Solving AI’s $3 Trillion Land and Water Problem

The artificial intelligence boom is consuming Earth’s resources at an alarming rate. The $3 trillion needed for new datacenters isn’t just a financial figure; it represents a massive new demand for land and, critically, water. Google’s “Project Suncatcher” is a direct attempt to solve this “terrestrial resources” crisis.
On Earth, AI datacenters are power-hungry behemoths that require constant cooling. This cooling is often done with water, sometimes millions of gallons per day for a single facility. This puts the AI industry in direct competition with agriculture and communities for a scarce resource.
Google’s plan is to move this infrastructure to an environment where water is irrelevant: space. By placing AI processors in orbit, the company “minimises impact on terrestrial resources.” The cooling challenge is replaced by a “thermal management” challenge in a vacuum, but it delinks AI’s growth from Earth’s water supply.
The same logic applies to land. The orbital datacenters would be powered by hyper-efficient solar panels, eliminating the need for sprawling solar farms on the ground or proximity to power plants. The only terrestrial footprint would be the ground stations for data communication.
This advantage is a key motivator not just for Google, but for competitors like Elon Musk and Starcloud. While the CO2 from rocket launches and astronomer objections are serious concerns, the potential to scale AI without consuming the planet’s land and water is a powerful, driving force.