
Hosted by Craig S. Smith
Written by a model from the transcript; not yet reviewed.
This episode explores how artificial intelligence, computer vision, and classical control algorithms are deployed in small military drones during the war in Ukraine. The guest, Alex Liannyi, CTO of NORDA Dynamics, explains how his company builds avionics and last-mile targeting modules for existing fixed-wing and quadcopter drones rather than manufacturing full aircraft. He and the host discuss the severe operational challenges caused by GPS jamming, radio horizons, and terrain obstacles, which force drones to operate autonomously during their final terminal guidance phase.
The discussion covers the pragmatic engineering choices behind using hobby-level single-board computers like Raspberry Pi instead of high-end edge AI chips, and how pilots maintain control over multiple drones simultaneously. Liannyi argues that while fully autonomous targeting is technologically close, retaining human approval in the loop remains essential for verifying targets and preventing false positives in combat environments.
The host introduces the topic of AI integration in drone systems and asks the guest about the presence of AI in their modules.
Discussion of GPS absence, radio horizon, and how these issues limit drone range and control during the Ukraine war.
Exploration of the limited use of neural networks, object detection on-device, and the need for pilot approval before engagement.
The conversation turns to future scenarios where drones operate within geofenced zones, awaiting operator approval, and the debate over fully autonomous attacks.
Discussion of the use of hobbyist boards, the challenges of edge computing, and the balance between speed and accuracy in AI models.
Alex explains how improved optics and gyro‑stabilized cameras could extend terminal guidance distance up to 2.5 km.
Final reflections on the readiness of technology, legal considerations, and the potential future of autonomous drone warfare.
Classical Algorithms Over Fancy AIinsight
The guest explains that their last-mile targeting systems do not rely on speculative artificial intelligence, instead using classical math and control theory.
Simultaneous Multi-Drone Pilot Controldata
The guest notes that specialized systems currently deployed allow a single military pilot to maintain active connection and control over thirty-two drones simultaneously.
Founding an Avionic Startupstory
The guest shares how his team of fifteen IT developers transitioned from volunteering to developing specialized drone avionics after the war started.
Overcoming GPS Jamminginsight
The guest details how the total absence of GPS navigation and radio signal loss due to the earth's curvature and terrain create major operational hurdles.
Using Raspberry Pi in Combatstrategy
The guest defends their choice of low-powered Raspberry Pi computers, stating that good engineering overcomes hardware limitations and runs algorithms in real time.
Collaborating With Manufacturersstrategy
Rather than competing with existing hardware builders, NORDA partners with about forty out of hundreds of drone manufacturers in Ukraine to supply software and modules.
“Technology is very close to that.”
“We are big fans of Raspberry Pi stuff.”
“One of the systems we use, it allows connection from one pilot to thirty two drones simultaneously.”
“If you can monitor them, you can control them.”
The AI systems guiding Ukrainian combat drones aren't running on expensive Nvidia chips. They're running on a Raspberry Pi Zero - a $15 hobby computer - and that single detail tells you more about how Ukraine's drone war is actually being fought than any headline about AI weapons. Craig Smith sits down with Alex Liannoy, CTO of NORDA Dynamics, for a technically specific and operationally candid conversation about what autonomous drone guidance looks like from inside the team building it. NORDA builds the avionics and targeting modules that plug into drones made by approximately 40 of Ukraine's hundreds of manufacturers, solving the two problems that make drone warfare hard at range: GPS denial from jamming, and the radio horizon that causes pilots to lose contact with their drone in the final 300 to 400 meters before ground level. NORDA's terminal guidance module takes over at up to one kilometer from the target, completing the approach autonomously while a human pilot's earlier lock defines the destination. The most counterintuitive finding in the episode is about what keeps humans in the loop, and it isn't ethics. Alex's argument is pragmatic: military doctrine requires proof that a drone hit its intended target. If you're monitoring the drone to verify the hit, you have enough connection to control it, which makes full autonomy less necessary than it might appear. That said, he's clear that the technology is "very close", one pilot already controls up to 32 drones simultaneously in some deployed systems, and target detection AI for notifying pilots of candidate targets is already in the field. The episode closes with a hardware finding that should recalibrate anyone's assumptions about AI compute requirements: after engineers mocked NORDA for using Raspberry Pi 4, they moved to the smaller, less powerful Raspberry Pi Zero. Both run real-time computer vision in active combat. The lesson Alex draws is precise: good engineering overcomes chip limitations, bad engineering can overload even a high-end processor. Subscribe to Eye on A. I. for weekly conversations with the people building and deploying the future of AI.
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