Is AI Generated Video Bad for the Environment? What the Evidence Really Says

AI generated video is moving rapidly into entertainment, from experimental short films and digital characters to visual effects, animation and online storytelling. The technology can give creators new ways to produce moving images without a traditional shoot, but creating those clips requires powerful computers working inside data centres.

Video is one of the more demanding forms of generative artificial intelligence, usually shortened to AI. Its environmental impact depends on the model, video length, quality, number of attempts, data centre and electricity supply, which means there is no single figure that describes every AI generated clip.

Why does AI video require so much computing?

Generating video is considerably more complicated than producing a few lines of text. A video contains a sequence of individual frames, and the AI system has to create movement while attempting to keep characters, faces, costumes, lighting, objects and locations consistent as the scene develops.

Longer clips and more complicated scenes can require additional processing. Higher quality outputs can also increase the workload, although the exact difference varies between systems rather than following one universal formula.

The International Energy Agency, known as the IEA, said in its April 2026 update that applications including AI video generation can require hundreds or even thousands of times more energy per query than simple text generation, depending on the model and complexity of the task.

That comparison does not mean every AI video always uses a thousand times more electricity than every text request. It shows how wide the difference between simple and demanding AI tasks can become.

How much electricity does one AI video use?

There is no reliable universal answer because different video generators work in different ways. The duration of a clip, model being used, complexity of the scene and number of generations needed before reaching a usable result can all affect the computing involved.

Research published in June 2026 by the United Nations University Institute for Water, Environment and Health, known as UNU INWEH, estimated that a high complexity AI video could use enough electricity to power a 10 watt light emitting diode, commonly known as an LED bulb, for around 42 hours.

That figure is an estimate produced using the researchers’ methodology rather than a meter reading that applies to every AI video. Its value is in showing how demanding video generation can become compared with simpler AI tasks.

Repeated attempts can make a difference

The finished video is not always the only generation involved. A creator may produce several versions because a character moves incorrectly, a face changes, an object disappears or the scene simply does not look right.

Imagine a visual effects team generating dozens of versions of the same shot while testing different movements, backgrounds or character actions. Each new version requires another request to the AI system, so the total computing involved in creating the final scene can be considerably greater than the energy associated with the one clip audiences eventually see.

This is why the environmental footprint of AI entertainment depends partly on workflow as well as technology. Two productions creating similar finished videos could use very different amounts of computing if one requires far more attempts.

Why does water matter?

The processors generating AI video produce heat and need cooling. Some data centres use water directly in their cooling systems, while water can also be associated with the electricity supplying the facility.

UNU INWEH estimated an electricity associated water footprint of around 4.1 litres for a high complexity AI video under the conditions used in its research. That does not mean 4.1 litres of water are physically poured into a cooling system every time someone generates a clip, because the calculation includes water connected with producing electricity.

Water use can vary substantially between locations and cooling systems. A data centre supplied by relatively low carbon electricity may still have a significant water footprint if its cooling technology requires substantial water, while another facility may use a different system with much lower demand.

What about carbon emissions?

The carbon footprint of AI video is closely connected to the electricity used to generate it. The same computing task can create different emissions depending on whether the data centre is supplied predominantly by renewable, nuclear, gas, coal or another source of electricity.

This is another reason why claims that every AI video creates one fixed amount of carbon should be treated cautiously. The model matters, but so do the physical location and energy supply behind it.

There is also a wider footprint from manufacturing the specialist processors and servers required to provide AI video at scale. Those machines need materials, water and energy before they are ever installed inside a data centre.

Could AI video also reduce some environmental costs?

Possibly, although there is not yet enough evidence to claim that AI video automatically makes entertainment production greener. Generative tools could reduce some physical production requirements in particular situations, while also giving smaller creative teams access to visual capabilities that once required considerably greater resources.

Research published in June 2025 by the British Film Institute, known as the BFI, and CoSTAR found generative AI already being explored across the UK screen industries. The research identified creative and efficiency opportunities while also highlighting energy consumption and carbon emissions as issues that need to be considered.

Any environmental saving would therefore need to be compared with the data centre electricity, hardware and cooling required by the AI alternative. Replacing part of a physical production with AI does not automatically guarantee a smaller overall footprint.

Isn't AI video becoming more efficient?

Yes. The IEA says improvements in computer hardware and software are reducing the amount of energy required for individual AI tasks, sometimes very quickly.

The difficulty is that better technology can also encourage far more use. If AI video becomes faster and cheaper, creators may generate dozens of versions where they previously produced only a handful, meaning total electricity demand could continue rising even while each individual generation becomes more efficient.

So is AI generated video bad for the environment?

AI generated video has a genuine environmental footprint and can require substantially more computing than simpler AI tasks. However, there is no single electricity, water or carbon figure that accurately describes every video because the impact changes according to the model, task, data centre and way the technology is used.

For entertainment, scale is likely to matter most. AI video could open significant creative opportunities for filmmakers and independent storytellers, but as it moves from experimentation towards widespread production use, understanding the resources behind those moving images will become increasingly important.

For more on how artificial intelligence is changing film, television, music and digital storytelling, explore SoapLandTV’s AI & Entertainment page.

Sources: International Energy Agency, April 2026; United Nations University Institute for Water, Environment and Health, June 2026; British Film Institute and CoSTAR, June 2025.