Infrastructure · Trend

Rising Risk of Weather Data Sabotage

Technology reporting flags increasing concern that weather data feeds critical to AI and operations can be sabotaged.

Trend strength 5/10

Momentum +5/q

Confidence medium

Status new

Forecast horizon

Weather data integrity becomes dual-use infrastructure security issue alongside AI forecast models.

Connections

Connections · 4

How this node ties into the rest of the map, and the evidence behind each link.

Both highlight dual-use infrastructure and capability pathways where AI-era systems amplify sabotage or misuse risk.

Weather-data integrity risk affects grid and infrastructure operations that also underpin AI energy planning.

Sabotage of shared weather inputs threatens AI- and forecast-dependent industrial, aviation, and grid operations.

MIT Technology Review covers the rising risk of weather data sabotage for critical decisions.

Signal sources

Dated facts from primary sources in this direction.

Training compute for frontier AI models grew ~4–5× per year from 2010 to 2024 — a trend Epoch projects to continue toward 2030.

Global data-centre electricity use is projected to roughly double from ~485 TWh (2025) to ~950 TWh by 2030 — about 3% of world electricity.

The price to query a GPT-3.5-level model fell from $20.00 to $0.07 per million tokens between Nov 2022 and Oct 2024 — a 280× drop in ~18 months.

DeepSeek disclosed its open-weight R1 reasoning model's final training run cost just $294,000; R1 was released under open weights on 20 Jan 2025.