worry that the age of noise will mark the era where we turn to machines to mediate this media sphere on our behalf. It follows a simple logic. To manage artificial information, we turn to artificial intelligence. But
It’s all based on these abstractions of the data about the past. This used to be the role of archivists. Archivists used to be the custodians of the past, and archives and curators, facing limited resources of space and time, often pruned what would be preserved. And this shaped the archives. The subjects of these archives adapt themselves to the spaces we make for them. Just as mold grows in the lightest part of a certain film, history is what survives the contours we make for it
We can’t save everything. But what history do we lose based on the size of our shelves?
Rather than new, the static is random, it is old patterns adapted to random noise. That’s distinct from newness. It is more true to say that the image is wrong. It is a hypothesis of an image that might exist in the static, based on all that has come before. The image that emerges is also noise, constrained by language and data. It references language and data to find clusters in that static. It is a prediction of what the image might be, a hypothetical image, constraining every possible image through the filter of our prompts. And all of these abstractions are wrong. No image made by image synthesis is true to the world, but every image is true to the data that informs it. It would be lovely to think of AI as creating something new. The age of noise offers us only a false reprieve from the information onslaught
The counterargument is that structured generation implies a degree of human control over the noise. If the model operates through deliberate abstraction rather than raw pattern recombination, the output is not simply a regurgitation of training data — it is shaped by the abstractions the user applies. The distinction matters: “feeding the machine” implies passivity, but directing generation through intentional framing is an active, curatorial act.Archives are far more than just data points. We’re using people’s personal stories and difficult experiences for this. There’s a beauty of lives lived and the horrors, too. Training images are more than data. There is more to our archives than the clusters of light-coloured pixels. Our symbols and words have meaning because of their context in collective memory. When we remove that, they lose their connection to culture. If we strip meaning from the archive, we have a meaningless archive. We have five billion pieces of information that lack real-world connections. Five billion points of noise. Rather than drifting into the mindset of data brokers, it is critical that we as artists, as curators, as policymakers approach the role of AI in the humanities from a position of the archivist, historian, humanitarian, and storyteller. That is, to resist the demand that we all become engineers and that all history is data science.
