Infrastructure · Trend

Nuclear Power Milestones Supporting AI Energy Demand

Nuclear fuel-cycle and power developments, including laser enrichment, relevant to rising AI electricity demand.

Trend strength 5/10

Momentum +5/q

Confidence medium

Status new

Forecast horizon

Laser enrichment and organ-bank-style industrial tech may indirectly ease AI energy and compute bottlenecks.

Connections

Connections · 1

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

Laser enrichment and nuclear fuel-cycle advances are covered alongside broader efforts to secure energy for compute growth.

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.