Sector
Climate and energy
AI already forecasts weather faster and, on many measures, more accurately than physics-based models. At the same time AI's own electricity use is growing fast. Both sides matter on the path to AGI.
Our predictions
Our view, with how sure we are. Each one links to the evidence it rests on.
What changes
Machine-learning models will become a standard part of national weather forecasting, including warnings for extreme weather.
On the road to AGI
Electricity and compute, more than ideas, will be the main limit on how fast AI capabilities grow over the next decade.
Progress around the world
Public results from other teams that shape our view.
· Stanford Institute for Human-Centered AI
AI Index 2026: capabilities jump, but unevenly
Agent success on Terminal-Bench rose from 20% to 77.3% in a year, and agents solved 93% of cybersecurity tasks, yet robots succeeded at only 12% of real household tasks. AI data-centre power capacity reached 29.6 GW, and four in five US students use AI for schoolwork.
What it means for AGI: digital abilities are racing ahead while physical abilities and governance lag.
· International Energy Agency
IEA: data-centre electricity use set to roughly double by 2030
Data centres used about 415 TWh in 2024. The IEA projects about 945 TWh by 2030, with AI the main driver, and notes large uncertainty.
What it means for AGI: energy and compute are becoming physical limits on how fast AI can scale.
Source: iea.org(opens external site)
· Google DeepMind (published in Nature)
AI weather model beats the leading physics-based ensemble forecast
GenCast, a machine-learning ensemble model, was more accurate than ECMWF's ENS on 97.2% of 1,320 targets tested, including extreme weather and cyclone tracks, and produces a 15-day forecast in minutes.
What it means for AGI: learned models can outperform decades of hand-built simulation in a complex physical system.
Source: nature.com(opens external site)
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