How the UK's Met Office, Google DeepMind, Nvidia, Microsoft, IBM, and startups leverage AI to make more accurate and detailed forecasts further into the future
so long as met data continues to flow freely https://www.ft.com/... via @ft
Context & Ripple Effects
The Met Office and DeepMind began with an AI collaboration focused on short-term storm and heavy-rain prediction, while the Met Office separately planned a UK weather-forecasting supercomputer with Microsoft. DeepMind then reported that GraphCast outperformed leading conventional systems at three- to 10-day forecasts, establishing a clearer benchmark for AI weather models.
This story expands that arc from individual partnerships and model results to a broader field spanning public forecasting, cloud providers, chip suppliers and startups. Its central constraint is equally important: progress depends on meteorological data continuing to circulate.
First-order effects
- The Met Office, DeepMind, Microsoft, IBM and startups can incorporate AI methods into forecasting workflows aimed at greater detail and longer lead times; Nvidia benefits where those workflows require accelerated AI computing.
- Forecast users gain access to forecasts positioned as more granular and longer-range, but model performance remains tied to the availability of underlying meteorological data.
Second-order effects
- Forecast providers face pressure to match AI-driven improvements in speed, resolution and forecast horizon, increasing the value of both specialized models and the computing stacks behind them.
- Cloud and hardware vendors become more embedded in weather forecasting as customers combine model development with large-scale compute, extending the logic of DeepMind's GraphCast benchmark beyond a single research result.
Third-order effects
- If open data access persists and AI forecast gains prove durable, weather prediction could shift toward a layered market in which public data, proprietary models and commercial compute are distinct sources of advantage.
- That would make meteorological-data governance and access a strategic issue: the same data-sharing foundation that supports public forecasting would also determine how broadly AI forecasting innovation can be distributed.
The trend: Weather forecasting is becoming an AI infrastructure market, where open observational data, specialized models and high-performance compute jointly determine competitive advantage.