In a major leap for artificial intelligence and robotics, computer scientist Peter Burke has introduced a project that uses generative AI to build autonomous drone software. Far from being a routine technical experiment, this initiative marks a pivotal shift in how we perceive machine intelligence and automation. By harnessing advanced AI models such as ChatGPT, Burke’s work showcases how robots can evolve beyond predefined programming, opening new possibilities for fully autonomous systems.
The project is designed around training a robot’s "brain" and hardware using generative AI, with minimal human supervision. “It’s a significant step forward,” Burke notes, drawing parallels to The Terminator’s portrayal of self-aware robots—while adding that his goal is to prevent such dystopian outcomes.
At the heart of the innovation lies a dual-robot framework: the AI models run on cloud-based laptops, while the drones execute their tasks through a Raspberry Pi Zero 2 W onboard computer. The models generate functional code, and the drones bring it to life. This combination gives drones autonomy while retaining the intelligence of advanced AI systems.
Burke’s system, called WebGCS, enables drones to host their own control dashboard on a small website, accessible online. This approach represents a clear departure from traditional drone control, offering both flexibility and independence from external operators.
The development process was rigorous, involving multiple “sprints” across different AI tools. Early attempts with models like Claude struggled with context limitations, while Gemini 2.5 and Cursor also posed challenges. Eventually, success came with the Windsurf model, which generated nearly 10,000 lines of code in just 100 hours. To put that into perspective, a similar project—Cloudstation—previously took Burke’s team four years to build. The comparison highlights the disruptive speed and efficiency AI brings to software prototyping.
Industry experts have taken note. Hantz Févry, CEO of spatial data firm Geolava, commended Burke’s ambition and the project’s alignment with the future of spatial intelligence. At the same time, he emphasized the importance of safeguards and ethical boundaries, pointing out that unchecked autonomy could pose risks.
Projects like Burke’s illustrate both the promise and the perils of generative AI. On one hand, they showcase how autonomous systems can transform industries; on the other, they raise urgent questions about ethics, regulation, and safety.
As AI innovation accelerates, the challenge will be balancing progress with responsibility. The ability for machines to independently develop and execute complex functions forces us to rethink issues of employment, security, and governance.