AI’s hidden power bill: what OpenAI and Google’s opaque energy figures mean for your strategy
OpenAI (~0.34 Wh/query) and Google (~0.24 Wh median/query) finally disclosed chat-only energy use for their flagship models. But without methods, model IDs, variance, or multimodal data (image/video/reasoning), enterprises face material uncertainty across sustainability reporting, regulatory exposure, capacity planning, and long-horizon data-center investments as AI demand scales.
Executive summary
- ESG and compliance risk: Incomplete disclosures undermine credible Scope 2/3 accounting and invite scrutiny under EU data center reporting rules and emerging SEC climate disclosures.
- Capex and opex volatility: Unknowns on workload variance (long context, reasoning, multimodal) make it hard to forecast power, cooling, and model serving costs at scale.
- Competitive edge through transparency: Vendors and users who meter and publish energy-per-task with clear methods can win enterprise trust and lower cost-to-serve.
Market context: rising demand, thin data
After months of pressure, OpenAI’s Sam Altman cited ~0.34 Wh per ChatGPT query; Google reported ~0.24 Wh median for Gemini chat. Neither shared measurement methods, model variants, or p95/p99 ranges, and both exclude image/video and advanced reasoning-workloads likely to consume more. A French startup, Mistral, shared emissions estimates but not standardized energy-per-task.
The stakes are large. If a service processes 2.5 billion prompts/day, even 0.3 Wh/query implies ~0.75 GWh/day (~274 GWh/year) before power-usage effectiveness (PUE) overhead-tens of millions of dollars in annual energy costs at typical tariffs. Meanwhile, Microsoft reports emissions up ~23% since 2020 amid AI buildouts, and a Lawrence Berkeley National Laboratory scenario warns AI could draw electricity comparable to 22% of US household use by 2028 if demand surges. An MIT group has also reported that most businesses are not yet seeing clear ROI from AI deployments.
Net: demand and infrastructure are accelerating, but energy transparency and monetization lag. That gap is where regulatory, reputational, and financial risk accumulates.
Opportunity analysis: turn opacity into advantage
Enterprises can differentiate by operationalizing energy as a first-class performance metric. Standardize internal “energy SLAs” per task (Wh/1k tokens or Wh/minute for audio/video) and require vendor disclosures by modality, model version, context length, and percentile (p50/p95) with clear methods and hardware details. Use these to guide model selection (small/efficient by default), prompt discipline (shorter contexts, caching), and workload routing (schedule flexible inference to off-peak/cleaner grids).
For builders and platforms, productize metering and show customers their energy/carbon per request; it strengthens enterprise sales, supports CSRD/SEC reporting, and reduces churn. For infrastructure teams, pursue sites with abundant clean power, heat reuse, and PUE ≤1.2; adopt efficient accelerators, quantization, speculative decoding, and batching to lower Wh per output.
Action items: what to do this quarter
- Update RFPs: Require per‑modality energy disclosures (p50/p95), measurement methods, hardware, region/grid intensity, and PUE; tie to SLAs.
- Instrument usage: Meter Wh per 1k tokens across your top 10 AI tasks; set efficiency guardrails (e.g., default to smaller models unless quality gap ≥X%).
- Integrate carbon into ROI: Add power and carbon price scenarios to AI business cases; stress‑test profitability under 2-3× demand and energy costs.
- Portfolio routing: Build policy to route workloads to lower‑energy models/regions automatically and cap context length by use case.
- Procure clean power: Lock PPAs/RECs for AI growth workloads; co‑locate new capacity near low‑carbon, reliable grids.
- Compliance readiness: Align reporting with GHG Protocol, EU Energy Efficiency Directive data center reporting, and anticipated SEC climate rules.
- Vendor incentives: Negotiate cost breaks tied to verified efficiency improvements (Wh/task) over contract term.
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