Lost in the Backrooms: Where Do All the Hallucinations Go?
The 1999 film The Matrix famously claimed that “no one can be told what the Matrix is.” Yet, shortly after, Morpheus explains the entire system as an exploitative machine that harvests humans for their body heat. It was a straightforward resolution to a seemingly complex premise.
The recent A24 Backrooms movie takes a different approach: it leaves its core premise largely unexplained.
There is no central architect (actually, there is only a failed architect trying to make sense of it) or exposition to make the space logically legible. The film simply places the viewer into the struggle of navigating a broken environment. We are left with damp carpet, peeling wallpaper, and the drone of fluorescent lights.
Since the film’s release, online discussions have debated what the space represents. On platforms such as Reddit or Hacker News, there are vivid discussions about whether this movie is a metaphor for decaying memory and trauma, or hallucinatory AIs.
As always, it is probably both, if not more, but I am very interested in the ideas of LLMs that ran wild and drained the electrical power from the furniture store.
The Clue in the “Sale” Signs
A key detail appears in an extended sequence focusing on retail “sale” signs scattered across an empty commercial space, pointing towards a reading of the Backrooms as a broken liminal space.
In physical reality, a retail sign is a standard example of mechanical reproduction — mass-printed and identical. In the Backrooms, however, these signs are inconsistent. Each one differs slightly in dimensions and alignment. They appear as individual variations rather than exact duplicates.
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For anyone working with machine learning, this feels familiar. This is not how mechanical printing fails; it reflects how a neural network operates.
Generative models do not retrieve stored files or copy templates. They sample from a vector space, predicting what a “sale sign” or an “office corridor” should look like based on statistical probabilities. Each generation produces a slight variation — a synthetic approximation that mimics surface patterns without grasping the underlying mechanics of how these objects are actually produced.
Latent Space in Drywall
This frames the setting differently. Rather than a supernatural dimension, the Backrooms functions like a physical manifestation of generative latent space.
Latent space is the mathematical realm within a neural network where concepts are mapped by proximity. The Backrooms translates this into drywall and drop ceilings. The architecture does not follow structural engineering; it follows statistical association. Corridors lead to dead ends and commercial spaces merge with residential basements because the system reproduces the visual syntax of architecture without any semantic understanding of a building’s function.
This suggests an interesting thought experiment for the AI era: when a model hallucinates — rendering a six-fingered hand or impossible room geometries — where do those errors go? In the logic of the film, they build the Backrooms.
Model Collapse and Automated Environments
This perspective also explains the entities in the film. In older sci-fi, AI threats often resemble the Terminator — conscious systems acting with intent. The Backrooms presents a different issue: model collapse.
Model collapse occurs when generative systems are recursively trained on their own synthetic data. As copies feed on copies, original features degrade, and the outputs become distorted. The multi-limbed “Still Life” entities in the film can be seen as failed generations — the result of a statistical model attempting to assemble a human form from degraded data.
The tension in The Backrooms comes from the futility of looking for human logic inside a statistical process. There is no mastermind to defeat or hidden message to decipher. Instead, the film illustrates the mechanics of an automated system generating an environment devoid of human context.
