Maven Smart System: How AI Compressed US Military Targeting in Iran Strikes to 1,000+ Hits in 24 Hours

During the first 24 hours of the assault on Iran, the US military struck more than 1,000 targets—nearly double the scale of the "shock and awe" campaign against Iraq over two decades ago. That acceleration traces directly to AI systems that dramatically speed up the targeting process. Chief among them is the Maven Smart System.

The origins of this capability go back to 2017, when Project Maven launched as an experiment in applying computer vision to drone footage. The program became a pivotal test case for military AI adoption. It also ignited controversy when Google—Project Maven's initial contractor—faced employee protests over the company's involvement in defense work. The backlash prompted internal debate over the ethical boundaries of AI in warfare, surfacing tensions between technological capability and corporate responsibility that remain unresolved.

Journalist Katrina Manson examines this inflection point in her new book, "Project Maven: A Marine Colonel, His Team, and the Dawn of AI Warfare." Her reporting traces how a small military team, working with Silicon Valley, built the system that eventually enabled targeting at a scale and speed previously unachievable. The operational output from the Iran strikes suggests that AI-assisted targeting has moved from experimental to routine. What remains less clear is how the same infrastructure will shape future conflicts, and whether the safeguards built into Maven's design will hold under the pressure of faster decision cycles.