Software could be the easiest fix for hyperscalers' AI power squeeze, researchers say
- ID
- 33217
- Status
- summarized
- Published
- 08 Oct 2026, 10:00 PM
- Fetched
- 08 Oct 2026, 10:07 PM
- Provider
- Tom's Hardware
- Category
- technology
- Original URL
- https://www.tomshardware.com/tech-industry/data-centers/software-could-be-the-easiest-fix-for-hyperscalers-ai-power-squeeze-researchers-say-data-center-demand-is-expected-to-rival-japans-electricity-usage-by-2030
- Source URL
- https://www.tomshardware.com/feeds/all
Summary
- Score
- 6.0
- Created
- 08 Oct 2026, 10:08 PM
- Tags
- Audience
- developersai_ml_learnersai_agent_userssaas_founders
What happened
A Tom's Hardware member-exclusive feature by Chris Stokel-Walker, published 8 October 2026, reports that the IEA expects roughly 945TWh of electricity to be consumed for AI by 2030 — about as much as Japan uses today — and that the industry's response has focused on hardware (more efficient GPUs, more grid supply). The researchers' counterpoint is that software is the easier lever, cutting back the work actually running on those machines. The piece also cites the Uptime Institute's 2025 survey finding that average PUE has barely changed across six successive years. The excerpt cuts off before the specific software techniques are detailed, so the concrete recommendations are not in the text provided.
Why it matters
If you pay for GPU or inference capacity, the constraint on your bill is shifting from chip supply to power and grid capacity, and the 945TWh-by-2030 figure plus six flat years of PUE means hardware efficiency alone is not absorbing that demand. The practical decision is to treat compute waste — idle accelerators, oversized models, unbatched requests — as a cost line now rather than waiting on the next GPU generation. For builders using regional cloud or colo capacity in Southeast Asia, tightening grid and power availability is likely to show up as allocation limits or pricing pressure before it shows up as anything you can fix in code.
Discussion angle
PUE has been essentially flat for six years — so if software is the 'easiest fix,' why hasn't it moved the needle? Ask people what they'd actually change in their own stack (batching, model sizing, scheduling off-peak, caching) to cut power per request, and whether their cloud bill has given them any signal to do it.