A Student Built a Map to Track AI Laws and Data Center Rules Around the World

Isabelle Reksopuro, an Indonesian-American student at the University of Washington, has created an interactive map that shows where governments around the world are making decisions about artificial intelligence and the physical buildings that power it. The map brings together information from multiple sources to create what looks like the first tool of its kind that lets you see AI policy and data center rules all in one place.

Reksopuro started the project because she was curious about rumors that Google was building data centers on public land in Oregon. That question led her to a bigger realization: governments everywhere are trying to figure out how to handle the rapid growth of AI infrastructure, and nobody had put all those decisions in one visual place.

Why Google's Oregon Data Centers Matter

Google's operations in Oregon show why Reksopuro's map fills a real gap. The company built its first major data center in The Dalles, Oregon in 2006, choosing the location because the Columbia River nearby provides both cheap power from hydroelectric dams and cool water for climate control. This decision created a model that Google would repeat for nearly twenty years.

Since then, Google has spent more than $2.4 billion in Oregon, with $1.8 billion going to new data centers. The company now runs three data centers in The Dalles and is building a fourth. In February, the third facility opened, bringing the total number of jobs to 200 people in the area.

These data centers have concrete benefits. New buildings cut the time it takes for data to travel to and from customers in the Pacific Northwest by up to 80 percent. Google also certified its facility for efficient energy management and made agreements with local power companies to help stabilize the electrical grid. The company has also donated money to Oregon State University to help it build out its own data center, extending the benefits beyond Google itself.

Who Decides Where Data Centers Go

Google's expansion in Oregon shows a tension that is playing out in cities and regions worldwide. Data centers are big employers and attract investment, but they also use enormous amounts of electricity and water. Governments have to decide: do we want these facilities, and if so, on what terms.

The situation is becoming more urgent. AI training and running AI systems require vastly more computing power than older software, so companies are in a race to build more data centers. Some regions are making it hard to build them with strict environmental rules. Others are offering tax breaks and fast-track approvals to attract this investment. Reksopuro's map lets you see these different approaches side by side.

This pattern is not entirely new. We saw it before when railroads were built across the country in the 1800s, and again when telephone and internet infrastructure rolled out in the 1990s. Each time, the same question came up: how much should government help companies build the infrastructure they want. What is different now is how fast it is happening and how directly the location of a data center shapes whether a region can participate in AI development at all.

How the Map Works

The map pulls information from Epoch AI, a database that tracks AI rules and laws in different countries and regions. Reksopuro added to this by collecting data center regulations from legislative websites—rules that often hide in zoning laws, environmental rules, or energy policy rather than AI-specific laws.

This combination addresses a real problem: AI rules are spread across many different types of regulations. A city might have zoning rules about buildings, environmental rules about water use, and energy rules about power consumption, but none of those might be labeled as "AI policy." The map connects these pieces so you can see the full picture.

What This Means for Where AI Develops

Data center capacity is becoming a bottleneck. Cloud companies like Amazon, Microsoft, and Google are competing fiercely to secure power supplies, cooling systems, and good government relationships so they can build more facilities. Where these resources are located increasingly determines which parts of the world can build and run AI systems.

Reksopuro's map shines a light on how policy decisions shape this competition. Regions with strict rules about data centers might find that AI companies build elsewhere. Regions with friendly policies might face challenges managing water use and power demand as more facilities arrive.

The broader context here is that we are watching policy shape where powerful technology gets built and controlled. As more AI systems get deployed in different countries, understanding the rules in each place matters more and more.

Reksopuro's project also shows something worth thinking about: one person with research skills and free web tools can make policy information much more transparent. As governments continue writing new AI rules at a rapid pace, tools like this map may become important ways for everyone—not just AI companies—to understand what is happening.