The Double-Edged Sword: Jamming and Machine Learning - MAJ Tom Berry

‘If an adversary is operating in a highly enabled headquarters and we’re not, we will fall behind instantly ...’

In this week’s episode, the host sits down with MAJ Tom Berry, a Signals Officer posted to Headquarters Joint Operations Command (HQJOC), to unpack the realities of jamming, machine learning, and the future of command-and-control on the modern battlefield. Building on a recent episode titled Tactical Communications with CAPT Jack Virtue, this conversation shifts from line-of-sight and beyond-line-of-sight communications to the complex world of electronic warfare, adaptation, and decision-making advantage.

We break down the assumptions many of us still hold about jamming — including the belief that enemy EW will simply “switch off” our command posts and force us back to maps and talcs. MAJ Berry explains why jamming rarely works like that, how most systems retain offline data even when real-time feeds are denied, and why jamming is a double-edged sword that exposes the emitter as much as the target. From GPS and SATCOM spoofing to tactical-level EW effects and A2/AD systems, he outlines the conditions, power requirements, and vulnerabilities that determine how and when jamming is actually effective.

The episode then explores mesh networks, distributed architectures, and what resilient, reconfigurable communications webs can offer a formation — and their limits. We discuss why mesh networks reduce bandwidth stress on BLOS communications, how they support tempo, and why even the best mesh still depends on a reliable BLOS hop.

Finally, we dive into machine learning and its role in enabling commanders and staff. MAJ Berry explains how ML helps find “needles in haystacks,” reduces the staff effort required to decypher useful information, and gives command post staff and commanders the space to create shock, surprise, and decision advantage. We also examine the tension between a commander’s information requirements and the creeping “1000-mile screwdriver” — what leaders need to see versus what they want to see. This episode challenges long-held assumptions about jamming and machine learning — and argues that if we consistently drop to map-and-compass we will be left behind by those armies embracing machine learning to accelerate their decision-making speed.

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