Nanoveu Limited announced a breakthrough in its drone technology through its subsidiary, EMASS. The company revealed that its ECS-DoT chip increased simulated drone flight time by 33%—from 60 to 80 minutes—without requiring any changes to the drone’s battery or airframe. This improvement was achieved in a hardware-in-the-loop test environment that ran a complete "sense–think–act" control cycle at 50 Hz, ensuring rapid and adaptive throttle and blade-pitch optimization. The key innovation is powered by embedded artificial intelligence, which uses sophisticated surrogate-driven power prediction and reinforcement learning algorithms. Operating on less than a milliWatt of power, the chip preserves almost all battery capacity for propulsion, making it exceptionally energy-efficient. The technology is designed to enhance flight duration and efficiency for a range of commercial drone applications across industrial inspection, logistics, precision agriculture, and defence. The ECS-DoT system holds promise not only for prolonged missions but also for providing real-time responsiveness and adaptive control without the need for offloading calculations to external systems. Further refinements are underway, with future milestones targeting a potential 40–70% improvement in flight time under diverse mission conditions. The company is also moving forward with expanded simulation testing and plans to conduct live flight trials to validate performance in real-world environments across various drone platforms. From a bullish perspective, these developments mark a significant step forward in drone technology, especially given the chip’s ability to enhance operational efficiency substantially through advanced AI while maintaining minimal energy overhead. The enhancements in flight time and the low-power, embedded AI approach are likely to appeal to industries looking to expand commercial drone applications in a growing global market. However, there is a bearish side to consider. The current results are based on simulation, and real-world implementation could present unforeseen challenges. Additionally, while the performance improvements are promising, further testing is needed to verify consistency and reliability across different operating conditions and drone classes before broader industry adoption can occur.