Infrastructure · Trend

Virtual Power Plants for AI Data Center Energy

MIT Technology Review examines how data centers can use demand flexibility ('flex') to manage grid stress and accelerate deployment.

Trend strength 5/10

Momentum +5/q

Confidence medium

Status new

Forecast horizon

VPP-based demand response will become a standard component of hyperscaler energy procurement strategies.

Connections

Connections · 1

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

Both trends address the energy and infrastructure constraints shaping where AI compute capacity is built globally.

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.