Why This Modernisation Cycle Is Qualitatively Different
The defence electronics sector has experienced multiple modernisation cycles over the past half-century, each driven by the combination of technology availability and threat evolution that defines military capability investment priorities. The Cold War drove the development of precision guidance, electronic warfare, and the C3ISR (command, control, communications, intelligence, surveillance, and reconnaissance) systems that distinguished Western military capability during that period. The post-Cold War decade saw sustained investment in the network-centric warfare concepts that sought to exploit information superiority as a force multiplier. The Global War on Terror period emphasised persistent surveillance, precision strike, and the protective systems — counter-IED electronics, vehicle armour integration, and tactical communication — demanded by counter-insurgency operations in Iraq and Afghanistan. Each of these cycles produced genuine technology advancement and genuine capability improvement, but each also operated within a relatively stable technology paradigm in which the fundamental nature of the electronic systems being developed and the industrial base that produced them evolved incrementally rather than being disrupted.
The current modernisation cycle is qualitatively different in its technology character because it involves not only the incremental improvement of existing defence electronics capabilities but the integration of commercial AI and machine learning technologies — whose development pace and commercial investment scale far exceed what defence-specific R&D could independently achieve — into military systems in ways that are creating genuine leaps in autonomous capability, sensor data exploitation, decision support speed, and the cognitive workload management of electronic warfare and C3ISR systems. The integration of commercial AI into defence electronics is happening across every functional domain simultaneously — in radar signal processing, in electronic warfare threat identification and response, in autonomous vehicle and platform control, in intelligence analysis and target recognition, and in the command and control systems that must synthesise information from all of these domains into the operational picture that commanders use to make decisions at speeds that peer adversary threats demand. The pace of AI capability development — driven by commercial investment that is orders of magnitude larger than defence R&D budgets — means that the defence electronics systems being designed and procured today will incorporate AI capabilities that did not exist in deployable form three years ago.
Electronic Warfare: The Most Contested Domain
The electronic warfare market — encompassing the systems that detect, characterise, and defeat electromagnetic threats through jamming, deception, and directed energy countermeasures — is experiencing investment growth at rates that reflect the recognition by Western defence establishments that their electronic warfare capability has atrophied relative to peer adversary investment during the two decades of counter-terrorism operations in which electronic warfare against state-level adversaries was not the operational priority. The demonstration of Russian electronic warfare capability in the Ukraine conflict — including the systematic jamming of GPS signals, the defeat of early-generation Ukrainian drone swarms through electronic attack, and the use of electronic intelligence systems to identify and target Ukrainian artillery positions through their electromagnetic emissions — has provided a contemporary operational data point on the importance of electronic warfare capability that has accelerated procurement and development investment in NATO and other allied defence establishments.
The technology transformation in electronic warfare is driven by the application of machine learning to the threat identification and countermeasure generation tasks that have historically been executed by threat libraries — pre-programmed databases of known emitter characteristics against which detected signals are matched. Machine learning-based electronic warfare — in which AI models trained on vast datasets of electromagnetic signal characteristics can identify and classify novel signals that do not match pre-programmed threat libraries, and can generate adaptive countermeasures optimised for specific signal characteristics in real time — provides a qualitatively different capability against the agile, frequency-hopping, and encoding-variable threats that peer adversaries have developed specifically to defeat library-based electronic warfare systems. The integration of AI into electronic warfare systems is the development that most clearly marks the current modernisation cycle as a departure from previous generations, and its commercial implications for the electronic warfare equipment market — in terms of both the development cost structure and the competitive advantage that superior AI development capability confers — are transforming the competitive landscape of the defence electronics industry.
Radar and Sensor Technology: The Active Electronically Scanned Array Revolution
Active electronically scanned array radar technology — in which the radar beam is steered electronically through the programmable phase control of thousands of individual transmit-receive modules rather than through the mechanical rotation of a conventional antenna — has been the dominant technology development in defence radar for the past two decades and continues to be the foundation on which current and next-generation radar systems are being built. AESA radar's ability to simultaneously perform multiple functions — air surveillance, missile approach warning, electronic attack, and communication through the same aperture — and to do so with the low probability of intercept characteristics that make the radar itself more difficult to detect and target has made it the radar architecture of choice across airborne, naval, and ground-based radar applications. The progressive reduction in the cost of the gallium nitride semiconductor devices that constitute the transmit-receive modules of AESA arrays — driven by the commercial investment in GaN for wireless infrastructure applications — is extending AESA radar from the most expensive combat aircraft and naval destroyer systems toward a broader range of military platform applications where its performance advantages are operationally valuable but whose cost constraints have historically excluded AESA from consideration.
Multi-function radar systems that integrate radar, electronic support measures, and electronic attack functions in a single aperture — eliminating the antenna farm of separate radar and electronic warfare systems that current platforms carry — represent the next generation of defence electronics integration that is being developed for the most advanced combat aircraft, naval platforms, and ground-based air defence systems. The processing capability that simultaneously manages multiple sensor functions through a shared aperture requires AI-assisted signal processing whose computational intensity far exceeds what conventional digital signal processors can support, creating demand for the specialised high-performance computing hardware and the associated AI software that constitutes the intelligence layer of the next-generation multi-function radar system. The defence electronics companies competing for the major radar development programmes — Northrop Grumman, Raytheon, Leonardo, Thales, and their joint venture and partnership arrangements — are investing in the GaN technology, the AI processing capability, and the systems integration expertise that next-generation multi-function radar requires, creating a competitive landscape in defence radar that is more technically demanding and more commercially concentrated than any previous generation of radar development.
Command and Control and the JADC2 Architecture
The Joint All-Domain Command and Control concept — the US military's framework for connecting sensors, shooters, and decision-makers across all military domains (air, land, sea, space, and cyber) through a unified data fabric that allows information from any sensor to be rapidly acted upon by any weapon or effector in the joint force — represents the command and control architecture challenge that is defining the most strategically significant and most commercially valuable electronics development programme in the current US defence budget. The technical challenge of JADC2 — achieving the data standards, communication protocols, cybersecurity, and AI-assisted decision support that allow information from thousands of disparate sensors and systems to be fused, prioritised, and presented to commanders at the speed of relevance in a contested electromagnetic environment — is the most complex software and systems integration challenge in defence electronics and the one that most directly determines the operational effectiveness of the integrated joint force that modern military strategy requires.
The commercial opportunity in JADC2 and equivalent multi-domain command architectures in allied defence establishments is large and is driving significant investment by both the traditional defence prime contractors — Lockheed Martin, Raytheon, BAE Systems, and L3Harris — and by the commercial technology companies — Microsoft, Amazon Web Services, Palantir, and a growing ecosystem of AI and data analytics companies — whose cloud, AI, and data integration capabilities are directly relevant to the data management and decision support requirements that JADC2 demands. The competition between traditional defence contractors and commercial technology companies for JADC2 programme roles is creating the most significant disruption in the defence electronics industry's competitive structure since the early days of network-centric warfare, as the capabilities that determine competitive advantage in multi-domain command architecture development are increasingly those that commercial technology development has produced faster and more cost-effectively than defence-specific development could achieve.