AI Video vs Images vs Text: Which Uses the Most Energy?

Artificial intelligence can now help write dialogue, create artwork, generate music and produce increasingly convincing video, giving entertainment companies and independent creators a growing range of new tools. What is much less visible is that these different forms of generative AI can require very different amounts of computing power and electricity.

That difference matters as AI moves further into film, television, visual effects and digital storytelling. Current research suggests that simple text tasks generally sit at the lower end of AI energy consumption, while complex image and especially video generation can demand considerably more processing.

Why different AI tasks use different amounts of energy

When someone asks an AI system to create something, computer processors perform calculations to produce the result. Running an already developed AI model in this way is known as inference, which in simple terms means the work the system does after receiving your request.

The amount of computing required depends on what you ask it to do. Producing a short piece of text is different from constructing a detailed image, while video adds multiple frames, movement and the challenge of keeping characters, objects and locations visually consistent.

The International Energy Agency, known as the IEA, said in its April 2026 update that newer applications including video generation and advanced reasoning can require hundreds or even thousands of times more energy per query than simple text generation. The exact difference depends on factors including the AI model and the complexity of the output, so it should not be treated as one fixed ratio.

Where does AI text sit?

Basic text generation is generally among the less energy intensive forms of generative AI. Even within text, however, a short answer and a long task involving more complicated reasoning can require very different amounts of processing.

The IEA says improvements in computer hardware and software have dramatically reduced the electricity required for individual AI tasks in recent years. It estimates that even if every conventional internet search worldwide were replaced by a simple AI text query, annual electricity demand would remain below four terawatt-hours.

A terawatt-hour, shortened to TWh, is one billion kilowatt-hours and is normally used when discussing electricity consumption on an industrial or national scale. For entertainment, text-based AI could include uses such as early script development, dialogue ideas, research or production planning, although how much energy is used will depend on the system and task involved.

AI images require more processing

Image generation asks an AI model to create detailed visual information rather than a sequence of words. Resolution, model choice, complexity and the number of attempts made before reaching a finished image can all affect the amount of computing required.

Research published in June 2026 by the United Nations University Institute for Water, Environment and Health, known as UNU-INWEH, demonstrates how large the differences between AI tasks can become. Using basic text classification as its starting point, its methodology estimated that a typical conversational AI query required around 200 times as much energy, while an AI-generated image required around 1,450 times the baseline.

Those are research estimates rather than universal rules for every AI platform. Different models and settings can produce substantially different results, and the figures are most useful for showing the scale of variation rather than calculating the exact electricity cost of every picture.

The researchers also estimated that the electricity associated with a typical generated image could power a 10-watt light-emitting diode, or LED, bulb for around 17 minutes. That comparison makes the number easier to picture, but it remains an illustrative estimate rather than a meter reading for every AI image created.

Why AI video can require much more

Video is particularly important for entertainment because it combines visual generation with movement over time. Instead of creating a single finished image, the system has to produce a sequence of frames while trying to keep characters, faces, costumes, lighting, locations and objects consistent as the scene develops.

That becomes increasingly demanding as clips become longer, resolutions improve and scenes become more complicated. A system creating detailed movement between several characters in a changing location has a very different job from generating one simple still image.

UNU-INWEH estimated that a high-complexity AI video could require enough electricity to power the same 10-watt LED bulb for around 42 hours. Again, that does not mean every AI video has the same energy footprint, because duration, quality, model choice and the number of generations required can all change the result.

The IEA reaches the broader conclusion that video generation can sit hundreds or thousands of times above simple text queries in energy demand. Together, the research makes clear why AI video generation is becoming an important part of the wider discussion around AI sustainability.

What does this mean for entertainment?

The British Film Institute, known as the BFI, and CoSTAR examined the growing use of generative AI across the UK screen industries in research published in June 2025. The report identified energy consumption and associated carbon emissions among the issues that film, television, animation and visual effects businesses may increasingly need to consider.

AI can also bring potential benefits, including faster workflows, new creative options and tools that may make some types of production more accessible. The environmental question is therefore not whether the entertainment industry should simply use or reject AI, but how those tools are used and whether their growing computing demands are understood.

Energy use could eventually become part of wider production decision-making alongside budgets, sustainability targets and workflow planning. A company generating dozens of AI versions of a scene, for example, may use considerably more computing overall than one producing only the few versions actually required.

More efficient AI does not always mean lower overall demand

AI systems are becoming more efficient, which is positive. Newer chips and improved software can reduce the electricity needed to complete individual tasks, sometimes dramatically.

The complication is that cheaper and faster generation can encourage much greater use. If AI video becomes efficient enough for creators to produce dozens of alternative scenes instead of two or three, total electricity demand could rise even while each individual generation uses less power.

This is sometimes called the rebound effect. In simple terms, efficiency makes something easier or cheaper to use, which can lead people to use much more of it.

So which type of AI uses the most energy?

Current evidence points to a clear general pattern: simple text generation usually requires less energy than image generation, while complex AI video can demand considerably more than either. There is no universal number that applies to every platform because models, settings, output quality and task complexity all make a difference.

For creators and entertainment companies, that distinction is more useful than treating every AI generation as environmentally equal. As AI becomes more common across film, television, music and digital storytelling, decisions about model choice, workflow design and how much content is generated could increasingly shape the environmental footprint behind what audiences eventually see on screen.

For more on how artificial intelligence is changing film, television, music and digital storytelling, explore SoapLandTV’s AI & Entertainment page. You can also find our latest explainers, industry coverage and stories following the growth of AI-created entertainment.

Sources: International Energy Agency’s April 2026 energy and AI update, United Nations University Institute for Water, Environment and Health research from June 2026, and British Film Institute and CoSTAR research from June 2025.