# MIT Study: AI-Generated Images Lose Attribution as Models Scale

> A new MIT CSAIL study lands in the middle of the AI copyright wars with an inconvenient finding. As image models get larger, they exhibit what the researchers call attribution decay: the influence of any single training image on a given output shrinks toward nothing, following an inverse power law. The practical consequence is strange. At sufficient scale, removing one image, or even every image from a particular artist or person, does not measurably change what the model generates. And if pulling data out does not alter the output, then by the study's logic that data was not really driving the result in the first place. That cuts in several directions at once. It complicates copyright and fair use claims that rest on pointing to specific stolen works, and it undercuts unlearning strategies that promise to scrub an individual from a model on request. The uncomfortable implication is that harm can be real and diffuse at the same time, spread so thinly across millions of images that no single source can claim it. Courts built to assign blame to identifiable works may struggle with a system where influence has been averaged away.

_Section: [Daily AI Updates](https://www.wortins.com/daily-ai) · Source: MIT News · Published Friday, August 21, 2026_

## Wortins' read

A new MIT CSAIL study lands in the middle of the AI copyright wars with an inconvenient finding. As image models get larger, they exhibit what the researchers call attribution decay: the influence of any single training image on a given output shrinks toward nothing, following an inverse power law. The practical consequence is strange. At sufficient scale, removing one image, or even every image from a particular artist or person, does not measurably change what the model generates. And if pulling data out does not alter the output, then by the study's logic that data was not really driving the result in the first place. That cuts in several directions at once. It complicates copyright and fair use claims that rest on pointing to specific stolen works, and it undercuts unlearning strategies that promise to scrub an individual from a model on request. The uncomfortable implication is that harm can be real and diffuse at the same time, spread so thinly across millions of images that no single source can claim it. Courts built to assign blame to identifiable works may struggle with a system where influence has been averaged away.

## Source

[Read the full story at MIT News](https://news.mit.edu/2026/when-ai-art-has-no-author-generated-images-often-cant-be-traced-to-training-data-0818)

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