$44.3 million. That is what the US Congress authorised for a single line item in the FY 2026 National Defence Authorisation Act: cognitive electromagnetic warfare research. The entire Pentagon budget runs past $1.5 trillion. This allocation is barely a rounding error. It is also the most telling signal in the entire defence budget about where the electromagnetic spectrum fight is heading.
Machine learning systems that sense, classify, jam, and adapt, all without a human in the loop, are transitioning from DARPA prototypes to operational deployment across the US Army, Air Force, and Navy. Cognitive electronic warfare is not a future concept. It is being fielded now.
From libraries to learning
For six decades, electronic warfare followed one playbook: detect a signal, match it against a pre-loaded threat library, fire the corresponding jamming technique. The system was only as good as its last intelligence update. When Russian and Chinese forces fielded software-defined radios capable of changing waveforms in milliseconds, that playbook became obsolete.
DARPA recognised the gap early. Two programs — Behavioural Learning for Adaptive Electronic Warfare (BLADE, awarded to Lockheed Martin) and Adaptive Radar Countermeasures (ARC, led by BAE Systems under a $35.5 million cumulative contract) — laid the intellectual foundation for cognitive EW. BLADE proved that a machine-learning system could encounter a novel communications waveform, characterise its properties, generate candidate jamming techniques, test them, and refine its approach, all within seconds. ARC tackled the harder problem of defeating cognitive radars that themselves adapt to interference.
The transition from DARPA research to operational capability has accelerated sharply since 2024. The Army Research Laboratory's FREEDOM programme — Fundamental Research for Electronic Warfare in Multi-Domain Operations — is funding the foundational science: RF sensing architectures, AI models optimised for embedded inference, and closed-loop EW techniques that sustain autonomous operation across the contested spectrum.
The operational layer
Three concurrent deployments illustrate how far cognitive EW has moved beyond the lab.
BEAST+. The Mastodon Beast+ system, a multi-channel EW device for detection, direction-finding, and attack of adversary signals, deployed with 3rd Infantry Division units during Exercise Combined Resolve 25-02. Soldiers used the system to detect signals across the electromagnetic spectrum, identify enemy positions, and apply countermeasures, all coordinated through an AI-driven interface that fused data from multiple sensors into a single operational picture.
REWSI. The Army's Rapid Electromagnetic Warfare and Signals Intelligence Commercial Solutions Offering, launched in June 2026, functions as a curated library of pre-vetted commercial cognitive EW capabilities. Commanders can pull off-the-shelf AI/ML modules into theatre at the speed adversaries change their electronic order of battle, bypassing the traditional multi-year acquisition cycle.
Spectrum Situational Awareness System. This Army capability gives commanders real-time visualisation of their own unit's electromagnetic signature, enabling signature management at the formation level, treating EW not as an isolated function but as a continuous operational discipline.
The industrial base
Every major Western defence prime now operates a cognitive EW programme.
Thales published a detailed white paper in April 2026 laying out its architecture for AI-integrated electromagnetic warfare, covering augmented warfighter decision-making, real-time optimisation of jamming signals, and automated decoy strategy generation. "The key issue with this technology will be making sure we do not become dependent on others," said Frantz Loutrel of Thales, framing cognitive EW as a sovereignty question.
BAE Systems completed Phase 3 of the ARC programme, transitioning cognitive countermeasure algorithms to fifth-generation fighter platforms. Northrop Grumman won a $200 million US Navy contract for AI-driven EW systems in 2024. Raytheon demonstrated the first AI/ML-powered Radar Warning Receiver for fourth-generation aircraft in February 2025, integrating cognitive algorithms for real-time threat detection and prioritisation. Leonardo partnered with UK-based Faculty AI to accelerate cognitive EW deployment, focusing on the Cognitive Intelligent Sensing (CoInS) framework.
Lockheed Martin opened a dedicated cognitive EW R&D facility in Maryland and is actively hiring AI/ML engineers for signal-processing applications across radar, communications, and EW, a rare public indicator of internal investment scale.
Will cognitive EW reach universal deployment by 2030?
The adversarial dimension
Cognitive EW creates a new layer of vulnerability. If an AI-driven jamming system can be deceived by adversarial signals crafted to cause misclassification, feeding it false data about threat characteristics to induce incorrect responses, then the EW fight becomes a machine-speed contest of adversarial learning.
Russian military researchers have published work on adversarial waveform generation: techniques for crafting electromagnetic emissions that deliberately confuse AI-based signal classifiers. Chinese PLA Strategic Support Force-affiliated institutions have published extensively on reinforcement learning for jamming strategy optimisation and GAN-based waveform generation. This is not theoretical. The practical application would be a radar system that, by subtly modulating its emissions, causes a cognitive EW system to misidentify it as a non-threat or apply an ineffective countermeasure.
The implication is a layered AI arms race within the electromagnetic spectrum: cognitive EW systems that learn to jam must also be hardened against adversarial inputs, requiring AI-based anomaly detection in the sensing layer, thorough training against adversarial examples, and human-in-the-loop verification for high-stakes jamming decisions.
Three scenarios
Scenario A — Full deployment by 2028 (50%): The US Army completes its Brigade Combat Team assessment of AI-enabled EW capabilities, the software integration challenges are solved through the REWSI commercial pipeline, and cognitive EW becomes standard equipment across all domains. The FY27 CJADC2 budget request, with $1.5 billion to industrialise Project Maven alone, provides the funding trajectory.
Scenario B — Software bottleneck delays (30%): The hardware is ready, but the AI models for the defence EW problem space remain too brittle for unsupervised deployment. Human-in-the-loop requirements limit cognitive EW to non-kinetic support roles. Full autonomy is pushed to 2032+.
Scenario C — Adversarial arms race dominates (20%): Adversarial AI techniques outpace defensive hardening. Cognitive EW systems are deployed but require constant model updates against adversarial waveforms, consuming disproportionate compute and personnel resources. The electromagnetic spectrum becomes a perpetual machine-speed contest with no lasting advantage for either side.
Signals to watch
Growing: VC investment in defence AI exceeded $15 billion in 2025 and is on pace to surpass $18 billion in 2026. Constelli raised $20 million (General Catalyst) for EW R&D. The convergence of commercial AI capability and defence EW requirements is producing a startup pipeline that did not exist three years ago.
Falling: The traditional threat-library model. No major Western defence programme has initiated a new static-library EW development since 2023. Cognitive EW is the default architecture for all new starts.
Emerging: University spinouts in cognitive EW signal processing. The SBIR/STTR pipeline now includes active topics for AI-based spectrum sensing, Rydberg atomic sensor integration, and autonomous EW behaviour validation, early indicators of where the next generation of capability will originate.
