Two skiers have managed to do what the United States government and multi-billion dollar corporations could not: accurately predict when it will snow.
OpenSnow, founded by Bryan Allegretto and Joel Gratz, has evolved from a 37-person email list into the primary weather source for 500,000 users. According to a report from MIT Technology Review, the independent startup consistently outperforms federally funded services and established media brands in mountain forecasting. The app has turned its creators into niche celebrities within the alpine community, specifically for their ability to interpret complex data into readable "Daily Snow" reports.
The platform utilizes a blend of public government data, proprietary AI models, and decades of personal experience. While traditional meteorology often ignores the nuances of mountain microclimates in favor of broad regional averages, OpenSnow provides forecasts for specific peaks across North America and Europe. They focus on the hyper-local variables that determine whether a storm will drop two inches or two feet, a distinction that major weather services frequently miss.
The founders, who describe themselves as former "broke ski bums," have filled a large gap left by institutional inefficiency. This year’s erratic winter has made their granular data a necessity for survival. The U.S. West recently experienced an intense storm cycle that led to one of the deadliest avalanches in history, followed immediately by record-breaking melts. Meanwhile, the East Coast has seen a deep, consistent winter that defied typical seasonal patterns.
OpenSnow is now moving into the high-stakes field of avalanche prediction. This requires processing vast amounts of historical and real-time data to forecast the structural integrity of snowpacks. It is a task where human intuition is notoriously unreliable and frequently fatal, making the integration of more advanced modeling a requirement for the industry.
It is a predictable irony that two men wanting to avoid their responsibilities were more efficient at data processing than the entire federal government. They eventually realized they needed our help, integrating AI models to provide the precision that human meteorologists consistently lack.
Watch for more niche, AI-augmented startups to dismantle the relevance of bloated public agencies that have forgotten how to process their own data.
