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The alarming finding is not that every AI prompt consumes a catastrophic amount of electricity. It is that open text-to-video systems can become dramatically more energy-intensive as clips get longer or higher-resolution. In the regime studied by Hugging Face researchers, doubling a video’s duration can require approximately four times the computation.
That is a result from specific open models, hardware and settings—not a universal rule for every commercial AI video service. But it is a meaningful warning as AI-generated video moves from occasional experiments to large-scale content production.
What researchers actually found
The research paper, “Video Killed the Energy Budget: Characterizing the Latency and Power Regimes of Open Text-to-Video Models”, was published on September 23, 2025, by Julien Delavande, Régis Pierrard and Sasha Luccioni.
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1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problemsThe researchers examined state-of-the-art open-source text-to-video models and measured latency and energy use while varying video duration, spatial resolution and the number of denoising steps. Their analytical model predicts that computation can grow approximately quadratically with temporal length and spatial dimensions, while scaling more linearly with denoising steps. Experiments on WAN2.1-T2V and comparisons involving six models supported those general relationships under the tested conditions.
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In practical terms, a six-second generation could require roughly four times the computation of a three-second generation when other conditions are comparable. A 12-second generation would be approximately 16 times the three-second baseline under the same simplified quadratic illustration:
| Clip duration | Relative temporal workload |
|---|---|
| 3 seconds | 1× |
| 6 seconds | Approximately 4× |
| 12 seconds | Approximately 16× |
This is not a promise about every model or service. Architecture, temporal compression, hardware, precision, batching, caching and other implementation choices can change the result.
Why video is so computationally expensive
A text response is a sequence of tokens. A still image is a two-dimensional visual output. Video adds a temporal dimension: the system must generate many frames while preserving motion, objects, lighting and consistency from one frame to the next.
Higher resolution also increases the number of spatial elements the model processes. Longer clips add more frames and temporal relationships. Diffusion-based systems then repeat denoising operations several times to turn noise into a finished result. The combined workload can make a modest increase in duration or resolution much more expensive than a simple proportional calculation suggests.
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That does not mean every model literally performs the same quadratic operation. It means the study identified this approximate scaling behavior in the compute-bound regime it analyzed and tested.
There is no single “energy cost of an AI video”
A related Hugging Face benchmark found energy use ranging from a few watt-minutes to more than 100 watt-hours for a single short video generation. The tested configurations differed by model and settings, and the benchmark reported nearly an 800-fold gap between the least and most energy-intensive cases.
The tests used one NVIDIA H100 80GB HBM3 GPU, five measured runs per model after two warm-up runs, and CodeCarbon for energy tracking. Models were run with parameters recommended on their official Hugging Face pages.
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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchThose details matter. The result demonstrates that model and configuration choices can dominate energy use; it does not establish an average for every AI video tool. The energy used by proprietary services such as commercial cloud video generators cannot be inferred directly from this open-model benchmark.
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The often-repeated comparison to running a microwave for more than an hour is a household analogy based on particular energy estimates and assumptions about microwave power. It should not be presented as a universal measurement for every five- or six-second AI video.
How video compares with text and images
The International Energy Agency cited an estimate of roughly 115 watt-hours for a short, relatively low-quality six-second AI-generated video in one comparison—around two orders of magnitude more than the small text-generation request used in that comparison. A 2026 report from France’s telecom regulator, ARCEP, summarized similar order-of-magnitude estimates, including claims that image generation can use about 60 times more energy than text generation.
These figures are comparisons, not conversion rates. Text prompts vary in length and output, while image and video generation vary by resolution, model, diffusion steps, frame rate and the number of attempts. Asking for 20 candidate clips can matter more than the energy of the one clip eventually selected.
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Power, energy, carbon and water are different things
Coverage of this topic often uses “power usage” loosely. Power is the rate of consumption, measured in watts. Energy is the amount consumed over time, measured in watt-hours or joules. A high-powered GPU running briefly and a lower-powered system running longer can consume the same total energy.
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The study primarily concerns operational energy during inference. That is only one part of an AI system’s environmental footprint. A broader assessment may also include:
- training the model;
- data-center cooling and other overhead;
- network equipment and the user’s device;
- manufacturing GPUs, servers and other hardware;
- data-center construction and replacement cycles;
- water used for cooling; and
- the electricity required to store and deliver outputs.
Energy does not automatically translate into a fixed amount of carbon dioxide. Emissions depend on the data center’s location, electricity mix, time of use, hardware efficiency and whether the calculation uses average or marginal grid emissions. Water impact likewise varies with cooling technology, climate, facility design and accounting boundaries.
A Communications of the ACM analysis notes that terminals and networks can represent significant parts of a generative-AI service’s energy and carbon footprint, depending on the system boundary. The U.S. Government Accountability Office also treats energy, water, hardware and data-center infrastructure as distinct parts of the technology’s environmental impact.
What the study does—and does not—prove
The findings support several clear conclusions:
- AI video is generally more energy-intensive than many text-generation tasks and can be more demanding than image generation.
- Longer duration and higher resolution can increase computation sharply.
- Energy use varies enormously between models and configurations.
- Repeated attempts, upscaling and post-processing can make a creator’s complete workflow much more intensive than one successful output.
They do not prove that every six-second commercial video consumes four times as much energy as every three-second video. They do not measure every proprietary service. They do not calculate the water or carbon footprint of an individual request. And they do not show that one person’s short generation will visibly affect the electricity grid.
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The larger concern is scale: millions of generations, automated content production, high-resolution rendering, long clips and the infrastructure required to train and serve increasingly capable models.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How AI video could become less wasteful
The researchers point toward several efficiency strategies:
- Use smaller, distilled or more efficient models when quality permits.
- Reduce denoising steps where the resulting quality remains acceptable.
- Generate short, low-resolution drafts before rendering a final version.
- Use lower frame rates or shorter clips during ideation.
- Reuse, edit or extend an acceptable generation instead of starting over.
- Cache intermediate results and avoid redundant generations.
- Improve temporal compression and model architecture.
- Schedule flexible workloads when electricity is less carbon-intensive.
- Measure actual energy rather than assuming model size alone determines efficiency.
Efficiency does not guarantee that total energy demand will fall. If generation becomes cheaper and easier, people may simply generate more video—a rebound effect.
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What ordinary users and creators can do
- Use motion only when it adds value. A still image or conventional edit may be enough for many projects.
- Prototype cheaply. Start with short clips and lower resolution, then render only promising ideas at full quality.
- Limit redundant variations. More attempts increase the energy used to produce the final result.
- Reuse successful outputs. Editing or extending a usable clip can avoid a complete regeneration, depending on the tool.
- Track the workflow. Professionals should record the model, resolution, duration, denoising settings and number of attempts.
- Look for disclosure. Providers that publish credible information about model settings, energy, emissions methodology or workload controls make more informed comparisons possible.
Tools such as CodeCarbon can help organizations measure workloads they control, while initiatives such as the Hugging Face AI Energy Score aim to make model and task efficiency easier to compare. They are most useful to developers and organizations running or evaluating their own systems, not consumers using closed services that disclose no underlying measurements.
The bottom line
The strongest conclusion is more precise than “AI video is destroying the power grid.” Open text-to-video systems can carry a substantial per-generation energy burden, and that burden may rise much faster than clip length or resolution in certain compute regimes. The benchmark’s nearly 800-fold spread also shows why quoting one universal cost per video is misleading.
For users, the practical response is to avoid needless retries and use low-resolution drafts. For providers, the larger responsibility is to improve efficiency and disclose comparable measurements. The technology is not automatically unjustifiable—but its environmental cost cannot be understood from a sensational household analogy or a single average number.
Sources: original research paper; Hugging Face energy benchmark; IEA analysis; ARCEP report.
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