Dark Output

Computer programs that generate texts, images, or program code on request are producing enormous amounts of material today. Some of it is never read, checked, or used by a human being. This exact material is called Dark Output. The name alludes to the fact that this content sits in the dark: it exists, but no one looks at it. Examples include automatically written reports that no one opens, or hundreds of generated image variants of which only one is chosen. The term is still young and is used with varying degrees of strictness in trade articles and consulting reports.

Costs that go unnoticed

Every response from an AI system costs computing time, electricity, and usually also money. For a single request, that amounts to fractions of a cent. But if a company generates millions of texts every day, this adds up. If a large portion of it is never read, the company is paying for work that creates no value.

Added to this is a risk that is harder to measure. Unchecked AI content can be factually wrong. If such a text ends up in a customer email, a proposal, or a piece of software, the error only comes to light much later. This is especially delicate with program code: an unnoticed security flaw can be exploited years later.

A third point concerns the internet as a whole. Automatically generated pages and articles fill search engines and platforms. New AI models continue to learn from such texts. If AI increasingly learns from AI-generated texts, quality declines over the long term. Experts refer to this as model collapse.

How dark output arises

The most common cause is automation without a recipient. A system generates, for example, a description for every product, a summary for every customer, a report for every day. This is set up once, and then it keeps running. Whether anyone still needs the results is often never checked.

A second cause is the principle of “generate a lot, keep little.” Anyone who needs an advertising image often has fifty variants computed and picks one. The other 49 are Dark Output. Something similar happens with chat conversations: several phrasings are tried out, and in the end only the last one is used.

What helps most against this is measurement. Companies log which generated files are actually opened, copied, or processed further. Whatever remains untouched for months gets shut down. Dark Output is easily confused with “Dark Data,” meaning unused collected data. The difference: Dark Data was measured or collected somewhere, while Dark Output was newly invented by a machine.

Where the term shows up in the news

In business news, Dark Output usually comes up in connection with the question of whether AI investments pay off. A company buys access to large models and reports rising usage. Critical analysts then ask how much of this usage actually reaches humans at all. High numbers are not proof of benefit.

In everyday life, one encounters the phenomenon without the technical term. The automatically generated weekly report from the school platform that no one reads is Dark Output. So is the AI summary beneath every email that gets skipped. Websites that were obviously written only for search engines also belong to this category.

A typical misconception is to consider Dark Output pure waste. The 49 discarded image variants do serve a purpose: they enable the selection. It only becomes problematic when content is neither read nor discarded, but simply keeps running, costing storage space, money, and trust.