Are Sora 2 and ChatGPT Consuming Too Much Power?

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The scale problem, in plain numbers

Data-center demand is climbing fast. AI is the accelerant. National grids are already planning for multi-gigawatt build-outs tied to model training and nonstop inference. Think of it this way: billions of daily prompts, millions of background API calls, and fleets of accelerators idling low but spiking high. The totals add up—quickly.

Why Sora-style video dwarfs text chat

Text-to-video diffusion unrolls many denoising steps across thousands of spatio-temporal tokens. Double the clip length or jump from 720p to 1080p and compute balloons. Minutes of coherent HD video can cost orders of magnitude more energy than a paragraph of text. That’s physics plus algorithms, not marketing.

What about ChatGPT?

Per-query energy is highly elastic:

  • Light prompts on efficient models: fractions of a watt-hour per answer.

  • Heavy prompts on larger models with long contexts: multiple watt-hours per response.
    Scale that by user volume and you start to see why utilities care. Each message is tiny; the ocean of messages is not.

Water matters too

Electricity is half the footprint; cooling is the other half. Data centers draw substantial water for heat rejection, with local impact depending on climate and cooling design. Arid sites feel it first. Smarter siting, liquid cooling, and heat reuse can blunt the damage.

Isn’t hardware getting better?

Yes—and that’s the bright spot. New accelerators push more tokens and frames per joule. Lower precision (FP8/FP4), mixture-of-experts routing, distillation, caching, and compiler/runtime tricks keep shaving the energy per output. Efficiency gains are real, but demand growth often outpaces them.

So… “too much”?

A practical framing:

  1. Energy intensity (micro):

    • Chat replies: sub-Wh to tens of Wh depending on model and prompt.

    • Video generations: far higher per minute, especially at HD and above.

  2. Total demand (macro):
    Aggregate AI load is now large enough to move national statistics. That makes power sourcing and grid planning policy questions, not just engineering details.

  3. Counterweights:

    • Efficiency: better chips, sparsity/MoE, lower precision, and caching.

    • Clean supply: colocate near abundant renewables or firm low-carbon power; shift flexible workloads to low-carbon hours.

    • Cooling & water stewardship: liquid/immersion cooling, heat reuse, accountable water metrics.

    • Transparency: standardized reporting—energy per 1K tokens, per video-minute, location-based emissions—not glossy averages.

The bottom line

  • Yes, the power draw is high, and for Sora-class video it’s dramatically higher per unit of content than chat.

  • Whether it’s “too much” depends on governance. If providers pair aggressive efficiency with verifiable low-carbon electricity and honest metrics, society can judge the tradeoffs. Without transparency, we’re guessing.

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