilani and Founder of YET Pvt. Ltd. He is a TEDx speaker and the author of books on Machine Learning and Machine Learning for Education. The views expressed are of the author and do not necessarily reflect the views of The News Analytics Herald.)
Key Takeaways
- Laser defence systems proved operational by destroying drones without missiles, reshaping naval combat economics.
• Digital twin platforms now monitor and predict the performance of complex fleets, aircraft, and power assets in real time.
• AI-enabled drone swarms executed coordinated strikes with minimal human input, redefining battlefield autonomy.
• Modern warfare increasingly depends on human trust in machine judgment, not just superior weapons.
• 2025 confirmed that rapid tech integration, not old doctrines, will decide victory in future wars.
