Can AI make high-precision weather forecasts a business?

WindBorne Systems recently secured $37 million in new funding to expand its operations. The company operates a network of roughly 600 custom weather balloons that drift around the world, gathering data from difficult locations, like the heart of a powerful storm. They feed this real-time information into their own forecasting model, a piece of software trained to predict atmospheric conditions. While they currently sell this information to government organizations like the U.S. National Weather Service and the military, they are now using this new investment to build a team dedicated to selling their weather insights to the private sector, such as investment firms looking to predict commodity price shifts.

Traditional weather forecasting requires a gargantuan amount of math. Scientists use a model—a digital simulation of the atmosphere—to crunch data from satellites and ground stations. Because the math involved in simulating air pressure, temperature, and moisture is so complex, it has historically required massive, energy-hungry supercomputers to run. WindBorne uses a different approach: they employ artificial intelligence to learn patterns in the atmosphere. Because these AI models are more efficient at identifying trends than older, brute-force simulation methods, they can run on much smaller computers. By combining this efficient software with their own unique, high-quality data from their balloon network, they can generate forecasts that are often more accurate and accessible than what the public sector produces.

The weather affects almost every major industry, from agriculture and shipping to finance, yet companies have struggled to act on weather data because it was too expensive or hard to interpret. In the past, a business might have needed a small army of meteorologists to translate raw government reports into useful business decisions. AI is lowering that barrier by bridging the gap between raw data and actionable advice. If WindBorne succeeds, it could mean that companies start treating weather not as an unpredictable nuisance, but as a standard data point for their daily operations. The real test is whether they can prove to private businesses that this information is worth paying for, turning a public necessity into a sustainable commercial product.